On-Farm Cropping Trials Northwest & West Central Minnesota ... · The Minnesota Wheat Research and Promotion Council – On-Farm Research Network, is supported by the Minnesota

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On-Farm Cropping Trials Northwest & West Central

Minnesota and 2016 Minnesota Wheat Research Review

MDA Funding provided through Agricultural Growth, Research

and Innovation (MDA-AGRI) Program

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2016 Wheat Research ReviewIn 2016 the Minnesota Wheat Research and Promotion Council allocated about $800,000 of the total $1,700,000 check-off income to wheat research projects. The 2016 reports from these projects are printed in this book.

Wheat Research Project Funding Process:Each year in September, the Minnesota Wheat Research and Promotion Council requests wheat research pre-proposals from researchers in Minnesota, North Dakota and South Dakota. Researchers are given an opportunity to meet with a small group of wheat growers to get feedback on project ideas. About 20 project pre-proposals are reviewed at the Prairie Grains Conference by a Research Committee of the Minnesota Wheat Council. This Committee listens to presentations from each researcher and then the Committee determines which ones should be asked to submit full proposals.

The proposals are evaluated on the following criteria: 1) Is it a priority for growers 2) Impact on Profitability 3) Probability of Success 4) Cost v.s. Benefit.

At the end of January the committee meets once again to review the full proposals and make funding recommendations to the Minnesota Wheat Research and Promotion Council.

In addition to the projects reports being printed and distributed through this booklet, some of the project researchersgive oral presentations at the Prairie Grains Conference, Best of the Best Workshops and Small Grains Updates –Wheat, Soybean and Corn. Also, some of the projects are reported in the Prairie Grains Magazine. The Minnesota Wheat Research Committee is made up of wheat growers, agronomists, unbias researchers and industry representatives.

Information about the committee and previously funded research can be found online at www.smallgrains.org. Click on the Research Committee tab.

On-Farm Research Network Project ReportsThe Minnesota Wheat Research and Promotion Council – On-Farm Research Network, is supported by the Minnesota wheat checkoff for a large portion of the operating budget. The Minnesota Department of Agricultural Growth, Research, and Innovation (MDA-AGRI) grant program has generously provided additional funds for this research.

The MDA -AGRI Program awards grants and other forms of financial assistance (for example, scholarships and cost share) to create agricultural jobs and profitable businesses. Investments are focused on livestock development; value added business and market development, farm to school, research, and renewable energy.

The funding for the On-Farm Research Network projects are funded through the AGRI - Crop Research Program. The purpose of the Crop Research Grant Program under AGRI is intended to generate applied crop research that will improve agricultural product quality, quantity, and/or value. Read more at https://www.mda.state.mn.us/grants/agri.aspx

County Soybean Trials – Marshall, Pennington, Red Lake and Polk Counties The data presented here is part of a coordinated effort by Minnesota County Soybean Growers to expand the amount of research information that soybean growers have access to in northwest Minnesota. These trials are funded by entry fees paid by the seed companies. The publication of the results are funded by the Minnesota Soybean Research and Promotion Council. The research leader is Bill Craig, Ag Service Director, and Marshall & Pennington Counties.

On-Farm Cropping Trials For NW & WC MNThe On-Farm Cropping Trials were not available at press time. You can see the all the on-line research reports and the NW and other reports across the state on the web at: http://www.extension.umn.edu/agriculture/crops-research/

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Table of Contents: Wheat Research Reports - Funded in part by the Minnesota Wheat Check-off

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Collaborative Research on Wheat Diseases: Bacterial Leaf streak, Root and Crown Rots and Viral Diseases of Wheat~ Ruth Dill-Macky, Dept. of Plant Pathology, U of M, St. Paul

Variation in Response to N and S Among Spring Wheat Genotypes Grown on Irrigated and Non-Irrigated Soils ~ Daniel Kaiser, Dept. of Soil, Water and Climate, U of M, St. Paul

Accelerated Breeding for Resistance to Fusarium Head Blight ~ Karl Glover, Plant Sciences, SDSU

Exploiting Genetic Variation for Wheat Improvement in the Northern Great Plains ~ Brian Steffenson, Dept of Plant Patholology, U of M, St. Paul

Using Sensors for Phenotyping and Assisting in Selection in Spring Wheat ~ Joel Ransom, Dept. of Plant Sciences, NDSU, Fargo

Developing Adapted Spring Wheat Cultivars to Better Serve MN Wheat Growers ~ Andrew Green, Dept. of Plant Sciences, NDSU, Fargo

Pre-breeding of HRWW to Achieve Multiple Disease Resistance ~ G. Francois Marais, Dept. of Plant Services, NDSU, Fargo

Spring Wheat Responses to Starter Fertilizer, Micronutrient and Root Inoculant ~ Armitava Chatterjee, Dept. of Soil Science, NDSU, Fargo

Minnesota Small Grains Pest Survey ~ Madeleine Smith, Dept. of Plant Pathology, NWROC, Crookston

Strategies for Meeting N Requirements of Wheat with New Fertilizers and Fertilizer Additives~ Joel Ransom, Dept. of Plant Genetics, NDSU, Fargo

Southern Minnesota Small Grains Research and Outreach Project ~ Jochum Wiersma, Dept. of Agronomy & Plant Genetics, NWROC, Crookston

Establishing Criteria for Applying Additional N In-Season ~ Joel Ransom, Dept. of Plant Sciences, NDSU, Fargo

University of Minnesota Wheat Breeding Program ~ James Anderson, Dept. of Agronomy and Plant Genetics, U of M, St. Paul

Red River On-Farm Yield Trials Summer Plot Tours ~ Jochum Wiersma, Dept. of Agronomy & Plant Genetics, NWROC, Crookston

Economics, Nitrogen Use Efficiency, and Effects of Nitrogen and Sulfur Fertilizer Level Combinations Applied to Spring Wheat in Minnesota ~ Jasper Teboh, Carrington Research Ext. Center, NDSU

2016 Minnesota Wheat, Barley and Oat Variety Performance – Preliminary Report

North Dakota Hard Red Spring Wheat Variety Trial Results for 2016 and Selection Guide - Preliminary Report

North Dakota Durum Wheat Variety Trial Results for 2016 and Selection Guide - Preliminary Report North Dakota Barley, Oat and Rye Variety Trial Results for 2016 and Selection Guide - Preliminary Report

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2016 Northwest Minnesota County Soybean Variety Research Trials

Minnesota Wheat Council - On-Farm Research Network

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Collaborative Research on Wheat Diseases: Bacterial Leaf Streak, Root and Crown Rots and Viral Diseases of Wheat

Ruth Dill-Macky, Department of Plant Pathology, U of M

Research Question

This project continued our research effort addressing emerging diseases of wheat that are responsible for yield and quality losses in the Upper Great Plains, includ-ing Minnesota. The project focused on three diseases; bacterial leaf streak (BLS); root and crown rots; and viral diseases.

Bacterial leaf streak of wheat, caused by Xanthomonas translucens pv. undulosa, is prevalent in Minnesota and is currently considered to be the second most important disease of wheat in the state, after Fusarium head blight (FHB). Managing BLS is difficult due to the lack of highly resistant cultivars and other effective tools, especially as fungicides are ineffective against bacteria. Our research aimed to improve our understanding of the disease and to develop methods for disease control. Our specific objec-tives related to BLS included: 1.1. Co-ordinate the BSL cooperative nursery, testing commercial cultivars from regional wheat breeding programs 1.2. Identify sources of resistance to BLS using field and greenhouse screens 1.3. Undertake genetic analysis of resistance to BLS in identified sources of resistance 1.4. Examine the role of common weeds and crop residues in the epidemiology of BLS 1.5. Examine genetic variation within pathogen populations 1.6. Disseminate information to wheat growersThe root and crown diseases of wheat may cause signifi-cant yield losses, although they frequently go unnoticed. Root diseases generally compromise the root system, affecting the ability of the plant to take up water and nutrients, thus root diseases are especially damaging in years when water is limiting during grain filling. Surveys from 2012 to 2015 identified several root rot pathogens impacting wheat crops in Minnesota. In this project we have continued research to identify the pathogens causing root disease and to understand the response of the wheat germplasm to these pathogens. Specific objectives on wheat and crown diseases included: 2.1. Validating and further developing screening methods for reaction to root rot pathogens 2.2. Screening commercial cultivars and advanced breeding lines for resistance to Fusarium crown rot (FCR) 2.3. Identify sources of resistance to FCR under field and greenhouse conditions 2.4. Disseminate information to wheat growers

2016 RESEARCH REPORT

Viral diseases such as barley yellow dwarf, caused by barley yellow dwarf virus (BYDV) and cereal yellow dwarf virus (CTDV); and wheat streak, caused by wheat streak mosaic virus (WSMV), have been severe in wheat in years where conditions are favorable to the insect and mite vec-tors that transmit these viruses. In this project we aimed to identify the viral threats to wheat production in the region and characterize the viruses identified. We also aimed to provide recommendations on the reaction of commercial varieties and to direct breeding efforts with respect to virus diseases. Specific objectives on virus diseases included: 3.1. Validate diagnostic tests that characterize the viruses found in association with wheat 3.2. Examine the distribution of cereal viruses in spring and winter wheat 3.3. Determine the occurrence and distribution of cereal viruses on non-wheat hosts 3.4. Develop management strategies for viral diseases 3.5. Disseminate information to wheat growersResults

Bacterial Leaf Streak:In 2016 we tested released varieties and advanced lines in a regional cooperative nursery (BLSCN). The 120 entries came from seven wheat breeding programs (3 public and 4 private) in the Upper Great Plains. The BLSCN was established at four locations (St Paul, Crookston, Fargo, ND and Brookings, SD). The data from all four locations indicate that significant differences were observed in these materials for their reaction to BLS under field conditions (See Table provided in the Appendix). The information obtained on the response of released varieties and elite germplasm will be utilized by regional wheat breeding programs to the benefit of growers. Information on the response of released germplasm to BLS collected in 2016 will be combined with previous data sets and the overall evaluations will be disseminated to Minnesota growers through the MN variety trials bulletin.

In 2016 we conducted surveys looking for BLS symptoms in wild grasses (wild oats, foxtail, barnyard grass and oth-ers) and in other grass hosts including intermediate wheat-grass, oats and wild rice in Minnesota. We made collec-tions of symptomatic tissues and are currently working to isolate and identify the bacteria associated with these host plants. Simultaneously we are working to establish greenhouse protocols to establish the host range of these isolates and thus establish if these are relevant to the Xan-thomonas population inciting BLS on wheat.

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We have characterized our entire isolate collection (includ-ing isolates from MN, ND and SD) with MLSA (multilocus sequence analysis). MLSA revealed that both Xanthomon-as translucens pv. undulosa (pathogenic on wheat and barley) and X. translucens pv. translucens (pathogenic on barley) are present in the region. Overall, isolates from wheat appear to be pathovar undulosa, though a couple isolates from wheat grouped with translucens isolates which suggests that host specificity may not be clearly defined. Isolates in the undulosa group are less geneti-cally diverse (monophyletic) compared to isolates within the translucens group (3 distinct clades). We intensively sampled two wheat fields that were naturally infected with BLS and obtained isolate collections (25 per field) that represent populations within a field site. MLSA of these isolates is in progress and results will be reported in future updates.

Root and Crown Diseases:Root rot disease work continued in 2016. We completed the isolation of fungal pathogens from the samples col-lected and have identified Fusarium species using mor-phological techniques. We are working toward developing protocols to confirm the identity of the isolated fungi using molecular and/or DNA sequencing. The results from the surveys conducted thus far (2012 onward) have deter-mined that the prevalence of species of Fusarium, particu-larly F. graminearum has increased compared to previous surveys. This has helped us prioritize research needs and has provided the isolates needed for establishing screen-ings for host resistance to root and crown rots. In 2016 we tested a protocol for screening materials in the field that, while effective in generating disease, was not suitable for experiments of the scale needed to effectively and reliably screen germplasm. Until we are able to establish a reliable inoculation protocol our ability to screen germplasm for re-sistance remains limited. Our preliminary findings indicate that resistance to Fusarium head blight (FHB or scab) is limited and independent of resistance to crown and root resistance, as has been reported in the scientific literature.

Virus Diseases:Efforts in 2016 focused on validating diagnostic tests that characterize the viruses found in association with wheat. Collections of barley yellow dwarf symptomatic material from 2013, 2014 and 2015 were used to refine the molec-ular diagnostic tools for Barley yellow dwarf virus (BYDV) and Cereal yellow dwarf virus (CYDV), the causes of bar-ley yellow dwarf. Molecular diagnostic tools are important because they represent the most efficient and sensitive methods for pathogen detection, as opposed to immuno-assays such as ELISA (enzyme-linked immunosorbent assay). In addition, there at least five different strains of BYD/CYDV, not all of which can be detected using ELISA. During the course of 2015 work, it became apparent that published protocols for molecular diagnostics of BYDV (Malmstrom and Shu, 2004) needed to be refined because these gave false negatives in some of our samples. Vari-ous methods were tested and a modified protocol estab-

lished that now appears to work consistently for diagnostic purposes in our region. In the spring of 2016, this method-ology was also adapted into a protocol for testing cereal aphids, in both bulked (many aphids) and individual (single aphid) samples. Testing winged aphid populations when they arrived in the southern part of Minnesota enabled es-timates to be made of the proportion of the vector popula-tion carrying BYDV/CYDV.

Refinements of the diagnostic assay has taken much longer than anticipated, but was crucial to the objectives of this work. We now have an assay for making greater progress on Objectives 3.2 and 3.3.

Results from the samples we collected, along with those collected by our NDSU and SDSU colleagues, indicate that the predominate strain of BYDV/CYDV in hard red spring wheat is BYDV-PAV. This finding in in accordance with data from surveys conducted over 10 years ago in the region. However, in other host species such as oat, BYDV-PAV was found less frequently and other strains such CYDV-RMV or CYDV-RPV seemed to be more com-mon. More extensive sampling of other host species will be required to determine if this finding is born out with a larger sample size and broader geographic context.

Application/Use

Our ability to determine the prevalence and impact of the diseases of wheat is essential to develop disease control strategies, including resistance. Developing resistant wheat germplasm will rely on collections of pathogens and the development of effective screening methods, both to identify sources of resistance and to introgress the re-sistance into adapted germplasm. In 2016 we have both utilized protocols we have developed to establish screen-ing nurseries for BLS and have continued work to improve screening protocols for BLS and the root diseases. Simi-larly, we have worked toward identifying the root rot fungi and viruses present in wheat.

Materials and Methods

Bacterial leaf streak: By the start of this project we had developed the basic protocols needed to work with BLS and developed a regional cooperative nursery (BLSCN) in which released cultivars and advanced lines from all wheat breeding programs (public and private) in the Upper Great Plains are being screened annually for resistance to BLS. Screening nurseries were also used to identify additional sources of resistance. Annual field screening nurseries are critical to the ultimate goals of the research - host resistance -and this work is being done cooperatively with Drs Shaukat Ali (South Dakota State University) and Zhaohui Liu (North Dakota State University).

continued on pages 6 - 7

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We examined populations of Xanthomonas in wheat for their host preference so that we can use this information to inform which isolates are selected for use in germplasm screening. To evaluate the contribution of weeds and crop residues to reservoirs of the BLS pathogen, collections of Xanthomonas were obtained from crop residues and common weed species, including wild rice throughout Minnesota. The host preference and genotype of each isolate was determined using molecular tools, including multi-locus sequence typing (MLST), a technique which was established in a previous project. Host range inocu-lation studies are being undertaken in the greenhouse to complement this work.

Wheat Root and Crown Diseases: Over the last five years we have developed a better understanding of the root and crown rots in wheat. Field surveys, conducted collabora-tively across the three states, have examined the distribu-tion and prevalence of root rot pathogens.

Fungal pathogens from the 2012-2016 surveys have largely been identified using morphological means and added to our collection. Efforts to identifying the fungi by DNA sequencing have continued. We have established work in the laboratory, greenhouse and field to test methods for inoculating the roots and stem bases of wheat plants with Fusarium spp. These efforts continued in 2016 and should ultimately facilitate our ability to screen breed-ing materials for reaction to the prevalent root rot patho-gens in the region. In 2016 a field nursery was established in St Paul to test the most promising inoculation protocols developed in the laboratory.

Virus Diseases: The data collected over the last three years has given us information on the strains prevalent in commercial crops around the state. Molecular methods for the identification of strains were used in strain identifica-tion. Surveys were conducted around the state in 2016 by sampling grasses in ditches next to fields with symptoms of BYDV. The molecular identification methods are being utilized to determine BYDV present in collected samples. Samples were also collected in South Dakota by Dr. Byamukama (South Dakota State University). The 2016 samples are also being evaluated for the presence of wheat streak mosaic virus in collaboration withDr. Byamukama.

Economic Benefit to a Typical 500 Acre Wheat Enterprise

We have demonstrated that bacterial leaf streak (BLS) is of economic importance to the wheat industry and data has been generated that a grower can use to select wheat varieties that are less susceptible to BLS. The data gathered from this project demonstrate that root rot and viral diseases are prevalent in commercial wheat fields in Minnesota. It appears that two root rot pathogens, Fusarium and Bipolaris, are abundant and that they likely contribute significantly to yield losses, particularly in years

when moisture is limiting in the latter part of the growing season. Similarly BYDV appears widespread in wheat and is likely impacting yields in some years. While this informa-tion does not provide any immediate benefit to the grower an awareness of the problem is a first step to the control of the root rots and viral diseases of wheat.

Related Research

This is a regional collaborative project involving patholo-gists in three states. We have established close relation-ships with research and extension plant pathologists and the wheat breeding programs (public and private) in each state. The regional wheat breeding programs have benefited the project by providing field observations of the distribution of diseases, collection of symptomatic plants for isolate collection and wheat germplasm. The wheat breeding programs in the region (public and private) have benefitted from knowledge of the reaction of released and advanced breeding lines to BLS.

Recommended Future Research

Bacterial leaf streak: Although we continue to make progress with BLS, additional research is needed. Our collaborative screening efforts have determined that the majority of our wheat cultivars and many advanced lines from the regional breeding programs are at least moder-ately susceptible to BLS and that there is generally less resistance in the regionally available germplasm than is desirable. We plan to continue using screening nurser-ies to test wheat lines for their response to BLS and plan in 2017 to expand the materials we are examining in the hopes of identifying additional and improved sources of resistance. Previous studies suggest that resistance to BLS is governed by multiple genes and quantitatively inherited; in addition, the evaluation of plant responses to BLS is challenging and influenced by environmental condi-tions. We anticipate continuing our studies examining the pathogen population, to determine the host range of the X. translucens pv. undulosa pathovars associated with BLS of wheat and other grasses.

Root Rots: The survey of root diseases we have already conducted have demonstrated that root rot pathogens are readily found in wheat crops in Minnesota and that they most likely have a significant negative impact on yield. We have made progress, albeit rather slow, in developing test-ing methods suitable for inoculating plants with Fusarium spp. in the field and greenhouse. As was anticipated from the start from this project, working with these root dis-eases has proven challenging. We plan to continue work in 2017 with a focus on developing a greenhouse-based screen suitable for high throughput phenotyping of germ-plasm.

Viruses: Efforts concerning BYDV and CYDV will continue in 2017. We plan to determine the role of grass species, other than crops, which are hosts for BYDV/CYDV using

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the improved molecular diagnostic techniques we have developed. This work will allow us to determine if the grasses growing in ditched and roadsides and other areas adjacent to commercial wheat crops are of concern in the epidemiology (survival and spread) of the disease in Min-nesota. We also plan to try to develop a quantitative PCR method suitable for the detection and quantification of BYDV/CYDV now that we have a consistent assay for mo-lecular detection. These efforts will be both informative to resistance breeding efforts and disease risk assessment.

Publications

Winter, M., Samuels P.L., Dong, Y and Dill-Macky. (2016). Deoxynivalenol (DON) and nivalenol (NIV) play a role as virulence factors for wheat root and stem base infection by Fusarium culmorum and F. graminearum. Abstract In Proceedings of the 2016 National Fusarium Head Blight Forum, St. Louis, Missouri, USA, December 4-6, 2016.

Anderson, J.A., Wiersma, J.J., Linkert, G.L., Reynolds, S., Kolmer, J.A., Jin, Y., Dill-Macky, R., and Hareland, G.A. (2015). Registration of ‘Rollag’ wheat. Journal of Plant Registrations, 9:201-207.

Variety St. Paul, MN Crookston, MN Fargo, ND Brookings, SD 4 Loc. MeanCromwell 3.50 2.75 5.25 4.25 3.94

LCS Trigger 3.50 2.75 6.25 3.50 4.00

Boost 3.75 3.50 5.00 4.00 4.06

Blade 4.75 2.50 5.75 3.50 4.13

Prosper 3.75 3.25 6.50 3.75 4.31

Prevail 4.25 3.00 5.75 4.50 4.38

LCS Hattrick 3.75 3.75 6.50 4.25 4.56

Advance 4.25 3.75 6.25 4.25 4.63

Shelly 3.75 3.50 8.00 3.75 4.75

Surpass 4.75 3.25 6.75 4.50 4.81

LCS Nitro 4.00 4.50 7.00 4.00 4.88

Forefront 4.75 4.25 6.50 4.25 4.94

LCS Iguaca 4.00 3.75 8.00 4.00 4.94

Barlow 5.25 3.00 7.00 4.75 5.00

Rollag 4.75 3.00 7.75 4.50 5.00

Bolles 3.50 5.00 7.25 5.00 5.19

Focus 5.00 4.00 8.00 4.25 5.31

Knudson 5.50 4.75 6.50 4.50 5.31

Glenn 5.00 4.00 7.25 5.25 5.38

LCS Prime 5.25 5.00 7.50 4.75 5.63

LCS Pro 5.00 4.50 8.00 5.00 5.63

Linkert 5.50 4.25 8.00 4.75 5.63

Norden 6.00 3.50 8.25 4.75 5.63

RB07 5.25 5.00 6.75 6.25 5.81

LCS Breakaway 6.25 3.75 7.75 5.75 5.88

LCS Powerplay 5.50 5.00 8.00 5.00 5.88

Samson 5.50 4.50 7.75 5.75 5.88

LCS Anchor 5.50 4.00 8.75 6.00 6.06

Select 6.50 6.00 8.00 6.25 6.69

Salgado, J.D., Ames, K., Bergstrom, G., Bradley, C., Byamu-kama, E., Cummings, J., Chapara, V., Chilvers, M., Dill-Macky, R., Friskop, A., Gautam, P., Kleczewski, N., Madden, L.V., Milus, E., Nagelkirk, M., Ransom, J., Ruden, K., Ste-vens, J., Wegulo, S., Wise, K., Yabwalo, D., and Paul, P.A. (2015). Robust management programs to minimize losses due to FHB and DON: a multi-state coordinated project. In: Proceedings of the 2015 National Fusarium Head Blight Forum, St. Louis, Missouri, USA, December 6-8, 2015, pp. 24-26.

Smith, M.J., Friskop, A., Arends, A., Chapara, V., Meyer, S., Schatz, B., Bergstrom, G.C., Cummings, J.A., Byamukama, E., Yabwalo, D., Bleakley, B., Murthy, N., Ruden, K., Brad-ley, C.A., Ames, K., Pike, J., and Bellm, R. (2015). Uniform Fungicide Trial Results for Management of FHB and DON. In: Proceedings of the 2015 National Fusarium Head Blight Forum, St. Louis, Missouri, USA, December 6-8, 2015, pp. 33.

Smith, M.J., Dill-Macky, R., Curland, R., Ishimaru, C.I, and Wiersma, J. (2015) Bacterial Leaf Streak and Black Chaff of Small Grains. University of Minnesota Extension Publication.

Mean BLS score (1-9 scale, where 9 is the highest level of disease) for named cultivars in the 2016 BLS cooperative nurseries established at four locations in 2016, along with the four-location mean. Values provided are the means of four replicates. The colors; dark green, light green and yellow, indi-cate where the values provided are 3, 2 and 1 standard deviations below the location mean, respectively, while the colors orange, red and maroon indicate that the values are 1, 2 and 3 standard deviations above the location mean, respectively.

Appendix

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Research Questions

1) Study the effect of sulfur rate on spring wheat grain yield and protein concentration and quality.

2) Determine whether spring wheat varieties differ in the potential response to sulfur fertilizer.

3) Evaluate if plant tissue analysis (flag leaf samples collected at anthesis) can indicate the responsiveness of spring wheat varieties to N or S

4) Determine the economic optimum nitrogen rate for spring wheat grown under irrigation.

Results

Wheat Sulfur Studies – Table 1 and 2 summarize plant-ing data and initial soil test information for the sulfur trials.

Statistical significance, by location, for spring wheat grain yield, grain protein concentration, and the total amount of protein produced per acre is summarized in Table 3. Main effect averages are given in Table 4 summarized by loca-tion and for the 3 location average for the 2016 growing season. As expected, yield differed consistently among the varieties at all locations. RB07 produced the great-est yield across sites followed in order by Mayville, Faller, Select, Glenn, and lastly, Vantage. Grain protein concen-tration was greatest for Vantage and Glenn while Faller producing the least. Total protein produced per acre was greatest for the top yielding variety. As in previous years, Staples had the highest average grain protein compared to the other two locations. It is unclear why Staples has had a higher potential protein concentration. However, the inverse relationship between protein and yield may explain the high protein concentration as wheat grain yield was substantially lower at Staples than the other two locations.

There was no detectable increase in yield from sulfur at the Crookston and Fergus Falls locations (Table 4). In 2014 the 7.5 lb S rate produced a significant yield in-crease at Staples and there was no further increase to the 15 lb rate. In 2015 grain yield was again increased at Staples but only for the 15 lb S rate. The actual yield increase was less at 4 bu/ac in 2015 than occurred in 2014 (2014 and 2015 data are included in previous reports and not shown in the current report). There was a yield decrease due to the application of the 15 lb S rate at Staples in 2016. The reason for the decrease was not

clear. In 2016, grain yield was increased when 15 lbs of S was applied at Fergus Falls. This response to S at Fergus Falls is the first that has been found outside of the Staples location.

Grain protein content was increased by sulfur at Staples. At Staples, the 7.5 and 15 lb S rate produced an increase of 0.2-0.3% protein. Average protein levels were above 14% so the increase in grain protein concentration would not have resulted in a discount. Grain protein produced on a per acre basis was only impacted by sulfur at Staples due to impacts of S on both grain yield and protein con-centration. Grain protein concentration and total protein produced per acre was not increased or decreased at Crookston or Fergus Falls.

Asparagine data was collected on samples taken from the 2014 sulfur studies (Table 5). Asparagine is important as it is an indicator of the production of acrylamide. Acrylamide can be produced during baking or frying and can have negative health impacts on humans. Asparagine content in the wheat grain varied among varieties across locations. Glenn typically had the lowest asparagine content fol-lowed by Vantage and Faller. Past research on hard red winter wheat has shown that sulfur can reduce the amount of asparagine in the grain. For hard red spring wheat, ap-plication of sulfur decreased asparagine content at Staples and Kimball. In both cases the 7.5 lb S rate produced the greatest decrease in asparagine content. It was interest-ing that the content of asparagine was higher at Staples than the other locations. The higher asparagine content at Staples could have been a result of higher protein content at Staples compared to the other locations. Samples were saved from the 2015 site to test for asparagine content if funds allow.

Wheat grain yield and grain protein concentration data were summarized across years for individual environ-ments of Crookston, Fergus Falls and Kimball, and Staples (Table 6). These environments were chosen as they represent, respectively, an increase in the likelihood of response to S fertilizer. Overall, grain protein and yield were only increased at Staples. The increase was a result of the 7.5 lb S per acre application rate. There was no further increase, on average, for the 15 lb S rate versus the 7.5 lb rate. On average, grain yield was increased by 4 bu/ac with an average increase of 0.5% in grain protein concentration at Staples. The lack of an increase in grain yield and protein concentration at the other two environ-ments indicates little potential of an economic benefit of S for wheat based on the current structure for marketing

Variation in Response to N and S Among Spring Wheat Genotypes Grown on Irrigated and Non-irrigated Soils

Daniel Kaiser, Department of Soil, Water and Climate, U of M


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spring wheat. It should be noted that the amount of S applied in the irrigation water was measured at Staples but the totals were negligible over the years studied. In other studies the amount of S supplied can be substantial thus the chance for a response to fertilizer S may be greater on sandy irrigated soils but the need for S may be low due to S supplied through irrigation water.

Figure 1 summarizes flag leaf sulfur concentration by variety across the six locations conducted between 2014 and 2016. One question we were to address is weather there were differences among the varieties in their distri-bution of sulfur in the flag leaf tissue following application of sulfur fertilizer. The varieties were selected because they showed some differences in preliminary surveys of variety trials. Flag leaf sulfur concentration was impacted by the application of sulfur across the sites but there was no indication that there was a differing effect by variety (no significant interaction between variety and sulfur rate). The lack of impact of variety on sulfur content could be as a result of the rates being used are too high to achieve dif-ferences or the fact that the previous research correlated sulfur content of individual varieties with the average sulfur concentration based on location. Grain S concentration was also measured (Figure 2) with similar results as flag leaf S concentration. Grain sub-samples were collected from 2016 but the samples are still being analyzed in the lab and the data are not available.The question remains as to whether flag leaf S concentration could be used to predict were S deficiencies will occur.

Figure 3 summarizes the relationship between relative yield produced by the control with no S versus the con-centration of S in the flag leaf tissue. Relative yield data was generated by dividing the yield of the control for each variety by the average yield of the S fertilized plots at each study location each year. Relative yield data was gener-ated to account for yield variation among the varieties. The dashed line represents the best fit relationship, which indicated that relative yield and flag leaf S were related but the prediction of yield by flag leaf S is poor (R2=0.09). The data for Staples were analyzed separately (not shown) and was found to better correlate but the relation-ship was linear so a critical level could not be established. Using 95% of maximum yield as a low end of a sufficiency range and the plateau as the upper end, the data indicates that a concentration ranging from 0.20-0.35% S should indicate that sufficient S is present at a location. Taking a sample at anthesis will not provide any useable data for the given growing season and can only be used in sub-sequent years as an indicator of where S deficiency may occur. There was no indication that the sufficiency level varied by variety thus the data in Figure 3 is shown across varieties.

Nitrogen Fertilization of Irrigated Wheat – Table 8 sum-marized pertinent soil test data collected prior to the study being established at Staples in 2016.

Data in Table 9 indicated that wheat grain yield, protein concentration, and protein yield were impacted by variety and nitrogen in 2016. There was on significant interaction for protein which indicates some variation in the response to N by variety in 2016. This interaction was not found for grain yield or total protein produced per acre which indicates that the amount of N required to maximize grain yield was the same for all varieties. Current N guidelines are based on yield goal recommendations. The data from 2016 as well as previous years indicates that there is no relationship between grain yield or protein production and the amount of N required. Grain protein concentration may be impacted due to variations in how protein is accumu-lated by varieties but the data supports the decoupling of yield goals with N guidelines for hard red spring wheat.

The N rate at the maximum return to nitrogen (MRTN) was calculated by year and for the three year average (Table 11). Since the protein concentration tended to be high (above 14%) discounts were not factored in to the calcula-tor. The actual MRTN value varied by year but the sug-gested N rate for a typical price ratio of 0.05-0.10 ranged from 130 to 155 lbs of N which is less than the MRTN previously calculated for non-irrigated wheat [200-250 lbs of applied N + residual N in the top two feet (data gener-ated though an unpublished study funded by AFREC)]. The residual N in the top 2 feet averaged around 25 lbs of N for Staples which was not substantial. One source that was not accounted for was the amount of N applied in the irrigation water which may account for the difference depending on the number of times irrigated and the N concentration in the irrigation water. Nitrate in the irrigation water also could account for the higher protein concentra-tions at Staples for both the N study and the S study.

Table 12 summarizes the statistical analysis of grain yield, protein concentration, protein yield (on a per acre basis), and flag leaf N concentration across years. Individual data for the measurements are summarized in Figures 4, 5, 6, and 7, respectively. Nitrogen rate influenced grain yield, protein yield, and flag leaf N but they did not interact. The probability values were close to the accepted level (P<0.10) for the interaction terms for all three variables. The interaction was significant for grain protein concen-tration. Figure 5 shows the impact of N on grain protein for each of the three varieties. From the data the variety RB07 appears to maximize protein around the 240 lb rate which the other two show increasing protein beyond the 300 lb N rate which was the highest applied in the study. The interaction indicates some evidence that the varieties differ in the accumulation of protein. However, the total protein produced was nearly identical among the varieties in which there was no difference in yield or total protein produced according to the three-year average (Figures 4 and 6). The variety Faller was the only one variety which produced less than 14% grain protein on average with 0 to 60 lbs of N applied.


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Flag leaf nitrogen concentration was measured and the data are reported in Figure 7. There was no indication that the varieties varied in their response to N in the flag leaf tissue. The P value was close to significance but the main effect of variety and nitrogen rate were the only significant factors for the study. The maximum N concentration in the flag leaf tissue was similar for Faller and RB07 and less for Mayville. There was no indication that the accumula-tion of N following fertilizer application varied by variety. Figure 8 summarizes relative grain yield and protein yield data versus flag leaf N to determine if the concentration of N in the flag leaf could be predictive of the two mea-surements. Relative data was generated to balance out genetic difference in yield and protein production among the varieties and among the years. In both cases there was a direct relationship between flag leaf N concentration and relative grain yield and protein yield of the three vari-eties. However, the relationship was linear in both cases making it difficult to determine an optimum N value from the figures. In both cases the values seem to cluster near 4.5% which may indicate that if more data were collected, the critical value would possibly have been within that range. The data also indicates that the critical value may be similar among the varieties. At the time of sampling the chance that applied N would increase yield is very low. Thus, tissue analysis may give an indication of the poten-tial reduction in yield or protein production but it will not indicate the achievable yield or protein produced on a per acre basis. Application and Use

Changes were made when the Fertilizer Guidelines for Wheat in Minnesota publication was updated in 2012. Some changes included a general framework of S guide-lines for eroded low organic matter soils (less than 2.0% organic matter in the top six inches). We did not have any non-irrigated locations with an organic matter con-centration 2.0% or less in the present study. We can only assume responses would be similar for irrigated and non-irrigated soils as long as the irrigation water is not providing available S to the plant which would lower the rate required. Currently, 25 lbs of S is suggested for wheat grown on sandy soil with low organic matter. If ir-rigated, our data suggests that wheat can benefit from S but the rate required is less, 10 lbs of S per acre should be sufficient for both irrigated and non-irrigated wheat. Cur-rently 10-15 lbs of S applied using a sulfate-S source is suggested for wheat grown on soils with an organic matter concentration of 3.0% or less. The data currently supports this suggestion.

There may be some benefit in baking quality when S fertilizer is applied regardless of whether there is a yield or grain protein concentration response to S. Until pro-tein premium/discounts reflect quality over the quantity there may be a limited impact to the bottom line of a wheat grower. Asparagine (an amino acid) data generated

from the 2014 data was used to look at as a precursor of acrylamide formation upon baking. The concentration of asparagine decreased when S was applied even without a resulting increase in grain yield or protein concentra-tion. Since grain S was typically increased when S was applied we can assume the production of cysteine and methionine (S containing amino acids) may be increasing when S is applied and may be offsetting the production of asparagine. Follow up research is needed to study the acrylamide issue, particularly looking at the impacts of S on asparagine and reducing sugars which are both important in the formation of acrylamide. Acrylamide is a known carcinogen that can be produced during baking at high temperatures.

Nitrogen response data for irrigated wheat indicates that 150 lbs of applied N split between an application after seeding and near jointing was sufficient for irrigated wheat. Some cautions on this data should be exercised as the amount of N in the irrigation water was not account-ed for in that value and there was significant variation in the economic optimum N rate by year. Local knowledge should always be used when determining how much N should be supplied based on all N sources to the plant.

Our goal for comparing the varieties was to determine if tissue sampling could be used to determine responsive-ness of varieties to N or S. Since there was no evidence that a variety by N or S interaction occurred, it is unlikely that the tissue data had much value in determining whether N or S would benefit one variety over another. One caution about this work is that since yield was only affect-ed at one location it is hard to draw hard conclusions un-less the effect can be replicated. More locations and one or two additional years of funding would greatly benefit this project to determine if similar effect can be replicated across sites and years. Overall, our data from this study and past research indicated that significant caution should be taken when using plant tissue samples for guiding fertil-izer application as a specific critical level can be difficult to determine for use and late season tissue samples cannot be used during the year collected and have more value for subsequent years’ management.


Small plot sulfur fertilization studies on non-irrigated soils will be established alongside two spring wheat variety trials. The locations were Crookston, Fergus Falls, and Staples. Staples was the only site where supplemental irrigation was supplied.

Six wheat varieties will be selected using the stability analysis conducted for spring wheat flag leaf tissue among varieties in 2011 and 2012. Variety selection will not be based on current planting trends or popularity. Two variet-ies will be selected that were considered in the high, aver-age, and low response to sulfur categories and that vary

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in protein and yield potential. The varieties selected are Faller, Vantage, Select, Glenn, Mayville, and RB07. Sulfur rates used will be a non-fertilized control (0 lb S), 7.5, and 15 lbs S per acre. Sulfur was applied to the soil surface after planting. The source of sulfur was ammonium sulfate (21-0-0-24). Nitrogen was applied to balance the rate of nitrogen applied with the high rate of ammonium sulfate. Nitrogen, phosphorus, and potassium were kept at non-limiting rates according to current recommendations.

The nitrogen trial consisted of only three of the varieties utilized in the S study (Faller, Mayville, and RB07) and six nitrogen rates (0, 60, 120, 180, 240, and 300 lbs of N per acre). Nitrogen was applied as urea (46-0-0) applied at two times with half of the nitrogen applied after seed-ing but before emergence and the remaining applied near jointing. Additional nutrients (P, K, and S) were applied as a pre-plant application. The varieties selected were done so based on previous flag leaf tissue data for N similar to selection characteristics outlined the preceding paragraph.

Soil samples were collected prior to fertilizer application at both sites at 0-6, 6-12, and 12-24” depths (1 compos-ite sample was collected per site). 0-6” samples were analyzed for P, K, pH, and organic matter. All samples were either analyzed for nitrate-N or sulfate-S depending on the study. Flag leaf samples were collected from the S and N studies at anthesis by sampling 25-30 leaves. Leaves were dried, ground, and analyzed for total N or S depending on the study. Grain yield was measured for all plots adjusted to 13% moisture and a sub-sample of grain will be collected and analyzed for protein concentration (reported at 12% moisture) by NIR. Grain samples for the S study will be analyzed for total S.


Assuming a wheat price of $6 per bushel, the response at Staples would result in an additional $24 per acre in added crop value across the varieties. If S cost was $0.50 per lb of S, the rate needed to increase yield (15 lb per acre) would cost a grower $7.50 per acre resulting in a net profit of $16.50 per acre and would total $8,250 for a 500 acre operation. When S is deficient, application of S is typically highly profitable for hard red spring wheat.

Even with the low total cost associated with the rate of S needed to increase yield, if a site is not responsive a grower should highly consider using money intended for S for nitrogen especially in years where yield potential and protein discounts are greater. Even with increases in grain protein due to S, the concentration of grain protein was above 14% thus an increase would not results in any significant economic benefit to a grower unless a premium is being paid for protein. The overall increase in protein on average was only one to two fifths of a percent. Overall,

S should not play a role in making decisions on fertilizer application for increasing grain protein concentration.

The economic benefit for the application of N is factored in to the data presented in Table 11. The values represented in Table 11 indicate the point at which a dollar invested in N will return a dollar in crop value. Rates applied beyond values will typically result in a negative return on average. These values are adjusted for the cost of N relative to the crop value. These data are meant to be an average value or a starting point and may need to be adjusted for other incidental sources of N or N loss in a given year. Only a single split application was used. Thus additional strate-gies using more splits may need to be research to deter-mine if two application of N is sufficient of if the N should be split more times over the growing season.

Related Research

A S study was concluded in 2009 which was funded by the Minnesota Wheat Growers that studied the effect of S source, rate, and timing for wheat grown on soils with relatively high concentration of organic matter. This current study provided supporting data for the previous research but focuses on questions received following the previous study on whether we would expect response to S to be greater for varieties which are greater yielding than Glenn which was used in the previous research. We are also following up on information collected in a study funded in 2011 and 2012 that included a survey of flag leaf tissue nutrient concentration. The current research will determine if there is any value in tissue concentration data and whether tissue concentration can help predict variety responsiveness to a specific fertilizer.

A study funded by AFREC is currently using the grain samples collected in this study for analysis of amino acids. This analysis will be used with NIR data to develop curves to screen for a rough estimate of amino acid concentrations for un-ground and ground samples. Our emphasis is to allow for us to screen for some of the sulfur amino acids by a quick and cheap method. For someone whom wants exact values there still is a need to run chemical analysis on the grain.


Further research work is needed to determine the potential health benefits that S fertilization may play in beyond po-tential for increase in grain yield and protein concentration. Research connecting the application of S with a decrease in acrylamide is needed for hard red spring wheat. The asparagine data presented in this report shows that there may be an impact of S in reducing asparagine but reduc-ing sugar concentrations are needed to give a full picture of the potential for acrylamide formation. In addition,

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measuring acrylamide directly on bread baked with wheat would be beneficial to regress acrylamide content with as-paragine and reducing sugars to determine if the relation-ship between the two is similar in hard red spring wheat as for other crops.

Monthly Total Precipation

Location County Soil Type Soil TextureSeeding

Date May June July------------inches--------

Crookston Polk Wheatville Sandy Loam 15-Apr 6.7 6.8 4.6Fergus Falls Otter Tail Formdale Clay Loam 13-Apr 1.0 1.6 7.2Staples Wadena Verndale Sandy Loam 13-Apr 1.7 1.9 10.3

Table 1. Trial location, planting information, and monthly total precipitation for spring wheat S rate studies.

Soil Test (0-6:) ƚ

Location P K SOM pH Sulfate-S ‡

---ppm---- ---%--- - --lb/ac--Crookston 5 117 4.1 8.2 208 §Fergus Falls 21 191 4.8 6.3 28Staples 16 69 1.8 6.8 16ƚ P, Bray-P1 phosphorus; K, ammonium acetate potassium; SOM, soil organic matter; pH, soil ph ‡ 0-2 foot soil sulfate-S § Sample was collected from 0-1’

Table 2. Spring soil test averages across replication for Spring wheat S trials.

Appendix

Publication

Kaiser, D.E., and J. Wiersma. 2016. Variation in response to sulfur among hard red spring wheat varieties. Agron. Abs. CD-ROM. ASA-CSSA-SSSA. Madison, WI.

Grain Yield ƚ Grain Protein ƚ Protein Yield ƚ Location V S VxS V S VxS V S VxSCrookston <0.001 0.81 0.66 <0.001 0.63 0.60 0.10 0.02 <0.001Fergus Falls 0.04 0.04 0.33 0.03 0.63 0.71 0.75 0.01 0.02Staples <0.001 <0.01 0.41 <0.001 <0.001 0.53 0.76 0.50 0.57

Table 3. Summary of statistical signficance of main effects of variety (V), sulfur rates (S), and their interaction (VxS) for spring wheat grain yield, protein concentration, and total protein produced per acre.

