LITHUANIAN UNIVERSITY OF AGRICULTURE

LITHUANIAN RESEARCH CENTRE FOR AGRICULTURE AND FORESTRY

Virmantas Povilaitis

ASPECTS OF CEREAL YIELD FORMATION IN AGROECOSYSTEMS OF DIFFERENT INTENSITY

Summary of doctoral dissertation

Biomedical sciences, Agronomy (06 B)

Akademija, 2011

The doctoral dissertation was prepared during the period 2006 – 2010 at the Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry.

Scientific supervisor: Dr. Sigitas Lazauskas (Institute of Agriculture, Lithuanian Research Centre

for Agriculture and Forestry, Biomedical Sciences, Agronomy – 06 B)

The dissertation will be defended at the Council of Agronomy Sciences at the Lithuanian University of Agriculture:

Chairman:

Prof. habil. dr. Zenonas Dabkevičius (Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, Biomedical Sciences, Agronomy – 06 B) Members:

Prof. habil. dr. Romualdas Juknys (Vytautas Magnus University, Biomedical Sciences, Ecology and Environmental sciences – 03 B)

Doc. dr. Steponas Raudonius (Lithuanian University of Agriculture, Biomedical Sciences, Agronomy – 06 B)

Habil. dr. Juozas Benediktas Staniulis (Institute of Botany of Nature Research Centre, Biomedical Sciences, Biology – 01 B)

Dr. Alvyra Šlepetienė (Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, Biomedical Sciences, Agronomy – 06 B) Opponents:

Doc., dr. Dalia Ambrazaitienė, (Klaipėda University, Biomedical Sciences, Ecology and Environmental Sciences – 03 B)

Prof. habil. dr. Gediminas Staugaitis (Agrochemical Research Laboratory, Lithuanian Research Centre for Agriculture and Forestry, Biomedical Sciences, Agronomy – 06 B)

Defence of the doctoral dissertation will take place at the public

meeting of the Council of Agronomy Sciences on the 19th of April, 2011 at 11.00 a.m. in room 261, central building of the Lithuanian University of Agriculture, Studentų st. 11, LT – 53361, Akademija, Kaunas distr., Lithuania.

The summary of the doctoral dissertation was distributed on the 19th of

March, 2011. The doctoral dissertation is available in the libraries of the Lithuanian

University of Agriculture, and the Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry.

LIETUVOS ŽEMĖS ŪKIO UNIVERSITETAS

LIETUVOS AGRARINIŲ IR MIŠKŲ MOKSLŲ CENTRAS ŽEMDIRBYSTĖS INSTITUTAS

Virmantas Povilaitis

MIGLINIŲ JAVŲ DERLIAUS FORMAVIMO ASPEKTAI SKIRTINGO INTENSYVUMO AGROEKOSISTEMOSE

Daktaro disertacijos santrauka

Biomedicinos mokslai, agronomija (06 B)

Akademija, 2011

Disertacija rengta 2006 – 2010 metais Lietuvos agrarinių ir miškų mokslų centro Žemdirbystės institute.

Mokslinis vadovas:

Dr. Sigitas Lazauskas (Lietuvos agrarinių ir miškų mokslų centro filialas Žemdirbystės institutas, biomedicinos mokslai, agronomija – 06 B)

Disertacija ginama Lietuvos žemės ūkio universiteto Agronomijos mokslo

krypties taryboje: Pirmininkas: Prof. habil. dr. Zenonas Dabkevičius (Lietuvos agrarinių ir miškų mokslų centras, biomedicinos mokslai, agronomija – 06 B). Nariai: Prof. habil. dr. Romualdas Juknys (Vytauto Didžiojo universitetas, biomedicinos mokslai, ekologija ir aplinkotyra – 03 B). Doc. dr. Steponas Raudonius (Lietuvos žemės ūkio universitetas, biomedicinos mokslai, agronomija – 06 B). Habil. dr. Juozas Benediktas Staniulis (Gamtos tyrimų centro Botanikos institutas, biomedicinos mokslai, biologija – 01 B). Dr. Alvyra Šlepetienė (Lietuvos agrarinių ir miškų mokslų centro Žemdirbystės institutas, biomedicinos mokslai, agronomija – 06 B).

Oponentai: Doc. dr. Dalia Ambrazaitienė, (Klaipėdos universitetas, biomedicinos mokslai, ekologija ir aplinkotyra – 03 B). Prof. habil. dr. Gediminas Staugaitis (Lietuvos agrarinių ir miškų mokslų centro Agrocheminių tyrimų laboratorija, biomedicinos mokslai, agronomija – 06 B).

Disertacija bus ginama viešame Agronomijos mokslo krypties tarybos

posėdyje 2011 m. balandžio 19 d. 11 val. Lietuvos žemės ūkio universiteto centrinių rūmų 261 auditorijoje.

Adresas: Lietuvos žemės ūkio universitetas, Studentų g. 11, LT-533361, Akademija, Kauno r., Lietuva. Tel.: (8-37) 752 254, faksas (8-37) 397 500.

Disertacijos santrauka išsiusta 2011 m. kovo mėn. 19 d. Disertaciją galima peržiūrėti Lietuvos žemės ūkio universiteto ir Lietuvos

agrarinių ir miškų mokslų centro filialo Žemdirbystės instituto bibliotekose.

INTRODUCTION The present world population of 6000 million is expected to reach 8100

million over the next two decades (Ali, Talukder, 2008). The population growth drives food demand, promotes intensification of agriculture, development of new production technologies and varieties more suitable for regional nature conditions. The present yield potential of cereal varieties is high and the measures of intensification are effective in Cambisols of temperate climate zone, where moisture supply is sufficient, however, intensification of production increases the risk of damaging the sustainability of environment. Increasing air temperature and variation of moisture regime, resulting from the climate change, raise additional specific questions. Deeper understanding and effective measures for monitoring and management of cereal growth process are needed for adaptation to changing climate and technological conditions.

Winter wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.) are among the major crops in Lithuania and in the world. The total area under winter wheat in Lithuania in 2009 was 397 thousand hectares and under spring barley 254 thousand hectares (Statistikos departamentas….2010). In 2008, winter wheat grain yield amounted to 4.7 t ha-1 and that of spring barley to 2.8 t ha-1 on average (Statistikos departamentas….2010). During the recent years, the land area of certified organic farms has been steadily increasing and in most of them cereal crops are cultivated. Cereal yields under organic management in Europe are typically by 60–70% lower than those under conventional management (Mader et al., 2007). In Lithuania, spring barley yield under intensive agriculture is by 94 – 106% higher than that under organic (Gužys, 2002 b). Evidently, at present, utilisation of technological and genetic potential of cereal varieties is insufficient; however, there is a shortage of evaluations based on research data. The reasons why the level of yield potential realisation is low are not always clear and lack of objective criteria complicates decisions on what level of management intensity it is expedient to apply.

