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1. Impact of coupled input data source-resolution and aggregation on contributions of high-yielding traits to simulated wheat yield

Author: Ehsan Eyshi Rezaei, Babacar Faye, Frank Ewert, Senthold Asseng, Pierre Martre, and Heidi Webber
Subjects: Medicine, Science
Abstract: Abstract High-yielding traits can potentially improve yield performance under climate change. However, data for these traits are limited to specific field sites. Despite this limitation, field-scale calibrated crop models for high-yielding traits are being applied over large scales using gridded weather and soil datasets. This study investigates the implications of this practice. The SIMPLACE modeling platform was applied using field, 1 km, 25 km, and 50 km input data resolution and sources, with 1881 combinations of three traits [radiation use efficiency (RUE), light extinction coefficient (K), and fruiting efficiency (FE)] for the period 2001–2010 across Germany. Simulations at the grid level were aggregated to the administrative units, enabling the quantification of the aggregation effect. The simulated yield increased by between 1.4 and 3.1 t ha− 1 with a maximum RUE trait value, compared to a control cultivar. No significant yield improvement (
Published: 2024
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2. Storage protein activator controls grain protein accumulation in bread wheat in a nitrogen dependent manner

Author: Anne Plessis, Catherine Ravel, Thierry Risacher, Nathalie Duchateau, Mireille Dardevet, Marielle Merlino, François Torney, and Pierre Martre
Subjects: Medicine, Science
Abstract: Abstract The expression of cereal grain storage protein (GSP) genes is controlled by a complex network of transcription factors (TFs). Storage protein activator (SPA) is a major TF acting in this network but its specific function in wheat (Triticum aestivum L.) remains to be determined. Here we generated an RNAi line in which expression of the three SPA homoeologs was reduced. In this line and its null segregant we analyzed GSP accumulation and expression of GSP and regulatory TF genes under two regimes of nitrogen availability. We show that down regulation of SPA decreases grain protein concentration at maturity under low but not high nitrogen supply. Under low nitrogen supply, the decrease in SPA expression also caused a reduction in the total quantity of GSP per grain and in the ratio of GSP to albumin-globulins, without significantly affecting GSP composition. The slight reduction in GSP gene expression measured in the SPA RNAi line under low nitrogen supply did not entirely account for the more significant decrease in GSP accumulation, suggesting that SPA regulates additional levels of GSP synthesis. Our results demonstrate a clear role of SPA in the regulation of grain nitrogen metabolism when nitrogen is a limiting resource.
Published: 2023
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3. Wheat DOF transcription factors TaSAD and WPBF regulate glutenin gene expression in cooperation with SPA.

Author: Marielle Merlino, Jean-Charles Gaudin, Mireille Dardevet, Pierre Martre, Catherine Ravel, and Julie Boudet
Subjects: Medicine, Science
Abstract: Grain storage proteins (GSPs) quantity and composition determine the end-use value of wheat flour. GSPs consists of low-molecular-weight glutenins (LMW-GS), high-molecular-weight glutenins (HMW-GS) and gliadins. GSP gene expression is controlled by a complex network of DNA-protein and protein-protein interactions, which coordinate the tissue-specific protein expression during grain development. The regulatory network has been most extensively studied in barley, particularly the two transcription factors (TFs) of the DNA binding with One Finger (DOF) family, barley Prolamin-box Binding Factor (BPBF) and Scutellum and Aleurone-expressed DOF (SAD). They activate hordein synthesis by binding to the Prolamin box, a motif in the hordein promoter. The BPBF ortholog previously identified in wheat, WPBF, has a transcriptional activity in expression of some GSP genes. Here, the wheat ortholog of SAD, named TaSAD, was identified. The binding of TaSAD to GSP gene promoter sequences in vitro and its transcriptional activity in vivo were investigated. In electrophoretic mobility shift assays, recombinant TaSAD and WPBF proteins bound to cis-motifs like those located on HMW-GS and LMW-GS gene promoters known to bind DOF TFs. We showed by transient expression assays in wheat endosperms that TaSAD and WPBF activate GSP gene expression. Moreover, co-bombardment of Storage Protein Activator (SPA) with WPBF or TaSAD had an additive effect on the expression of GSP genes, possibly through conserved cooperative protein-protein interactions.
Published: 2023
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4. Evidence for increasing global wheat yield potential

Author: 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
Subjects: 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.
Published: 2022
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5. Diverging importance of drought stress for maize and winter wheat in Europe

Author: Heidi Webber, Frank Ewert, Jørgen E. Olesen, Christoph Müller, Stefan Fronzek, Alex C. Ruane, Maryse Bourgault, Pierre Martre, Behnam Ababaei, Marco Bindi, Roberto Ferrise, Robert Finger, Nándor Fodor, Clara Gabaldón-Leal, Thomas Gaiser, Mohamed Jabloun, Kurt-Christian Kersebaum, Jon I. Lizaso, Ignacio J. Lorite, Loic Manceau, Marco Moriondo, Claas Nendel, Alfredo Rodríguez, Margarita Ruiz-Ramos, Mikhail A. Semenov, Stefan Siebert, Tommaso Stella, Pierre Stratonovitch, Giacomo Trombi, and Daniel Wallach
Subjects: Science
Abstract: Drivers of crop yield variability require quantification, and historical records can help in improving understanding. Here, Webber et al. report that drought stress will remain a key driver of yield losses in wheat and maize across Europe, and benefits from CO2 will be limited in low-yielding years.
Published: 2018
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6. Proteomic Data Integration Highlights Central Actors Involved in Einkorn (Triticum monococcum ssp. monococcum) Grain Filling in Relation to Grain Storage Protein Composition