Grain Yield ƚ Grain Protein ƚ Protein Yield ƚ

Variety CR FF ST AVG CR FF ST AVG CR FF ST AVG---Bushels/ac (@13%)----- -----%(@12%)------ ------pounds/ac (13%)-------

Faller 94.4a 83.7b 40.5d 73.0bc 14.2e 15.9b 18.3e 16.1e 804 797b 444c 682dGlenn 86.0b 83.3b 43.9c 71.1cd 15.5b 16.5a 19.5b 17.2b 799 822b 513b 711bcMayville 86.6b 89.2a 45.5bc 73.6b 15.2c 16.1ab 18.7c 16.7c 788 861a 510b 720abRB07 95.6a 84.2b 51.6a 77.1a 14.5d 15.8b 18.4de 16.2de 834 803b 572a 737aSelect 86.0b 83.5b 47.7b 72.4bc 14.8d 15.8b 18.5cd 16.4d 764 788b 530b 694cdVantage 83.2b 815b 44.7c 69.8d 15.9a 16.5a 19.8a 17.4a 795 805b 530b 710bcS Rate (lb/ac)0 89 832b 46.5a 73 15.0 16.2 18.3b 16.5b 799 807b 510b 7057.5 88 828b 47.4a 73 15.0 16.0 19.0a 16.7ab 790 792b 840a 70815 89 86.7a 43.1b 73 15.0 16.1 19.2a 16.8a 802 839a 500b 714

Table 4. Summary of hard spring wheat garin yield, grain protein concentration, and protein production per acre for individual varieties at Crookston (CR), Fergus Falls (FF), and Staples (ST) Minnesota during 2015. Average values were calculated for variety and sulfur main effects by and across locations (AVG).

ƚ within columns for each main effect, numbers followed by the same letter are not statistically significant at P<0.05 probibility level.

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Figure 1. Flag leaf S concentration summarized by variety across six locations from 2014-2016

Figure 2. Grain S concentration summarized by variety across six locations from 2014-2015

Figure 3. Prediction of relative wheat grain yield using flag leaf S concentration

Grain Yield ƚ

Variety CR K ST AVG-----umol/gram (@14%)------------

Faller 3.5c 3.9c 8.6b 5.2cGlenn 3.3c 3.1d 5.1c 3.5dMayville 4.6b 5.8a 10.4a 6.6abRB07 3.7c 5.2b 10.1a 6.3bSelect 5.4a 5.2b 10.3a 6.9aVantage 3.1c 3.7c 8.1b 4.9cS Rate (lb/ac)0 3.7 4.7a 9.7a 6.0a7.5 4.0 4.4b 8.6b 5.5b15 4.0 4.3b 8.0b 5.3b

ƚ within columns for each main effect, numbers followed by the same letter are not statistically significant at P<0.05 probablily level.

Table 5. Effect of variety and sulfur rate on asparaginecontent in the wheat grain collected from 2014 at Crookston, Kimbal and Staples. Grain Yield Grain Protein Conc.

Environment Variety Sulfur VxS Variety Sulfur VxS-----------------------P>F-----------------------

Crookston <0.001 ns ns <0.001 ns nsKimball/FF <0.01 ns ns <0.001 ns nsStaples <0.001 0.01 ns <0.001 <0.001 0.05 ƚAll <0.001 0.03 ns <0.001 <0.01 ns

ƚ Grain protein was not increased by S for Fall and RB07.

Table 6. Summary of statistics for the analysis of grain yield and protein concentration across years for each given environment.

S Rate Yield Proteinlb S/ac -bu/ac- -----%-----

0 63.6b 17.7b7.5 67.6a 18.1a15 67.0a 18.3a

Table 7. Summary of Grain Yield and Protein Data collected at Staples from 2014-2016

Soil Test (0-6") ƚ

Location P K SOM pH Nitrate-N ‡

----ppm---- ----%--- --lb/ac--Staples 14 80 1.5 7.3 24

ƚ P. Bray-P1 phosphorus; K, ammonium acetate potassium; SOM, soil organic matter; pH, soil pH.‡ 0 to 2 foot soil nitrate-N

Table 8. Spring soil test averages across replications for Spring wheat N trial

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Figure 4. Effect of nitrogen on grain yield of three hard red spring wheat varieties grown under irrigation.

Figure 5. Effect of nitrogen on grain protein concen-tration of three hard red spring wheat varieties grown under irrigation.

Grain Yield ƚ Grain Protein ƚ Protein Yield ƚLocation V N VxN V N VxN V N VxN

--------------------------------------------------------------P>F-----------------------------------------------------------Staples 0.05 <0.001 0.15 <0.001 <0.001 <0.01 0.02 <0.001 0.17

Table 9. Summary of statistical significance of main effects of variety (V), nitrogen rate (N), and their interaction (VxN) for spring wheat grain yield, protein concentration, and total protein produced per acre during 2016.

Table 10. Summary of hard red spring wheat grain yield, grain protein concentration, and protein production per acre for individual varieties at Staples Minnesota from 2014-2016. Average values were calculated for data across six nitrogen rates and across years (AVG).

Grain Yield ƚ Grain Protein ƚ Protein Yield ƚ2014 2015 2016 AVG 2014 2015 2016 AVG 2014 2015 2016 AVG-----bushels/ac (@13%)-------- -------------% (@ 12%)------------ -------pounds/ac (@13%)---------

Faller 69a 62a 36b 56 16.7b 14.0b 16.4c 15.7c 699a 543b 363b 534Mayville 60c 63a 36b 53 17.8a 14.7a 17.6a 16.7a 646b 577a 387b 538RB07 63b 57b 41a 54 17.7a 14.7a 16.9b 16.4b 667b 527b 424a 539

ƚ within column, numbers followed by the same letter are not statistically significant at P<0.05 probability level.

Ratio of Price N: Price per bushel of spring wheatLocation 0.00 0.05 0.10 0.15 0.20 0.25

---------------lb N/acre------------------------2014 164 148 130 113 95 782015 238 205 171 137 103 702016 98 88 77 67 56 46Average 179 154 131 109 86 64

Table 11. Summary of economic optimum nitrogen rates using the maximum return to N model for irrigated HRSW at Staples, MN in 2014-2016 assuming no discounts.

Grain Yield ƚ Grain Protein ƚ Protein Yield ƚ Flag Leaf %N ƚLocation V N VxN V N VxN V N VxN V N VxN

--------------------------------------------------P>F----------------------------------------------Staples 0.26 <0.001 0.18 <0.001 <0.001 <0.01 0.94 <0.001 0.17 <0.001 <0.001 0.14

Table 12. Summary of statistical significance of main effects of variety (V), nitrogen rate (N), and their interaction (VxN) for spring wheat grain yield, pretoin concentration, total protein produced per acre, and flag leaf N concentration across years (2014-2016).

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Figure 6. Effect of nitrogen on grain protein yield of three hard red spring wheat varieties grown under irrigation.

Figure 7. Effect of nitrogen on flag leaf nitrogen concentration at anthesis of three hard red spring wheat varieties grown under irrigation.

Figure 8. Relationship between flag leaf tissue N concentration for samples collected at anthesis and relative yield and relative protein yield (both expressed as % of maximum) across three wheat varieties for an irrigated soil at Staples, MN.

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Research Questions

Complete resistance to Fusarium Head Blight (FHB) is unavailable, yet genetic variability for resistance is well documented. Steady progress toward increasing resistance levels has been demonstrated by breeding programs through the implementation of largely repeatable FHB screening procedures. Breeding programs must sustain efforts to simultaneously select resistant materials with desirable agronomic characteristics. The objective of this program is to use traditional plant breeding and selection techniques to develop hard spring wheat germplasm and cultivars that possess agronomic characteristics worthy of release in addition to acceptable levels of FHB resistance. Results

Entries retained in the advanced yield trial (AYT) are thought to be at least moderately resistant to FHB. Those that do not perform adequately are generally discarded after the first year of AYT observation. 2016 AYT results are presented in the appendix. Thirty-five experimental breed-ing lines were tested along with thirteen check cultivars dur-ing the 2016 growing season. Of the thirty-five experimental lines, twenty nine had FHB disease index (DIS) values that were less than the test average. Twenty five of the twenty nine entries also had Fusarium damaged kernel (FDK) values that were below average. Among these twenty five, eighteen produced more grain than average, and both test weight and protein content of seven were also better than average. One of these seven, SD4579, is presently being increased for the second year and may be considered for release as a new cultivar in fall 2017. Although slightly low on the scales of test weight and protein concentration, the experimental line SD4465 is also being increased for the second year and may also be considered for release in fall 2017. SD4546 and SD4625 are presently being increased for the first year and may be considered for cultivar release in fall 2018.

Application and Use

With the progression of time, increases in FHB resistance levels should help to prevent devastating loses to growers caused by severe FHB outbreaks.


Focused efforts to increase resistance began within this program after the 1993 FHB epidemic in the spring wheat production region. Both mist-irrigated greenhouse and field screening nurseries were established and disease evalu-ation methods were developed. Breeding materials are evaluated for FHB resistance using three generations per year: two in the greenhouse and one in the field. We have the capacity to screen as many as 4,500 individual hills in

Accelerated Breeding for Resistance to Fusairum Head BlightKarl D. Glover, Plant Science Department, SDSU


the greenhouse. We also have 4 acres in the field under mist-irrigation. Both the field and greenhouse nurseries are inoculated with grain spawn (corn that is infested with the causal fungus) and spore suspensions. Mist-irrigation is used to provide a favorable environment for infection. Approximately 25 percent of the experimental populations possess Fhb1 as a source of resistance. Most of what remains are crosses with various “field resistant” advanced breeding lines. Experimental materials are advanced through the program in the following fashion;Year 1 Field Space planted F2 populationsYear 1 Fall greenhouse F2:3 hillsYear 1 Spring greenhouse F3:4 hillsYear 2 Field F4:5 progeny rowsYear 2 Off-season Nursery F5:6 progeny rowsYear 3 Field F5:7 Yield Trials (1 replication, 2 locations)Year 4 Field F5:8 Yield Trials (2 replications, 5 locations)Year 5 Field Advanced Yield Trials (3 reps, 8 locations)

F2 populations are planted in the field and individual plants are selected. These are advanced to the fall greenhouse where seed from each plant is sown as individual F2:3 hills and evaluated for FHB resistance. Four plants from each of the top 25% of the hills are advanced to the spring green-house. They are sown as individual F3:4 hills and evaluated for FHB resistance. Those with FHB resistance nearly equal to or better than ‘Brick’ are advanced to the mist-irrigated field nursery as F4:5 progeny rows. They are evaluated again for resistance and general agronomic performance. Plants are selected within the superior rows and sent to New Zealand as F5:6 progeny rows for seed increase. A portion of seed from each selected plant is also grown in the fall greenhouse to confirm its resistance. If the FHB resistance of an F5:6 line is confirmed, then the respective progeny row is harvested in New Zealand. In the following South Dakota field season, the selected lines are tested in a two replication, multi-location yield trial. Those that have agronomic performance and yield similar to current cultivars are included in more advanced, multi-location, replicated yield trials the following year. In year 5, lines advanced through this portion of the program are included in the AYT along with entries from the traditional portion of the program. Performance data with respect to DIS and FDK, along with agronomic potential from the 2016 AYT are presented in Table 1 of the appendix. Economic Benefit to a Typical 500 Acre Wheat Enterprise

The presence of FHB inoculum within fields and favorable weather conditions are just two factors that heavily influ-ence whether this disease becomes problematic. Imme-diate economic benefits are therefore difficult to assess. When conditions become favorable for disease presence, however, cultivars with elevated FHB resistance levels can help to reduce potentially serious grower losses.

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Entry FHBDIS

IndexTombstone

(%)Grain Yield

(bu/ac)Test Weight

(lb/bu)Grain Protein

(%)Head Date

(D>6/1)Plant Height

(Inches)

SD4582 11.50 11.17 47.87 62.36 15.21 15.47 37.60

BRICK 12.02 12.83 52.32 62.20 14.71 11.60 34.19

FOREFRONT 12.05 11.67 54.44 61.41 14.88 13.60 35.92

FOCUS 12.09 11.17 52.94 62.24 15.31 11.40 35.76

SD4465 12.20 18.33 54.17 61.36 14.49 15.07 34.11

SD4546 12.76 15.00 53.18 62.15 15.01 11.93 34.01

SD4539 13.11 19.17 54.02 61.07 14.71 17.33 36.05

SD4660 13.32 19.17 46.35 62.30 15.89 16.20 35.11

SD4631 13.39 17.50 51.99 62.33 14.80 16.00 35.82

SELECT 13.40 18.33 52.71 62.12 14.63 11.73 34.03

SD4623 13.58 15.83 46.20 61.79 15.38 16.60 40.22

SURPASS 13.70 16.67 55.55 60.55 14.60 12.60 31.96

SD4595 14.27 17.50 52.81 62.27 14.94 14.93 35.42

PREVAIL 14.29 17.50 58.92 60.92 14.08 13.73 32.30

SD4659 14.30 15.00 47.31 61.78 15.70 16.00 35.63

BOOST 14.36 17.50 52.96 60.27 15.21 18.73 33.77

SD4393 14.60 15.83 52.34 61.44 15.22 15.53 31.83

SD4625 14.98 16.67 55.50 61.83 14.52 17.40 33.12

SD4587 15.17 14.17 51.20 61.91 15.34 15.20 36.71

SD4607 15.40 14.17 50.80 62.54 15.08 15.60 36.37

ADVANCE 15.50 19.17 52.82 61.20 14.03 17.07 30.47

SD4579 15.60 16.67 52.61 61.49 15.11 16.33 34.40

SD4650 16.02 25.83 53.90 60.61 14.64 16.87 35.06

SD4628 16.06 16.67 52.56 61.93 15.35 16.87 35.47

SD4557 16.09 22.50 51.98 60.87 13.83 14.00 32.51

SD4671 16.27 21.67 52.30 60.97 14.56 16.33 33.72

SD4492 16.45 18.33 51.82 61.91 14.58 13.00 34.61

SD4597 16.57 14.17 52.72 62.16 14.15 14.80 36.03

SD4529 16.68 20.00 55.06 61.93 15.30 16.33 36.42TRAVERSE 17.02 30.00 54.31 58.38 13.97 16.00 35.47

SD4681 17.15 18.33 52.74 62.40 14.92 14.53 35.50

BRIGGS 17.28 22.50 51.80 60.94 15.15 12.93 33.43

SD4472 17.52 25.00 49.75 60.47 15.18 14.60 31.26

SD4493 17.92 20.83 52.41 62.06 14.67 14.87 32.15

SD4684 18.01 19.17 51.85 62.50 15.19 13.60 35.13

SD4624 18.16 19.17 55.10 61.78 14.84 16.47 30.89

STEELE-ND 18.50 21.67 50.99 60.85 15.07 16.73 34.66

SD4416 18.58 20.83 53.61 61.16 15.15 15.33 33.25

SD4673 18.73 20.00 51.01 61.73 14.65 11.87 32.93

SD4514 19.10 23.33 53.39 61.75 15.07 17.33 37.73

SD4403 19.25 17.50 53.28 60.84 15.00 17.07 34.66

SD4543 20.27 17.50 51.11 62.18 15.31 15.20 37.13

FALLER 20.88 27.50 49.65 58.95 13.85 18.47 33.96

SD4676 20.92 23.33 51.79 61.43 14.97 17.93 31.96

SD4575 21.27 24.17 52.95 60.80 14.57 17.93 36.65

SD4649 21.31 20.83 54.04 61.50 14.73 15.60 36.60

OXEN 31.08 32.50 52.61 59.43 14.73 15.73 32.15

SD4678 33.00 37.50 51.58 61.17 14.73 19.87 31.26

MEAN 16.70 19.41 52.36 61.42 14.85 15.42 34.49

LSD (0.05) 4.31 5.67 1.90 0.34 0.26 0.82 0.87

cv 25.06 26.89 4.39 1.45 3.05 12.87 5.93

Table 1. South Dakota State University advanced yield trial spring wheat entries ranked according to FHB disease index values (lowest to highest – collected at Brookings) presented along with agronomic data obtained from three replication trials conducted at eight test environments in 2016.

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Research Questions

Crop improvement is predicated on exploiting genetic variation. Without this variation, breeders cannot advance germplasm for any of the important traits of interest to growers. This project seeks to answer the question: what degree can we enhance economically important traits in wheat using diverse germplasm from the USDA Spring Wheat Core Collection?

This germplasm enhancement project is based on nested association mapping (NAM) and was initiated in 2013. It is a long-term and broad-based program that will provide a rich source of genetic diversity for many traits that are or may become important to wheat growers in the region. This includes, but is not limited to: yield, protein content, milling and baking quality, root growth, stand establishment, nitrogen use efficiency, water use efficiency, and disease and insect resistance. Results

Summary of population development activities. Based on single nucleotide polymorphism (SNP) marker data provided by the Triticeae Coordinated Agricultural Project (TCAP), the Spring Wheat Core Collection held by the USDA-ARS National Small Grains Collection was grouped into four subpopulations based on their degree of related-ness. We then selected 409 accessions that represent the greatest genetic and geographic diversity in the Spring Wheat Core Collection. These 409 accessions were desig-nated as the “Spring Wheat Diversity Collection” (SWDC) and evaluated in the field for various traits. As expected, a wide range of phenotypic diversity was observed for many traits in the SWDC. Together with UM wheat breeder Jim Anderson, we winnowed down the 409 accessions of the SWCC to the workable number of 30 based on: a) genetic diversity as assayed by SNP markers, b) desirable pheno-types in field nurseries (i.e. normal heading date, short-stature, good straw strength, disease resistance, etc.), and c) diversity for geographic origin. These 30 Nested Asso-ciation Mapping Parental Selects (NAMPS) were sown in the 2013 fall greenhouse for crossing with cultivar RB07, selected by Jim Anderson as the recurrent parent. In De-cember 2013, the first crosses of the NAMPS were made with RB07 in the greenhouse. All but five of these crosses were successful; thus, we developed a 25-parent NAM population from the accessions listed in Table 1. Crossed seed from these hybridizations were planted in the 2014 winter greenhouse for backcrossing to RB07. This was done to recover more of the superior genetic constitution

Exploiting Genetic Variation for Wheat Improvement in the Northern Great Plains

Brian Steffenson, Dept. of Plant Pathology, U of M


of RB07 since some of the NAMPS are not adapted to the Midwest production region. About 100 BC1 crossed seeds from each cross were obtained and planted in the 2014 fall greenhouse with harvest occurring at the end of December. One arbitrarily selected seed (single seed de-scent) from each of ~2,500 BC1F2 plants was grown in the 2015 winter greenhouse and harvested as BC1F3 seed in April 2015. Another generation advance of this population (harvesting BC1F4 seed) was made during the 2015 spring greenhouse season. To further increase homozygosity in the NAM population and at the same time increase seed for the larger field plots sown at Crookston in 2016, we planted BCIF4 seed in the 2015 fall greenhouse. The BC1F5 seed from these BCIF4 plants were harvested in December 2015 and planted in the 2016 spring green-house. See the timetable and status summary for the proj-ect under the Recommended Future Research section.

Activities in 2016. The BC1F5 seeds harvested from BC1F4 plants in December 2015 were sown in the 2016 spring greenhouse. The BC1F6 seeds from these BC1F5 plants were harvested in March 2016 and prepared for spring planting at Crookston. The final number of lines in the Min-nesota Nested Association Mapping Population (MNAMP) was 2,240. MNAMP was planted on May 16, 2016 at the Northwest Research and Outreach Center in Crookston. Individual lines were planted as paired 4 ft. long rows spaced 24 inches apart. During the course of the season, data were collected on the following agronomic traits: days to heading, plant height, spike length, and number of kernels per spike. Natural infections of several impor-tant wheat diseases occurred in the nursery. Therefore, we also assessed the reaction of each MNAMP line and respective parents to the bacterial leaf streak (BLS) (Xan-thomonas translucens), leaf rust (Puccinia triticina), and stripe rust (Puccinia striiformis f. sp. tritici) pathogens.

A wide range of phenotypic diversity was observed for all of the agronomic traits scored in 2016. First, with respect to the parents, days to heading of the exotic parents ranged from 51 (PI 519465) to 70 (PI 282922) compared to the recurrent parent of RB07 at 50 (Table 1). For plant height, the exotic parents ranged from 58 (PI 519465) to 98 cm (PI 205714) in comparison to RB07 at 79 cm. Spike length for the exotic parents ranged from a low of 4.2 cm (PI 199806) to a high of 10.5 cm (PI 345693) with RB07 at 7.0 cm. For nearly every one of the progeny families in the MNAMP, transgressive segregants (i.e. progeny exhibiting extreme phenotypes that exceed those of the parents) were observed for days to heading, plant height and spike length (Table 2). Moreover, in most every case,

Page 19

the extreme phenotypes occurred in both directions: i.e. fewer and greater days to heading, shorter and taller plant heights, and shorter and longer spikes than the respective parents. The few exceptions where transgressive segre-gants in both directions were not found include MNAMP family #13 for plant height and families #6, #18, #27, and #30 for spike length (Table 2). Transgressive segregants are useful in breeding. For example, if a breeder wanted to increase the length of the spike and hence seed num-ber in the program, he/she might consider selecting for the crossing block transgressive progeny from family #18 with a spike length of 10.5 cm (Table 2), a significant increase over the 7.0 cm spike length of RB07 (Table 1).

In addition to the agronomic traits, we also observed phe-notypic diversity in the parents for reaction to BLS. RB07 is moderately susceptible to BLS giving a reaction of 5 on a 0 to 9 scale (Table 1). PI 278392 is highly resistant (reaction=1) to this disease (Table 1) and can therefore serve as a valuable donor for BLS resistance in its de-rived MNAMP family #15 (Table 2). Other families showed strong transgressive segregation for resistance to BLS and can also serve as sources of resistance.

The NAMPS were grown out in 2015 at St. Paul, and the seed harvested from these plots was sent to the Wheat Quality Laboratory at North Dakota State University under the direction of Dr. Senay Simsek. One striking result from this analysis was the high protein levels observed in some of the parents, several of which had levels exceeding 17.5%. This test will be repeated on the NAMPS grown in 2016.

The MNAMP was hand-harvested using a sickle and then threshed with a Vogel threshing machine during the week of August 22-26. Seed of each line has been cleaned and will then be assessed for test weight, 1000 kernel weight, protein level, etc. In the fall greenhouse 2016, BC1F6 seed was planted for increase. The derived BC1F7 generation seed will be planted and evaluated for various agronomic and disease reaction traits in the field in 2017. To gener-ate molecular markers for mapping traits of interest, we will subject the MNAMP to genotype by sequencing. This service will be performed by the USDA-ARS Regional Genotyping Laboratory in Raleigh, NC, under the direction of Dr. Gina Brown-Guedira. We have already collected tissue from BC1F6 plants and sent them to North Carolina. Application and Use

The germplasm developed from this project will serve as superior, adapted parental material for regional breed-ing programs aiming to enhance wheat for many different traits, including but not limited to yield, protein content, milling and baking quality, root growth, stand establish-ment, nitrogen use efficiency, water use efficiency, and disease and insect resistance. Our first year of field data demonstrated that useful genetic diversity exists in the

MNAMP for yield components, quality traits, and disease resistance.


The MNAMP will be planted again in the field in spring 2017 and assessed for the following traits: heading date, plant height, spike length, number of kernels per spike, and lodging. In addition, we will assess the field reaction of lines to any diseases that develop in the nursery. For the 2017 field trial, the plot size will be larger to generate sufficient seed quantities for milling and baking tests at NDSU. In 2015, the mapping parents were evaluated to the widely virulent African stem rust (Puccinia graminis f. sp. tritici) races of TTKSK (isolate synonym Ug99) and TRTTF inside the Biosafety Level-3 (BSL-3) greenhouse. Significantly, several NAMPS were resistant to either race TTKSK or TRTTF, and one was resistant to both (PI 519465). These results hold great promise for enhanc-ing the resistance of Minnesota wheat varieties to widely virulent African stem rust races. Additionally, several NAMPS possess resistance to the stripe rust and leaf rust pathogens. We will evaluate each of the MNAMP families whose parents exhibit differences in reaction to these rust pathogens. The data from these phenotyping tests will be merged with the genotyping data to provide valuable information on the number and chromosomal location of genes controlling resistance. These data will be valuable for introgressing the resistance traits into the Minnesota breeding program.

The NAMPS have been distributed to other researchers in the region so they can phenotype the germplasm for traits of interest. These cooperators include Ruth Dill-Macky and Madeleine Smith at the University of Minnesota; Francois Marais, Shaobin Zhong & Senay Simsek at North Dakota State University and Karl Glover, Shaukat Ali, and Bill Berzonsky at South Dakota State University. We expect to receive phenotype data from their trials in the near future and will provide seed of any MNAMP families whose par-ents exhibit differences in the target traits under study.


Cultivars bred with one or more of the enhanced traits derived from the MNAMP will increase profitability for wheat producers in the region. The level of economic benefit will depend on the trait considered. Several exotic parents have protein levels exceeding 17%. If higher protein levels can be bred into new cultivars, the premium paid to producers could be substantial. Incorporating new resistance to a disease like BLS also can provide signifi-cant benefits during epidemic years. It is important to note that this pre-breeding project has a longer-term horizon for results. In this respect, it is similar to breeding programs since it will take several years before growers will realize direct economic benefits. continued on pages 20 - 22

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Related Research

To effectively map and transfer genes controlling traits in the MNAMP, genotyping must be done. Currently, geno-typing by sequencing is the best method for generating a sufficiently large number of molecular markers at a reasonable cost. The Minnesota Wheat Research and Promotion Council kindly provided funds for service so we can complete mapping work on select traits in 2017. Other colleagues in the region also have expressed a strong interest in evaluating the MNAMP for specific traits of importance to their programs. They have been sent seed of the NAMPS so they can choose which segregat-ing families to pursue for genetic analyses. We will make public all of the data we have generated in this project via the T3 database administered by USDA-ARS.


Now that the MNAMP is developed, we will evaluate it for many traits of importance to regional wheat producers, including agronomic traits (yield, lodging, etc.), milling and baking quality (flour yield, protein, absorption, mixing time, loaf volume, etc.), and disease resistance (rusts, root rots, bacterial leaf streak, etc.). Other colleagues in the region also have expressed a strong interest in evaluating the MNAMP for specific traits of importance to their programs. We will assist them by providing seed and genotype by sequencing data for their research. To follow the steps and progress of this research, I have provided the following timeline and status update.

Timetable and Status of the Minnesota Nested Association Mapping Population Project

2013 Fall GH: --Plant NAMPS in August-September, make crosses

to RB07 in November and harvest crossed (F1) seed in December. (Status: Completed)

2014 Winter GH: --Plant crossed seed in late December-early January,

make backcrosses to RB07 in March, and harvest BC1 crossed seed in April. (Status: Completed)

--Establish a genetically pure seed increase of the original 25 NAMPS. (Status: Completed)

2014 Summer field: --Plant BC1 crossed seed from each cross combination

in the field at St. Paul in April. (Status: Postponed until fall greenhouse season to ensure no populations are lost due to weather-related calamities).

--Disease and agronomic trait assessments of original 25 NAMPS and RB07. (Status: Completed)

2014 Fall GH: --BC1 crossed seed (generating BC1F1 plants) was

planted from each cross combination in the greenhouse

and harvested in December (represents 1st selfed genera-tion). (Status: Completed)

--Collate and analyze data taken on the NAMPS from the field. (Status: Completed)

2015 Spring GH: --BC1F2 seed was planted (for single seed descent)

and harvested in April (represents 2nd selfed generation). (Status: Completed)

--NAMPS were screened against African stem rust races TTKSK and TRTTF at the seedling stage. (Status: Completed)

--Seed of NAMPS and RB07 were distributed to coop-erators around the region so they can test them for traits of interest. Parents that differ from RB07 for a particular trait can be mapped in the derived MNAMP families. (Status: Pending)

2015 Late spring-Summer GH: --BC1F3 seed planted in April and harvested in July and

August (3rd selfed generation). (Status: Completed)--Collate all data collected on the NAMPS and RB07 by

our cooperators and by us. (Status: Pending)

2015 Summer Field --Disease and agronomic trait assessments of original

25 NAMPS and RB07. (Status: Completed)

2015 Fall GH: --Plant BC1F4 seed in greenhouse in August-September

and harvest in December. (4th selfed generation) (Status: Completed)

--Test NAMPS and RB07 for leaf rust reaction at the seedling stage. (Status: Completed)

2016 Winter GH: --Plant BC1F5 seed in greenhouse in January and har-

vest in March (5th selfed generation). (Status: Completed)

2016 Summer field: --Plant BC1F6 lines of the MNAMP (and parents) at

Crookston and obtain year 1 phenotype data from the field. (Status: Completed)

--Collate all data collected on the MNAMP and parents by our cooperators and by us. (Status: Pending)

2016 Fall GH: --Plant BC1F6 seed, extract DNA from seedlings, and

send to USDA-ARS for genotype by sequencing (Status: Completed)

--Plant NAM population (and parents) segregating for various traits and obtain first experiment phenotype data from the greenhouse. (Status: In progress)

2016 Winter GH:--Analyze genotype by sequencing data for the MNAMP. --Plant MNAMP (and parents) segregating for various

traits and obtain second experiment phenotype data from the greenhouse.

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2017 Summer field: --Plant MNAMP (and parents) at Crookston and obtain

year 2 phenotype data from the field. --Collate all data collected on the NAM population (and

parents) by our cooperators and by us.

2017 Fall: --Analyze data.--Identify and distribute advanced lines with enhanced

traits to regional breeders for crossing in their programs.--Write up manuscripts for publication.--Continue evaluations of derived materials until variety

candidates are identified.

2017-2019: --Continue evaluation of the MNAMP for other traits of

interest to regional producers.--Distribute complete MNAMP to cooperators who are

interested in exploiting specific traits important to their programs.

--Conduct mapping studies of additional traits in the MNAMP.

--Conduct validation studies of identified genes in ad-vanced populations of the Minnesota wheat improvement program.

--Continue evaluations of derived materials until variety candidates are identified.

LID Accession Origin Days to Heading

Plant Height

Spike Length Protein % BSL

3 CItr 14819 Eritrea 55 80 8.5 15.2 44 CItr 15006 Nepal 56 89 9.0 19.9 65 PI 62364 Venezuela 55 79 8.2 16.1 56 PI 153785 Brazil 55 92 9.2 13.8 68 PI 181458 Finland 70 70 7.0 15.3 49 PI 189771 Tunisia 55 86 7.7 15.6 410 PI 193938 Brazil 57 87 8.0 16.3 411 PI 199806 Peru 56 64 4.2 17.6 512 PI 205714 Peru 63 98 8.7 18.7 513 PI 213602 Argentina 55 66 6.0 18 314 PI 220455 Egypt 61 69 6.0 17.3 315 PI 278392 Palestine 59 75 7.2 16.7 116 PI 282922 Argentina 70 76 8.0 16.9 717 PI 344018 Angola 57 71 6.5 17.6 318 PI 345693 Belarus 57 97 10.5 15.9 520 PI 374254 Mali 60 75 8.0 14.4 321 PI 384403 Nigeria 55 73 7.0 15.4 422 PI 430750 Yemen 53 69 9.5 14.7 223 PI 449298 Spain 52 70 9.0 14.6 324 PI 519465 Zimbabwe 51 58 8.0 18 425 PI 519580 Chile 53 74 7.0 15.2 426 PI 520033 Kenya 54 81 7.0 17.6 427 PI 520371 Syria 51 71 10.0 15.4 629 PI 565238 Bolivia 51 74 8.5 15.3 530 PI 623147 Iran 55 86 5.5 16.6 5P1 RB07 USA 50 79 7.0 15.8 5

Table 1. Data for days to heading, plant height, spike length, protein level and bacterial leaf streak (BLS) reaction for the Minnesota Wheat Nested Association Mapping parents.

LID=Laboratory ID number. Accessions are designated by Cereal Investigation numbers for Triticum or Plant Introduc-tion numbers. Origin=Country of origin. Days to heading were the number of days from planting to when 50% of spikes in the row were half-emerged from boot. Plant height & spike length were measured in centimeters. Protein content data were provided by Senay Simsek at North Dakota State University A 0-9 scale was used for assessing the reaction of lines to bacterial leaf streak where 0=most resistant and 9=most susceptible.

Appendix

Page 22

Table 2. Mean, range and standard deviation for data on days to heading, plant height, spike length and bacterial leaf streak (BLS) reaction in families of the Minnesota Wheat Nested Association Mapping Population.

Family # Days to Heading Plant Height Spike Length Bacterial Leaf StreakMean Range Std Mean Range Std Mean Range Std Mean Range Std

3 53.5 49-75 5.5 80.8 43-57 8.03 7.6 5.5-10.0 0.9 2.7 1-6 1.24 51.8 43-72 4.6 78.6 59-98 8.2 7.5 5.2-9.2 0.9 3.2 1-7 1.45 52.4 49-69 4.6 77.3 62-102 8.04 7.3 5.5-9.0 0.7 3.1 1-6 1.16 53.3 48-72 4.5 82.7 65-107 8.1 7.8 6.7-9.2 0.6 2.6 0-7 1.28 54.4 48-65 3.3 80.1 55-106 10.1 7.2 5.5-9.5 0.7 2.6 1-5 0.99 51.9 48-59 2.9 79.2 51-102 9.3 7.6 6.3-9.5 0.7 2.6 1-5 0.9

10 52.3 49-70 4.1 81.4 66-104 8.1 7.3 5.5-9.2 0.7 2.5 1-5 0.911 52.3 49-68 4.3 75.5 54-99 8.1 6.7 3.0-9.7 1.7 3.1 1-8 1.312 52.7 48-71 3.8 76.9 63-99 8.1 7.3 5.5-9.2 0.7 3.2 1-6 1.113 53.4 49-68 3.7 77.3 66-103 7.1 7.1 5.2-8.2 0.6 2.9 1-7 1.414 54.1 49-70 4.1 77.5 57-104 10.7 7.2 5.7-9.0 0.7 3.1 1-7 1.215 51.7 48-75 4.3 76.4 60-92 6.5 7.1 5.5-9.0 0.7 3.2 1-7 1.316 52.6 47-70 3.2 77.4 59-102 8.4 7.4 5.5-9.0 0.7 2.4 1-7 1.117 54.4 48-70 6.2 75.8 54-97 8.1 7.2 5.5-9.2 0.7 2.8 1-6 1.118 52.9 49-65 2.7 81.3 65-112 8.7 7.7 6.0-10.5 0.8 2.8 1-6 1.220 51.8 49-60 2.8 76.1 43-108 8.7 7.2 4.5-9.0 0.8 3.8 0-9 1.621 50.5 48-66 2.8 77.5 58-100 8.5 7.3 5.3-9.0 0.7 4.1 2-9 1.522 54.2 48-69 2.8 77.1 44-113 14.1 7.8 6.2-9.2 0.6 3.3 1-7 1.223 51.8 48-67 2.8 73.3 58-85 5.1 7.4 5.5-10.0 0.7 2.7 1-5 1.124 52.4 48-64 2.8 67.6 55-83 7.1 7.1 5.5-9.0 0.8 2.9 1-6 0.925 52.9 49-69 2.8 78.3 60-105 8.1 7.5 5.7-9.0 0.7 2.9 1-6 1.026 52.1 49-65 2.8 69.7 53-86 8.6 6.7 4.7-9.0 0.7 2.8 1-7 1.127 53.1 48-57 2.8 77.1 51-106 11.7 7.3 5.7-10.0 0.8 2.7 1-6 1.129 51.5 48-62 2.8 79.5 63-101 7.4 7.4 6.0-9.5 0.7 3.1 1-7 1.230 53.2 48-65 2.8 81.1 62-103 9.4 7.3 5.5-9.0 0.8 3.1 1-6 1.1

Family number refers to the exotic parent (see respective numbers in Table 1) used in the cross with RB07. For ex-ample, family #18 was derived from the cross between PI 345693 and RB07. The mean (average), range (highest and lowest values) and standard deviation (amount of variation in dataset) are provided for data on the four assessed traits within each family. Days to heading were the number of days from planting to when 50% of spikes in the row were half-emerged from boot. Plant height & spike length were measured in centimeters. A 0-9 scale was used for assessing the reaction of lines to bacterial leaf streak where 0=most resistant and 9=most susceptible.

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Research Questions

A continuous pipeline of new, higher yielding hard red spring wheat (HRSW) varieties with tolerance to biotic and abiotic stresses is needed to sustain viable spring wheat production in the region. Developing new spring wheat cultivars is a costly endeavor and requires the evaluation of thousands of lines for every variety that is released. Not only is the process of selection time consuming and expensive, it is also not an exact science and the possibil-ity of discarding genotypes that could become highly suc-cessful varieties is quite high. Our research question is; can the use of canopy spectral reflectance, and other indi-ces obtained from vehicle-mounted sensors help breeders to select genotypes with beneficial characteristics, such as superior yield and stress tolerance. Results

Prior to and shortly after flowering, data from the sensor suite described below were collected from the four most advanced yield trials planted by the NDSU spring wheat breeder at two locations in North Dakota. We have not yet completed the analysis of the data, as it requires multiple steps, and we are still learning the geographic information system (GIS) procedures that are essential to the research in software such as ArcMap and Quantum GIS. The pri-mary result that we can report at this stage is that the plat-form designed for gathering the data seems to be working, that the system of data collection is also functional, and that researchers are now trained in how to best implement this type of research from several national training meet-ings and conferences.

Application and Use

The primary application of this research will be to assist breeders in identifying genotypes that may have desir-able traits: such as high yield, maturity, stalk strength, and stress tolerant. Once we completely analyze the data, we will have a better idea on how well some of the measurements predict the performance of advanced lines and will work towards integrating some of these sensor-based phenotyping measurements into earlier stages of the HRSW breeding program with the intent of improving selection efficiencies.

Using Sensors for Phenotyping and Assisting in Selection in Spring Wheat

Joel Ransom, Dept. of Plant Sciences, NDSU



A suite of sensors developed by Holland Scientific (Lin-coln, NE, USA) and used by other breeding programs in the USA, was chosen for use in this project. The system is a combination of active and passive sensors consisting of a three-band active optical sensor, a multi-parameter data acquisition sensor and geospatial data logger. The active optical sensor will provide measurements for red, red-edge and near infrared reflectance, red and red-edge normalized difference vegetation indices (NDVI and NDRE) and estimation models for leaf area index (LAI), plant canopy chlorophyll content (CCC) and optical sen-sor-to-plant distance (plant height). The multi-parameter sensor will provide measurements for passive upwelling and down-welling photosynthetic active radiation (PAR), passive temperature for both canopy and ambient air, humidity and atmospheric pressure. These sensors were mounted on a four-wheeler and connected to a Trimble RTK system so that all measurements were georefer-enced. Data were collected from four advanced yield trials, replicated at Casselton and Prosper, North Dakota. Data from the sensors will be correlated to yield and protein data from these trials.


At this stage, this research will have no direct economic benefit at the farm level. If this system of phenotyping ad-vanced lines proves to be helpful to breeders, the number of lines that are identified, released, and used by produc-ers, will determine the value of this research.

Related Research

This is a topic of intense interest in the breeding and ge-netics research circles. There are a number of programs using this type of methodology.


We need an additional year to gain the expertise and experience to effectively evaluate the current platform as to its future usefulness. No significant changes in our research approach is proposed for the next year. Once this first year’s data analysis is completed, we will be able to move through data analysis much faster, allowing for practical applications to the HRSW breeder in-season.

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Research Questions

Applied breeding for superior spring wheat cultivars remains the focus of the North Dakota State University breeding program. A higher proportion of selection pres-sure is placed on environments in eastern North Dakota, and western Minnesota. The primary objectives for the breeding program include:

- High grain yield and balanced end-use quality to maintain international competitiveness as a spring wheat market class.

- Strong straw and shorter plant height to withstand higher input management, and higher rainfall environ-ments common to the target region.

- Maintenance of high levels of resistance to Fusarium head blight, and work to integrate both Type I (resistance to infection) and Type II resistance (resistance to infection spread throughout the head) in our germplasm.

- Durable resistance to leaf diseases, including leaf rust, stripe rust, and bacterial leaf streak.

Results

The experimental line ND825 has been approved for pre-release. The line is half ‘Glenn’ by parentage, and pos-sesses high levels of FHB resistance, but is shorter than Glenn, with stiffer straw. It has slightly lower yield potential than high yielding cultivars like ‘Faller’ and ‘Prosper’, but it has shown very strong grading characteristics in experi-mental testing, with consistently high grain protein, grain virtuousness, and test weight. A release decision will be made in January 2017. Seed has been increased and will be available, if released.

In 2016, around 600 breeding populations were evaluated and advanced through phenotypic selection in eastern North Dakota. 150-200 crosses were made targeting eastern ND and western MN during the spring crossing cycle, and around 300 are expected during the current greenhouse crossing cycle. These crosses were made with goals of shorter plant height, greater straw strength, FHB resistance, and high end-use quality.

Funds provided also helped to supplement the shuttle breeding program for early generation materials. Our program currently grows segregating populations in Yuma, Arizona, Christchurch, New Zealand, and Puerto Rico. Our winter nurseries are bigger this year than ever in the past.

Funds provided also supported two yield trials of elite breeding material at Alvarado and Wolverton, MN. Results

Developing Adapted Spring Wheat Cultivars to Better Serve MN Wheat Growers

Andrew Green, Department of Plant Sciences, NDSU

6 2016 RESEARCH REPORT

from these trials, as well as three locations in eastern ND, are presented in the Appendix.

Application and Use

Information collected from line and variety testing can be used in two primary ways. First, data is used in the breed-ing program to make selections which are targeted for the geographic region of interest. Secondly, the published data from state variety trials in eastern North Dakota can be used for variety selection by producers. Publicizing data can also be of use to fellow researchers as they ask and answer relevant agronomic questions about current variety offerings.


Early generation materials - Segregating populations are currently all grown at two locations in Cass County, ND. Beginning in 2017, three segregating generations will be grown exclusively in a winter shuttle program. This will greatly expedite the creation of new experimental lines.

Yield testing - Elite yield trials are grown at seven loca-tions in North Dakota, and two locations in Minnesota.

Disease screening- All experimental lines are screened for resistance to stem rust and leaf rust. All lines and advanced segregating populations are also screened in a misted, inoculated Fusarium head blight nursery. Oppor-tunistic notes for other diseases and abiotic conditions are also recorded.

Uniform nursery - The breeding program submits materi-als to the USDA Uniform Regional Nursery, and Uniform Regional Scab Nursery. Reports from these nurseries are available publically. Our program grows three locations of the Uniform Regional Nursery.

Quality evaluation A great strength of our program lies in our cooperation with the NDSU Wheat Quality Lab, led by Dr. Senay Simsek. Following is a summary of quality tests, by year in the breeding program.

Preliminary Yield Trial - Grain Protein, Test WeightIntermediate Yield Trial - Grain Protein, Test Weight, MixographAdvanced Yield Trial - Grain Protein, Test Weight, Mixograph, Mill and BakeElite Yield Tria l- Grain Protein, Test Weight, Mixograph, Farinograph, Mill and Bake

Page 25

Our goal is to release high quality varieties which consis-tently grade well, to protect producers from discounts. Par-ticularly for our target geographic area our goal are lines with consistently high grain protein, even in years with ex-ceptional yield potential. Maintaining and improving upon the milling and baking quality of released varieties will help protect the integrity of the market class for all spring wheat producers. High test weight and grain protein, along with consistent yield potential, helps to manage risk at the point of sale.

Averages for preliminary quality data are included in the Appendix data for the MN and ND locations.


Acreage of NDSU developed varieties is down in relation to previous years, due increased acreage of quality of-ferings from the University of Minnesota breeding pro-gram, like Linkert and Bolles. Still, an estimated 16.74%

Appendix

of the MN wheat acreage was sown to NDSU varieties in 2016. Based on an estimated farm value of $4.50 per bushel, this represented a value of over $1 million. Hav-ing a strong testing program in the target region ensures that producers have competitive choices which are well adapted to their area.

