6 results on '"Phillip D. Alderman"'
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2. Evidence for increasing global wheat yield potential
- Author
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Jose Rafael Guarin, Pierre Martre, Frank Ewert, Heidi Webber, Sibylle Dueri, Daniel Calderini, Matthew Reynolds, Gemma Molero, Daniel Miralles, Guillermo Garcia, Gustavo Slafer, Francesco Giunta, Diego N L Pequeno, Tommaso Stella, Mukhtar Ahmed, Phillip D Alderman, Bruno Basso, Andres G Berger, Marco Bindi, Gennady Bracho-Mujica, Davide Cammarano, Yi Chen, Benjamin Dumont, Ehsan Eyshi Rezaei, Elias Fereres, Roberto Ferrise, Thomas Gaiser, Yujing Gao, Margarita Garcia-Vila, Sebastian Gayler, Zvi Hochman, Gerrit Hoogenboom, Leslie A Hunt, Kurt C Kersebaum, Claas Nendel, Jørgen E Olesen, Taru Palosuo, Eckart Priesack, Johannes W M Pullens, Alfredo Rodríguez, Reimund P Rötter, Margarita Ruiz Ramos, Mikhail A Semenov, Nimai Senapati, Stefan Siebert, Amit Kumar Srivastava, Claudio Stöckle, Iwan Supit, Fulu Tao, Peter Thorburn, Enli Wang, Tobias Karl David Weber, Liujun Xiao, Zhao Zhang, Chuang Zhao, Jin Zhao, Zhigan Zhao, Yan Zhu, and Senthold Asseng
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yield increase ,radiation use efficiency ,wheat potential yield ,crop model ensemble ,global food security ,Environmental technology. Sanitary engineering ,TD1-1066 ,Environmental sciences ,GE1-350 ,Science ,Physics ,QC1-999 - Abstract
Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.
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- 2022
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3. Exploring long-term variety performance trials to improve environment-specific genotype x management recommendations: A case-study for winter wheat
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Romulo P. Lollato, Jeffrey T. Edwards, J.E. Lingenfelser, Alan J. Schlegel, Allan K. Fritz, S.H. Unêda-Trevisoli, Trevor J. Hefley, Erick DeWolf, D. Marburger, Jerry Johnson, Guorong Zhang, Phillip D. Alderman, S.M. Jones-Diamond, Lucas Berger Munaro, Scott D. Haley, Lucas A. Haag, Kansas State Univ, Colorado State Univ, Oklahoma State Univ, and Universidade Estadual Paulista (Unesp)
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0106 biological sciences ,Yield (finance) ,Management practices ,Drought tolerance ,Soil Science ,Sowing ,04 agricultural and veterinary sciences ,Biology ,Crop rotation ,01 natural sciences ,Term (time) ,Fungicide ,Tillage ,Exploratory analysis ,Agronomy ,040103 agronomy & agriculture ,Trait ,G x E x M ,0401 agriculture, forestry, and fisheries ,Long-term data ,Agronomy and Crop Science ,Conditional inference trees ,010606 plant biology & botany - Abstract
Made available in DSpace on 2020-12-10T20:07:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-09-15 Kansas Wheat Alliance Kansas Agricultural Experiment Station (KAES) The complex and interactive effects of genotype (G), environment (E), and management (M) can be a barrier to the development of sound agronomic recommendations. We hypothesize that long-term variety performance trials (VPT) can be used to understand these effects and improve regional recommendations. Our objective was to explore long-term VPT data to improve management and variety-selection recommendations using winter wheat (Triticum aestivum L.) in the U.S. central Great Plains as a case-study. Data of grain yield, variety, and trial management were collected from 748 wheat VPT conducted in the states of Colorado, Kansas, and Oklahoma over nineteen harvest years (2000-2018) and 92 locations, resulting in 97,996 yield observations. Using 30-yr cumulative annual precipitation and growing degrees days, we partitioned the study region into 11 contiguous sub-regions, which we refer to as growing adaptation regions (GAR). We used variance