14 esa congress 5 9 september 2016 edinburgh, scotland14th esa congress 5–9th september 2016...

14th ESA Congress 5–9th September 2016 Edinburgh, Scotland

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IMPACTS OF CLIMATE CHANGE AND AGRONOMIC MANAGEMENT ON CROP YIELDS, SOIL ORGANIC CARBON AND SOIL NITROGEN: AN MODEL ENSEMBLE APPROACH B. DUMONT 1 – B. BASSO 2 – I. SHCHERBAK 2 – S. ASSENG 3 – S. BASSU 4 – C. BIERNATH 5 – K. BOOTE 6 – D. CAMMARANO 7 – G. DE SANCTIS 8 – J.-L. DURAND 9 – F. EWERT 10 – S. GAYLER 11 – P. GRACE 12 – R. GRANT 13 – J. KENT 14 – P. MARTRE 15 – C. NENDEL 16 – K. PAUSTIAN 14 – E. PRIESACK 5 – D. RIPOCHE 17 – A. RUANE 18 – P. THORBURN 19 – J. HATFIELD 20 – J. JONES 21 – C. ROSENZWEIG 18

1 Department Terra & AgroBioChem, Gembloux Agro-Bio Tech, ULg-GxABT, 2 Passage des Déportés, 5030 Gembloux, Belgium. Email: [email protected]; Tel : +32.81.62.21.43

2 Department of Geological Sciences, Michigan State University, MI, USA 3 Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, USA 4 INRA-AgroParisTech, Thiverval-Grignon, France 5 Institute of Biochemical Plant Pathology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg,

Germany 6 Department of Agronomy, University of Florida, Gainesville, FL, USA 7 The James Hutton Institute, Invergowrie, Scotland, UK 8 European Commission - Joint Research Center, Ispra, Italy 9 INRA-URP3F, Lusignan, France 10 Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany 11 Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany 12 Institute for Future Environments, Queensland University of Technology, Brisbane, Queensland, Australia 13 Earth Sciences, University of Alberta, Edmonton, AB, Canada 14 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA 15 INRA, UMR759 LEPSE, Montpellier, France 16 Institute of Landscape Systems Analysis, ZALF, Leibniz-Centre for Agricultural Landscape Research, Muencheberg, Germany 17 INRA-AGROCLIM, Avignon, France 18 Climate Impacts Group, NASA Goddard Institute for Space Studies, New York, NY, USA 19 CSIRO Ecosystem Sciences, Dutton Park, Queensland, Australia 20 USDA-ARS National Soil Tilth Laboratory for Agriculture and the Environment, Ames, IA, USA 21 Department of Agricultural & Biological Engineering, University of Florida, Gainesville, FL, USA.

Introduction The assessment of climate change impacts on global and local crop production is known to be inherently uncertain (Asseng et al., 2015). Crop simulations models are playing an increasing role in research related to the soil-plant-atmosphere continuum and have proven to be useful tools in climate impact studies (Palosuo et al., 2011). Recent maize (Bassu et al., 2014) and wheat (Asseng et al., 2015) multi-modelling ensembles have predicted grain yield declines across the world in response to global temperature increases. But none of these models have been used in continuous mode (Basso et al., 2015), rather annually re-initializing pre-seasons soil conditions. In an effort to evaluate the long-term impacts of soil management on crop production under climate change conditions, we present the results of a multi-modelling ensemble simulations where models were run under continuous mode without the annual reinitialization. Materials and Methods Five maize models and seven wheat models involved respectively in the maize- and wheat-pilot initiatives of the Agricultural Model Intercomparison and Improvement Project (AgMIP) were run under reinitialized and sequential running mode. The same respective factorial climatic modification protocols were followed (Asseng et al., 2015; Bassu et al., 2014). Additionally, modelers were asked to simulate conventional tillage and no-tillage management. Modelling ensemble approaches were used to analyze the impacts of the interactions between soil, climate and management on soil organic carbon (SOC), soil nitrogen content (NO3

-) and crop yields under the maize-fallow and wheat-fallow crop rotations. Results and Discussion Under continuous mode, all models agreed in the direction of the changes. With increases in temperature, the results showed that N-NO3

- would increase whilst SOC would decrease. However, important differences between simulated yields with annual reinitialization and continuous mode were found. When models were run in continuous mode, model ensemble highlighted that yields were overall higher when CO2 increased, independently of the temperature treatment. Soil N-NO3

- was lower under higher CO2 levels and was found to increase with temperature, while SOC modification rates were the same under different CO2 treatments.


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Simulated yields were slightly lower in no-till compared to conventional tillage. The main effects of no-till was observed in maize-fallow rotation, with lower N-NO3

- level and lower SOC decrease observed under no-till system, leading to higher level of SOC remaining in the soil. Conclusions The execution of crop models under continuous mode allows for a better understanding of the interactions between soil, climate and crop management and for designing crop and soil adaptation and mitigation strategies to climate change impacts. Acknowledgements This work was supported by the AgMIP (http://www.agmip.org/) project.

References Asseng S. et al.: 2015. Rising temperatures reduce global wheat production. Nature Climate Change, 5:143–147. Basso B. et al.: 2015. Can impacts of climate change and agricultural adaptation strategies be accurately quantified if crop models are

annually re-initialized? Plos One, 10(6): e0127333. 12 pp. Bassu S. et al.: 2014. How do various maize crop models vary in their response to climate change factors? Global Change Biology,

20(7):2301–2320. Palosuo T. et al.: 2011. Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop

growth models. European Journal of Agronomy, 35(3):103–114.


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CLIMATE CHANGE IMPACT ON WHEAT S. ASSENG1 – F. EWERT2 – P. MARTRE3,4 – A. MAIORANO3 – R.P. RÖTTER5 – G. O’LEARY6 – G. FITZGERALD7 – C. GIROUSSE8,9 – M.A. BABER10 – M.P. REYNOLDS11 – P.J. THORBURN12 – K. WAHA13 – A.C. RUANE14 – P.K. AGGARWAL15 – M. AHMED16,17 – J. BALKOVIC18,19 – B. BASSO20,21 – A. BERGER22 – C. BIERNATH23 – M. BINDI24 – D. CAMMARANO25 – A.J. CHALLINOR26,27 – G. DE SANCTIS28 – J. DOLTRA29 – B. DUMONT20 – E. EYSHI REZAEI2 – E. FERERES30 – R. FERRISE24 – M. GARCIA-VILA30 – S. GAYLER31 – G. HOOGENBOOM1,16 – R.C. IZAURRALDE32,33 – M. JABLOUN34 – C.D. JONES32 – B.T. KASSIE1 – K.C. KERSEBAUM35 – C. KLEIN36 – A.K. KOEHLER26 – B. LIU37 – S. MINOLI38 – M. MONTESINO SAN MARTIN39 – C. MÜLLER38 – S. NARESH KUMAR40 – C. NENDEL35 – J.E. OLESEN34 – T. PALOSUO5 – J.R. PORTER39,41 – E. PRIESACK36 – D. RIPOCHE42 – M.A. SEMENOV43 – C. STÖCKLE16 – P. STRATONOVITCH43 – T. STRECK31 – I. SUPIT44 – F. TAO45 – M. VAN DER VELDE28 – D. WALLACH46 – E. WANG47, – H. WEBBER2 – J. WOLF44 – P. WOLI16 – Z. ZHANG48 – Y. ZHU37

1Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL 32611, USA, Email: [email protected] 2Institute of Crop Science and Resource Conservation INRES, University of Bonn, 53115, Germany 3INRA, UMR0759 Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, F-34 060 Montpellier, France 4Monptellier SupAgro, UMR0759 Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, F-34 060 Montpellier, France. 5Environmental Impacts Group, Natural Resources Institue Finland (Luke), FI-01301 Vantaa, Finland 6Grains Innovation Park, Department of Economic Development Jobs, Transport and Resources, Horsham 3400, Australia7Agriculture Research Division, Department of Economic Development, 402-404 Mair St Ballarat, Victoria 3350 Australia 8INRA, UMR1095 Genetic, Diversity and Ecophysiology of Cererals (GDEC), F-63 100 Clermont-Ferrand, France 9Blaise Pascal University, UMR1095 GDEC, F-63 170 Aubière, France. 10World Food Crops Breeding (wheat & oat), 3105 McCarty Hall B, Department of Agronomy, IFAS, University of Florida, Gainesville, FL32611, USA 11CIMMYT Int. Adpo, D.F. Mexico 06600, Mexico 12CSIRO Agriculture Flagship, Brisbane, Queensland 4102, Australia 13CSIRO, Queensland Biosci Precinct St Lucia, St Lucia, Qld 4067, Australia 14NASA Goddard Institute for Space Studies, New York, NY 10025 15CGIAR Research Program on Climate Change, Agriculture and Food Security, International Water Management Institute, New Delhi-110012, India 16Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA 17Department of agronomy, PMAS Arid Agriculture University Rawalpindi Pakistan 18International Institute for Applied Systems Analysis, Ecosyst Serv & Management Program, A-2361 Laxenburg, Austria 19Comenius University, Faculty of Natural Science, Department of Soil Science, Bratislava 84215, Slovakia 20Department of Geological Sciences, Michigan State University East Lansing, Michigan 48823, USA 21W.K. Kellogg Biological Station, Michigan State University East Lansing, Michigan 48823, USA 22Instituto Nacional de Investigaciones Agropecuarias, Estacionn Experimental La Estanzuela, Colonia 70000, Uruguay 23Institute of Soil Ecology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, D-85764, Germany 24Department of Agri-food Production and Environmental Sciences (DISPAA), University of Florence, I-50144 Florence, Italy 25James Hutton Institute, Invergowrie, Dundee, DD2 5DA, Scotland, UK 26Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS29JT, UK 27CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Centre for Tropical Agriculture (CIAT), A.A. 6713, Cali, Colombia. 28European Commission Joint Research Center, via Enrico Fermi, 2749 Ispra, 21027 Italy 29Cantabrian Agricultural Research and Training Centre (CIFA), 39600 Muriedas, Spain 30IAS-CSIC and University of Cordoba, Apartado 3048, 14080 Cordoba, Spain 31Institute of Soil Science and Land Evaluation, University of Hohenheim, 70599 Stuttgart, Germany 32Dept. of Geographical Sciences, Univ. of Maryland, College Park, MD 20742, USA 33Texas A&M AgriLife Research and Extension Center, Texas A&M Univ., Temple, TX 7650, USA 34Department of Agroecology, Aarhus University, 8830 Tjele, Denmark 35Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, 15374 Müncheberg, Germany 36Institute of Biochemical Plant Pathology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, D-85764, Germany 37College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, 210095, China 38Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany 39Univ Copenhagen, Dept Plant & Environm Sci, DK-2630 Taastrup, Denmark 40Centre for Environment Science & Climate Resilient Agriculture, IARI PUSA, New Delhi 110 012, India 41Univ Greenwich, Nat Resources Inst, Greenwich, England 42INRA,US1116 AgroClim, F-84 914 Avignon, France 43Computational and Systems Biology Department, Rothamsted Research, Harpenden, Herts, AL5 2JQ, UK 44Plant Production Systems & Earth System Science, Wageningen University, 6700AA Wageningen, The Netherlands 45Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China 46INRA, UMR 1248 Agrosystèmes et développement territorial (AGIR), 31326 Castanet-Tolosan Cedex, France 47CSIRO Agriculture Flagship, Black Mountain, ACT 2601, Australia 48State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China †Authors after A.C.R. are listed in alphabetical order.


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Introduction Global agricultural output, including wheat as the most important food crop (FAO, 2015), must increase by 70 percent to meet the food needs of the global population expected in 2050 (Godfray et al., 2010). Therefore, it is important to develop innovative crop management and breeding strategies that are more sustainable, more productive than current strategies and take into account future climate change. Crop simulation models are an important tool in agricultural sciences as they deal dynamically with multiple cropping system factors. Crop models are the most important tools to quantify climate change impacts and the impact of adaptation options. Results on climate change impact on wheat from the Agricultural Model Intercomparison and Improvement Project for Wheat (AgMIP-Wheat) will be presented. Materials and Methods Multi-model (up to 34) intercomparisons, improvements and applications were organized with wheat models from across the world. Detailed field experimental data from several locations, including contrasting growing environments, wide temperature gradients, extreme heat stress and elevated atmospheric CO2 were used for model testing and improvements. Input information was harmonized to enable model comparisons. Model uncertainties were quantified and compared with other uncertainties in climate impact assessments. Results and Discussion Multi-model intercomparisons have shown that many wheat crop models can simulate grain yields and growth dynamics under a range of growing conditions, but simulation results vary. Crop model uncertainties increase with higher temperatures and with combined increased temperature and increased CO2, however, multi-model ensemble medians have proven to be good predictors of observed data in various growing environments (Asseng et al., 2013). Using multi-model ensemble median simulations showed that global wheat production declines by 6% for each degree of global temperature increase (Asseng et al., 2015). Building on the temperature impact study, new simulations from AgMIP-Wheat will be presented that explore the impact of interactions of increased temperature, elevated CO2 and rainfall changes on yield and grain quality, and the possible role of genetic adaptations. In conclusion, multi-model ensembles linked to field data can now provide comprehensive global impact assessments for wheat production. References Asseng, S. et al.: 2015. Rising temperatures reduce global wheat production. Nature Climate Change, 5(2):143–147. Asseng, S. et al.: 2013. Uncertainty in simulating wheat yields under climate change. Nature Climate Change, 3(9):827–832. Godfray, H.C.J. et al.: 2010. Food Security: The Challenge of Feeding 9 Billion People. Science, 327(5967):812–818. Rosenzweig, C. et al.: 2013. The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies.

Agricultural and Forest Meteorology, 170:166–182.


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EVIDENCE FOR IMPROVED SEED YIELD IN FIELD-DROUGHTED OILSEED RAPE (BRASSICA NAPUS L.) FOLLOWING APPLICATION OF POLYMERS OVER THE REPRODUCTIVE STAGE M. FARALLI – I.G. GROVE – M.C. HARE – P.S. KETTLEWELL

Crop and Environment Sciences, Harper Adams University, Newport, Shropshire TF10 8NB, UK, Email: [email protected]

Introduction Oilseed rape (OSR) is a crop of major importance worldwide. However, environmental factors such as drought are severely reducing the yield potential and restraining the geographical growing expansion. Conventional crop improvement is limited due to the extensive bottleneck over the breeding history leading to narrow genetic diversity and broad environmental susceptibility (Guo et al., 2015). A few minor successes have been obtained by Wang et al. (2005) via genetic engineering where down-regulation of farnesyl-transferase which controls ABA sensitivity in the stomata guard cells was successfully used to promote faster stomatal closure and thus improve water-use efficiency in water-limited conditions. However, despite these efforts, a conventional drought tolerant variety is still far from being achieved and thus crop management tools are required. Film-forming antitranspirants (polymers) were considered a promising agronomic tool to preserve water in plants and thus avoid yield decreases under water deficit: however their stomata-blocking property was strongly related to a decrease in CO2 assimilation (Kettlewell, 2014). In recent years, however, new works have shown physiological improvements on different crops under drought conditions, especially when polymers are applied at low dose rates and prior to the most drought-sensitive stage, leading to significant benefits in terms of yield (Kettlewell, 2014; Abdullah et al., 2015). Materials and Methods Field experiments in 2014/2015 investigated the effectiveness of two different film forming polymers (1 L ha-1 Nu-Film P, NFP a.i. 96% poly-1-p menthene; 1 L ha-1 Vapor Gard, VG a.i. 96% d-1-p menthene) applied at GS 6.0 (flowering) to reduce transpiration rate and yield losses in droughted OSR. Drought was imposed by installing polytunnels over the experimental plots at the end of February 2015. Stomatal conductance to water vapour was analysed with a porometer (AP4, Delta-T, Cambridge, UK) and leaf water potential with Scholander pressure chamber (SKPM 1405/50, Skye Instruments Ltd, UK). Plots were harvested at complete maturity and the seeds were oven-dried to reach 9% moisture content. Results and Discussion Under drought conditions, leaf adaxial stomatal conductance of VG sprayed plots was significantly (P<0.001) lower than that of the un-sprayed (Figure 1A) and leaf CO2 assimilation rate was statistically un-affected (P>0.05, data not shown).

Figure 1. Leaf adaxial stomatal conductance (A) and leaf water potential (LWP, B) of well-watered (WW), water-stressed (WS), water-stressed NFP-treated (WS+NFP) and water-stressed VG-treated (WS+VG) OSR plots at GS 6.0 (n=15 ±SED from ANOVA).

Leaf water potential of VG- and NFP-sprayed plots were significantly (P<0.001) less negative with respect to the un-sprayed droughted plots (Figure 1B).