Perhaps more importantly, the use of NDSU developed germplasm and testing results should help other public breeding programs, like University of Minnesota. In past years, germplasm exchange has been limited, but it is the goal of our program to work cooperatively with other breeding programs in the region, particularly the public programs.

Related Research

Agronomic data and preliminary quality data for eastern ND Red River Valley testing sites from the ND State variety trial are presented in the Appendix.

Entry Plant Height Test Weight Grain Yield Grain Protein Mixographin. lbs/bu bu/ac % 1-9

Barlow 30.4 62.1 55.1 14.8 5 Bolles 29.8 59.9 48.7 15.6 5 Elgin-ND 30.6 60.1 46.7 14.4 4 Faller 29.8 60.4 52.1 14.0 3 Glenn 30.6 62.9 40.3 14.5 5 Prosper 30.4 60.7 54.2 13.9 3 SY Soren 26.3 59.3 43.5 14.7 4 ND804 29.4 62.1 53.5 14.4 5 ND827 32.0 61.6 50.0 14.4 3 ND828 32.1 62.6 49.6 15.5 5 ND829 31.6 61.6 49.8 14.8 3 ND830 30.0 62.4 49.4 15.0 5 ND831 29.5 63.4 44.5 15.2 5 ND832 31.1 63.0 45.8 14.8 5 ND833 30.1 62.9 47.5 15.3 5 ND834 34.8 60.8 41.3 15.6 6 NDHRS16-12-12 34.0 62.3 45.0 15.2 5 NDHRS16-12-13 32.0 62.8 52.0 15.3 6 NDHRS16-12-16 30.0 62.6 56.8 14.2 5 NDHRS16-12-17 29.0 61.8 51.0 15.0 5 NDHRS16-12-18 31.0 63.0 42.6 14.7 5

Table 1. 2016 Elite Yield Trial, averaged across MN locations at Alvarado and Wolverton.

Table continued on page 26

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NDHRS16-12-19 31.6 62.0 52.3 14.9 4 NDHRS-16-12-22 33.8 61.7 49.0 14.6 5 NDHRS-16-12-24 34.8 61.1 44.6 14.9 7 NDHRS-16-12-25 31.5 63.3 42.3 14.1 6 NDHRS-16-12-26 31.5 63.2 42.4 14.6 5 NDHRS-16-12-27 32.6 63.4 44.5 14.3 5 NDHRS-16-12-28 31.1 63.5 42.8 14.5 5 NDHRS-16-12-29 30.1 61.0 47.3 14.8 5 NDHRS-16-12-31 31.5 62.9 51.3 14.3 5 NDHRS-16-12-32 33.1 61.6 36.0 14.5 5 NDHRS-16-12-33 31.4 62.2 44.5 14.9 4 NDHRS-16-12-34 32.4 61.6 46.7 14.5 5 NDHRS-16-12-35 30.9 60.8 45.7 15.0 3 NDHRS-16-12-36 31.3 61.0 49.5 14.6 5 NDHRS-16-12-37 31.6 62.2 50.4 14.3 3 NDHRS-16-12-38 33.3 62.9 53.8 14.6 6 NDHRS-16-12-39 31.9 59.1 49.7 15.3 6 NDHRS16-12-4 32.4 61.3 48.2 15.0 4 NDHRS-16-12-41 31.1 61.9 47.6 14.6 4 NDHRS-16-12-42 34.3 61.2 50.4 15.8 5 NDHRS-16-12-43 30.8 63.2 41.7 15.0 6 NDHRS-16-12-44 31.8 63.2 46.1 14.5 7 NDHRS-16-12-45 30.8 61.2 42.5 14.9 6 NDHRS-16-12-46 33.3 61.4 43.9 14.5 5 NDHRS-16-12-47 29.5 61.4 38.7 14.3 5 NDHRS-16-12-48 31.4 60.3 38.0 14.6 5 NDHRS-16-12-49 32.8 62.9 41.4 15.6 5 NDHRS16-12-5 30.3 62.3 44.6 14.7 5 NDHRS-16-12-51 30.1 62.3 52.0 15.3 5 NDHRS-16-12-52 30.8 61.3 47.4 15.9 5 NDHRS-16-12-53 32.8 63.2 41.0 15.5 5 NDHRS-16-12-54 32.3 63.4 48.6 14.6 6 NDHRS-16-12-55 32.0 62.0 45.4 15.1 7 NDHRS16-12-6 28.4 59.1 45.1 14.9 5 NDHRS16-12-7 29.8 60.6 44.3 15.0 4 Average 31.3 61.8 46.8 14.8 4.6

Entry Plant Height Test Weight Grain Yield Grain Protein Mixographin. lbs/bu bu/ac % 1-9

Table 1 continued

Page 27

Entry Plant Height Test Weight Grain Yield Grain Protein Mixograph in lb/bu bu/ac % 1-9Barlow 32.6 61.5 64.0 14.7 4 Bolles 31.4 59.7 56.7 16.5 6 Elgin-ND 33.8 60.1 61.8 14.4 4 Faller 32.4 60.8 67.8 13.5 2 Glenn 33.4 62.9 53.9 14.7 4 Prosper 33.0 60.7 67.0 13.8 3 SY Soren 29.9 59.3 54.4 14.4 4 ND804 31.9 61.9 64.6 14.3 4 ND827 34.5 61.8 64.3 14.5 4 ND828 33.7 62.4 61.0 15.3 4 ND829 34.3 61.7 62.4 14.9 3 ND830 31.8 62.2 59.7 15.1 5 ND831 32.7 63.0 56.1 15.3 5 ND832 33.8 63.1 56.7 15.0 5 ND833 32.4 62.9 59.2 15.3 4 ND834 36.5 60.2 47.7 15.6 7 NDHRS16-12-12 36.1 62.1 57.0 15.1 5 NDHRS16-12-13 33.8 62.4 60.8 15.2 6 NDHRS16-12-16 32.7 62.1 66.7 14.0 4 NDHRS16-12-17 32.0 61.7 63.1 14.3 4 NDHRS16-12-18 33.6 63.0 54.6 14.4 6 NDHRS16-12-19 33.7 61.9 64.4 14.4 3 NDHRS-16-12-22 35.9 61.3 59.2 14.5 4 NDHRS-16-12-24 36.2 60.6 54.2 15.2 5 NDHRS-16-12-25 34.1 63.0 54.2 14.4 6 NDHRS-16-12-26 34.2 63.0 54.5 15.0 6 NDHRS-16-12-27 34.4 63.0 55.6 15.1 6 NDHRS-16-12-28 33.2 63.2 53.9 15.0 6 NDHRS-16-12-29 33.3 61.0 60.8 15.1 5 NDHRS-16-12-31 33.5 62.6 62.1 14.5 5 NDHRS-16-12-32 35.4 61.5 51.3 14.8 5 NDHRS-16-12-33 33.8 62.3 58.5 15.2 4 NDHRS-16-12-34 34.6 61.7 62.1 14.3 4 NDHRS-16-12-35 33.5 60.8 60.3 15.0 2 NDHRS-16-12-36 33.5 60.9 63.4 15.0 4 NDHRS-16-12-37 33.8 61.9 60.7 14.9 5 NDHRS-16-12-38 35.3 62.6 63.0 14.8 5 NDHRS-16-12-39 33.9 59.5 61.3 15.9 6 NDHRS16-12-4 34.8 61.4 61.9 15.2 4

Table 2. 2016 Elite Yield Trial, averaged across three eastern North Dakota locations.

Table continued on page 28

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Table 2 continued

NDHRS-16-12-41 33.2 61.7 59.4 14.6 3 NDHRS-16-12-42 36.4 60.9 58.2 15.9 4 NDHRS-16-12-43 33.7 63.2 55.7 14.8 5 NDHRS-16-12-44 33.9 63.1 56.8 14.6 5 NDHRS-16-12-45 33.7 61.2 53.2 15.1 6 NDHRS-16-12-46 36.1 61.2 54.6 14.9 5 NDHRS-16-12-47 32.7 61.9 58.1 14.5 5 NDHRS-16-12-48 33.9 60.6 51.1 14.8 5 NDHRS-16-12-49 34.4 63.1 55.0 15.7 5 NDHRS16-12-5 32.5 62.2 61.4 14.7 4 NDHRS-16-12-51 32.7 61.9 60.1 15.4 4 NDHRS-16-12-52 32.6 61.7 61.2 16.3 4 NDHRS-16-12-53 35.4 63.1 52.0 15.5 6 NDHRS-16-12-54 33.7 63.2 58.6 14.9 5 NDHRS-16-12-55 34.3 61.9 54.5 15.1 5 NDHRS16-12-6 30.9 59.3 62.2 15.0 4 NDHRS16-12-7 31.4 60.5 55.7 14.9 4 Average 33.7 61.8 58.7 14.9 5

Entry Plant Height Test Weight Grain Yield Grain Protein Mixograph in lb/bu bu/ac % 1-9

On a 1-9 scale, with higher numbers indicating superior baking quality. For these locations in 2016, Faller scored 2, Glenn scored 4, and Bolles scored 6.

Measured in bushels per acre. Faller averaged 52.1, Glenn averaged 42.3, and Bolles averaged 48.7.

0.0

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Frequency Distribution for Mixograph at 3 Eastern ND locations, 2016 Elite Yield Trial

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Frequency Distribution for Average grain yield across two MN locations, 2016 Elite Yield Trial



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Research Questions

A new NDSU HRWW breeding program was started in 2011. Current hard red winter wheat germplasm is gen-erally lacking in resistance to major diseases such as Fusarium head blight, leaf rust, stem rust, bacterial leaf streak, tan spot and Septoria nodorum blotch. In the past five years, useful resistance has been transferred from many diverse sources of spring wheat and less winter-har-dy winter wheat. The acquired genes occur mostly singly in diverse introgression lines that will now be utilized for the systematic development of inbred lines with broader resistance spectra and superior winter survival.

Gene pyramiding is being attempted at two levels: First, very specific pyramids are being constructed. The pri-mary aim is to attain complex FHB or rust resistance in winter-hardy backgrounds (primarily Jerry and Norstar). Such plants are being used as cross parents to rapidly disseminate multiple resistance genes coupled with cold-hardiness into the breeding population; however, they are unlikely to directly produce commercially useful geno-types. Second, more broad-based pyramids are also being pursued. These are based on annual characterization of the previous seasons’ cross progeny by using molecular markers, bio-testing and yield assessment (field). Prom-ising and more complex gene combinations involving a broader range of pests are being identified and follow-up crosses are made to produce still better combinations. These crosses aim to establish a broader base of back-ground variability and have stronger potential for direct production of commercially useful genotypes.

The best pyramided genotypes from both attempts are annually employed as parents to produce 600-700 cross combinations in the general pedigree breeding program.

Results

Specific (narrow genetic base) gene pyramids Confirmation of earlier derived 2-gene pyramids: SSD11M221-24-1 (= RWG10/Jerry), 14K456-K-1 and 14K-456-L-5 (= CM82036/2* Jerry) were found to contain both Fhb1 and Qfhs.ifa-5A, while DH172 (=CM82036/Jerry) contains only Fhb1. Phenotypic results showed that the four lines have significantly reduced severity of infection, vomit toxin accumulation and FDK as compared to susceptible controls. Lines with both Fhb1 and Qfhs.ifa-5A performed better than lines with Fhb1 only. 14K456-K-1 was the most resistant and comparable to the spring donor parent, CM82036. Lines SSD11M228-19-1 and SSD11M228-57-2 carry resistance from PI277012 (QT-

L5AS and QTL5AL) with SSD11M228-57-2 being the more resistant of these two winter habit lines. However, it is not clear whether one or both genes had been transferred.

Pyramiding of Fhb1, Qfhs.ifa-5A, QTL5AS and QTL5AL: Cross 15K353 was made and 406 F2 plants tested for the presence of Fhb1 and Qfhs.ifa-5A. Fhb1 could be detected with marker Umn10, while each of markers Gwm304, Gwm293 and Barc186 could be used to detect Qfhs.ifa-5A. Neither the QTL5AS markers nor the QTL5AL markers appeared to be suited for marker selection. As a result, progeny phenotyping will be done in an attempt to select genotypes with pyramided resistance QTL from PI277012. Also, it is not clear from the published data whether one of the genes may be the same as Qfhs.ifa-5A. In total, 69 Fhb1 homozygous lines were selected, including 19 Fhb1, Qfhs.ifa-5A homozygotes; 33 Fhb1 homozygotes segregating for Qfhs.ifa-5A; and 17 Fhb1 homozygotes. Four F2:3 progeny of each of the plants in the two homozygous classes were raised and leaf sam-ples of each were cut for single nucleotide polymorphism (SNP) analysis and detection of the 1BL.1RS transloca-tion. Upon ripening the plants were harvested separately. The 136 F4 families thus derived will now be subjected to FHB evaluation utilizing 15-20 plants per family. The over-all means of the Qfhs.ifa-5A (+) and Qfhs.ifa (-) groups will be compared to evaluate the effect of this QTL. Similarly, the 1BL.1RS (+) means will be compared with 1BL.1RS (-) means to determine whether the results were affected by its segregation. Finally, the within family variation and mean values will be compared to search for evidence of the segregation of additional FHB resistance QTL and an attempt will be made to find evidence of co-segregation with mapped SNP loci.

Pyramiding Fhb1 with a 3A QTL from the spring wheat Frontana: The F1:15M16 (= Norstar-Fhb1//Frontana/Norstar) heterozygous for Fhb1 and QTL3A was obtained and has been planted for derivation of F2. Approximately 100-200 F2 plants will be typed for Fhb1 and ±25-50 homozygotes will be identified; these will then be charac-terized with respect to absence/presence of QTL3A.

Pyramiding Fhb1 & Qfhs.ifa-5A with Fhb6: The F1:14M7 (= Fhb6/Jerry//Accipiter) was marker tested to derive F1 suitable for continued marker-aided selection of Qfhs.ifa-5A. The selected plants were crossed with 14K456-K-1 (Fhb1 and Qfhs.ifa-5A). The hybrid progeny will be used to establish populations for the recovery of the desired pyramid.

Pre-breeding of HRWW to Achieve Multiple Disease ResistanceG. Francois Marais, Dept. of Plant Services, NDSU



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Pyramids involving durable (slow-rusting) leaf rust resistance genes: 414 new doubled haploids derived from eight crosses were increased and field planted (fall 2016) in un-replicated plots or single rows. Marker data suggest-ed that 18 lines have Fhb1 in combination with the durable leaf rust resistance genes Lr34, Lr46 and Lr68; some lines have Fhb1 plus two (38 lines) or one (21 lines) durable resistance gene(s). 53 lines have the three durable resis-tance genes but not Fhb1. The lines will be evaluated for cold hardiness and phenotype and the best will be used for continued field evaluation, new crosses and pyramid-ing.

Broad genetic base pyramids: New (2016) crosses for continued gene pyramid-ing: Ten new F1 combinations were produced and supplied to Heartland Plant Innovations for the development of 400 doubled haploids. Twenty six new F2 populations were produced for the eventual development of 350-400 new single seed descent (SSD) inbred lines. These crosses were intended to produce new combinations of resistance genes in winter-hardy, well adapted genetic backgrounds. Each cross involved an FHB resistant parent with 1-2 of the resistance QTL Fhb1, Qfhs.ifa-5A, and the PI277012-derived 5AS and 5AL QTL. The second parent was chosen to have resistance to one or more pests (leaf rust, stem rust, stripe rust, wheat stem sawfly, bacterial leaf streak). 2015 Crosses: F2 and F3 from an additional 26 crosses were used to initiate single seed descent (SSD) inbreeding. The material was first subjected to screen-ing with mixed leaf and stem rust inoculum. The more resistant seedlings were transferred to a greenhouse and evaluated for FHB resistance. Random F3 and F4 plants were increased and 396 rows and plots were planted in the field for evaluation of phenotype and cold-hardiness. 31 of the lines are also being analyzed with markers to detect known, major resistance loci. The inbred lines that will be derived from the above crosses will be selected to obtain subsets of new breeding parents having more complex resistance com-binations. The best combinations from each annual cycle will be used in further crosses to derive still more complex pyramids.

Use of gene combinations in the breeding program crossing blocks: The overall purpose of these crosses is to initially establish Fhb1 as the baseline FHB resistance in the crossing block and to systematically add to it additional FHB, rust, leaf spot, bacterial leaf streak and wheat stem sawfly resistance. 22 parental lines with moderate to very good winter-hardiness and one or more of the FHB resis-tance QTL Fhb1, Qfhs.ifa-5A and the PI277012-derived 5AS and 5AL QTL were crossed with 46 F1, inbred lines and cultivars that varied widely in terms of FHB resistance, cold tolerance, known and unknown leaf and stem rust

resistance genes. 662 crosses were made. The F1 was increased and the F2 field planted for selection in 2017. Five newly identified lines with broad resistance to tan spot and Septoria nodorum blotch; 14 lines with stripe rust resistance and 5 lines with broad stem rust resistance were incorporated for the first time in crosses involving cold-hardy parents with some level of FHB resistance. Application and Use

The accumulation of multiple favorable genes for disease resistance, yield, adaptation and processing quality in a breeding population is a formidable task that is only achieved through decades of un-interrupted, meticulous planning; strict phenotypic and statistical evaluation and selection. A well established, successful breeding program is built on an extensive base of phenotypic and genetic data and highly productive elite germplasm. Not surpris-ingly, such breeding populations are extremely valuable and well-guarded. The new NDSU HRWW program is only in its sixth year of development and clearly will require many more years of genetic improvement before it can be fully commercially competitive. To speed up introgression and genetic improvement, optimal use should be made of modern breeding tools such as accelerated inbreeding and in particular, marker-facilitated selection. The genetic material and gene pyramids that are being pursued here are absolutely necessary to ensure that the breeding effort will reach commercial competitiveness within a reasonable period of time.


No pre-determined procedure is followed for gene pyra-miding as each set of target genes and donor parents presents a unique situation in terms of crossing strategy and marker/bio-test application. In addition, a pyramiding scheme often needs to be modified as new results be-come available. Standard plant breeding methodologies (convergent- and backcrosses; doubled haploid production and modified (with selection) single seed descent inbreed-ing) are primarily being followed in the pyramiding at-tempts. Molecular marker characterization (DNA extraction and use of marker systems such as microsatellite, SCAR, EST, SNP, etc.) is an integral part of the pyramiding at-tempts. Phenotypic evaluations are being done including seedling leaf and stem rust resistance screening, green-house FHB type II resistance screening; seedling resis-tance to tan spot and Septoria nodorum blotch. Phenotyp-ing is necessary in cases where no appropriate markers are available or it is used to confirm marker results. Annual field yield trials and processing quality assessments are being done as appropriate. Naturally occurring diseases and response to stresses such as winter damage are recorded in field trials and provide valuable information on genetic background differences among pyramids.

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The disease-causing pathogens targeted in the project an-nually cause significant wheat yield losses in the Northern Great Plains and even modest changes in the average level of resistance in new cultivars will be of considerable benefit to producers. The targeted diseases include some that are notoriously difficult to breed resistance for (for ex-ample tan spot, bacterial leaf streak, SNB and FHB) since resistance/insensitivity is based on numerous quantitative trait loci each making only a small contribution to the total resistance phenotype.

The project aims to assemble a wide spectrum of useful known and new resistance and adaptation genes through pre-breeding in winter-hardy genetic backgrounds. The majority of the target genes are not currently available in the HRWW primary breeding pool. Pre-breeding is being applied to gradually improve the general genetic back-ground in which the newly introduced genes occur and to concentrate/assemble them into more complex combina-tions that will be more useful in pedigree breeding. This will make it possible to also develop new cultivars with bet-ter resistance gene combinations and yield stability.

Related Research

A hard red winter wheat pedigree breeding program was initiated at NDSU during 2011. Annually, 500-700 new crosses are being made among winter wheat parents. Many of the known genes for resistance to the rusts, FHB, tan spot, SNB and BLS are not available in winter-hardy genetic backgrounds that are adapted to North Dakota. Furthermore the resistance genes often occur singly in very diverse and poorly adapted backgrounds mak-ing it even more difficult to combine multiple genes in a single line. This pre-breeding program is meant to directly supplement and facilitate the pedigree breeding effort.


Breeding of a new cultivar that incorporates multiple advantageous genes affecting many different traits, is dif-ficult. The odds of being able to select such genotypes in a pedigree breeding program can be improved vastly if the breeding population gets continuously enriched with those target genes. In a well-established breeding program this stage of development is normally achieved following de-cades of selection and intercrossing of superior selections. However, the new NDSU program is being developed from scratch and therefore totally lacks such structure. Since a breeding cycle takes about 9 years, the NDSU program cannot even generate field-tested inbred lines that can serve as new parents (akin to an established program) un-til 2019. The current pre-breeding effort is therefore crucial

to fill the void until 2019 and beyond. It aims to hasten the process of gene acquisition and base population enrich-ment and therefore should:

a. Continue to speedily acquire and evaluate new resistance and adaptation genes and increase the frequencies of those genes within the pedigree base population.

b. Build ever more complex and versatile gene combinations in genetically diverse and high yielding backgrounds (new breeding parents) that would hasten its dissemination in the breeding base population.

b. Build ever more complex and versatile gene combina-tions in genetically diverse and high yielding backgrounds (new breeding parents) that would hasten its dissemination in the breeding base population.

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Research Questions

Can we increase spring wheat yield and protein content with phosphorus, sulfur, copper, zinc and root inoculant in addition to recommended NPK fertilizers?

Results

Significant increase in yield over recommended NPK fertilizers was observed only in 2015 growing season (Fig. 1). Application of copper, zinc and sulfur had significantly higher yield (76 Bu/ac) than recommended NPK applica-tion (70 Bu/ac) at 90% significance level. Highest yield was achieved with 11-52-0 at 40 lb P2O5/ac and inoculant treatment in 2014 and 2015, respectively. Significant increase in protein content was also observed with ad-dition of copper and sulfur together over recommended rate of NPK. However, grain protein content did not show any increase over recommended NPK in 2015 and 2016. Grain nutrient concentrations did not increase over recom-mended NPK applications.

Application and Use

Our study indicates that additions of either 11-52-0 at 40 lb/ac or integrated use of copper (1 lb/ac), zinc (1 lb/ac) and sulfur (10 lb/ac) with recommended NPK have potential to increase yield and protein content.


Integrated use of copper, zinc and sulfur or 11-52-0 at 40 lb/ac increased yield 6 Bu/ac in 2015. So, these treat-ments have potential to increase the profit by $8.00/Bu×6 Bu/ac×500 ac= $24,000.


Field experiments were conducted during 2014-2016 growing season near Glyndon, MN on a Beardon Silty Clay Loam soil. Initial soil properties are presented in Table 1. Eleven treatments of various combinations were arranaged in a randomized complete block design with four blocks. The treatments were: 1. Control (no fertilizer applied); 2. Recommended NPK; 3. Starter fertilizer (11-52-0) @ 40 lb/ac with recommended NPK; 4. Sulfur @10 lb/ac (as ammonium sulfate) with recommended NPK ; 5. Copper @ 1 lb/ac with recommended NPK; 6. Zinc @ 1 lb/ac with recommended NPK; 7. Copper + sulfur (as CuSO4 matching the amount of Cu and S with treatment 4 and

5) with recommended NPK; 8. Zinc + sulfur (as ZnSO4 matching the amount of Zn and S with treatment 5 and 6) with recommended NPK; 9. Copper + zinc + sulfur (as CuSO4 and ZnSO4 matching the amount of Cu, Zn and S with treatment 4, 5 and 6) with recommended NPK;10. Root inoculant (Trichoderma spp.) with recommended NPK; and 11. Root inoculant+ (Trt. 9: copper + zinc+ sulfur) with recommended NPK. Individual treatment plots measured 10 feet wide and 30 feet long.

The initial nutrient status of the soil was presented in Table 1. The soybean stubbles were removed from the field before the application of the treatments. All of the above mentioned treatments were mid-row banded, and spring wheat was planted on May 16, April 18, April 28 in 2014, 2015, and 2016, respectively, with an 8’ wide, small plot sized grain drill. ND Wheat variety Glenn was planted in 2014 and Faller was planted in 2015 and 2016, at the seeding rate of 2 bushels per acre. Overall germination and plant stands were very good. Husky herbicide was applied one time for weed control on the spring wheat. At physiological maturity, the middle five rows of each plot were harvested using the small plot combine harvester on August 24, August 4, and August 15 in 2014, 2015 and 2016, respectively. Wheat grains were dried at 60˚C for 3 days and adjusted to 14 % moisture level before record-ing grain yield. Grain protein content was analyzed using Infratec 1241 Grain analyzer (FOSS analytical AB, Hoga-nas, Sweden). Statistical analyses were performed using PROC-ANOVA procedure for RCBD in SAS 9.3 (SAS Institute Inc, Cary NC). Means comparisons were con-ducted at the 90% confidence level using Fisher’s least significance difference method.

Related Research

Soon, Y.K., G.W. Clayton, and P.J. Clarke. 1997. Content and uptake of phosphorus and copper by spring wheat: Ef-fect of environment, genotype, and management. Journal of Plant Nutrition. 20(7&8): 925-937.

Franzen, D.W., M.V. McMullen, and D.S. Mosset. 2008. Spring wheat and durum yield and disease responses to copper fertilization of mineral soils. Agronomy Journal. 100(2): 371-375.

Rehm, G.W. 2008. Response of hard red spring wheat to copper fertilization. Communications in Soil Science and Plant Analysis. 39:2411-2420.

Spring Wheat Responses to Starter Fertilizer, Micronutrient and Root Inoculant

Armitava Chatterjee, Dept. of Soil Science, NDSU


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I strongly suggest to conduct more research on spring wheat yield and protein response to additions of phospho-rus, sulfur, copper and zinc particularly in light textured, low organic matter soils. I also suggest to explore the effect of Trichoderma spp. inoculation on spring wheat root growth, yield and nutrient uptake particularly in saline conditions.

Publications

Chatterjee, A., Thapa, R. 2016. Can We Increase Spring Wheat (Triticum aestivum L.) Grain and Protein Yields with Urease and Nitrification Inhibitors? ASA-CSSA-SSSA An-nual Meeting, Nov. 6-9, Phoenix, AZ (Poster).

BD (Mg m-3)

pH OM g kg-1

NO3-N Kg ha-1

Olsen-P mg Kg-1

K mg Kg-1

S kg ha-1

Cu mg Kg-1

Zn mg Kg-1

CEC Cmol+ kg-1

1.28 8.4 47 15 19 240 18 1.37 1.44 26.1

Table 1. Basis initial surface (0-15 cm depth) soil properties of field site at Glyndon, MN.

continued on page 34

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Research Questions

Spring wheat is a major crop for producers in Northwest Minnesota and small grains have had increasing interest from producers across the rest of the state for the past eight years. Diseases, along with insect pests, have long been detrimental to the quantity and quality of the crop. One of the key elements to successful production centers is the timely and correct identification of these diseases and insect problems followed by implementation of appro-priate management strategies.

Results

Two disease scouts were hired and one based at the Northwest Research and Outreach Center Crookston and one located near Fergus Falls, MN. With the support of a grant from the Minnesota Soybean Research and Promo-tion council, a third scout was placed in Southern MN.

Scout training on disease and insect identification and scouting methods was conducted at the Northwest Re-search and Outreach Center in early May for all three scouts. Scouts were trained in disease and insect iden-tification, methods of assessing disease incidence and severity, and insect sampling. Scouts then proceeded to scout weekly from late May approximately 20 -30 fields per week in their area monitoring disease incidence and severity and the presence of insect pests.

In late May, early June wheat was generally at the Zadocks 20-30 growth stage (GS) (the beginning of tiller-ing to pseudo stem erection). Rye scouted in Anoka and Isanti counties was far more advanced at Zadocks GS 69 (flowering complete). Tan spot was prevalent in Swift and Grant counties with incidence ranging between 15 and 26% per field with one field as high as 44%. However, severity was generally low (10-15%) at this stage of the season. Leaf rust was detected at low incidence and se-verity in a few fields in Wright, Norman and Clay counties. Low incidences of Septoria spot blotch were reported in Ottertail County and low incidences (2-8%) Barley yellow dwarf were reported in Pope, Swift and Ottertail Counties. Stripe rust was noted in winter wheat trials at the Universi-ty of Minnesota Southwest Research and Outreach Center at Lamberton.

In southern counties, low incidences of Army worm were also reported. Cereal aphids were abundant in the southern part of the state in winter cereals, but also were starting to be reported in low number in hard red spring wheat varieties. Molecular testing of aphids for detection of

Barley yellow dwarf virus in the Smith lab, suggested that although some of these aphids were viruliferous (carrying virus), the proportion of the early influx of aphids carrying BYDV was low.

As the month of June progressed, leaf rust continued to be detected in some wheat fields in southern MN and as the disease developed, a few fields had high incidences in Anoka and Isanti counties (90-96%0, the disease sever-ity was around 20-30%. Tan spot, Septoria spot blotch and Barley yellow dwarf remained the prevalent diseases present in central Minnesota. Wheat had progressed to Zadocks GS 65 in northern counties, rye in the southern part of the state ranged from Zadocks GS 70-85. The first scouting report of Fusarium head blight was made in a field in Wilkin county in spring wheat which had already completed flowering, incidence and severity were both re-corded 20% in this field. By the end of June, cereal aphids had moved in to northern counties in reasonable numbers in Kittson and Marshal counties. Stripe rust was still being sporadically reported but conditions had become much less favorable for stripe rust with warmer temperatures, and development had greatly slowed or halted. In many areas development of leaf rust also halted due to drier conditions. Incidence of Fusarium head blight was starting to increase in southern Minnesota as conditions during and post flowering in many areas had seen periods of high relative humidity. Tan spot continued to be prevalent at 20-30% incidence in central Minnesota, with severities in a similar range.

By mid-July the wheat ( and other small grains such as oats and rye) in the southern part of the state ( Stearns, Hennepin and Shurburn counties) was well in to ripen-ing and so scouting more or less ceased in these areas. In central and Northern Minnesota wheat was in the early milk stages of grain ripening. At this time growers were noting discolored awns indicative of Fusarium head blight infection under conditions of high relative humidity. In ar-eas which had received large precipitation events, the evi-dence of Fusarium head blight infection was more marked. This varied greatly by field indicative of the interaction of varietal resistance, maturity date and weather conditions in certain areas, influencing correct timing of fungicide applications.

Scouting continued until early August in the northern part of the state where symptoms of infection by Bacterial leaf streak (BLS) were reported. It is typical for BLS to become apparent after heading and this was certainly the case this year.

Minnesota Small Grains Pest SurveyMadeleine Smith, Dept. of Plant Pathology, NWROC



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From the scouting effort in 2016 it is clear that the ma-jor leaf diseases early in the season were tan spot and Septoria spot blotch. Although there was evidence of both stripe and leaf rust in the southern part of the state, these diseases did not become prevalent as in previous years, largely due to environmental conditions not being con-ducive for either disease to take hold. Cereal aphids did move up from the southern part of the state and did carry Barley yellow dwarf throughout the state although inci-dences appeared to be low and molecular testing showed that this year the early influx of aphids from the southern US did not have a large proportion of viruliferous individu-als. Fusarium head blight was again an issue in the state, helped by high relative humidity (RH) days (frequently reaching 100% RH) from consistent and frequent rainfall in many parts of the state. BLS in the latter part of the season was prevalent although in many cases the disease was not largely visible until after heading had occurred. Oat crown rust was prevalent in oats although not neces-sarily reaching high levels of disease severity, especially where fields had been sprayed with fungicide for control.

Due to the generally lower numbers of barley acres planted verses wheat, the number of barley fields scouted was relatively lower. The main disease identified in fields was net blotch.

Map images generated in collaboration with North Da-kota State University showing the results of the combined Integrated Pest Management (IPM) program scouting ef-forts throughout the season can be viewed at https://www.ag.ndsu.edu/ndipm. Figures included in this report show final end of season data for the main diseases noted in Minnesota. Fig. 1 shows the locations scouted throughout the season in Minnesota and North Dakota as well as the final growth stages at the end of the season.

Fig. 1. Final wheat growth stages and locations scouted for the 2016 growing season in North Dakota and Minnesota

Fig. 2. Incidence of BYDV during the 2016 growing season in North Dakota and Minnesota.

Fig. 3. Incidence of Leaf rust in % as the final number of instances from the 2016 growing season in North Dakota and Minnesota.

Fig. 4. Incidence of Stripe rust during the 2016 growing season North Dakota and Minnesota.

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Fig. 5. Incidence of Septoria spot blotch duringthe 2016 growing season in Norht dakota and Minneosta.

Fig. 6. Scab severity Index at locations scouted in the 2016 growing season in North Dakota and Minnesota.

Application and Use

The application of the disease scouting is to provide timely information to growers to raise awareness of disease issues in their areas. Growers can then make informed decision about whether to spray pesticides for disease and insect control and what products are best to use given the current scouting reports on disease. This can save the grower both application costs by not spraying when there is not threat of disease or insect problems and not wasting application and product costs on products that may not be appropriate to the current pest. Over a period of several growing seasons, growers are able to discern how fre-quently diseases occur in their area.


The MN survey was conducted according to the same protocol followed by the NDSU IPM survey so the output could be merged and reflect a regional effort.

Each scout surveyed 7-10 fields per day for a total of 20-30 per week throughout the growing season starting in mid/late May and extending into mid-August.

Scouts were trained at the Northwest Research and Outreach Center in disease and insect identification for all common small grains pests.

Disease incidence and severity, as well as the extent of any insect pests, were assessed as scouts walked a large ‘W’ pattern through each field, observing incidence and severity at the five points of the ‘W’. Disease incidence was recorded as the presence or absence of a disease or insect per total number of plants examined per field and expressed as %. Severity of disease was assessed by % leaf area affected by the disease and averaged per field.

Field cards indicating general growth stage, crop and field condition and general notes as well as disease severity and incidence as well as the frequency if insect pests were recorded for each filed surveyed. Where identification needed to be confirmed, plant samples were collected and sent back to the smith lab for further laboratory testing.

Data was collated each week and sent to NDSU for inclu-sion in the weekly regional distribution maps including North Dakota and Minnesota.

Notifications were sent out via scabsmart, Minnesota Association of wheat growers disease forecasting site as well as at grower meetings held throughout the summer in locations around MN.

Related Research

This project continues the highly successful Minnesota program which has been conducted over the last three years previously funded by the MWRPC. Data collected from this project dovetails with the NDSU IPM survey program to create a regional picture of disease incidence and distribution.

In addition, this project also feeds in to the Upper Great Plains Wheat Pathology Collaboration (UGPWP) recently funded by MWRPC by both providing current informa-tion on pathogen and insect distribution and population structures, but also by providing an insight in to upcom-ing disease issues in the state that inform future research aims and objectives of this interdisciplinary plant pathology team.

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Research Questions

Nitrogen fertilizer is usually the single most expensive input used in wheat production. Losses of N can be sub-stantial through leaching and denitrification, particularly in the Red River Valley region. This project seeks to answer the question: Can nitrogen stabilizing technologies im-prove nitrogen efficiency and allow for greater flexibility in the timing of N application, without the risk of serious N loss to the environment? Though this was a single season project, it continues research that had been conducted for two previous seasons. Results

The research conducted included several factors (rates, sources and timing of application of N); a rather complex trial to summarize in this space. Yields in general were very high; 2016 was a phenomenal year for yield and ap-parently for soil N mineralization as evidenced by the very high yields in the unfertilized checks at Red Lake Falls (RLF) and Casselton (see table in annex). Differences in yield and protein were small when comparing sources of N at the same rate and timing. Spring applications tended to be only slightly better for yield and protein than the fall applications of the same fertilizer type and rate. Averaged over locations, the 100% rate of ESN numerically had the highest yield and protein. At RLF, the addition of Agro-tain to the treatments receiving UAN at the 6 leaf stage improved the effectiveness of this treatment. At the Ada location, ESN tended to provide greater yields and protein at most timings and rates compared to urea alone. This site had the coarsest textured soil, so leaching potential was greatest, which may explain the reason for the rela-tively better response of ESN. The results obtained this season will be further analyzed with the results of previous seasons along with the weather data in order to develop a better understanding of how weather impacts the re-sponses of these N sources and timings. Nevertheless, generally it appears that the results from this season were similar to those obtained in previous seasons. Application and Use

It is difficult to make recommendations from a single year of data, even with the results of three locations. The data do suggest that there is only a very small added value in some of the more expensive fertilizer sources that “sta-bilize” nitrogen. Relative to the additional cost, in most cases the added value may not always cover the extra cost of these inputs. One factor that will impact the re-

Strategies for Meeting N Requirements of Wheat with New Fertilizers and Fertilizer Additives

Joel Ransom, Department of Plant Sciences, NDSU


turns to these products over urea alone is the premium/discount of the protein at the elevator. With the very high yields obtained this season, a moderate discount/premium may be enough to make the use of ESN in a site with high potential for nitrogen loss (like Ada) profitable.


Experiments were established in three locations, Casselton, Ada and Red Lake Falls, in 2016. There were 24 treatments total: ESN in the fall (all fall applications were made after the soil temperature had dropped below 50 degrees); urea in the fall; part ESN part urea compared to all ESN in the fall and in the spring; ESN in the fall compared to urea plus Instinct in the fall, and a control treatment of no added N. Rates of nitrogen were also included. Yield and grain protein were measure at harvest.


Given the fact that the results discussed here were from a single year of experimentation, we are not able to calculate an economic benefit to the farmer with any confidence. In some environments, the use of nitrogen extending technology may actually cost more than can be covered by the added value of either the yield or the pro-tein. Given the high yields this year, it appears that some profit from the use of ESN and perhaps Instinct could have be obtained, provided that there is at least a modest protein premium/discount at the elevator.

Related Research

The research that we have been conducting that deals with predicting the need for extra nitrogen and nitrogen timing is complementary to this research. We are also conducting research with ESN in wheat and corn, with different timings of application in the fall.


A more complete analysis of the three years of data is needed in order to better understand what type of environ-ments stabilized nitrogen will be profitable.

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Publications

Feland, C. and J. Ransom. 2016. Impact of nitrogen type, timing, and additives on grain protein in hard red spring week. Abstract of presentation at the Nitrogen Use Efficiency Conference, Boise, Idaho, August 2016. Calli Feland won first prize for the poster she presented.

Appendix

N Rate Timing FertilizerProtein Yield

Casselton, ND

Ada, MN

RLF, MN Combined

Casselton, ND

Ada, MN

RLF, MN Combined

g kg-1 bu/ac0% NA None 12.3 11.7 10.9 11.6 70.9 53.6 72.6 69.150% October ESN 13.3 11.9 11.2 12.1 79.3 81.3 86.5 82.475% October ESN 14.0 11.9 12.0 12.7 81.8 82.7 104.9 90.2100% October ESN 14.3 12.7 12.6 13.2 78.7 90.8 96.5 88.750% October Urea 13.4 11.8 11.4 12.2 81.0 80.8 92.7 84.875% October Urea 13.6 12.4 12.0 12.7 76.9 85.8 100.5 87.7100% October Urea 13.9 12.5 12.4 13.0 79.7 83.6 102.4 88.6100% October 75:25 14.3 12.8 12.6 13.2 82.4 94.4 107.5 94.7100% October 50:50 14.5 12.8 12.5 13.3 83.9 85.3 102.0 90.475% October U + Instinct 13.6 12.3 11.6 12.5 82.9 77.0 98.4 86.1100% October U + Instinct 13.4 12.9 12.8 13.0 75.2 88.4 101.5 88.450% Spring 100% ESN 14.0 12.2 12.3 12.8 83.0 80.8 95.5 86.075% Spring 100% ESN 13.9 12.9 12.3 13.1 81.4 91.7 100.4 91.1100% Spring 100% ESN 14.3 13.5 13.4 13.7 82.8 97.1 112.1 97.350% Spring 100% Urea 13.5 12.1 11.3 12.3 80.1 91.1 89.8 87.075% Spring 100% Urea 14.3 12.5 12.5 13.1 78.8 89.4 102.1 90.4100% Spring 100% Urea 14.2 13.0 12.5 13.2 85.7 84.6 101.5 90.3100% Spring 75:25 14.2 13.3 12.6 13.4 82.6 90.3 102.8 92.0100% Spring 50:50 14.5 13.4 12.8 13.6 76.5 88.4 105.7 90.275% Spring Urea + Instinct 14.4 12.9 12.0 13.1 86.4 91.5 97.0 91.6100% Spring Urea + Instinct 14.2 13.0 12.7 13.3 84.9 93.1 103.5 93.6100% Spring 50:50 at 6 leaf 14.2 13.1 11.9 13.1 81.1 88.8 103.9 91.2100% Spring 50% UAN +A 14.3 13.3 12.9 13.5 79.9 90.3 108.4 92.9200 lb N Spring Urea 14.6 13.6 13.4 13.9 81.0 91.5 111.5 94.7

LSD 0.05 0.9 0.5 0.4 0.4 7.0 12.2 8.4 6.1

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Research Questions

The objectives of the grant were to:1) Establish variety performance evaluation trials for

HRSW and HRWW near Montgomery, Kimball and Ben-son.

2) Organize extension programming focused on small grains production and management in southern Minne-sota.

Results

The Southern Wheat Tour encompasses the winter exten-sion programming efforts for small grains in central and southern Minnesota. These meetings were held in Cold Spring, LeCenter, Slayton, and Benson in the third week of February and were attended by nearly 150 producers. The average yield across the 3 locations funded by this grant was 86 bu/acre and 76 bu/acre for spring wheat and winter wheat, respectively. The variety trials near Mont-gomery suffered substantial lodging due to torrential rains and 50 mph. straight-line winds. All three locations served as sentinel plots for the small grains pest surveys and were used for summer plot tours that were attended by 30 producers. Detailed results can be found in tables 1 and 2 (Appendix I).

Application/Use

Hard red spring, winter wheat, winter rye, barley, and oats have been grown in central and southern Minnesota for decades but not in large acreages. Producers in these regions are now incorporating more intense manage-ment systems to maximize yield and quality on their small grain acres with genetics, input products, and fertility systems on productive soils including irrigation. The rising awareness of cover crops, crop rotation benefits, current economic markets and recent years have contributed to an increased awareness of the agronomic benefits and economic opportunities of small grains. Producers with dedicated intense production of small grains have dem-onstrated the ability to do so very successfully with yield and quality. Testimony of individual producers during plot tours and workshops suggest that 90 bushel spring and winter wheat and 150 bushel oat are routinely attained in production fields. This replicates what our research and demonstrations plots have documented. This underscores the importance that the University of Minnesota conducts high quality yield trials that demonstrate the maximum attainable yield rather than simply demonstrate relative differences between cultivars.

Material and Methods

The winter wheat and winter rye variety trials with 24 and 20 entries, respectively, were seeded on October 1st, 2015 near Montgomery and Kimball. The spring wheat, oats, and barley variety trials with 54, 29, and 18 entries, respectively, were seeded adjacent to the winter cereals at the same two locations in early April. In addition, a spring variety trial was seeded near Benson in the second week of April. All trials used a Randomized Complete Block design with 3 replications. Field preparations and some of the fertility management were done by the cooperators with planting, weed control, data collection, and harvest completed by the research group.


Spring and winter wheat should be an essential part of Minnesota’s agriculture. Providing small grain producers with the latest and most recent production and manage-ment information and educating producers which cultivars are best suited for their production system are critical to the economic well-being of Minnesota. A 10% increase in yield equates to nearly $14,000 in gross returns for a 500 acre wheat enterprise at today’s market prices.