Crop simulation models, intensively elaborated during the last decades, turned into handy tools for estimating likely effects of interactions of climate, soil and management on yield and its development, simulation of processes in the system atmosphere – plant – soil (Bacsi, Zemankovics, 1995; Naud et al., 2008). Different models have been developed for a variety of purposes in agriculture (Brisson, et al., 2004; van Ittersum et al., 2003; Abrahamsen, Hansen, 2000; Franko et al., 1995; Diekkrüger, Arning, 1995; Porter, 1993). The DSSAT model has been widely used in the world for simulation of yield and parameters of main plant growth processes (IBSNAT, 1994; Eitzinger et al., 2008). The model relatively well simulated grain yield in the countries under temperate climate zone of the Central Europe – Czech Republic, Hungary (Harnos, Kovacs, 1999; Trnka et al., 2006a; Holden, Brereton, 2006). The results of model performance on yield simulation in the northern regions are controversial. The model simulated the yield with lower accuracy under

condition of North-Germany (Langensiepen et al., 2008). The study conducted in Poland suggests that additional measures are needed to improve accuracy of simulation (Kuchar et al. 2004). There is limited experience in Lithuania of application of models in agronomic research, and modelling in studies of agroecosystems’ productivity has been applied only fragmentary.

Water and nitrogen are the main factors conditioning utilisation of crop yield potential (Nanga et al. 2008; Pala, et al. 2007). Nitrogen nutrition is an especially important yield formation factor in the northern temperate climate zone, where due to excess of moisture supply crops rarely suffer shortage of water. However, changing climate, more frequent invasions of tropical weather masses, increase the probability of dry periods when plants can experience water shortage stress. Effect of climatic factors and particularly climate change on the yield formation process, efficacy of intensification measures have not been comprehensively, especially quantitatively, investigated. Studies are needed for more complex understanding of the mechanisms, creation of methods of stress identification and reduction of grower’s risks.

Some of the most productive soils of Lithuania – Cambisols are located in the zone of interaction of the cool littoral and humid continental air masses (Maracchi G. et al. 2005) and this situation suggests higher variation in cereal yield formation and yield potential realisation level.

Hypothesis of work Formation of canopy and yield of cereals under conditions of

temperate climate zone with periodic excess of moisture depends on growing intensity, especially supply of nitrogen, and temporary water shortage stresses. Nitrogen and water shortage induced stresses can be identified and their effect on productivity of cereals assessed combining methods of field experiments and modelling.

Research objective The study was designed to explore the aspects of yield formation of

winter wheat and spring barely, grown in the agroecosystems of different intensity on an Endocalcari- Epihypogleyic Cambisol in the northern latitudes of temperate climate and to assess the suitability of DSSAT v4.0.2.0 model for the simulation of yield formation process.

The object of study – spring barley (Hordeum vulgare L.) and winter wheat (Triticum aestivum L.).

Research tasks: 1. To investigate the effect of growing intensity, applying fertilisers

according to normative for target yield, on spring barley and winter wheat leaf index, biomass and grain yield formation.

2. To quantitatively assess accumulation of nitrogen and carbon in the biomass during vegetation.

3. To explore the effect of water and nitrogen induced stresses on productivity of photosynthesis and to evaluate feasibility of DSSAT v4.0.2.0 model for the diagnosis.

4. To estimate likely effect of climate change on winter wheat and spring barely yield.

Propositions to be defended in the dissertation: 1. Cereal crops grown under conventional technology and applied

with nitrogen fertilisers develop higher leaf index and photosynthetic potential, which correlates with grain and biomass yield, compared with cereals grown under organic agroecosysytem.

2. The quantitative parameters of biomass accumulation are determined by cultivation intensity, and the stress induced by nitrogen and water shortage, which highly influences this process, can be simulated by the DSSAT v4.0.2.0 model.

3. Under the effects of climate change, resulting in increasing air temperature, the growing season of cereals can become shorter, and without the application of adaptation measures, can lead to grain yield reduction.

Originality of the research work. Cereals grown under conventional technology on Cambisols in the

northern latitudes of temperate climate develop higher leaf area index and higher photosynthetic potential, which closely correlate with grain and biomass yield, compared with those grown under organic system. The DSSAT v4.0.2.0 model relatively well simulated the grain yield of spring barley and winter wheat in the favourable years under conventional technology and could be used for yield and yield development simulation. The shortage of water decreases the photosynthetic productivity and it could be identified by model’s simulations. The increasing air temperature as influenced by climate change will result in shorter growing season and without the application of adaptation measures can even reduce grain yield.

Practical application. Results of analysis of winter wheat and spring barley yield formation

and of water and nitrogen stress can be used to improve growing technologies in fertile soils. The changes of nitrogen concentration in cereals during growing period in different agroecosystems can help to specify diagnostic measures of plant nutrition. Yield changes simulated by the DSSAT v4.0.2.0 model under different growing and ambient conditions help to improve forecast of grain yield in Cambisols and to calculate likely impact of climate change on cereal yield.

Approval of the dissertation work. The main research results have been published in 3 scientific articles in refereed ISI WOS or Master List data base, 4 in other scientific journals and 2 in conference proceedings.

Scope and volume of the dissertation. The dissertation is written in the Lithuanians language. It comprises 91 pages and is composed of: an

introduction, literature overview, materials and methods, results and discussion, conclusions, list of references, list of publications on the dissertation topic. The dissertation includes 15 tables, 61 figures. A total of 238 literature references have been used.

EXPERIMENTAL MATERIAL AND METHODS Experimental site. The field experiments were carried out at the

Lithuanian Institute of Agriculture, in Valinava long-term experiment (55.22° N, 23.51° E) in 2007 – 2009. This experiment was established in 1991 by A. Bučienė (Bučienė, 2003, Bučienė et al., 2007). The experimental site is situated on the terrace of the Dotnuvele River and occupies 4.4 ha. The prevailing soil is light loam, Endocalcari – Epihypogleyic Cambisol (CMg-p-w-can), carbonates are at 40 – 60 cm depth. Soil pH – 7.2, Content of total N – 0.18 %; available phosphorus (P2O5) – 151 mg kg-1, available potassium (K2O) – 119 mg kg-1.