Table 1. Seed yield production (t ha-1) at 9% moisture (n=3) of well-watered (WW), water-stressed (WS), water-stressed NFP-treated (WS+NFP) and water-stressed VG-treated (WS+VG) OSR plots at GS 6.0 (±0.24, SED from ANOVA)

OSR Seed Yield At 9% Moisture (t ha-1)

Treatments At GS 6.0 WW WS WS + NFP WS +VG 4,26 1,52 2.46 3.19


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Significant increases in droughted OSR seed yield compared to the un-sprayed plots were recorded when the polymer applications were made at GS 6.0 in NFP- and, to a greater extent, VG-treated plots (Table 1). Conclusions The experiment shows the potential for an on-farm application of the film-forming treatment VG for yield protection against drought periods over the OSR reproductive phase. WS plots showed a 2.74 t ha-1 (2.5-fold) seed yield reduction with respect to the WW plants. NFP- and VG-treated plots exhibited an increase in seed yield by 0.94 and 1.67 t ha-1 with respect to the WS. In conclusion, the polymer application may be an additional management strategy available for OSR yield protection under drought, but further research is needed to evaluate this in a wider range of environments. References Abdullah A.S. – Aziz M. – Siddique K. – Flower K.: 2015. Film antitranspirants increase yield in drought stressed wheat plants by

maintaining high grain number. Agricultural Water Management, 15:11–18. Guo Y.M. – Turner N.C. – Chen S. – Nelson M.N. – Siddique K.H.M. – Cowling W.A.: 2014. Genotypic variation for tolerance to transient

drought during the reproductive phase of Brassica rapa. Journal of Agronomy and Crop Science, 201:267–179. Kettlewell P.S.: 2014. Waterproofing wheat – a re-evaluation of film antitranspirants in the context of reproductive drought physiology.

Outlook in Agriculture, 43:25–29. Wang Y. – Ying J. – Kuzma M. – Chalifoux M. – Sample A. – McArthur C. – Huang Y.: 2005. Molecular tailoring of farnesylation for

plant drought tolerance and yield protection. The Plant Journal, 43:413–424.


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AGRONOMIC IMPROVEMENT OF SORGHUM DROUGHT TOLERANCE USING FILM ANTITRANSPIRANTS J.T. SAMAILA – M. CROOK – I.G. GROVE – P.S. KETTLEWELL Crop and Environment Sciences, Harper Adams University, Newport, Shropshire, UK, Email: [email protected]

Introduction Sorghum is the fifth most important cereal crop world-wide with 62 million tonnes produced in 2013 (FAO, 2015) and Nigeria is the world’s third largest producer. Sorghum is recognised as a remarkably drought tolerant crop with the capacity to thrive in hot and dry climates. However, in spite of its being drought tolerant, it is sensitive to water deficits at certain growth stages (Reddy et al., 2011). Significant yield reductions were recorded when water stress occurred during booting and flowering stages (Craufurd & Peacock, 1993) and at microsporogenesis and milk dough stages of panicle development. In Northern Nigeria sorghum production is adversely affected especially by terminal drought stress resulting in little grain being harvested as farmers use the photoperiod sensitive sorghum varieties which flower at the end of the rains. As measures to improve drought tolerance in sorghum farmers adopt planting of early maturing and drought resistant varieties and mixed cropping systems. However, management of hybrid varieties under field conditions is complex for the farmers and the use of irrigation as a strategy is not common due to water scarcity and costs. The application of film-forming antitranspirants is not practiced in Nigeria as a measure for combating water stress in crops. Yet such a technique accomplishes the current needs of farmers for a technology that is easy and cheap, sustainable and adaptable to rain fed crops and which can sustain sorghum through terminal drought without significant yield loss due to drought. This study investigates the potential of film-forming antitranspirants as an agronomic technique for improving sorghum drought tolerance. Recent evidence have shown mitigation of reproductive stage water stress leading to yield and yield component increase in wheat upon application of film-forming antitranspirants (Kettlewell, 2014; Abdallah et al., 2015). Materials and Method The experimental design was a 2 × 2 factorial with six replicates for a total of 24 plants situated in a glasshouse set at day temperature of 28°C and night temperature at 31°C and a minimum day length of 12 h. The plants were grown in 10 L pots filled with sandy loam soil mixed with 10% John Innes no. 2 compost Keith singleton. Pots were arranged in a randomised complete block design and automatic drippers fixed for irrigation. One antitranspirant, Vapor Gard® product by Miller chemical, Pennsylvannia USA containing 96% di-1-p-methene active ingredient and 4% inert materials was used. Drought was imposed on droughted pots by withholding irrigation, at a stage when the flag leaf was visible (GS 5) and the flag leaf was sprayed with the film-antitranspirant. Measurements of stomatal conductance were taken over a period of 29 days, after which normal irrigation schedule resumed when about half of the plants have started to bloom (GS 6). Plants were later harvested at physiological maturity, panicles were threshed and seeds were oven dried to almost constant weight and measurements taken of yield and yield components. Results and Discussion The interaction between drought and antitranspirants showed significant effects (P=0.032) on the adaxial stomatal conductance in sorghum (data not shown).


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Table 1. Yield and yield components of sorghum given either drought or antitranspirant, or both in the glasshouse

Treatments Yield Plant-1 (G)

Seed Number Plant-1

Weight Seed-1 (Mg)

Droughted 28.1 459 61

Irrigated 25.9 570 47.6

Unsprayed 25.1 545 59.5

Sprayed 28.9 484 49.2

Irrigated Sprayed 27 522 53.5

Irrigated Unsprayed 24.8 618 41.6

Droughted Sprayed 30.8 445 65.4

Droughted Unsprayed 25.4 472 56.7

D.F 5 5 5

Sed 3.15 80.7 11.51

Cv% 11.7 15.7 21.2

P-Values

Antitranspirants 0.117 0.265 0.143

Drought 0.364 0.052 0.062

Antitranspirant.Irrigation 0.505 0.524 0.819 Although drought did not have any significant effect on yield plant-1, it had a significant effect in reducing seed number plant-1(P=0.052), and a borderline significant effect in increasing weight seed-1 (P=0.062). Antitranspirants did not have any significant effect on yield and yield components of sorghum. The interaction between drought and antitranspirants was not significant for yield and yield components of sorghum (Table 1). Conclusion In this experiment, drought imposed during booting and flowering reduced seed number, but weight per seed compensated by increasing. Antitranspirant has not shown potential for use in alleviating drought in sorghum in this experiment, but further work is planned to study this in more depth. References Craufurd P. – Peacock J.: 1993. Effect of heat and drought stress on sorghum (Sorghum bicolor). II. Grain yield. Experimental Agriculture,

29(01):77–86. FAOSTAT.: 2015. http://faostat.fao.org. 2014 Kettlewell P.S.: 2014. Waterproofing wheat–a re-evaluation of film antitranspirants in the context of reproductive drought physiology.

Outlook on Agriculture, 43(1):25–29. Reddy B.V. – Ramesh S. – Reddy P.S. – Kumar A.A.: 2009. 3 genetic enhancement for drought tolerance in sorghum. Plant Breeding

Reviews, 31:189.


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EFFECTS OF CLIMATE AND MANAGEMENT ON CROP PHENOLOGY Ehsan EYSHI REZAEI – Stefan SIEBERT – Frank EWERT

Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D-53115 Bonn, Germany, Email: [email protected]

Introduction Change in timing of phenological events is one of the most important bio-indicators of climate change (Menzel et al., 2006). However, little is known on combined and sole effects of climate and crop management on crop phenology and whether these effects can be generalized for different crops. To fill the gap of knowledge, we have analyzed effects of climate and management on phenology of winter rye and winter rapeseed in the period 1960–2013 across Western Germany. Materials and Methods Phenology observations of winter rapeseed (day of sowing, emergence, flowering, maturity and harvest) and winter rye (day of sowing, emergence, heading, yellow ripeness and harvest) for the period 1960–2013 were obtained from the German Meteorological Service and interpolated to 1 km × 1 km resolution across Western Germany. We have no data for maturity date of winter rapeseed before year 1992. Daily mean temperature at 1 km × 1 km for the study period was derived from Zhao et al., 2015. Trends in the timing of phenological stages and the length of phenological phases were analyzed by segmented, piecewise linear regression. The significance levels of trends before and after the break point were tested by F-tests. Results and Discussion There was no significant trend before the estimated break point (year 1978) for the length of vegetative period of both crops. However, the length of the vegetative phase showed a strong decline after break point for winter rapeseed (-4.8 days per decade) and a moderate negative trend (-1.3 days per decade) for winter rye (Figure 1) similar to the increase in mean temperature trend (0.4°C per decade) over the study region. The length of reproductive phase of winter rye has significantly shortened by 1.2 days per decade for the period from 1977 to 2013. In contrast, the reproductive phase of winter rapeseed showed an increasing trend (1.9 days per decade) in the period 1992–2013 (Figure 1). This is in contrast to the expected trend and indicates modifications of varieties and change in management overcompensating for the effects of the warming trend on the length of the reproductive phase. Our results showed only 1 and 9 days shortening in vegetative and reproductive phases of winter rye, respectively from 1960 to 2013 (Figure 2). On the other hand, for winter rapeseed, the length of the period from emergence to flowering declined by 14 days and the length of the period from flowering to harvest increased by 26 days between 1960 and 2013 (Figure 2). The period between maturity/yellow ripeness and harvest remarkably extended for winter rye but not for winter rapeseed during the study period (Figure 2).

Figure 1. Trends in the length of vegetative (a and b) and reproductive (c and d) phases of winter rapeseed and winter rye for the period 1960-2013 across Western Germany. BP = break point, ns = non-

significant break point and trend, *, ** and *** = significance at 5, 1 and 0.1% probability levels, respectively.


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Conclusions The differences in the change of the phenological development and in the factors determining these changes for the two crops winter rapeseed and winter rye highlight, that findings for specific crops should not be generalized. Instead, crop specific analysis is required as a basis for climate impact studies and adaptation scenarios. Acknowledgements Research support was provided by a grant from the German Research Foundation DFG (grant no. EW 119/5-1).

References Menzel A. et al.: 2006. European phenological response to climate change matches the warming pattern. Global Change Biology, 12, 1969–

1976. Zhao G. et al.: 2015. Demand for multi-scale weather data for regional crop modeling. Agriculture for Meteorology, 200:156–171.

238 288 338 388 438 488 538

SO EM HD YR HA

MA

SO EM FL HA

1960

1992

2013Win

ter

rye

1960

1992

2013

Win

ter

rap

esee

dSO: SowingEM: EmergenceHD: HeadingFL: FloweringYR: Yellow ripenessMA: MaturityHA: Harvest

Day of year238 288 338 23 73 123 173

Figure 2. Changes in occurrence of phenological events of winter rye and winter rapeseed for years 1960, 1992 and 2013 calculated by piecewise linear regression of mean occurrence of phenological

events across Western Germany on year.


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FOOD PRODUCTION UNDER CLIMATE CHANGE: CAN AGRICULTURAL LANDSCAPES MITIGATE EMISSIONS? Mariana C. RUFINO 1, 2 – Patric BRANDT 2

1 Lancaster Environment Centre, Lancaster University, UK, Email: [email protected] 2 Climate Change and Energy, Centre for International Forestry research (CIFOR), Nairobi, Kenya

Introduction Livestock production largely drives both the demand for arable land and agricultural emissions worldwide (Havlik et al., 2014). World leaders committed in Paris to mitigate future climate change, but how can mitigation be implemented, given competing demands on the land? Reducing emissions, achieving food security, and preserving ecosystem requires careful landscape planning (Brandt et al., 2015). This paper explores options to achieve production and mitigation targets in tropical landscapes. The Kenyan dairy sector sustains the rural livelihoods of many producers. Animal numbers and demands for feeds will increase in the future, leading to higher emissions of greenhouse gases (GHGs) due to direct emissions from livestock and the conversion of natural ecosystems. Food insecurity concerns justify assessing measures that mitigate GHG emissions and support provisioning of food. This study shows a spatially explicit assessment of options for the dairy sector, including trade-offs between production and mitigation. Materials and Methods We calculated emissions intensities from dairy production in Kenya expressed in CO2eq per kg fat and protein corrected milk (FPCM). Emission estimates use tier 1 to 3 methods by IPCC (2006), including enteric fermentation, manure management, and managed soils for six livestock systems (Robinson et al., 2011). The dynamic model LivSim (Rufino et al., 2009) simulates milk yields, faecal and urine excretion for each system. The HeapSim model simulates the dynamics of manure decomposition during storage, including the nutrient losses, and manure application (Rufino et al., 2007). An additional module calculates the GHG emissions from animals and manures. The models run using existing (baseline) and alternative management practices (scenarios). Scenarios include diets with higher quality, using grain and other concentrated feeds. Results and Discussion Analyses showed high emission intensities varying from 3.7 kg CO2eq kg FPCM-1 head-1 yr-1 in semi-arid systems to 3.5 kg CO2eq kg FPCM-1 head-1 yr-1 in the cooler highlands. All scenarios reduced emission intensities for enteric fermentation and manure management (CH4), and increased milk production up to 845 kg FPCM animal-1 yr-1. However, emission intensities increased for manure management (N2O). In addition, land-use change emissions increased due to higher demands for land to grow fodder crops in all scenarios. The supplementation of concentrates (scenarios “N50+C8”, “N50+G50+C8”) led to indirect emissions from soils and transport.

Figure 1. GHG emission intensities of modeled baseline and feeding scenarios. Scenario “N50+C8” substitutes pasture grass by napier grass

(50% total DMI) and adds 8 kg day-1 concentrate (dairy meal) during lactation. Scenario “N50+G50” substitutes pasture grass by napier grass and maize stover by maize silage (50% total DMI). Scenario “N50+G50+C8” substitutes pasture grass by napier grass and maize

stover by maize silage (50% total DMI) and adds 8 kg day-1 concentrate (dairy meal) during lactation.


12

Conclusions Introducing management practices that increase productivity of cows is useful to reduce direct emissions from dairy. In the case of animal production, improving the quality of feeds can lead to increased demand on land, and expansion of croplands into forest and other natural ecosystems. This consideration must be built into landscape analyses of management options, to serve both food security and climate change mitigation. References Brandt P. – Kvakić M. – Butterbach-Bahl K. – Rufino M.C.: 2015. How to target climate-smart agriculture? Concept and application of the

consensus-driven decision-making framework "targetCSA". Agricultural Systems, (In press). Havlík P. – Valin H. – Herrero M. – Obersteiner M. – Schmid E. – Rufino M.C. – Mosnier A. – Böttcher H. – Frank S. – Fritz S. – Fusse S.

– Kraxner F. – Notenbaert A.M. – Thornton P.: 2014 Climate change mitigation through livestock system transitions. Proceedings of the National Academy of Sciences, USA, 111(10):3709–3714.

IPCC.: 2006. IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme. Japan: The Intergovernmental Panel on Climate Change.

Robinson T.P. – Wint G.R. – Conchedda G. – Van Boeckel T.P. – Ercoli V. – Palamara E. – Cinardi G. – D’Aietti L. – Hay S.I. – Gilbert M.: 2014. Mapping the global distribution of livestock. PLoS ONE, 9(5): e96084.

Rufino M.C. – Herrero M. – Van Wijk M.T. – Hemerik L. – de Ridder N. – Giller K.E.: 2009 Lifetime productivity of dairy cows in smallholder mixed systems of the highlands of Central Kenya. Animal, 3: 1044–1056.

Rufino M.C. – Tittonell P. – Van Wijk M.T. – Castellanos-Navarrete A. – Delve R.J. – de Ridder N. – Giller K.E.: 2007 Manure as a key resource within smallholder farming systems: analysing farm-scale nutrient cycling efficiencies with the NUANCES framework. Livestock Science, 112:273–287.


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PARTICIPATORY APPROACH FOR IDENTIFYING AND PRIORITISING LOCAL ADAPTATION STRATEGIES TO CLIMATE CHANGE IN SOUTHERN SPAIN E. GARCÍA-PONCE 1 – R. OLIVA 2 – R. GALLARDO 2 – H. GÓMEZ-MACPHERSON 1

1 Instituto de Agricultura Sostenible, CSIC, Córdoba, Spain, Email: [email protected] 2 Department of Agricultural Economics, University of Cordoba, Córdoba, Spain

Introduction Spain lies in an area of special vulnerability to climate change. Predictions highlight a significant increase in temperature, mainly in summer. The average annual precipitation is expected to reduce along with an increase in extreme events (REDIAM, 2014). Thus, measures that address adaptation are required to be adopted by farmers. Several farm management measures taken by farmers are relevant as climate adaptation options. However, farming decisions might not be motivated by climate change concerns due to other priorities. In this context, we examine farmers’ perceptions and responses to climate change, as well as farmers’ local adaptation goals. Results are based on a case study in Southern Spain. Materials and Methods The study area is located in the Guadalquivir Valley in Western Andalusia, Southern Spain. This research used a participatory learning and action (PLA) process. The approach was adapted from Willaume et al. (2014). Survey and workshops were organized to collect information. Questions were based on Niles et al. (2015) and Bryan et al. (2009). A supporting tool to evaluate and prioritise farmers’ adaptation options was included using the analytical hierarchy process (AHP) (Saaty, 1980). Results and Discussion Most of the interviewed farmers perceived that global climate is changing and that it poses risks to agriculture, but without considering that agriculture is contributing to global warming clearly. Regarding local climate, farmers hardly noticed climate trends because local weather conditions are highly variable from year to year. Higher temperature is the local long-term change that has been clearly perceived while there was no common perception of a rainfall trend (Fig. 1). Among climate risks, less seasonal rainfall is perceived to be the most damaging to their farming system (Fig. 2). All interviewed farmers practiced conservation agriculture but did not mention it as a climate adaptation strategy. This was probably because climate adaptation was not a determinant of adoption; rather, they adjust or adopt their practices according to dynamic local conditions, giving priority to minimizing economic and agronomic risks as reported by Carmona et al. (2015) for the same study region.

During the workshop, farmers (n=10) and others stakeholders (n=7) identified several adaptation measures to climate change. The two main options were conservation agriculture and the construction of a small reservoir to collect runoff water. However, their implementation presents constraints, mainly lack of appropriate technology, investment and adapted legislation, as well as technical support and knowledge.