Related Research

These trials are an integral part of the University of Min-nesota Spring Wheat, Barley, and Oat Breeding Programs and the Extension's Commodity Crops Team program-ming efforts. The rye variety trials are part of a Minnesota Department of Agriculture grant entitled ‘The Flavor and Agronomic Performance of Winter Rye for the Craft Distill-ers in Minnesota’.

Publications

Results of yield trials for spring and winter wheat, barley, and oats are part of the variety trial results that will be published in the on-line publication '2016 Minnesota Field Crop Trials’. The 2015 trial results were published in:

1. Anderson J.A, J.J. Wiersma, S. Reynolds, R. Caspers, and C. Springer. 2015. Hard Red Spring Wheat. In: 2015 Minnesota Field Crop Trials. Minnesota Agricultural Ex-periment Station Publication MP 121-2016. University of Minnesota, St. Paul, MN. 2. Smith, K., E. Schiefelbein, J.J. Wiersma, R. Dill-Macky, M. Smith, and B. Steffenson. 2015. Barley. In: 2015 Min-

Southern Minnesota Small Grains Research and Outreach ProjectJochum Wiersma, Dept. of Agronomy & Plant Genetics, NWROC


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nesota Field Crop Trials. Minnesota Agricultural Experi-ment Station Publication MP 121-2016. University of Minnesota, St. Paul, MN.3. Wiersma, J.J. and J.A. Anderson. 2015. Hard Red Winter Wheat. In: 2015 Minnesota Field Crop Trials. Min-nesota Agricultural Experiment Station Publication MP 121-2016. University of Minnesota, St. Paul, MN.

4. Wiersma, J.J., R. Dill-Macky, and H. Rines. 2015. Oat. In: 2015 Minnesota Field Crop Trials. Minnesota Agri-cultural Experiment Station Publication MP 121-2016. University of Minnesota, St. Paul, MN.

Appendix

Table 1 – Grain yield, test weight, and grain protein of 38 named HRSW varieties at three on-farm trial locations in southern Minnesota in 2016.

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12: APPENDIX

Table 1 – Grain yield, test weight, and grain protein of 38 named HRSW varieties at three on-farm trial locations in southern Minnesota in 2016.


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Table 2 – Grain yield, test weight, and grain protein of 23 named HRWW varieties at two on-farm trial locations in southern Minnesota in 2016

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Table 2 – Grain yield, test weight, and grain protein of 23 named HRWW varieties at two on-farm trial locations in southern Minnesota in 2016

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Research Questions

Grain protein content can dramatically impact the value of spring wheat at the elevator. Generally, in high yield-ing years, protein is low and protein premiums/discounts are high. Being able to predict the likelihood of a high yield early enough in the season to allow for additional N to be applied to increase grain protein could increase the profitability of growing wheat, particularly when the newer, higher yielding cultivars that inherently have relatively low protein are grown. Our research questions are: Using in-season NDVI, can we predict protein levels far enough prior to harvest so as to inform the decision as to whether additional N is needed and what kind of protein and yield response can be expected from N applications at various crop growth stages?

Results

We have not yet finalized the research that attempts to predict protein and yield with in-season sensors or crop growth modeling. We plan to focus on this during the coming months now that all of the data have been col-lected. The 2016 growing season was very favorable for yield and, we surmise, for mineralization of N in the soils, at least at the Red Lake Falls site where the unfertilized check yielded 62 bu/acre (see tables in annex). Adding 30 lbs/acre of nitrogen regardless of type or timing tended to increase yield, more so at Ada than at Red Lake Falls. Adding 60 lbs/acre of additional N, however, gave little or no response above that seen from the 30 lbs/acre rate. Protein increased with the addition of extra N, with the higher rate generally providing higher levels of protein regardless of fertilizer type and timing. Though loca-tions varied somewhat, urea and UAN appeared to have roughly the same effect on protein when applied at similar growth stages. In Ada, the highest protein at 30 lbs/acre of N was with the foliar application of UAN, but this treatment was similar to other sources of extra N at the Red Lake Falls location. This response is contrary to what has been observed in previous research.

Application/Use

The data from this research demonstrates the value of ap-plying extra nitrogen in-season in order to increase protein but less so for increasing yield. It also suggests that the type and timing of the N fertilization is less critical to the type of response obtained. These data need to be looked at in conjunction with the results from previous seasons in order to determine how the environment influences the outcome. This was an exceptional year for yield and N mineralization, so this year’s data must be viewed in light of that.

Establishing Criteria for Applying Additional N In-SeasonJoel Ransom, Department of Plant Sciences, NDSU



Field experiments were established in three locations to mimic field conditions with slightly less than optimal fertilization in order to verify the effect of various in-season N timings, rates, and sources on the yield and protein of spring wheat. In-season additions of N were applied at the 6-leaf, boot, and post-flowering stages. Yield and protein were measured for the various nitrogen additions rela-tive to the untreated check. Using historical weather from NDAWN, yield will be predicted at various N application timings using the DSSAT Crop Growth model (those data were not reported here as this research is still in process). Also, with the aid of an N rich strip, data from sensors will be obtained at these same growth stages to see if a relationship between NDVI and yield and protein can be established and therefore used as a predictive tool for the need of an in-season nitrogen application.


If the market offers a $0.50 premium/discount per percent of protein, applying an additional 30 lbs of N in-season could return more than $17,000 in a 500-acre field. The actual net benefit would be impacted by the actual yield and the premium/discount in the market for the extra protein and minus any application costs.

Related Research

The nitrogen use efficiency research that we have been conducting is supportive of this research. Since NDVI data were also collected in these other trials, those data can be used to verify the results obtained from this research with regards to predicting protein. The on-farm research group also conducted some in-season nitrogen strip trials.


We recommend an additional year of research with the incorporation of drones in fields with N rich strips as a way of monitoring in-season N needs.

Publications

Rellaford, M. and J. Ransom. 2016. Can in-season NDVI be used to predict grain protein in spring wheat? Abstract of presentation at the Nitrogen Use Efficiency Conference in Boise Idaho, August 2016.


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Appendix

Treatment Ada Red Lake Falls70% rate urea at planting (77 lbs/acre) 60.3 69.770% rate urea at planting + 30 lbs N as urea extra at planting (107 lbs/acre) 69.7 74.770% rate urea at planting (77 lbs/acre) + 30 lbs N as urea 4 to 5 leaf stage 70.3 75.070% rate urea at planting (77 lbs/acre) + 60 lbs N as urea 4 to 5 leaf stage 73.9 75.270% rate urea at planting (77 lbs/acre) + 30 lbs N as urea at the boot stage 69.7 73.270% rate urea at planting (77 lbs/acre) + 60 lbs N as urea at the boot stage 69.9 75.170% rate urea at planting (77 lbs/acre) + 30 lbs N of UAN at flowering 70.6 72.570% rate urea at planting (77 lbs/acre) + 30 lbs N as UAN 4 to 5 leaf stage 72.2 73.170% rate urea at planting (77 lbs/acre) + 60 lbs N as UAN 4 to 5 leaf stage 74.1 73.170% rate urea at planting (77 lbs/acre) + 30 lbs N as UAN at the boot stage 71.9 74.470% rate urea at planting (77 lbs/acre) + 60 lbs N as UAN at the boot stage 70.9 75.4200 lbs. urea at planting 67.0 74.5Check 47.6 62.5

Table 1. Yield of spring wheat at two locations, 70% base rate plus extra N at different timings.

Treatment Ada Red Lake Falls70% rate urea at planting (77 lbs/acre) 12.1 12.870% rate urea at planting + 30 lbs N as urea extra at planting (107 lbs/acre) 12.7 13.170% rate urea at planting (77 lbs/acre) + 30 lbs N as urea 4 to 5 leaf stage 12.9 13.470% rate urea at planting (77 lbs/acre) + 60 lbs N as urea 4 to 5 leaf stage 13.2 13.570% rate urea at planting (77 lbs/acre) + 30 lbs N as urea at the boot stage 13.2 13.570% rate urea at planting (77 lbs/acre) + 60 lbs N as urea at the boot stage 13.7 13.470% rate urea at planting (77 lbs/acre) + 30 lbs N of UAN at flowering 13.2 13.170% rate urea at planting (77 lbs/acre) + 30 lbs N as UAN 4 to 5 leaf stage 12.9 12.470% rate urea at planting (77 lbs/acre) + 60 lbs N as UAN 4 to 5 leaf stage 13.4 13.570% rate urea at planting (77 lbs/acre) + 30 lbs N as UAN at the boot stage 12.6 13.070% rate urea at planting (77 lbs/acre) + 60 lbs N as UAN at the boot stage 13.4 13.8200 lbs. urea at planting 13.0 13.6Check 11.4 11.0

Table 2. Protein of spring wheat at two locations, 70% base rate plus extra N at different timings.

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Research Questions

The objectives of this proposal are to i) develop improved varieties and germplasm combining high grain yield,disease resistance, and end-use quality; and ii) provide performance data on wheat varieties adapted to the state ofMinnesota.

Results

During the 2015/2016 crossing cycle, 290 crosses were made. The State Variety Trial, which contained 38 re-leased varieties, 11 University of Minnesota experimental lines, and 2 experimental lines from other programs was grown at a total of 15 locations in 2016. During the 2016 growing season, another 213 advanced experimental lines were evaluated in advanced yield trials at 11 locations. An additional 468 lines were evaluated in preliminary yield tri-als at 2 locations. A total of 7,077 yield plots were harvest-ed in 2016. Fusarium-inoculated, misted nurseries wereestablished at Crookston and St. Paul. Inoculated leaf rust nurseries were conducted at Crookston and St. Paul and astem rust nursery was also conducted at St. Paul. The dis-ease nurseries involve collaboration with agronomists andpathologists at Crookston and with personnel from the Plant Pathology Department and the USDA-ARS. Data from the yield and disease nurseries are summarized and published in Prairie Grains and the MAES’s 2016 Minne-sota Field Crop Trials bulletin.

MN11325-7 (Faller//00H04*J3/MN03130-1-62) was released as ‘Shelly’ in 2016. Shelly is a mid-late maturity hard red spring wheat that is competitive for grain yield with the highest yielding varieties in the region, but with higher protein. Shelly is moderately resistant to impor-tant diseases such as leaf rust, bacterial leaf streak, and Fusarium head blight. Straw strength is average, rated as a ‘5’. Shelly is resistant to preharvest sprouting and has exhibited acceptable end-use quality characteristics. Another advanced experimental line that is a candidate for release in the next year is MN10261-1. Data summaries of MN10261-1, recent U of MN releases, and popular variet-ies are shown in Table 1.

Application/Use

Experimental lines that show improvement over currently available varieties are recommended for release.Improved germplasm is shared with other breeding pro-grams in the region. Scientific information related toefficiency of breeding for particular criteria is presented at local, regional, national, and international meetings andpublished.


All yield nurseries are grown in small, replicated plots (typi-cally 40-75 sq. ft. harvested area per plot). Fusariuminocu-lated nurseries at Crookston and St. Paul consist of single 4 to 6 ft. rows, with 1 to 3 replications. Fusarium infectedcorn seed or spray-applied macroconidia are used as inoculum. The plot areas are misted periodically to maintain a high humidity environment for at least three weeks after anthesis. Leaf and stem rust nurseries are spray inoculated with spore suspensions and surrounded by a border seeded to mixture of susceptible varieties to further increase disease pressure.


Choice of variety is one of the most important decisions growers make each year. The development of high-yield-ing varieties that are resistant to the prevalent diseases and have good end-use quality are necessary to increase grower profit and protect against constantly changing pathogens and pests. As an example, a new variety that yields 4% higher will produce 3 extra bushels in a field that averages 75 bu/A. At current market prices that equates toapproximately an additional $7,000- in gross revenue for a 500 acre wheat enterprise.

Related Research

These funds provide general support for our breeding/genetics program. Additional monetary support for breed-ingrelated research in 2016 came from the Minnesota Agricultural Experiment Station, and the U.S. Wheat and Barley Scab Initiative via USDA-ARS.


We will continue to operate the breeding program using similar methodologies in the future, but are also exploringthe integration of genomic selection with DNA markers to more efficiently select for important traits and speed ourrate of genetic progress. If successful, I anticipate genom-ic selection being a routine feature of our breedingprogram, using even lower cost DNA marker systems in the future.

University of Minnesota Wheat Breeding ProgramJames Anderson, Department of Agronomy & Plant Genetics, U of M



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Entry

% State YieldTest Wt(lbs/bu)

Protein(%)

StrawStr.

BakingQuality PHS

LeafRust

StripeRust

Bact.Leaf Str. ScabRelease 2016 MN 2016 2 yr 3 Yr

Yr. Acreage bu/A 2 yr 2 Yr 1-9 1-9 1-9 1-9 1-9 1-9 1-9SY Valda 2015 3.0 90 93 – 60.2 13.8 4 – 3 – 2 – 4LCS Albany 2009 1.6 89 92 91 59.9 13.3 5 6 4 2 3 6 4Shelly 2016 0.5 88 90 88 60.1 13.8 5 5 1 4 1 4 4Prosper 2011 10.2 82 86 87 59.9 13.6 6 5 2 5 5 4 5Faller 2007 6.0 80 85 86 59.6 13.4 5 5 1 5 5 4 4SY Ingmar 2014 3.1 88 87 85 60.7 14.7 4 2 2 3 2 3 4MN10261-1 N/A N/A 82 86 85 61.2 14.7 5 3 1 1 1 3 3SY Soren 2011 4.6 84 84 82 59.5 14.7 4 4 2 2 2 4 5Forefront 2012 2.4 77 82 82 60.6 14.6 6 5 3 2 2 3 3Bolles 2015 8.8 79 82 81 59.4 15.9 4 1 1 1 1 4 4WB-Mayville 2011 13.1 81 83 81 59.7 14.6 3 3 3 3 3 6 7Linkert 2013 27.85 77 83 80 60.4 15.0 2 1 2 4 1 4 5Rollag 2011 2.4 76 81 79 60.8 14.8 3 6 1 4 1 4 3

Table 1. Comparison of Bolles, MN10261-1, and Shelly with the most popular varieties in Minnesota. Varieties are sorted from highest to lowest yielding based on 3 Year yield.

Publications

Anderson, J.A. J. Wiersma, R. Dill-Macky, J. Kolmer, M. Rouse, and Y. Jin., M. Smith, and L. Dykes. 2016.Hard Red Spring Wheat. In Minnesota Field Crop Trials, University of Minnesota Agricultural Experiment Station.

Anderson, J.A. J. Wiersma, S. Reynolds, L. Miller, C. Olson, R. Dill-Macky, J. Kolmer, M. Rouse, and Y. Jin.2016. Spring Wheat. In Preliminary Report 24: 2016 Wheat, Barley, and Oats Variety Performance inMinnesota Preliminary Report, Edited by Jochum Wiersma.

Bajgain, P., M.N. Rouse, T.J. Tsilo, G.K. Macharia, S. Bhavani, Y. Jin Y, and J.A. Anderson. 2016. Nested Asso-ciation Mapping of Stem Rust Resistance in Wheat Using Genotyping by Sequencing. PLoS ONE 11(5): e0155760. doi:10.1371/journal.pone.0155760.

Gao, L., M.K. Turner, S. Chao, J. Kolmer, and J.A. Ander-son. 2016. Genome Wide Association Study of

Seedling and Adult Plant Leaf Rust Resistance in Elite Spring Wheat Breeding Lines. PLoS ONE11(2): e0148671. doi:10.1371/journal. pone.0148671.Rawat, N., M.O. Pumphrey, S. Liu, X. Zhang, V.K. Tiwari, K. Ando, H.N. Trick, W.W. Bockus, E. Akhunov,J.A. Anderson, and B.S. Gill. 2016. Wheat Fhb1 encodes a chimeric lectin with agglutinin domains and a pore-form-ing toxin-like domain conferring resistance to Fusarium head blight. Nature Genetics. doi:10.1038/ng.3706.

Turner, M.K, Y. Jin, M.N. Rouse, and J.A. Anderson. 2016. Stem Rust Resistance in ‘Jagger’ Winter Wheat.Crop Sci. 56:1–7.

Turner, M.K., J.A. Kolmer, M.O. Pumphrey, P. Bulli, S. Chao, and J.A. Anderson. 2016. Association mapping of leaf rust resistance loci in a spring wheat core collection. Theor Appl Genet. doi:10.1007/s00122-016-2815-y.

Zhang, X., M.N. Rouse, I.C. Nava, Y. Jin, and JA. Ander-son. 2016. Development and verification of wheatgermplasm containing both Sr2 and Fhb1. Mol. Breeding. 36:85.

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Research Questions

Organize summer plot tours continue at a number of the locations of the Red River On-Farm Yield Trials with the goal to educate growers about emerging pest issues and the attributes of new HRSW cultivars.

Results

The plot tours were held in the second week of July at the trial locations near Fergus Falls, Oklee, Strathcona, and Hallock. Approximately 250 producers attended the plot tours. The cooperation and support of the individual coop-erators and/or the county crop improvement committees to provide refreshments and food cannot be underestimated.

Application/Use

Summer plot tours continue to be an effective tool to educate growers not only about the individual variety’s attributes but also are an excellent tool to educate growers about emerging issues such as Bacterial Leaf Streak.


Spring wheat is and should be an essential to agriculture in northwest Minnesota. Providing small grain producers with the latest and most recent production and manage-ment information and educating producers which cultivars are best suited for their production system are critical to the economic well-being of northwest Minnesota and the state as a whole. A 10% increase in yield equates to nearly $14,000 in gross returns for a 500 acre wheat enterprise at today’s market prices. Therefore, variety selection is a first important step towards maximizing an economic return.

Red River On-Farm Yield Trials Summer Plot ToursJochum Wiersma, Dept. of Agronomy & Plant Genetics, NWROC


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Research Questions

1.Does sulfur enhance wheat grain yield and grain protein?2.Can sulfur impact wheat N use or N removal from soil?

3.How does sulfur application affect the net return on the farmer’s investment on sulfur fertilizer?

Results

Grain yield and protein content were assessed in re-sponse to N and S rates, and the effect of their interac-tion was analyzed at several locations. From the analysis of variance (ANOVA), there was no interaction between N and S rates for yield and protein. Sulfur application in 2016 had no statistically significant effect on grain yield and protein of wheat at Ada (Table 1), and Red Lake Falls (RLF) (Table 2). At Thief River Falls (TRF), yield responded significantly to S (p<0.05) but not protein (Table 3). Averaged across N rates, grain yield at TRF increased by a significant 5.4 and 6.4 bu/a for the 10 and 20 lbs of S treatments, respectively, when compared to the S control (0 lbs S added) treatment. The neutral detergent fiber (NDF) of the grain, whose level in livestock ration influ-ences their intake, was significantly increased with S at 20 lbs, compared to the check. Nitrogen rates had significant impact on yields and protein at each site. At Ada, 180 lbs of N produced significantly higher yields (80 bu/ac) than the lower N rates (Table 1). At RLF, 60 lbs N produced the highest yield (87 bu/a). But the control produced just 7 bu less, and with statistically similar yields compared to 120, 180 and 240 lbs N. High N mineralization (3.6% SOM) in 2016 probably explains the high yields with no added N. The N mineralized was however, not enough to meet the level of protein produced with fertilizer N at higher rates. At TRF, 120 lbs N produced the highest yield and protein (Table 3). There was no obvious explanation why the protein at 240 lbs N was significantly lower than the other treatments (Table 3), despite producing similar yields as the lower N fertilizer rates. As expected, the strongest response to S and N was at Ada and TRF, where the soils had lower clay content (sandy loam soils) compared to the loam soil of RLF. The average yield and protein response across Ada and TRF, for the 2015 and 2016 growing sea-sons showed that S improved yields (Figure 1) and grain protein (Figure 2).

Flag leaf N and S showed significant interaction between S and N rates. Nitrogen accumulation in flag leaf (table 3) suggests that when N fertilizer is applied N accumulation is favored when S is applied. But when N is no applied, and probably very deficient, N uptake in plant is not im-

Economics, Nitrogen Use Efficiency, and Effects of Nitrogen and Sulfur Fertilizer Level Combinations Applied to Spring Wheat in Minnesota

Jasper Teboh, NDSU Research Extension Center, Carrington


proved by S. The interaction between S and N on flag leaf S suggests that S accumulation increased with application of higher rates of N.

Based on the method (see materials and methods section) used to determine net gain or loss in income, when S was applied in 2016, the farmer would have incurred a net loss of income at almost every N treatment level used, other than at 120 lbs N at RLF and TRF. The highest net rev-enue was recorded at TRF, where $84/ac was estimated at 120 lbs N and 10 lbs S, followed by $66/ac at 20 lbs of S, compared to $39 at 0 lbs added S (Figure 3). The net return at RLF was $5 at 10 lbs S, but with a net loss of $8 at 20 lbs S. At Ada, there was a surprising net loss of $43/ac with 10 lbs added S, meanwhile a net gain of $10 was recorded without S or with S at 20 lbs. Grain N removed has not yet been determined for 2016. In 2015, grain N removed, and averaged across N rates was, 116 lbs/a without S, 122 lbs at 10 lbs S, and 120 lbs N, at 20 lbs S.

This study supports the recommendation that wheat is more likely to respond to S, on lighter soils, and for soils with low SOM (typically less than 3.5%) content. Applica-tion of S at about 10 lbs or less would probably justify the cost than rates above 10 lbs. This is supported by incon-sistent responses where, at Ada (2016), yield and protein response was weak whereas in 2015, both yield and pro-tein showed strong response to S and N. In 2015 at TRF, wheat yield did not respond to S and N, contrary to 2016. showed significant response to S was if S is to be applied annually. from this study were that, S increased grain yields and protein in 2015 and 2016 at Ada and TRF with sandy loam soils. The economic impact of S fertilizer ap-plication, can be significantly affected by the farmer’s deci-sion on how much N fertilizer to use. Our results showed that the farmer has a better chance of a net positive return on S application if they are using the state N recommen-dation for their respective locations. There was a higher N removed by the grain with application of S.

Application/Use

When farmers apply sulfur to their wheat crop, it is im-portant that they consider factors such as soil type, soil organic matter, and market price. The main determinant of wheat yield and quality is N, but S will often enhance yield and protein in sandy/lighter soils and in soils where SOM is low. Since soil test for sulfur is not well predictive of crop response, annual S application must be weighed against the cost, considering that most of the soil S is present in the organic form that, under suitable soil conditions (warm

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spring, adequate soil moisture), will supply the crop needs. Determining the magnitude of yield or protein increase due to S, can serve the farmer well if the impact of S on yields and quality over a period of several years is inconsistent and shows that N fertility and other essential nutrients should be the primary concern. Material and Methods

The trial was conducted for the second year, on three framers’ fields located at Ada, Red Lake Falls (RLF), and Thief River Falls (TRF) in the Red River Valley Area of Minnesota. At Ada, the soil type was a Ulen Sandy loam (2.4% SOM), planted to the variety, Faller on April 13th. Faller was also planted on April 13th at RLF on a Wheat-ville loam containing 3.6% SOM. At TRF, the variety Pros-per was planted on a Foxhome sandy loam soil containing 2.6% SOM. This trial evaluated the response of spring wheat to S at different levels of N. The fertilizer treatments were five levels of N, including a control, at 0, 60, 120, 180, and 240 lbs N. Three S treatment levels were, 0, 10, and 20 lbs imposed in a RCBD within each N treatment, in a split-plot arrangement where, N levels were whole plots and S levels as sub-plots. Each treatment was replicated four times. Flag leaf samples were collected and grain samples sent for S and N analysis, and biomass at Ada (not yet available). Net economic return was calculated using the price of grain at 14% protein at $4.91/bu, with premium above 14% protein and equal to 15% every 0.2% given at $0.06/bu, premium > 15 and equal to 16% protein every 0.2% at $0.02/bu, and discount down between 14 and 11% at every 0.2% reduction at $0.06/bu. The recom-mended N rate for each site was determined from the Uni-versity of MN N fertilizer recommendation calculator, using a yield goal of 80 bushels at Ada and RLF, and 70 bushels at TRF. Grain was used was $4.55/bu (also using previous crop N credit of 20 lbs following soybeans, and residual soil test). The net returns were calculated by taking the dif-ference from the baseline N recommended rate calculated above without S applied, and the protein and yields were estimated for this baseline from the regression equations for respective site in 2016. Revenues did not include other variable cost of production besides fertilizer cost and ap-plication cost. Data were subjected to analysis of variance (ANOVA) using the Mixed Model analysis in SAS. S rates were analyzed as fixed effects, and the replicate x N rates as random effects.


The economic impact of S fertilizer application, can be sig-nificantly affected by the farmer’s decision on how much N fertilizer to use. Our results showed that farmers have a better chance of a net positive return on S application if they are using the state N recommendation for a given location. A dollar lost/ac is $500 for 500 acres. From the TRF results, $45 gain over the control, at 10 lbs S and N

rate of 120 lbs/ac would mean an increase in income of almost $22,500/ year

Related Research

This research was in its second year. Results from 2015 study were presented at the 2015 Prairie Grains Confer-ence, and available at: http://smallgrains.org/wp-content/uploads/2015/12/2015TebohSulfur.pdf

Other research work trials to be continued in 2017, will be quantifying the relative contribution of S, P, and K. The first year (2016) study showed S increased wheat yield by three bushels, P, by a bushel, and no increase with potassium applied. How each of these elements affects the impact of the other in single or multiple combination, is to be verified at two locations.


The far reaching implication of lost income by the farmer, due to inadequate management of S or N can have signifi-cant implications on their farm business. Our observations from these results need to be verified with more research data for, consistency. Another year of funding will help advance our understanding, to support the fertilizer recommendations used by the farmers.

References

Fertilizer Recommendations for Agronomic Crops in Minnesota: http://www.extension.umn.edu/agriculture/nutrient-man-agement/nutrient-lime-guidelines/fertilizer-recommenda-tions-for-agronomic-crops-in-minnesota/wheat/#table-1

continued on pagea 50 - 52

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Lb/aYield Protein Ash Fiber Starchbu/a % % % %

N Rate (N)0 60c 11.54b 1.46 2.786 83.1a60 80b 11.74b 1.46 2.808 83.0a120 77b 13.04a 1.49 2.793 82.1b180 87a 13.32a 1.50 2.816 81.9bc240 83ab 13.6a 1.51 2.813 81.5cMean 77 12.66 1.48 2.80 82.33

S Rate (S)0 78 12.65 1.48 2.796 82.310 77 12.60 1.48 2.808 82.520 77 12.73 1.49 2.806 82.2Mean 77 12.66 1.48 2.80 82.33

ANOVA ------------------------------------------------------------P-Values---------------------------------------------------N Rate (N) <.0001 <.0001 0.0234 2.796 <.0001S Rate (S) 0.6094 0.831 0.3565 2.808 0.4417N x S 0.5705 0.7063 0.9936 2.806 0.3331Means within each column and treatment with different letters are significant (P <0.05) according to the Tukey’s HSD test. P-values < 0.05 indicate the treatment had significant effect on the measured variable

Table 1. Effect of N and S, on wheat grain yield and quality (Ada, MN, 2016)

Lbs/aYield Protein TWT NDVI Fiber Starch Ash NDFbu/a % lb/bu % % % % %

N rate (N)0 80b 10.28c 56.4ab 0.857b 2.98a 81.62b 1.419c 8.908a60 87a 12.34b 57.20a 0.881a 2.92b 82.10a 1.464b 7.897bc120 87a 13.30a 55.3a 0.886a 2.93ab 81.51b 1.477b 7.753c180 86ab 13.72a 54.5b 0.889a 2.96ab 80.85c 1.486ab 7.974bc240 86ab 13.99a 54.5b 0.887a 2.92b 80.73c 1.516a 8.505abMean 85 12.73 55.60 0.880 2.94 81.4 1.472 8.207

S Rate (S)0 86 12.74 55.6 0.878 2.95 81.5 1.465 7.881b10 84 12.72 55.5 0.882 2.93 81.3 1.474 8.363ab20 85 12.72 55.6 0.881 2.94 81.3 1.479 8.378aMeans 85 12.73 55.582 0.880 2.94 81.362 1.472 8.207

ANOVA -----------------------------------------------------P-Values-----------------------------------------------------------------N Rate (N) 0.0275 <.0001 0.0134 <.0001 0.0105 <.0001 <.0001 0.0002S Rate (S) 0.4412 0.9974 0.9732 0.3552 0.5051 0.2025 0.2955 0.0263N x S 0.6314 0.3255 0.6938 0.4488 0.3909 .06024 0.4313 0.9510Means within each column and treatment with different letters are significant (P < 0.05) according to the Tukey’s HSD test. P-values < 0.05 indicate the treatment had significant effect on the measured variable

Table 2. Effect of N and S, on wheat grain yield and quality (Red Lake Falls, MN, 2016)

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Fig 1. Grain protein response of wheat to S at different N rates averaged across Ada and TRF and across years (2015, 2016)

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Fig 2. Grain protein response of wheat to S at different N rates averaged across Ada and TRF and across years (2015, 2016)

15.0

Yield Protein NDVI Flag leaf S Flag leaf N N:SLb/ac bu/a % % %N Rate 3.58c 17.31b 3.58c0 40.18b 15.34a 0.746b 4.16b 18.04ab 4.16b60 49.62ab 15.58a 0.85a 4.33ab 18.18ab 4.33ab120 63.12a 16.00a 0.878a 4.39ab 19.27a 4.39ab180 54.4a 15.26a 0.854a 4.54a 18.97a 4.54a240 57.08a 14.06b 0.832a 4.20 18.35 4.20Mean 52.9 15.25 0.83 % % %

S Rate0 48.95 15.23 0.816b 0.265b 4.18b 20.71a10 54.41 15.30 0.837a . . .20 55.28 15.21 0.844a 0.203a 4.22a 15.99bMean 52.88 15.25 0.832 0.234 4.18

ANOVA --------------------------------------P-Values----------------------------------N Rate 0.0030 <.0001 0.0001 0.0018 <.0001 0.0246S Rate <.0001 0.5785 <.0001 <.0001 0.209 <.0001N x S Rate 0.1908 0.1645 0.08 <.0001 0.0305 <.0001Means within each column and treatment with different letters are significant (P < 0.05) according to the Tukey’s HSD testP-values < 0.05 indicate the treatment had significant effect on the measured variable

Table 3. Effect of N and S, on wheat grain yield and quality (TRF, 2016)

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Fig 5. Grain N removed in wheat in response to S applied and averaged across N rates (Ada, 2015)

Fig 4. Net revenue from S and N application to wheat. Estimated by sbtracting revenue of each treatment from that of the baseline (recommended N of 130 lbs at 0 lbs S, RLF

Fig 3. Net revenue from S and N application to wheat. Estimated by sbtracting revenue of each treatment from that of the baseline (x-axis) recmmended N (135 lbs N) at 0 lbs S, at TRF

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2016 Wheat, Barley, and Oat Variety Performance in Minnesota- Preliminary Report

Higher hopes in early spring of a greater upward pricing potential for corn relative to other commodities, a wetter and slightly later spring, and lack of contracts offered for both barley and oats resulted in a decline in Minnesota’s small grain acreage across the board. Spring wheat, oat, and barley declined by 11%, 25%, and 30% respectively when compared to 2015 acreages. The 2016 growingseason was also markedly different from 2015, with tem-peratures at or above normal for much of the growing season but especially during the critical grain fill period and excess precipitation during the latter part of the growing season in many parts of the state. Nonetheless, the average grain yield for spring wheat was estimated at 59 bu/acre, the second highest ever recorded while the state’s average winter wheat yield was estimated at 56 bu/acre, a new state record. The state’s averages for barley and oats were 66 and 68 bu/acre, respectively.

Planting started in earnest in the second week of April with nearly a quarter of the spring wheat having been seeded by April 17th. This was about a week behind last year’s pace but on-par with the 5-year average. The pace of planting slowed in the second half of April as showerscombined with excess soil moisture in especially the north-ern half of the Red River Valley yielded only a few days suitable for field work. By May 1st, nearly two thirds of the spring wheat, half of the barley and 80% of the oat acre-age had been seeded. A pace well behind 2015’s pace but still slightly ahead of the 5-year average. The development of the crop initially followed much the same pattern as planting progress; slower than 2015’s pace but ahead of the 5 year average. This gap in crop developmentwas closed by the end of June as average temperatures started trending above normal from mid-June on. It was especially the higher night time temperatures that acceler-ated the crop development. This trend continued through much of the month of July and by the end of July the spring wheat crop had surpassed both the 5-year aver-age as well as last year’s pace. Harvest therefore started earlier than last year and by the first week of August nearly half of the wheat, barley, and oats had already been harvested. Unfortunately untimely rains in the northern half of the Red River Valley resulted in a scenario much like the 2015 harvest with spring wheat and barley left standing well past harvest ripeness before finally being able to get harvested. USDA’s July 1 and August 1 yield forecasts mirrored the differences between the 2015 and 2016 seasons. Whereas in 2015 USDA initially estimated the average spring wheat yield to be 62 bu/acre on July 1st it adjusted this number upwards to 64 bushels by August 1st . In contrast, this year the USDA estimated the aver-age spring wheat yield to be 63 bu/acre on July 1st only to downgrade it to 60 bu/acre by August 1st.

Disease and pest problems in 2016 yielded a few surpris-es. First, wheat stem saw fly was confirmed in a few fields immediately following harvest as the lodged grain had caught some growers by surprise. Although not a new pest to Minnesota, wheat stem saw fly has not been an economic pest problem in recent memory. Fusarium root and crown rot were prevalent in some areas. Conditions were conducive for this disease early in the season and in a few cases growers experienced seedling death and areas of poor stand establishment in the worst affected areas. More noticeable, however, were the white heads late in the season that are indicative of latent infections. Incidences and severity of stripe rust, leaf rust and crown rust were generally low across the state. Tan spot and Septoria leaf blotch were most prevalent but losses were likely minimal as producers have largely adopted a dual timing fungicide program. Bacterial leaf streak, BLS, was again prevalent in many areas, especially those which had experienced hail damage and more episodes of windier, stormier weather. In many cases the disease was severe on flag leaves and thus likely having an impact on grain yield. The higher dew points and overall wetter conditions resulted in damages by Fusarium head blight (FHB) to be substantial in the northern half of the Red River Valley. Not only was this because conditions were conducive to FHB development, but also because the persistent episodes of rain made it impossible for growers to apply fungicide at the crucial time for control and producers are being dis-counted for the presence of DON in the harvested grain.

The quality of the wheat, barley and oats is average to slightly above average. Preliminary reports from US Wheat Associates indicate that grain protein is equal to the 2015 crop with lower test weight, but an overall grade of No. 1 DNS (Dark Northern Spring).

INTRODUCTION

Successful small grain production begins with selection of the best varieties for a particular farm or field. For that reason, varieties are compared in trial plots on the Min-nesota Agricultural Experiment Station (MAES) sites at St. Paul, Rosemount, Waseca, Lamberton, Morris, and Crookston. In addition to the six MAES locations, trials are also planted with a number of farmer cooperators. The cooperator plots are handled so factors affecting yield and performance are as close to uniform for all entries at each location as possible.

The MAES 2016 Wheat, Barley, and Oat Variety Perfor-mance in Minnesota Preliminary Report 24 is presented under authority granted by the Hatch Act of 1887 to the Minnesota Agricultural Experiment Station to conduct performance trials on farm crops and interpret data for the public.

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The MAES and the College of Food, Agricultural and Natural Resource Sciences (CFANS) grants permission to reproduce, print, and distribute the data in this publication - via the tables - only in their entirety, without rearrange-ment, manipulation, or reinterpretation. Permission is also granted to reproduce a maturity group sub-table provided the complete table headings and table notes are included. Use and reproduction of any material from this publication must credit the MAES and the CFANS as its source.

VARIETY CLASSIFICATIONS

Varieties are listed in the tables alphabetically. No other distinction or classification is used to group varieties. Seed of tested varieties can be eligible for certification, and use of certified seed is encouraged. However, cer-tification does not imply a recommendation. Registered and certified seed is available from seed dealers or from growers listed in the ‘Minnesota Crop Improvement As-sociation 2017 Directory’, available through the Minnesota Crop Improvement Association office in St. Paul or online at http://www.mncia.org

INTERPRETATION OF THE DATA

The presented data are the preliminary variety trial infor-mation for single (2016) and multiple year (2014-2016) comparisons in Minnesota. The yields are reported as a percentage of the location mean, with the overall mean (bu/acre) listed below. Two-year and especially one-year data are less reliable and should be interpreted with cau-tion. In contrast, averages across multiple environments, whether they are different years and/or locations, provide a more reliable estimate of mean performance and are more predictive of what you may expect from the variety the next growing season. The least significant difference or LSD is a statistical method to determine whether the observed yield difference between any two varieties is due to true, genetic differences between the varieties or due to experimental error. If the difference in yield between two varieties equals or exceeds the LSD value, the higher yielding one was indeed superior in yield. If the difference is less, the yield difference may have been due to chance rather than genetic differences, and we are unable to dif-ferentiate the two varieties. The 10% unit indicates that, with 90% confidence, the observed difference is indeed a true difference in performance. Lowering this confidence level will allow more varieties to appear different from each other, but also increases the chances that false conclu-sions are drawn.

THE AUTHORS AND CONTRIBUTORS

This report is written, compiled, and edited by Dr. Jochum Wiersma, Small Grains Specialist. The contributing authors/principal investigators are:

Dr. James Anderson, Wheat Breeder, Department of Agronomy & Plant Genetics, St. Paul; Dr. Kevin Smith,

Barley Breeder, Department of Agronomy & Plant Genet-ics, St. Paul; Dr. Tyler Tiede, Oat Breeder, Department of Agronomy & Plant Genetics, St. Paul; Dr. Ruth Dill-Macky, Plant Pathologist, Department of Plant Pathology, St. Paul.Dr. James Kolmer, USDA-ARS, Cereal Disease Labora-tory, St. Paul; Dr. Howard Rines, USDA-ARS, Department of Agronomy & Plant Genetics, St. Paul; Dr. Matt Rouse, USDA-ARS, Cereal Disease Laboratory, St. Paul; Dr. Madeleine Smith, Extension Plant Pathologist, Northwest Research & Outreach Center, Crookston; Dr. Brian Stef-fenson, Plant Pathologist, Department of Plant Pathology, St. Paul; Dr. Yue Jin, USDA-ARS, Cereal Disease Labora-tory, St. Paul.

Matt Bickell, Robert Bouvette, Dave Grafstrom, Mark Hanson, Tom Hoverstad, Lance Miller, Chris Olson, Steve Quiring, Curt Reese, Susan Reynolds, Dimitri von Ruckert, Edward Schiefelbein, Galen Thompson, and Donn Vellek-son supervised fieldwork at the various sites. Special thanks are also due to all cooperating producers.

SPRING WHEATJames Anderson, Jochum Wiersma, Susan Reynolds, Lance Miller, Chris Olson, Ruth Dill-Macky, James Kolmer, Matt Rouse, and Yue Jin

The severe lodging encountered in 2015 was the under-lying reason why acreage of Linkert nearly doubled to almost 28% of the acreage. Simultaneously Faller and Prosper’s acreage precipitously dropped by half to just over 16% of the acreage. Three public varieties, namely Boost, Shelly, and Surpass, in addition to LCS Prime were released in 2016 and their single and multi-year data has been added to the tables. First-time entrants in the 2016 trials were Dyna-Gro Ambush, LCS Anchor, HRS 3616 (Croplan Genetics), TCG-Cornerstone, TCG-Spitfire, and TCG-Wildfire. Testing of Barlow, MS Stingray, and Samson was discontinued.

The results of the variety performance evaluations are summarized in Tables 1 through 7. The average yield across the seven southern testing locations was 78 bu/acre in 2016. This compares to an average of 81 bu/acre in 2015 and a three-year average of 79 bu/acre. The five northern locations averaged 86 bu/acre in 2016 compared to 91 bu/acre last year and 88 bu/acre for the three-year average.

Tables 4, 5, and 6 present the relative grain yield of tested varieties in 1, 2, and 3-year comparisons. LCS HRS 3419, HRS 3530, LCS Albany, LCS Nitro, SY Valda, and Shelly were the highest yielding varieties in both the south as well as the northern half of the state in both single year and multiyear comparisons. Higher yielding cultivars tend to be lower in grain protein. Variety selection is one ap-proach to avoid discounts for low protein, but N fertility management remains paramount to maximize grain yield and grain protein.

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The varietal characteristics are presented in Tables 1, 2, and 3. Table 3 summarizes all the disease reactions for individual varieties. Varieties that are rated 4 or lower are considered the best defense against a particular disease. Varieties that are rated 7 or higher are likely to suffer significant economic losses under even moderate disease pressure. Table 7 provides insight of how variet-ies respond to the use of fungicides. In Lamberton, Morris, Crookston, and Roseau the State Variety Trials are grown in duplicate. The duplicate trial is treated with fungicides at Feekes 5, 9, and 10.51 to eliminate, to the extent possible, any fungal pathogens that potentially can reduce grain yield and quality. Averaged across varieties, the use of fungicide increased grain yield by 6 bu/acre in the north-ern locations and 7 bu/acre in southern locations in 2016, relatively small differences compared to previous years. Individual varieties may have very different responses to fungicide, depending on their level of susceptibility to and intensity of fungal diseases. Use the information in Tables 3 and 7 to gain an understanding of how individual varieties should be managed to reduce the yield losses caused by fungal pathogens such as tan spot, leaf and stripe rust, and FHB. For example, WB9507 yielded a third more when protected with fungicide, largely due to its high susceptibility to stripe rust.

The foliar disease rating represents the total complex of leaf diseases other than the rusts, and includes the Septoria complex and tan spot. Although varieties may differ from their response to each of those diseases, the rating does not differentiate among them. Therefore, the rating should be used as a general indication and only for varietal selection in areas where these diseases histori-cally have been a problem or if the previous crop is wheat or barley. Control of leaf diseases with fungicides may be warranted, even for those varieties with an above average rating.

Bacterial Leaf Streak cannot be controlled with fungicides. Variety selection of more resistant varieties is the only recommended practice at this time if you have a history of problems with this disease. Boost, Focus, Forefront, LCS Breakaway, Prevail, SY Ingmar, and SY Rowyn offer the best resistance while varieties like HRS 3419, LCS Albany, LCS Nitro, RB07, WB-Mayville, and WB9507 have a rating of 6, indicating that they are the most consistently affected by the disease.

Variety selection for 2017 is a balance between yield potential, disease responses, and grain quality. As the past growing season demonstrated in the most northern tier of Minnesota counties, vigilance against FHB remains paramount as economic losses can quickly add up with varieties rated 6 or higher. Forefront, and Rollag provide the best resistance against FHB. Forefront has good adaptation to southern locations and both Forefront and Rollag are competitive varieties for the northern locations.Bolles, Boost, Faller, Focus, HRS3530, LCS Albany,

LCS Iguacu, LCS Prime, Norden, Prevail, RB07, Shelly, Surpass, SY Ingmar, SY Rowyn, SY Valda, and WB9507 are all varieties with a rating of 4 for FHB. Combined, this group of varieties includes some of the top yielders and varieties with higher grain protein content such as Bolles and Rollag.

BARLEYKevin Smith, Ruth Dill-Macky, Jochum Wiersma,Madeleine Smith, Brian Steffenson, and Ed Schiefelbein

The results of the state yield trials are summarized in Table 8. The average yield across the four testing loca-tions (Crookston, Morris, St. Paul, and Roseau) was 95 bu/acre in 2016. This is 21 bushels lower than the state average in 2015. The highest yields were in Crookston and the lowest in Roseau.