Description of field experiments. Spring barley (Hordeum vulgare L.) ‘Luoke’ and winter wheat (Triticum aestivum L.) ‘Ada’ were grown in a crop rotation typical of Middle Lithuania: spring barley intercropped with red clover, red clover, winter wheat and spring rape. Crops were grown under three levels of intensity: a) conventional, b) integrated and c) organic. Agroecosystems were repeated twice in space, each occupied approximately 786 m2 (32.2 m long, and 24.4 wide) and contained 6 sub-plots (44 m2 – 20 m long, 2.2 m wide).

Cereals in conventional and integrated agroecosystems were applied with herbicides, fungicides and insecticides and in organic system were grown without application of industrial fertilisers and plant protection measures. In conventional system, winter wheat was fertilised with N110P90K140 for a target yield of 6 – 7 t ha-1, and spring barley – with N100P80K150 for 5 t ha-1 yield. Crops in integrated system were applied with lower rates of fertilisers – N70P40K60 for winter wheat and N45P60K120 for spring barley. Winter wheat was sown in the middle of September and spring barley at the end of April at a density of 400 kernels m-2. The red clover ‘Arimaičiai’ (rate 10 kg ha-1) was intercropped after sowing of spring barley. Herbicides were applied once, fungicides and insecticides once or twice, depending on pest incidence. Spring barley and winter wheat were harvested at full maturity separately from each sub-plot with a grain harvester ‘Wintersteiger’. Grain yield was adjusted to 14% moisture level and recalculated to t ha-1.

Leaf area and biomass measurements. Samples of spring barley and winter wheat were taken biweekly, six times per growing season. Samples were collected from 3 sub-plots of each agroecosystem, from an area of 0.13 m2 (two adjacent crop rows 0.5 m long). The number of plants was assessed and sample was divided in two parts: five plants were taken for leaf area measurements and the rest were used for biomass measurements and chemical analyses. All leaves, with green surface more than 50%, were detached from the selected plants,

scanned and leaf area was measured with a computer program ‘WinFolia pro v2004 a’. Other plants were dried and biomass was assessed. Photosynthetic potential (leaf area duration) was calculated using the equation of Power et al. (1967). Net photosynthetic productivity (g m-2 day-1) was calculated using the equation described in literature (Šlapakauskas and P. Duchovskis, 2008). All leaf area and biomass results are presented per square meter.

Chemical analysis of biomass. The dried plant samples were milled with a "Retsch ZM 200” mill. Nitrogen content of the biomass was measured by Kjeldahl method. Carbon content was determined by a chemical method: 0.05 g of dry biomass sample was placed into the 100-ml flat-bottomed flask, mixture of chromium was added, flask was placed in the thermostat (+160 °C) and heated for 30 minutes. Later content of the flasks was cooled, the solution was poured into a 200 ml flask and allowed to stand overnight. The absorbance of solution was determined using an automatic spectrophotometer UV/Ws Cary 50 at the wavelength of 590nm using glucose as a standard.

Soil moisture measurements. Soil moisture sensors ‘Watermark’ were installed in the soil in spring barley and winter wheat in each agroecosystem at the 20 and 40 cm depths. The technical characteristics of the soil moisture sensors ‘Watermark’ allow measurements of soil moisture in the range from 0 to 200 Centibars (kPa). The measurements are reliable at soil temperature > +16°C.

Statistical analysis of grain yield, leaf area index, biomass, carbon and nitrogen concentrations was performed using ANOVA completely randomized design (Clewer and Scarisbrick, 2001). Level of significance was assessed with Fisher’s (F) and Student’s criterion (t). Interdependence of individual characteristics was assessed using linear correlation and regression.

Modelling. The DSSAT v4.0.2.0 model was chosen for this study because it has been used relatively successfully for more than a decade for cereal crop simulations under different climate and soil conditions, including climate change, and provides a number of parameters of relevant crop growth processes. For efficient performance under local pedo-climatic conditions the model requires preparatory work associated with input database compilation and testing.

Database compilation comprised comprehensive input of local meteorological and soil conditions and details of growing techniques. Input of meteorological data – daily minimum and maximum air temperature (°C), rainfall (mm) was obtained from the Dotnuva weather station. The data of daily solar radiation (MJ m-2 d-1) were obtained from the Kaunas weather station and were adjusted to experimental site location by correcting these values from sun shine hours obtained in Dotnuva, with simulation model ‘AgroMetShell 1.57’. The carbon dioxide concentration was set to 375 ppm, the level which corresponds to actual values measured at present in cereal crop stands in Valinava experiment. Soil data input was derived from the parameters of the soil

profile prevailing in the experimental site (Table 1). In order to convert values of soil texture data from N. Kačinskis to FAO method, the equation proposed by J. Mažvila et al. (2006) was used. The content of organic carbon (Corg.) was derived from humus content using the equation described by Орлов (1990). Data of growing techniques required for model input reflected actual operations performed in the field. Crop genetic coefficients were selected from the model’s data base.

Table 1. Soil property for model’s input from the Valinava field site. Depth, cm

Soil particles 0 – 30 30 – 50 50 – 80 80 – 100 Clay, % 21.00 17.92 16.02 12.00 Silt % 11.86 24.88 24.00 19.00

Organic carbon, % 1.45 1.10 0.40 0.30 Total nitrogen, % 0.18 0.13 0.08 0.05

Testing of the DSSAT v4.0.2.0 model was performed on the data from

field experiments conducted at the Lithuanian Institute of Agriculture in Dotnuva with spring barley in 1990 – 1991 and with winter wheat in 1989-1991.

Cereals crop simulations as affected by climate change. Most of the

published scenarios of the expected climate change are similar (Olesen, Bindi, 2002, Christensen and Christensen, 2007; IPPC-2007, Ekholm et al. 2010, Patil et al., 2010). Based on this material, several scenarios for simulating spring barley and winter wheat performance were prepared. Meteorological data of 2007 in Dotnuva, with average air temperature higher by 1.7°C than the climatic norm of 1961-1990, were used as a baseline for simulations. For simulations we used two levels of CO2 concentrations – 375 ppm and 485 ppm. Scenario for spring barley: the air temperature increases by 1, 2, or 3°C; amount of rainfall per growth season is not changing, increasing or decreasing by 15 mm in comparison to 2007. For winter wheat the same air temperature regimes as for spring barley were applied, however, the amount of rainfall varied within a wider range - decreased or increased by 50 mm.