Figure 1. Perceived changes in climate over time for farmers (n = 10).

Figure 2. Degree of farmers’s climate change concerns about specific climate related risks (n = 10).


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Conclusions Although farmers participating in this research are innovative, more technical support and research are required for adopting new adaptation measures and for improving agricultural practices. In particular, it is required to improve some elements of conservation agriculture systems; e.g. residue management and crop diversification. Knowledge and applied scientific research with farmers were among the main determinants affecting farmers’ awareness of climate change and decisions to adapt their farming systems to its predicted risks. Acknowledgements We are very grateful to the farmers that made this work possible. This research is partly funded by EU Climate-CAFE project within the FACCE-ERA-NET+ programme and FEDER funds.

References Bryan E. – Deressa T.T. – Gbetibouo G.A. – Ringler C.: 2009. Adaptation to climate change in Ethiopia and South Africa: options and

constraints. Environmental Science & Policy, 12:413–426. Carmona I. – Griffith D.M. – Soriano M.A. – Murillo J.M. – Madejón E. – Gómez-Macpherson H.: 2015. What do farmers mean when they

say they practice conservation agriculture? A comprehensive case study from southern Spain. Agriculture, Ecosystems & Environment, 213:164–177

Mustajoki J. – Hämäläinen R.P.: 2000. Web-HIPRE: global decision support by value tree and AHP analysis. INFOR, 38(3):208–220 Saaty T.L.: 1980. The Analytic hierarchy process. New York: McGraw-Hill. Niles M.T. – Lubell M. – Brown M.: 2015. How limiting factors drive agricultural adaptation to climate change. Agriculture, Ecosystems

and Environment, 200:178–185. REDIAM.: 2014. Consejería de Medio Ambiente y Ordenación del territorio, Junta de Andalucía. Climate change scenarios available on:

http://www.juntadeandalucia.es/medioambiente/site/rediam/portada/ Willaume M. – Rollin A. – Casagrande M.: 2014. Farmers in southwestern France think that their arable cropping systems are already

adapted to face climate change. Regional Environment Change, 14:333–345.


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CLIMATE SMART AGRICULTURE IN WESTERN SIBERIA – POTENTIAL OF NO-TILL IN SPRING WHEAT PRODUCTION Insa KÜHLING – Dieter TRAUTZ

Working Group Sustainable Agro-Ecosystems, Faculty of Agricultural Sciences and Landscape Architecture, Osnabrück University of Applied Sciences, Am Krümpel 31, 49090 Osnabrück, Germany, Email: [email protected]

Introduction The Western Siberian grain belt is of global significance in terms of agricultural production as well as carbon sequestration and biodiversity preservation. Regional downscaling of general circulation models predict increasing drought risks and water scarcity for this area (Degefie et al., 2014; Tchebakova et al., 2011). Additionally, significant land-use changes took place after the dissolution of the USSR and collapse of the state farm system. Land-use intensity in Tyumen province (Western Siberia, Russian Federation) continuously decreased on grassland whilst on cropland increasing intensity by recultivation of abandoned cropland and rising fertilizer inputs have been observed since 2003 (Kühling et al., 2016). Together, these changing conditions lead to upcoming challenges for sustainable agriculture in this semi-arid environment (Alcamo et al., 2007). For sustainable land management practices, strategies for adapted crop production are needed, in particular for enhanced water-use efficiencies. Within the interdisciplinary German-Russian research project ‘SASCHA’ (SASCHA, 2015), we evaluated in agronomic field trials the potential of no-till in spring wheat production as one contribution to a resilient agricultural system under changing climate conditions. Materials and Methods A factorial field trial with three replications was installed on 10 ha near Ishim (Russia, 56.17˚N, 69.49˚E), growing a regional spring wheat variety (dominant crop) on clay-loam soils (Phaeozems) under on-farm conditions. Over three years (2013–2016) we observed effects of tillage (conventional/no-till) and seeding (high/low seed rate and deep/shallow seed placement) adjustments with regular soil moisture measurements (TDR) and agronomic traits (phenological development, yield, quality). Results and Discussion Results after 3 years showed a significant effect of tillage on soil water content over the entire growing seasons. Soil moisture was on average 34.4% higher under no-till conditions compared to conventional tillage (Figure 1A). Differences in yield were only in 2015 significantly higher (Figure 1B). The other seeding adjustments did not significantly affect plant development and yield parameter. During the three years, the weather conditions during the growing seasons were different from the long-term average: Temperature sums were 8–20% lower than the long-term mean (1576 growing degree days from 1981–2010, base 5°C). Furthermore, the precipitation was higher by 4–53% compared to long-term mean (265 mm during the growing season).

Figure 1. Comparison of no-till and conventional tillage in relation to the overall mean for average volumetric water content (VWC) over three years (n=313) (A) and grain yield for each individual year (B). Tukey HSD-test, different letters indicate significant differences at

p=0.001; n.s.: not significant; **: P=0.01; Boxes (A): lower to upper quartile with median, Whiskers: min/max; error bars (B): 1 standard error of the mean.


16

Our results are in accordance with those from a global meta-analysis about no-till potential by Pittelkow et al. (2015), where especially for wheat production under dry and rainfed conditions positive yield effects are reported. Conclusions The findings of our trials revealed a general potential for no-till spring wheat production in Western Siberia under exceptional cold and wet conditions. Under the predicted climate change scenarios, a clearer yield effect seems to be likely. Acknowledgements This work was conducted as part of project SASCHA (‘Sustainable land management and adaptation strategies to climate change for the Western Siberian grain belt’). We are grateful for funding by the German Government, Federal Ministry of Education and Research within their Sustainable Land Management funding framework (funding reference 01LL0906D).

References Alcamo J. – Dronin N. – Endejan M. – Golubev G. – Kirilenko A.: 2007. A new assessment of climate change impacts on food production

shortfalls and water availability in Russia. Global Environmental Change, 17: 429–444. Degefie D.T. – Fleischer E. – Klemm O. – Soromotin A.V. – Soromotina O.V. – Tolstikov A.V. – Abramov N.V.: 2014. Climate extremes

in South Western Siberia: past and future. Stoch. Environmental and Research Risk Assessment, 28:2161–2173. Kühling I. – Broll G. – Trautz D.: 2016. Spatio-temporal analysis of agricultural land-use intensity across the Western Siberian grain belt.

Science in the Total Environment, 544:271–280. Pittelkow C.M. – Linquist B.A. – Lundy M.E. – Liang X. – van Groenigen K.J. – Lee J. – van Gestel N. – Six J. – Venterea R.T. – van

Kessel C.: 2015. When does no-till yield more? A global meta-analysis. Field Crops Research, 183:156–168. SASCHA.: 2015. SASCHA - Sustainable land management and adaptation strategies to climate change for the Western Siberian Grain Belt.

http://www.uni-muenster.de/SASCHA/en (accessed 3.10.15). Tchebakova N.M. – Parfenova E.I. – Lysanova G.I. – Soja a.J.: 2011. Agroclimatic potential across central Siberia in an altered twenty-first

century. Environmental Research Letters, 6:045207.


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EFFECT OF ELEVATED CO2 ON NITRATE ASSIMILATION AND GROWTH OF COMMON BEAN, WHEAT AND PERENNIAL RYEGRASS Mitchell ANDREWS 1 – Stuart LARSEN 1 – James D MORTON 1 – Leo M CONDRON 1 – Peter D KEMP 2

1 Faculty of Agriculture and Life Sciences, Lincoln University, Canterbury, New Zealand, Email: [email protected] 2 Institute of Agriculture and Life Sciences, Massey University, Palmerston North, New Zealand

Introduction Nitrate (NO3

-) is the main form of nitrogen (N) taken up and assimilated by most crop plants in cultivated soils (Andrews et al., 2013). It has been reported that high atmospheric carbon dioxide (CO2) concentrations inhibit photoreduction of NO3

- in shoots and in some cases growth of C3 plants but do not affect their respiratory driven NO3

- reduction in roots (Bloom et al., 2012). Common bean (Phaseolus vulgaris), wheat (Triticum aestivum) and perennial ryegrass (Lolium perenne) assimilate a substantial, usually major, proportion of their NO3

- taken up in the shoot under high NO3

- supply (Andrews et al., 2013). The primary objective of the work was to determine if elevated atmospheric CO2 concentration inhibits total plant NO3

- assimilation and growth of the C3 crop species common bean, wheat and perennial ryegrass under limiting and/or optimum NO3

- supply. Materials and Methods The experiments were conducted across two Conviron BDW 120 plant growth rooms (Thermo-Fisher, Auckland, NZ). The lighting system in both growth rooms consisted of 48 400 W metal halide bulbs (Venture Ltd, Mount Maunganui, NZ) in combination with 48 soft tone, soft white 100 W incandescent bulbs (Philips, Auckland, NZ) mounted behind a Perspex barrier 2.4 m above floor level. The PAR at the pot surface was 950 mol photons m-2 s-1 with light levels ramped for 60 mins to simulate dawn/ dusk. A top-down airflow pattern, with controlled flow of outdoor air, maintained ambient CO2 conditions (390 ppm CO2) within one growth room. The second growth room was maintained at 760 ppm CO2 with G214 food grade CO2 (BOC, Auckland, NZ) added as required. The CO2 levels in the cabinets were measured using PP Systems WMA-4 Gas Analysers (John Morris Scientific, Auckland, NZ). Daytime relative humidity was maintained at 65% and night time humidity peaked at 80%. Common bean, wheat and perennial ryegrass were grown from seed in 1 L volume pots (one to four plants per pot depending on species and experiment) containing a 1:1 volume mix of vermiculite and perlite which was flushed every 2 days or 1 day on last 2 weeks of experiments with basal nutrient solution (Andrews et al., 1999) containing the appropriate NO3

- treatment. The treatments were 0.5, 1, 2, 3, 4, 6, 8 and 10 mol m-3 NO3

- supplied as potassium nitrate. Potassium concentration was made equal in all treatments by the addition of potassium sulphate as required, but sulphate was not balanced. Plants were harvested 33–49 days after sowing (depending on experiment), dried at 60C for 7 days and shoot and root DW determined. Shoot and root material was then ground and 200 mg samples analysed for N in an Elementar Vario-Max CN analyser (Hanau, Germany). Results and Discussion At both limiting and optimum NO3

- supply, carbon assimilation and hence total plant dry weight for common bean, wheat and perennial ryegrass were substantially greater in elevated than ambient atmospheric CO2 concentration. For all three species, NO3

- uptake and assimilation were also greater at elevated CO2 but the relative increase in N assimilation was less than for C assimilation thus the final concentration of N in the plant (tissue %N) was less at elevated than ambient CO2. Nitrogen utilisation efficiency (dry matter or C per unit N) was substantially greater at elevated CO2. These findings have different impacts for the different crops and their uses. For example, bread wheat producers who get a premium price for grain that is high in protein, would get an increase in yield but may need to add additional N fertiliser to counter the reduction in grain protein concentration. However, for perennial ryegrass pastures, greater production linked to lower N concentration could result in reduced N losses from the system without loss in production. Conclusions Elevated CO2 does not inhibit but promotes total plant NO3

- assimilation and growth of the C3 crops common bean, wheat and perennial ryegrass. Nitrogen utilization efficiency of NO3

- fed plants was greater at elevated CO2. Reduced tissue %N under elevated CO2 would have different impacts on crops depending on their uses.


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References Andrews M. – Raven J.A. – Lea P.J.: 2013. Do plants need nitrate? The mechanisms by which nitrogen form affects plants. Annals of

Applied Biology, 163:174–199. Andrews M. – Sprent J.I. – Raven J.A. – Eady P.E.: 1999. Relationships between shoot to root ratio, growth and leaf soluble protein

concentration of Pisum sativum, Phaseolus vulgaris and Triticum aestivum under different nutrient deficiencies. Plant Cell and Environment, 22:949–958.

Bloom A.J. – Asensio J.S.R. – Randall L. – Rachmilevitch S. – Cousins A.B. –Carlisle E.A.: 2012. CO2 enrichment inhibits shoot nitrate assimilation in C3 but not C4 plants and slows growth under nitrate in C3 plants. Ecology, 93:355–367.


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PHOTOPERIOD INSENSITIVITY EFFECT ON WHEAT FLORET DEVELOPMENT AND GRAIN YIELD Paula PRIETO 1 – Helga OCHAGAVIA 1 – Roxana SAVIN 1 – Gustavo A. SLAFER 1, 2

1 Department of Crop and Forest Sciences and AGROTECNIO (Center for Research in Agrotechnology), University of Lleida, Av. Rovira

Roure 191, 25198, Lleida, Spain, Email: [email protected] 2 ICREA (Catalonian Institution for Research and Advanced Studies), Spain.

Introduction Greater genetic gains of wheat yield are urgently needed. Understanding the physiology of the main yield components (mainly number and weight of the grains) is relevant. Flowering time and particularly the length of stem elongation may be relevant traits determining differences in yield. It is well known that photoperiod affects flowering time and that photoperiod response is determined by Ppd-D1a, Ppd-B1a and Ppd-A1a, located on the short arms of group 2 chromosomes. However, how photoperiod genes affect the length of particular component phases of flowering time and rates of floret development has been scarcely studied. We aimed to characterize floret development patterns, dynamics of floret primordia (resulting in numbers of fertile florets at anthesis and grains at maturity) as affected by the action of particular Ppd alleles. Materials and Methods Two field experiments (growing seasons 1 and 2) were carried out at Bell-lloc d’Urgell, Lleida, NE Spain. Ten near isogenic lines (NILs) differing in number (4 single, 5 double and 1 triple) and variant of Ppd alleles produced by the John Innes Centre and Paragon (common background) were sown. From the onset of stem elongation onwards, plants were sampled twice or thrice a week and the spikes dissected under microscope. Floret primordia of the central spikelets were counted and their stages of development determined. At anthesis and maturity, a sample of each plot was harvested and the number of fertile florets and grains counted, and grains weighed to determine yield. Results and Discussion The effect of the photoperiod insensitivity was much clearer in the first than in the second growing season. In the first season, photoperiod insensitivity alleles reduced the time of floret development in comparison with Paragon (Fig. 1A). The reduced time of floret development resulted in a decrease in the number of total fertile florets (Fig. 1B) the effect being stronger towards the apical spikelet positions (Fig. 1B). In the second season the differences were less evident (Fig. 1C,D).

Figure 1. (Left) Number of living floret primordia in the central spikelet against thermal time from anthesis (A,C); and number of fertile

florets per spikelet along the entire spike (B,D) during both seasons.

0 1 2 3 4 50

10

20

30A+DParagon

-1000 -500 0 500 1000

A+D

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-1000 -500 0 500 10000

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0 1 2 3 4 5

A+DParagon

Number of fertile florets per spikelet

Thermal time from anthesis (°C d)

Growing season 1 Growing season 2

A

B

C

D

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f li

vin

g fl

oret

pri

mor

dia

(sp

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et-1

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pos

itio

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Figure 2. (Right) Number of fertile florets (A,E) number of grains (B,F), grain weight (C,G) and grain yield (D,H) among the NILs during both seasons, dashed lines represent Paragon. Inset each panel the different lines with equal number of Ppd genes were averaged.

Consistently, during the first season the number of fertile florets was lower in all of the NILs than in Paragon (Fig. 2A). This resulted in a reduction in the number of grains/m2 (Fig. 2B), affecting the spike fertility. However, as the grains of most of the NILs were heavier than those of Paragon (Fig. 2C), grain yield was similar between the NILs and Paragon (Fig. 2D). On the other hand, the lack of effect seen in the second year was reflected in most of the NILs on the number of fertile florets/m2 (Fig. 2E), number of grains/m2 (Fig. 2F), grain yield (Fig. 2H) but not in the thousand grain weight (Fig. 2G). Conclusions The absence of effect in the second season may involve some photoperiod x temperature interaction. When there was an effect, manipulating time to flowering through introgressing photoperiod sensitivity caused a reduction in the period of floret development and as a consequence reduced spike fertility. Acknowledgements Funding was provided by ADAPTAWHEAT, an EU project and the Spanish Ministry of Science and Innovation project AGL2012-35300. PP and HO held scholarships from the University of Lleida and the Spanish Ministry of Science, respectively.