The yield data in Table 8 were collected from advanced yield trials that contain the important varieties for the region planted in five locations in the state. Yield data is presented as percent of the mean of the varieties listed in the table. The mean of the varieties is presented in bush-els per acre. Innovation, ND Genesis and Rasmusson were the highest yielding varieties followed by ABI Balster and Lacey based on the 3 year state averages (Table 8). Pinnacle is the most lodging resistant line and Robust and Quest are the least (Table 9). The two-rowed varieties Conlon, ND Genesis and Pinnacle had the plumpest grain while Celebration was the thinnest. Grain proteins for the six-rowed varieties ran from 13.0% (Rasmusson) to 13.9% (Robust). While the two-rowed varieties had 11.1% (ND Genesis) to 13.1% (Conlon) protein.

Table 10 describes the reaction of the currently grown varieties to the six major diseases in the region. Disease reaction is based on at least two years of data and scored from 1–9 where 1 is most resistant and 9 is most suscep-tible. Bacterial Leaf Streak (BLS) cannot be controlled by fungicides and there are only minimal differences in resis-tance among the current varieties. Conlon and Celebra-tion have the best net blotch resistance while Quest and Conlon have the best FHB resistance among the varieties presented. Pinnacle appeared to be hit particularly hard with net blotch this year so a timely fungicide application is particularly important for this variety. OATSTyler Tiede, Jochum Wiersma, Ruth Dill-Macky, and Dimitri von Ruckert

The results of the variety performance evaluations are summarized in Tables 11 through 14. The oat performance evaluations were grown in 10 Minnesota locations, in-cluding Waseca, LeCenter, Lamberton, St. Paul, Kimball, Morris, Fergus Falls, Crookston, Stephen, and Roseau in 2016. The greatest challenge during this past growingseason in both the oat performance evaluations and

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commercial production was again lodging. Consequently, data from the trials in St. Paul, Fergus Falls, Morris, and Roseau were not included in the results. The performance evaluations were treated with a fungicide when the flag leaf was fully extended (Feekes 9). The average yield across the testing locations was 133 bushels per acre in 2016. This compares to an average of 170 bushels per acre in 2014 and a three-year average of 144 bushels per acre. While Hayden, the 2015 release from SDSU, sur-passed Deon for yield in 2016, the later remained the top yielding variety across the state in the multi-year compari-sons (Table 14).

Relative maturity, measured as the number of days to heading, plant height, and resistance to lodging have been converted to a 1-9 scale to allow for easier interpretation of the data (Table 11). Differences among varieties for all three characteristics are generally much less in the south-ern half of the state or when seeding is delayed. In the northern half of the state the differences among varieties widen as is also the case when seeding is early. Earlier varieties tend to perform relatively better in the southern parts of the state, while later maturing varieties usually have a yield advantage in the north. Varieties with lodging scores greater than 4 should be chosen with caution as lodging problems reduce yield, quality, and harvestability. This is especially important if your soils are highly fertile. The extensive lodging encountered across the state will increase the emphasis on straw strength in the variety selection process for next year. Deon provides some of the best straw strength available in oats, but when condi-tions are favorable even Deon will encounter substantial lodging.

Quality traits are also presented on a 1-9 scale (Table 12). Groat percentage is an important consideration for grain production (perhaps as important as grain yield) regard-less whether the crop is intended for food or feed. It is defined as the percentage of germ, bran, and endosperm in proportion to the whole seed on a weight basis (Table 12).

The disease ratings are based on inoculated screening nurseries for crown rust and smut on the University of Minnesota’s St. Paul campus and for Barley yellow dwarf (or red leaf of oats) on the University of Illinois’ Champaign Urbana campus (Table 13). Consider most oat varieties to be moderately to very susceptible to crown rust. Other fun-gal pathogens that commonly cause yield losses in oats in Minnesota are stem rust of oats and Septoria leaf blotch. No ratings are presented on these disease due to insuf-ficient data at this time. The use of a fungicide at Feekes 9 is warranted if crown rust is present in the lower canopy and the variety has a crown rust rating of 4 or higher. A fungicide application at Feekes 9 will also provide excellent protection against Septoria leaf blotch but will likely be too early to protect against stem rust. Even when a fungicide application is made at Feekes 9, expect some yield losses

due to crown rust with the most susceptible cultivars if conditions for crown rust remain favorable during the grain fill period. Therefore, selecting a less susceptible culti-vars like Deon and Ron is still prudent. Seed treatment should be used for smut-susceptible varieties. Varieties susceptible to Barley yellow dwarf (a rating of 6 or higher) should be avoided in the southern half of the state or when planting is delayed as viruliferous aphids are more likely to arrive early enough in the crops development to cause economic damages.

Descriptions of oat varieties covered by the U.S. Plant Variety Protection Act include a PVP designation. When PVP is followed by the notation (94), seed of that variety may not be sold by a grower, not even to a relative or neighbor, without the express permission of the variety’s developer/owner. If the PVP application is pending, consider the variety as having PVP (94) protection. Using oats for cover crop does not exempt the buyer from the legal obligation to purchase only certified or registered classes of seed. Proper selection of oat varieties requires consideration of the anticipated growing conditions, the pests that might be encountered in a specific production situation, the purpose for growing the crop and its eventual usage. Specific growing situations will dictate the priority and emphasis given to each trait included in the tables.

Tables on pages 57 - 67

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Variety Origin1

Days to Height StrawPVP Status Heading2 Inches2 Strength3

Bolles 2015 MN PVP (pending) 62.1 32.3 4Boost 2016 SDSU PVP (pending) 62.7 29.6 5Chevelle 2014 Meridian Seeds PVP (94) 58.9 30.6 4Dyna-Gro Ambush 2016 Dyna-Gro PVP (pending) 58.4 31.8 5Elgin-ND 2013 NDSU PVP (94) 59.8 35.9 6Faller 2007 NDSU PVP (94) 61.6 32.9 5Focus 2015 SDSU PVP (pending) 55.6 36.4 7Forefront 2012 SDSU PVP (94) 57.6 37.2 6Glenn 2005 NDSU PVP (94) 57.7 35.3 5HRS 3361 2013 CROPLAN by WinField PVP (94) 60.3 31.3 3HRS 3419 2014 CROPLAN by WinField PVP (pending) 63.8 31.7 3HRS 3504 2015 CROPLAN by WinField PVP (pending) 62.4 28.7 3HRS 3530 2015 CROPLAN by WinField PVP (pending) 61.8 35.0 5HRS 3616 2016 CROPLAN by WinField PVP (pending) 60.2 31.4 4LCS Albany 2009 Limagrain Cereal Seeds PVP (94) 63.4 30.3 5LCS Anchor 2016 Limagrain Cereal Seeds PVP (pending) 58.0 28.9 5LCS Breakaway 2012 Limagrain Cereal Seeds PVP (94) 59.8 30.2 4LCS Iguacu 2014 Limagrain Cereal Seeds PVP (94) 62.3 31.0 4LCS Nitro 2015 Limagrain Cereal Seeds PVP (94) 62.3 31.1 5LCS Prime 2016 Limagrain Cereal Seeds PVP (pending) 58.1 32.5 5Linkert 2013 MN PVP (94) 59.9 29.3 2Norden 2012 MN PVP (94) 60.3 30.5 3Prevail 2014 SDSU PVP (94) 57.9 32.8 4Prosper 2011 NDSU PVP (94) 61.6 33.5 6RB07 2007 MN PVP (94) 59.0 31.7 5Rollag 2011 MN PVP (94) 59.2 29.9 3Shelly 2016 MN PVP (pending) 62.0 30.6 5Surpass 2016 SDSU PVP (pending) 57.0 33.7 7SY Ingmar 2014 AgriPro/Syngenta PVP (94) 60.5 31.0 4SY Rowyn 2013 AgriPro/Syngenta PVP (94) 59.3 30.5 5SY Soren 2011 AgriPro/Syngenta PVP (94) 59.6 29.7 4SY Valda 2015 AgriPro/Syngenta PVP (94) 60.1 30.7 4TCG-Cornerstone 2016 21st Century Genetics PVP (pending) 60.5 29.1 3TCG-Spitfire 2016 21st Century Genetics PVP (pending) 63.5 30.1 3TCG-Wildfire 2016 21st Century Genetics PVP (pending) 60.7 33.6 4WB-Mayville 2011 WestBred PVP (94) 58.8 28.8 3WB9507 2013 Westbred PVP (94) 59.8 33.3 5WB9653 2015 Westbred PVP (94) 61.8 29.1 4Mean 60.2 31.6

1 Abbreviations: MN = Minnesota Agricultural Experiment Station; NDSU = North Dakota State University Research foundation; SDSU = South Dakota Agricultural Experiment Station. 2 2016 data. 3 1-9 scale in which 1 is the strongest straw and 9 is the weakest. Based on 2014-2016 data. The rating of newer entries may change by as much as one rating point as more data are collected.

Table 1. Origin and agronomic characteristics of hard red spring wheat varieties in Minnesota in single-year (2016) and multiple-year comparisons.

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VarietyTest Weight (Lb/Bu) Protein (%)1 Baking Pre-Harvest

2016 2 yr 2016 2 yr Quality2 Sprouting3

Bolles 58.3 59.4 15.8 15.9 1 1Boost 58.7 59.0 14.6 14.8 3 4Chevelle 59.1 60.2 12.9 13.2 – 3Dyna-Gro Ambush 60.2 – 14.7 – – –Elgin-ND 58.9 59.6 14.4 14.6 3 2Faller 58.6 59.6 13.2 13.4 5 1Focus 60.1 61.3 15.1 15.0 3 4Forefront 59.8 60.6 14.7 14.6 5 3Glenn 61.0 62.0 14.8 15.0 1 1HRS 3361 58.6 59.4 13.8 13.9 3 2HRS 3419 58.5 58.8 12.9 13.2 6 4HRS 3504 58.9 58.9 13.3 13.7 – 1HRS 3530 59.5 60.2 14.4 14.4 – 2HRS 3616 58.8 – 15.0 – – –LCS Albany 59.3 59.9 13.3 13.3 6 4LCS Anchor 58.5 – 14.9 – –LCS Breakaway 59.5 60.6 14.9 14.8 5 2LCS Iguacu 60.1 60.7 12.5 12.9 7 2LCS Nitro 58.7 59.6 13.1 13.2 4 4LCS Prime 58.0 59.4 12.9 13.2 – 2Linkert 59.6 60.4 14.9 15.0 1 2Norden 60.2 61.3 13.9 14.0 4 1Prevail 59.5 60.3 14.1 14.1 4 4Prosper 58.9 59.9 13.4 13.6 5 2RB07 58.6 59.8 14.5 14.5 3 2Rollag 59.9 60.8 14.7 14.8 6 1Shelly 59.4 60.1 13.4 13.8 5 1Surpass 58.8 59.6 14.5 14.6 – 2SY Ingmar 59.9 60.7 14.3 14.7 2 2SY Rowyn 59.7 60.3 13.7 13.8 3 3SY Soren 59.1 59.5 14.6 14.7 4 2SY Valda 59.6 60.2 13.7 13.8 – 3TCG-Cornerstone 58.9 – 14.6 – – –TCG-Spitfire 57.7 – 13.5 – – –TCG-Wildfire 58.9 – 14.3 – – –WB-Mayville 58.9 59.7 14.7 14.6 3 3WB9507 58.1 58.8 13.4 13.5 3 4WB9653 59.0 59.6 13.3 13.5 – 1Mean 59.3 60.7 14.1 14.2No. Environments 9 11 9 111 12% moisture basis. 2 2014-2015 crop years. 3 1-9 scale in which 1 is best and 9 is worst. Values of 1-3 should be considered as resistant.

Table 2. Grain quality of hard red spring wheat varieties in Minnesota in single-year (2016) and multiple-year comparisons.

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Variety Leaf Rust Stripe Rust2 Stem Rust3Bacterial

Leaf Streak4Other Leaf Diseases5 Scab

Bolles 1 1 2 4 4 4Boost 2 2 3 2 7 4Chevelle – 1 1 – 6 5Dyna-Gro Ambush – – – – 4 –Elgin-ND 2 2 2 5 5 5Faller 5 5 2 4 4 4Focus 3 3 3 3 7 4Forefront 2 2 4 3 4 3Glenn 5 1 1 4 5 3HRS 3361 3 3 3 4 4 5HRS 3419 4 1 1 6 3 5HRS 3504 – 2 2 – 4 6HRS 3530 – 3 1 – 4 4HRS 3616 – – – – 5 –LCS Albany 2 3 3 6 5 4LCS Anchor – – – – 6 –LCS Breakaway 3 2 2 3 5 5LCS Iguacu 4 5 2 4 4 4LCS Nitro 4 2 2 6 6 5LCS Prime – 4 2 – 6 4Linkert 4 1 1 4 4 5Norden 2 1 1 4 4 4Prevail 2 1 5 2 6 4Prosper 5 5 2 4 4 5RB07 2 2 2 6 6 4Rollag 4 1 2 4 5 3Shelly 4 1 2 4 4 4Surpass – 2 5 – 5 4SY Ingmar 3 2 1 3 5 4SY Rowyn 3 1 1 2 6 4SY Soren 2 2 1 4 4 5SY Valda – 2 1 – 4 4TCG-Cornerstone – – – – 5 –TCG-Spitfire – – – – 4 –TCG-Wildfire – – – – 4 –WB-Mayville 3 3 2 6 7 7WB9507 8 8 3 6 3 4WB9653 – 2 2 – 4 51 1-9 scale where 1=most resistant, 9=most susceptible. 2 Based on natural infections in 2015 at Kimball, Lamberton, and Waseca. 3 Stem rust levels have been very low in production fields in recent years, even on susceptible variet-ies. 4 Bacterial leaf streak symptoms are highly variable from one environment to the next. The rating of newer entries may change by as much as one rating point as more data is collected. 5 Combined rating of tan spot and septoria.

Table 3. Disease reactions1 of hard red spring wheat varieties in Minnesota in multiple-year comparisons.

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VarietyCrookston Fergus Falls Hallock

2016 2-Year 3-Year 2016 2-Year 3-Year 2016 2-Year 3-YearBolles 95 94 96 98 97 99 89 92 92Boost 97 92 92 98 94 97 86 93 93Chevelle 103 103 – 106 102 – 99 96 –Dyna-Gro Ambush 105 – – 100 – – 106 – –Elgin-ND 89 94 95 95 96 97 89 87 93Faller 97 95 104 95 105 108 102 106 109Focus 89 94 94 100 96 95 105 100 100Forefront 94 98 99 89 91 97 97 100 101Glenn 88 89 87 97 94 90 99 94 95HRS 3361 106 103 101 101 103 104 97 96 97HRS 3419 108 111 109 95 101 109 109 112 110HRS 3504 102 102 – 111 108 – 108 102 –HRS 3530 100 104 – 95 103 – 110 112 –HRS 3616 100 – – 105 – – 97 – –LCS Albany 111 107 110 105 108 110 103 106 106LCS Anchor 96 – – 95 – – 94 – –LCS Breakaway 105 104 98 99 100 93 101 96 96LCS Iguacu 107 105 106 94 93 101 111 107 104LCS Nitro 104 104 103 102 103 109 104 101 100LCS Prime 96 92 – 104 111 – 93 101 –Linkert 97 102 99 96 95 91 97 99 98Norden 103 101 99 100 99 98 98 97 98Prevail 96 99 98 97 98 104 105 103 102Prosper 100 100 105 99 104 107 102 106 108RB07 104 102 102 98 97 97 91 92 93Rollag 102 105 103 98 99 98 106 103 102Shelly 103 105 106 106 100 105 102 103 103Surpass 101 102 – 98 100 – 102 99 –SY Ingmar 99 97 99 106 102 101 106 102 100SY Rowyn 100 101 104 101 104 106 101 102 102SY Soren 102 103 102 107 97 99 102 96 97SY Valda 108 108 – 107 108 – 119 118 –TCG-Cornerstone 96 – – 99 – – 89 – –TCG-Spitfire 102 – – 111 – – 101 – –TCG-Wildfire 101 – – 97 – – 91 – –WB-Mayville 96 97 93 107 101 95 92 95 96WB9507 104 99 103 97 104 107 112 111 111WB9653 103 100 – 110 109 – 106 96 –

Mean (Bu/Acre) 102.1 92.2 93.5 87.6 98.7 92.1 79.6 85.4 86.3LSD (0.10) 3.4 3.7 3.7 3.9 3.5 3.7 4.3 4.0 4.0

Table 4 Relative grain yield of hard red spring wheat varieties in northern Minnesota locations in single-year (2016) and multiple-year comparisons (2014-2016).

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VarietyOklee Perley1 Roseau Stephen1 Strathcona1

2016 2-Year 3-Year 2-Year 2016 2-Year 3-Year 2-Year 2-YearBolles 97 98 100 97 99 100 101 95 103Boost 93 98 96 94 83 94 95 94 84Chevelle 103 104 – – 85 84 – – –Dyna-Gro Ambush 103 – – – 104 – – – –Elgin-ND 106 104 101 93 104 92 93 95 94Faller 103 101 104 104 110 107 105 107 105Focus 96 99 96 97 97 104 105 95 94Forefront 94 93 95 103 105 99 100 98 101Glenn 93 96 97 92 103 105 102 98 100HRS 3361 101 100 101 97 105 97 100 93 99HRS 3419 108 109 110 106 131 120 116 108 111HRS 3504 100 100 – – 93 94 – – –HRS 3530 105 103 – – 114 107 – – –HRS 3616 96 – – – 83 – – – –LCS Albany 110 107 110 104 120 110 108 106 105LCS Anchor 95 – – – 74 – – – –LCS Breakaway 94 95 98 103 92 98 97 101 100LCS Iguacu 101 100 105 107 111 111 110 109 106LCS Nitro 102 102 103 101 114 105 105 100 106LCS Prime 109 107 – – 105 107 – – –Linkert 96 96 96 95 85 94 97 101 103Norden 99 101 101 97 84 101 97 99 93Prevail 98 97 98 101 105 106 105 99 101Prosper 103 100 104 106 119 111 109 105 106RB07 98 99 99 98 83 88 92 99 97Rollag 95 94 95 100 81 84 88 92 97Shelly 107 107 107 108 119 111 107 98 107Surpass 103 101 – – 102 104 – – –SY Ingmar 102 99 98 100 99 101 98 101 100SY Rowyn 100 101 102 98 103 96 97 100 102SY Soren 100 99 99 95 86 100 102 99 102SY Valda 110 109 – – 121 108 – – –TCG-Cornerstone 92 – – – 91 – – – –TCG-Spitfire 103 – – – 109 – – – –TCG-Wildfire 105 – – – 100 – – – –WB-Mayville 98 97 99 97 86 91 96 94 95WB9507 90 93 98 106 108 99 100 104 103WB9653 97 99 – – 96 95 – – –Mean (Bu/Acre) 99.4 101.2 98.4 95.4 63.0 73.8 78.7 72.8 77.6LSD (0.10) 3.5 3.4 3.5 3.6 5.5 4.7 4.4 4.7 4.41 Data from 2016 sites at Perley (hail), Stephen and Strathcona (excessive water) were excluded from analyses. 2-year data for these locations is from 2014 and 2015.

Table 4 (continued) Relative grain yield of hard red spring wheat varieties in northern Minnesota locations in single-year (2016) and multiple-year comparisons (2014-2016).

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VarietyBenson Kimball LeCenter


Mean (Bu/Acre) 113.0 109.2 109.9 64.6 81.1 84.1 78.4 82.9 74.3LSD (0.10) 3.6 8.0 6.5 19.9 12.5 9.3 14.6 14.6 12.0

Table 5. Relative grain yield of hard red spring wheat varieties in southern Minnesota locations in single-year (2016) and multiple-year comparisons (2014-2016).

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VarietyLamberton Morris St. Paul Waseca

2016 2-Year 3-Year 2016 2-Year 3-Year 2016 2-Year 3-Year 2016 2-Year 3-YearBolles 101 95 97 97 97 99 102 103 104 99 99 104Boost 106 97 99 101 97 103 103 98 99 102 106 106Chevelle 99 101 – 109 109 – 106 105 – 107 101 –Dyna-Gro Ambush 102 – – 107 – – 97 – – 93 – –Elgin-ND 84 90 94 99 100 100 99 100 99 99 91 87Faller 109 109 108 88 92 97 76 89 95 92 98 98Focus 96 103 103 95 94 101 77 88 91 100 98 96Forefront 98 102 101 91 98 102 95 90 95 96 98 101Glenn 85 93 93 81 84 90 81 78 78 89 93 90HRS 3361 107 102 101 97 100 99 95 98 99 111 112 111HRS 3419 116 113 114 115 115 111 114 116 113 115 115 115HRS 3504 107 108 – 115 115 – 101 102 – 112 107 –HRS 3530 105 104 – 104 97 – 108 107 – 119 126 –HRS 3616 93 – – 104 – – 104 – – 96 – –LCS Albany 114 107 111 105 103 106 107 112 114 102 104 111LCS Anchor 75 – – 86 – – 91 – – 83 – –LCS Breakaway 89 90 91 99 93 94 105 102 96 91 94 91LCS Iguacu 103 101 102 98 98 99 112 116 116 103 92 95LCS Nitro 110 106 107 107 110 108 126 123 119 110 105 109LCS Prime 97 103 – 100 95 – 86 86 – 86 82 –Linkert 85 88 89 94 96 93 109 105 99 92 95 97Norden 96 97 98 99 100 97 105 103 101 88 93 98Prevail 105 103 101 89 95 102 99 103 105 117 118 120Prosper 109 105 105 95 92 100 86 94 98 96 99 104RB07 89 93 97 95 95 94 96 98 94 81 89 87Rollag 84 89 91 98 101 98 94 94 94 75 79 80Shelly 112 109 109 118 115 107 115 115 113 114 112 112Surpass 101 100 – 104 102 – 79 78 – 112 108 –SY Ingmar 110 105 104 106 100 103 113 100 100 105 94 94SY Rowyn 109 107 106 108 110 113 111 106 104 113 107 110SY Soren 89 90 96 102 93 97 113 106 103 112 98 97SY Valda 111 110 – 113 112 – 103 106 – 105 102 –TCG-Cornerstone 93 – – 94 – – 105 – – 93 – –TCG-Spitfire 99 – – 102 – – 103 – – 89 – –TCG-Wildfire 103 – – 103 – – 99 – – 98 – –WB-Mayville 100 96 93 98 105 97 105 106 103 94 99 95WB9507 107 100 102 76 80 89 87 103 106 112 101 106WB9653 106 106 – 109 115 – 102 106 – 110 110 –

Mean (Bu/Acre) 72.0 84.4 83.3 78.3 71.6 74.7 65.7 76.7 74.1 74.2 61.5 53.6LSD (0.10) 9.2 8.6 7.1 10.1 10.2 11.4 12.3 11.6 11.6 11.1 15.9 14.2

Table 5. (continued) Relative grain yield of hard red spring wheat varieties in southern Minnesota locations in single-year (2016) and multiple-year comparisons (2014-2016).

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VarietyState North South


Mean (Bu/Acre) 81.5 85.0 83.3 86.2 89.4 87.5 78.0 81.0 79.0LSD (0.10) 4.8 3.4 2.5 6.0 4.4 2.9 6.8 4.9 3.9No. Environments 12 27 42 5 13 21 7 14 21

Table 6. Relative grain yield of hard red spring wheat varieties in Minnesota in single-year (2016) and multiple-year comparisons (2014-2016).

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Variety

NORTH SOUTH2016 2-year 3-year 2016

Conv Int Conv Int Conv Int Conv IntBolles 79.6 86.4 80.3 89.4 84.8 93.5 74.4 82.5Boost 75.8 78.0 76.9 86.8 80.5 92.8 77.6 79.2Chevelle 79.3 87.7 78.4 94.1 – – 78.3 84.7Dyna-Gro Ambush 86.4 91.2 – – – – 78.5 76.9Elgin-ND 78.2 83.4 77.2 86.9 81.3 92.1 69.2 75.0Faller 84.1 93.0 83.3 99.1 89.9 105.1 73.7 87.6Focus 76.0 80.5 81.8 88.5 85.1 93.6 71.7 82.9Forefront 80.8 87.7 81.4 85.4 85.5 92.3 70.6 74.4Glenn 77.2 80.8 79.5 84.9 80.7 89.8 62.1 68.7HRS 3361 87.1 91.3 83.5 91.6 86.7 97.7 76.6 86.6HRS 3419 96.5 100.7 95.6 99.5 96.8 105.7 86.9 88.7HRS 3504 81.5 82.9 81.5 92.7 – – 83.3 90.1HRS 3530 87.3 89.7 87.4 94.5 – – 78.5 90.4HRS 3616 77.1 83.9 – – – – 74.2 81.4LCS Albany 94.4 98.9 90.1 100.0 93.7 103.7 82.3 86.7LCS Anchor 72.3 75.4 – – – – 60.7 69.7LCS Breakaway 82.4 86.3 84.3 93.4 83.9 97.1 71.2 75.6LCS Iguacu 89.8 92.4 89.3 92.6 92.8 99.1 75.6 87.2LCS Nitro 89.0 97.7 86.8 94.4 89.8 100.5 81.4 82.0LCS Prime 82.1 89.7 82.0 97.0 – – 73.8 85.1Linkert 76.2 80.0 81.5 89.0 84.5 93.9 67.2 78.5Norden 79.1 80.2 83.8 87.2 84.4 92.5 73.3 76.3Prevail 82.0 86.2 84.9 93.9 87.3 97.0 72.8 84.4Prosper 88.6 89.3 86.9 93.7 91.9 101.0 76.5 87.4RB07 79.0 84.5 79.4 92.5 83.7 97.9 69.2 67.8Rollag 77.6 86.9 79.4 92.8 82.7 96.4 68.6 73.3Shelly 90.2 97.0 89.3 100.7 91.8 104.4 86.8 94.0Surpass 83.6 83.9 85.4 90.2 – – 77.0 80.7SY Ingmar 81.7 90.7 81.9 95.4 84.8 98.0 81.2 87.3SY Rowyn 83.6 89.1 81.9 94.6 86.6 98.7 81.6 90.4SY Soren 79.1 88.7 84.3 92.9 87.6 97.7 71.8 81.2SY Valda 93.5 94.6 89.5 97.0 – – 84.1 90.1TCG-Cornerstone 77.5 83.9 – – – – 70.3 76.7TCG-Spitfire 86.2 91.0 – – – – 75.7 80.6TCG-Wildfire 82.9 85.9 – – – – 77.7 78.2WB-Mayville 76.2 89.5 78.3 95.6 81.3 98.7 74.6 83.8WB9507 87.0 96.7 82.4 99.6 87.5 103.2 68.2 92.6WB9653 82.7 84.6 – – – – 80.7 94.1Mean (Bu/Acre) 82.5 87.7 83.0 92.5 86.0 97.2 75.2 82.4LSD (0.10) 9.9 12.5 7.1 7.9 5.5 5.8 8.1 6.8No. Environments 2 2 4 4 6 6 2 2

Table 7. Grain yield (bushels per acre) of hard red spring wheat varieties grown under conventional and intensive management.

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Table 7. (continued) Grain yield (bushels per acre) of hard red spring wheat varieties grown under conventional and intensive management.

Variety

SOUTH STATE2-year 3-year 2016 2-year 3-year

Conv Int Conv Int Conv Int Conv Int Conv IntBolles 74.9 81.1 77.6 83.1 77.0 84.4 77.6 85.2 81.2 88.3Boost 75.5 80.1 79.8 83.1 76.7 78.6 76.2 83.4 80.2 87.9Chevelle 81.7 87.0 – – 78.8 86.2 80.1 90.5 – –Dyna-Gro Ambush – – – – 82.5 84.1 – – – –Elgin-ND 73.8 78.6 76.7 81.2 73.7 79.2 75.5 82.8 79.0 86.7Faller 78.7 90.1 81.2 90.2 78.9 90.3 81.0 94.6 85.6 97.6Focus 76.9 82.5 80.4 84.1 73.9 81.7 79.4 85.5 82.8 88.9Forefront 77.9 80.1 80.0 81.6 75.7 81.1 79.7 82.8 82.8 87.0Glenn 69.4 72.1 72.4 76.6 69.7 74.8 74.4 78.5 76.6 83.2HRS 3361 79.2 83.0 79.2 83.3 81.8 89.0 81.4 87.3 83.0 90.5HRS 3419 89.0 92.7 88.9 93.1 91.7 94.7 92.3 96.1 92.9 99.4HRS 3504 86.9 90.5 – – 82.4 86.5 84.2 91.6 – –HRS 3530 78.6 89.2 – – 82.9 90.1 83.0 91.9 – –HRS 3616 – – – – 75.6 82.6 – – – –LCS Albany 82.1 91.1 85.6 89.7 88.3 92.8 86.1 95.6 89.7 96.7LCS Anchor – – – – 66.5 72.6 – – – –LCS Breakaway 71.4 79.6 73.2 82.3 76.8 80.9 77.9 86.5 78.6 89.7LCS Iguacu 78.0 88.7 79.4 88.6 82.7 89.8 83.7 90.7 86.1 93.9LCS Nitro 84.0 83.8 84.9 87.9 85.2 89.8 85.4 89.1 87.4 94.2LCS Prime 77.2 90.8 – – 78.0 87.4 79.6 93.9 – –Linkert 71.5 79.9 72.0 79.4 71.7 79.3 76.5 84.4 78.3 86.7Norden 76.8 79.6 76.9 81.1 76.2 78.3 80.3 83.4 80.7 86.8Prevail 77.6 86.0 80.0 84.9 77.4 85.3 81.2 89.9 83.6 90.9Prosper 77.3 89.4 81.1 89.6 82.6 88.4 82.1 91.5 86.5 95.3RB07 73.3 77.6 75.6 79.7 74.1 76.1 76.4 85.0 79.7 88.8Rollag 73.5 77.0 74.4 77.9 73.1 80.1 76.5 84.9 78.6 87.2Shelly 87.2 91.8 85.6 89.8 88.5 95.5 88.2 96.2 88.7 97.1Surpass 78.7 83.7 – – 80.3 82.3 82.1 86.9 – –SY Ingmar 80.1 85.4 81.6 86.5 81.4 89.0 81.0 90.4 83.2 92.2SY Rowyn 84.7 88.7 86.3 89.7 82.6 89.8 83.3 91.6 86.5 94.2SY Soren 71.4 82.5 76.3 83.7 75.5 85.0 77.8 87.7 82.0 90.7SY Valda 86.8 91.6 – – 88.8 92.3 88.2 94.3 – –TCG-Cornerstone – – – – 73.9 80.3 – – – –TCG-Spitfire – – – – 80.9 85.8 – – – –TCG-Wildfire – – – – 80.3 82.1 – – – –WB-Mayville 78.3 82.1 75.0 81.6 75.4 86.7 78.3 88.8 78.1 90.2WB9507 70.8 91.5 75.9 92.0 77.6 94.6 76.6 95.5 81.7 97.6WB9653 – – – – 81.7 89.3 – – – –Mean (Bu/Acre) 77.8 84.5 78.9 84.6 78.8 85.0 80.4 88.5 82.5 90.9LSD (0.10) 7.3 7.0 5.1 5.0 6.1 6.9 5.1 5.2 3.7 3.8No. Environments 4 4 6 6 4 4 8 8 12 12

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Page 69

North Dakota Hard Red Spring Wheat Variety Trial Results for 2016 and Selection GuideJoel Ransom, Andrew Green, Senay Simsek, Andrew Friskop, Matt Breiland, Tim Friesen, Zhaohui Liu and Shaobin Zhong (NDSU Main Station); John Rickertsen (Hettinger Research Extension Center); Eric Eriksmoen (North Central Research Extension Center, Minot); Bryan Hanson (Langdon Research Extension Center); Glenn Martin (Dickinson Research Extension Center); Gautam Pradhan (Williston Research Extension Center); Mike Ostlie (Carrington Research Extension Center)

Hard red spring (HRS) wheat was harvested from 6.2 mil-lion acres in 2016, down slightly from 2015. The average yield of spring wheat was 47 bushels/acre (bu/a), a bushel lower than last year.

SY Soren was the most popular HRS wheat variety in 2016, occupying 15.4 percent of the planted acreage, followed by SY Ingmar (11.5), Elgin-ND (8.7), Barlow (8.0) Glenn (7.9), Faller (7.3) and Prosper (6.6). SY Soren and SY Ingmar were released by Syngenta/AgriPro. All other varieties are NDSU releases.

Spring wheat generally was planted early in 2016 due to an early spring. Temperatures were moderate during much of the growing season, which helped the development of relatively high yield potential. We did not experience the yield losses due to the yellow rust that we saw in 2015. Scab caused elevated levels of DON in a few regions. Excessive moisture in NE North Dakota reduced yield and increased damage due to scab.

Successful wheat production depends on numerous fac-tors, including selecting the right variety for a particular area. The information included in this publication is meant to aid in selecting that variety or group of varieties. Char-acteristics to consider in selecting a variety may include yield potential, protein content when grown with proper fer-tility, straw strength, plant height, reaction to problematic pests (diseases, insects, etc.) and maturity. Every grow-ing season differs; therefore, when selecting a variety, we recommend using data that summarize several years and locations. Choose the variety that, on average, performs the best at multiple locations near your farm during several years.

Selecting varieties with good milling and baking quality also is important to maintain market recognition and avoid discounts. Hard red spring wheat from the northern Great Plains is known around the world for its ex-cellent end-use quality. Millers and bakers consider many factors in determining the quality and value of wheat they purchase. Several key parameters are: high test weight (for optimum milling yield and flour color), high falling num-ber (greater than 300 seconds indicates minimal sprout

damage), high protein content (the majority of HRS wheat export markets want at least 14 percent protein) and ex-cellent protein quality (for superior bread-making quality as indicated by traditional strong gluten proteins, high baking absorption and large bread loaf volume).

Gluten strength, and milling and baking quality ratings, are provided for individual varieties based on the results from the NDSU field plot variety trials. These ratings are ap-plied to varieties grown for multiple years at seven NDSU Research Extension Centers across the state to provide producers and end users with end-use performance data. The wheat protein data often are higher than obtained in actual production fields but can be used to compare differ-ences among varieties. The agronomic data presented in this publication are from replicated research plots using experimental designs that enable the use of statistical analysis. These analyses en-able the reader to determine, at a predetermined level of confidence, if the differences observed among varieties are reliable or if they might be due to error inherent in the experimental process.

The LSD (least significant difference) values beneath the columns in the tables are derived from these statistical analyses and apply only to the numbers in the column in which they appear. If the difference between two varieties exceeds the LSD value, it means that with 95 or 90 percent confidence (LSD probability 0.05 or 0.10), the higher-yielding variety has a significant yield advantage. When the difference between two varieties is less than the LSD value, no significant difference was found between those two varieties under those growing conditions.

NS is used to indicate no significant difference for that trait among any of the varieties at the 95 or 90 percent level of confidence. The CV stands for coefficient of variation and is expressed as a percentage. The CV is a measure of variability in the trial. Large CVs mean a large amount of variation that could not be attributed to differ-ences in the varieties. Yield is reported at 13.5 percent moisture, while protein content is reported at 12 percent moisture content.

Presentation of data for the entries tested does not imply approval or endorsement by the authors or agencies con-ducting the test. North Dakota State University approves the reproduction of any table in the publication only if no portion is deleted, appropriate footnotes are given and the order of the data is not rearranged. Additional data from county sites are available from each Research Extension Center at www.ag.ndsu.edu/varietytrials/spring-wheat.


Page 70

Reaction to Disease 4

VarietyAgent

or Origin 1Year

ReleasedHeight

(inches)Straw

Strength 2

Days to

Head 3Stem Rust 5

Leaf Rust

Stripe Rust 5

Tan Spot

Bact. Leaf

Head Scab

Barlow ND 2009 35 6 62 R MS M MS MS/S MBolles MN 2015 32 4 66 R/MR MR MR MR S MBoost SD 2016 30 5 64 NA NA NA NA NA MRBrennan AgriPro 2009 30 4 62 R MR M MS S MSDuclair6 MT 2011 31 4 65 R MR NA S S MSEgan MT 2014 35 NA 65 NA NA NA NA NA NAElgin-ND ND 2012 36 5 65 R MS M MS MS/S MFaller ND 2007 35 5 65 R S S MS MS MFocus SD 2015 35 5 60 R MR/MS MS S MS/S MRForefront SD 2012 37 5 61 R/MR MR MS S S MRGlenn ND 2005 37 4 61 R MS M MS M/MS MRHRS 3361 Croplan 2013 33 3 65 NA MR MS NA NA MHRS 3378 Croplan 2013 32 4 64 NA MR MS NA NA MHRS 3419 Croplan 2014 32 2 68 NA MR R NA NA MRHRS 3504 Croplan 2015 31 3 67 NA NA NA NA NA NAHRS 3530 Croplan 2015 36 4 68 NA MS S NA NA NAHRS 3616 Croplan 2016 32 4 64 NA NA NA NA NA NAJenna AgriPro 2009 32 4 66 R MR M M M/MS MLCS Albany Limagrain 2008 32 5 67 R MR MS R MS MLCS Anchor Limagrain 2016 31 3 64 NA NA NA NA NA NALCS Breakaway Limagrain 2011 32 5 63 R R MS MR MS MLCS Iguacu Limagrain 2014 33 3 66 R MS MS R MS/S MLCS Nitro Limagrain 2015 32 4 65 R/MR R MR R M MLCS Powerplay Limagrain 2011 33 5 65 R MS M S MS MLCS Prime Limagrain 2015 33 4 61 NA MS NA NA NA MRLCS Pro Limagrain 2015 32 5 66 R MS NA S S MLinkert MN 2013 31 2 63 R MR R MR MS/S MMott 6 ND 2009 36 3 66 R MS MS MS S MSMS Chevelle Meridian 2014 30 5 63 R MR MR MS MS MMS Stingray Meridian 2013 35 3 67 R MS S R M MRND901CLPlus 7 ND 2010 36 4 60 MR MR/MS NA NA NA MPrestige Pulse-USA 2015 31 3 62 NA MS NA NA NA NAPrevail SD 2014 31 4 64 R/MR/MS MR MR MR M/MS MProsper ND 2011 35 5 65 R MS S MS MS/S MRB07 MN 2007 32 5 62 R MS NA MR S MRRedstone Pulse-USA 2014 32 3 67 NA R NA NA NA NARollag MN 2011 32 3 63 R MR/MS R R M/MS MRShelly MN 2016 34 5 65 NA MR/MS NA NA NA MSurpass SD 2016 31 5 59 NA MR/MS NA NA NA MR

Table 1. North Dakota hard red spring wheat variety descriptions, agronomic traits, 2016.

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Table 1. (continued) North Dakota hard red spring wheat variety descriptions, agronomic traits, 2016.

SY Ingmar Syngenta/AgriPro 2014 31 3 64 R MR MS MS S M

SY Rowyn Syngenta/AgriPro 2013 31 4 62 R MR M R M/MS M

SY Soren Syngenta/AgriPro 2011 30 3 63 R MR MS R S M

SY Tyra6 Syngenta/AgriPro 2011 31 5 62 R R R MS S S

SY Valda Syngenta/AgriPro 2015 31 4 64 R R MS MS S MR

SY605CL7 Syngenta/AgriPro 2009 34 7 62 R/MR MR/MS NA MS NA S

TCG-Cornerstone

21st Century Genetics

2015 31 4 64 NA MR/MS NA NA NA MS

TCG-Spitfire21st

Century Genetics

2015 36 4 66 NA MS NA NA NA MS

TCG-Wildfire21st

Century Genetics

2015 37 5 64 NA MS NA NA NA MS

Velva ND 2011 35 4 63 R MS MS R M/MS MSWB9312 WestBred 2016 30 4 63 NA MR NA NA NA NAWB9507 WestBred 2013 32 5 61 R/MR MR S MR S MRWB9653 WestBred 2015 31 4 65 R/MR MR S MS S MWB-Mayville WestBred 2011 30 3 63 R R MS MS S S1 Refers to agent or developer: MN = University of Minnesota; MT = Montana State University; ND = North Dakota State University; SD = South Dakota State University; Bold varieties are those recently released, so data is limited and rating values may change.2 Straw Strength = 1 to 9 scale, with 1 the strongest and 9 the weakest. These values are based on recent data and may change as more data become available. 3 Days to Head = the number of days from planting to head emergence from the boot averaged from several locations. 4 R = resistant; MR = moderately resistant; M = intermediate; MS = moderately susceptible; NA = Not adequately tested; S = susceptible. 5 Fargo stem rust nursery inoculated with Puccinia graminis f. sp. Tritici races TPMK, TMLK, RTQQ, QFCQ and QTHJ. 6 Solid stemmed or semisolid stem, imparting resistance to sawfly. 7 CL = Refers to a Clearfield variety, with tolerance to the BeyondTM family of herbicides.

Reaction to Disease 4

VarietyAgent

or Origin 1Year

ReleasedHeight

(inches)Straw

Strength 2

Days to

Head 3Stem Rust 5

Leaf Rust

Stripe Rust 5

Tan Spot

Bact. Leaf

Head Scab

Page 72

Variety

Planted 2016

Eastern ND OBS1

TestWeight

Protein12% MB

VitreousKernels

FallingNumber

FarinographStability

FarinographAbsorption

LoafVolume

Mill and Bake

Quality Rating

(% area) (lb/bu) (%) (%) (seconds) (minutes) (%) (cc) (1-5 Stars)2

Barlow 2.8 5 62.3 13.8 70 366 7.3 64.7 946 ***Bolles -- 5 61.9 14.6 83 401 12.1 61.5 966 ****Elgin-ND 5.9 5 61.5 13.4 66 390 8.3 62.6 920 ***Faller 12.6 5 61.5 12.6 63 394 7.2 61.3 908 ***Glenn3 5.0 5 64.1 14.5 78 375 10.2 64.2 958 *****Linkert 8.6 5 61.6 14.3 54 420 13.8 62.0 980 ****Prevail -- 5 61.6 13.3 46 396 6.6 60.7 958 **Prosper 8.4 5 61.6 12.3 48 383 7.2 61.0 872 ***Rollag 4.1 5 62.6 14.4 80 448 5.7 66.3 879 *SY Ingmar 11.9 5 62.2 14.0 78 417 8.8 61.3 1004 ***SY Rowyn 1.4 5 61.7 12.8 54 439 11.8 59.7 948 ****SY Soren 12.4 5 62.4 13.7 51 433 7.1 62.3 963 ***WB9507 -- 3 59.0 12.7 58 430 9.4 60.6 947 ***WB-Mayville 6.6 5 61.0 13.7 68 389 8.2 62.5 928 ***Analyses conducted at the NDSU Hard Red Spring Wheat Quality Laboratory in Fargo, N.D. 1 Observations. 2 Mill and Bake Quality Rating scale 1 to 5, with 1 being low and 5 being superior. 3 Glenn is the current Wheat Quality Council check variety for comparing new experimental lines and newly released varieties. 2015 Carrington data omitted due to poor quality.

Table 2. Analytical milling and baking data from field plot variety trials at Carrington, Casselton and Langdon in 2014 and 2015 (unless otherwise noted).