Biomass energy value. Energy potential of biomass was calculated following the coefficient of Power and Murphy (2008) – 14.8 MJ kg-1. The energy value of bio-ethanol was assumed 21 MJ l-1. A calculation of bioethanol yield from wheat grain was based on coefficient (0.367) provided by Рухлядева et al. (1979) and from barley grain (333 l per ton) by Murphy and Power (2008). These values are similar to those obtained in the Lithuanian biofuel plants.

Climatic conditions. The winter of 2007 was changeable in terms of temperatures: the weather in January was very mild and the air temperature was 10°C higher than climatic norm. The spring was very early and changeable. Dry weather prevailed. Like in spring, the weather in summer was changeable – cool weather alternated with periods of warm weather, while dry weather alternated

with rainy spells. In September, warm weather prevailed, in the third ten-day period it was dry. In November and December, the air temperature was higher than climatic norm.

In 2008, the winter was short, with no permanent snow cover and no permanent frost. The spring was early, dry and in terms of temperature, changeable weather prevailed. Warm and dry weather prevailed during the largest part of summer. In September, the weather was changeable – unusually warm during the first ten-day period, cool during the second ten-day period and warmer again during the third-ten day period.

In 2009, the winter was changeable in terms of temperatures, wintry cold weather alternated with warmer spells. February was the coldest month. The spring was extremely early. Warm and dry weather was characteristic of April. In May, the weather was cool. The dry weather that started in April persisted during the first half of May. With the rainfall at the end of the month, the rainy period started (with short intervals), which persisted throughout the entire summer period.

EXPERIMENTAL RESULTS AND DISCUSSION

Testing of the DSSAT v4.0.2.0 model Winter wheat. In the first stage of the study, the DSSAT v4.0.2.0 model

was validated against the data of the field experiments with winter wheat ‘Širvinta 1’ carried out during the period 1989–1991. The winter wheat was applied with ammonium nitrate (N60) at different growth stages (from the beginning of the vegetation in spring until the beginning of heading). Simple linear regression was computed to determine R2 value between observed and simulated grain yield data. Comparison of simulated and measured grain yield values in three cropping seasons, involving results from the treatments where winter wheat was applied with N fertilisers in spring, showed good match judging from the correlation (R2 = 0.81) and data scatter according to 1:1 line (Figure 1). However, in the plots without N fertilisation a good fit between measured (3.69 t ha-1) and simulated (3.57 t ha-1) values was demonstrated only in 1990, when crop stand severely thinned out during the winter, while in 1989 and 1991 the grain yield was markedly underestimated.

012345678

0 2 4 6 8Actual, t ha-1

Sim

ulat

ed, t

ha-1

Figure 1. The relationship between actual and simulated grain yield of

the winter wheat ‘Širvinta 1’ Dotnuva, 1989–1991 Spring barley. Spring barley was grown under two levels of intensity:

1) low (without mineral fertilisers and pesticides for palnt protection) and 2) medium (with mineral fertilises: N45P65 and sprayed with herbicides and fungicides. Barley was drilled at two rates: 3.0 and 4.5 million seeds per ha-1. The DSSAT v4.0.2.0 model overestimated period from drilling to maturity of spring barley up to six days in 1990. In our study, the correlation between simulated and actual spring barley grain yield was very strong and significant (r = 0.95, p <0.01). Significant correlation was found also between simulated and actual number of stems (r = 0.85, p <0.01). Some literature data suggest that DSSAT model successfully simulated the yield of spring barley in different locations (the coefficient of determination was from 0.64 to 0.86), but overestimated the yield in less favourable years (Štastna et al., 2002).

In 1991, the DSSAT v4.0.2.0 model overestimated the period from drilling to maturity up to seven days. The significant correlation was found between the actual and simulated grain yield (r = 0.91; p<0.01) and number of stems (r = 0.74, p <0.01). These results are rather similar to those obtained in other studies in the temperate climate zone on fertile soils (Czech Republic, Austria).

Leaf area index, photosynthetic potential and grain yield The second series of the experiments was conducted during 2007 –

2009 with the spring barley cv. ‘Luoke’ and winter wheat cv. ‘Ada’ under three levels of intensity: a) conventional, b) integrated, c) organic.

Spring barley. Formation of leaf area index in all experimental years followed the same pattern, with the higher values measured under conventional agroecosystem and much lower under organic. The largest differences between agroecosystems were measured when spring barley reached stage BBCH 32 – 49 – leaf area index under conventional growing peaked on average at 4.0 m2 m-

Y= 0.9295x+0.1667 R2= 0.8125; p <0.01

1:1 line

2, while under organic only at 2.0 m2 m-2 (Figure 2). Later leaf area index and differences between growing systems gradually decreased. The plant protection measures and applied nitrogen fertilisers helped to preserve healthy leaves longer in the conventional and integrated agroecosystems. The reason of leaf area decrease is not only diseases but also shortage of water, nitrogen, phosphorus, and sulphur (Caviglia, Sadras, 2001; Salvagiotti, Miralles, 2008; Panda et al., 2003; Dagdelen et al., 2006).

Spring barley

0.0

1.0

2.0

3.0

4.0

5.0

BBCH 21 BBCH 32 BBCH 49 BBCH 73 BBCH 77 BBCH 85Growth stage

Leaf

are

a in

dex,

m 2

m -2

ConventionalIntegrated Organic

Figure 2. Development of leaf area index of spring barley under

different growing intensity, average of 2007- 2009, Dotnuva Three years’ data of photosynthetic potential and grain yield were

subjected to combined analyses of variance (Table 2). For photosynthetic potential the effect of management was significant, whereas there was no effect of the year or interaction between year and management on this parameter. Photosynthetic potential of spring barley under conventional growing was on average by 38 and 87 m2 m-2 higher than that under integrated and organic growing. These results are in a good agreement with the patterns of leaf area index development. The photosynthetic potential significantly correlated with spring barley grain yield (r = 0.69; p<0.05). Combined analyses of variance showed significant effect of management, year and interaction between year and management on grain yield. Nonetheless, the same trend is apparent: grain yield under conventional and integrated growing was on the same level, but higher, than that under organic growing.