Author: Emmanuelle Bancel, Titouan Bonnot, Marlène Davanture, David Alvarez, Michel Zivy, Pierre Martre, Sébastien Déjean, and Catherine Ravel
Subjects: albumin-globulin, data integration, nitrogen, proteomic, grain development, storage protein, Plant culture, SB1-1110
Abstract: Albumins and globulins (AGs) of wheat endosperm represent about 20% of total grain proteins. Some of these physiologically active proteins can influence the synthesis of storage proteins (SPs) (gliadins and glutenins) and consequently, rheological properties of wheat flour and processing. To identify such AGs, data, (published by Bonnot et al., 2017) concerning abundance in 352 AGs and in the different seed SPs during grain filling and in response to different nitrogen (N) and sulfur (S) supply, were integrated with mixOmics R package. Relationships between AGs and SPs were first unraveled using the unsupervised method sparse Partial Least Square, also known as Projection to Latent Structure (sPLS). Then, data were integrated using a supervised approach taking into account the nutrition and the grain developmental stage. We used the block.splda procedure also referred to as DIABLO (Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies). These approaches led to the identification of discriminant and highly correlated features from the two datasets (AGs and SPs) which are not necessarily differentially expressed during seed development or in response to N or S supply. Eighteen AGs were correlated with the quantity of SPs per grain. A statistical validation of these proteins by genetic association analysis confirmed that 5 out of this AG set were robust candidate proteins able to modulate the seed SP synthesis. In conclusion, this latter result confirmed that the integrative strategy is an adequate way to reduce the number of potentially relevant AGs for further functional validation.
Published: 2019
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7. Methodology to assess the changing risk of yield failure due to heat and drought stress under climate change

Author: Tommaso Stella, Heidi Webber, Jørgen E Olesen, Alex C Ruane, Stefan Fronzek, Simone Bregaglio, Sravya Mamidanna, Marco Bindi, Brian Collins, Babacar Faye, Roberto Ferrise, Nándor Fodor, Clara Gabaldón-Leal, Mohamed Jabloun, Kurt-Christian Kersebaum, Jon I Lizaso, Ignacio J Lorite, Loic Manceau, Pierre Martre, Claas Nendel, Alfredo Rodríguez, Margarita Ruiz-Ramos, Mikhail A Semenov, Pierre Stratonovitch, and Frank Ewert
Subjects: climate risk assessment, climate change impact, wheat, maize, crop model, relative distribution, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract: While the understanding of average impacts of climate change on crop yields is improving, few assessments have quantified expected impacts on yield distributions and the risk of yield failures. Here we present the relative distribution as a method to assess how the risk of yield failure due to heat and drought stress (measured in terms of return period between yields falling 15% below previous five year Olympic average yield) responds to changes of the underlying yield distributions under climate change. Relative distributions are used to capture differences in the entire yield distribution between baseline and climate change scenarios, and to further decompose them into changes in the location and shape of the distribution. The methodology is applied here for the case of rainfed wheat and grain maize across Europe using an ensemble of crop models under three climate change scenarios with simulations conducted at 25 km resolution. Under climate change, maize generally displayed shorter return periods of yield failures (with changes under RCP 4.5 between −0.3 and 0 years compared to the baseline scenario) associated with a shift of the yield distribution towards lower values and changes in shape of the distribution that further reduced the frequency of high yields. This response was prominent in the areas characterized in the baseline scenario by high yields and relatively long return periods of failure. Conversely, for wheat, yield failures were projected to become less frequent under future scenarios (with changes in the return period of −0.1 to +0.4 years under RCP 4.5) and were associated with a shift of the distribution towards higher values and a change in shape increasing the frequency of extreme yields at both ends. Our study offers an approach to quantify the changes in yield distributions that drive crop yield failures. Actual risk assessments additionally require models that capture the variety of drivers determining crop yield variability and scenario climate input data that samples the range of probable climate variation.
Published: 2021
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8. Extreme lows of wheat production in Brazil

Author: Rogério de Souza Nóia Júnior, Pierre Martre, Robert Finger, Marijn van der Velde, Tamara Ben-Ari, Frank Ewert, Heidi Webber, Alex C Ruane, and Senthold Asseng
Subjects: climate change, extreme weather, food price and food security, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract: Wheat production in Brazil is insufficient to meet domestic demand and falls drastically in response to adverse climate events. Multiple, agro-climate-specific regression models, quantifying regional production variability, were combined to estimate national production based on past climate, cropping area, trend-corrected yield, and national commodity prices. Projections with five CMIP6 climate change models suggest extremes of low wheat production historically occurring once every 20 years would become up to 90% frequent by the end of this century, depending on representative concentration pathway, magnified by wheat and in some cases by maize price fluctuations. Similar impacts can be expected for other crops and in other countries. This drastic increase in frequency in extreme low crop production with climate change will threaten Brazil’s and many other countries progress toward food security and abolishing hunger.
Published: 2021
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9. RulNet: A Web-Oriented Platform for Regulatory Network Inference, Application to Wheat -Omics Data.

Author: Jonathan Vincent, Pierre Martre, Benjamin Gouriou, Catherine Ravel, Zhanwu Dai, Jean-Marc Petit, and Marie Pailloux
Subjects: Medicine, Science
Abstract: With the increasing amount of -omics data available, a particular effort has to be made to provide suitable analysis tools. A major challenge is that of unraveling the molecular regulatory networks from massive and heterogeneous datasets. Here we describe RulNet, a web-oriented platform dedicated to the inference and analysis of regulatory networks from qualitative and quantitative -omics data by means of rule discovery. Queries for rule discovery can be written in an extended form of the RQL query language, which has a syntax similar to SQL. RulNet also offers users interactive features that progressively adjust and refine the inferred networks. In this paper, we present a functional characterization of RulNet and compare inferred networks with correlation-based approaches. The performance of RulNet has been evaluated using the three benchmark datasets used for the transcriptional network inference challenge DREAM5. Overall, RulNet performed as well as the best methods that participated in this challenge and it was shown to behave more consistently when compared across the three datasets. Finally, we assessed the suitability of RulNet to analyze experimental -omics data and to infer regulatory networks involved in the response to nitrogen and sulfur supply in wheat (Triticum aestivum L.) grains. The results highlight putative actors governing the response to nitrogen and sulfur supply in wheat grains. We evaluate the main characteristics and features of RulNet as an all-in-one solution for RN inference, visualization and editing. Using simple yet powerful RulNet queries allowed RNs involved in the adaptation of wheat grain to N and S supply to be discovered. We demonstrate the effectiveness and suitability of RulNet as a platform for the analysis of RNs involving different types of -omics data. The results are promising since they are consistent with what was previously established by the scientific community.
Published: 2015
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10. Global Nitrogen Needs to Improve Wheat Yield Under Climate Change