Variety

Planted 2016

Western ND OBS 1

TestWeight

Protein12% MB

VitreousKernels

FallingNumber



LoafVolume

Mill and Bake

Quality Rating

(% area) (lb/bu) (%) (%) (seconds) (minutes) (%) (cc) (1-5) 2 Barlow 11.8 7 61.7 13.7 58 346 8.3 62.8 939 ***Bolles -- 7 59.7 15.0 52 392 24.6 60.8 978 ****Elgin-ND 10.8 7 60.9 13.2 38 374 9.6 61.0 876 ***Faller 3.4 7 60.5 12.7 43 374 7.6 60.1 870 ***Glenn3 9.9 7 63.3 13.8 64 360 9.7 61.8 924 *****Linkert 0.7 7 60.9 13.9 43 396 20.7 60.8 933 ****Prevail -- 7 60.2 12.7 27 311 8.7 56.9 903 **Prosper 5.3 7 60.6 13.1 41 362 7.7 60.7 924 ***Rollag 2.3 6 61.5 14.1 49 450 6.8 64.6 888 *SY Ingmar 11.1 5 61.8 14.4 61 408 10.2 60.3 974 ***SY Rowyn 0.7 6 61.0 13.2 45 408 19.6 59.1 941 ****SY Soren 17.6 6 62.0 14.0 40 411 7.8 61.0 959 ***WB 9507 -- 5 58.9 13.6 42 370 8.3 61.9 976 ***WB Mayville 0.5 6 61.1 13.8 54 381 9.6 61.9 931 ***Analyses conducted at the NDSU Hard Red Spring Wheat Quality Laboratory in Fargo, N.D. 1 Observations 2 Mill and Bake Quality Rating scale 1 to 5, with 1 being low and 5 being superior. 3 Glenn is the current Wheat Quality Council check variety for comparing new experimental lines and newly released varieties. 2015 Dickinson data omitted due to poor quality.

Table 3. Analytical milling and baking data from field plot variety trials at Dickinson, Hettinger, Minot and Williston in 2014 and 2015. (unless otherwise noted).

Page 73

Variety

Planted 2016

Western ND OBS 1

TestWeight

Protein12% MB

VitreousKernels

FallingNumber



LoafVolume

Mill and Bake

Quality Rating

(% area) (lb/bu) (%) (%) (seconds) (minutes) (%) (cc) (1-5) 2 Barlow 11.8 7 61.7 13.7 58 346 8.3 62.8 939 ***Bolles -- 7 59.7 15.0 52 392 24.6 60.8 978 ****Elgin-ND 10.8 7 60.9 13.2 38 374 9.6 61.0 876 ***Faller 3.4 7 60.5 12.7 43 374 7.6 60.1 870 ***Glenn3 9.9 7 63.3 13.8 64 360 9.7 61.8 924 *****Linkert 0.7 7 60.9 13.9 43 396 20.7 60.8 933 ****Prevail -- 7 60.2 12.7 27 311 8.7 56.9 903 **Prosper 5.3 7 60.6 13.1 41 362 7.7 60.7 924 ***Rollag 2.3 6 61.5 14.1 49 450 6.8 64.6 888 *SY Ingmar 11.1 5 61.8 14.4 61 408 10.2 60.3 974 ***SY Rowyn 0.7 6 61.0 13.2 45 408 19.6 59.1 941 ****SY Soren 17.6 6 62.0 14.0 40 411 7.8 61.0 959 ***WB 9507 -- 5 58.9 13.6 42 370 8.3 61.9 976 ***WB Mayville 0.5 6 61.1 13.8 54 381 9.6 61.9 931 ***Analyses conducted at the NDSU Hard Red Spring Wheat Quality Laboratory in Fargo, N.D. 1 Observations 2 Mill and Bake Quality Rating scale 1 to 5, with 1 being low and 5 being superior. 3 Glenn is the current Wheat Quality Council check variety for comparing new experimental lines and newly released varieties. 2015 Dickinson data omitted due to poor quality.

Casselton Prosper Langdon Avg. eastern N.D.Variety 2016 3 Yr. 2016 3 Yr. 2016 3 Yr. 2016 3 Yr.

--------------------------------------------------(bu/a)---------------------------------------------------------------Barlow 80.0 73.9 73.3 71.4 59.6 72.9 71.0 72.7Bolles 69.9 -- 65.8 -- 62.6 73.8 66.1 --Boost 79.0 -- 67.3 -- 60.5 72.6 68.9 --Elgin-ND 78.0 76.9 74.1 70.0 64.7 75.9 72.3 74.3Faller 72.0 77.9 80.8 74.2 79.0 82.9 77.3 78.3Focus 85.0 -- 70.8 -- 53.1 66.2 69.6 --Glenn4 67.1 67.5 66.9 69.7 64.3 71.6 66.1 69.6HRS 3361 78.3 -- 81.7 -- 70.5 73.8 76.8 --HRS 3419 82.7 -- 86.4 -- 78.9 83.5 82.7 --HRS 3504 82.3 -- 85.3 -- 73.2 -- 80.3 --HRS 3530 87.8 -- 93.6 -- 78.1 -- 86.5 --HRS 3616 76.7 -- 75.4 -- 65.9 -- 72.7 --LCS Anchor -- -- -- -- 59.5 -- -- --LCS Breakaway 79.0 75.6 79.1 74.8 70.9 73.9 76.3 74.8LCS Iguacu 76.6 79.7 85.3 77.9 75.7 79.9 79.2 79.2LCS Nitro 82.2 -- 82.4 -- 77.8 81.3 80.8 --LCS Prime 73.3 -- 75.2 -- 68.7 -- 72.4 --LCS Pro 79.3 -- 74.8 -- 67.8 -- 74.0 --Linkert 72.0 74.9 75.5 73.2 62.8 73.5 70.1 73.9MS Chevelle 76.0 72.8 78.5 75.6 67.7 79.7 74.1 76.0MS Stingray 87.1 82.6 87.8 73.1 75.6 77.0 83.5 77.6Prestige 79.1 -- 73.1 -- 67.8 -- 73.3 --Prevail 82.4 -- 88.4 -- 65.9 74.8 78.9 --Prosper 75.4 75.8 75.5 72.6 77.8 80.5 76.2 76.3Redstone 80.1 -- 75.2 -- 75.9 -- 77.1 --Rollag 75.5 79.3 74.1 73.5 71.2 77.1 73.6 76.6Shelly 82.7 -- 81.6 -- 71.1 -- 78.5 --Surpass 89.3 -- 82.2 -- 58.5 -- 76.7 --SY Ingmar 83.1 80.4 76.6 71.4 70.0 77.0 76.6 76.3SY Rowyn 85.6 81.0 88.5 77.4 67.2 77.3 80.4 78.6SY Soren 78.0 72.5 70.8 67.0 68.9 76.0 72.6 71.8SY Valda 87.8 -- 91.1 -- 78.4 -- 85.8 --TCG-Cornerstone 70.9 -- 75.9 -- 58.3 -- 68.4 --TCG-Spitfire 75.5 -- 80.2 -- 58.2 -- 71.3 --TCG-Wildfire 84.3 -- 79.2 -- 64.2 -- 75.9 --Velva 71.4 69.5 64.5 61.4 52.6 -- 62.8 --WB9312 83.0 -- 87.6 -- -- -- -- --WB9507 83.6 -- 81.9 -- 76.6 74.7 80.7 --WB9653 86.7 -- 90.4 -- 76.1 -- 84.4 --WB-Mayville 78.8 71.6 79.3 72.6 56.9 68.1 71.7 70.8Mean 79.4 75.7 78.9 72.2 68.0 75.8 75.4 75.1CV% 6.8 -- 7.1 -- 8.7 -- 6.4 3.6LSD 0.05 7.3 -- 7.5 -- 8.1 -- 7.8 4.6LSD 0.10 6.1 -- 6.3 -- 6.8 -- 6.3 3.8

Table 4. Yield of hard red spring wheat varieties grown at three locations in eastern North Dakota, 2014-2016.

Page 74

Table 5. Yield of hard red spring wheat varieties grown at four locations in western North Dakota, 2014-2016

Variety

Dickinson Hettinger Minot Williston Avg. western N.D.

2016 3 Yr. 2016 3 Yr. 2016 3 Yr. 2016 3 Yr. 2016 3 Yr.----------------------------------------------------------(bu/a)----------------------------------------------------------

Barlow 48.5 69.6 48.4 64.6 63.6 64.8 52.8 38.6 53.3 59.4Bolles 47.5 68.4 44.0 64.8 70.2 68.1 45.6 -- 51.8 --Boost 52.3 68.6 50.6 66.1 66.0 -- 48.5 40.8 54.4 --Duclair 49.8 67.4 48.5 -- 84.4 74.5 53.0 41.6 58.9 --Egan 45.4 -- 51.6 -- 71.0 -- 48.4 -- 54.1 --Elgin-ND 48.5 69.6 48.5 70.5 80.0 71.6 57.2 43.0 58.6 63.7Faller 47.1 70.1 43.4 72.3 84.2 81.0 53.5 39.5 57.1 65.7Focus 49.6 -- 48.2 66.0 65.3 -- 54.7 -- 54.5 --Glenn4 51.2 68.3 49.1 63.1 76.4 67.3 52.7 38.7 57.4 59.4HRS 3361 55.3 67.8 44.6 66.1 90.2 72.5 51.8 -- 60.5 51.6HRS 3419 60.5 69.0 54.7 79.8 83.8 77.9 56.9 -- 64.0 --HRS 3504 65.0 -- 48.3 -- 83.6 -- 59.8 -- 64.2 --HRS 3530 59.1 -- 43.7 -- 73.5 -- 54.0 -- 57.6 --HRS 3616 48.9 -- 48.6 -- 75.6 -- 52.2 -- 56.3 --LCS Anchor 51.7 -- 52.6 -- 72.5 -- 55.2 -- 58.0 --LCS Breakaway 51.2 72.6 48.4 66.3 86.9 73.4 49.8 38.2 59.1 62.6LCS Iguacu 54.0 71.0 47.5 69.5 75.4 71.3 50.7 41.6 56.9 63.4LCS Nitro 57.0 74.4 44.9 74.0 109.1 85.7 52.5 42.4 65.9 69.1LCS Prime 60.5 -- 49.1 -- 77.7 -- 55.9 -- 60.8 --LCS Pro 60.7 76.9 49.7 64.6 81.2 71.5 57.1 45.3 62.2 64.6Linkert 52.9 70.4 43.6 62.6 77.5 69.1 51.3 41.8 56.3 61.0Mott 57.7 -- 46.4 63.9 66.9 68.5 53.3 42.7 56.1 43.8MS Chevelle 52.1 69.4 47.8 71.7 86.7 73.5 57.8 -- 61.1 --MS Stingray 45.1 65.4 43.2 71.0 99.1 86.4 55.4 -- 60.7 --ND901CLPlus 49.5 65.4 41.5 58.1 69.9 68.2 53.0 40.6 53.5 58.1Prestige 54.1 -- 45.8 -- 74.2 -- 51.4 -- 56.4 --Prevail 57.5 65.4 49.3 70.6 64.2 71.6 57.9 45.4 57.2 63.3Prosper 55.0 71.6 36.0 64.2 93.8 82.4 51.2 41.1 59.0 64.8Redstone 56.6 -- 47.0 -- 86.2 -- 56.9 -- 61.7 --Rollag 52.6 71.5 47.3 67.8 74.6 73.2 53.7 41.1 57.1 63.4Shelly 48.7 -- 50.9 -- 71.2 -- 51.6 -- 55.6 --Surpass 56.2 -- 49.7 -- 68.6 -- 54.8 -- 57.3 --SY Ingmar 56.7 73.6 48.1 65.7 84.6 78.0 51.9 42.9 60.3 65.1SY Rowyn 58.6 72.7 48.8 69.4 70.5 75.1 50.8 39.5 57.2 64.2SY Soren 50.1 69.2 50.1 69.4 68.6 68.3 54.4 40.7 55.8 61.9SY Tyra 51.9 71.9 46.6 66.3 74.2 -- 53.8 42.6 56.6 45.2SY Valda 63.5 -- 49.6 -- 77.1 -- 55.4 -- 61.4 --SY605CL 45.9 69.2 49.0 67.9 73.3 70.7 55.2 41.6 55.9 62.4

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TCG-Cornerstone 52.0 -- 43.2 -- 60.7 -- 47.5 50.9 --TCG-Spitfire 59.7 -- 52.0 -- 87.7 -- 55.0 63.6 --TCG-Wildfire 52.0 -- 45.3 -- 71.5 -- 53.4 55.6 --Velva 55.3 72.8 43.0 63.4 80.0 70.6 58.9 46.5 59.3 63.3WB9312 56.1 -- 39.1 -- 77.3 -- 48.9 -- 55.4 --WB9507 52.8 71.0 36.2 64.9 73.3 73.6 52.8 42.2 53.8 62.9WB9653 61.8 -- 45.8 -- 87.5 -- 56.3 -- 62.9 --WB-Mayville 53.7 73.5 45.9 61.9 69.7 67.5 52.6 42.5 55.5 61.4Mean 53.7 70.2 46.9 67.1 77.4 73.2 53.4 41.7 57.9 60.9CV % 10.4 -- 7.1 -- 10.5 -- 7.1 -- 10.0 6.9LSD 0.05 7.6 -- 4.7 -- 13.1 -- 5.3 -- 8.1 6.1LSD 0.10 6.4 -- 4.0 -- 11.0 -- 4.4 -- 6.7 5.1

Table 5. (continued) Yield of hard red spring wheat varieties grown at four locations in western North Dakota, 2014-2016

Variety

Dickinson Hettinger Minot Williston Avg. western N.D.

2016 3 Yr. 2016 3 Yr. 2016 3 Yr. 2016 3 Yr. 2016 3 Yr.----------------------------------------------------------(bu/a)----------------------------------------------------------

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Variety Casselton Prosper Dickinson Hettinger Langdon Minot Williston State Avg.------------------------------------------------------------(%)---------------------------------------------------------------

Barlow 13.9 15.3 14.8 14.5 14.8 14.8 14.2 14.6Bolles 15.3 16.9 16.3 15.8 15.9 15.4 16.0 15.9Boost 14.4 15.5 15.7 14.7 15.0 15.2 15.2 15.1Duclair 14.0 14.8 13.9 13.5 -- 14.6 13.9 --Egan 15.2 15.5 15.1 14.8 -- 15.8 15.7 --Elgin-ND 13.7 14.9 13.9 14.6 14.9 14.9 14.1 14.4Faller 13.4 14.1 14.1 13.2 13.6 13.8 13.4 13.7Focus 13.5 15.2 14.5 13.4 14.3 14.3 13.9 14.2Glenn 4 13.7 14.8 15.3 14.9 14.7 15.0 13.9 14.6HRS 3361 13.5 14.3 14.0 13.6 13.8 14.4 13.8 13.9HRS 3419 13.0 13.8 12.2 13.4 13.3 13.3 14.0 13.3HRS 3504 13.8 14.3 12.7 13.4 13.4 14.5 13.3 13.6HRS 3530 14.4 15.0 14.4 14.2 14.3 14.2 14.0 14.4HRS 3616 14.4 15.9 14.3 14.3 15.3 15.4 14.7 14.9LCS Anchor -- -- 15.5 13.9 14.7 14.9 14.6 --LCS Breakaway 14.3 15.4 13.4 13.6 14.1 15.0 14.9 14.4LCS Iguacu 12.3 13.3 12.8 12.3 12.4 13.7 13.1 12.8LCS Nitro 13.1 14.0 13.8 13.4 13.3 13.3 13.5 13.5LCS Prime 13.1 13.9 13.3 13.0 13.5 13.0 12.8 13.2LCS Pro 14.4 15.4 13.7 14.0 14.4 14.9 13.5 14.3Linkert 14.3 15.2 14.7 14.7 15.1 15.4 14.6 14.9Mott 13.9 14.9 12.5 14.5 -- 14.7 15.1 --MS Chevelle 13.0 14.3 12.6 12.9 13.4 13.4 13.2 13.3MS Stingray 11.6 12.8 15.0 12.6 11.6 12.2 12.3 12.6ND901CLPlus 14.9 15.8 14.9 15.2 -- 16.6 15.5 --Prestige 14.0 14.7 13.8 13.4 14.5 14.7 14.1 14.2Prevail 13.3 14.3 13.7 13.6 14.3 13.9 13.6 13.8Prosper 13.3 14.3 14.2 13.3 13.8 14.2 13.9 13.9Redstone 13.6 14.8 12.4 13.4 13.4 13.2 13.7 13.5Rollag 14.2 15.6 14.2 14.5 14.8 15.2 14.7 14.7Shelly 13.0 14.4 13.4 13.7 14.0 14.0 13.4 13.7Surpass 13.9 14.9 13.6 13.4 14.2 13.9 13.5 13.9SY Ingmar 14.1 15.1 14.0 14.3 15.1 14.4 14.5 14.5SY Rowyn 13.9 14.2 13.6 13.8 14.1 14.3 14.0 14.0SY Soren 14.0 15.0 14.5 14.5 14.5 14.8 14.6 14.6SY Tyra 13.7 14.5 13.3 13.3 -- 13.6 13.6 --SY Valda 13.5 14.3 12.9 13.4 13.9 14.0 13.6 13.7SY605CL 14.4 15.9 14.1 14.2 -- 14.7 14.2 --TCG-Cornerstone 14.4 15.0 14.8 14.3 14.5 14.8 15.4 14.7

Table 6. Protein at 12 percent moisture of hard red spring wheat varieties grown at seven locations in North Dakota, 2016.

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TCG-Spitfire 13.9 14.6 14.3 13.8 14.2 14.3 14.0 14.2TCG-Wildfire 14.2 14.8 14.2 13.7 14.2 14.9 14.1 14.3Velva 14.2 14.9 14.3 13.7 14.8 14.4 13.8 14.3WB9312 12.6 12.8 12.8 12.2 -- 12.8 13.2 --WB9507 13.7 14.5 13.7 13.3 13.7 15.0 14.1 14.0WB9653 13.3 14.1 14.3 13.2 13.4 14.0 13.7 13.7WB-Mayville 14.6 15.3 15.1 13.9 14.7 14.9 14.6 14.7Mean 13.8 14.7 14.0 13.8 14.2 14.4 14.1 14.1CV % 3.5 2.5 5.1 3.7 2.8 3.1 3.4 2.9LSD 0.05 0.7 0.5 1.4 0.7 0.6 0.7 0.7 0.4LSD 0.10 0.6 0.4 1.2 0.6 0.5 0.6 0.6 0.3

Table 6. (continued) Protein at 12 percent moisture of hard red spring wheat varieties grown at seven locations in North Dakota, 2016.

Variety Casselton Prosper Dickinson Hettinger Langdon Minot Williston State Avg.------------------------------------------------------------(%)---------------------------------------------------------------

Variety Casselton Prosper Dickinson Hettinger Langdon Minot Williston State Avg.------------------------------------------------------------(lb/bu)----------------------------------------------------

Barlow 62.4 61.8 60.3 59.0 60.0 61.9 59.3 60.7Bolles 60.6 59.3 58.2 58.8 60.2 60.9 56.5 59.2Boost 61.4 61.0 61.1 57.6 58.5 59.6 57.5 59.5Duclair 59.9 59.4 57.9 56.2 -- 59.6 56.6 --Egan 59.6 58.4 55.9 56.4 -- 57.9 55.9 --Elgin-ND 61.0 60.2 57.8 56.8 59.2 60.6 57.8 59.1Faller 60.3 61.1 58.5 57.1 60.8 60.1 56.5 59.2Focus 62.9 62.8 62.1 59.6 61.2 60.6 59.8 61.3Glenn4 63.9 63.4 59.2 60.1 63.0 63.2 60.7 61.9HRS 3361 60.3 59.8 59.9 57.6 58.7 60.1 56.6 59.0HRS 3419 60.5 60.1 59.2 57.3 59.1 59.5 55.1 58.7HRS 3504 60.9 60.0 61.4 56.7 58.2 60.1 56.9 59.2HRS 3530 62.3 62.2 60.5 55.4 60.8 60.5 56.8 59.8HRS 3616 60.4 60.1 61.3 57.2 59.4 60.9 57.4 59.5LCS Anchor -- -- 60.9 59.4 59.3 61.6 59.1 --LCS Breakaway 62.7 62.3 62.6 58.8 61.7 61.9 59.4 61.3LCS Iguacu 62.0 62.6 60.2 60.2 60.7 60.5 58.7 60.7LCS Nitro 60.9 60.1 57.6 57.3 60.0 60.4 56.1 58.9LCS Prime 62.1 62.5 60.1 57.5 60.9 61.4 59.4 60.6LCS Pro 62.4 61.4 62.1 58.3 61.1 61.6 57.6 60.6Linkert 61.4 61.2 61.0 57.8 59.7 60.8 58.1 60.0Mott 61.7 61.1 60.8 58.5 -- 59.5 57.5 --

Table 7. Test weight of hard red spring wheat varieties grown at seven locations in North Dakota, 2016.

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MS Chevelle 61.1 60.1 59.4 58.0 58.5 61.2 58.3 59.5MS Stingray 60.2 59.8 58.5 55.7 58.8 55.5 56.9 57.9ND901CLPlus 61.3 61.1 61.8 57.7 -- 60.7 58.6 --Prestige 60.7 60.3 59.1 58.3 59.4 60.1 56.1 59.1Prevail 61.5 61.2 60.2 59.0 59.3 60.2 57.7 59.9Prosper 60.3 60.5 59.5 54.9 60.9 60.8 57.3 59.2Redstone 61.4 59.9 58.9 56.2 60.1 60.3 58.1 59.3Rollag 62.5 61.7 60.7 58.8 61.3 61.2 57.6 60.5Shelly 62.2 61.1 60.5 56.8 59.7 61.0 58.3 59.9Surpass 61.0 61.4 60.7 58.8 59.4 61.0 57.8 60.0SY Ingmar 62.0 60.6 62.4 58.8 60.9 61.1 57.7 60.5SY Rowyn 61.6 61.6 60.5 58.3 60.1 61.1 57.1 60.0SY Soren 60.6 59.4 59.5 57.9 60.6 61.4 58.4 59.7SY Tyra 60.0 58.7 61.0 57.6 -- 57.3 58.6 --SY Valda 61.7 61.9 61.0 57.7 60.5 57.4 57.9 59.7SY605CL 61.7 60.4 58.7 59.1 -- 62.0 59.1 --TCG-Cornerstone 61.2 60.9 59.2 57.1 59.5 61.5 57.5 59.6TCG-Spitfire 59.7 59.4 60.6 56.1 59.0 58.6 57.2 58.7TCG-Wildfire 61.8 60.7 58.6 58.5 59.4 59.8 58.3 59.6Velva 60.2 59.3 60.1 56.2 57.7 59.1 58.2 58.7WB9312 60.5 61.6 61.4 55.8 -- 61.3 57.8 --WB9507 59.3 59.3 59.2 53.9 59.2 59.0 55.1 57.9WB9653 61.2 60.8 60.4 56.8 58.5 60.5 57.6 59.4WB-Mayville 62.0 61.6 60.1 55.8 58.9 59.6 57.7 59.4Mean 61.2 60.8 60.0 57.6 59.9 60.3 57.7 59.6CV % 1.0 1.3 2.3 2.3 1.1 1.3 0.6 1.5LSD 0.05 0.8 1.1 1.9 1.8 1.0 1.3 0.5 1.0LSD 0.10 0.7 0.9 1.6 1.5 0.8 1.1 0.4 0.8

Table 7. (continued) Test weight of hard red spring wheat varieties grown at seven locations in North Dakota, 2016.

Variety Casselton Prosper Dickinson Hettinger Langdon Minot Williston State Avg.------------------------------------------------------------(lb/bu)----------------------------------------------------

Page 79

North Dakota Durum Wheat Variety Trial Results for 2016 and Selection GuideJoel Ransom, Elias Elias, Andrew Friskop, Tim Friesen, Zhaohui Liu and Frank Manthey (NDSU Main Station); John Rickertsen (Hettinger Research Extension Center); Eric Eriksmoen (North Central Research Extension Center, Minot); Bryan Hanson (Langdon Research Extension Cen-ter); Gautam Pradhan (Williston Research Extension Cen-ter); Mike Ostlie (Carrington Research Extension Center)

Durum was planted on 1.46 million acres in North Dakota in 2016, up significantly from the 1.1 million acres planted in 2015. The average yield is estimated at 40.5 bushels per acre, up from last year. If this estimate is accurate this will represent the highest average yield ever reported for durum in North Dakota. The most commonly grown variet-ies in 2016 and the percent of the acreage they occupied were Divide (21.5), Carpio (17.0), Alkabo (11.2), Joppa (10.3), Tioga (8.0), Lebsock (7.8), Mountrail (5.5), and Grenora (3.8).

Durum varieties are tested each year at multiple sites throughout North Dakota. The relative performance of these varieties is presented in table form. Variety per-formance data are used to provide recommendations to producers. Some varieties may not be included in the tables due to insufficient testing or lack of seed availability, or they offer no yield or disease advantage over similar varieties. Yield is reported at 13.5 percent moisture, while protein content is reported at 12 percent moisture.

The agronomic data presented in this publication are from replicated research plots using experimental designs that enable the use of statistical analysis. These analyses enable the reader to determine, at a predetermined level of confidence, if the differences observed among varieties are reliable or if they might be due to error inherent in the experimental process. The LSD (least significant differ-ence) numbers beneath the columns in tables are derived from these statistical analyses and only apply to the num-bers in the column in which they appear. If the difference between two varieties exceeds the LSD value, it means that with 95 or 90 percent confidence (LSD probability 0.05 or 0.10), the higher-yielding variety has a significant yield advantage. When the difference between two varieties is less than the LSD value, no significant difference occurs between those two varieties under those growing conditions.

The abbreviation NS is used to indicate no significant dif-ference for that trait among any of the varieties at the 95 or 90 percent level of confidence. The CV is a measure of variability in the trial. The CV stands for coefficient of varia-tion and is expressed as a percentage. Large CVs mean a large amount of variation that could not be attributed to differences in the varieties.

Presentation of data for the entries tested does not imply approval or endorsement by the authors or agencies con-ducting the test. North Dakota State University approves the reproduction of any table in the publication only if no portion is deleted, appropriate footnotes are given and the order of the data is not rearranged. Additional data from county sites are available from each Research Extension Center at www.ag.ndsu.edu/varietytrials/durum. Use data from multiple locations and years when selecting a variety.


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VarietyAgent or Origin 1

Year Released

Height (inches)

Straw Strength 2

Days to Heading 3

Reaction to Disease4

Stem Rust

Leaf Rust Foliar

Bact. Leaf

StreakHead Scab

AC Commander Can. 2002 32 5 68 R R MS NA NAAC Napoleon Can. 2001 40 5 68 R R S NA NAAC Navigator Can. 1999 32 5 66 R R M NA SAlkabo ND 2005 36 2 67 R R M MS MSAlzada5 WB 2004 30 6 63 R R S NA VSBelzer ND 1997 39 5 66 R R M NA MBen ND 1996 39 3 67 R R MR MS S6

Carpio ND 2012 37 5 69 R R M MS/S MCDC Verona Can. 2010 38 4 69 R R MR NA SDG Max DGP 2008 38 5 66 R MR MR NA MSDG Star DGP 2007 37 4 64 R R M NA NADilse ND 2002 37 5 68 R R M M MSDivide ND 2005 38 5 68 R R M MS/S MGrande D’Oro WB/DGP 2005 37 4 68 R R M NA NAGrenora ND 2005 35 5 67 R R M MS/S MSJoppa ND 2013 39 5 68 R R M MS MKyle Can. 1984 39 7 68 R MR M NA NALebsock ND 1999 37 3 67 R R M MS MSMaier ND 1998 37 5 67 R R M NA S6

Mountrail ND 1998 37 5 68 R R M MS S6

MS-Dart Meridian 2015 37 5 68 NA NA NA NA NAPierce ND 2001 38 5 67 R R MS MS SPlaza ND 1999 29 7 68 R R M NA MSRugby ND 1973 38 5 64 R R MR NA S6

Silver MT 2012 31 5 62 NA NA NA NA NAStrongfield Can. 2004 37 6 68 R R MS NA STioga ND 2010 39 4 68 R R M MS MSVT Peak Viterra 2010 37 6 68 NA NA NA NA NAWales WB 2008 36 3 67 R R M NA S6

WB-Belfield WB 2011 30 2 62 R R S NA SWesthope WB 2009 36 3 67 R R MS NA S1 Refers to agent or developer: Can. = Agriculture Canada, WB = Westbred, ND = North Dakota State University, DGP = Dakota Growers Pasta, Montana State = MT.2 Straw Strength = 1-9 scale, with 1 the strongest and 9 the weakest. Based on recent data. These values may change as more data become available.3 Days to Heading = the number of days from planting to head emergence from the boot. Averaged from several loca-tions and years.4 R = resistant; MR = moderately resistant; M = intermediate; MS = moderately susceptible; S = susceptible; VS = very susceptible; NA = Not adequately tested. Foliar Disease = reaction to tan spot and septoria leaf spot complex. 5 Alzada has a disease-resistance package that makes it more adapted to drier growing conditions (for example, western North Dakota).6 Indicates yields and/or quality often have been higher than would be expected based on visual symptoms. NA = Not adequately tested.

Table 1. Descriptions and agronomic traits of durum wheat varieties grown in North Dakota, 2016.

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VarietyTest

WeightVitreousKernels

LargeKernels

FallingNumber

WheatProtein 1

GlutenIndex 2

PastaColor 3

SpaghettiFirmness

OverallQuality 4

(lb/bu) (%) (%) (sec) (%) (1-12) (g-cm)AC Commander 59.5 93 52 495 14.1 89 9.0 5.4 AverageAC Navigator 60.0 93 59 486 14.2 68 8.9 5.4 GoodAlkabo 61.2 86 51 399 13.8 47 9.0 4.8 GoodAlzada 5,6 59.1 91 62 467 14.2 88 8.5 5.3 AverageCarpio 61.0 82 59 447 13.9 91 8.9 5.1 GoodDivide 60.7 88 51 442 14.1 76 8.9 4.9 GoodGrenora 60.3 92 52 424 13.9 63 8.8 5.0 GoodJoppa 60.7 87 44 405 13.5 83 9.2 4.8 GoodMaier 60.4 92 47 399 14.7 54 8.8 5.2 GoodMountrail 59.8 91 42 417 14.4 22 8.4 4.4 AveragePierce 60.8 94 44 406 14.3 60 8.8 5.0 GoodStrongfield 60.2 89 52 426 14.7 65 8.7 5.1 GoodTioga 60.7 88 57 402 13.8 76 8.7 5.1 GoodAverage 60.3 90 51 432 14.1 68 8.8 5.1For all numbered footnotes, refer to bottom of Table 3.

Table 2. Durum wheat variety quality descriptions, milling and processing data averaged for five years (2010-2015) from drill strips (33 locations/year).

VarietyTest

WeightVitreousKernels

LargeKernels

FallingNumber

WheatProtein1

GlutenIndex 2

PastaColor 3

SpaghettiFirmness

OverallQuality 4

(lb/bu) (%) (%) (sec) (%) (1-12) (g-cm)AC Commander 5 58.9 90 38 596 14.2 91 9.3 4.2 AverageAC Navigator 59.6 89 39 576 14.0 75 9.2 4.1 AverageAlkabo 62.1 82 47 469 13.5 46 9.4 3.4 GoodAlzada6 58.8 86 56 545 14.4 85 8.7 4.0 AverageCarpio 61.6 78 54 517 14.1 92 9.1 3.8 GoodDivide 61.4 86 48 501 14.2 75 9.1 3.8 GoodGrenora 60.9 87 51 505 13.8 58 9.2 3.8 GoodJoppa 61.5 85 40 479 13.4 83 9.4 3.6 GoodLebsock5 61.7 89 41 509 14.1 32 8.8 3.7 GoodMaier 61.4 87 44 483 14.3 55 9.1 4.0 GoodMountrail 60.6 91 39 491 14.1 18 9.1 3.4 AveragePierce 61.8 91 44 483 14.2 60 9.1 3.7 GoodStrongfield 60.6 89 48 516 14.8 65 8.9 3.9 GoodTioga 61.4 87 55 468 13.8 77 9.2 3.8 GoodAverage 60.9 87 46 510 14.1 65 9.1 3.81 Wheat protein is reported on a 12 percent moisture basis. 2 Gluten index is unitless. Numbers less than 15 = very weak and greater than 80 = very strong gluten proteins. 3 Pasta Color Score: Higher number indicates better color, with 8.5+ typically considered good. 4 Overall Quality is determined based on agronomic, milling and spaghetti processing performance. 5 Average of 31 drill strips instead of 33 for other varieties in Table 1. Lebsock average of five locations and AC Commander average of six locations instead of seven for other varieties in Table 3. 6 Alzada has good quality when grown in environments where it is adapted. Low test weight can affect quality in some environments.

Table 3. Durum wheat variety quality descriptions, milling and processing data for 2015 at seven locations in the drill strips.

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VarietyLangdon Dickinson Hettinger Minot Williston Average

2016 3 Yr. 2016 3 Yr. 2016 3 Yr. 2016 3 Yr. 2016 3 Yr. 2016 3 Yr.-------------------------------------------------------------(bu/a)------------------------------------------------------------

AC Commander 45.4 64.1 47.6 61.2 35.0 54.8 61.1 63.6 39.6 33.8 45.7 55.5AC Navigator 35.2 57.2 43.7 57.8 36.3 52.5 55.7 54.1 37.3 34.0 41.6 51.1Alkabo 51.2 68.8 53.1 65.2 33.3 62.0 72.3 76.4 36.4 33.3 49.3 61.1Alzada 37.1 59.2 39.9 52.1 34.4 44.3 60.9 53.4 35.5 32.7 41.6 48.3Ben 44.5 65.3 43.7 61.1 31.6 55.0 61.8 67.1 36.8 31.8 43.7 56.1Carpio 43.0 69.0 47.7 59.1 32.2 61.4 72.5 71.8 35.3 33.6 46.1 59.0CDC Verona 36.3 60.7 52.5 62.3 33.7 59.8 59.7 60.0 37.7 31.4 44.0 54.8Divide 35.0 65.5 54.2 68.4 33.4 65.9 64.2 69.4 41.4 34.5 45.6 60.7Grenora 40.9 67.7 51.5 68.2 33.4 59.3 69.1 69.6 39.4 34.2 46.9 59.8Joppa 42.9 70.4 56.7 68.7 41.1 68.5 78.3 73.9 32.7 32.3 50.3 62.8Lebsock 52.5 68.1 52.8 64.6 35.6 56.0 66.7 71.2 41.5 32.2 49.8 58.4Maier 37.2 64.5 45.1 64.2 30.4 52.0 66.1 67.1 34.1 -- 42.6 --Mountrail 37.9 68.3 50.3 67.3 31.8 63.9 72.5 73.7 33.0 31.9 45.1 61.0Pierce 41.3 65.5 48.2 64.6 35.1 51.6 61.3 68.0 35.0 31.0 44.2 56.1Rugby 31.8 57.2 45.8 60.9 25.5 53.2 59.1 61.6 35.5 30.2 39.5 52.6Strongfield 32.7 61.0 51.2 62.5 35.5 60.6 45.7 59.0 38.0 31.5 40.6 54.9Tioga 37.1 65.4 51.5 68.2 34.3 65.2 51.7 63.8 43.0 37.5 43.5 60.0VT Peak 55.2 70.4 51.7 -- 35.1 64.9 77.4 73.5 38.9 34.2 51.7 --Mean 41.0 64.9 49.3 63.3 33.8 58.4 64.2 66.5 37.3 32.9 45.1 57.2CV % 11.1 -- 9.6 17.2 -- 6.7 -- 9.4 -- 10.9 6.1LSD 0.05 6.3 -- 6.8 8.4 -- 7.7 -- 5.6 -- 6.2 4.0LSD 0.10 5.3 -- 5.7 7.1 -- 6.4 -- 4.7 -- 5.2 3.4

Table 4. Yield of durum wheat varieties at five Research Extension Centers in North Dakota, 2014-2016.

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Variety

Langdon Dickinson Hettinger Minot Williston Average

Test Wt.Test Wt. Protein

Test Wt. Protein

Test Wt. Protein

Test Wt. Protein

Test Wt. Protein 1

lb/bu lb/bu % lb/bu % lb/bu % lb/bu % lb/bu %AC Commander 52.5 59.0 13.8 58.9 13.4 56.3 14.6 57.6 18.7 56.9 13.9

AC Navigator 52.9 60.4 14.3 60.7 12.8 55.7 14.4 57.6 18.7 57.5 13.8Alkabo 56.6 61.4 12.3 59.3 12.4 57.2 13.6 56.6 19.3 58.2 12.8Alzada 51.4 59.7 13.8 59.4 12.3 57.4 13.7 56.3 18.7 56.8 13.3Ben 55.3 60.9 13.5 59.3 12.3 57.9 15.1 57.2 19.4 58.1 13.6Carpio 55.6 61.5 14.1 56.1 12.6 57.9 14.0 57.2 19.0 57.7 13.6CDC Verona 55.5 60.5 12.9 58.8 12.7 56.3 15.4 56.8 21.0 57.6 13.7Divide 53.6 60.1 12.7 59.6 12.6 57.5 14.5 57.1 19.5 57.6 13.3Grenora 54.2 58.5 12.3 58.8 12.5 56.5 15.0 57.3 17.9 57.1 13.3Joppa 55.4 60.8 11.9 58.7 11.8 57.5 13.7 57.1 18.9 57.9 12.5Lebsock 57.2 61.9 12.3 60.4 11.6 58.5 14.4 57.6 18.0 59.1 12.8Maier 53.7 61.5 12.3 58.5 12.7 57.5 15.2 56.6 20.7 57.6 13.4Mountrail 54.4 59.0 12.1 58.9 12.1 57.2 14.1 55.3 19.7 57.0 12.8Pierce 56.7 61.8 13.1 60.4 12.2 58.3 14.2 57.5 18.9 58.9 13.2Rugby 54.3 60.3 13.4 59.1 12.7 57.6 14.1 57.9 18.2 57.8 13.4Strongfield 53.2 58.7 11.9 58.2 13.3 54.1 15.6 56.8 20.1 56.2 13.6Tioga 53.2 61.4 12.9 59.8 13.0 55.1 14.7 58.2 17.7 57.5 13.5VT Peak 58.6 61.9 13.4 61.1 12.1 59.0 14.4 58.9 19.4 59.9 13.3Mean 54.7 60.5 12.9 59.2 12.5 57.1 14.5 57.2 19.1 57.7 13.3CV % 1.5 1.4 4.8 2.2 8.6 1.1 1.8 1.1 5.2 1.7 4.4LSD 0.05 1.2 2.0 1.2 1.8 1.5 1.0 0.4 1.0 1.6 1.3 1.0LSD 0.10 1.0 1.6 1.0 1.5 1.3 0.8 0.3 0.8 1.3 1.1 0.81 Williston protein not included in average.

Table 5. Test weight and protein of durum wheat varieties at five Research Extension Centers in North Dakota, 2016.

Page 84

North Dakota Barley, Oat and Rye Variety Trial Results for 2016 and Selection GuideJoel Ransom, Robert Brueggeman, Rich Horsley, Mike McMullen, Paul Schwarz and Andrew Friskop (NDSU Main Station); Blaine Schatz, Steve Zwinger and Mike Ostlie (Carrington Research Extension Center); Glenn Martin (Dickinson Research Extension Center); John Rickertsen (Hettinger Research Extension Center); Eric Eriksmoen (North Central Research Extension Center, Minot); Bryan Hanson (Langdon Research Extension Center); and Gautam Pradhan (Williston Research Extension Center)

Barley, oat and rye varieties currently grown in North Dakota are described in the following tables. Successful production of these crops depends on numerous factors, including selecting the right variety for a particular area. Characteristics to evaluate in selecting a variety are: yield potential in your area, test weight, straw strength, plant height, reaction to problematic diseases and maturity.

Selecting varieties with good quality also is important to maintain market recognition. Because malting barley usu-ally is purchased on an identity-preserved basis, produc-ers are encouraged to determine which barley varieties are being purchased by potential barley buyers before se-lecting a variety. When selecting a high-yielding and good-quality variety, use data that summarize several years and locations. Additional data from county sites are available at www.ag.ndsu.edu/varietytrials/ and from each Research Extension Center.

The agronomic data presented in this publication are from replicated research plots using experimental designs that enable the use of statistical analysis. The LSD (least sig-nificant difference) numbers beneath the columns in tables are derived from these statistical analyses and apply only to the numbers in the column in which they appear. Differences between two varieties exceeding the LSD value means that with 95 or 90 percent confidence (LSD probability 0.05 or 0.10), the higher-yielding variety has a significant yield advantage. The abbreviation NS is used to indicate that no statistical difference occurs between va-rieties. The CV is a measure of variability in the trial. The CV stands for coefficient of variation and is expressed as a percentage. Large CVs mean a large amount of varia-tion could not be attributed to differences in the varieties.

Presentation of data for the entries tested does not imply approval or endorsement by the authors or agencies con-ducting the test. North Dakota State University approves the reproduction of any table in this publication only if no portion isdeleted, if appropriate footnotes are given and if the order of the data is not rearranged.


Page 85

Variety Use 1 Origin 2Year

ReleasedAwn

Type 3

Rachilla Hair

Length 4Aleurone

Color Height Straw

StrengthRelative Maturity

Reaction to Disease5

Stem Rust

Spot-form Net

BlotchSpot

BlotchNet

BlotchSIX-ROWEDCelebration M/F BARI 2008 S S White M.short Strg. Med. S MS MR/R MS/S

Drummond M/F ND 2000 S L White M.short V.strg. Med. S MR MR/R MS/S

Innovation MT BARI 2009 S L White M.short Strg. Med. S MS MR/R MS/S

Lacey M/F MN 1999 S S White M.short Strg. Med. S MR MR/R MS/S

Legacy M/F BARI 2000 S L White Med. Strg. M.late S MS MR/R MS/S

Quest6 M/F MN 2010 S L White M.short V.strg. Med. S MR MR/R MS/S

Rasmusson M/F MN 2008 S S White M.short Strg. Med. S MS MR/R MS/S

Robust F MN 1983 S S White Med. M.strg. Med. S MS/S MR/R MS/S

Stellar-ND M/F ND 2005 S L White M.short V.strg. Med. S MS MR/R MS/S

Tradition M/F BARI 2003 S L White M.short V.strg. Med. S MS MR/R MS/S

TWO-ROWED

AACSynergy M/F Syngenta 2015 R L White M.short Strg. M.late MR MR/R MR MR

ABI Balster M/F BARI 2015 R L White M.short Med. Med. NA MR NA NA

ABI Growler M/F BARI 2015 R L White M.short M.strg. Med. NA MS/S NA NA

AC Metcalfe M Canada 1997 R L White Med. Med. Late S MS MS S

CDC Copeland M Canada 1999 R L White Tall Med. Late S MS MS MR

CDC Meredith M Canada 2008 R L White Med. Med. Late MR MR S MS

Conlon7 M/F ND 1996 S L White M.short Med. M.early S MR MS MR/R

Conrad M BARI 2007 R L White Tall M.weak Late S MS NA NA

Eslick F MT 2003 R L White Med. M.weak M.late S NA MS NA

Harrington8 F Canada 1981 R L White Med. M.weak Late S S S MS

Haxby F MT 2003 R L White Med. Med. Med. S MS MS NA

Hockett M/F MT 2008 R L White Med. Med. Med. S NA NA NA

LCS Genie M Limagrain S S White Short V.strg. Med. NA MS NA NA

LCS Odyssey M/F Limagrain R S White Short Med. Med. NA MS NA NA

Lilly F Germany NA R L White Short M.strg. Late S MS/S S MR/R

ND Genesis9 M/F ND 2015 S L White Med. M.strg. M.late S MR MR MS

Pinnacle M/F ND 2006 S L White Med. Strg. M.late S S MR MS

Rawson F ND 2005 R L White Med. Med. Med. S MS MR MS

Scarlett M Germany 1995 R L White Short Med. Late S NA S MR

Sunshine F Germany NA R L White Short M.strg. Late S S S MS

Specialty

Wanubet SP MT 1990 H L White Med. Weak Late S NA S S1 M = malting; MT = being tested in plant-scale tests for malting and brewing quality; F = feed; SP = special uses (hull-less).2 BARI = Busch Agricultural Resources Inc.; MN = University of Minnesota; MT = Montana State University; ND = North Dakota State University.3 R = rough; S = smooth; H = hull-less.4 S = short; L = long.5 R = resistant; MR = moderately resistant; MS = moderately susceptible; S = susceptible; NA = not available.6 Moderately resistant to Fusarium head blight.7 Lower DON accumulations than other varieties tested.8 Recommended as a malting barley in western U.S.9 Bold indicates newly released in 2016.