0

20

40

60

80

100

p<0.01

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* * * * * * * * * * * * * * * * * * * * * * * *

Num

ber

of fer

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flo

rets

(10

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-2)

p<0.001

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p=0.25

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*

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B

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SA

+B

SA

+B

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+D

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B

+D

SB

+D

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5

10

15

20

25

p=0.12

D

GY

(t/h

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p=0.05

0

20

40

60

80

100

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ber

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s (1

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) p<0.001

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50

100p=0.01

C

* * ** ** *** * ***

Tho

usan

d gr

ain

wei

ght (g

)

p<0.001

* * * * * * *

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B

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SA

+B

SA

+B A+D

+D

CS

B

+D

SB

+D

CS

A+B

p=0.04

H

* ** *

p<0.01

*

p=0.22F p=0.51

p<0.01G

** ** ** ***** * **

p<0.01

** *

Growing season 1

SingleDoubleTriple

p=0.35

Growing season 2


21

EARLINESS PER SE EFFECTS ON DEVELOPMENTAL PROCESSES, FINAL LEAF AND SPIKELET NUMBER Helga OCHAGAVIA 1 – Paula PRIETO 1 – Roxana SAVIN 1 – Gustavo A. SLAFER 1, 2

1 Department of Crop and Forest Sciences and AGROTECNIO, (Center for Research in Agrotechnology), University of Lleida, Av. Rovira

Roure 191, 25198 Lleida, Spain, Email: [email protected] 2 ICREA, Catalonian Institution for Research and Advanced Studies, Spain

Introduction Wheat adaptation depends on flowering time, which in turn involves three developmental sub-phases: (i) vegetative (ii) early reproductive and (iii) late reproductive (Slafer & Rawson, 1994). Major genetic effects depend upon sensitivity to photoperiod (Ppd genes) and vernalization (Vnr genes). A third group of genes is responsible for smaller variation in flowering time (Eps genes), and are critical for fine-tuning adaptation. The Eps effects on duration of the three sub-phases, on the dynamics of initiation/appearance of leaves and spikelets and on numbers of leaves (FLN), spikelets and tillers have been mostly overlooked, and assessing them was the main objective of this study. Materials and Methods Field experiments were carried out during 2012–13 and 2013–14 growing seasons in Catalonia (NE Spain), sown in autumn (optimal). Treatments consisted of four pairs of near isogenic lines (NILs) for Eps genes on chromosome 1D (developed by John Innes Centre; Norwich, UK) from the cross Spark × Rialto. Each pair has a NIL homozygous at the QTL for the Spark (earliness) or Rialto allele (lateness). Phenological stages were determined according to the Zadoks’ scale (Zadoks et al., 1974). Plants were dissected weekly to determine the stage of apex development (Kirby & Appleyard, 1984) and to count cumulative numbers of primordia (Figure 1). The number of leaves (main shoot) and tillers was recorded weekly.

Figure 1. Schematic description of dissection of plants.

Results and Discussion Time to heading was reduced by Eps early alleles, but the difference was significant only in the first season (Figure 2A). As expected, the effects were relatively small. When decomposing time to heading into component phases, the early alleles seemed to have reduced only the late reproductive phase, again significantly only in the first season (Figure 2B). This is relevant as it is during this period when potential yield of the crop is being determined. Delay on heading time caused by late alleles was mainly explained by lengthening the phyllochron of late-appearing leaves (it was 129±2 and 137±2 °C d leaf-1 for early and late alleles, respectively). That is why these Eps alleles affected mostly the length of the late reproductive phase.


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Figure 2. Thermal time to heading (A), thermal time from emergence to floral initiation (B, black bars), from floral initiation to terminal spikelet (B, white bars), from terminal spikelet to heading (B, grey bars) for NILs with Eps early and late alleles in both growing seasons.

As these alleles did not affect the vegetative phase, advancing heading by the early alleles produced no effects on FLN (11.9±0.1 for early vs 12.0±0.1 for late in 2012–13, 11.0±0.2 for early and 11.4±0.2 for late in 2013–14). Spikelet number was not affected either (22.7±0.2 for early vs 23.6±0.2 for late in 2012–13, 23.5±0.2 for early vs 23.3±0.3 for late in 2013-14) as these alleles did not affect the spikelet initiation phase. Dynamics of tillering and tiller mortality were also quite similar between pairs implying that final number of spikes per plant were insensitive to the action of these alleles. Conclusions Eps early alleles reduced time to heading mainly by inducing a shorter phyllochron of late-appearing leaves determining a shorter period of stem elongation. Then they could be potentially used to fine-tune time to heading with no effects in the canopy structure, as they did not affect numbers of leaves or tillers. Acknowledgements Funding provided by EU and the Spanish “National Plan” grants. HO and PP held scholarships from the Spanish Ministry of Science and the University of Lleida, respectively.

References Kirby E.J.M. – Appleyard M.: 1984. Cereal development guide, 2nd Edn. Coventry: Arable Unit, National Agricultural Centre. Slafer G.A. – Rawson H.M.: 1994. Intrinsic earliness and basic development rate assessed for their response to temperature in wheat.

Euphytica, 83:175–183. Zadoks JC. – Chang TT. – Konzak CF.: 1974. A decimal code for the growth stages of cereals. Weed Research, 14:415–421.


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EFFECTS OF CONSERVATION AGRICULTURE ON SOIL QUALITY, CARBON SEQUESTRATION AND CROP PERFORMANCE IN AN IRRIGATED MAIZE-BASED SYSTEM Carlos SALAMANCA 1 – José M. MURILLO 2 – Helena GÓMEZ-MACPHERSON 1

1 Agronomy Department, Institute for Sustainable Agriculture (IAS, CSIC), Av. Menéndez Pidal, s/n, Campus Alameda del Obispo, 14004

Córdoba, Spain, Email: [email protected] 2 Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS, CSIC), Av. Reina Mercedes 10. Sevilla, Spain

Introduction Conservation agriculture (CA) encompasses minimal soil disturbance, permanent soil ground cover and crop diversification. Its advantages include increased water holding capacity, reduced runoff and erosion, increased carbon sequestration, improved soil structure, increased biological diversity and better energy use efficiency (FAO, 2015). Additionally, CA is regarded as effective at reducing greenhouse gas emissions (GHGs). Nevertheless, CA has not being adopted in annual crop-based systems in southern Spain, mostly due to a knowledge gap in crop management, particularly in irrigated systems. This study aims to underline the benefits of CA in terms of crop performance, soil quality and carbon sequestration in an irrigated maize-based system. Materials and Methods A field study was conducted during 2015 in a long-term experiment established in 2007 in Alameda del Obispo (Córdoba, Spain) to compare permanent beds (PB) in which plant residues were unaltered, and conventional beds (CB) with ploughing and residue incorporation (Boulal et al., 2012). The experimental design is RCB with three blocks. Each experimental unit had an area of 387 m2. Maize (Zea mays L.) sowing took place on 18 May with 0.86 m row spacing, with the harvest occurring at 20 WAP (weeks after planting). Grain yield, harvest index and other yield components, and above-ground dry matter (AGDM) were estimated from manually harvested samples following Boulal et al. (2012). Soil properties were measured before planting to study the long-term tillage effects. Soil bulk density (BD), organic matter (SOM) and organic carbon stocks (SOCs) (Schwager & Mikhailova, 2002) were measured for seven soil layers down to 0.65 m depth, and resistance to penetration was recorded every 0.01 m down to 0.6 m depth using a penetrometer connected to a data-logger (Agüera & Gil, 1991). Fluxes of three GHGs, carbon dioxide (C-CO2), methane (C-CH4) and nitrous oxide (N-N2O), were measured simultaneously for the first time in the long-term experiment, using the chamber-based method (Parkin & Venterea, 2010), making three extractions (at 0, 30 and 60 mins) per chamber assuming a linear increase in gas concentration. Readings were made by gas chromatography and corrected for soil temperature. Results and Discussion In 2015, 8 years after establishing the experiment, soil bulk density did not vary between treatments throughout the profile, but penetration resistance was significantly higher under PB than CB down to 35 cm soil depth (Fig. 1). This compaction, plus the cooler soil due to residues in PB were probably the causes of lower AGDM after establishment (P < 0.05; data not shown). Nevertheless, grain yield did not vary significantly between treatments, averaging 1372 g m-2, nor did other yield components.

Figure 1. Effects of CB and PB on soil penetration resistance prior to planting throught the 0-60 cm soil profile. ** and *** refer to P

values < 0.01 and < 0.001 respectively; ns: not significant

Soi

l Dep

th (

m)

Penetration Resistance (MPa)

CB PB

*** ***

***

**

ns

ns


24

SOM and SOCs were slightly higher throughout the profile in PB than CB, but differences were significant only for the top 10 cm soil layer (P<0.05, data not shown), denoting higher SOM protection under PB in the topsoil layer. A few days after tilling the soil in CB, GHG effluxes were not significantly different betweeb treatments, although C-CO2 fluxes were generally lower under PB than CB, and higher N-N2O fluxes after fertilization were found under PB than CB. On the other hand, C-CH4 fluxes were equally negative under both treatments. Results so far indicate an optimistic trend in terms of GHGs emissions reduction by the use of PB. Conclusions Provisional results of this study show that CA in annual crops under irrigation conditions might pose an interesting cropping management system towards a climate change scenario, although there is need for a more complete description of GHG effluxes and soil properties developments with time in order to get more conclusive results in the long-term and provide data for modelling purposes. Acknowledgements This work has been supported by the Spanish Ministry of Economy and Competitiveness (Project AGL2013-49062-C4-2R) and FEDER funds.

References Agüera J.V. – Gil J.R.: 1991. Penetrometro portatil para la medida del indice de cono. 23 Conferencia Internacional de Mecanización

Agraria, Zaragoza, Spain, pp. 199–206. Boulal H. – Gómez-Macpherson H. – Villalobos F.J.: 2012. Permanent bed planting in irrigated Mediterranean conditions: short-term

effects on soil quality, crop yield and water use efficiency. Field Crops Research, 130:120–127. FAO.: 2015. The main principles of conservation agriculture. In: http://www.fao.org/ag/ca/1b.html (accessed 03.16). Parkin T.B. – Venterea R.T.: 2010. USDA-ARS GRACEnet project protocols, chapter 3. Chamber-based trace gas flux

measurements. Sampling Protocols. USDA-ARS, Fort Collins, CO, 3-1. Schwager S.J. – Mikhailova E.A.: 2002. Estimating variability in soil organic carbon storage using the method of statistical

differentials. Soil Science, 167(3):194–200.


25

EVALUATION OF CAMELINA SATIVA (L.) CRANTZ AS OILSEED CROP IN TWO ENVIRONMENTS OF CENTRAL AND NORTHERN ITALY Luciana G. ANGELINI 1 – Lara FOSCHI 1 – Silvia TAVARINI 1 – Luca LAZZERI 2

1 Department of Agriculture, Food and Environment, University of Pisa, Italy, via del Borghetto 80, Pisa, Email: [email protected] 2 Research Centre for Industrial Crops (CREA-CIN), Bologna, Italy

Introduction Camelina (Camelina sativa (L.) Crantz) has gained considerable attention in Europe and North America as a potential oilseed feedstock for advanced biofuels (i.e.aviation fuel) and bioproducts (Shonnard et al., 2010; Kim et al., 2015). Camelina seeds contain also different compounds, among which glucosinolates (GLs) make the resulting oil cake interesting for the production of value added chemicals (Matthäus & Angelini, 2005). The objectives of the following study were to characterize the production potential of camelina in two environments of central (Pisa, Tuscany) and northern (Bologna, Emilia Romagna) Italy, traditionally devoted to cereal and sunflower cultivation. Materials and Methods A three-year field experiment (2013–2015) was conducted in Pisa (low Arno valley, 43°40’ N; 10°19’ E; 5 m a.s.l) and in Bologna (Po valley, 44°32′ N, 11°29′ E, 29 m. a.s.l.) in loamy soils (clay content 13.4±3.2 and 33.4±0.9 g 100g-1 soil, respectively for Pisa and Bologna), with medium soil organic matter content (about 1.9 g kg-1 soil). In Pisa, camelina was sown between March and May and harvested at the end of June; in Bologna camelina was sown in October and harvested from end of May-beginning of June. The cropping techniques and mechanisation methods were defined in relation to the specific characteristics of the area according to integrated production methods. At maturity, three sample areas of 1 m2 were collected within each experimental field and for each crop, to assess harvestable crop yields. The seed oil content was determined by Soxhlet extraction method and the ISO 5508 method (1998) was used for fatty acid composition. GLs were determined according to the EU official ISO 9167-1 method. Data were analysed by a two-way ANOVA (environment E, year Y, and ExY). Results and Discussion Seed and oil yield were higher in Pisa than in Bologna. This was probably due to differences in the sowing dates. In fact, previous experiments showed that winter sown camelina can be damaged by frost in the initial vegetative growth stages (Angelini, 2012). The harvest index found in Pisa was significantly higher than in Bologna due to the lower dry matter accumulation in the vegetative tissues in spring sown crops during the 2nd and 3rd year of field trial. Very high amounts of GLs were found, with significant variation among years and environments (Fig. 1); the values reached in both environments were among the highest between those found in the literature (Schuster and Friedt, 1998).

Table 1. Seed, above ground lignocellulosic biomass and oil yield in Pisa and Bologna

Seed Yield (t ha-1) Above ground Biomass (t ha-1) Oil Yield (kg ha-1) Year Pisa Bologna Pisa Bologna Pisa Bologna 2013 1.31 0.94 3.04ab 2.74b 521.4 378.8 2014 1.23 0.91 2.57bc 3.68a 449.0 358.5 2015 1.44 0.60 2.68b 1.79c 417.3 230.4

Environment 1.33A 0.82B 2.76 3.07 462.6A 322.6B

Means followed by different letters are significantly different at P<0.05. Lowercase letters for ExY effect; capital letters for E effect.


26

Figure 1. Crude protein and glucosinolates content of camelina seeds in Pisa and Bologna field experiments. Bars followed by different letters are significantly different at P<0.05.

Conclusions This study highlights the good crop adaptability, seed and oil yield, protein and GL content of camelina under the pedo-climatic conditions of central and northern Italy, evidencing a clear effect of location and climate on seed yield and its quality. The high amount of glucosinolates makes the resulting oil cake suitable for the production of value added chemicals. Acknowledgements Work carried out within the Project “SUSCACE - Research activity Agriculture for bio-products Axbb”, financed by Italian Ministry of Agricultural, Food and Forestry Policies (MiPAAF) and coordinated by CREA-CIN of Bologna, Italy.

References Angelini L.G.: 2012. Exploitation of non-conventional biodiesel oil crops for Southern European cropping systems. Proceedings of the 20th

European Biomass Conference, pp. 550–560. Kim N. – Li Y. – Sun X.S.: 2015. Epoxidation of Camelina sativa oil and peel adhesion properties. Industrial Crops and Products, 64:1–8. Matthäus B. – Angelini L.G.: 2005. Anti-nutritive constituents in oilseed crops from Italy. Industrial Crops and Products, 21:89–99. Schuster A. – Friedt W.: 1998. Glucosinolate content and composition as parameters of quality of camelina seed. Industrial Crops and

Products, 7:297–302. Shonnard D.R. – Williams L. – Kalnes T. N.: 2010. Camelina-derived jet fuel and diesel: Sustainable advanced biofuels. Environmental

Programme for Sustainable Energy, 29:382–392.


27

INTERNAL FACTORS OF LUPINUS POLYPHYLLUS INVASIVITY IN WARMING CLIMATE CONDITIONS Sigita JURKONIENĖ – Virgilija GAVELIENĖ – Danguolė ŠVEGŽDIENĖ – Jūratė DARGINAVIČIENĖ – Jurga JANKAUSKIENĖ – Nijolė ANISIMOVIENĖ Institute of Botany of Nature Research Centre, Akademijos str. 2, 08412 Vilnius, Lithuania, Email: [email protected]

Introduction In Lithuania, the invasive lupin, Lupinus polyphyllus Lindl., occupies many natural habitats, changing their composition or destroying them completely. Ability to adaptively alter morphological, anatomical and physiological traits to local environmental variations is especially well expressed by all invasive plants. Because of global warming, many aspects of plant development and growth can be affected. The processes determining invasiveness are not equally expressed at all invasive plants (Erfmeier et al., 2011). Strategies of investigation of alien plant internal factors determining their invasiveness are at an early stage. The main goal of the work was to evaluate intercellular chains determining survival of invasive species in warming climate conditions. Materials and Methods Test objects were one of the seven most dangerous invasive plant species for Lithuanian ecosystems, namely L. polyphyllus, and non-invasive L. luteus L. IAA content and status were measured employing TLC, HPLC methods (Shimadzu PROMINENCE LC-20). ATPase activity was assessed according liberated Pi. The simulation of climate warming was an increase of air temperature from +25°C to 35°C. Results and Discussion The results of comparative study of changes of phytohormone IAA content and status showed that during early phases of growth and development of invasive species, they were supplied by more IAA from reserve tissues of germinating seeds (cotyledons) and possibly for a longer time of growth. Preliminary results of identification of IAA metabolites suggest that the major amount of IAA-amides is characteristic for invasive lupin in comparison with non-invasive lupin (Table 1). The invasive species was characterized as having major amount of ethylene, and intensified the utilization of resources of IAA-amide complexes for maintaining IAA homeostasis under the unfavorable environmental conditions. IAA increased growth of invasive lupin roots more intensively at 25C than at 30C. The roots of both species grew independently of the temperature of environment during the early phases of development (up to 2 days), but gravitropic movement of L. polyphyllus roots was stronger at 25C (Figure 1). The better facilities for survival of invasive species has been revealed by investigations of the activity of barrier membrane ATP-ases and electrochemical potential differences. It was shown that invasive plant was capable to maintain the differences of transmembrane electrochemical potential in the conditions of lower hydrolysis of ATP (Darginavičienė, Jurkonienė, 2013).