Author: Pierre Martre, Sibylle Dueri, Jose Rafael Guarin, Frank Ewert, Heidi Webber, Daniele Calderini, Gemma Molero, Matthew Reynolds, Daniel Miralles, Guillermo Garcia, Hamish Brown, Mike George, Rob Craigie, Jean-Pierre Cohan, Jean-Charles Deswarte, Gustavo Slafer, Francesco Giunta, Davide Cammarano, Roberto Ferrise, Thomas Gaiser, Yujing Gao, Zvi Hochman, Gerrit Hoogenboom, Leslie A. Hunt, Kurt C. Kersebaum, Claas Nendel, Gloria Padovan, Alex C. Ruane, Amit Kumar Srivastava, Tommaso Stella, Iwan Supit, Peter Thorburn, Enli Wang, Joost Wolf, Chuang Zhao, Zhigan Zhao, and Senthold Asseng
Subjects: Meteorology and Climatology
Abstract: Increasing global food demand will require more food production without further exceeding the planetary boundaries, while at the same time adapting to climate change. We used an ensemble of wheat simulation models, with improved yield traits from the highest-yielding wheat genotypes to quantify potential yield gains and associated N requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The improved yield traits increased yield by 16% with current N fertilizer applications under both current climate and mid-century climate change scenarios. To achieve the full yield potential, a 52% increase in global average yield under a mid-century high warming climate scenario (RCP8.5), fertilizer use would need to increase fourfold over current use, which would unavoidably lead to higher environmental impacts from wheat production. Our results show the need to improve soil N availability and N use efficiency, along with yield potential.
Published: 2024

11. An AgMIP framework for improved agricultural representation in integrated assessment models

Author: Alex C Ruane, Cynthia Rosenzweig, Senthold Asseng, Kenneth J Boote, Joshua Elliott, Frank Ewert, James W Jones, Pierre Martre, Sonali P McDermid, Christoph Müller, Abigail Snyder, and Peter J Thorburn
Subjects: AgMIP, climate impacts, crop models, integrated assessment models, agricultural impacts, emulators, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract: Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.
Published: 2017
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12. The coordination of leaf photosynthesis links C and N fluxes in C3 plant species.

Author: Vincent Maire, Pierre Martre, Jens Kattge, François Gastal, Gerd Esser, Sébastien Fontaine, and Jean-François Soussana
Subjects: Medicine, Science
Abstract: Photosynthetic capacity is one of the most sensitive parameters in vegetation models and its relationship to leaf nitrogen content links the carbon and nitrogen cycles. Process understanding for reliably predicting photosynthetic capacity is still missing. To advance this understanding we have tested across C(3) plant species the coordination hypothesis, which assumes nitrogen allocation to photosynthetic processes such that photosynthesis tends to be co-limited by ribulose-1,5-bisphosphate (RuBP) carboxylation and regeneration. The coordination hypothesis yields an analytical solution to predict photosynthetic capacity and calculate area-based leaf nitrogen content (N(a)). The resulting model linking leaf photosynthesis, stomata conductance and nitrogen investment provides testable hypotheses about the physiological regulation of these processes. Based on a dataset of 293 observations for 31 species grown under a range of environmental conditions, we confirm the coordination hypothesis: under mean environmental conditions experienced by leaves during the preceding month, RuBP carboxylation equals RuBP regeneration. We identify three key parameters for photosynthetic coordination: specific leaf area and two photosynthetic traits (k(3), which modulates N investment and is the ratio of RuBP carboxylation/oxygenation capacity (V(Cmax)) to leaf photosynthetic N content (N(pa)); and J(fac), which modulates photosynthesis for a given k(3) and is the ratio of RuBP regeneration capacity (J(max)) to V(Cmax)). With species-specific parameter values of SLA, k(3) and J(fac), our leaf photosynthesis coordination model accounts for 93% of the total variance in N(a) across species and environmental conditions. A calibration by plant functional type of k(3) and J(fac) still leads to accurate model prediction of N(a), while SLA calibration is essentially required at species level. Observed variations in k(3) and J(fac) are partly explained by environmental and phylogenetic constraints, while SLA variation is partly explained by phylogeny. These results open a new avenue for predicting photosynthetic capacity and leaf nitrogen content in vegetation models.
Published: 2012
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13. Wheat Crop Traits Conferring High Yield Potential May Also Improve Yield Stability Under Climate Change

Author: Tommaso Stella, Heidi Webber, Ehsan Eyshi Rezaei, Senthold Asseng, Pierre Martre, Sibylle Dueri, Jose Rafael Guarin, Diego N L Pequeno, Daniel F Calderini, Matthew Reynolds, Gemma Molero, Daniel Miralles, Guillermo Garcia, Gustavo Slafer, Francesco Giunta, Yean-Uk Kim, Chenzhi Wang, Alex C Ruane, and Frank Ewert
Subjects: Meteorology and Climatology, Earth Resources and Remote Sensing
Abstract: Increasing genetic wheat yield potential is considered by many as critical to increasing global wheat yields and production, baring major changes in consumption patterns. Climate change challenges breeding by making target environments less predictable, altering regional productivity and potentially increasing yield variability. Here we used a crop simulation model solution in the SIMPLACE framework to explore yield sensitivity to select trait characteristics (radiation use efficiency [RUE], fruiting efficiency and light extinction coefficient) across 34 locations representing the world’s wheat-producing environments, determining their relationship to increasing yields, yield variability and cultivar performance. The magnitude of the yield increase was trait-dependent and differed between irrigated and rainfed environments. RUE had the most prominent marginal effect on yield, which increased by about 45 % and 33 % in irrigated and rainfed sites, respectively, between the minimum and maximum value of the trait. Altered values of light extinction coefficient had the least effect on yield levels. Higher yields from improved traits were generally associated with increased inter-annual yield variability (measured by standard deviation), but the relative yield variability (as coefficient of variation) remained largely unchanged between base and improved genotypes. This was true under both current and future climate scenarios. In this context, our study suggests higher wheat yields from these traits would not increase climate risk for farmers and the adoption of cultivars with these traits would not be associated with increased yield variability.
Published: 2023
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14. The Extreme 2016 Wheat Failure in France