component analysis, gradient boosted trees, and conditional inference trees to explore the management and variety trait effects within each GAR. For the variety trait analysis, the VPT dataset was reduced to account for varieties for which 17 agronomic traits and 11 disease/insect reaction ratings were available (65,264 yield observations). GAR accounted for 46 % of the total variation in grain yield, M for 32 %, residuals (including interactions) for 13 %, year for 7 %, and G for 2 %. Conditional inference trees identified interactions among management practices and their effects on yield within each GAR. For instance, water regime was the most important practice influencing wheat yield in the semi-arid western portion of the study region, followed by sowing date and fungicide. In dryland trials, there was typically an interaction between fungicide, sowing date, and tillage system, depending on GAR. Other management practices (e.g. dual-purpose management, crop rotation, and tillage practice) also significantly affected yield, depending on GAR. The main variety trait associated with increased yields depended on region and management combination. For instance, drought tolerance was the most important trait in dryland trials while stripe rust tolerance was more relevant in irrigated trials in the semi-arid region. In this research, we demonstrated an approach that uses widely available long-term VPT data to improve management and variety selection recommendations and can be used in other regions and crops for which long-term VPT data are available. Kansas State Univ, Dept Agron, Manhattan, KS 66506 USA Kansas State Univ, Dept Stat, Manhattan, KS 66506 USA Kansas State Univ, Dept Plant Pathol, Throckmorton Hall, Manhattan, KS 66506 USA Colorado State Univ, Dept Soil & Crop Sci, Ft Collins, CO 80523 USA Oklahoma State Univ, Dept Plant & Soil Sci, Stillwater, OK 74078 USA Sao Paulo State Univ, Dept Crop Prod, Jaboticabal, SP, Brazil Sao Paulo State Univ, Dept Crop Prod, Jaboticabal, SP, Brazil Kansas Wheat Alliance: GAGR004805BG5828
- Published
- 2020
4. The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations
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L. A. Hunt, David B. Lobell, Phillip D. Alderman, Alex C. Ruane, Zhigan Zhao, T. Palosuo, Mohamed Jabloun, Margarita Garcia-Vila, Andrew J. Challinor, Reimund P. Rötter, Jordi Doltra, Dominique Ripoche, Jeffrey W. White, Bing Liu, Jakarat Anothai, Fulu Tao, Katharina Waha, Eckart Priesack, Sebastian Gayler, Pierre Stratonovitch, Andrea Maiorano, Davide Cammarano, Christoph Müller, Bruno Basso, Ehsan Eyshi Rezaei, Senthold Asseng, Claas Nendel, Joost Wolf, Curtis D. Jones, Ann-Kristin Koehler, Matthew P. Reynolds, Enli Wang, Belay T. Kassie, Christian Biernath, Soora Naresh Kumar, Pierre Martre, Frank Ewert, Iwan Supit, Jørgen E. Olesen, Gerrit Hoogenboom, Giacomo De Sanctis, Thilo Streck, Elias Fereres, Yan Zhu, Kurt Christian Kersebaum, Mikhail A. Semenov, Claudio O. Stöckle, Benjamin Dumont, Roberto C. Izaurralde, Peter J. Thorburn, Garry O'Leary, and Pramod K. Aggarwal
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Simulations ,0106 biological sciences ,Water en Voedsel ,klim ,01 natural sciences ,Heat stress ,Crop ,heat stress ,Anthesis ,Yield (wine) ,wheat ,Life Science ,Cultivar ,field experimental data ,Biomass (ecology) ,WIMEK ,Water and Food ,Field experimental data ,Sowing ,04 agricultural and veterinary sciences ,PE&RC ,Productivity (ecology) ,Agronomy ,Plant Production Systems ,Plantaardige Productiesystemen ,Wheat ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Environmental science ,Water Systems and Global Change ,simulations ,Cropping ,010606 plant biology & botany - Abstract
All data are available via DOI http://doi.org/10.7910/DVN/ECSFZG, he data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.