Figure 1. The gravitropic response of L. polyhyllus (a) and L. luteus (B) roots after 90° reorientation at different temperature

Table 1. IAA and its reserve compounds stocks in L. polyhyllus and L. luteus leaves

IAA status L. luteus

(IAA, µg/10 g fresh weight) L. polyphyllus (invasive)

(IAA, µg/10 g fresh weight) IAA (free form) 278 ± 19 290 ± 12 IAA-ester 166 ± 19 191 ± 23 IAA-amide 417 ± 11 819 ± 12 IAA metabolites 21.2 ± 0.8 12.2 ± 3.2

0

20

40

60

80

100

0 1 2 3 4

Time (h)

An

gle

of

curv

atu

re (

deg

rees

)

25°C

30°C

0

20

40

60

80

100

0 1 2 3 4

Time (h)

An

gle

of

curv

atu

re (

deg

rees

)

25°C

30°C


28

Conclusions Cotyledons of the invasive lupin contain more IAA than those of the non-invasive yellow lupin. Invasive lupins sprouts are better supplied with IAA from the cotyledons from IAA-reserve compounds. A specific IAA-conjugate for invasive lupin was detected. Higher ethylene content was found in invasive lupin sprouts than in non-invasive. IAA promotes root growth of invasive lupin more strongly at 25°C than at 30°C. The invasive lupin maintains transmembrane electrochemical potential in the lower energy liberated by ATP hydrolysis. Alterations in root cell growth provoked by elevated temperature can determine the negative modifications of root system which may led to the decrease of invasive lupine population. References Darginavičienė J. – Jurkonienė S.: 2013: Characteristics of transmembrane proton transport in the cells of Lupinus polyphyllus. Central

European Journal of Biology, 8:461–469. Erfmeier A. – Bohnke M. – Bruelheide H.: 2011: Secondary invasion of Acer negundo: the role of phenotypic responses versus local

adaptation. Biological Invasions, 13:1599–1614.


29

THE PERFORMANCE OF AQUACROP MODEL IN CHICKPEA CROP (CICER ARIETINUM L.) SOWN ON DIFFERENT DATES IN NORTHEASTERN SOUTH AFRICA M.T. MUBVUMA 1, 2 – J.B.O. OGOLA 2 – T. MHIZHA 3

1 Department of Soil and Plant Sciences, Great Zimbabwe University, P. Box 1235, Masvingo, Zimbabwe 2 Department of Plant Production, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa, Email:

[email protected] 3 Department of Physics, University of Zimbabwe. P. O. Box MP 167, Mt Pleasant Zimbabwe

Introduction Chickpea (Cicer arietinum L.) is a drought-tolerant legume crop that is widely grown in tropical and subtropical regions. Possible negative effects of climate change on future productivity of the crop are well documented (Singh et al., 2013). An increase in intensity and severity of drought, frost and heat waves may seriously alter the current planting dates in northeastern South Africa and consequently affect future yield of chickpea. Climate change is expected to cause a decrease in the length of the growing season in the Lowveld regions of NE South Africa (Tadross et al., 2009). Farmers’ choice of planting date (PD) may thus be an important adaptation strategy to ameliorate the effects of changing climate conditions on the length of the growing season and crop productivity. This study aimed at calibrating and validating the performance of the AquaCrop model, to simulate attainable yields in chickpea crop under varying planting dates in NE South Africa. Materials and Methods Model calibration data was obtained from two experiments of contrasting water regime (Expt. I & II, respectively), sown in the 2014 winter season at Thohoyandou, South Africa, whilst data for model validation was obtained from 2015 planting season at the same site. Expt. I was well watered throughout the season while Expt. II was watered only at sowing, flowering and at pod formation. Both experiments were laid out in a split-plot design with PD (1, 14 and 28 May 2014) as main plots replicated three times and chickpea genotypes (Range 1, Range 3, Range 4 and Range 5) as sub-plots. Model manual data and user-specific parameters were measured from the field experiments. Model evaluation criteria involved comparing experimental data from the field and the simulated results after model validation. Performance of the AquaCrop model was assessed using linear regression analysis (R2), the Relative Root Mean Square Error (RRMSE), Spearman’s rank order correlation coefficient (r) and the index of agreement (d-index). Linear regression and Spearman product correlation coefficient (r) analyses were conducted using Genstat 17, whilst RRMSE and d-index were calculated according to Katerji et al. (2013). We considered the model excellent when the RRMSE was less than 10%, good when the RRMSE was between 10 and 20%, fairly acceptable when the RRMSE was between 20% and 30%, and poor when the value of RRMSE was greater than 30% (Katerji et al., 2013). A d-index value of 1 indicated perfect agreement between the simulated and observed data, 0 indicated no agreement, and a value of 0.5 was considered fairly acceptable (Xiangxiang et al., 2013). Results and Discussion The calibrated and validated AquaCrop model was able to predict biomass, evapotranspiration (ET) and canopy cover (CC) for all planting dates (Table 1). The model was sensitive to different planting dates and showed significant yield variation that was attributed to PD. Statistical indicators showed excellent model performance in Expt. I, a fair performance in Expt. II, and a good agreement index in both experiments. These results suggest that AquaCrop may be used for strategic planning when deciding optimal sowing dates for the chickpea crop.


30

Table 1. Model performance and quality evaluation statistics

Above Ground Biomass Evapotranspiration Canopy Cover

Water

Regime Statistics

Early

Planting

Date

Normal

Planting

Date

Late

Planting

Date

Early

Planting

Date

Normal

Planting

Date

Late

Planting

Date

Early

Planting

Date

Normal

Planting

Date

Late

Planting

Date

D 0.72 0. 65 0.63 0.62 0.58 0.52 0.75 0.69 0.64

Irrigated

Experiment RRMSE (%) 16 18 17 27 28 30 18 19 24

( n = 36) R2

0.78 0.72 0.65 0.69 0.61 0.56 0.74 0.70 0.67

R 0.82 0.76 0.67 0.61 0.57 0.45 0.84 0.74 0.64

D 0.74 0.67 0.61 0.66 0.55 0.50 0.68 0.58 0.53

Dryland

Experiment RRMSE (%) 20 21 29 23 28 30 25 30 35

(n = 36) R2

0.62 0.57 0.51 0.61 0.56 0.47 0.58 0.53 0.44

R 0.69 0.66 0.55 0.58 0.55 0.51 0.68 0.61 0.54 *n is total number of data points, d is the agreement index, r is the Spearman’s correlation coefficient, R2 is the linear regression.

Conclusion The model predicted the measured parameters fairly well and picked out differences in CC, ET and yield that were attributed to time of planting and genotype, so it may be suitable for assessing the effect of planting date on chickpea yield under climate change scenarios. References Katerji N. – Campi P. – Mastrorilli M.: 2013. Productivity, evapotranspiration and water use efficiency of corn and tomato crops simulated

by AquaCrop under contrasting water stress conditions in the Mediterranean region. Agricultural Water Management, 130:14–26. Singh P. – Nedumaran S. – Boote K.J. – Gaur P.M. – Srinivas K. – Bantian M.C.C.: 2014. Climate change impacts and potential benefits of

drought and heat tolerance in chickpea in South Asia and East Africa. European Journal of Agronomy, 52:123–127. Tadross M. – Suarez P. – Lotsch A. – Hachigonta S. – Mdoka M. – Unganai L. – Lucio F. – Kamdonyo F. – Muchinda M.: 2009. Growing-

season rainfall and scenarios of future change in southeast Africa: implications for cultivating maize. Climate Research, 40:147–161. Xiangxiang W. – Wang Q. – Fan J. – Fu Q.: 2013. Evaluation of the AquaCrop model for simulating the impact of water deficit and

different irrigation regimes on the biomass and yield of winter wheat grown on China’s Loess Plateau. Agricultural Water Management, 129:95–104.


31

PHYSIOLOGICAL PHENOTYPING OF MAPPING POPULATION AIMING AT INCREASING CHILLING TOLERANCE OF SORGHUM AS A FUTURE ENERGY CROP IN EUROPE Franciszek JANOWIAK 1 – Katarzyna KACZANOWSKA 1 – Agnieszka DOMAGAŁA 1 – Hai-Chun JING 2 – Wubishet A. BEKELE 3 – Birgit SAMANS 3 – Rod J. SNOWDON 3 1 The F. Górski Institute of Plant Physiology, Polish Academy of Sciences, Niezapominajek 21, 30-239 Kraków, Poland, Email: [email protected] 2 Institute of Botany, Chinese Academy of Sciences, Nanxincun 20, Haidian District, Beijing, China 3 Department of Plant Breeding, Justus Liebig University Giessen, 35392 Giessen, Germany Introduction In view of the effects of global climate changes on plant growth conditions in Europe, specific traits of sorghum (Sorghum bicolor (L.) Moench) make some of its cultivars (sweet sorghum) a promising candidate for a future bioenergy crop in this region. The main limiting factor seems to be low tolerance of sorghum seedlings to low temperature and possible seedling damage caused by cold spells occurring in April and May. However, thanks to very broad genetic variability of this trait in the sorghum species, it should be possible to breed new hybrids aiming at higher chilling tolerance (Bekele et al., 2014). The aim of the presented research was physiological characterization of mapping population (JE) consisting of 176 lines and their parental lines Ji-2731 Etian at the seedling stage under chilling and recovery conditions aiming at the identification of potential traits differentiating the genotypes’ responses to low temperature stress. Materials and Methods For each line three pots – each with 13 planted seeds – were set up in growth rooms. After growing for 21 days under 25/20°C (day/night) the seedlings at the 3rd leaf stage were exposed to five-day chilling (13/10°C, day/night) and then recovered for five days at control temperature. Directly before and during the chilling treatment as well as during recovery the following physiological parameters were measured for the 1st, 2nd, and 3rd leaf: photosynthetic efficiency by chlorophyll a fluorescence, stomata status by porometer, osmotic potential by osmometer, leaf ABA content by ELISA, chlorophyll content by SPAD 502 Plus, and chilling injury as electrolyte leakage. Results and Discussion The obtained results show enormous differences in physiological parameters between the lines of the mapping population (Figure 1). These differences are much bigger than the differences between the parental lines, with a number of lines scoring significantly above or below the parameter values of the parental lines. The differences among the lines are more pronounced during recovery than under chilling. The most pronounced genotypic differences are in the extent of osmotic adjustment under chilling and during recovery.

Figure 1. Photosynthetic efficiency measured as chlorophyll a fluorescence parameter effective quantum yield of PSII (YIELD) in JE

mapping population of sorghum. Horizontal lines show differences between parental lines under different treatments.


32

Conclusions Large differentiation among lines in JxE mapping population in the measured physiological traits, particularly in photosynthetic efficiency and osmotic adjustment, forms the basis for successful genetic selection of sorghum genotypes and breeding of new cultivars with increased tolerance to chilling stress, suitable for cultivation under European climatic conditions. Acknowledgements This research was supported by the National Centre for Research and Development (NCBR), Warsaw, Poland, in the frame of the program ERANET Bioenergy, Project No. ERA-NET-BIOENERGY-3/2013.

Reference Bekele W.A. – Fiedler K. – Shiringani A. – Schnaubelt D. – Windpassinger S. – Uptmoor R. – Friedt W. – Snowdon R.J.: 2014. Unravelling

the genetic complexity of sorghum seedling development under low-temperature conditions. Plant, Cell & Environment, 37:707–723.


33

EXPLORING RYE-SOYBEAN DOUBLE CROPPING SYSTEMS AS A CLIMATE CHANGE ADAPTATION STRATEGY R. BLOCH 1, 2 – J. BACHINGER 1 – M. RECKLING 1, 3 – J. SCHULER 2, 4 – P. ZANDER 4 1 Institute of Land Use Systems, Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374 Müncheberg,

Germany, Email: [email protected] 2 Eberswalde University for Sustainable Development, Germany 3 4Institute of Socio-Economics, Leibniz Centre for Agricultural Landscape Research (ZALF).

Introduction Climate change in the northeastern parts of Germany is expected to cause an increase in average annual temperatures, shifts in seasonality of precipitation (decreased in summer, increased in winter), accompanied by an increased evaporation. For agriculture this means a prolonged vegetation period which is characterized by a lack of water in the summer, but a higher proliferation in the winter months (Reyer et al., 2012). The potential climate change adaptation strategies were discussed and identified in a case study with two networks of farmers (organic, conventional) that followed a participatory learning and action process (PLA). As an outcome of this process, double cropping systems were one of the selected adaptation strategies. Double cropping systems lead to the possibility of harvesting two main crops in one season. Usually crops with higher temperature needs (e.g. maize, soybean) are cultivated after a winter crop which is harvested as an early fodder crop (e.g. winter rye, legume-grass-mixture). However, there is little experience on such systems in this region. The expected advantages of these systems under climate change are i) a more productive use of the longer vegetation period and ii) a risk distribution between the both crops. However, double cropping systems need more precipitation or additional irrigation for the second crop. Soybean is a new crop in this region profiting from the expected longer and warmer vegetation period which is also characterized by a later sowing date as compared to other regional crops. However, it has a high water demand and is difficult for weed control. Erosion risk can be high due to the need for mechanical weed control in organic farming. The central question is therefore how to implement soybean in conventional and organic farming systems. In the following the design of three new double cropping systems will be presented. Materials and Methods For this study, three different designs of double cropping systems for rye-soybean have been implemented at the ZALF research station in Müncheberg as a first explorative test of the practicability of such strategies. The experiments have been set up on long plots. Two low input systems with reduced tillage practices (knife roller for organic farming (B), zero tillage for conventional farming (C) were compared to a standard practice with ploughing (A) (see Table 1). Both low input systems are designed to avoid water losses from ploughing and to reduce the erosion risk through the establishment of a mulch layer. All systems were managed with and without irrigation. Table 1. Different types of double cropping systems (soybean after winter rye) with different treatments before soybean, cultivar: Merlin; 74

seeds m2

Cropping System Date Of Growth

Stop Rye

Use Of Winter Rye

Soy Bean Sowing

Date

Soy Bean Harvest

Date A) Standard Conventional

Tillage (plough) and herbicide application; no mulch

11.5.15 Fodder 13.5.15 05.10.15

B) Low Input 1 Organic

No-tillage (knife roller and direct seeding); no herbicide

application 28.5.15

Mulch For Weed

Suppression 28.5.15 22.10.15

C) Low Input 2 Conventional

No-tillage (glyphosate application and direct seeding)

29.4.15 Mulch 13.5.15 05.10.15

Results and Discussion In general, the extreme dry conditions in 2015 showed that irrigation is strongly needed for minimizing risks in soy bean production under the climatic conditions of North-East Germany. Overall, the systems results in the following yield performance (C) > (A) > (B). System (A) and especially at (B) the late harvest or termination of the winter rye caused a severe water deficit for the following soy bean. The highest yield of soybean was achieved with the system (B) where winter rye was killed much earlier, and water losses through ploughing were avoided. The knife roller practice (B) showed the lowest yield even with irrigation and a complete yield


34

loss without irrigation which was due to late sowing date and the high water consumption of the winter rye (water deficit). The weed suppression effect of the rolled-down winter rye, which looks rather high, was nevertheless sufficient from the viewpoint of organic farming. However, the standard practice provides a sufficient fodder yield with a sufficient soybean yield. Conclusions Given these preliminary results, conventional cash crop farmers would opt for the low input system (C). For organic farmers the observed strong weed suppression effect of the low input system (B) in addition with the saving of any tillage and mechanical weed control seems to be promising only at ground water influenced soils which will be further investigated with on-farm trails. Results from a second experimental year will be presented at the ESA conference in Sept 2016. Acknowledgements The work was financed by the German Federal Ministry of Education and Research; the Brandenburg Ministry of Sciences, Research and Cultural Affairs and the FACCE-ERA-NET+ project Climate-CAFE (PTJ-031A544).

Reference Reyer C. – Bachinger J. – Bloch R. – Hattermann F.F. – Ibisch P.L. – Kreft S. – Lasch P. – Lucht W. – Nowicki C. – Spathelf P. – Stock M.

– Welp M.: 2012. Climate change adaptation and sustainable regional development: a case study for the Federal State of Brandenburg, Germany. Regional Environmental Change, 12:523–542.


35

YIELD, GRAIN NUMBER AND THOUSAND GRAIN WEIGHT PLASTICITY IN MAIN STEM AND TILLER OF WHEAT Jaime HERRERA 1 – Daniel CALDERINI 2

1 Graduate School, Faculty of Agricultural Sciences, Universidad Austral de Chile, Email: [email protected] 2 Institute of Plant Production and Protection, Universidad Austral de Chile.

Introduction Researchers and studies predict problems for global food security. Growing populations, climatic conditions, transformation of food into energy and other situations, press the increase in food production in the future. For wheat, researchers predict increases in demand at a rate of around 1.7% per annum until 2050 (Reynolds et al., 2012). However, annual increases in production are less than 1.1% and in some regions the production is stagnant (Rosegrant & Cline, 2003). Increasing the yield can mitigate the gap between real and potential increase in production, and requires an increased understanding of the physiological components that determine the grain weight and grain number, to design new strategies for growing and scientific study. For example, studies postulate that the number of grains has reached its maximum potential (Quintero et al., 2014), and as an alternative is to increase yield through the grain weight. The objective of this study is to compare the yield, grain number and grain weight plasticity of wheat under five sowing conditions. Materials and Methods Two spring wheat cultivars (Bacanora and Kambara), were sown to field in EEAA (Universidad Austral de Chile), Valdivia, Chile, during 2012–13, 2013–14 and 2014–15 under three seeding rates (control 370 plants m-

2, low seed rate 44 plants m-2, and 370 plants m-2 with thinning at booting) and two temperature conditions (nocturnal temperature was increased by 6°C with greenhouse and electric heaters), 10 days before anthesis and 15 days after anthesis. The plots were arranged in a split-split plot design, with three replications. At harvest, one linear meter was collected and separated into main stems and tillers, then both groups were dissected into stem, grain, chaff and leaf. The weight was determined after drying at 60°C for 48 h. Yield, grain number, and thousand grain weight were calculated and analyzed by ANOVA. The plasticity was estimated through the quotient of the treatment variance compared to 10% and 90% of the yield. Results and Discussion Total yield had a plasticity between 0.1 and 0.4, and under the best crop conditions, the yield exceeds 12 t ha-1. The main stems had low plasticity but produced more than 6 t ha-1 under adverse conditions, while the low seeding rate produced less than 2 t ha-1. Tillers had a wide response of yield (2 to 12 t ha-1; Figure 1A). The grain number can increase under optimal condition, but to increase the plasticity decreased the grain number. Furthermore, grain number is supported by the main stems principally with the exception of low seeding rate (Figure 1B). The thousand grain weight had a wide range of plasticity, in poor crop condition the grain weight was stable (~40 g). However, grain weight could increase from 50 g to over 60 g. The wide response of the yield in tillers, show that wheat have a high potential for generation and development of tillers. This character could stimulate wheat plants, provided that the plant has the potential to sustain its maturity, to increase the yield. The difference in the grain number in main stem is zero a low seeding rate or little between treatments. The wheat plant prioritizes the development and maturity of the main stem, affecting variability and grain number in tillers. The grain weight under adverse conditions could indicate a minimum grain weight, but maximum weight could increase through different sowing conditions.