Author: Rogério de S Nóia Júnior, Jean-Charles Deswarte, Jean-Pierre Cohan, Pierre Martre, Marijn van der Velde, Remi Lecerf, Heidi Webber, Frank Ewert, Alex C Ruane, Gustavo A Slafer, and Senthold Asseng
Subjects: Meteorology and Climatology, Earth Resources and Remote Sensing
Abstract: France suffered in 2016 the most extreme wheat yield decline in recent history, with some districts losing 55% yield. To attribute causes, we combined the largest coherent detailed wheat field experimental dataset with statistical and crop model techniques, climate information and yield physiology. The 2016 yield was composed of up to 40% fewer grains that were up to 30% lighter than expected across eight research stations in France. The flowering stage was affected by prolonged cloud cover and heavy rainfall when 31% of the loss in grain yield was incurred from reduced solar radiation and 19% from floret damage. Grain filling was also affected as 26% of grain yield loss was caused by soil anoxia, 11% by fungal foliar diseases and 10% by ear blight. Compounding climate effects caused the extreme yield decline. The likelihood of these compound factors recurring under future climate change is estimated to change with a higher frequency of extremely low wheat yields.
Published: 2023
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15. The nitrogen price of improved wheat yield under climate change

Author: Pierre Martre, Sibylle Dueri, Jose Guarin, F Ewert, Heidi Webber, Daniel Calderini, Gemma Molero, Matthew Reynolds, Daniel Miralles, Guillermo Garcia, Hamish Brown, Mike George, Rob Craigie, Jean-Pierre Cohan, Jean-Charles Deswarte, Gustavo Slafer, F Giunta, Davide Cammarano, Roberto Ferrise, Thomas GAISER, Yujing Gao, Zvi Hochman, Gerrit Hoogenboom, Leslie A Hunt, Kurt Kersebaum, Claas Nendel, Gloria Padovan, Alex Ruane, Tommaso Stella, Iwan Supit, Amit Srivast, Peter Thorburn, Enli Wang, Joost Wolf, Chuang Zhao, Zhigan Zhao, and Senthold Asseng
Abstract: Increasing global food demand will require more food production without further exceeding the planetary boundaries, while at the same time adapting to climate change. We used an ensemble of wheat simulation models, with sink-source improved traits from the highest-yielding wheat genotypes to quantify potential yield gains and associated N requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The sink-source traits emerged as climate neutral with 16% yield increase with current N fertilizer applications under both current climate and mid-century climate change scenarios. To achieve the full yield potential, a 52% increase in global average yield under a mid-century RCP8.5 climate scenario, fertilizer use would need to increase fourfold over current use, which would unavoidably lead to higher environmental impacts from wheat production. Our results show the need to improve soil N availability and N use efficiency, along with yield potential.
Published: 2023
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16. Dissecting durum wheat time to anthesis into physiological traits using a QTL-based model

Author: Pierre Martre, Rosella Motzo, Anna Maria Mastrangelo, Daniela Marone, Pasquale De Vita, and Francesco Giunta
Abstract: Fine tuning crop development is a major breeding avenue to increase crop yield and for adaptation to climate change. In this study, we used a model that integrates our current understanding of the physiology of wheat phenology to predict the development and anthesis date of a RILs population of durum wheat with genotypic parameters controlling vernalization requirement, photoperiod sensitivity, and earlinessper seestimated using leaf stage, final leaf number, anthesis date data from a pot experiment with vernalized and nonvernalized treatments combined with short- and long-day length. Predictions of final leaf number and anthesis date of the QTL-based model was evaluated for the whole population of RILs in a set of independent field trials and for the two parents, which were not used to estimate the parameter values. Our novel approach reduces the number of environments, experimental costs, and the time required to obtain the required data sets to develop a QTL-based prediction of model parameters. Moreover, the use of a physiologically based model of phenology gives new insight into genotype-phenology relations for wheat. We discuss the approach we used to estimate the parameters of the model and their association with QTL and major phenology genes that collocate at QTL.HighlightWe used a modeling framework integrating our current understanding of the physiology of wheat phenology to dissect durum wheat time to anthesis into physiological traits and link them to QTL.
Published: 2023
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17. Sources of Uncertainty in Simulating Crop N2o Emissions Under Contrasting Environmental Conditions

Author: Sibylle Dueri, Joël Léonard, Florent Chlebowski, Pablo Rosso, Michael Berg-Mohnicke, Claas Nendel, Fiona Erhardt, and Pierre Martre
Published: 2023
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18. Drought exerts a greater influence than growth temperature on the temperature response of leaf day respiration in wheat (Triticum aestivum)

Author: Peter E.L. van der Putten, Xinyou Yin, Pierre Martre, Liang Fang, and Paul C. Struik
Subjects: reassimilation, Crop Physiology, photorespiration, Chemistry, Physiology, Respiration, Water, temperature, Plant Science, acclimation, PE&RC, Droughts, winter wheat, Plant Leaves, Horticulture, water stress, climate change, Centre for Crop Systems Analysis, Temperature response, Chlorophyll fluorescence, Triticum
Abstract: We assessed how the temperature response of leaf day respiration (Rd) in wheat responded to contrasting water regimes and growth temperatures. In Experiment 1, well-watered and drought-stressed conditions were imposed on two genotypes; in Experiment 2, the two water regimes combined with high (HT), medium (MT) and low (LT) growth temperatures were imposed on one of the genotypes. Rd was estimated from simultaneous gas exchange and chlorophyll fluorescence measurements at six leaf temperatures (Tleaf) for each treatment, using the Yin method for non-photorespiratory conditions and the non-rectangular hyperbolic fitting method for photorespiratory conditions. The two genotypes responded similarly to growth and measurement conditions. Estimates of Rd for non-photorespiratory conditions were generally higher than those for photorespiratory conditions but their responses to Tleaf were similar. Under well-watered conditions, Rd and its sensitivity to Tleaf slightly acclimated to LT but did not acclimate to HT. Temperature sensitivities of Rd were considerably suppressed by drought, and the suppression varied among growth temperatures. Thus, it is necessary to quantify interactions between drought and growth temperature for reliably modelling Rd under climate change. Our study also demonstrated that the Kok method, a currently popular method for estimating Rd, underestimated Rd significantly and should be abandoned.HighlightLeaf day respiration (Rd) acclimated little to growth temperature, but significantly to drought by reducing its thermal sensitivity. The Kok method underestimates Rd and should no longer be used.
Published: 2022