Table 1. 2016 North Dakota barley variety descriptions.

Page 86

Variety

Carrington Langdon Average Eastern N.D.TestWt.

Yield TestWt.

Yield Test Yield2016 3 Yr. 2016 3 Yr. Wt. 2016 3 Yr.

(lb/bu) -----(bu/a)----- (lb/bu) -----(bu/a)----- (lb/bu) -----(bu/a)-----SIX-ROWEDCelebration 46.2 57.1 95.5 47.1 110.5 128.1 46.7 83.8 111.8Innovation 46.7 54.8 91.8 46.3 112.9 126.3 46.5 83.9 109.1Lacey 47.1 56.6 90.6 46.9 115.9 126.0 47.0 86.3 108.3Quest 47.1 48.1 86.6 45.9 107.2 120.2 46.5 77.7 103.4Stellar-ND 46.1 54.8 89.6 48.8 104.0 119.3 47.5 79.4 104.5Tradition 47.8 49.2 88.2 46.8 108.1 124.1 47.3 78.7 106.2TWO-ROWEDAAC Synergy 47.1 48.7 -- 48.0 112.5 -- 47.6 80.6 --ABI Balster 46.2 55.8 -- 43.7 91.8 -- 45.0 73.8 --ABI Growler 45.5 52.2 -- 45.4 94.4 -- 45.5 73.3 --CDC Meredith 44.1 51.1 -- 44.7 90.3 -- 44.4 70.7 --Conlon 50.2 52.8 91.1 49.7 76.4 104.4 50.0 64.6 97.8LCS Genie 47.8 51.9 -- 43.5 77.2 -- 45.7 64.6 --LCS Odyssey 47.7 53.1 -- 39.9 70.8 -- 43.8 62.0 --ND Genesis 48.4 55.6 90.3 47.3 104.6 -- 47.9 80.1 --Pinnacle 47.4 55.7 91.5 47.9 105.8 125.3 47.7 80.8 108.4Rawson 48.4 53.0 89.2 46.8 106.6 117.5 47.6 79.8 103.4SY Sirish 48.7 53.2 -- 44.1 87.5 -- 46.4 70.4 --Mean 47.7 52.6 90.5 46.4 100.3 121.2 47.1 76.5 105.9CV % 1.7 10.8 -- 2.6 6.4 -- 3.8 13.1 --LSD 0.05 1.2 NS -- 1.7 9.1 -- 3.7 NS --LSD 0.10 1.0 NS -- 1.4 7.6 -- 3.1 NS --

Table 2. Yield and test weight of barley varieties at two locations in eastern North Dakota, 2014-2016.

Page 87

VarietyCarrington Langdon Average Eastern N.D.

Plump Protein Plump Protein Plump Protein(%) (%) (%) (%) (%) (%)

SIX-ROWEDCelebration 88.0 16.3 92.3 13.9 90.2 15.1Innovation 86.3 16.4 91.0 13.5 88.7 15.0Lacey 82.5 15.8 90.7 13.9 86.6 14.9Quest 80.5 16.0 85.0 13.0 82.8 14.5Stellar-ND 86.4 14.9 95.5 13.3 91.0 14.1Tradition 83.5 15.8 88.7 13.9 86.1 14.9TWO-ROWEDAAC Synergy 88.9 15.4 94.3 11.9 91.6 13.7ABI Balster 83.5 16.2 83.7 12.8 83.6 14.5ABI Growler 88.2 16.6 83.9 12.4 86.1 14.5CDC Meredith 75.1 15.7 85.8 12.2 80.5 14.0Conlon 97.6 14.9 96.1 12.7 96.9 13.8LCS Genie 92.2 15.9 82.9 12.4 87.6 14.2LCS Odyssey 92.3 15.6 82.0 12.0 87.2 13.8ND Genesis 92.5 14.2 96.2 10.9 94.4 12.6Pinnacle 92.8 14.7 96.1 12.5 94.5 13.6Rawson 97.0 13.9 96.9 12.2 97.0 13.1SY Sirish 93.7 15.6 85.0 13.0 89.4 14.3Mean 89.7 15.3 90.8 12.5 89.1 14.1CV % 5.0 4.4 4.2 5.3 5.1 3.9LSD 0.05 6.3 0.9 5.4 0.9 9.6 1.2LSD 0.10 5.3 0.8 4.5 0.8 8.0 1.0

Table 3. Plump and protein of barley varieties at two locations in eastern North Dakota, 2016.

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Variety

Dickinson Hettinger Minot WillistonAverage Western

N.D.Test Yield Test Yield Test Yield Test Yield Test Yield Test YieldWt. 2016 3 Yr. Wt. 2016 3 Yr. Wt. 2016 3 Yr. Wt. 2016 3 Yr. Wt. 2016 3 Yr.

(lb/bu) ---(bu/a)--- (lb/bu) ---(bu/a)--- (lb/bu) ---(bu/a)--- (lb/bu) ---(bu/a)--- (lb/bu) ---(bu/a)---SIX-ROWEDCelebration 49.1 74.3 100.2 43.3 61.4 84.4 49.2 92.4 91.1 48.6 87.1 68.1 47.6 78.8 86.0Innovation 50.8 77.8 103.8 44.4 62.1 90.5 50.5 94.0 90.3 47.4 73.9 61.5 48.3 77.0 86.5Lacey 50.6 75.2 106.4 45.4 59.5 86.0 51.1 84.8 88.3 48.6 77.6 63.7 48.9 74.3 86.1Quest 49.9 74.5 99.8 43.7 64.4 80.3 49.8 101.1 91.8 49.2 76.7 66.6 48.2 79.2 84.6Stellar-ND 49.3 75.2 108.5 44.0 62.5 90.4 48.9 96.0 89.7 47.8 66.6 58.5 47.5 75.1 86.8Tradition 51.1 69.5 97.2 46.5 63.3 89.1 50.3 89.0 83.4 48.6 77.9 68.0 49.1 74.9 84.4TWO-ROWEDAAC Synergy

50.9 90.3 -- 44.7 75.7 -- 50.2 114.5 -- 48.9 81.8 -- 48.7 90.6 --

ABI Balster

49.9 92.4 -- 43.9 73.2 -- 50.0 108.6 -- 47.1 76.3 -- 47.7 87.6 --

ABI Growler

49.1 86.9 -- 43.4 68.0 -- 49.7 103.8 -- 49.2 75.2 -- 47.9 83.5 --

CDC Meredith

49.5 94.9 -- 41.9 68.2 -- 49.2 110.2 -- 48.3 82.1 -- 47.2 88.9 --

Conlon 51.5 54.2 88.1 46.5 60.4 87.1 52.1 93.5 94.1 49.5 71.4 61.9 49.9 69.9 82.8LCS Genie

51.0 97.1 -- 45.9 67.0 -- 50.6 107.5 -- 50.3 81.9 -- 49.5 88.4 --

LCS Odyssey

50.4 92.7 -- 43.2 70.7 -- 49.7 126.1 -- 49.5 75.9 -- 48.2 91.4 --

ND Genesis

50.8 88.7 113.2 45.8 69.0 95.5 50.5 110.4 106.7 49.8 77.1 62.8 49.2 86.3 94.6

Pinnacle 52.5 80.3 112.3 47.8 64.6 94.4 51.7 104.7 92.9 50.6 83.3 71.8 50.7 83.2 92.9Rawson 51.4 73.9 106.3 45.8 66.1 87.1 50.6 95.8 94.0 50.8 83.0 66.7 49.7 79.7 88.5SY Sirish 51.9 91.4 -- 45.8 71.4 -- 50.9 118.4 49.9 78.1 -- 49.6 89.8 --Mean 50.6 81.7 103.6 44.8 66.3 88.5 50.3 103.0 92.2 49.1 78.0 65.0 48.7 82.3 87.3CV % 0.9 9.2 -- 1.5 7.5 -- 1.0 7.4 -- 1.7 6.6 -- 1.4 7.7 --LSD 0.05 0.7 10.6 -- 1.0 7.2 -- 0.8 12.7 -- 1.2 7.4 -- 1.0 9.0 --LSD 0.10 0.6 8.8 -- 0.8 6.0 -- 0.7 10.6 -- 1.0 6.2 -- 0.8 7.5 --

Table 4. Yield and test weight of barley varieties at four locations in western North Dakota, 2014-2016.

Page 89

VarietyDickinson Hettinger Minot Williston

Average Western N.D.

Plump Protein Plump Protein Plump Protein Plump Protein Plump Protein-----------------------------------------------------(%)----------------------------------------------------------------------------

SIX-ROWEDCelebration 98.3 15.2 89 13.4 95 13.9 75.8 15.6 89.5 14.5Innovation 99.3 14.5 90 12.6 98 13.1 73.3 16.4 90.2 14.2Lacey 99.0 14.8 90 13.0 96 13.0 73.9 15.9 89.7 14.2Quest 98.5 14.2 86 13.5 92 13.5 76.3 16.1 88.2 14.3Stellar-ND 98.6 14.1 89 13.1 96 12.9 67.0 17.3 87.7 14.4Tradition 99.1 15.6 93 12.6 95 13.0 66.7 15.7 88.5 14.2TWO-ROWEDAAC Synergy 98.8 13.6 94 12.1 97 12.7 76.9 15.1 91.7 13.4

ABI Balster 97.4 13.9 89 11.8 95 12.7 52.8 17.2 83.6 13.9

ABI Growler 97.3 14.6 88 12.6 95 13.5 74.8 16.7 88.8 14.4CDC Meredith 97.3 13.4 85 11.9 95 12.1 66.5 15.7 86.0 13.3

Conlon 98.9 14.5 98 13.0 98 13.3 89.5 16.0 96.1 14.2LCS Genie 98.6 13.2 93 12.6 96 12.4 73.7 15.6 90.3 13.5LCS Odyssey 99.1 12.4 93 10.9 97 11.9 79.3 15.9 92.1 12.8

ND Genesis 99.0 13.2 97 11.9 98 11.3 91.4 14.2 96.4 12.7

Pinnacle 99.1 13.9 96 11.4 98 10.9 88.5 13.8 95.4 12.5Rawson 99.2 13.6 96 12.6 97 12.7 92.9 14.0 96.3 13.2SY Sirish 99.1 12.8 92 11.3 97 13.0 71.0 16.1 89.8 13.3Mean 98.6 14.0 92 12.4 96 12.7 75.9 15.7 90.6 13.7CV % 0.3 5.5 2.6 4.3 0.9 3.8 8.7 47.4 5.3 4.5LSD 0.05 0.5 1.6 3.4 0.7 1 0.8 9.3 1.0 6.9 0.9LSD 0.10 0.4 1.3 2.9 0.6 1 0.7 7.8 0.8 5.7 0.7

Table 5. Plump and protein of barley varieties at four locations in western North Dakota, 2016.

Page 90

Variety Origin1Year

ReleasedGrainColor Height

StrawStrength Maturity2

Reaction to Diseases

Bu/Wt. Protein5StemRust3

CrownRust3

BarleyY.Dwf4

AAC Justice

AAFC/MN

2015 White Tall Strong L S R NA Good NA

AC Assiniboia

AAFC 1997 Red Med. Strong L S S T Good M/L

AC Kaufman

AAFC 2000 Yellow Tall Strong L S S MT V.good M/L

AC Pinnacle AAFC 1999 White Tall Med. L S S S V.good L

Beach ND 2004 White Tall M.strg. M/L S MR/MS MS V.good MBuff SD 2002 Hull-less Med. M.strg. L S MR/MS MT Good HCDC Dancer Sask. 2000 White Tall Strong L S MS S V.good M

CDC Minstrel Sask. 2006 White Tall M.strg. L S S S Good M

CDC Weaver

Sask. 2005 Yellow Med. M.strg. L S S S Good M

Deon MN 2013 Yellow Tall Strong L S R T V.goodFurlong AAFC 2003 Red Tall M.strg. L S S T V.good MGoliath SD 2013 White Tall Med. L NA MR/MS NA Good MHayden SD 2015 White Med Med M S MR/MS MT V Good MHiFi ND 2001 White Tall Strong L MR/MS S T Good MHorsepower SD 2012 White Short Strong E/M MS S MT V.good M/HHytest SD 1986 White Tall M.strg. E S MS S V.good HJury ND 2012 White Tall M.strg. M R S MT V.good MKilldeer ND 2000 White Med. Strong M S MS MT Good MLeggett AAFC 2005 White Tall Strong L MR R S Good MLoyal SD 2000 Ivory Tall M.strg. L S MR T Good M/HMaida ND 2005 Yellow Med. Strong M R S MS V.good M/HMorton ND 2001 White Tall V.strg. L S S MT V.good MNewburg ND 2011 White Tall Med. L R S MT Good MOtana MT 1977 White M.tall M.weak L S S S V.good M/LPaul ND 1994 Hull-less V.tall Strong L R MR/MS T Good HRockford ND 2008 White Tall Strong L S S MT V.good MSesqui MN 2001 Yellow M.tall Strong L S S T Good MShelby 427 SD 2008 White Med. Strong E S S NA V.good NASouris ND 2006 White Med. Strong M MS S MS V.good MStallion SD 2006 White Tall Med. L S MR NA V.good MStark ND 2004 Hull-less Tall M.strg. L R MR/MS T V.good MStreaker SD 2008 Hull-less Tall M.weak M S R/MR NA V.good M/HSummit AAFC 2008 White Med. Strong L S S MT Good M1 AAFC = Agriculture & Agri-Food Canada; MN = University of Minnesota; ND = North Dakota State University; SD = South Dakota State University; Sask. = University of Saskatchewan; MT = Montana State University. 2 E = early; M = medium; L = late. 3 R = resis-tant; MR = moderately resistant; MS = moderately susceptible; NA = not available; S = susceptible. 4 Barley Yellow Dwarf Virus; S = susceptible; MS = moderately susceptible; MT = moderately tolerant; T = tolerant; NA = not available. Varieties rated MT or T have a relatively good degree of protection against barley yellow dwarf virus.5 H = high; M = medium; L = low.

Table 6. 2016 North Dakota oat variety descriptions.

Page 91

Variety

Fargo Edgeley Langdon Average Eastern N.D.Test Yield Test Yield Test Yield Test YieldWt. 2016 3 Yr. Wt. 2016 3 Yr. Wt. 2016 3 Yr. Wt. 2016 3 Yr.

(lb/bu) ---(bu/a)--- (lb/bu) ---(bu/a)--- (lb/bu) ---(bu/a)--- (lb/bu) --(bu/a)---AC Pinnacle 36.1 132.8 169.0 38.8 181.3 178.9 34.9 151.2 169.6 36.6 155.1 172.5Beach 40.3 122.6 155.0 42.4 174.5 162.3 37.0 146.1 152.9 39.9 147.7 156.7Beta-Gene 39.4 134.6 168.9 39.4 159.0 158.5 -- -- -- -- -- --CDC Dancer 39.4 135.6 152.1 39.5 171.9 153.2 36.3 131.6 160.9 38.4 146.4 155.4CDC Minstrel 37.6 135.0 167.8 37.1 168.9 159.4 33.2 145.3 160.9 36.0 149.7 162.7CS Camden -- -- -- -- -- -- 34.1 174.0 -- -- -- --Deon 40.1 148.1 170.0 41.3 183.6 165.9 35.5 161.6 170.4 39.0 164.4 168.8Furlong 36.9 130.9 149.1 -- -- -- 37.0 151.6 165.0 -- -- --GM423 35.7 14.01 -- -- -- -- -- -- -- -- -- --Goliath 41.4 136.6 159.3 42.3 170.3 163.4 36.2 131.7 155.8 40.0 146.2 159.5Hayden 39.8 144.1 -- 42.7 173.2 -- 38.3 133.8 -- 40.3 150.4 --HiFi 38.8 125.1 143.6 40.4 164.1 159.4 35.8 139.4 156.4 38.3 142.9 153.1Horsepower 41.5 131.5 145.8 40.9 150.7 142.5 -- -- -- -- -- --Hytest 41.1 121.6 138.7 42.9 134.1 -- 38.5 102.2 122.7 40.8 119.3 130.7Jury 41.4 134.0 164.9 41.9 159.3 157.2 34.9 127.8 148.3 39.4 140.4 156.8Killdeer 37.0 129.9 165.6 -- -- -- 35.9 154.3 164.6 -- -- --Leggett 38.5 140.1 168.9 40.4 153.2 154.0 37.1 157.0 170.5 38.7 146.7 164.5Newburg 40.4 137.4 156.9 40.0 172.6 160.8 33.7 139.4 159.0 38.0 149.8 158.9Otana 38.4 117.9 122.4 38.5 143.2 133.7 34.3 100.0 126.6 37.1 120.4 127.6Paul1 42.4 79.7 102.2 45.6 118.7 116.7 44.2 98.5 119.8 44.1 99.0 112.9Rockford 41.0 139.8 142.5 41.6 163.0 155.3 36.8 124.8 141.7 39.8 142.5 146.5Souris 40.2 130.9 141.2 39.3 168.4 153.8 34.8 136.2 152.5 38.1 145.2 149.2Stallion 41.5 132.3 157.4 41.2 155.5 149.1 35.9 106.6 141.3 39.5 131.5 149.3Mean 39.6 132.3 152.1 40.9 162.2 154.4 36.2 134.5 152.2 39.1 141.0 151.6CV % 5.7 6.4 -- 1.7 7.3 -- 3.1 12.4 -- 3.2 6.6 --LSD 0.05 3.7 13.6 -- 1.1 19.2 -- 1.5 23.8 -- 2.0 14.9 --LSD 0.10 3.1 11.4 -- 0.9 16.1 -- 1.3 19.9 -- -- -- --1 Hull-less varieties. When comparing yield of hull-less oat varieties with varieties with hulls, multiply the yield of the hull-less oats by 1.35 (the hull of a hulled kernel comprises 35 percent of the weight).

Table 7. Yield and test weight of oat varieties at three locations in eastern North Dakota, 2014-2016.

Page 92

Variety

Dickinson Hettinger Minot Williston Average Western N.D.Test Yield Test Yield Test Yield Test Yield Test YieldWt. 2016 3 Yr. Wt. 2016 3 Yr. Wt. 2016 3 Yr. Wt. 2016 3 Yr. Wt. 2016 3 Yr.

(lb/bu) ----(bu/a)---- (lb/bu) ----(bu/a)---- (lb/bu) ----(bu/a)---- (lb/bu) ----(bu/a)---- (lb/bu) ----(bu/a)----AC Pinnacle 37.1 150.3 176.0 36.9 77.4 143.8 40.3 150.9 138.0 45.2 103.3 90.9 39.9 120.5 137.2

Beach 38.1 130.9 138.8 38.2 59.6 131.8 42.2 144.4 150.9 47.5 86.0 64.3 41.5 105.2 121.5

CDC Dancer 37.0 135.3 149.1 37.3 62.4 135.0 37.8 140.1 143.5 45.7 101.1 84.1 39.5 109.7 127.9

CDC Minstrel 35.5 133.9 159.2 33.7 65.9 147.9 39.4 159.6 151.4 45.8 105.8 82.7 38.6 116.3 135.3

Deon 35.5 126.9 150.9 36.0 64.0 146.0 41.5 164.8 142.5 45.7 82.3 76.8 39.7 109.5 129.1Furlong 34.0 130.5 153.1 33.5 59.0 148.8 40.0 152.7 146.1 44.2 101.1 74.3 37.9 110.8 130.6Goliath 36.4 103.5 130.9 37.5 54.6 142.6 40.8 150.2 134.4 47.0 77.7 -- 40.4 96.5 --Haydon 36.6 140.7 -- 37.0 68.9 -- 41.6 160.6 -- -- -- -- -- -- --HiFi 34.4 123.3 142.3 36.0 61.6 124.1 38.9 154.2 131.5 45.3 92.4 72.3 38.7 107.9 117.6Hytest 37.3 108.3 132.0 39.7 53.7 102.5 41.4 144.2 135.2 46.8 77.0 60.9 41.3 95.8 107.7Jury 37.1 111.2 132.1 36.2 55.5 142.4 39.9 136.4 130.4 46.3 81.6 74.3 39.9 96.2 119.8Killdeer 35.4 125.3 142.4 36.3 61.6 144.6 40.6 129.2 126.4 45.3 111.9 81.6 39.4 107.0 123.8Leggett 36.0 133.4 160.2 35.9 61.5 143.1 39.2 136.8 134.7 45.4 104.4 77.0 39.1 109 128.8Newburg 36.0 115.9 145.7 35.7 63.9 146.1 41.3 147.6 133.4 46.3 84.6 75.7 39.8 103 125.2Otana 36.9 126.1 144.0 36.4 63.8 133.8 40.0 136.3 122.1 45.4 86.2 80.1 39.7 103.1 120.0Paul1 38.6 91.8 109.7 41.1 46.2 109.1 45.0 102.0 111.6 51.9 62.6 52.2 44.2 75.7 95.7Rockford 36.6 133.2 150.6 38.7 65.3 150.5 42.2 128.7 120.2 46.5 100.5 75.9 41.0 106.9 124.3Souris 35.8 111.5 143.3 36.6 64.0 120.8 40.9 145.7 126.6 46.3 93.0 76.0 39.9 103.6 116.7Stallion 39.1 118.4 143.0 37.4 59.6 125.7 38.0 121.6 143.5 46.2 92.1 62.9 40.2 97.9 118.8Mean 35.7 123.7 144.6 36.0 61.5 135.5 40.0 142.4 134.6 45.7 91.3 74.2 40.0 104.7 122.2CV % 3.8 9.0 -- 3.6 9.1 -- 1.8 8.8 -- 5.9 1.2 -- 2.5 8.5 7.0LSD 0.05 1.9 15.5 -- 1.8 8.0 -- 1.2 21.0 -- 7.6 0.7 -- 1.4 12.2 12.0LSD 0.10 1.6 12.9 -- 1.5 6.7 -- 1.0 17.5 -- 6.4 0.6 -- 1.2 10.1 10.01 Hull-less varieties. When comparing yield of hull-less oat varieties with varieties with hulls, multiply the yield of the hull-less oats by 1.35 (the hull of a hulled kernel is 35 percent of the weight).

Table 8. Yield and test weight of oat varieties at four locations in western North Dakota, 2014-2016.

Page 93

Variety Origin 1Year

Released HeightStraw

Strength MaturitySeed Color

Seed Size

Test Weight

Winter Hardiness

AC Hazlet Canada 2006 Med. Good Med. Blue Large Good V.goodAC Rifle Canada 1994 Short V.good Med. Blue Med. Med. V.goodAC Remington Canada 1998 Short V.good Med. NA2 Med. Good GoodAroostok USDA 1981 Tall Fair Early NA Small High V.goodEnsi Finland 1933 Tall Fair Late NA Small Low NADacold ND 1989 Med. Good3 V.late Bl-grn. Med. Low GoodFrederick SD 1984 Tall Fair Late Tan Med. High GoodHancock WI 1979 Tall Good Med. Tan Large High Fair4

Musketeers Canada 1980 Tall Fair M.early Blue Large Med. V.goodND Dylan ND 2016 Tall Good Med Blue Med. High V. goodPrima Canada 1984 Tall Good Med. Blue Large Med. V.goodRymin MN 1973 Tall V.good Late Grn-gray Large High Fair4

Spooner WI 1993 Tall V.good Med. Tan Large High GoodWheeler MI 1971 Tall Fair Med. NA Large Low GoodWrens Abruzzi GA 1953 Tall Fair Early NA Small High Good1 ND = North Dakota State University; SD = South Dakota State University; WI = University of Wisconsin; MN = University of Minnesota; MI = Michigan State University. GA = Georgia. 2 NA = not available. 3 Under certain environments, lodging has been observed. 4 Varieties with fair winter hardiness should not be seeded in bare soil.

Table 9. 2016 North Dakota winter rye variety descriptions.

Variety

Hettinger Langdon Minot Average

TestWt.

Seed YieldTestWt.

Seed YieldTestWt.

Seed YieldTestWt.

Seed Yield

2016 3-yr. 2016 20162-yr. Avg. 2016 2/3-yr Avg.

(lb/bu) --(bu/a)-- (lb/bu) --(bu/a)-- (lb/bu) --(bu/a)-- (lb/bu) --(bu/a)--Aroostok 55.4 45.6 48.6 52.6 55.6 53.7 59.0 53.8 53.9 53.4 51.2Dacold 52.6 72.9 77.3 52.7 76.5 53.4 80.6 75.2 52.9 76.7 76.3Hancock 54.2 59.9 65.7 54.3 75.8 54.8 78.3 73.6 54.4 71.3 69.7Musketeer 54.3 70.7 -- 54.0 64.7 55.0 81.9 -- 54.4 72.4 --ND Dylan -- -- -- 52.3 84.0 54.4 95.2 83.4 53.4 89.6 --Rymin 54.2 62.1 -- 53.5 65.8 54.8 77.5 -- 54.2 68.5 --Spooner 55.5 57.3 56.9 54.2 67.4 54.5 79.3 67.5 54.7 68.0 62.2Mean 54.4 61.4 62.1 53.4 70.0 54.4 78.8 70.7 54.0 70.1 64.8CV % 1.0 9.0 -- 1.6 8.3 0.8 4.6 -- 1.2 6.1 --LSD 0.05 0.8 8.0 -- 1.2 8.5 0.6 5.2 -- 1.1 7.3 --LSD 0.10 0.7 6.7 -- 1.0 7.0 0.5 4.3 -- 0.9 6.0 --

Table 10. Yield and test weight of winter rye varieties at four locations in North Dakota, 2014-2016.

Page 94

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Page 95

Minnesota Wheat Council – On-Farm Research Network

A Final Report for the 2016 Growing Season

ByLauren Proulx MN Wheat, Agronomist, CCA and On-Farm Research Coordinator

andDr. Grant Mehring MN Wheat Research Director NDSU Dept. Plant Sciences & ND Corn Council

MInnesota Wheat Research & Promotion Council

Page 96

The Minnesota Wheat Research and Promotion Council – On-Farm Research Network, is supported by the Min-nesota wheat checkoff for a large portion of the operating budget. The Minnesota Department of Agricultural Growth, Research, and Innovation (MDA-AGRI) grant program has generously provided additional funds for this research.

The On-Farm Research Network (OFRN) has been im-pacted by the direction of the leadership within the pro-gram, and today’s progress is in part owed to past leaders including Dave Willis, Dr. Garth Kruger, and Dr. Dave Graf-strom. The advisory committee to the OFRN, with mem-bers including Dave Willis, Dave Grafstrom, Tony Brateng, Tim Osowski, Steve Lacey, and Ryan Casavan, continues to be invaluable in providing steering to the direction of the OFRN.

We were fortunate to have some of the products needed for our research donated. This helped to control variability, reduce the time and costs associated with doing the trials for the participants, and increases our chances of a suc-cessful research season. The producers that we collabo-rate with want to do the research to get the answers they need. However, they often have work of a greater impor-tance to their business going on at the same time as the trial work and as a result, the research may get eliminated. We put great focus on making the trials as efficient and streamlined as possible for the participants, so that we can to keep our success rate high and participants continu-ously willing to do research with us for years to come.

BASF donated 25 gallons of Limus, a urease inhibitor for the topdressing nitrogen trial. All but one of the topdress trial participants received the free product.

TeeJet Technologies donated their SJ3 fertilizer nozzles for the six topdressing trial participants who did not own them already and for two of Dave Grafstrom’s small plot research sprayers. Syngenta donated 25 gallons of Palisade EC for our plant growth regulator trial. Palisade is the newest plant growth regulator on the market and most widely used for wheat in the USA.

http://www.lh-agro.de/english/home/products/spray-products/fertilizer-spray-nozzles/stream-jet--sj3-fertilizer-nozzles.aspx

ACKNOWLEDGEMENTS

Lastly, the initiative and willingness of the growers who have been in the OFRN through hosting trials or provid-ing support has been essential to our current successes. Without willing producers, looking to improve production on their operations, this network would not be what it is today. Their excitement and encouraging words keep us going.

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Topdressing Nitrogen as UAN Near the Boot Growth Stage Introduction | Materials and Methods | Results and Discussion | Conclusions............................................Pages 98 - 100

Plant Growth Regulator Application in HRSW Introduction | Materials and Methods | Results and Discussion | Conclusions............................................Pages 101 - 103

Optimum Seeding Rates for the HRSW Varieties ~ Linkert and Bolles Introduction | Materials and Methods | Results and Discussion | Conclusions............................................Pages 104 - 108

ESN Compared with Urea as a Nitrogen Fertilizer Source Introduction | Materials and Methods | Results and Discussion | Conclusions............................................Pages 109 - 110

N-Serve Nitrification Inhibitor for Anhydrous Ammonia Application Introduction | Materials and Methods | Results and Discussion | Conclusions............................................Page 111

TABLE OF CONTENTS

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TOPDRESSING NITROGEN AS UAN NEAR THE BOOT GROWTH STAGE

INTRODUCTIONWheat producers in Minnesota (MN) are applying nitro-gen in quantities to reach maximum yield, with economi-cally viable sources and equipment. A large proportion of producers put the entire wheat crops nitrogen requirement on at the pre-plant stage, in either the fall or spring. With knowledge of the wheat plants nitrogen use timeline, research has shown that sidedressing or topdressing nitro-gen applications might result in maximum yields, though not always enough to pay for the extra application costs associated with two nitrogen applications. Past small plot research at North Dakota State University (NDSU) has found that a UAN topdress at the boot stage, which is later than previously recommended, might be a practice to benefit both protein and yield.

The objective of this research is to compare the applica-tion of 100% nitrogen at the pre-plant timing with 100% ni-trogen at the pre-plant timing plus 30 lbs acre-1 of nitrogen as UAN with a urease inhibitor near the boot growth stage.

MATERIALS AND METHODS This experiment was set up in 11 locations throughout NW MN in 2016. The site descriptions are in Table 1. There were two treatments in this trial, including 100% nitrogen rate at the pre-plant timing, and 100% nitrogen rate at the pre-plant timing plus an application of 30 lbs acre-1 of nitro-gen in the form of urea ammonium nitrate (UAN) with the urease inhibitor Limus (BASF). All applications were made with 10 gallon per acre streaming nozzles to mitigate plant tissue phytotoxicity, also known as burning, from the nitro-gen fertilizer. The pre-plant nitrogen application for each individual producer’s optimum nitrogen rate was up to them, with his or her preferred nitrogen form and applica-tion methods. Additionally, nitrogen applications differed in that one producer used variable rates across the trial field while all others used a blanket N rate.

At all sites, the experiment was set up in a randomized complete block design, with between two and five repli-cates. The replicates were not always randomized, with treatments often alternating between the control and the treatment. The topdress nitrogen application was done by the participant as close to the boot stage as the field conditions and farm workload allowed. Knowing that the timing would be critical and difficult to have at the same exact timing at all fields, the general guideline for applica-tion was after the main stem had two nodes but before awns had protruded from the sheath. Additionally, the ap-plication was intended to be sprayed before a forecasted rain event. Rain gauges were installed and monitored by the producer, to understand rainfall amounts on the field,

and specifically rainfall after the topdress application. Data from Climate Fieldview’s website was also included and analyzed.

We took stand counts from multiple spots within each plot at almost all locations as a gauge of the producer’s plant stand and overall field quality. We used producer machinery to harvest the trials and plot weights taken with a weigh wagon that we provided, with yields adjusted to 13.5% grain moisture. As the grain augured out of the weigh wagon into the truck, we collected sub-samples of the grain from each plot with an attachment to the weigh wagon’s auger that takes a small stream of continuous grain from the grain off-loading through the auger. Sub-samples were immediately analyzed for harvest moisture and grain test weight with a Dickey John mini-GAC plus. Sub-samples were analyzed for grain protein at the North-ern Crops Institute with a Perten NIR and adjusted to 12% moisture, providing accurate protein levels for each plot.

RESULTS AND DISCUSSIONResults from the 11 locations that could be taken to har-vest are presented in Table 2. There is success in simply getting 11 producers to all apply topdress nitrogen applica-tions during the difficult spray season that 2016 was. Top-dressing nitrogen did not have an effect on the test weight of the grain, with average test weight across all environ-ments registering quite high at 61.9 lb bu-1. The applica-tion of topdress nitrogen did not improve yield above the control where no nitrogen was applied at the boot stage. There were two environments that when taken alone, had yield that was significantly impacted by the nitrogen application, however the results were opposite in the two environments.

Our initial hypothesis was that a late application of nitro-gen at the boot stage could perhaps increase yield at the same time as protein, however this result was only seen at one environment. The environment where additional nitrogen decreased yield can be in part explained by within field variability, as this application of UAN is not known to have phytotoxic effects on the wheat plant. An increase in protein was the other expected result of this trial, and that result shined through. When combined over all 11 environments there was a 0.2%, or one-fifth, increase in protein when the topdress nitrogen was applied. Among all of those environments there were five that had between 0.4-0.6% increases with the topdress nitrogen, and five that had no increase, or even a non-significant numerical decrease in protein with the topdress N. When taken as a whole, the combined 11 locations did not produce enough extra protein with the topdress to warrant considering the

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extra nitrogen application in today’s wheat market. How-ever, the five locations that showed greater increases in protein levels with the topdress approached the increase needed to pay for the application if there is a sizeable pro-tein premium/discount in place.

The results from the topdress N trials this year contrast with what was found in prior small plot research in North Dakota (ND) and MN. The environments the trial was done in during the 2016 growing season had a warm April. Farmers were able to get into the fields early and the nice weather likely increased the amount of nitrogen generated through mineralization so these plots were likely not short on nitrogen. With the results we saw in 2016, an alteration to the protocol for 2017 could be a true sidedress applica-tion where the preplant nitrogen is reduced in strips and then balanced out with an early season UAN application.

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TreatmentLocation

1 2 3 4 5 6 7 8 9 10 11 Combined---------------------------------------------lb bu-1---------------------------------------------

No Topdress N 63.5 64.1 60.3 60.2 62.9 61.1 62.3 60.1 62.5 62.0 63.7 62.0With Topdress N 63.4 63.8 59.9 59.9 62.7 59.4 63.0 61.0 62.3 61.7 63.1 61.8LSD (0.05) NS NS NS NS NS NS NS NS NS NS NS NS

------------------------------------------------%------------------------------------------------No Topdress N 12.8 13.5 15.1 13.4 14.5 14.3 13.1 14.0 13.5 13.3 14.3 13.8With Topdress N 12.7 13.5 15.2 14.0 14.9 14.8 13.2 14.5 13.9 13.6 14.2 14.0LSD (0.05) NS NS NS 0.51 NS 0.52 NS 0.43 0.24 0.17 NS 0.17

---------------------------------------------bu ac-1---------------------------------------------No Topdress N 97.5 98.2 54.9 54.3 80.4 50.9 82.2 54.9 87.4 89.5 77.0 74.7With Topdress N 99.1 99.0 55.8 58.3 76.0 51.3 81.9 55.9 88.3 89.1 73.4 75.4LSD (0.05) NS NS NS 1.59 2.12 NS NS NS NS NS NS NS

NS – non-significant difference at the 95% confidence level.LSD – least significant difference, if the means differ by more than the LSD number the numbers are statistically different.

Table 2. Effect of topdressing nitrogen at the boot stage compared with no topdress of nitrogen on test weight, grain protein, and yield, at 11 diverse environments throughout NW MN and combined over all 11 environments.

lbs product 28-0-0

rate applied per gallonweight per gallon

per ac @10 gal/ac

product cost per ac

10 10.67 106.7 19.72

Location

1 2 3 4 5 6 7 8 9 10 11 Combined 11

------------------------------------------------------------------------%------------------------------------------------------------------------

No Topdress N Protein 12.8 13.5 15.1 13.4 14.5 14.3 13.1 14.0 13.5 13.3 14.3 13.8

With Topdress N Protein 12.7 13.5 15.2 14.0 14.9 14.8 13.2 14.5 13.9 13.6 14.2 14.0

Protein gain/loss -0.1 0 0.1 0.6 0.4 0.5 0.1 0.5 0.4 0.3 -0.1 0.2

$ Protein gain/bu1 -0.04 0 0 0.24 0.08 0.1 0.04 0.1 0.16 0.12 -0.02 0.08

Topdress Yield 99.1 99.0 55.8 58.3 76.0 51.3 81.9 55.9 88.3 89.1 73.4 75.4

Total $ Gain topdress for protein/acre ($) -3.96 0.00 0.00 14.00 6.08 5.13 3.28 5.59 $4.12 $0.69 -1.47 6.03

Application Costs 2bu/ac ($)2 36.12 36.12 36.12 36.12 36.12 36.12 36.12 36.12 36.12 36.12 36.12 36.12

Financial outcome ($) -40.08 -36.12 -36.12 -22.12 -30.04 -30.99 -32.84 -30.53 -22.00 -25.43 -37.59 -30.09

Table 3. Economic analysis of the topdress nitrogen application of all locations individually and the combined analysis, NW MN, 2016.

1 Protein premium of $0.04 per fifth above 14% up to 15%, and a protein discount of $0.08 per fifth below 14%.2 September wheat price of $4.20. Considering 2 bu/ac lost due to tire tracks from application.

Conclusions The additional 30 lbs per acre of nitrogen as UAN in combination with a urease inhibitor, did not improve yield beyond the control, however did increase protein by 0.2% combined over all locations. Even if 0.5% protein increase was gained, as was found in at least two indi-vidual environments, without the additional yield this timing and application does not seem to make more sense than

an equal application of nitrogen at the later post-anthesis growth stage, where between 0.5-1.0% protein increases are routinely gained, without significant change to yield. An additional year of research as robust as 2016 will help to solidify whether the boot stage topdress nitrogen applica-tion is an economically viable agronomic practice

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PLANT GROWTH REGULATOR APPLICATION IN HRSW

INTRODUCTIONSignificant attention has been paid in recent years to the negative aspects of growing tall or weak-strawed wheat varieties. Generally speaking, producers have options from between two classes of wheat varieties; semi-dwarf containing one of the two common semi-dwarf alleles, and wild-type or tall cultivars. Practically, producers would look to the heights reported in variety testing results to make decisions about plant height. In hard red spring wheat (HRSW), increased plant height is often associated with higher grain yield, though there are exceptions. Higher yielding HRSW varieties are often prone to lodging, where due to low straw strength, stem lodging takes a plant from being completely erect, to leaning over. Increased lodging will decrease plant yield. In addition to decreasing grain yield, lodging results in a more challenging harvesting scenario. Often combine speeds will need to be reduced to combine as low as needed to collect the lodged wheat spikes, which also increases risk of colliding with exposed rocks in the field.

Producers have several agronomic practices that can decrease stem lodging, with the most widely used method in recent years being selecting a variety with strong straw strength. Recently, attention in Minnesota has turned to using a plant growth regulator chemical application in season as a practice to reduce lodging. Plant growth regulators have been commonplace in parts of Europe and South American for small grains, and are now being considered in the wheat regions of the USA. Plant growth regulators decrease the internode distance in wheat and can thicken the stems of the plant, successfully shortening the plant. In the U.S. few chemicals are offered as plant growth regulators, though a few choices do exist.

The objective of this research is to understand the effects of the plant growth regulator Palisade EC (Syngenta) on yield, height, lodging, and general combinability in HRSW.

Location1 2 3 4 5 6

Hendrum Fertile Fertile Dorothy Red Lake Falls St. HilairePlanting Date N/A 4/13 4/13 4/18 4/22 4/28Harvest Date 8/1 8/6 8/6 8/9 8/22 8/13Previous Crop Beets Soybean Soybean Soybean Soybean SoybeanSoil Type Bearden Fargo Chapett Knute Chapett Knute Glyndon Hecla Borup ClearwaterVariety Prosper Forefront Forefront Digger Prosper/ Faller ProsperPalisade Date/Stage

6/2 Feekes 7 5/26 Feekes 6.5-7

6/7 Feekes 7.5-8

6/8 Feekes 7-8

6/7 Feekes 7

Table 4. Agronomic details for all six locations of the palisade growth regulator trial in 2016

Specifically, the hypothesis is that a plant growth regulator application will protect plant yield and increase combinabil-ity when an environment for severe lodging is encoun-tered.

MATERIALS AND METHODSWe implemented this trial at six locations in Minnesota, with one trial only having one replication, so we included it in the combined analysis but not as its own location due to no replication. We had a seventh location on durum wheat but it received severe hail and did not go to yield for research. The trial consisted of an application of Palisade EC growth regulator (Syngenta) compared with no chemi-cal application. The participants applied Palisade at a rate of 12 fluid ounces per acre, which is in the middle of the application range of 10.5-14.4 lb ai per acre. The Palisade was sprayed as close to the Feekes 7 growth stage as spray conditions allowed. Feekes 7 is the growth stage where two stem nodes are visible above the ground.

We took stand counts from multiple spots within each plot at almost all locations as a gauge of the producers plant stand. Plant height measurements close to maturity were collected within each plot. We used producer machinery to harvest the trials and measured plot weights with a weigh wagon, with yields adjusted to 13.5% grain moisture. As the grain augured out of the weigh wagon into a truck, we collected sub-samples of the grain from each plot with an attachment to the weigh wagon auger that takes a small stream of continuous grain from the grain off-loading through the auger. Two of the locations were weighed with the participant’s grain cart and the sample was taken by manually holding a tube into the stream of grain com-ing out of the auger. With a Dickey John mini-GAC plus testing device, we immediately analyzed each sample for harvest moisture and grain test weight. We analyzed the samples for protein at the Northern Crops Institute with a Perten NIR and adjusted to 12% moisture.

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RESULTS AND DISCUSSIONThe growth regulator Palisade impacted plant height, grain test weight, and yield combined over six diverse environ-ments in NW MN in 2016. The impact of Palisade on plant height was clear in our research. Palisade reduced plant height by 4.6, 3.5, 2.7, and 3.1 inches at locations 1, 2, 3, and 5, respectively, and 3.6 inches combined over all locations where we measured height. Lodging was observed at all locations, but the exact effect of Palisade on lodging was difficult to quantify over the large plot size. At location 3, the researchers saw no lodging (score of 1) in the palisade treated plots, and an average lodging of a 45 degree angle on the plant stem (4-5 lodging score) in the untreated plots, as an example of the lodging seen. Palisade increased the test weight of the grain by 0.7 lb bu-1 combined over all locations, which was numerically evident at all locations. This result of an increase in test

Table 5. Effect of the plant growth regulator Palisade EC (Syngenta) applied at the two nodes above ground growth stage in HRSW on test weight, grain protein, and yield, at 5 diverse environments throughout NW Minnesota and combined over all 6† environments, 2016.