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Figure 1. Plasticity of the yield (A), grain number (B) and thousand grain weight (C) in total, main stem (Vp) and tillers (Mac) of wheat crop. The figure show the plasticity estimate respect to 10% and 90% the value of measurements. The black line estimate the linear

regression.


36

Conclusions A wide range of plasticity affect tillers and produce a high range of yield. The grain number was stable and sustained by main stem. The plasticity into grain weight can affect? Acknowledgements Acknowledgments to project FONDEF D09I / 1125 and CONICYT scholarship.

References Reynolds M. – Foulkes J. – Furbank R. – Griffiths S. – King J. – Murchie E. – Parry M. – Slafer G.: 2012. Achieving yield gains in wheat.

Plant, Cell & Environment, 35(10):1799–1823. http://doi.org/10.1111/j.1365-3040.2012.02588.x Rosegrant M.W. – Cline S.a.: 2003. Global food security: challenges and policies. Science, 302:1917.

http://doi.org/10.1126/science.1092958. Quintero et al.: 2014. Trade-off between grain weight and grain number and key traits for increasing potential grain weight in CIMCOG

population. In Proceedings of the 4th International Workshop of the Wheat Yield Consortium. CENEB, CIMMYT, Cd. Obregón, Sonora Mexico, 24–25 March 2014. Mexico. Eds M.P. Reynolds et al.


37

IRRIGATION MANAGEMENT STRATEGY BY POTATO CULTIVAR IN CASE OF RESTRICTED AVAILABLE W ATER VOLUME Sophie GENDRE 1 – Cyril HANNON 2 – Jean-Marc DEUMIER 3

1 Agronomy Research Unit, Arvalis-Institut du végétal, 6 chemin de la côte vieille 31450 Baziège, FRANCE, Email:

[email protected] 2 North France Unit, Arvalis-Institut du végétal, 2 chaussée Brunehaut, 80200 Estrées-Mons, FRANCE 3 Retired Agronomy Research Unit, Arvalis-Institut du végétal

Introduction Water is a vital resource subject to incessantly increasing demands. More and more watersheds in France are suffering from restrictions during irrigation period. Based on this idea, regulation is changing (Environmental code R211 article) in France. Instead of flow management, watersheds are managed with a volume divided between all the users. In France, 40% of potato production is irrigated (RGA, 2010) and irrigation is often required in the industrial production contracts. Irrigation is a central issue in potato production for France. Giving this, the research question asked was: if I have a restricted volume, how can I optimize its application? Materials and Methods To create an "optimal" strategy with an insufficient volume, potato physiology knowledge has been used. Potato yield is formed during two important main phases during the crop cycle: tuber formation and tuber filling. In case of lack of water, the idea is to adapt irrigation strategy (dose and frequency) to the outlet. This study is based on three (3) years of field trials (2012, 2013 and 2014). We tested different strategies on three potato cultivars: 'Lady Claire', 'Marabel' and 'Innovator'. Two reference strategies were set up: well irrigated and no irrigation. Then, three strategies were tested with a restricted volume: well irrigated and stop when defined volume is over (T2), beginning irrigation during tuber filling with dose and frequency adaptation (T3), beginning irrigation 65 days after planting with dose and frequency adaptation (T4). To adapt dose and frequency, we used the IRMA® model (Leroy et al., 1997) to calculate a frequency water need for potato in the region where trials took place. Then, to pilot irrigation, we used the ratio of available water volume to need volume 9 years out of 10 (IRMA® exits – cf. Figure 1).

Figure 1. Water consumption for 'Lady Claire'.

Results and Discussion The 3 years were rainy (Figure 2) during all the cycle (2012 and 2014) or only at the beginning (2013).


38

Figure 2. Climatic hydric deficit Saint Quentin (02, France) Figure 3. Total 2013 yield for the three cultivars.

Due to the rainfall in 2012 and 2014, experiments were not conclusive. Only the 2013 experiment allowed us to gain relevant data. In 2013, 'Marabel', with low biomass and early senescence, had a negative impact from water stress during biomass setting up and at the end of tuber filling. On the other hand, 'Lady Claire' and 'Innovator', with more biomass and later senescence, tolerated better a water stress during biomass setting up and at the end of tuber filling, showing that there is more irrigation flexibility for these cultivars. Conclusions This work is only a first step to answer the question of restricted irrigation volume. These conclusions need to be continued with more field trials coupled with digital experiments which would allow us to try different irrigation strategies and validate them in the field. References Lerbourg J.: 2012. Des surfaces irrigables en baisse à partir de 2000. Agreste Primeur, n°292. Leroy P. – Deumier J.M. – Jacquin C.: 1997. Un simulateur de l’organisation des chantiers d’irrigation. Perspectives Agricoles, 228:76–83. Gendre S.: 2015. Vers des stratégies et des conduites d’irrigation par variété. Pomme de terre Hebdo, n°1093.


39

GRAIN N-ACQUISITION AND GRAIN PROTEIN CONCENTRATION IN WHEAT AS AFFECTED BY FREE AIR CO2 ENRICHMENT AND N FERTILIZATION Markus DIER – Remy MANDERSCHEID – Jan SIKORA – Martin ERBS – Hans-Joachim WEIGEL Thünen-Institute of Biodiversity, Bundesallee 50, 38116 Braunschweig, Email: [email protected]

Introduction Elevated atmospheric CO2 ([eCO2]) is known to increase growth and grain yield of C3 crops and has repeatedly been shown to decrease cereal grain protein concentration (Taub et al., 2008; Myers et al., 2014) with potential consequences, i.a. for baking quality (Wieser et al., 2008). However, the mechanisms behind the CO2-induced changes in grain protein and the interaction with the N supply of the plants are still open. Here we investigated the effect of free air CO2 enrichment [eCO2] on key processes determining grain N acquisition and thus grain protein concentration in winter wheat. This included the remobilization of N originating from pre-anthesis uptake and N acquisition during grain filling. To test possible interactions between [eCO2] and N-fertilization, three NO3

- based fertilization regimes were applied comprising a deficiency, a standard and an excessive variant. Materials and Methods The experiment was conducted with winter wheat (cultivar 'Batis') on a field site at the Thünen-Institute in Braunschweig in 2014 and 2015. Treatments consisted of three plots (“rings”) with free air CO2 enrichment ([CO2] ~ 600 ppm) and three ambient plots ([CO2] ~ 390 ppm). NO3- based fertilization was carried out with calcium ammonium nitrate at 40, 180 and 320 kg ha-1 in 2014 and 35, 200 and 320 kg ha-1 in 2015. The fertilization variants were randomized within the plots. Irrigation was carried out to keep usable field capacity in the range of 60% and 90%. In both years, crop growth and plant N-concentration was measured during five destructive harvests. Results and Discussion In 2014 [eCO2] increased grain yield (Table.1) by 12, 16 and 19% in the N-deficiency, -standard and -excessive variant, respectively. Grain protein concentration of these variants were reduced by [eCO2] by 2, 6 and 4%. While the [eCO2] effect on grain yield was in accordance with the results of other studies (Weigel & Manderscheid, 2012), the [eCO2]-induced decrease of grain protein concentration was considerably smaller than in other investigations (e.g. Taub et al., 2008; Myers et al., 2014). Similar to grain yield, [eCO2] increased the amount of N remobilized from the vegetative organs to the grain (13%, 18% and 8% for the N-deficiency, -standard and -excessive variant, respectively) and its efficiency, the amount of N remobilized to the grain divided by the amount of N at anthesis (Figure 1). [eCO2] did not have a significant effect on N-acquisition during grain filling. However, it increased post-anthesis N-acquisition in the N-excess variant by 26%.

Table 1. Effect of [eCO2] and N fertilization on grain yield and grain protein concentration

N-fertilization Grain yield (g m-2) Grain protein concentration (%) Ambient CO2 Elevated CO2 Ambient CO2 Elevated CO2

40 kg ha-1 472 528 7.9 7.7 180 kg ha-1 817 949 10.6 10.0 320 kg ha-1 818 971 12.3 11.9

Figure 1. Effect of [eCO2] and N fertilization on N-remobilization (A), its efficiency (B) and N-uptake during grain filling (C). ANOVA results for the CO2 and N treatment: ns, not significant; (*), P<0.1; **, P<0.01; ***, P<0.001; data from 2014

Conclusions Our data from one growing season show that [eCO2] increased grain N-acquisition. This points to the possibility that under our field conditions grain protein concentration is only slightly affected by [eCO2].


40

Acknowledgements This project is founded by the German Research Foundation.

References Myers S.S. – Zanobetti A. – Kloog I. et al.: 2014. Increasing CO2 threatens human nutrition. Nature, 510:139–143. Taub D.R. – Miller B. – Allen H.: 2008. Effects of elevated CO2 on the protein concentration of food crops: a meta-analysis. Global Change

Biology, 14:565–575. Weigel H.J. – Manderscheid R.: 2012. Crop growth responses to free air CO2 enrichment and nitrogen fertilization: rotating barley, ryegrass,

sugar beet and wheat. European Journal of Agronomy, 43:97–107. Wieser H. – Manderscheid R. – Erbs M. et al.: 2008. Effects of elevated atmospheric CO2 concentrations on the quantitative protein

composition of wheat grain. Journal of Agricultural and Food Chemistry, 56:6531–6535.


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GROWTH AND GENOME ANALYSIS OF 98 BARLEY (HORDEUM VULGARE) GENOTYPES EXPOSED TO ELEVATED CO2 UNDER FIELD CONDITIONS Esther MITTERBAUER 1, 2 – Matthias ENDERS 2 – Jürgen BENDER 1 – Martin ERBS 1 – Antje HABEKUß 2 – Benjamin KILIAN 3 – Frank ORDON 2 – Hans-Joachim WEIGEL 1 1 Thünen Institute of Biodiversity, Bundesallee 50, 38116 Braunschweig, Germany, Email: [email protected] 2 Institute of Resistance Research and Stress Tolerance, Federal Research Centre for Cultivated Plants, Erwin Baur-Str. 27, 06484 Quedlinburg, Germany 3 Department of Genebank, Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK), Correns-Str. 3, 06466 Gatersleben, Germany Introduction The growth-enhancing effects of elevated atmospheric CO2 concentrations [eCO2] have repeatedly been shown to differ between crop species (Ainsworth & McGrath, 2010). However, only little is known about intraspecific variabilities in the response of crop species to eCO2 (Ziska et al., 1996; Manderscheid & Weigel, 1997) and the underlying genetics of different growth responses, which is particularly true for cereals. If there are differences in the eCO2 response between different genotypes, it may be of interest whether targeted exploitation of the additional CO2 resource by plant breeding might contribute to optimize future crop yields (Ziska et al., 2012). In a 2-year field experiment with 98 winter barley genotypes, we tested their growth responses to [eCO2] and conducted genome wide association studies (GWAS) in order to identify quantitative trait loci (QTL) involved in this response. Materials and Methods The experiments were carried out over two growing seasons in an experimental field site in Braunschweig, Germany in open-top chambers (OTC) under ambient (~ 400 ppm) and elevated (~ 700 ppm) CO2 concentrations. The 98 barley genotypes were cultivated in a greenhouse until the two-leaf stage and then transplanted into open field plots within an established winter barley canopy that served as a shelter crop. Each plot comprised 26 rows of different genotypes with 12 plants per genotype and 0.12 m row spacing. At crop maturity, total above-ground biomass (AGB) and culm, leaf and ear biomass were measured, and genome wide association studies (GWAS) were conducted in order to identify quantitative trait loci (QTL) involved in this response. Results and Discussion Averaged across all genotypes, eCO2 enhanced AGB by ca. 15%, and culm and ear biomass by 18 % and 15 %, respectively. However, these results were significant only for AGB and culm biomass. Leaf biomass did not respond to eCO2. The AGB response to eCO2 of the individual genotypes ranged from c. -36% to c. +95% compared to ambient CO2 (Fig.1), showing wide variability in growth responses. In the GWAS, 51 associations between markers and the relative changes (biomass under eCO2/ biomass under ambient air) of total aboveground biomass, ear biomass, culm biomass, and leaf biomass were detected, located across all chromosomes. Mapping and annotation of the sequences resulted in the identification of genes and proteins potentially responsible for biomass alterations under eCO2.

Figure 1. Relative changes (E/A = elevated / ambient CO2) of aboveground biomass of 98 barley genotypes grown under different CO2

concentrations (400 ppm CO2/ 700 ppm CO2). The names and origins of the five most positively or negatively responding genotypes are also given.


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Conclusions The present study, which for the first time tested a large set of barley genotypes under close-to-field-conditions to [eCO2], revealed a wide range of variability of growth responses of the genotypes that might be exploited by breeding using respective markers.

References Ainsworth E. – McGrath, J.M.: 2010. Direct effects of rising atmospheric carbon dioxide and ozone on crop yields. Advances in Global

Change Research, 37:109–130. Manderscheid R. – Weigel H.J.: 1997. Photosynthetic and growth responses of old and modern spring wheat cultivars to atmospheric CO2

enrichment. Agriculture, Ecosystems & Environment, 64:65–73. Ziska L.H. – Manalo P.A. – Ordonez R.A.: 1996. Intraspecific variation in the response of rice (Oryza sativa L.) to increased CO2 and

temperature: Growth and yield response of 17 cultivars. Journal of Experimental Botany, 47:1353–1359. Ziska L.H. – Bunce J.A. – Shimono H. et al.: 2012. Food security and climate change: on the potential to adapt global crop production by

active selection to rising atmospheric carbon dioxide. Proceedings of the Royal Society Biological Sciences, Series B, 279:4097–4105.


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EVALUATION OF THE EFFECTS OF PARTIAL ROOT ZONE DRYING IRRIGATION ON MAIZE YIELD AND YIELD COMPONENTS Mahmoud RAEINI-SARJAZ – Mohammad HOSSEINI – Ali SHAHNAZARI

Department of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran, PO-Box 578, Email: [email protected]

Introduction Climate change is a threat for crop production in arid and semiarid regions (Solomon et al., 2007). Water deficit is one of the most important problems in crop production throughout the world. Decline of water resources and fresh water reservoirs makes it hard to supply the total plant water requirements (Jackson et al., 2001). In arid and semiarid regions, farming could be possible if water could be available, then water deficit could be the main challenge in agriculture. In recent decades, the subject of regulated deficit (DI) irrigation and partial root drying irrigation (PRD) has gained attention for increasing water use efficiency (WUE) in many plant species (Sepaskhah & Ahmadi, 2010). The majority of Iran’s land is in arid and semiarid regions, where the application of water in agriculture should be highly efficient. So, in a changing climate the emphasis here is on elevating water use efficiency for strategic crops. Materials and Methods The effect of water deficit stress on maize single cross (SC704) (Zea mays L.) yield was evaluated on a silt-sandy soil during the 2012 farming season in Mahmoudabad, 5 km south of the Caspian shore, Mazandaran province, Iran. A randomized complete block design with four treatments of 1) Full irrigation (FI), as control treatment, which plants received 100% of field capacity (FC); 2) Partial root drying irrigation (PRD), where plants received 80% of FC (PRD80); 3) Partial root drying irrigation, where plants received 50% of FC (PRD50) and 4) Deficit irrigation, where plants received 50% of FC (DI50). Data were analyzed using SAS software and SNK post-hoc test was employed to compare treatment means. Results and Discussion The effects of water treatment on maize yield and water use efficiency (WUE) were highly significant (P<0.001). The PRD80 treatment consumed 25% less water than the FI (control) treatment during its growing season, while its yield was not significantly different from FI treatment (Table 1). The yields of the high water stressed treatments, PRD50 and DI50, were significantly lower than those of FI and PRD80. The mean water use efficiency (WUE) of PRD80 was significantly different from that of other treatments and the lowest value was achieved in the well-watered treatment (FI) (Table 1). The substantial compensatory effect of partial root drying zone irrigation on yield and WUE indicates that ABA and stomatal regulation in favouring of photosynthesis to transpiration could be the key (Hu et al., 2008).