Table 2. The results of ANOVA of photosynthetic potential and grain yield of spring barley grown under different intensity. Photosynthetic potential,

m 2 m-2 Grain yield, t ha-1

Agro ecosystem 2007 2008 2009 2007 2008 2009 Conventional 162 140 191 4.37 5.07 4.72 Integrated 127 111 139 4.42 4.87 5.46 Organic 82 80 70 2.82 4.43 2.81

Analyses of variance F value

Source of variance Photosynthetic potential Grain yield Year 1.62 11.8* Agro ecosystem 29.86** 94.72** Interaction 1.32 13.65**

Significant: * - p< 0.05; ** - p < 0.01 Winter wheat. Like for spring barley, formation of leaf area index in all

years of the study followed similar pattern, with the higher values measured under conventional agroecosystem and much lower under organic (Figure 3). The largest differences between agroecosystems were measured when winter wheat reached stage BBCH 31 – 34 – leaf area index under conventional growing peaked on average at 4.5 m2 m-2, while under organic only at 2.5 m-2 m-

2. Like for spring barley, during later stages of development leaf area index and differences between growing systems gradually decreased.

Winter wheat

0.0

1.0

2.0

3.0

4.0

5.0

BBCH 30 BBCH 31 BBCH 34 BBCH 57 BBCH 75 BBCH 85Growth stage

Leaf

are

a in

dex,

m 2 m

-2

ConventionalIntegratedOrganic

Figure 3. Development of leaf area index of spring barley under

different growing intensity, average of 2007- 2009, Dotnuva

Winter wheat photosynthetic potential and grain yield was higher than that of spring barley; however, combined analyses of variance of three years’ data resulted in similar results (Table 3). For photosynthetic potential, only effect of management was significant and this result was in a good agreement with data of leaf area index. Photosynthetic potential of winter wheat under conventional growing was on average by 27 and 79 m-2 m-2 higher than that under integrated and organic growing. There was very strong correlation between photosynthetic potential and grain yield (r = 0.95; p<0.01). The same trend was indicated in literature (Dunphy et al. 1984, Richards, 2000, Zhang et al. 2006). Combined analyses of variance showed significant effect of management, year and interaction between year and management on grain yield. The variation in grain yield and effect of management on this parameter across years is apparent and reflects climatic fluctuations. In 2008, differences between crop management systems were much higher than in 2007 or 2009.

Table 3. The results of ANOVA of photosynthetic potential and grain

yield of winter wheat grown under different intensity. Photosynthetic potential, m2 m -2 Grain yield, t ha-1 Agro ecosystem

2007 2008 2009 2007 2008 2009

Conventional 196 257 191 6,27 8,15 6,86 Integrated 161 216 186 6,19 7,67 6,86 Organic 132 83 111 4,79 3,95 5,05

Analyses of variance F value

Source of variance

Photosynthetic potential Grain yield

Year 1.47 51.03** Agro ecosystem

15.25** 316.99**

Interaction 1.53 34.81** Significant: ** - p < 0.01 The biomass, carbon and energy value of cereal crops. Spring barley. There was significant correlation (R2=0.85, p<0.01)

between biomass accumulation of spring barley and photosynthetic potential in the field experiments in 2007 – 2009 (Fig. 4). The 1 m2 increase of photosynthetic potential increased spring barley biomass by about 7 g m-2. The time of leaf duration and its working is related with photosynthetically active radiation (Gimenez et al., 2002; Li et al., 2008). In 2007, the highest biomass

yield of spring barley was produced under conventional agroecosystem (1028 g m-2), it was 27% more than under integrated agroecosystem and 32% more than grown under organic agroecosystem. In 2008, biomass yield of spring barley was similar to that in conventional and integrated agroecosystems – 1012 and 1095 g m-2, however under organic management it was lower – 927 g m-2. In 2009, biomass yield of spring barley was similar to that in 2007.

y = 6.8876x + 17.856R2 = 0.8597; p<0.01

0200400600800

1000120014001600

0 25 50 75 100 125 150 175 200 225Photosynthetic potential m2 m-2

Bio

mas

s, g

m -2

Figure 4. Relationship between biomass accumulation of spring barley

and photosynthetic potential in 2007 – 2009, Dotnuva Winter wheat. Significant correlation (R2=0.72, p<0.01) was

established between winter wheat biomass increase and photosynthetic potential in the field experiments in 2007 – 2009 (Fig. 5). In 2008, the highest yield of winter wheat biomass was in conventional and integrated agro ecosystems 2309 g m-2 and 2389 g m-2, respectively. Under organic management, the highest yield of winter wheat biomass (1353 g m-2) was in 2007.

y = 5.4464x - 297.98R2 = 0.720; p<0.01

0

400

800

1200

1600

2000

2400

2800

0 50 100 150 200 250 300 350 400Photosynthetic potential, m2 m-2

Biom

ass,

g m

-2

Figure 5. The relationship between winter wheat biomass increase and

photosynthetic potential in 2007 – 2009, Dotnuva The highest photosynthetic productivity and biomass accumulation in

relation to the sum of active temperatures (the sum of daily temperatures during the period with an average daily temperature above 10 °C) was in cereals grown in conventional agroecosystem. Calculations showed that the highest potential of carbon yield and the highest value of biomass energy were also in cereals

under conventional management, while the lowest – in crops grown under organic management.

Calculated value of biomass energy was higher in winter wheat (up to 200 GJ ha-1) than in spring barley (close to 100 GJ ha-1). The energy accumulated in winter wheat grain under conventional management was 80 GJ ha-1 and under organic – 60 GJ ha-1. The energy value of spring barley grain was 60 GJ ha-1 in conventional agroecosystem, and 40 GJ ha-1 in organic agroecosystem. The estimated energy value of ethanol of winter wheat grain under conventional management was 50 GJ ha-1 and of spring barley – 30 GJ ha-

1. Under organic management energy value of bioethanol in both crops was about 60% lower.

Water and nitrogen stress. During the study period, temporary water shortage occurred several times per season. Water stress simulated by the DSSAT v4.0.2.0 model correlated relatively well with actual readings of irrometers. The correlation coefficient of three years data in spring barley was 0.85, p<0.05, and in winter wheat - r = 0.80, p<0.05.

Simulation by the DSSAT v4.0.2.0 model showed, that for transpiration plants under conventional management used by 9 – 24% more water in spring barley and by 20 – 32% in winter wheat than under organic management. However, under organic management, more water was used for evaporation. These trends are confirmed by the results from our other studies, led by D. Feizienės (Feizienė et al., 2008).