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- 2017
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5. Similar estimates of temperature impacts on global wheat yield by three independent methods
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Frank Ewert, Jakarat Anothai, P. V. Vara Prasad, Davide Cammarano, Curtis D. Jones, Elias Fereres, Margarita Garcia-Vila, Soora Naresh Kumar, Eckart Priesack, Phillip D. Alderman, Andrew J. Challinor, Reimund P. Rötter, Alex C. Ruane, Christian Folberth, Gerrit Hoogenboom, Pierre Martre, Roberto C. Izaurralde, Fulu Tao, Pramod K. Aggarwal, Mohamed Jabloun, Jordi Doltra, Joshua Elliott, Christoph Müller, Bing Liu, Iurii Shcherbak, Jeffrey W. White, Bruno Basso, Senthold Asseng, Pierre Stratonovitch, Peter J. Thorburn, Claas Nendel, Taru Palosuo, Joost Wolf, Ann-Kristin Koehler, Thilo Streck, Jørgen E. Olesen, David B. Lobell, Kurt Christian Kersebaum, Delphine Deryng, L. A. Hunt, Garry O'Leary, Katharina Waha, Giacomo De Sanctis, Daniel Wallach, Yan Zhu, James W. Jones, Elke Stehfest, Mikhail A. Semenov, Christian Biernath, Claudio O. Stöckle, Thomas A. M. Pugh, Matthew P. Reynolds, Enli Wang, Bruce A. Kimball, Erwin Schmid, Iwan Supit, Zhigan Zhao, Michael J. Ottman, Sebastian Gayler, Cynthia Rosenzweig, Ehsan Eyshi Rezaei, Gerard W. Wall, National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Potsdam Institute for Climate Impact Research (PIK), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], Computation Institute, Loyola University of Chicago, Department of Environmental Earth System Science and Center on Food Security and the Environment, Stanford University, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, CGIAR Research Program on Climate Change, Agriculture and Food Security, Borlaug Institute for South Asia, CIMMYT, Consultative Group on International Agricultural Research (CGIAR), Department of Plant and Soil Sciences, Mississippi State University [Mississippi], Department of Plant Science, Faculty of Natural Resources, Prince of Songkla University (PSU), Department of Geological Sciences, University of Oregon [Eugene], W. K. Kellogg Biological Station (KBS), Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Institute of Soil Ecology [Neuherberg] (IBOE), Helmholtz-Zentrum München (HZM), The James Hutton Institute, Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, European Commission - Joint Research Centre [Ispra] (JRC), Cantabrian Agricultural Research and Training Centre, Department of Agronomy, Purdue University [West Lafayette], Department of Geography, University of Liverpool, Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Institute of Soil Science and Land Evaluation, University of Hohenheim, AgWeatherNet Program, Washington State University (WSU), Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], US Arid-Land Agricultural Research Center, United States Department of Agriculture, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, Landscape & Water Sciences, Department of Environment of Victoria, The School of Plant Sciences, University of Arizona, Natural resources institute Finland, Institute of Ecology, German Research Center for Environmental Health, Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), School of Geography, Earth and Environmental Sciences [Birmingham], University of Birmingham [Birmingham], Birmingham Institute of Forest Research (BIFoR), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Center for Development Research (ZEF), Environmental Impacts Group, Georg-August-University [Göttingen], Universität für Bodenkultur Wien [Vienne, Autriche] (BOKU), Computational and Systems Biology Department, Rothamsted Research, Biotechnology and Biological Sciences Research Council, Netherlands Environmental Assessment Agency, Department of Biological Systems Engineering, University of Wisconsin-Madison, PPS, WSG and CALM, Wageningen University and Research [Wageningen] (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), USDA-ARS, Arid-Land Agricultural Research Center, China Agricultural University (CAU), National High-Tech Research and Development Program of China (2013AA100404), the National Natural Science Foundation of China (31271616, 31611130182, 41571088 and 31561143003), the National Research Foundation for the Doctoral Program of Higher Education of China (20120097110042), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China Scholarship Council., IFPRI through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat, the Agricultural Model Intercomparison and Improvement Project (AgMIP), Agricultural & Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation, University of Bonn-Division of Plant Nutrition, Stanford University [Stanford], Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Prince of Songkla University, Texas A&M AgriLife Research and Extension Center, Natural Resources Institute Finland, Georg-August-Universität Göttingen, Wageningen University and Research Center (WUR), China Agricultural University, Division of Plant Nutrition-University of Bonn, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), University of Florida, Potsdam Institute for Climate Impact Research ( PIK ), Leibniz Centre for Agricultural Landscape Research, Institute for Landscape Biogeochemistry, Center for Climate Systems Research [New York] ( CCSR ), Écophysiologie des Plantes sous Stress environnementaux ( LEPSE ), Institut National de la Recherche Agronomique ( INRA ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Consultative Group on International Agricultural Research ( CGIAR ), W.K. Kellogg Biological Station, Institute of Soil Ecology [Neuherberg] ( IBOE ), Helmholtz-Zentrum München ( HZM ), James Hutton Institute, Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, European