TreatmentLocation

1 2 3 4 5 Combined-------------------------------------------------------Height (inches)--------------------------------------------

No Palisade 32.5 39.7 NA 33.2 33.8 35.6Palisade 27.9 36.2 NA 30.5 30.7 32.0LSD (0.05) 1.63 2.1 NA NS 3.0 2.2

--------------------------------------------------------Test Weight (lb bu-1)----------------------------------------------No Palisade 61.8 62.1 62.4 62.3 60.7 61.7Palisade 62.3 62.7 62.6 63.0 62.3 62.4LSD (0.05) NS NS NS NS 0.7 0.2

-------------------------------------------------------------Protein (%)----------------------------------------------------No Palisade 13.7 12.8 12.7 13.9 12.4 13.2Palisade 13.5 12.6 12.6 14.1 12.4 13.2LSD (0.05) NS NS 0.1 NS NS NS

----------------------------------------------------------Yield (bu ac-1)------------------------------------------------------No Palisade 65.2 87.4 84.7 78.9 83.5 78.3Palisade 68.8 91.8 88.2 78.6 86.9 81.3LDS (0.05) NS 1.6 1.5 NS 2.0 2.1

NS – non-significant difference at the 95% confidence level.LSD – least significant difference, if the means differ by more than the LSD number the numbers are statistically different.† 6 environments includes one that did not have replication within the trial, so we included it in the combined analysis but did not report numeric means in for the location alone.

weight with Palisade has been replicated in other Palisade trials, however not in all years or locations. Protein was not impacted by Palisade, as expected. We found a significant increase in grain yield with Palisade compared to the untreated check at all locations besides 1 and 4. When combined over all six locations, a 3.0 bu ac-1 yield increase was found with Palisade above the untreated check. At location 4 there was no yield increase from Palisade, so analyzing the combined analysis without that location increased the yield advan-tage with Palisade to 3.7 bu ac-1 (data not reported). When looking at an input that has a cost attached to it, a cost-benefit analysis is useful for determining if the input makes economic sense. When looking at a 3 bu ac-1 increase with Palisade compared to the untreated check, the yield increase does not pay for the application cost (Table 6).

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Table 6. Economic analysis of the palisade application of all locations individually and the combined analysis, NW MN, 2016.

Location1 2 3 4 5 Combined

No Palisade 65.2 87.4 84.7 78.9 83.5 78.3With Palisade 68.8 91.8 88.2 78.6 86.9 81.3Yield gain/loss 3.6 4.4 3.5 -0.4 3.4 3.0$ Yield gain/bu 14.94 18.36 14.69 -1.51 14.31 12.60Application Costs1 $28.40 $28.40 $28.40 $28.40 $28.40 $28.40Financial outcome -$13.46 -$10.04 -$13.71 -$29.91 -$14.09 -$15.80

1 September wheat price of $4.20. Considering 2 bu/ac lost due to tire tracks from application. Palisade cost of $1 per ounce at a $12 oz per acre rate.

CONCLUSIONSCombined across all locations the application of the growth regulator Palisade decreased plant height by 3.6 inches, and increased yield by 3.0 bushels per acre. When looking at the economic analysis the application of Palisade was not profitable at any individual location or the combined results, but if was not so far off as to discourage the use of the chemical for other purposes. In a more lodging-prone environment, the benefit of wheat that has lodged less would add into the financial outcome quite significantly.

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OPTIMUM SEEDING RATES FOR THE HRSW VARIETIES LINKERT AND BOLLES

INTRODUCTIONAt the Small Grain Update meetings in January of 2016, there were many questions from producers about what rate to seed the newer HRSW varieties. It takes three years to get enough data to identify the best seeding rate for a newly released variety. Additionally, on farm results carried out by producers often differ from small plot results, specifically that higher yields are achieved by increasing their seeding rate beyond that of what the small plot research shows as optimum.

Seeding rate in the field has an effect on lodging and yield, with certain varieties being more negatively impacted by increased seeding rates than others. When deciding on a seeding rate, producers often default to what has worked in past growing seasons. However, this line of thought comes with some challenges. If the kernel weight, or number of seeds in a pound has changed in a seedlot from year to year, seeding rates in bushels per acre would not adjust for that change. If producers do not get a seed weight test done on a seedlot, then they could be seeding at much different rate than what they intend to, or that they seeded in previous years.

For this trial, we picked two recently released varieties Linkert and Bolles, that have been increasing in popularity according to yearly variety surveys in MN, to do a seeding rate study. Our objective was to test the yield response curve at three seeding rates to determine the optimum seeding rate for maximum yield, and the most economic seeding rate.

MATERIALS AND METHODSWe implemented the seeding rate trial at seven total loca-tions in 2016, with four locations being with the variety Linkert and three locations being with the variety Bolles. The treatments for this study consisted of three seeding rates at 1.0 million, 1.5 million and 2.0 million seeds per acre. There were between three and four replicates at every environment. One location planted some additional seeding rates of 0.75 million and 1.75 million seeds per

Table 7. Agronomic details for three locations of Bolles seeding rate trial in 2016, NW MN, 2016.Location

1 2 3Roseau Campbell Red Lake Falls

Planting Date 5/8 3/28 4/20Harvest Date 8/31 7/20 8/15Previous Crop Soybean Soybean SoybeanSoil Type Borup Zippel Foldahl Antler Mustinka Hecla Borup

acre, and a treatment of as high as their equipment would allow, which was 2.262 million seeds per acre (data not shown).

At all sites, the trials were set up in a randomized com-plete block design although not all of the treatments were randomized in each replication. We took stand counts twice at each location. The second stand count taken was to account for delayed seed germination due to poor early seed germination and stand establishment across NW MN due to dry planting conditions and infrequent rainfall. A stand loss estimation was made with the formula: stand loss = ((live seed planted – initial plant population)/live seed planted)*100. At all sites except for location 1 in both the Linkert and Bolles trial, no stand loss percentage was added into the seeding rate calculation, however at location 1 for both trials, 10% stand loss was added into the seeding rate.

As the HRSW crop was nearing physiological maturity, we did stem or head counts from each plot. We did this by measuring out three feet of row, from at least three locations in each strip and counting the number of wheat spikes or stems. The strips are usually a half a mile long and with time constraints, we normally took the counts within a quarter mile. At two of the locations during harvest we collected head measurements to aid in explaining the yield results.

We used our weigh wagons and the producer’s equipment for harvesting the plots and the yields were adjusted for moisture to 13.5 %. Grain samples were collected to ana-lyze for protein, test weight and moisture. The grain sam-ples were collected in two different ways; with an attach-ment on the auger of the weigh wagon that collects the subsample as the grain is being unloaded into the truck, or from the bottom of the weigh wagon’s auger through a hole cut in the side. With a Dickey John mini-GAC plus testing device, we immediately analyzed each sample for harvest moisture and grain test weight. We analyzed the samples for protein at the Northern Crops Institute with a Perten NIR and adjusted to 12% moisture.

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Location1 2 3 4

Roseau Waukon Argyle DorothyPlanting Date 5/5 4/14 4/25 4/15Harvest Date 8/31 8/6 8/22 8/3Previous Crop Soybean Soybean Soybean SoybeanSoil Type Borup Augsburg Hamerly Vallers Fargo Fargo

Table 8. Agronomic details for four locations of Linkert seeding rate trial in 2016, NW MN, 2016.

RESULTS AND DISCUSSIONThere was not a perfect stand of spring wheat established at any of the seven seeding rate trial sites. The estab-lished plant stand was taken to verify how far off from the desired plant stand the actual stand was (Table 9). Com-bined over all locations the stand loss in the Bolles trial ranged from 14.7-22.0%, and in the Linkert trial ranged from 15.6-24.9%. This estimation is live seeds that did not make it to the growth stage from when we went into the field to count plant stand, and is the percent stand away from the desired seeding rate a treatment was. Head counts were done near harvest, and showed that all seed-ing rates led to a similar number of wheat spikes per acre, with Bolles averaging 2.0 million heads per acre and Link-ert averaging slightly higher at 2.2 million heads per acre. The stand counts and head counts combined to provide an estimate of stems per individual plant. Combined over all locations for each variety, stems per plant decreased as seeding rate increased. Bolles ranged from 1.3-1.9 stems per plant and Linkert ranged from 1.5-2.6 stems per plant, from lowest to highest seeding rates, respectively. It is important not to take this as absolute evidence that Linkert tillers more than Bolles, as the two varieties were in sepa-rate trials at geographically spread sites.

At all individual locations for both trials, and combined over all locations within a trial, seeding rate did not affect test weight or protein. The hypothesis was that yield would

be affected by varying the seeding rate. In all locations and combined over all locations for a trial, except for the Linkert trial location 4, the yields were flat across the three seeding rates. Yield did not increase or decrease as seed-ing rates increased. These yield results are evidence that in 2016, increasing the seeding rate beyond 1.0 million seeds per acre did not lead to an increased yield. Addi-tionally, using a rough economic analysis (Table 10 and 12), the most economic treatment was the lowest seeding rate in both the Linkert and Bolles trials. The head count data leads us to think that enough stems per acre were produced regardless of seeding rate because of the wheat varieties ability to compensate with tillering, which made yields similar.

CONCLUSIONSStand loss was in the same range as has been reported in many previous small plot research trials in the region, so producers should be aware that their seeding rates should reflect the potential live seeds that do not make it through early season conditions. The varieties Linkert and Bolles had the capacity to tiller at levels that did not leave any significant differences in heads per acre between seeding rate treatments. Seeding rate did not impact test weight, grain protein, or yield in the combined analysis for all trials. The treatment with the highest net income was the lowest seeding rate averaged across all locations in both trials.

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TreatmentLocation

1 2 3 Combined----------------------------------------------Population (plants acre-1) ---------------------------------------

1,000,000 1,014,464 826,672 787,307 876,1481,500,000 1,455,872 1,198,384 1,099,648 1,251,3012,000,000 1,775,312 1,517,824 1,393,920 1,562,352LSD (0.05) 226,063 223,230 225,779 111,783

-----------------------------------------Stand loss away from seeding rate (%)-------------------------------1,000,000 4.9 17.3 21.3 14.71,500,000 7.3 20.1 26.7 18.22,000,000 11.2 24.1 30.3 22.0LSD (0.05) NS NS NS NS

-------------------------------------------Stems at harvest (Spikes acre-1) ----------------------------------1,000,000 1,657,216 1,670,381 2,431,616 1,919,7381,500,000 1,717,232 1,742,400 2,431,616 1,963,7492,000,000 1,930,192 1,918,963 2,516,800 2,121,985LSD (0.05) NS NS NS NS

-----------------------------------------------Tillering (Stems plant-1) ------------------------------------------1,000,000 1.7 2.1 3.1 1.91,500,000 1.2 1.6 2.3 1.42,000,000 1.1 1.4 1.8 1.3LSD (0.05) 0.3 0.2 0.6 0.3

----------------------------------------------------Test Weight (lb bu-1)---------------------------------------1,000,000 60.9 61.0 59.8 60.51,500,000 61.7 60.8 59.8 60.82,000,000 61.2 60.9 59.5 60.5LSD (0.05) NS NS NS 0.07

-----------------------------------------------Protein (%)---------------------------------------------1,000,000 13.9 16.3 15.5 15.21,500,000 13.7 16.2 15.2 15.12,000,000 13.6 16.3 15.3 15.0LSD (0.05) NS NS NS NS

---------------------------------------------------Yield (bu ac-1)--------------------------------------------1,000,000 53.9 67.2 74.4 65.01,500,000 53.2 65.6 74.4 64.22,000,000 51.3 67.2 73.4 64.1LSD (0.05) NS NS NS NS

Table 9. Effect of seeding rate in the HRSW variety Bolles on initial plant population, stand loss, stems per acre, stems per plant, test weight, protein, and yield at 3 diverse locations throughout NW and WC MN and combined over all three environments, 2016.


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Table 10. Economic analysis of the yield results for the Bolles seeding rate trial combined over all locations.

Seeding Rate Seeding Rate1 Seed cost2 Yield Gross Income3 Net IncomeSeeds/ac -Bushels/ac- --$/acre-- -Bushels/ac- ----$/ac---- ----$/ac----1,000,000 1.5 18.0 65 261.3 243.301,500,000 2.2 26.4 64.2 258.08 231.682,000,000 2.9 34.8 64.1 257.68 222.88

Table 11. Economic analysis of the yield results for the Bolles seeding rate trial combined over all locations.

Seeding Rate Seed Rate 1 Seed cost 2 Yield Gross Income 3 Net IncomeSeeds/ac -Bushels/ac- --$/acre-- -Bushels/ac- ----$/ac---- ----$/ac----1,000,000 1.5 18.0 70.7 284.21 266.211,500,000 2.2 26.4 71.2 286.22 259.822,000,000 2.9 34.8 71.2 286.22 251.42

1 Estimated.2 Certified seed cost of $12.00 per bushel of HRSW.3 October wheat price of $4.02.

1 Estimated.2 Certified seed cost of $12.00 per bushel of HRSW.3 October wheat price of $4.02.

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Location1 2 3 4 Combined

----------------------------------------Population (plants acre-1) ----------------------------------------1,000,000 818,928 787,952 882,816 921,536 846,6651,500,000 1,169,344 894,432 1,113,200 1,299,056 1,118,5872,000,000 1,597,200 1,236,136 1,77,1440 1,746,272 1,597,471LSD 341,420 261,180 267,009 425,944 176,132

---------------------------------Stand loss away from seeding rate (%)----------------------------------1,000,000 18.1 21.2 11.7 7.8 15.61,500,000 22.0 40.4 25.8 13.4 24.92,000,000 20.1 38.2 11.4 12.7 20.0LSD NS NS NS NS NS

----------------------------------------Stems at harvest (Spikes acre-1) -------------------------------------1,000,000 2,205,104 1,866,304 1,874,048 2,514,864 2,123,7361,500,000 2,241,888 1,972,784 2,094,752 2,681,360 2,257,2722,000,000 2,319,328 2,102,496 2,292,224 2,656,192 2,329,833LSD NS NS NS NS NS

--------------------------------------------------------Tillering (Stems plant-1) -------------------------------------------1,000,000 2.8 2.5 2.1 2.8 2.61,500,000 1.9 2.2 1.9 2.1 2.02,000,000 1.5 1.7 1.3 1.5 1.5LSD 1.1 NS 0.6 0.9 0.7

----------------------------------------------------Test Weight (lb bu-1)-----------------------------------------------1,000,000 62.1 61.1 61.9 62.8 62.01,500,000 61.5 61.3 62.0 62.6 61.82,000,000 61.7 61.6 61.4 63.0 61.9LSD NS NS NS NS NS

---------------------------------------------------Protein (%)--------------------------------------------------1,000,000 13.7 14.1 14.4 14.1 14.21,500,000 13.6 14.5 14.5 14.1 14.12,000,000 13.4 13.6 14.3 14.2 14.0LSD NS NS NS NS NS

-------------------------------------------------------Yield (bu ac-1)------------------------------------------1,000,000 58.2 74.2 56.6 93.2 70.71,500,000 58.6 74.0 56.4 96.0 71.22,000,000 55.4 75.6 59.9 93.4 71.2LSD NS NS NS 1.0 NS

Table 12. Effect of seeding rate in the HRSW variety Linkert on initial plant population, stand loss, stems per acre, stems per plant, test weight, protein, and yield at 4 diverse environments throughout NW MN and combined over all four environments, 2016.


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ESN COMPARED WITH UREA AS A NITROGEN FERTILIZER SOURCE

INTRODUCTIONAgriculture in Minnesota is prone to variable and adverse weather conditions, which leads to nitrogen that can become vulnerable to losses. A one-time nitrogen fertilizer application in the late fall or early spring is quite common for producers, but may result in nitrogen that is not avail-able when the plants need it most during the growing season. This set of circumstances has led to interest in products that protect the nitrogen, such as environmen-tally smart nitrogen (ESN), which is a controlled release product. Additionally, low grain protein in spring wheat has led to alternative nitrogen applications rather than the one-time nitrogen application. The theory behind ESN is that there should be more nitrogen available later in the season than compared to the standard nitrogen sources, which could lead to increased yield or protein.

The objective for this research was to evaluate the ef-fectiveness of 100% ESN with 100% urea at the same nitrogen rate in NW MN.

MATERIALS AND METHODSWe implemented this trial at two locations in MN. Both locations applied 100% of their nitrogen in the fall as ESN or as urea, and had four replicates. At both sites the trials were set up in a randomized complete block design, al-though not all of the treatments were randomized in each replication.

We used our weigh wagons and the producer’s equipment for harvesting the plots and the yields were adjusted for

moisture to 13.5 %. Grain samples were collected to ana-lyze for protein, test weight and moisture. The grain sam-ples were collected in two different ways; with an attach-ment on the auger of the weigh wagon that collects the subsample as the grain is being unloaded into the truck, or from the bottom of the weigh wagon’s auger through a hole cut in the side. With a Dickey John mini-GAC plus testing device, we immediately analyzed each sample for harvest moisture and grain test weight. We analyzed the samples for protein at the Northern Crops Institute with a Perten NIR and adjusted to 12% moisture.

RESULTS AND DISCUSSION Both of these fields experienced extreme weather events. One looked beautiful all year and then a couple of weeks before harvest a windstorm flattened the crop. This partici-pant shared concerns about harvesting flat wheat plants and was not sure if he could accurately harvest the trial, however he did and we are thankful to have the results. The other trial had crusting, poor emergence, and severe waterlogging through harvest. Stressed environments have higher potential for a product like ESN to be effec-tive, however neither of these sites showed a protein or yield difference between urea or ESN. With these results there is no way that the added cost of ESN was recovered through extra protein or yield, which is a part of the larger probability of getting your investment back when using nitrogen stabilizers. The results from these two trials are part of the larger collection of data from this network that has mostly shown ESN to not be an effective nitrogen tool for increasing protein or yield.

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Location1 2

Alvarado Red Lake FallsPlanting Date 4/14 4/12Harvest Date 8/17 8/3Rainfall (in) 19.72 12.39Previous Crop Soybean SoybeanSoil Type Bearden Colvin Hattie Reis ClearwaterVariety Mayville SY SorenN Timing 10/21 11/3

Table 13. Agronomic details for both locations of the ESN vs. urea nitrogen trial in 2016.

Table 14. Effect of using 100% ESN or 100% urea as the nitrogen source at pre-plant timing, on test weight, grain protein, and yield, at two diverse environments throughout NW Minnesota and combined over both environments, 2016.

Location1 2 Combined

----------------------------------------Test Weight (lb bu-1)----------------------------------------ESN 59.8 63.2 61.5Urea 60.0 63.2 61.6LSD (0.05) NS NS NS

---------------------------------------------------Protein (%)-------------------------------------------ESN 14.3 12.6 13.4Urea 14.1 12.7 13.4LSD (0.05) NS NS NS

----------------------------------------Yield (bu ac-1)--------------------------------------------------ESN 55.1 95.6 75.4Urea 57.0 94.1 75.5LSD (0.05) NS NS NS

NS – non-significant difference at the 95% confidence level.LSD – least significant difference, if the means differ by more than the LSD number the numbers are statisticallydifferent.

CONCLUSIONSNeither treatment of ESN or urea provided a protein or yield improvement. ESN is a product that depends largely on the right environmental stresses for nitrogen loss to be an effective management tool. Additionally, even though this trial looked at 100% of the nitrogen as ESN, the more preferred practice currently is to mix only a portion of the overall nitrogen as ESN.

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USING A NITRIFICATION INHIBITOR FOR ANHYDROUS AMMONIA APPLICATION

INTRODUCTIONThe environment that wheat is subject to in Minnesota production systems is one where nitrogen losses due to the environment are common. Anhydrous ammonia (AA) comes with concerns for losses just as other nitro-gen sources such as urea and UAN do. One tested and proven product on the market to use with AA for farmers is N-Serve, a nitrification inhibitor. It is supposed to have an approximately 90-day effective period when the soils are 40 degrees Fahrenheit or warmer. Producers in NW MN like to spread out their workload so they try to apply some of their fertility requirements in the fall, where the product N-Serve has utility.

The objective for this research trial is to determine if a nitrification inhibitor improves yield in spring wheat above the same rate of N without the nitrification inhibitor, and compared to a higher rate of nitrogen.

MATERIALS AND METHODS We had this trial at one location near Roseau, Minnesota, on a Borup-Glyndon loam. The location applied 100% of their nitrogen in the fall as anhydrous ammonia, at either 100 or 135 lbs N per acre and with and without N-Serve at the 100 lb rate of nitrogen, with four replications. The trial was set up in a randomized complete block design.

Table 15. Effect of using fall applied anhydrous ammonia with N-Serve at 100 lbs N acre-1 compared to 100 lbs N acre-1 or 130 lbs N acre-1 alone on test weight, protein, and yield at one location in NW MN, 2016.

TreatmentLocation

1-Test Weight (lb bu-1)-

100 lbs N 61.3100 lbs N + NSERVE 61.0135 lbs N 61.6LSD (0.05) NS

-------Protein (%)------100 lbs N 12.9100 lbs N + NSERVE 12.7135 lbs N 13.0LSD (0.05) NS

----Yield (bu ac-1)-----100 lbs N 75.0100 lbs N + NSERVE 68.1135 lbs N 70.8LSD (0.05) NSNS – non-significant difference at the 95% confidence level.LSD – least significant difference, if the means differ by more than the LSD number the numbers are statistically different.

CONCLUSIONS Adding N-Serve did not improve grain yield or protein at the 100 lb N rate. Additionally, the results suggest that the100 lbs per acre N rate was the optimum N rate as well, as an additional 135 lbs of N per acre did not lead to increased yield or protein.

We used our weigh wagons and the producer’s equipment for harvesting the trial, and the yields were adjusted for moisture to 13.5%. Grain samples were collected to ana-lyze protein, test weight and moisture. The grain samples were collected with an attachment on the auger of the weigh wagon that collects the subsample as the grain is being unloaded into the truck, or from the bottom of the weigh wagon’s auger through a hole cut in the side. With a Dickey John mini-GAC plus testing device, we immedi-ately analyzed each sample for harvest moisture and grain test weight. We analyzed the samples for protein at the Northern Crops Institute with a Perten NIR and adjusted to 12% moisture.

RESULTS AND DISCUSSIONAt this single location, no treatment gave an advantage in test weight, protein, or grain yield. It is not impossible to find statistical significance with just one location of a trial. However, this location had too much variability to let any treatment shine through, even with 6.9 bushels per acre difference in yields between two different treatments. This trial would have benefitted from a few more locations to help make the results more robust.

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2016 Northwest Minnesota County Variety

Research Trials

• Polk County Soybean-Corn Growers• Pennington/Red Lake County Soybean-Corn Growers• Marshall County Soybean-Corn Growers

Variety Plot Trial Booklet Funded by MSRPC

and the Soybean Checkoff

Variety Trial Organizers & Participants:

• Bill Craig, Ag Service Director, Marshall & Pennington Cnty, Project Lead • Russ Severson, Crookston, MN, Project Support• Nathan Johnson, U of M Extension, Project Support• Jim Stordahl, U of M Extension, Project Support• Howard Person, Thief River Falls, Project Support• Dr. Grant Mehring, NDSU Assistant Professor, Statistical Analysis

Special Thanks to:

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• Bill Craig, Ag Service Director, Marshall & Pennington Cnty, Project Lead • Russ Severson, Crookston, MN, Project Support• Nathan Johnson, U of M Extension, Project Support• Jim Stordahl, U of M Extension, Project Support• Howard Person, Thief River Falls, Project Support• Dr. Grant Mehring, NDSU Assistant Professor, Statistical Analysis

Marshall County: Bill Craig – Chairman, Warren, MN [email protected]

Jerred Copp, Vice Chairman, Warren, MN [email protected]

Cecil Deschene, Secretary, Argyle, MN [email protected] Rodney Liedberg, Treasurer, Newfolden, [email protected] Nathan Potucek, Argyle, MN [email protected]

Philip Olson, Plot Coordinator, Warren, [email protected]

Cooperator/Location: Tony Johnson Farm, Alvarado, MN

Planting Date: May 6, 2016 Harvest Date: October 3, 2016

Pennington/Red Lake Counties:Kyle Mehrkens, Chair, Thief River Falls, MN [email protected]

Tom Scholin, Vice President, Thief River Falls, MN, [email protected]

Darin Asp, Secretary, Thief River Falls, MN [email protected]

Garrett Novak, Treasurer, St. Hilaire, MN [email protected]

Kevin Amiot, MSGA State Delegate, Red Lake Falls, MN, [email protected] Matt Knutson, Plot Coordinator, Red Lake Falls, MN, [email protected] Cooperator/Location: Kyle Mehrkens Farm, Thief River Falls, MN

Planting Date: May 6, 2016 Harvest Date: September 30, 2016

Polk County:Elliott Solheim, Chairman, Crookston, MN, [email protected]

Wayne Olson, Vice President, Fosston, MN [email protected] Rick Roed, Secretary/Treasurer, Fosston, MN [email protected] Mike Skaug, MSGA State Delegate, Beltrami, MN [email protected] Kevin Krueger, Membership, East Grand Forks, MN, [email protected]

Russ Severson, Associate Director, Crookston, MN, [email protected]

Cooperator/Location: Ellsworth Danielson Farm, Fosston, MN

Planting Date: May 5 2016 Harvest Date: September 20, 2016

Coordinated by the following Soybean Counties

County Soybean Variety Trial Locations

AlvaradoThief River Falls

Fosston

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Coordinated County Variety Trials and Research Trials:The data presented here is part of a coordinated effort by Minnesota County Soybean Growers to expand the amount of research information that soybean growers have access to in northwest Minne-sota. These trials are funded by entry fees paid by the seed companies.

There were three MN soybean counties across northern MN that participated in this coordinated effort. The results of these trials will be disseminated in the On-Farm Cropping Trials Booklet which will be available at the Prairie Grains Conference, December 8, 2016 and at future county meetings.

About This Variety Plot Trial:The County Soybean Variety Plots are randomized small plot trials. They utilized three replicated blocks in each location. The soybean plots were planted with a Haldrup small plot cone planter and harvested with a small plot Zurn combine. For weed control, the plots were sprayed with glyphosate by the farmer-cooperator using commercial size equipment, utilizing driving lanes through the plots.

Data Interpretation:Statistics are a mathematical tool used to summarize and interpret groups of numbers. In these tables we used a LSD (least significant difference) test to determine if differences in yield are due to genetic differ-ences between varieties or due to other causes such as variability in soil type or fertility, or other environmental factors.

If the difference between two varieties exceeds the LSD value, with 95 percent probability, the higher yielding variety is significantly different in yield. If the difference between two varieties is less than the LSD value, then the variety yields are considered the same. The LSD number is also a measure of variabil-ity within a trial; and a large number indicates there is more variability in a location compared to a location with a small LSD number.

Coefficient of Variation (CV) is an indicator of how much variability there was within the soybean trial location (uneven seeding rate, emergence, insect damage, disease, soil type etc.) that was not due to any effect of the varieties. A CV of less than 15 indicates a very uniform trial site; therefore, differences in soybean yields are the result of varieties rather than other external factors.

Relative Maturity:Relative maturity ratings are provided for each entry. These ratings consist of a number for the maturity group designations (000, 00, 0, 1, 2) followed by a decimal and another number, ranging from 0-9, which indicates a ranking within each maturity group. For example the entry MN0101 indicates a 0.1, making it an early group 0, while MN0901, with a 0.9 rating, is the latest group 0.

County Variety Trials and Plot Tours: In 2016 county soybean varietal trials were conducted in Marshall, Pennington/Red Lake, and Polk coun-ties. The plots are conducted as random, replicated trials. The trial results are published in booklet and online form for all soybean growers and seed companies to use to provide selection information to im-prove soybean production within each county. The county data, along with university plot data, can provide useful yield information for purchasing top yielding varieties that improve soybean production and profitability within the region. County plot tours, sponsored by the University of Minnesota Extension and Minnesota Soybean Research & Promotion Council, were held in August. The plot tours allowed growers to view the plots and learn about soybean varieties from seed company representatives. Production updates were also presented by the University of Minnesota Extension researchers. Note: Varieties containing an X are Roundup Ready2Xtend soybeans containing Dicamba and Glyphosate tolerant genetics.

County Collaborators: Bill Craig, Ag Services Director, Marshall & Pennington Counties & Russ Severson, Polk County Soybean Growers, Associate Director

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Characteristics of Soybean Varieties and Variety Placement Across Zones

PHYTOPHTHORA ROOT ROT is a destructive soil borne disease that can cause soybean stand loss and reduced plant productivity. The primary means of managing this disease is to plant varieties that are resistant to the pathogen. This is a bit of a ‘cat and mouse’ game since there are over 55 races of this disease and approximately 8 single resistance genes, designated as Rps genes that are used in soybean that offer different spectrums of control. Each Rps gene offers control of several races of phytophthora but no gene offers control of all races. The key to managing this disease is to know which Rps gene is used in each soybean field you plant and make an annual evaluation of how well it is performing. For example, if the soybean variety you have chosen has a Rps 1k gene and you plant it in two fields and you notice phytoph-thora is very low in field A but is pretty noticeable in spots in field B, you want to make field notes to avoid using the Rps 1K gene in field B in future soybean variety selections. SOYBEAN CYST NEMATODE (SCN) is a highly damaging pest of soybean. Surveys indicate this pest is expanding its range in NW Minnesota and you should be testing your soil to determine if it is present. Crop rotation and planting SCN resistant varieties are the primary means for managing this microscopic roundworm.

Company VarietyRelative Maturity

SCN Trait

Phytophthora Gene

ng = No Gene

Seed Treatment (See chart above for

reference)

Channel 00806R2 00.8 - 3 9, 10, 21

Dyna-Gro S005RY87 00.5 - 3 5, 8, 19, 21

Dyna-Gro S007XT27 00.7 1 3 9, 10, 18, 21

Integra 20097 00.9 - - 8, 5, 19

Legacy LS-00835 00.8 1 5 8, 5, 19

Legend LS-007R653 00.7 - ng 8, 5, 19

Legend LS-004R752 00.4 - 5 8, 5, 19

Northstar NS-0080R2 00.6 - 3 19, 9, 5, 21

Northstar NS-0072R2 00.7 - 3 19, 9, 5, 21

Northstar NS-0081NR2 00.8 1 5, 3 19, 9, 5, 21

NuTech 6008 R2 00.8 - - 8, 5, 19

Peterson 16R008N 00.8 - 5, 3 16

Proseed 50-08 00.8 1 3 19

Proseed XT6007 00.7 1 3 19

REA R00727 00.7 - - 9, 10, 21

Syngenta S007-Y4 00.5 - 3 8, 5, 19

Wensman W30065NR2 0.06 1 5, 3 5, 8, 19, 21

Early Soybean Varieties - 00.8 and Earlier

SEED TREATMENTS: 1-16: Fungicides / 17-19: Insecticides / 20: Inoculants / 21: Other

Ref #: Treatment Ref #: Treatment Ref #: Treatment

1 Azoxystrobin 8 Mefenoxam 15 Trichoderma harzianum Rifai

2 Bacillus pumilus 9 Metalaxyl 16 Trifloxystrobin

3 Bacillus subtilis 10 Pyraclostrobin 17 Clothianidin

4 Captan 11 Streptomyces griseoviridis 18 Imidacloprid

5 Fludioxonil 12 Streptomyces lydicus 19 Thiamethoxam

6 Ipconazole 13 Thiabendazole 20 Bradyrhizobium japonicum

7 Mancozeb 14 Thiram 21 Other

In the Seed Treatment column on the form: List each of the seed treatments present on the variety. (I.e. If the variety is treated with CruiserMaxx Plus (mefenoxam, fludioxonil, thiamethoxam) you would put 8,5,19 in the box. If the seed treatment list does not include one of the compounds use the number 21.)

Phytophthora

Ref #: Gene:

1 Rps 1a

2 Rps 1b

3 Rps 1c

4 Rps 1k

5 Rps 3

6 Rps 4

7 Rps 6

SCN Trait:

Ref #: Trait

1 PI88788

2 Peking

3 Other

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Medium Soybean Varieties - 00.9 - 0.3


SCN Trait

Phytophthora Gene ng = No Gene


reference)

Channel 0209R2 0.2 - 3 9, 10, 21

Channel 0205R2 0.2 - 3 9, 10, 21

Dairyland DSR-0225/R2Y 0.2 - 3 5, 8, 19, 20

Dairyland DSR-0305/R2Y 0.3 - 4 5, 8, 19, 20

Dairyland DSR-C918/R2Y 00.9 - 4 5, 8, 19, 20

Dyna-Gro S03RY36 0.3 - 3 5, 8, 19, 21

Dyna-Gro S01RY86 0.1 - 3 5, 8, 19, 21

Integra 20126 00.9 - - 8, 9, 19

Integra 20087 0.1 - - 8, 5, 19

Legacy LS-0334 RR2 0.3 - 4 8, 5, 19

Legacy LS-0214 RR2 0.2 - - 8, 5, 19

Legacy LS-0135 RR2 0.1 - 3 8, 5, 19

Legend 03R650 0.3 - 5 8, 5, 19

Legend 01R656 0.1 - 3 8, 5, 19

Northstar 0111R2 0.1 - 3 19, 9, 5, 21

NuTech 6021 0.2 - - 8, 5, 19

Partners Brand PB00961 00.9 1 4 8, 5, 19

Partners Brand PB0361 0.3 - - 8, 5, 19

Partners Brand PB0251 0.2 - 5 8, 5, 19

Peterson Farms 16R01 0.1 - 3 16

Peterson Farms 17R008N 00.9 - 3 16

Pioneer P02T54R 0.2 - 4 -

Prairie Brand PB-0397R2 0.3 - - 5, 8, 19, 21

Prairie Brand PB-0146R2 0.1 1 3 5, 8, 19, 21

Prairie Brand PB-00856R2 00.9 1 3, 5 5, 8, 19, 21

REA R0216 0.2 - 3 9, 10, 21

Syngenta S02-B4 0.2 - 4 8, 5, 19

Wensman W1037RX 0.3 - 3 5, 8, 19, 21

Wensman W3018R2 0.1 - 3 5, 8, 19, 21


SCN Trait

Phytophthora Gene ng = No Gene


reference)

Channel 0507R2 0.5 1 3 9, 10, 21

Dairyland DSR-0619/R2Y 0.6 1 5 5, 8, 19, 20

Latham 0685 0.6 1 5 5, 8

Latham 0485 0.4 1 5 5, 8

NuTech 6048 0.4 - 4 8, 5, 19

Pioneer P06T28R 0.6 - 4 -

Proseed XT 607 0.7 1 3 -

REA R0815 0.8 1 3 9, 10, 21

REA 64G94 0.4 1 - 9, 10, 21

Syngenta S06-Q9 0.6 1 - 8, 5, 19

Wensman W1048NRX 0.4 1 5 5, 8, 19, 21

Late Soybean Varieties - 0.4 and later

Page 117

EA

RLY

MA

TU

RIT

YM

ED

IUM

M

AT

UR

ITY

Variety trial results for three NW Minnesota counties and the combined analysis for soybean yield, 2016.

Brand Variety Relative Maturity Polk (bu/ac)Pennington/Red Lake (bu/acMarshall (bu/ac)Combined (bu/ac)

Legacy LS-0334 RR2 0.3 61.5 70.8 59.3 63.9

Dairyland DSR-0225/R2Y 0.2 55.7 67.1 64.6 62.5

Northstar 0111R2 0.1 60.8 71.4 54.6 62.3

Prairie Brand PB-0397R2 0.3 60.3 68.2 57.2 61.9

Legend 03R650 0.3 57.6 66.0 61.7 61.8

Wensman W1037RX 0.3 54.7 59.8 70.0 61.5


Dyna-Gro S03RY36 0.3 54.5 68.7 58.9 60.7

Peterson Farms 16R01 0.1 55.7 67.8 58.7 60.7

Wensman W3018R2 0.1 55.2 69.6 55.1 60.0

REA R0216 0.2 49.3 71.2 58.0 59.5

Dairyland DSR-0305/R2Y 0.3 60.8 66.3 51.2 59.5

Legend 01R656 0.1 51.3 68.8 56.8 59.0

Channel 0209R2 0.2 49.5 68.9 57.8 58.7

Dyna-Gro S01RY86 0.1 52.9 65.6 57.0 58.5

Channel 0205R2 0.2 49.4 66.8 59.3 58.5

Partners Brand PB0361 0.3 60.3 58.1 54.5 57.6


Legacy LS-0135 RR2 0.1 55.1 75.8 38.0 56.6

Legacy LS-0214 RR2 0.2 52.3 67.9 46.5 55.5

Integra 20126 00.9 53.7 61.9 48.5 54.7

Dairyland DSR-C918/R2Y 00.9 53.6 60.6 44.7 53.0

Peterson Farms 17R008N 00.9 50.6 59.2 46.8 52.2


Syngenta S02-B4 0.2 46.4 60.2 44.7 50.4


Integra 20087 0.1 45.9 60.8 37.1 47.9

NuTech 6021 0.2 48.9 56.4 34.5 46.6

Pioneer P02154R 0.2 46.9 58.4 14.3 39.9

Average 53.5 65.5 50.8 56.6

CV 9.7 8.9 10.2 9.5

LSD (0.05) 8.5 9.3 8.6 10.8

Top Third 56.5-61.5 69.4-75.8 51.5-70.0 56.0-63.9

Mid Third 51.2-56.4 62.9-69.3 32.9-51.4 47.9-55.9

Bottom Third 45.9-51.1 56.4-62.8 14.3-32.8 39.9-47.8

Variety trial results for two* NW Minnesota counties and the combined analysis for soybean yield, 2016.

Brand Variety Relative Maturity Polk (bu/ac) Pennington/Red Lake (bu/ac) Combined (bu/ac)

Proseed 50-08 00.8 60.2 69.6 64.9

Legacy LS-00835 00.8 56.5 70.7 63.6

Peterson 16R008N 00.8 56.8 70.2 63.5

Integra 20097 00.9 55.4 70.7 63.0

Northstar NS-0081NR2 00.8 55.4 67.3 61.4

Legend LS-004R752 00.4 51.1 71.0 61.1

Dyna-Gro S005RY87 00.5 52.3 68.5 60.4

Channel 00806R2 00.8 47.3 71.6 59.4

Legend LS-007R653 00.7 50.0 65.4 57.6

Northstar NS-0072R2 00.7 54.3 60.6 57.4

Proseed XT6007 00.7 46.6 68.0 57.3

NuTech 6008 R2 00.8 48.0 65.7 56.8

REA R00727 00.7 48.0 64.3 56.1

Northstar NS-0080R2 00.6 46.0 65.1 55.6

Syngenta S007-Y4 00.5 43.7 67.6 55.6

Wensman W30065NR2 00.6 42.1 65.4 53.8

Dyna-Gro S007XT27 00.7 42.1 63.6 52.9

Average 50.3 67.4 58.8

CV 8.8 7.5 8.1

LSD (0.05) 7.4 8.7 7.8

Top Third 54.4-60.2 68.0-71.6 61.0-64.9

Mid Third 48.3-54.3 64.3-67.9 57.0-60.9

Bottom Third 42.1-48.2 60.6-64.2 52.9-56.9

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Soybean Production Research - MN Soybean Research & Promotion CouncilMission: To help farmers turn discoveries from science into higher crop yields and enhance profit potential in the field.

Why it matters:Unbiased production research information is vital to farmers across Minnesota. Because fewer public dollars are spent on agricultural research and extension, projects supported by the Production action team make valuable management information and new soybean cultivars available to farmers across the state.

Research Funding: In 2016, the Production action team recommended 21 projects for funding. Checkoff dollars are leading the way to increasing soybean yield and enhancing environmental stewardship. Funded projects included developing genetic resistance to SCN, Soybean Aphid and Sudden Death Syndrome, development of biological control for soybean cyst nematode, enhancing soybean aphid management, optimizing soybean plant nutrition management and continued technology transfer program support for control of herbicide resistant weeds, optimizing soybean pest (insect and disease) management and improving soil health.

U of M Soybean Breeding Position:Farmer leaders and Minnesota Soybean production staff participated in the process with the Department of Agronomy and Plant Genetics at the University of Minnesota to identify and hire a new soybean breeder for Minnesota. Dr. Aaron Lorenz began in March 2015. He worked closely with Dr. Jim Orf to transfer duties as Orf moves toward retirement after more than 30 years of service to Minnesota soybean farmers in the development of new cultivars and germplasms in 2016.

Conservation Tillage Conference and Soil Health Field Day:The Production action team co-sponsored the University of Minnesota Conservation Tillage Conference and the Soil Health Field Day, which demonstrated the impacts of conservation tillage, soil salinity and other agronomic practices on Soil Health. Farmers could see actual compaction following various tillage practices via soil pits excavated in the field. Agronomic practices were evaluated for crop and soil health responses. Several different equipment manufacturers demonstrated equipment to minimize tillage effects and provide in-furrow cover crop planting methods.

Production Breakout:Projects Subject 8 Agronomic research and technology transfer 3 Disease and Pest Management 2 Insect Management 8 Soybean breeding, molecular genetics and functional genomics

Variety trial results for three NW Minnesota counties and the combined analysis for soybean yield, 2016.

Brand Variety Relative Maturity Polk (bu/ac)Pennington/Red Lake (bu/ac)Marshall (bu/ac)Combined (bu/ac)

REA R0815 0.8 57.4 62.5 71.7 63.5

Proseed XT 607 0.7 54.0 66.2 67.5 62.9

Dairyland DSR-0619/R2Y 0.6 51.1 63.1 60.7 59.8

Check 57.7 57.7 62.1 59.3

Syngenta S06-Q9 0.6 55.3 63.9 61.1 58.8

Latham 685 0.6 52.4 61.6 57.3 57.7

Pioneer P06T28R 0.6 52.5 60.8 57.1 57.2

Latham 485 0.4 50.9 59.7 58.7 56.9

Wensman W1048NRX 0.4 51.2 62.6 55.7 55.9

REA 64G94 0.4 51.3 60.2 52.9 55.5

NuTech 6048 0.4 51.5 60.6 52.4 54.3

Channel 0507R2 0.5 53.3 65.8 47.4 54.1

Average 53.2 62.0 58.7 58.0

CV 5.4 6.3 8.6 7.8

LSD (0.05) 5.8 NS 8.3 6.5

Top Third 55.5-57.7 63.5-66.2 63.8-71.7 60.5-63.5

Mid Third 53.2-55.4 60.6-63.4 55.6-63.7 57.3-60.4

Bottom Third 50.9-53.1 57.7-60.5 47.4-55.5 54.1-57.2

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Channel- www.channel.com

Dairyland Seed Company - www.dairylandseed.com

Dyna-Gro - www.dynagroseed.com

Integra Seeds - www.integraseed.com

Latham Hi-Tech Seeds - www.lathamseeds.com

Legacy Seed - www.legacyseeds.com

Legend Seeds - www.legendseeds.net

NorthStar Genetics - www.northstargenetics.com

NuTech Seed - www.nutechseed.com

Partners Brand - www.partnersbrandseed.com

Peterson Farms Seed - www.petersonfarmsseed.com

Pioneer - www.pioneer.com

Prairie Brand - www.prairiebrand.com

Proseed Inc. - www.proseed.net

REA Hybrids - www.rea-hybrids.com

Syngenta - www.syngenta-us.com/crops/soybeans

Wensman Seed Company - www.wensmanseed.com

Thank you to the following seed companies for participating in the 2016 Soybean Variety Trials:

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Sponsors that help fund this book and the research in it are theMinnesota Wheat Research & Promotion Council, Minnesota Soybean

Research and Promotion Council and the Minnesota Department of Agricultural Growth, Research, and Innovation (MDA-AGRI) grant program.

MDA Funding provided through Agricultural Growth, Research

and Innovation (MDA-AGRI) Program

Documents

On-Farm Cropping Trials Northwest & West Central Minnesota ... · The Minnesota Wheat Research and Promotion Council – On-Farm Research Network, is supported by the Minnesota