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VALIDATING PREDICTION MODELS FOR ON-FARM ALFALFA FORAGE YIELDS

Principal Investigator: Marisol Berti, North Dakota State University (NDSU)

Co-authors: M. Kazula, D. Samarappuli, O. Teuber, J. Lukaschewsky, A. Aponte, A. Peterson,

W. Yang, and C. Ciria

Collaborators:

County agents/ Agronomists

S. Knoke, E. Gaugler, M. Knudson, K. Hain, J. Trottier, J. Henessy, J. Lemer, B. Kiser, F.

Brummer, K. Hoppe, K. Froehlich, R. Wald, J. Dhuyvetter, K. Egeland, T. Becker, M. Vig, M.

Seykora, L. Berg, A. Johnson, L. Lubenow, S. Gerhardt, S. Lahman, and R. Buetow.

Farmers

Shuman, A. Johnson, S. Haugen, Lee, Landies, Schun, Schlecht, Ridke, Dense, Ichler, J. Trottier,

C. Montgomery, M. Becker, H. Kerr, A. Ridl, D. Roise, D. Fiest, Knudson, D. Bichler, Williams,

D. Carter, and D. Heinz.

Introduction

Predicting actual on-farm alfalfa forage yields is difficult but necessary. Very few studies

actually have been able to predict forage yield. Existing alfalfa yield prediction models for the

first harvest of the year are based on winter temperature cycles, spring growing degrees, and

days in hardening period (Durling et al., 1995). This model was able to explain about 65% of the

variability in forage yield. Hall et al. (2004) determined that plant density on average declined

from 100 to 25 plants m-2 in four years. Stem density decreased the first two years but it was

stable at 350 stems m-2 thereafter.

Predicting yield potential at each harvest by counting plants or stems, will give farmers a forage

yield estimate useful to decide the availability of forage. Also, prediction is necessary for alfalfa

multi-peril insurance claims. Crop adjustors need better prediction models to determine the yield

potential of alfalfa and the loss caused by flooding or drought.

Current prediction losses used by RMA for multi-peril insurance use a historical seasonal forage

yield and requires to determine the actual forage yield. Coverage level is based on a percentage

of the actual production history (APH), which can range from 50 to 75% of APH (RMA, 2014).

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Predicting forage yield by counting plants or stems would eliminate the need of a farmer or

adjustor to have to weigh the forage yield to claim insurance. Validation of the models developed

on farmer’s field is necessary before these models can be used.

Objectives

1 Determine the relationship between plant and stem density, and forage yield.

2. Develop an accurate and efficient method to appraise alfalfa for multi-peril insurance.

3. Validate prediction models developed on-farm.

Materials and Methods

The Midwest Forage Association and National Crop Insurance Services provided funds for a

four year project. The project funded in 2016, was the last stage of the project corresponding to

the validation of the models developed from the in-plots research studies from 2013-2016

Experiments were planted at Fargo, Prosper, and Carrington, ND in 2013 and at Fargo and

Prosper in 2014. Experimental design was a RCBD with six seeding rates (0.9, 4.5, 8.9, 13.4,

17.9, and 22.3 lbs/acre of pure live seed) and four replicates. Each plot had 8 rows, 6 inches

apart and 20 ft long. In each plot, stem and plant were counted in fall and spring in a 1 m2

quadrant. Forage yield was evaluated in each harvest in 2013, 2014, 2015, and 2016. The

seeding year had two cuts while the first, second and third production years had typically four

cuts. Forage yield was taken with a plot forage harvester from a 3.2 x 20 ft wide area in each plot

and for each harvest. Targeted harvest stage was, first cut late bud, second cut 10% bloom, and

third and fourth cut at 25% bloom.

All samples were weighed, dried and ground to 1-mm mesh and analyzed for forage quality with

a NIRS (XDS analyzer Foss, Denmark).

All data was analyzed by location, year, and cut and then combined using PROC Mixed of SAS

across locations. Seeding rate, plant density, year and cut were fixed variables while location and

blocks were random. Regression models for forage yield response to plant stand and stem

density by stand age (Seeding Year, Year 1, Year 2 and Year 3) and harvest cut (1, 2, 3, and 4).

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Resulting models include four years of plant density studies at three locations (n= 1700) in North

Dakota.