19. Global wheat production could benefit from closing the genetic yield gap

Author: Nimai Senapati, Mikhail A. Semenov, Nigel G. Halford, Malcolm J. Hawkesford, Senthold Asseng, Mark Cooper, Frank Ewert, Martin K. van Ittersum, Pierre Martre, Jørgen E. Olesen, Matthew Reynolds, Reimund P. Rötter, and Heidi Webber
Subjects: Plant Production Systems, Plantaardige Productiesystemen, Life Science, Animal Science and Zoology, PE&RC, Agronomy and Crop Science, Food Science
Abstract: Global food security requires food production to be increased in the coming decades. The closure of any existing genetic yield gap (Yig) by genetic improvement could increase crop yield potential and global production. Here we estimated present global wheat Yig, covering all wheat-growing environments and major producers, by optimizing local wheat cultivars using the wheat model Sirius. The estimated mean global Yig was 51%, implying that global wheat production could benefit greatly from exploiting the untapped global Yig through the use of optimal cultivar designs, utilization of the vast variation available in wheat genetic resources, application of modern advanced breeding tools, and continuous improvements of crop and soil management.
Published: 2022
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20. Needed global wheat stock and crop management in response to the war in Ukraine

Author: Rogério de S. Nóia Júnior, Frank Ewert, Heidi Webber, Pierre Martre, Thomas W. Hertel, Martin K. van Ittersum, and Senthold Asseng
Subjects: Plant Production Systems, Ecology, Hunger, Plantaardige Productiesystemen, Food security, War, PE&RC, Ukraine, Wheat export, Safety, Risk, Reliability and Quality, Safety Research, Food Science
Abstract: The war in Ukraine threatened to block 9% of global wheat exports, driving wheat prices to unprecedented heights. We advocate, that in the short term, compensating for such an export shortage will require a coordinated release of wheat stocks, while if the export block persists, other export countries will need to fill the gap by increasing wheat yields or by expanding wheat cropping areas by 8% in aggregate. We estimate that a production increase would require an extra half a million tons of nitrogen fertilizer, yet fertilizer prices are at record levels, driven by rising energy prices. Year-to-year variability plus more frequent climate change-induced crop failures could additionally reduce exports by another 5 to 7 million tons in any given year, further stressing global markets. Without stabilizing wheat supplies through judicious management of stocks and continuing yield improvements, food and national security are at risk across many nations in the world.
Published: 2022
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21. Extreme lows of wheat production in Brazil

Author: Marijn van der Velde, Tamara Ben-Ari, Pierre Martre, Rogério de Souza Nóia Júnior, Frank Ewert, Heidi Webber, Alex C. Ruane, Robert Finger, Senthold Asseng, Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Department of Management, Technology, and Economics [ETH Zürich] (D-MTEC), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), European Commission - Joint Research Centre [Ispra] (JRC), Centre International de Recherche sur l'Environnement et le Développement (CIRED), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École des hautes études en sciences sociales (EHESS)-AgroParisTech-École des Ponts ParisTech (ENPC)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), and Technical University of Munich (TUM)
Subjects: 2. Zero hunger, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0303 health sciences, 010504 meteorology & atmospheric sciences, Renewable Energy, Sustainability and the Environment, Public Health, Environmental and Occupational Health, Climate change, 15. Life on land, 01 natural sciences, ddc, 03 medical and health sciences, Extreme weather, Geography, climate change, 13. Climate action, extreme weather, Climatology, food price and food security, Food price and food security, Production (economics), 030304 developmental biology, 0105 earth and related environmental sciences, General Environmental Science
Abstract: Wheat production in Brazil is insufficient to meet domestic demand and falls drastically in response to adverse climate events. Multiple, agro-climate-specific regression models, quantifying regional production variability, were combined to estimate national production based on past climate, cropping area, trend-corrected yield, and national commodity prices. Projections with five CMIP6 climate change models suggest extremes of low wheat production historically occurring once every 20 years would become up to 90% frequent by the end of this century, depending on representative concentration pathway, magnified by wheat and in some cases by maize price fluctuations. Similar impacts can be expected for other crops and in other countries. This drastic increase in frequency in extreme low crop production with climate change will threaten Brazil's and many other countries progress toward food security and abolishing hunger. ISSN:1748-9326 ISSN:1748-9318
Published: 2021
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22. Invited review: Intergovernmental Panel on Climate Change, agriculture, and food—A case of shifting cultivation and history

Author: Pete Smith, Pierre Martre, Stuart Mark Howden, Andrew J. Challinor, Christian Bugge Henriksen, John R. Porter, Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Plant and Environmental Sciences, University of Gothenburg (GU), University of Leeds, Climate Change Institute at the Australian National University, Australian National University (ANU), 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), Institute of Biological and Environmental Sciences, University of Aberdeen, and Agropolis Fondation
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Crops, Agricultural, 0106 biological sciences, Livestock, Mitigation policy, 010504 meteorology & atmospheric sciences, Natural resource economics, [SDE.MCG]Environmental Sciences/Global Changes, Climate Change, Climate change, adaptation, 010603 evolutionary biology, 01 natural sciences, Food Supply, mitigation, Shifting cultivation, Animals, Humans, Environmental Chemistry, Adaptation, SolACEWP1, 0105 earth and related environmental sciences, General Environmental Science, 2. Zero hunger, Global and Planetary Change, Food security, Ecology, IPCC, business.industry, Agriculture, food security, 15. Life on land, Impact, climate change, Geography, 13. Climate action, impact, Food processing, business, Working group, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, policy
Abstract: International audience; Since 1990, the Intergovernmental Panel on Climate Change (IPCC) has produced five Assessment Reports (ARs), in which agriculture as the production of food for humans via crops and livestock have featured in one form or another. A constructed database of the ca. 2,100 cited experiments and simulations in the five ARs was analyzed with respect to impacts on yields via crop type, region, and whether adaptation was included. Quantitative data on impacts and adaptation in livestock farming have been extremely scarce in the ARs. The main conclusions from impact and adaptation are that crop yields will decline, but that responses have large statistical variation. Mitigation assessments in the ARs have used both bottom-up and top-down methods but need better to link emissions and their mitigation with food production and security. Relevant policy options have become broader in later ARs and included more of the social and nonproduction aspects of food security. Our overall conclusion is that agriculture and food security, which are two of the most central, critical, and imminent issues in climate change, have been dealt with an unfocussed and inconsistent manner between the IPCC five ARs. This is partly a result of not only agriculture spanning two IPCC working groups but also the very strong focus on projections from computer crop simulation modeling. For the future, we suggest a need to examine interactions between themes such as crop resource use efficiencies and to include all production and nonproduction aspects of food security in future roles for integrated assessment models.
Published: 2019
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23. Climate change impact and adaptation for wheat protein