Table 1. Mean effects of water shortage treatments on yield and yield components Treatments Received

water (mm) Yield (kg ha-1)

WUE (kg ha-1

mm-1)

Leaf length (cm) Ear length (cm)

Plant height (cm)

Leaf dew deposition (g m-2)*

FI 630 8.4a 13.3c 125a 25.2a 290a 117

PRD80 500 7.4a 14.8b 117a 24.6a 270ab 119

PRD50 320 5.7b 17.8a 112b 21.6b 242bc 117

DI50 320 4.7b 14.7b 100c 20.5b 230c 121

The effects of water treatments on leaf length, ear length and plant height were statistically significant (P<0.001). Mean leaf length and ear length of FI and PRD80 were significantly higher than those of water-stressed treatments of PRD50 and DI50. Mean stalk height of well-watered (FI) and moderately watered treatments (PRD80) was also significantly higher than that of highly water stressed treatment of DI50 (Table 1). It is well known that the more the necessary agronomic inputs are provided, the higher the leaf area index (LAI) and the more yield relative to stressed plants. In PRD irrigation treatment, although water consumption decreased, it did not translate to a linear decline of LAI and grain yield. It could be due to the role of ABA elevation and stomatal conductance regulation. During calm, clear sky nights, dew deposited on leaves and finally dropped onto soil close to the corn stem. The mean amount of deposited dew on leaf was similar on all water treatments.


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Conclusions Partial root zone drying irrigation, in comparison to conventional deficit irrigation, could benefit maize growth and development. This benefits could be due to efficient use of agronomic inputs, such as nutrition and water. In harsh climates of arid and semiarid regions due to low water availability and higher chance of drought, PRD method could save water with little reduction in yield. References Hu T. – Kang S. – Yuan L. – Zhang F. – Li Z.: 2008. Effects of partial root-zone irrigation on growth and development of maize root

system. Acta Ecologica Sinica, 28:6180–6188. Jackson R.B. – Carpenter S.R. – Dahm C.N. – McKnight D.M. – Naiman R.J. – Postel S.L. – Running S.W.: 2001. Water in a Changing

World. Issues in Ecology, 9:1–18. Sepaskhah A.R. – Ahmadi S.H.: 2010. A review on partial root-zone drying irrigation. International Journal of Plant Production, 4(4):241–

258. Solomon S. – Qin D. – Manning M. – Chen Z. – Marquis M. – Avery K. et al.: 2007. Climate Change 2007: The Physical Science Basis.

Contributions of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.


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HEAT AND DROUGHT TOLERANCE EVALUATION IN POTATO (SOLANUM TUBEROSUM L.) CULTIVARS Krystyna RYKACZEWSKA

Department of Potato Agronomy, Plant Breeding and Acclimatization Institute, Div. Jadwisin, 05-140 Serock, Email: [email protected] Introduction Heat stress due to increased temperature is an agricultural problem in many areas in the world. In potato production, the adverse effects of this stress can be mitigated by developing cultivars with improved thermotolerance using various genetic approaches for this task, however, a thorough understanding of physiological responses of plants to high temperature is imperative. Potato is characterized by specific temperature requirements. The limits and optimal values for the growth of the above-ground part of the plant and for the tubers are different. At a temperature higher than optimum, a reduction or complete inhibition of tuber formation and the intensified development of aboveground part of plants take place (Monneveux, 2014; Rykaczewska, 2015). The aim of the study was to assess the response of chosen early potato cultivars to high temperature and drought during the different stages of plant growth in the experiment under controlled and field conditions. Materials and Methods In 2014 a pot experiment was carried with following cultivars: 'Lord', 'Miłek' (very early), 'Gwiazda', 'Hubal' (early), 'Tetyda', and 'Oberon' (medium early). The impact of high temperature day/ night 38°C/ 25°C was tested in four periods: I – June 1–15, II–June 16–30, III–July 1–15 and IV–July 16–30. In these periods, half of the plants were watered to a level close to optimal (Favourable Soil Moisture), while the other half remained without irrigation (Soil Drought). The control combination consisted of potato plants grown throughout the whole season under conditions close to optimal. Measures of tolerance of the potato cultivars to high temperature during the growing season were a decrease in yield, number of tubers per plant and the presence of physiological defects in the tubers in comparison with the control combination. In 2015, a field experiment was carried out at Jadwisin (52°28’44”N, 21°02’38”E) with cultivars registered recently in the Polish List of Varieties: 'Viviana' (very early), 'Bohun' (early), 'Bogatka', 'Honorata', 'Laskara', 'Lavinia', 'Malaga', 'Otolia' (medium early). The field trial was set up in a randomized complete block design with three replicates. Planting took place on April 28th on a poor clayey sand of a good agricultural suitability. Weather conditions were monitored using a Campbell Weather Station (Campbell Scientific Inc.). During the growing season, the plant phenological stages were observed. Directly after harvest, tubers with physiological defects including tubers chronologically younger (immature), tubers with multidirectional deformations and with gemmations and tuber size were determined. The results of the experiments were analyzed with ANOVA using a model of statistics program in Statistica. Means were separated with Tukey’s test at 5% P-value. Results and Discussion In 2014 significant impacts of the tested factors on the height of plants, chlorophyll a fluorescence in leaves, yield, number of tubers, mass of individual tuber, tubers deformations and immature tubers were found. It was demonstrated, however, that potato cultivars’ response to high temperature during the growing season is dependent on the growth stage when the stress occurs. The earlier it occurs, the more negative its impact on the growth and total yield. Among the tested cultivars, ‘Tetyda’ was the most tolerant to high temperature acting on the plants during the growing season. This cultivar was characterized by a relatively small decrease in the total yield and tuber size in relation to control and by a low level of tuber deformations. These studies were continued in the following year and the finalization of the results is in progress. In 2015, thermal conditions in the experimental field were similar to those that were determined in pot experiment (Table 1).

Table 1. Meteorological factors during growing season in the year of study Meteorological factor

Month April May June July August September

Total rainfall in mm 27.8 39.5 15.4 62.6 8.6 36.6 Mean air temperature in °C 8.3 12.9 17.5 19.6 22.5 15.1 Maximum air temperature in °C 20.4 23.5 28.4 33.9 35.2 29.0 Number of days with max. temp. > 25°C 0 1 12 16 24 3 Number of days with max. temp.> 30°C 0 0 0 7 16 1


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The high maximum temperatures during the day from June to August and higher level of rainfall in July contributed to secondary vegetation lasting throughout August until mid-September. Summing up all the physiological defects of tubers and the evaluation of their share in the total yield allowed to assess the tolerance of tested cultivars to heat and drought during the growing season. It was found that most tolerant cultivars were 'Otolia', 'Honorata' and 'Bohun'. Conclusions The indication of tolerant cultivars allow their selection for cultivation in regions with higher temperatures and allow their use in breeding programmes of new genotypes. Acknowledgements The research was partly funded by the Ministry of Agriculture and Rural Development.

References Monneveux P. – Ramírez D.A. – Awais Khan M. – Raymundo R.M. – Loayza H. – Quiroz R.: 2014. Drought and heat tolerance evaluation

in potato (Solanum tuberosum L.). Potato Research, 57:225–247 Rykaczewska K.: 2015 The effect of high temperature occurring in subsequent stages of plant development on potato yield and tuber

physiological defects. American Journal of Potato Research, 92:339–349.


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OPTIMAL SEEDING DATES AND SEEDING RATES OF WINTER WHEAT IN EASTERN CANADA FRANCIS ALLARD

1 – ANNE VANASSE

1 – DENIS PAGEAU

2 – GILLES TREMBLAY

3 – JULIE DURAND

4 –

ÉLIZABETH VACHON 5

1 Département de phytologie, Université Laval, Québec, QC, Canada G1V 0A6 e-mail: [email protected] 2 Agriculture et Agroalimentaire Canada, Normandin, QC, Canada G8M 4K3 3 Centre de recherche sur les grains inc. (CÉROM), Saint-Mathieu-de-Beloeil, QC, Canada G1P 3W8 4 Semican inc., Princeville, QC, Canada G6L 4K7 5 Moulins de Soulanges, Saint-Polycarpe, QC, Canada J0P 1X0

Introduction Challenging climate of Canada is a major problem for the winter survival of winter wheat. The yield of winter wheat could allow a gain of 25% compared to spring wheat (RGCQ, 2012). The use of cover crops, like winter wheat, is also recognized to improve soil structure and fertility (Biederbeck, 1998). A study conducted by Hall (2014) indicated that there was a significant effect of seeding rate on winter wheat yield. However, the interaction between planting dates and seeding rate was significant. Because there are many contrasting production areas and climates in eastern Canada, it is important to identify the best seeding date and seeding rate adapted to these locations. The purpose of this study is to determine the optimal seeding dates and seeding rates of winter wheat to improve winter survival and productivity in growing conditions in eastern Canada. Materials and Methods Trials were conducted from 2014 to 2016. Three winter wheat cultivars were evaluated at four seeding dates (early September – mid September- late September -mid-October) and four seeding rates (250-350-450-550 grains m-2). The trials were seeded at four locations with contrasting climates. The winter survival, yield and yield components (number of spikes m-2 and 1000 grain weight) were determined. Results and Discussion The winter survival was excellent on two sites in 2015. At the site located near Quebec City (2800 degree-days > 0ºC), the highest yields were obtained with early and mid-September seeding date (average of 6.5 t ha-1, Figure 1). Those high yields could be explained by high spike density and 1000-grain weight with these planting dates (Table 1). Seeding rates had no effect on grain yield for the earlier seeding dates, low seeding rates tend to tiller more, providing a high number of spikes m-2 and therefore makes no difference of yield regarding the high seeding rate (Table 2). For the latest seeding date (mid-October), the highest seeding rate (550 grains m-2) produced the best yield (Figure 1.). At the site located near Montreal (3270 degree-days > 0ºC), seeding dates from mid-September to late September gave the highest yields (average of 6.9 t ha-1), considering high spike density and 1000-grain weight (Table 1). There was no effect of the seeding rates on grain yield; spikes per plant decreasing with higher seeding rates (Table 2).

Figure 1. Effect of seeding dates and seeding rates on yield of winter wheat (Quebec, 2015).


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Table 1. Winter wheat yield components according to seeding dates (Quebec and Montreal, 2015)

Table 2. Spikes per plant at Quebec and Montreal sites (2015)

Spikes per plant

Seeding rates (grains m‐2) Quebec Montreal

250 1.89 a 2.14 a

350 1.54 b 1.56 b

450 1.20 c 1.35 c

550 1.04 d 1.17 d

Conclusions The results obtained at these four locations over two years indicated that early (Quebec site) and intermediate (Montreal site) seeding dates gave the highest grain yield. Seeding rates seem to have a little impact on winter wheat productivity. References RGCQ. : 2012. Résultats 2012 et recommandations 2013 des RGCQ, pp.41–43. Biederbeck et al.: 1998. Soil quality attributes as influenced by annual legumes used as green manure. Soil Biology and Biochemistry, 30(8–9):1177–1185. Hall B.: 2014. Eastern Canada: Winter wheat seeding rate × date interactions. Crops and Soils, 47(4):16–17.

Seeding dates Number of spikes m-2 1000 grain weight (g) Number of spikes m-2 1000 grain weight (g)Early September 576 ab 41.9 a 552 45.2 aMid September 602 a 41.4 a 583 45.0 aLate September 525 b 40.0 b 592 44.8 bMid October 461c 38.1 c 587 42.4 c

Quebec Montreal


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DO WE NEED HEAT STRESS TOLERANCE IN FUTURE GENOTYPES OF WHEAT? Henry M. BARBER 1 – Martin LUKAC 1 – Mike J. GOODING 2

– Mikhail A. SEMENOV 3

1 School of Agriculture, Policy and Development, University of Reading, Berkshire, RG6 6AR, UK, Email: [email protected] 2 Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Ceredigion SY23 3FL, UK 3 Computational and Systems Biology Department, Rothamsted Research, Harpenden, Herts AL5 2JQ, UK

Introduction The response of wheat to heat waves during reproductive phases of growth needs further clarification and these responses need to be better integrated into crop simulation models, such as the crop model ‘SIRIUS’ (Semenov et al., 2014). Wheat is well known to be susceptible to heat stress around booting and anthesis (Barnabas et al., 2008), although more research is required to elucidate specific growth stage effects (Barber et al., 2015). This project aims to improve current knowledge through the combination of phenotypic and genotypic analysis with crop modelling. The project revolves around two cultivars of wheat, 'Savannah' (Rht-D1b + 1BL/1RS) and 'Renesansa' (Rht8c +Ppd-D1a) and 62 Doubled Haploid lines from the two parents. 'Savannah' has high yield potential in North West Europe with low bread making quality, recommended for the UK in 1998. 'Renesansa', listed in 1995, had high yield and bread making quality in southern Europe. Information gained from phenotypic work, controlled environment experiments and crop modelling will serve as a basis for searching for beneficial traits suitable for future European climates. Materials and Methods A replicated field phenotyping study to enable calibration of the cultivars to the crop model, ‘SIRIUS’ took place, along with heat stress experiments on pot-grown plants exposed to heat around the reproductive phases of growth in controlled environments. Results and Discussion Both cultivars were found to be susceptible to heat stress at booting, 'Savannah' particularly so. 'Renesansa' was more susceptible around flowering; heat stress led to significant decreases in grain number per plant. Yield compensation through increased grain size was variable and not sufficient to offset yield losses. Double Gaussian models fitted to the heat stress responses displayed in Figure 1 show peak grain losses occurred 18 days and 2 days before anthesis. Data from 3 years of field phenotyping (Barber et al., 2015) calibrated the lines to the model ‘SIRIUS’, with the results in Figure 1 used as a basis for the heat stress response. CMIP5-based climate scenarios were generated across four European sites. Simulation runs of the Sirius model with newly calibrated parameters showed that yield losses due to heat stress are likely to increase three fold in susceptible European varieties in central European locations, such as Debrecen by 2090. However, even by 2090, heat stress is unlikely to cause significant yield losses in Northern European sites like Hamburg in Germany and Rothamsted in the UK.


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Figure 1. Effects of increasing day temperature from 20oC to 35oC in successive 1-day transfers to controlled environment cabinets on yield

components per pot (four whole plants per pot) of two cultivars of winter wheat.

Conclusions Research presented here highlights the issue of heat tolerance, as an essential trait needed in future wheat breeding strategies. The ability to maintain high yield potential under future climates is vital in order to feed increasing human populations. Acknowledgements The BBSRC and the University of Reading support this work.

References Barber H.M. – Carney J. – Alghabari F. – Gooding M.J.: 2015. Decimal growth stages for precision wheat production in changing

environments? Annals of Applied Biology, 166:355–371. Barnabas B. – Jaeger K. – Feher A.: 2008. The effect of drought and heat stress on reproductive processes in cereals. Plant, Cell and

Environment, 31:11–38. Semenov M.A. – Stratonovitch P. – Alghabari F. – Gooding M.J.: 2014. Adapting wheat in Europe for climate change. Journal of Cereal

Science, 59:245–256.


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DEEP ROOTING IN A WHEAT DOUBLED HAPLOID POPULATION WITH INTROGRESSION FROM WILD EMMER Christina K. CLARKE 1 – Peter J. GREGORY 1 – Mike GOODING 2

– Martin LUKAC 1

1 School of Agriculture, Policy and Development, University of Reading, Berkshire RG6 6AR, UK [email protected] 2 Institute of Biological, Environmental and Rural Sciences, Aberystwyth University Introduction Wheat root systems may not be optimal for the acquisition of resources, especially to access subsoil water, due to excessive root growth in surface layers and inadequate soil exploration at depth. In modern wheat crops, root length density (RLD) exceeds that needed for uptake of water and nitrate, with common values varying between 3 and 6 cm cm-3 in the upper 30 cm with less than 1 cm cm-3 below 40 cm (Hoad et al., 2004). The investment in fine roots at depth and a smaller abundance of surface roots would give a greater economic return in terms of accessing more water and nitrogen (King et al., 2003). The diversity of root length at depth within a doubled haploid (DH) population of 'Shamrock' × 'Shango' has been studied in the field and in controlled environment conditions. 'Shamrock' has recent introgression from wild emmer (Triticum dicoccoides) and the population is known to segregate for some of the traits that derive from this introgression. This includes a Viridescent trait which causes delayed senescence and a longer grain filling period, due to a stay green effect. 'Shamrock' has been shown to have significantly greater and repeatable RLD at depth compared to other elite wheat cultivars (Ford et al., 2006). Materials and Methods Field root cores were collected from parent plots at anthesis 2014 and for the whole DH population in 2015 using a window sampler 73 mm in diameter. The field trial was a two randomised block design with parent plots replicated four times. Canopy data was collected for the population over the two field seasons to measure thermal time to senescence and photosynthetically active radiation (PAR) interception. Selected lines were grown in rhizotrons (100 cm × 30 cm × 5 cm) laid at a 30° angle in a glasshouse in a three-replicate randomised block design. Soil was packed to a bulk density of 1.2 g cm-3 and roots were traced twice a week on acetate sheets before the experiment finished at GS29. Roots were sieved from the soil to calculate actual RLD and root dry weight (RDW). A genetic linkage map of the population has been produced using SNP markers which allow the identification of quantitative trait loci (QTL). Results and Discussion 'Shamrock' had significantly greater RLD at 60 and 70 cm depths than 'Shango' in 2014. The DH lines differed significantly for RLD, RDW and root diameter at depths between 50 and 80 cm in 2015 (Fig 1). Lines with the Viridescent trait had delayed senescence and intercepted more PAR over the season. QTL were found explaining a significant proportion of the phenotypic variation for rooting and canopy traits. Eight QTL were found on five linkage groups for RLD, RDW and root diameter, which explained between 7.8 and 14.1% of the phenotypic variation. Co-located QTL on chromosome 2B indicates the relationship between the Viridescent trait and the stay green and increased PAR interception charecteristics. DH lines grown in rhizoboxes differed for the Viridescent trait and lines with the trait exhibited significantly greater RLD between 40 and 80 cm depths (Fig 2.). Wild emmer was also grown in the rhizotrons and had singificantly greater RLD in the 20–60 cm layers and a significantly lower root diameter, which was also exhibited in the lines with the Viridescent trait.