On-farm validation 2016:

In 2016, 24 farmer’s alfalfa fields were sampled for forage yield, plant and stem counts in each

cut. Sampling was conducted by NDSU Extension service county agents and farmers. Three 1-

m2 samples/field/cut were taken, bagged and sent to NDSU main campus for evaluation. A

evaluation sheet and a short-video were developed with instructions on how to sample. Burlap

bags, tags and written instructions were sent to county agents. Evaluation sheet is included as an

Appendix to this report. The link to the instruction video can be found in

https://youtu.be/yhn8u_FAgXo

Samples were ground to 1 mm mesh and forage quality was analyzed with an NIRS. ArcGis

10.4 map software was used to create the forage yield map and sampling areas.

Results

With the data collected between 2013 and 2016 several regression models were developed. The

maximum forage yield in the Seeding Year, Year 1, Year 2, and Year 3 was reached with 73, 52,

36, and 36 plants/m2 (Fig. 1, Table 1) and 583, 497, 433, and 428 stems/m2, (Fig. 2, Table 2),

respectively. Prediction models generated with the controlled experiments from 2013-2016

indicated that stems/m2 predicted yield slightly better than plants/m2 (Fig. 2, Table 2).

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Fig. 1. Total seasonal forage yield response to plant density in the seeding year, Year1, Year 2

and Year 3.

Table 1. Predicted alfalfa plant density for maximum forage yield values in different

production years.

Production year Max. yield No. plants

Tons/A plants/m2

Seeding year 2.64 73

Year 1 5.92 52

Year 2 5.51 36

Year 3 7.18 36

Forage yield is the sum of all cuts in a year (2 cuts Seeding year, 4 cuts Years1-3)

y = 1.56 + 0.029x -0.0002x2

r² = 0.94

y = 2.90 + 0.115x - 0.0011x2

r² = 0.99

y = 2.60 + 0.16 - 0.0022x2

r² = 0.95

y = 3.38 + 0.21x - 0.0029x2

r² = 0.95

0

1

2

3

4

5

6

7

8

0 10 20 30 40 50 60 70 80 90 100

Tota

l sea

sona

l for

age

yiel

d ( t

ons/

acre

)

Number of plants/m2

Seeding Year Year 1

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Fig. 2. Total seasonal forage yield response to stem density in the seeding year, Year1, Year 2,

and Year 3.

y = 0.914 + 0.007x - 6E-06x2

r² = 0.97

y = -2.37 + 0.0298x - 3E-05x2

r² = 0.99

y = -0.327 + 0.026x - 3E-05x2

r² = 0.97

y = -3.56 + 0.051x - 6E-05x2

r² = 0.98

0

1

2

3

4

5

6

7

8

0 100 200 300 400 500 600 700

Tota

l sea

sona

l for

age

yiel

d (t

ons/

acre

)

Number of stems/m2

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Table 2. Predicted alfalfa stem density for maximum forage yield values for different

production years.

Max. yield No. stems

Tons/A stems/m2

Seeding year 2.96 583

Year 1 5.03 497

Year 2 5.31 433

Year 3 7.41 428

On-farm validation

Samples from farms in 2016 varied in age (1->5), number of cuts in the season (1-3), harvest

stage (bud to full bloom), and plant height at harvest (17-38”) (Table 3). In general forage yield

in 2016 was low due to lack of rain. It is clear that fields harvested only once have much lower

yield than those harvested thrice. The one-cut fields are coincident with the below normal

rainfall in the months of June and July (Fig. 4 and Fig. 5).

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Table 3. Characteristics, total seasonal forage yield, plant and stem density and plant height of 24

alfalfa fields sampled.

County No.