Author: Mohamed Jabloun, Pierre Martre, Garry O'Leary, Dominique Ripoche, Bruno Basso, Pramod K. Aggarwal, Daniel Wallach, Matthew P. Reynolds, Marijn van der Velde, John R. Porter, Heidi Webber, Enli Wang, Frank Ewert, Joost Wolf, Christian Klein, Belay T. Kassie, Christian Biernath, Margarita Garcia-Vila, M. Ali Babar, Pierre Stratonovitch, Yujing Gao, Glenn J. Fitzgerald, Davide Cammarano, Bing Liu, Peter J. Thorburn, Fulu Tao, Andrew J. Challinor, Reimund P. Rötter, Christine Girousse, Zhigan Zhao, Christoph Müller, Ann-Kristin Koehler, Jørgen E. Olesen, Elias Fereres, Iwan Supit, Andrea Maiorano, Marco Bindi, Sebastian Gayler, Kurt Christian Kersebaum, Giacomo De Sanctis, Alex C. Ruane, Rosella Motzo, Juraj Balkovic, Manuel Montesino San Martin, Roberto Ferrise, Mikhail A. Semenov, Claudio O. Stöckle, Soora Naresh Kumar, Gerrit Hoogenboom, Benjamin Dumont, Ehsan Eyshi Rezaei, Mukhtar Ahmed, Senthold Asseng, Thilo Streck, Yan Zhu, R. Cesar Izaurralde, Katharina Waha, Ahmed M. S. Kheir, Taru Palosuo, Liujun Xiao, Sara Minoli, Eckart Priesack, Heidi Horan, Curtis D. Jones, Francesco Giunta, Zhao Zhang, Claas Nendel, International Food Policy Research Institute (US), CGIAR (France), European Commission, Institut National de la Recherche Agronomique (France), National Natural Science Foundation of China, Federal Ministry of Food and Agriculture (Germany), Biotechnology and Biological Sciences Research Council (UK), Innovation Fund Denmark, China Scholarship Council, Ministero delle Politiche Agricole Alimentari e Forestali, Academy of Finland, Finnish Ministry of Agriculture and Forestry, Federal Ministry of Education and Research (Germany), Department of Agriculture and Water Resources (Australia), University of Melbourne, Grains Research and Development Corporation (Australia), National Institute of Food and Agriculture (US), German Research Foundation, Gorgan 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), 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), European Food Safety Authority = Autorité européenne de sécurité des aliments, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Georg-August-University = Georg-August-Universität Göttingen, Centre for Biodiversity and Sustainable Land-use [University of Göttingen] (CBL), Department of Economic Drt and Resources, Grains Innovation Park, Agriculture Victoria Research, Department of Economic Development, Jobs, Transport and Resources, Faculty of Veterinary and Agricultural Science [Melbourne], Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Department of Agricultural Sciences, University of Naples Federico II = Università degli studi di Napoli Federico II, World Food Crops Breeding, Department of Agronomy, IFAS, University of Florida [Gainesville] (UF), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Soils, Water and Environment Research Institute, Agricultural Research Center (ARC), Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), International Maize and Wheat Improvement Centre [Inde] (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Biological Systems Engineering, University of Wisconsin-Madison, Department of Agronomy, University of El-Tarf, Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Department of Soil Science, Faculty of Natural Sciences, Comenius University in Bratislava, W. K. Kellogg Biological Station (KBS), Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department of Earth and Environmental Sciences [Ann Arbor], University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Institute of Biochemical Plant Pathology, Research Center for Environmental Health, Helmholtz Zentrum München = German Research Center for Environmental Health, Department of Agri‐food Production and Environmental Sciences (DISPAA), Università degli Studi di Firenze = University of Florence (UniFI), 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, Collaborative Research Program from CGIAR and Future Earth on Climate Change, Agriculture and Food Security (CCAFS), International Center for Tropical Agriculture, GMO Unit, European Food Safety Authority, Department Terra & AgroBioChem, Gembloux Agro‐Bio Tech, Université de Liège, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Department of Crop Sciences, Instituto de Agricultura Sostenible - Institute for Sustainable Agriculture (IAS CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, Food Systems Institute [Gainesville] (UF|IFAS), Department of Geographical Sciences, College Park, 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], Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Biochemical Plant Pathology [Neuherberg], German Research Center for Environmental Health - Helmholtz Center München (GmbH), National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Member of the Leibniz Association, Potsdam Institute for Climate Impact Research (PIK), Department of Plant and Environmental Sciences [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Centre for Environment Science and Climate Resilient Agriculture [New Delhi], Indian Agricultural Research Institute (IARI), Natural Resources Institute Finland (LUKE), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), University of Lincoln, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Rothamsted Research, Biotechnology and Biological Sciences Research Council (BBSRC), Water & Food and Water Systems & Global Change Group, Wageningen University and Research [Wageningen] (WUR), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Joint Research Centre (IPTS), Commission Européenne, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), CSIRO Agriculture and Food (CSIRO), Plant Production Systems, State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University (BNU), Department of Agronomy and Biotechnology, China Agricultural University (CAU), National Research Foundation for the Doctoral Program of Higher Education of China, Grant/Award Number: 20120097110042, International Food Policy, European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Georg-August-University [Göttingen], Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen - Georg-August-Universität Göttingen, University of Naples Federico II, Helmholtz-Zentrum München (HZM), Universtiy of Florence, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Natural Resources Institute Finland, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Wageningen University, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agricultural & Biological Engineering Department, University of Florida [Gainesville], Georg-August-Universität Göttingen, University of Goettingen, Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Research Program on Climate Change, Agriculture and Food Security, BISA‐CIMMYT, Consultative Group on International Agricultural Research (CGIAR), Comenius University [Bratislava], Helmholtz Zentrum München, Institute of Crop Science and Resource Conservation INRES, University of Bonn, IAS‐CSIC, Universidad de Cordoba, Institute for Sustainable Food Systems, Texas A&M AgriLife Research and Extension Center, Aarhus University, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), UE Agroclim (UE AGROCLIM), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Wageningen University and Research Center (WUR), Beijing Normal University, and China Agricultural University
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0106 biological sciences, 010504 meteorology & atmospheric sciences, Water en Voedsel, 01 natural sciences, grain protein, adaptation au milieu, climate change adaptation, climate change impact, food security, wheat, Co2 concentration, adaptation to the environment, Triticum, General Environmental Science, 2. Zero hunger, changement climatique, Global and Planetary Change, Food security, Ecology, Temperature, food and beverages, Adaptation, Physiological, Droughts, Nitrogen, Climate Change, Climate change, 010603 evolutionary biology, blé, Crop production, Food Quality, Grain quality, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Environmental Chemistry, Grain Proteins, global change, 0105 earth and related environmental sciences, WIMEK, Water and Food, Global change, Carbon Dioxide, Models, Theoretical, 15. Life on land, Agronomy, 13. Climate action, Grain yield, Environmental science, Water Systems and Global Change, Protein concentration
Abstract: Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production., B.L received support from the International Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the CGIAR Research Program on Wheat. A.M. received support from the EU Marie Curie FP7 COFUND People Programme, through an AgreenSkills fellowship under grant agreement no. PCOFUND‐GA‐2010‐267196. PM, A.M., D.R., and D.W. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). L.X. and Y.Z. were supported by the National High‐Tech Research and Development Program of China (2013AA100404), the National Natural Science Foundation of China (31271616), the National Research Foundation for the Doctoral Program of Higher Education of China (20120097110042), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). F.T. and Z.Z. were supported by the National Natural Science Foundation of China (41571088, 41571493 and 31561143003). R.R. received support from the German Ministry for Research and Education (BMBF) through project SPACES‐LLL. Rothamsted Research receives support from the Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat programme [BB/P016855/1]. M.J. and J.E.O. were supported by Innovation Fund Denmark through the MACSUR project. L.X. and Y.G. acknowledge support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan Univ. of Agric. Sci. & Natur. Resour. for his support. R.P.R., T.P., and F.T. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM) and from the Academy of Finland through the projects NORFASYS (decision nos. 268277 and 292944) and PLUMES (decision nos. 277403 and 292836). K.C.K. and C.N. received support from the German Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through BMBF. G.J.O. and G.J.F. acknowledge support from the Victorian Department of Economic Development, Jobs, Transport and Resources, the Australian Department of Agriculture and Water Resources, The University of Melbourne and the Grains Research Development Corporation, Australia. P.K.A.'s work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organizations.. B.B. received financial support from USDA NIFA‐Water Cap Award 2015‐68007‐23133. F.E. acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of Food and Agriculture (2815ERA01J) and from the German Science Foundation (project EW 119/5‐1).
Published: 2019
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24. Leaf elongation response to blue light is mediated by stomatal-induced variations in transpiration in Festuca arundinacea