The highest nitrogen concentration in plant biomass was in cereals grown in conventional agroecosystem. The nitrogen concentration steadily decreased during the growing season both in spring barley and winter wheat. The DSSAT model’s simulations showed that under organic management nitrogen stress occured in early growth stages. Besides, in spring barley in several cases the simulated nitrogen stress coincided with moisture stress. In winter wheat, simulated nitrogen stress was detected in spring even before water stress occurred.

Nitrogen is the most important limiting factor for biomass production both in natural and managed ecosystems. Plants affected by the nitrogen stress produce lower number of tillers and develop smaller leaf area (Gastal et al., 2002; Lemaire et al., 2008) and in this way indirectly limit absorption of photosynthetic radiation. Studies performed in Lithuania showed, that conditions of optimal nitrogen supply in cereals are achieved, when nitrogen concentration in plants at the booting stage is above 3.2 – 3.9% (Плюпелите и др., 1986). Greenwood et al., (1990), Lemaire et al., (2008), Danytė and Igras (2008) and other researchers used an equation for calculation of theoretical bottom-line nitrogen concentration for non-limited plant growth:

N = aWb, where, [1]

N –a critical (non-limiting plant growth) nitrogen concentration;

a – constant, representing the plant non limiting N%, when W=1t× ha-1; W – crop weight, (t ha-1) b – slope, characterising the decrease of N % during plant growth. The solid line in Figures 6 and 7 is calculated according to this

equation and marks the optimal concentrations of nitrogen which are necessary for growth, non-limited by this element.

Spring barley, conventional agroecosystem

0

1

2

3

4

5

6

0 1.2 2.4 3.6 4.8 6 7.2 8.4 9.6

Biomass, t ha -1

N, %

Critical nitrogen concentration, %"2007 2008 2009

Spring barley, organic agroecosystem

0

1

2

3

4

5

6

0 0.9 1.8 2.7 3.6 4.5 5.4 6.3 7.2 8.1 9 9.9

Biomass, t ha -1

N, %

Critical nitrogen concentration, %"2007 2008 2009

Figure 6. Critical and actual concentrations of nitrogen in spring barley

grown under agroecosystems of different intensity. Dotnuva 2007 – 2009. In Fig. 6, actual nitrogen concentrations, measured 6 times per spring

barley growth period, are plotted against theoretical line of non-limited growth. The figure shows, that nitrogen concentrations in spring barley plants grown under conventional management were closer to theoretical line, than under organic management. We can assume that nitrogen supply of crops under conventional management was somewhat lower, especially in the second half of growth period, than is needed for maximum realisation of yield potential, whereas under organic management, shortage of nitrogen was evident from the earliest growth stages. These results are in a good agreement with simulations performed by the DSSAT v4.0.2.0 model. The largest differences between actual values and theoretic line were found in 2008. Among the reasons of larger inadequacy, droughty weather conditions can be mentioned.

In winter wheat, a similar pattern to that in spring barley was found. Under organic management, nitrogen concentration in plants during the whole growth period was much lower than critical (Figure 7). Under conventional management, nitrogen concentrations were closer to theoretic line than under organic management. However, actual values were almost in all cases lower than critical, especially at later growth stages. The results of the DSSAT

v4.0.2.0 model simulation showed nitrogen stress too. It is clear that nitrogen was an important limiting factor for realisation of winter wheat yield potential in our experiments.

Winter wheat, conventional agroecosystem

0

1

2

3

4

5

6

7

0.1 1.3 2.5 3.7 4.9 6.1 7.3 8.5 9.7Biomass, t ha -1

N, %

Critical nitrgen concentration, %"

2007

2008 2009

Winter wheat, organic agroecosystem

0

1

2

3

4

5

6

7

0.1 1.3 2.5 3.7 4.9 6.1 7.3 8.5 9.7Biomass, t ha -1

N, %

Critical nitrogen concentration, %2007 2008 2009

Figure 7. The critical and actual concentrations of nitrogen in winter

wheat grown under agroecosystems of different intensity. Dotnuva 2007 – 2009 Cereal yield changes as affected by climate change. Simulation of

spring barley and winter wheat grain yield as affected by climate change was performed according to scenarios, with different levels of average air temperature, CO2 concentration and sum of precipitation during growth period. No extreme events were planned and currently applied crop growing technologies were used, including dates of sowing, varieties, etc. For this reason results of simulations can be treated as very preliminary estimations of possible effects on grain yield caused by changing climate.

Simulation showed that spring barley can be affected by climate change more negatively than winter wheat. According to pessimistic scenarios, when air temperature increases by 3°C above 2007 level, which is by 1.7°C higher then climatic norm, spring barley yield can decrease by as much as 40% under conventional growing, by 22% under integrated and by 10% under organic. Under the same climate scenarios, winter wheat yield can decrease by 15%. The higher carbon dioxide concentration can alleviate negative impact of higher air temperate on grain yield. Sorter growth can be mentioned among the reasons of lower yield levels under higher air temperatures. An increase in average air temperature by 1 °C can reduce growth period of cereals by approximately 5 days, and an increase by 3°C can result in shorter growth period by 14 days in spring barley and by 11 days in winter wheat.

Many studies were aimed to examine the likely impacts of climate change on cereal crops productivity. However, it is still unclear if a range of

conventional adaptive measures, such as changing of sowing time or varieties, can be effective in order to completely adapt spring barley and winter wheat to new climate conditions.

Conclusions 1. The winter wheat grown under conventional agrotechnology

developed a leaf area index of above 3 from BBCH 31 till BBCH 57, the spring barley from BBCH 32 till BBCH 71. The leaf area index of cereals grown under organic agroecosystem did not reach 3. The potential of photosynthesis significantly correlated with grain yield and biomass of winter wheat (respectively r = 0.95 and r = 0.87) and spring barley (respectively r = 0.69 and r = 0.68). The potential of photosynthesis was higher in the winter wheat than spring barley, and higher under conventional agroecosystem than organic.

2. The quantitative parameters of cereal crop biomass accumulation are affected by growing intensity. The highest photosynthetic productivity was in the conventional agroecosystem.

3. The DSSAT v4.0.2.0 model relatively well simulated the grain yield of spring barley and winter wheat grown under conventional agrotechnology in favourable meteorological conditions. Significant correlation between simulated and actual grain yield of winter wheat (r = 0.90) and spring barley (r = 0.81) was established. The yield of cereal crops was simulated with lower accuracy in drier year and grown without nitrogen fertilisers.