Commission - Joint Research Centre [Ispra] ( JRC ), International Institute for Applied Systems Analysis ( IIASA ), Washington State University ( WSU ), Texas A and M University ( TAMU ), Leibniz Centre for Agricultural Landscape Research (ZALF), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung ( IMK-IFU ), Karlsruher Institut für Technologie ( KIT ), School of Geography, Earth & Environmental Science and Birmingham Institute of Forest Research, University of Birmingham, International Maize and Wheat Improvement Center ( CIMMYT ), Bonn Universität [Bonn], University of Natural Resources and Life Sciences, University of Wisconsin-Madison [Madison], Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Commonwealth Scientific and Industrial Research Organisation, Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Université de Toulouse (UT)-Université de Toulouse (UT), Helmholtz Zentrum München = German Research Center for Environmental Health, Natural Resources Institute Finland (LUKE), Georg-August-University = Georg-August-Universität Göttingen, Universität für Bodenkultur Wien = University of Natural Resources and Life [Vienne, Autriche] (BOKU), Biotechnology and Biological Sciences Research Council (BBSRC), and Institute of geographical sciences and natural resources research [CAS] (IGSNRR)
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0106 biological sciences ,[SDV.SA]Life Sciences [q-bio]/Agricultural sciences ,[ SDV.BV ] Life Sciences [q-bio]/Vegetal Biology ,régression statistique ,010504 meteorology & atmospheric sciences ,impact sur le rendement ,klim ,Atmospheric sciences ,01 natural sciences ,incertitude ,wheat ,uncertainty ,[ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences ,2. Zero hunger ,changement climatique ,Regression analysis ,statistical regression ,simulation ,PE&RC ,[ SDE.MCG ] Environmental Sciences/Global Changes ,sécurité alimentaire ,Plant Production Systems ,modèle de récolte ,Yield (finance) ,comparaison de modèles ,[SDE.MCG]Environmental Sciences/Global Changes ,Climate change ,Environmental Science (miscellaneous) ,Earth System Science ,blé ,température ,Life Science ,[SDV.BV]Life Sciences [q-bio]/Vegetal Biology ,réchauffement climatique ,global change ,0105 earth and related environmental sciences ,Hydrology ,WIMEK ,Global temperature ,business.industry ,Crop yield ,Global warming ,Climate Resilience ,13. Climate action ,Agriculture ,Klimaatbestendigheid ,Plantaardige Productiesystemen ,Environmental science ,Leerstoelgroep Aardsysteemkunde ,Climate model ,business ,Social Sciences (miscellaneous) ,010606 plant biology & botany - Abstract
The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security. The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.
- Published
- 2016
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6. Improving Crop Adaptation to Climate Change through Strategic Crossing of Stress Adaptive Traits
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Gemma Molero, Maria Tattaris, Phillip D. Alderman, Sivakumar Sukumaran, C.M. Cossani, and Matthew P. Reynolds
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education.field_of_study ,biology ,business.industry ,Physiology ,Population ,Climate change ,food and beverages ,phenomics ,Quantitative trait locus ,biology.organism_classification ,Biotechnology ,remote sensing ,Phenomics ,breeding ,General Earth and Planetary Sciences ,Adaptation ,education ,Triticeae ,Association mapping ,business ,Selection (genetic algorithm) ,General Environmental Science - Abstract
Crossing programs based on phenomics have resulted in a new generation of drought adapted wheat lines based on strategic crossing of complementary physiological traits (PT) that have been included in CIMMYT's international distribution system since 2010. New PT lines have shown superior performance over conventional material in most international environments. For example, in the 17th SAWYT the average yield of PT lines was larger than the group of conventionally bred lines at 75% of international sites. This ongoing effort has involved broadening the genetic base of conventional wheat genepools through extensive use of genetic resources, including landraces and products of inter-specific hybridization with members of the Triticeae tribe. One of the prerequisites for successful application of phenomics in breeding is the establishment of reliable screening tools and platforms that can precisely measure expression of physiological traits in realistic field environments. Genetic gains associated with selection for canopy temperature and spectral water indices have shown that such remotely sensed traits can serve as proxies that reliably estimate water relations characteristics impacting on yield. The first aerial remote sensing platforms for large scale genetic resource screening was developed at CIMMYT in Mexico and more than half of the accessions of the World Wheat Collection have been screened. These high throughput field phenotyping tools have application in gene discovery and QTL for both drought and heat adaptive traits have been identified on 4 different chromosomes of the Seri/Babax RILs population, showing for the first time a common genetic basis for these key abiotic stresses. Similarly the phenology- controlled ‘Wheat Association Mapping Initiative’ panel has been used for gene discovery work. To define the best constellation of traits for application in breeding -and determine priorities for genetic understanding- it is necessary to develop conceptual models of adaptive traits that highlight wheat's genetic limitations under water limitation; pre-breeding serves as a practical tool to test different models.
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