cuts

Stand

Age

City Total

yield

Plants

Cut1

Stems

Cut 1

Height

Cut 1

Yrs. tons/A plants/m2 stems/m2 inches

Benson 2 4 Minnewaukan 2.97 20.0 443 33.0

Bowman 1 na Bowman 2.05 13.7 288 30.7

Cass 2 1 Galesburg 2.73 52.0 579 23.3

Cavalier 1 na Langdon 2.51 61.3 400 38.3

Dickey 2 na Ellendale 2.90 43.0 381 27.3

Eddy 2 5 McHenry 3.42 19.0 508 25.0

Emmons 1 2 Linton 1.54 38.0 444 28.3

Foster 2 >5 Carrington 3.91 24.7 492 28.0

Griggs 3 5 Cooperstown 5.81 15.7 378 30.0

Logan 1 2 Napoleon 1.38 39.3 429 20.7

McHenry 2 2 Velva 3.43 42.0 456 20.7

Mountrail 1 na Stanley 1.19 14.3 247 15.7

Nelson 1 3 Niagara 1.57 30.0 504 21.7

Nelson 2 3 Lakota 2.86 26.7 465 23.7

Pembina 2 3 Walhalla 4.05 43.3 366 30.7

Rolette 2 1 Rolla 5.20 39.3 371 29.0

Sargent 1 1 Milnor 1.55 49.3 638 24.7

Stark 1 >5 Dickinson 1.14 24.7 140 23.0

Steele 3 4 Primrose 2.87 35.3 359 19.7

Steele 2 >5 Enger 2.21 48.3 562 23.7

Stutsman 1 1 Streeter 1.45 27.3 339 26.3

Stutsman 1 4 Streeter 1.37 34.7 321 24.0

Towner 3 2 Rock Lake 3.74 36.0 511 29.7

Ward 2 2 Minot 2.55 34.3 365 17.3

LSD (0.05) 0.85 15.8 125 7.6

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Fig. 3 shows the total forage yield distribution of fields sampled. The larger the green circle the

higher the total seasonal forage yield. Low yielding fields were those harvested only once

because of less than normal rainfall in the months of June and July (Fig. 4 and Fig. 5).The high

variability in the data collected on-farm made difficult to generate one-fit-all models.

Fig. 3. Distribution and forage yield on 24 alfalfa fields in North Dakota in 2016.

3.50->4.50 3.00-3.49 2.00-2.99 1.50-1.99 1.00-1.49

Forage yield (tons/acre)

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Fig. 4. Percent of normal rainfall in June 2016.

Fig. 5. Percent of normal rainfall in July 2016.

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Models fitted for the first harvest only are shown in Fig. 6 (plants/m2) and Fig. 7 (stems/m2).

Plants/m2 was not a predictor and stems/m2 was slightly better. The predicted forage yield

versus observed forage yield was significant only for stems/m2 (Fig. 8). Predicted forage yield

was not reduced significantly for > 350 stems/m2.

Fig. 6. Total seasonal forage yield response to plant density on-farm, n=24.

y= 2.89 + 0.0009xr² = 0.01

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0

Fora

ge y

ield

(ton

s/ac

re)

Plants/m2

Total yield vs. plants

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Fig. 7. Total seasonal forage yield response to stem density on-farm, n=24.

Fig. 8. Predicted forage yield versus observed on-farm forage yield using stems/m2, n=24.

y= -2.08+ 0.0224x - 3E-05x2

r² = 0.20

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

0 100 200 300 400 500 600 700

Fora

ge y

ield

(ton

s/ac

re)

Stems/m2

Total yield vs. stems

>350 stems/m2

y= 4.06 + 1.58x - 0.18x2

r² = 0.28

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

0.00 1.00 2.00 3.00 4.00 5.00

Pred

icte

d fo

rage

yie

ld (t

ons/

acre

)

Forage yield (tons/acre)

Predicted yield vs. Forage yield (with stems/m2)

>350 stems/m2

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Forage quality

Forage quality did not change with plant or stem density in the experimental trials (data not

shown). Many farmers believe that higher plant density will produce finer stems with less fiber

and thus improved quality, but forage quality depends much more on the leaf/stem ratio of the

alfalfa not on the thickness of stems. Greater leaves content improves forage quality.

Variations in forage quality of alfalfa from different farm fields was mainly due to growth stage

at harvest (Table 4). In Fig. 9 is clear there is no relationship between plant height and forage

quality components. The lowest RFQ values (89-120) correspond to fields harvested in the first

cut at 30-80% bloom.

Fig. 9. Relationship between plant height at harvest in cut1 and forage quality components

(RFQ, CP, NDF)

y = 156 -0.58xr² = 0.015

y = 22 - 0.047xr² = 0.01

y = 42.2 + 0.13xr² = 0.02

020406080

100120140160180200

0 5 10 15 20 25 30 35 40 45

RFQ

, CP,

and

NDF

(%)

Plant height at harvest (inches)

RFQ CP NDF

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Table 4. Forage quality from the first harvest of 24 alfalfa fields sampled in North Dakota