Author: Didier Combes, Pierre Martre, Abraham J. Escobar-Gutiérrez, Jean-Louis Durand, Romain Barillot, Tom De Swaef, Cédric Perrot, Elzbieta Frak, Eric Roy, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Research Institute for Agricultural, Fisheries and Food (ILVO), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Region Poitou-Charentes, European Project: 609398,EC:FP7:PEOPLE,FP7-PEOPLE-2013-COFUND,AGREENSKILLSPLUS(2014), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)
Subjects: Festuca, 0106 biological sciences, 0301 basic medicine, Vapor Pressure, Physiology, Vapour Pressure Deficit, Water flow, stomata, Irradiance, Tiller (botany), Plant Science, 01 natural sciences, hydraulic model, transpiration, 03 medical and health sciences, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Transpiration, biology, Chemistry, fungi, Water, food and beverages, Plant Transpiration, plant hydraulics, biology.organism_classification, photomorphogenesis, Plant Leaves, Horticulture, 030104 developmental biology, grasses, leaf elongation, Plant Stomata, Photomorphogenesis, Elongation, light, Festuca arundinacea, 010606 plant biology & botany, Blue light
Abstract: Reduced blue light irradiance is known to enhance leaf elongation rate (LER) in grasses, but the mechanisms involved have not yet been elucidated. We investigated whether leaf elongation response to reduced blue light could be mediated by stomata-induced variations of plant transpiration. Two experiments were carried out on tall fescue in order to monitor LER and transpiration under reduced blue light irradiance. Additionally, LER dynamics were compared with those observed in the response to vapour pressure deficit (VPD)-induced variations of transpiration. Finally, we developed a model of water flow within a tiller to simulate the observed short-term response of LER to various transpiration regimes. LER dramatically increased in response to blue light reduction and then reached new steady states, which remained higher than the control. Reduced blue light triggered a simultaneous stomatal closure which induced an immediate decrease of leaf transpiration. The hydraulic model of leaf elongation accurately predicted the LER response to blue light and VPD, resulting from an increase in the growth-induced water potential gradient in the leaf growth zone. Our results suggest that the blue light signal is sensed by stomata of expanded leaves and transduced to the leaf growth zone through the hydraulic architecture of the tiller.
Published: 2021
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25. Differential sensitivity to temperature and evaporative demand in wheat relatives