4. The water stress simulated by the DSSAT v4.0.2.0 model significantly correlated with actual values of irometer. The aggregate correlation coefficient of measurements in the winter wheat was – r = 0.80, in spring barley r = 0.85. The results of water balance simulation showed that more water is used for transpiration in conventional agroecosystem and more for evaporation in the organic agroecosystem.

5. The concentration of nitrogen in biomass decreased during the growing season under all intensities of agroecosystems, the critical level was reached earliest in the cereal crop under organic agroecosystem. The results of the DSSAT v4.0.2.0 model simulations showed that cereal crops experience shortage of nitrogen at different times under different agroecosystems. The earliest and most severe nitrogen stress begins in cereal crop grown under organic agroecosystem.

6. The increasing mean air temperature resulting from climate change will reduce the length of growth of cereals, and without any adjustments in agrotechnology, will reduce grain yield. The results of model simulation showed that spring barley would respond to temperature increase more sensitively than winter wheat.

7. The energy potential of cereals per hectare depends on growing intensity and energy conversion pathway. The winter wheat crop produces

higher grain and biomass yield and therefore has a higher energy and bioethanol yield potential compared with spring barley.

List of Publications Articles in journals indexed in ISI Master List database: 1. Povilaitis V., S. Lazauskas, V. Mašauskas, Š. Antanaitis (2008).

Vasarinių miežių derliaus modeliavimo DSSAT v 4.0.2.0 modeliu galimybės. Žemdirbystė=Agriculture, t.95, Nr. 2, p. 88 – 97;

2. Povilaitis V., S. Lazauskas (2010) Winter wheat productivity in relation to water availability and growing intensity. Žemdirbystė=Agriculture, vol. 97, No 3, p. 59 - 68.

3. Feizienė D., V.Povilaitis, G. Kadžienė (2008). Springtime soil surface respiration and soilvapour flux in different long-term agro-ecosystems. Ekologija Nr. 54 (4), p. 216 – 226.

Publications in reviewed journals: 1. Povilaitis V., Lazauskas S., Kriščiukaitienė I. (2009). Javų

derlingumo prognozavimas pagal tikėtinus klimato kaitos scenarijus. Žemės ūkio mokslai T.16. Nr. 3-4, p. 224 – 229.

2. Povilaitis V., Lazauskas S., Duchovskis P. (2009). Possibilities to simulate productivity of spring barely using model DSSAT v4. Vagos, Nr. 82(35), p. 22 – 27.

3. Povilaitis V., Lazauskas S. (2009). Javų derlingumo Lietuvoje prognozavimas pagal tikėtinus skirtingus klimato kaitos scenarijus. Ekonomika ir vadyba: aktualijos ir perspektyvos. Šiaulių universitetas, mokslo darbai. T. 3 (16). p. 172-177.

4. Povilaitis V., Tilvikienė V., Lazauskas S., Kadžiulienė Ž. (2010). Javų bioenergetinis potencialas vidurio Lietuvoje. Ekonomika ir vadyba: aktualijos ir perspektyvos. Šiaulių universitetas, mokslo darbai. T. 3 (19). p. 103-109.

Other publications 1. Povilaitis V. (2007). Vasarinių miežių biomasės formavimasis

skirtingo auginimo intensyvumo sąlygomis. Jaunimas siekia pažangos, Nr. 2. Kaunas - Akademija, p. 62 – 65.

2. Povilaitis V. (2009). Vasarinių miežių vegetacijos trukmės ir derliaus modeliavimas modeliu DSSAT v4.0.2.0. Jaunimas siekia pažangos. Nr.3. Doktorantų mokslinės konferencijos straipsnių rinkinys, p. 48 – 51.

Reziumė Lauko eksperimentai ir modeliavimo skaičiavimai buvo atlikti 2006 –

2010 metais Lietuvos žemdirbystės institute (Dabar Lietuvos agrarinių ir miškų mokslų centras).

Hipotezė. Javų lapijos ir derliaus formavimasis vidutinių platumų klimato zonoje, kurioje yra periodinis drėgmės perteklius, priklauso nuo auginimo intensyvumo, ypač aprūpinimo azotu, ir laikino drėgmės trūkumo sukeltų stresų. Azoto ir vandens trūkumo augaluose sukelti stresai ir jų poveikis javų produktyvumui gali būti nustatomi derinant lauko eksperimento ir modeliavimo metodus.

Darbo tikslas ištirti žieminių kviečių ir vasarinių miežių, auginamų skirtingo intensyvumo agroekosistemose vidutinių platumų klimato zonos šiauriniame regione esančiame giliau karbonatingame sekliai glėjiškame rudžemyje, augimo ir derliaus formavimosi ypatumus, įvertinti modelio DSSAT v4.0.2.0 tinkamumą siekiant apskaičiuoti derliaus formavimosi procesus.

Darbo uždaviniai: 1. Ištirti skirtingo auginimo intensyvumo, tręšiant pagal

normatyvus planuojamam derliui, poveikį vasarinių miežių ir žieminių kviečių lapų indekso, biomasės ir grūdų derliaus formavimuisi.

2. Nustatyti kiekybinius azoto ir anglies kaupimosi biomasėje pokyčius vegetacijos metu.

3. Ištirti vandens ir azoto trūkumo sukeltų stresų pasireiškimą migliniuose javuose ir įvertinti galimybes juos diagnozuoti modeliu DSSAT v4.0.2.0.

4. Įvertinti tikėtiną klimato kaitos poveikį žieminių kviečių ir vasarinių miežių derlingumui.

Tyrimų objektas – vasarinis miežis (Hordeum vulgare L.), žieminis

kvietys (Triticum aestivum L.). Mokslinis naujumas. Nustatyta, kad vidutinių platumų klimato zonos

šiaurinėje dalyje esančiuose rudžemiuose pagal tradicinę technologiją auginami migliniai javai formuoja didesnį nei auginami ekologiškai lapų indeksą ir turi didesnį fotosintezės potencialą, kuris glaudžiai koreliuoja su grūdų ir biomasės derliumi. Modelis DSSAT v4.0.2.0 sąlygiškai gerai skaičiuoja žieminių kviečių ir vasarinių miežių grūdų derlių vyraujant palankioms meteorologinėms sąlygoms ir juos auginant pagal tradicinę žemdirbystės technologiją, todėl gali būti taikomas derliaus ir jo formavimosi procesų kiekybiniams skaičiavimams. Trumpalaikiai vandens stygiaus stresai laikinai sumažina grynąjį fotosintezės produktyvumą ir juos galima identifikuoti modeliuojant. Dėl klimato kaitos poveikio didėjanti vidutinė oro temperatūra gali trumpinti miglinių javų vegetaciją ir, netaikant adaptacinių priemonių, net gali mažinti grūdų derlių.