County City CP ADF NDF ADL NDFD Ash RFQ

Benson Minnewaukan 23.2 30.0 40.5 8.0 50.9 8.4 179

Bowman Bowman 18.1 33.2 46.1 9.0 41.4 6.6 131

Cass Galesburg 14.0 47.1 58.3 11.8 37.0 5.9 89

Cavalier Langdon 20.6 39.4 47.8 9.2 45.7 8.6 126

Dickey Ellendale 20.7 35.8 46.5 8.7 45.8 7.5 141

Eddy McHenry 20.9 31.1 39.2 7.1 50.5 8.1 179

Emmons Linton 19.6 36.3 46.2 9.0 43.9 7.6 139

Foster Carrington 21.7 34.0 44.0 8.3 45.8 7.9 146

Griggs Cooperstown 23.6 32.2 42.5 7.8 49.9 5.9 175

Logan Napoleon 21.0 28.6 39.6 7.0 48.4 7.2 179

McHenry Velva 21.1 34.2 45.7 8.6 44.0 7.7 141

Mountrail Stanley 25.2 31.1 40.5 8.2 40.5 7.5 155

Nelson Niagara 18.3 42.3 53.5 10.7 39.2 5.6 115

Nelson Lakota 18.2 42.6 53.5 10.7 39.7 6.0 110

Pembina Walhalla 22.7 35.0 44.8 8.4 46.4 7.3 151

Rolette Rolla 20.4 44.0 50.7 10.1 41.9 8.3 107

Sargent Milnor 20.4 37.4 44.7 9.0 41.2 8.1 136

Stark Dickinson 19.5 33.7 45.2 8.8 39.4 6.5 133

Steele Grand Forks 23.4 32.9 42.0 7.9 45.5 7.9 166

Steele Grand Forks 22.1 33.1 41.9 8.4 42.5 6.5 158

Stutsman Streeter 19.3 37.5 48.2 9.2 41.9 7.6 123

Stutsman Streeter 21.9 35.1 47.2 8.2 44.0 8.8 133

Towner Rock Lake 20.4 38.7 48.7 9.0 43.2 8.2 130

Ward Minot 22.7 29.6 40.5 7.3 46.3 7.9 168

LSD (0.05) 3.2 7.4 8.1 1.7 3.2 1.1 34

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Conclusions

o Maximum forage yield in the Seeding year, Year 1, Year 2, and Year 3 was reached

with 73, 52, 36, and 36 plants/m2 and 583, 497, 433, and 428 stems/m2, respectively.

o Predictive models generated with the controlled experiments from 2013-2016

indicated that stems/m2 predicts yields slightly better than plants/m2.

o The high variability in the data collected on-farm made it difficult to generate one-fit-

all models.

o Both, the models developed with controlled experiments from 2013-2016 and the

models from on-farm samples in 2016 predicted that forage yield is maximized above

350 stems/m2.

o Forage quality was not affected by plant and stem density or plant height.

Acknowledgments

This research was possible thanks to the funding provided by the Midwest Forage Association

Research Program and the National Crop Insurance Services (Dr. Mark Zarnstorff). My most

sincere thanks to all the undergraduate and graduate students who spend numerous hours

counting plants and stems and grinding samples. Last but not least this research would not have

been possible with the collaboration of county agents, research extension centers, and farmers.

References

Durling, J.C., O.B. Hesterman, and C.A. Rotz. 1992. Predicting first-cut alfalfa yields from

preceding winter weather Hall, M.H. C.J. Nelson, J.H. Coutts, and R.C. Stout. 2004. Effect of seeding rate on alfalfa stand

longevity. Agron. J. 96:717-722. Risk Management Agency, 2014 Forage Seeding. Iowa, Minnesota, and Wisconsin. St. Pual

Regional Office.

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SUPPLEMENTAL INFORMATION

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Field Information

Field InfoAgent name Sampling date

Contact Person Alfalfa planting date ( month/Year)

County Irrigation (Y/N)

Township Cutting (circle)

Range, Section, quarter Note: Please, sample only Monday through Wednesday if shipping samples directly to Fargo GPS location (optional) GPS can be with phone GPS app or other

City

Zip code

e-mail agent

SHIP VIA: SHIP DATE:

Sample #plant

height (in) Growth stage Comments

Example

1 20 10%bloom

2 22 90% LV

3 19 90% EV

Your records

1

2

3

Send to:One of the following area specialists. Area specialists involved in the project Fara Brummer (Streeter) , Leslie Lubenow ( Langdon), or Ryan Buetow(Dickinson), or send them to Fargo Check List

Materials: measuring tape, flags or stakes, pencil, something to cut the plants (scissors, knife, scythe), burlap bags, tags ( Bags and tags will be sent to you or your area specialist)INSTRUCTIONS:

5. In each square sample 10 stems and determine growth stage and record it on the table above (see Sheet #3. Plant growth stage)6. In each square take the plant height take the tallest stem in the square and measure it from the base to the top.