Figure 1. Histogram showing distribution of RLD in the DH haploid population grown in the field.

Figure 2. RLD of selected lines with () or without the Viridescent trait () grown in rhizotrons. Error

bars are + and – S.E.D.


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Conclusions The diversity in rooting traits within this DH haploid population provides a valuable resource to investigate genotypic and phenotypic influences of rooting at depth in a wheat crop, which can improve subsoil water and nitrogen resource acquisition to sustain and improve yields in a future climate. Acknowledgements The author acknowledges AHDB and the University of Reading for funding this project, JIC for providing the DH population, ADAS for providing equipment, the technician team at Reading Crops Research Unit and Bristol University for collaboration with genetics work.

References Ford K.E. – Gregory P.J. – Gooding M.J. – S. Pepler S.: 2006. Genotype and fungicide effects on late-season root growth of winter wheat.

Plant Soil, 284:33–44. Hoad S.P. – Russel G. – Kettlewell P.S. – M. Belshaw M.: 2004. Root system management in winter wheat: practices to increase water and

nitrogen use. HGCA Project Report No. 351. King J. et al.: 2003. Modelling cereal root systems for water and nitrogen capture: Towards an economic optimum. Annals of Botany,

London, 91:383–390.


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BUILDING RESILIENCE TO CLIMATE CHANGE THROUGH THE SHARP TOOL: LESSONS LEARNED THROUGH BEST PRACTICES IN CLIMATE ADAPTATION IN SUB-SAHARAN AFRICA John CHOPTIANY 1 – Maria HERNANDEZ LAGANA 2 – David COLOZZA 2 – Benjamin GRAUB 2

1 Food and Agriculture Organization of the United Nations (FAO), Rome. Viale delle Terme di Caracalla 00153 Rome, Italy, (+39) 06

57051; Email: [email protected] 2 Food and Agriculture Organization of the United Nations (FAO)

Introduction The Self-evaluation and Holistic Assessment of climate Resilience for farmers and Pastoralists (SHARP) tool, developed by FAO in 2013, is being used to better understand the resilience levels and corresponding practices used by farmers and pastoralists and was developed in response to a lack of existing effective tools (Dixon & Stringer, 2015; ODI, 2015). The tool works through an offline Android-based application to let smallholder producers self-assess (through a facilitator if needed) their level of resilience at the household level in a participatory manner. The tool holistically assesses indicators that span governance, environment, social, economic and general agricultural practices using agro-ecosystem indicators developed by Cabell & Oelofse (2012). SHARP is being used in eight countries in sub-Saharan Africa, which has enabled geographical, climatic and agricultural practices to be compared to determine their impacts on resilience levels of small agricultural producers. Surveys collected at different points in time have been gathered, from which initial changes over time are being drawn. We present both the challenges associated with developing and implementing the SHARP tool in diverse environments and the insights gained. Results and Discussion We have found that different agricultural practices and socioeconomic contexts have varying impacts on resilience and that there are no easy solutions (see Table 1). Overall, it has been observed that smallholder producers in selected communities from Angola and South Sudan present low levels of climate resilience and are not well prepared to face extreme weather events. Low resilience levels could be explained by the scarcity of knowledge on agricultural practices, both for crops and livestock, that may limit their ability to have a more diversified system, enhance productivity and build resilience. Besides, the precarious financial status (e.g. low saving rates, low access to markets), together with the low diversification of income sources limit people’s capacity to diversity their risks, by investing in farm and nonfarm-related assets and activities, making them more vulnerable to unexpected climate shocks and making it harder to cope with those type of events. In general, a strong social component has been noticed in most communities. This suggests that relatively high levels of trust and cooperation, as well as active participation of members of household in the decision-making process and in-farm activities have been factors positively contributing to individual’s and systems’ resilience.


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Table 2. Mean resilience levels for the four subset of indicators. Means and standard deviations are shown for the three components of questions: academic scoring, self-assessed adequacy and self-assessed importance.

Que

stio

n

Section/Variable Surveys

Academic scoring

Self-assessment of adequacy

Self-assessment of importance

Final mean level of resilience

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Production

2 Household 249 6.26 1.25 4.40 2.41 1.82 2.36 12.48 3.16

3 Production types 249 5.61 3.00 3.05 2.00 2.28 2.84 10.94 3.99 4 Aquaculture - - - - - - - - - 5 Crops 249 5.15 3.11 3.08 2.25 1.89 2.51 9.54 5.25 6 Livestock practices 248 1.83 2.88 3.49 2.50 1.39 2.29 3.46 5.52 7 Seed/breed sources 244 5.48 3.56 3.46 2.14 1.68 2.32 10.43 4.59 8 Trees and agroforestry 248 3.51 2.38 3.68 2.44 2.19 2.89 9.30 3.98 9 Record keeping 248 3.30 3.26 1.70 2.54 2.70 3.08 7.71 5.28

10 Access to climate change, cropping practices, and

meteorological data 248 4.31 2.89 2.33 1.99 2.84 2.79 9.48 4.04

11 Animal disease control practices 83 5.83 3.00 3.83 3.06 1.49 2.15 11.13 4.88 12 Synthetic pesticide use 246 5.57 1.71 1.15 2.19 3.83 3.78 10.45 4.37

Environment 13 Water access 248 4.03 2.88 4.08 2.61 1.10 2.15 9.21 4.63 14 Land access 248 6.01 2.46 3.45 1.97 2.03 2.77 11.47 4.03 15 Land management practices 248 5.03 4.45 2.90 2.53 2.03 2.46 9.90 5.96 16 Leguminous plants 248 2.94 4.52 1.87 2.61 2.87 3.06 7.62 6.16 17 Fertilizers 220 3.75 1.81 1.66 1.94 3.04 2.74 8.35 3.78

Social 18 Group membership 248 1.69 2.91 2.62 2.58 3.86 3.50 8.12 4.48 19 Meals 244 5.86 1.73 3.41 2.16 1.40 2.07 10.56 3.26 20 Disturbances - - - - - - - - - 21 Veterinary access 83 4.22 4.24 2.44 2.56 2.23 2.73 8.89 7.00 23 Trust and cooperation 247 6.66 3.33 4.07 2.53 1.71 2.28 12.44 5.03 24 Household decision-making 245 6.29 2.89 5.10 2.25 1.89 2.28 13.17 4.51

Economic 22 Market information access 245 4.29 3.79 2.74 2.37 2.38 2.93 9.38 5.27 25 Income sources 240 4.31 4.27 3.32 2.19 1.90 2.44 9.41 4.96 26 Nonfarm income generating activities 248 3.16 3.51 2.06 2.44 2.68 3.33 7.89 4.82 27 Savings 248 1.17 2.99 1.64 2.62 1.92 2.79 4.72 5.26

Conclusions Internal and external remedial actions should be promoted to improve resilience levels, taking into consideration people’s needs, priorities and constraints. Action should be made at the community level, where producers can discuss and learn about best crop and livestock management practices. The selection of practices should be site-specific, focusing on (native) crops and livestock species and local climate conditions and threats. The structure of groups, (e.g. cooperatives, farmers’ organizations), could be a strategy to increase access to local markets, while constructing stronger social networks. External actions may also be needed to reduce the vulnerability of people to climate change. These may include the promotion of micro financing programmes or the use of project funding to offer smallholder producers more resources to invest in their systems (e.g. irrigation systems, paddocks, energy-saving stoves), as well as non-farm activities to diversify their income sources. Dissemination of climate and weather information as well as market prices should be strengthened by meteorological offices to increase the community’s level of knowledge on markets and weather impacts.

References Cabell J.F. – Oelofse M.: 2012. An Indicator Framework for Assessing Agroecosystem Resilience. Ecology and Society, 17. Dixon J.L. – Stringer L.C.: 2014. Towards a theoretical grounding of assessment tools to strengthen resilience of smallholder farming

systems. Resources, 4(1):128–154; Doi: 10.3390/resources4010128 ODI.: 2015. A comparative overview of resilience measurement frameworks: analysing indicators and approaches. Eds E.L.F. Schipper and

L. Langston. Working paper 422. www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/9754.pdf.


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DISTRIBUTION OF SOIL CARBON IN ARABLE SOILS IN SCOTLAND UNDER DIFFERENT TILLAGE PRACTICES J.L. BROWN 1 – R. STOBART 2 – P.D. HALLETT 3 – N.L. MORRIS 2 – T.S. GEORGE 1 – A.C. NEWTON 1

–T.A. VALENTINE 1 – B. McKENZIE 1 1 The James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK, Email: [email protected] 2 NIAB, Morley, Wymondham, Norfolk NR18 9DF, UK 3 Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen AB24 3UU, UK

Introduction Mouldboard ploughing affects soil bulk density, aeration, drainage, thermal regimes, and soil microorganisms (Sun et al., 2011). Tillage breaks up plant residues, may degrade previously protected organic matter and potentially release CO2 to the atmosphere (Farina et al., 2011). Reduced tillage through either no till or non-inversion tillage to shallower depths (minimum till) may be a climate change mitigating strategy due to its potential to restore soil organic carbon (Lal, 2015) and decrease fuel used during tillage. However, the positive effects of reduced tillage may have been overestimated due to a sampling bias towards the soil surface. Other sources of bias are the reporting of soil carbon as a concentration without taking into account differences in soil bulk density, the effect of stone content and the length of time the management practices have been in place (Angers & Eriksen-Hamel, 2008). The objective of this study was to assess the impacts of four tillage treatments on the content and depth distribution of carbon in soils from replicated long-term experimental arable plots in Scotland. Materials and Methods A split-plot design experiment was planted with barley at The James Hutton Institute, Dundee, Scotland in 2003. The treatments were mouldboard ploughing (P) to 20 cm; no till (N); minimum tillage (M) to a depth of 7 cm; and compaction (C) by ploughing to 20 cm and driving the tractor over with a raised plough. Soil samples were taken in August 2013 at five depths intervals to a depth of 60 cm. Total carbon was determined on ball-milled soil. Statistical analyses were performed using REML and least significant differences (LSD) at P<0.05. Results and Discussion The maximum BD occurred below the plough depth (25–30 cm) in the C and P treatments but was not significantly different between them. Correcting for stone content produced significant differences at the plough depth (higher BD in C than in M and N treatments). Total soil carbon content was significantly different for treatment (P=0.006) and for depth (P<0.001) but not for their interaction (P=0.174). LSD showed that carbon content was significantly greater in the P treatment than in all other treatments and in the plough depth than all other depths. Although the interaction between treatment and depth was not statistically significant, mean C values were higher at this depth in the P and the C treatments (Figure 1) as reported in other studies (Angers & Eriksen-Hamel, 2008; Dolan et al., 2006).

Figure 1. Carbon content in volume at different depths and tillage treatments.

The similarities in carbon content between P and M and N treatments when the whole profile is considered are consistent with other studies but the pattern of greater carbon in the soil surface for reduced tillage was not seen (Sun et al., 2011; West & Post, 2002).


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Conclusions Carbon content was greater in the conventional plough treatment than all other treatments and at the plough depth. Acknowledgements Support is acknowledged from: AHDB (Project 3786: Platforms to test and demonstrate sustainable soil management: integration of major UK field experiments,) and The Scottish Government Rural and Environment Science and Analytical Services (RESAS) (Sustainable Agriculture - Plants programme).

References Angers D.A. – Eriksen-Hamel N.S.: 2008. Full-inversion tillage and organic carbon distribution in soil profiles: A meta-analysis. Soil

Science Society of America Journal, 72:1370–1374. Dolan M.S. – Clapp C.E. – Allmaras R.R. – Baker J.M. – Molina J.A.E.: 2006. Soil organic carbon and nitrogen in a Minnesota soil as

related to tillage, residue and nitrogen management. Soil & Tillage Research, 89:221–231. Farina R. – Seddaiu G. – Orsini R. – Steglich E. – Roggero P.P. – Francaviglia, R.: 2011. Soil carbon dynamics and crop productivity as

influenced by climate change in a rainfed cereal system under contrasting tillage using EPIC. Soil & Tillage Research, 112:36–46. Lal R.: 2015. Sequestering carbon and increasing productivity by conservation agriculture. Journal of Soil & Water Conservation, 70: 55A–

62A. Sun B. – Hallett P. – Caul S. – Daniell T.J. – Hopkins D.W.: 2011. Distribution of soil carbon and microbial biomass in arable soils under

different tillage regimes. Plant & Soil, 338:17–25. West T.O. – Post W.M.: 2002. Soil organic carbon sequestration rates by tillage and crop rotation: A global data analysis. Soil Science

Society of America Journal, 66:1930–1946.


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SPRING BARLEY SPLIT ROOT SYSTEMS FOR EVALUATING RHIZOSPHERE SOIL CARBON AND NITROGEN CYCLING UNDER DIFFERENT FERTILISERS J.P. PARKER 1 – A. BASLEY 2 – J.M. CLOY 1 1 Crops and Soil Systems, Scotland’s Rural College, Peter Wilson Building, King’s Buildings, West Mains Road, Edinburgh EH9 3JG,

Email: [email protected] 2 Wardell Armstrong LLP, City Quadrant, 11 Waterloo Square, Newcastle Upon Tyne NE1 4DP, UK Introduction The use of nitrogen (N) fertilisers in agricultural systems can lead to increased greenhouse gas (GHG) emissions, N runoff and leaching from soils. This research used a novel split root design to study plant-soil-rhizosphere interactions in high vs. low N input systems. To test whether the soil rhizosphere facilitated N utilisation by spring barley (Hordeum vulgare), differences in carbon (C) and N cycling between split root barley systems receiving inorganic and organic fertiliser treatments on one side of the split root system (no fertiliser on the other side) were investigated. Emissions of nitrous oxide (N2O), carbon dioxide (CO2) and methane (CH4) from the soils were determined for both sides of the split root systems alongside plant productivity indicators and soil nutrient contents. Materials and Methods Spring barley seeds were germinated for 4 days. Two main roots were divided and fixed in place on either side of a plastic divider. The columns were packed with arable soil: sandy loam, pH 6, low N content, bulk density ~ 1.1 g cm-3. Sewage sludge (SS), cattle slurry (CS), and ammonium nitrate (AN) treatments were applied to one side of the column at a rate of 50 kg ha-1 available N, with a non-fertilised control. Columns were replicated in a randomised block design in a greenhouse. Headspace gas samples were taken every 2 days and analysed using gas chromatography. Shoot and root biomass were measured and rhizosphere soil NH4

+-N and NO3--N contents

were determined colorimetrically after 28 days of plant growth. Results and Discussion Harvested barley shoot biomass values, used here as an indicator of plant productivity, were highest for the CS treatment (5.5 ± 0.4 g for CS compared with 2.89 ± 0.5 g for SS and 2.58 ± 0.5 g for AN). The cumulative N2O (see Fig. 1) and CO2 flux trends over the 28 day barley growth period followed the order SS > CS > control ≈ AN. Cumulative N2O and CO2 fluxes from the SS treated soils were significantly greater (4- to 7-fold, P=0.003 for N2O, 3- to 5-fold, P=0.005 for CO2) than those from the other treatments.

Figure 1. Cumulative N2O fluxes from treated and untreated split-root system soils under different fertilisers.

For SS-treated soils, cumulative N2O and CO2 fluxes from the treated side were higher than those from the untreated side. This was not the case for the CS and AN-treated systems, where the difference was not significant. Variations in treated (high N) vs untreated (low N) rhizosphere soil N2O and CO2 flux trends suggest that alternative rhizosphere C and N cycling processes took place in the SS-treated soils. Destructive harvesting revealed that SS soils were moist with a sulphurous smell, suggesting anaerobic waterlogged conditions and increased denitrification activity. There was a significant correlation (P<0.001) between total mineral N contents of fertilised soils and cumulative N2O fluxes, indicating that NH4

+/NO3--N content within the soil was a driving

force for elevated N2O emissions. Soil NO3--N dominated the soil mineral N pool, suggesting that denitrification

was the dominant soil microbial process responsible for N2O emissions. Soil respiration is a major component of soil-atmosphere CO2 exchange and the amount is linked to soil properties such as temperature, moisture and


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nutrient availability. These factors are likely to have affected CO2 emissions from the soils studied here. CS- and AN-treated soils demonstrated a more established root rhizosphere, suggesting improved efficiency of root respiration under these treatments. Conclusion Overall soil rhizosphere C and N cycling and the magnitude of barley-planted soil GHG emissions depends upon fertiliser type. The GHG emissions and soil mineral N contents measured for rhizosphere soils receiving CS and AN fertilisers were similar in magnitude and suggest that the combination of AN and CS, rather than use of high GHG-emitting SS, would be a potential strategy for increasing soil fertility and crop yields in a manner that keeps GHG emissions to a minimum. However, the agricultural system, climate and soil conditions such as soil type, structure and water properties must also be considered.

14 esa congress 5 9 september 2016 edinburgh, scotland14th esa congress 5–9th september 2016...

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