Ginamieji teiginiai 1. Pagal tradicinę technologiją auginami ir azotu tręšiami javai nuo

krūmijimosi tarpsnio formuoja didesnį nei auginami ekologiškai lapų indeksą ir

fotosintezės potencialą, kuris glaudžiai koreliuoja su javų grūdų ir biomasės derliumi.

2. Biomasės kaupimosi kiekybinius parametrus lemia auginimo intensyvumas, o šį procesą reikšmingai veikiančius azoto ir vandens trūkumo sukeltus stresus galima apskaičiuoti modeliu DSSAT v4.0.2.0.

3. Dėl klimato kaitos poveikio smarkiai didėjanti vidutinė oro temperatūra gali trumpinti miglinių javų vegetacija ir, netaikant adaptacinių priemonių, net mažinti javų derlingumą.

Darbo praktinė reikšmė Žieminių kviečių ir vasarinių miežių derliaus formavimosi ir vandens

bei azoto trūkumo sukeltų stresų analizės rezultatai padės patikslinti auginimo technologijas šalies našiuose dirvožemiuose. Azoto koncentracijos augaluose kitimas vegetacijos laikotarpiu skirtingo intensyvumo agroekosistemose leidžia patikslinti augalų mitybos diagnostikos priemones. Kompiuteriniu modeliu DSSAT v4.0.2.0 nustatyti derliaus pokyčiai esant įvairioms auginimo ir aplinkos sąlygoms padės tiksliau prognozuoti javų derlingumą rudžemiuose, kiekybiškai apskaičiuoti tikėtiną klimato kaitos poveikį javų derliui.

Išvados 1. Lauko eksperimentų metu visais tyrimų metais pagal tradicinę

technologiją auginti žieminiai kviečiai suformavo didesnį kaip 3 lapų indeksą pasiekus BBCH 31 tarpsnį ir išlaikė iki BBCH 57 tarpsnio, o vasariniai miežiai – nuo BBCH 32 iki BBCH 71 tarpsnio. Tačiau ekologinėje sistemoje javų lapų indeksas nepasiekė 3. Fotosintezės potencialas esmingai ir glaudžiai koreliavo su žieminių kviečių ir vasarinių miežių grūdų derliumi (atitinkamai r = 0,95 ir r = 0,69), biomasės derliumi (r = 0,87 ir r = 0,68). Jis buvo didesnis tradicinėje nei ekologinėje sistemoje, o žieminių kviečių pasėlyje didesnis nei vasarinių miežių.

2. Miglinių javų anglies ir biomasės kaupimosi kiekybinius parametrus lėmė auginimo intensyvumas, o didžiausias grynasis fotosintezės produktyvumas buvo tradicinėje agroekosistemoje.

3. Modelis DSSAT v 4.0.2.0 sąlygiškai gerai apskaičiavo žieminių kviečių (1989–1991 m. eksperimentų metu) ir vasarinių miežių (1990–1991 m.) grūdų derlių vyraujant palankioms meteorologinėms sąlygoms ir juos auginant pagal tradicinės žemdirbystės technologijas. Nustatyta esminė glaudi koreliacija tarp išauginto faktinio ir apskaičiuoto žieminių kviečių (r = 0,90) bei vasarinių miežių (r = 0,81) grūdų derliaus. Azotu netręštų ar nepalankiomis meteorologinėmis sąlygomis augusių javų derlių modelis apskaičiavo mažesniu tikslumu.

4. Modelio DSSAT v4.0.2.0 apskaičiuotas vandens trūkumo sukelto streso augaluose lygis esmingai koreliavo su irometrų rodytu drėgmės stygiumi dirvožemyje (koreliacijos koeficientas žieminių kviečių pasėlyje – r =

0,80, vasarinių miežių – r = 0,85). Modelio skaičiavimai parodė, kad tradicinėje javų auginimo agroekosistemoje santykinai daugiau vandens sunaudojama transpiracijai, o ekologinėje – evoporacijai.

5. Visose agroekosistemose azoto koncentracija augaluose vegetacijos metu nuosekliai mažėjo. Nuo krūmijimosi tarpsnio ryškus azoto trūkumas buvo nustatomas ekologiškai auginamuose migliniuose javuose. Pagal tradicinę technologiją augintuose javuose šio elemento koncentracija buvo daug didesnė, tačiau daugeliu atvejų buvo mažesnė nei optimali teorinė riba, būtina maksimaliam augalo augimui. Modelio DSSAT v4.0.2.0 skaičiavimai parodė panašias tendencijas.

6. Dėl klimato kaitos poveikio smarkiai didėjanti vidutinė oro temperatūra gali trumpinti miglinių javų vegetaciją ir, nekeičiant auginimo technologijų, net mažinti grūdų derlių. Modelio skaičiavimai parodė, kad į temperatūros didėjimą jautriau reaguotų vasariniai miežiai nei žieminiai kviečiai.

7. Miglinių javų grūdų ir biomasės vieno hektaro energinis potencialas priklauso nuo auginimo intensyvumo ir energijos gavimo būdo. Žieminiai kviečiai išaugina didesnį biomasės ir grūdų derlių, todėl turi didesnį energijos potencialą ir bioetanolio išeigą nei vasariniai miežiai.

Trumpos žinios apie disertantą Virmantas Povilaitis gimė 1982 m. vasario 12 d. Žagariškių kaime,

Joniškio r. sav. 2000 m. baigė Žagarės vidurinę mokyklą ir įstojo į Šiaulių universitetą. 2004 m. baigė taikomosios ekologijos ir aplinkotyros bakalauro studijas. 2004 m. įstojo į Lietuvos žemės ūkio universitetą. 2006 metais apgynė agronomijos magistro darbą. 2006 m. pradėjo doktorantūros studijas Lietuvos žemdirbystės institute (dabar LAMMC Žemdirbystės institutas). 2009 metais stažavosi Čekijos respublikos Ekologijos ir biologijos sistemų institute prie Čekijos respublikos mokslų akademijos. Nuo 2006 m. dirba jaunesniuoju mokslo darbuotoju Augalų mitybos ir agroekologijos skyriuje.