9. Harvest all plants aboveground biomass and place it in the burlap bag, label both tags provided with sampling date, field name, county and the sample number ( 1 , 2, or 3), place one inside and tie one outside the

10. Pack the three bags (sample #1, #2, and #3) in the burlap bags

11. Remove the flags

12. Send the bags to you area specialist the same or next day. Notify them , if they are close to your area they maybe be able to pick them up13. If you prefer to ship them to Fargo directly let me know.14. The second cut will be in the same field as cut1 repeat steps 2-11

15. Do the same if the farmer cuts the field a third time.

Pictures would be nice but not requiredNote : the alfalfa has to be sampled for EVERY cut the producer makes this year . Do not cut the plants unti l you have followed steps 1-8

1st 2nd 3rd 4th

Ground

8. Record number of stems per plant in the sheet #2 ( Count sheet)

7. Proceed to count stems. Count the number of stems in each plant in the square.

4. Mark the three square meters 100 x 100 cm, each corner with a flag or stake. Select one square of each low, medium and high plant density

1. Select the field ( sample one week before the farmer cuts the alfalfa or at Late bud- 10% bloom stage)

2. Complete the field information above

3 Select an area to sample, walk from the edge of the field at least 30 ft

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Total stems and plants are calculatedin the last two columns.

Example add as many columns as plants you have

Sample Plant 1 Plant 2 Plant 3 Plant 4 Plant 5 Plant 6 Plant 7 Plant 8 Plant 9 Plant 10 Plant 11 Plant 12 Plant 13 Plant 14 Plant 15 Plant…… Total stems Total plan

1 6 2 3 15 4 7 2 4 20 3 6 7 4 15 13 111 15

Plant 16 Plant 17 Plant 18 Plant 19 Plant 20 Plant 21 Plant 22 Plant 23 Plant 24 Plant 25 Plant 26 Plant 27 Plant 28 Plant 29 Plant 30

Your dataSample Plant 1 Plant 2 Plant 3 Plant 4 Plant 5 Plant 6 Plant 7 Plant 8 Plant 9 Plant 10 Plant 11 Plant 12 Plant 13 Plant 14 Plant 15 Plant……

1 0 0Plant 16 Plant 17 Plant 18 Plant 19 Plant 20 Plant 21 Plant 22 Plant 23 Plant 24 Plant 25 Plant 26 Plant 27 Plant 28 Plant 29 Plant 30 0 0

Plant 31 Plant 32 Plant 33 Plant 34 Plant 35 Plant 36 Plant 37 Plant 38 Plant 39 Plant 40 Plant 41 Plant 42 Plant 43 Plant 44 Plant 45

2 Plant 1 Plant 2 Plant 3 Plant 4 Plant 5 Plant 6 Plant 7 Plant 8 Plant 9 Plant 10 Plant 11 Plant 12 Plant 13 Plant 14 Plant 15

Plant 16 Plant 17 Plant 18 Plant 19 Plant 20 Plant 21 Plant 22 Plant 23 Plant 24 Plant 25 Plant 26 Plant 27 Plant 28 Plant 29 Plant 30

0 0Plant 31 Plant 32 Plant 33 Plant 34 Plant 35 Plant 36 Plant 37 Plant 38 Plant 39 Plant 40 Plant 41 Plant 42 Plant 43 Plant 44 Plant 45

3 Plant 1 Plant 2 Plant 3 Plant 4 Plant 5 Plant 6 Plant 7 Plant 8 Plant 9 Plant 10 Plant 11 Plant 12 Plant 13 Plant 14 Plant 15

Plant 16 Plant 17 Plant 18 Plant 19 Plant 20 Plant 21 Plant 22 Plant 23 Plant 24 Plant 25 Plant 26 Plant 27 Plant 28 Plant 29 Plant 30

Plant 31 Plant 32 Plant 33 Plant 34 Plant 35 Plant 36 Plant 37 Plant 38 Plant 39 Plant 40 Plant 41 Plant 42 Plant 43 Plant 44 Plant 45

if you have more than 45 plants in a sample just add them below

Mail the Excel file to marisol.berti@ndsu.edu

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