Author: Serge Zaka, Stéphane Leveau, Pierre Martre, Boris Parent, ITK [Clapiers], Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: 0106 biological sciences, Physiology, Range (biology), leaf expansion, Aegilops, vapour pressure deficit (VPD), Plant Science, Biology, 01 natural sciences, transpiration, Crop, 03 medical and health sciences, wheat, Triticum, 030304 developmental biology, Transpiration, 2. Zero hunger, 0303 health sciences, Genetic diversity, food and beverages, temperature, Interspecific competition, genetic diversity, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, evaporative demand, Agronomy, leaf growth, Trait, Ploidy, human activities, 010606 plant biology & botany, Diversity (business)
Abstract: There are potential sources of alleles and genes currently present in wheat-related species that have the potential to be introduced into wheat breeding programs targeting current and future hot and dry climates. However, to date neither the intra- nor the interspecific diversity of the responses of leaf growth and transpiration to temperature and evaporative demand have been investigated in across a significant range of wheat-related species. By analysing 12 groups of wheat-related species and subspecies, we were able to examine the multi-dimensional structure of the genetic diversity for traits linked to plant vegetative structures and their development, and to leaf expansion and transpiration, together with their responses to ‘non-stressing’ ranges of temperature and evaporative demand. In addition to providing new insights on how genome type, ploidy level, phylogeny, and breeding pressure act together to structure this genetic diversity, our study also provides new mathematical formalisms and associated parameters for trait responses across a wide range of genetic diversity in wheat-related species. This will potentially allow crop models to predict the impact of this diversity on yield, and thus to indicate potential sources of varietal improvement for modern wheat germplasms through interspecific crosses.
Published: 2021
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26. Omics data reveal putative regulators of einkorn grain protein composition under sulfur deficiency

Author: Titouan Bonnot, Victor Hatte, Philippe Leroy, Catherine Ravel, Pierre Martre, Annick Moing, Marie Pailloux, Mireille Dardevet, Emmanuelle Bancel, Yves Gibon, Camille Bénard, Catherine Deborde, Marie-Laure Martin-Magniette, Natalia Falagán, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of California [Riverside] (UC Riverside), University of California (UC), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Biologie du fruit et pathologie (BFP), Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut des Sciences des Plantes de Paris-Saclay (IPS2 (UMR_9213 / UMR_1403)), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Mathématiques et Informatique Appliquées (MIA Paris-Saclay), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Ministère de l'Enseignement Supérieur et de la Recherche, France AgriMer, French Fund Supporting Plant 46 Breeding (FSOV), ANR-10-BTBR-0003,BREEDWHEAT,Développer de nouvelles variétés de blé pour une agriculture durable(2010), ANR-11-INBS-0010,METABOHUB,Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation(2011), ANR-10-LABX-0040,SPS,Saclay Plant Sciences(2010), European Project: 613556,EC:FP7:KBBE,FP7-KBBE-2013-7-single-stage,WHEALBI(2014), University of California [Riverside] (UCR), University of California, Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Mathématiques et Informatique Appliquées (MIA-Paris), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-AgroParisTech-Université Paris-Saclay, and Ecole Nationale Supérieure des Mines de St Etienne-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0106 biological sciences, Triticum monococcum, Physiology, [SDV]Life Sciences [q-bio], Sulfur metabolism, Plant Science, Biology, Einkorn (Triticum monococcum), Composition de la protéine du grain, 01 natural sciences, Nitrogen 69 and sulphur nutrition, Transcriptome, [SDV.GEN.GPL]Life Sciences [q-bio]/Genetics/Plants genetics, Gene Expression Regulation, Plant, Genetics, Metabolome, Grain Proteins, Gene, Triticum, Research Articles, Plant Proteins, 2. Zero hunger, Regulation of gene expression, Grain protein composition, Plant physiology, food and beverages, Réseau de régulation, Metabolism, Diploidy, Regulatory networks, Métabolome, Biochemistry, Ploidy, Sulfur, 010606 plant biology & botany
Abstract: International audience; Understanding the molecular mechanisms controlling the accumulation of grain storage proteins in response to nitrogen (N) and sulfur (S) nutrition is essential to improve cereal grain nutritional and functional properties. Here, we studied the grain transcriptome and metabolome responses to postanthesis N and S supply for the diploid wheat einkorn (Triticum monococcum). During grain filling, 848 transcripts and 24 metabolites were differentially accumulated in response to N and S availability. The accumulation of total free amino acids per grain and the expression levels of 241 genes showed significant modifications during most of the grain filling period and were upregulated in response to S deficiency. Among them, 24 transcripts strongly responded to S deficiency and were identified in coexpression network analyses as potential coordinators of the grain response to N and S supply. Sulfate transporters and genes involved in sulfate and Met metabolism were upregulated, suggesting regulation of the pool of free amino acids and of the grain N-to-S ratio. Several genes highlighted in this study might limit the impact of S deficiency on the accumulation of grain storage proteins.Sulfur deficiency in einkorn grain leads to upregulation of genes acting in sulfur transport and metabolism that may coordinate the pool of free amino acids necessary for storage protein synthesis.
Published: 2020
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27. Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models

Author: Kurt Christian Kersebaum, Frank Ewert, Carlos Gregorio Hernández Díaz-Ambrona, Pierre Martre, Fulu Tao, Tapio Salo, Camilla Dibari, Xenia Specka, Lucía Rodríguez, Roberto Ferrise, Amit Kumar Srivastava, G. Padovan, Taru Palosuo, Davide Cammarano, Margarita Ruiz-Ramos, M. Ines Minguez, Alan H. Schulman, Mikhail A. Semenov, Thomas Gaiser, Claas Nendel, Reimund P. Rötter, Jukka Höhn, Viikki Plant Science Centre (ViPS), Institute of Biotechnology, Natural Resources Institute Finland (LUKE), Georg-August-University [Göttingen], Centre for Biodiversity and Sustainable Land Use (CBL), Universidad Politécnica de Madrid (UPM), Rothamsted Research, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Department of Agronomy, Purdue University [West Lafayette], Crop Science Group, INRES, Rheinische Friedrich-Wilhelms-Universität Bonn, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Helsinki, FACCE-MACSUR knowledge hub (031A103B), Academy of Finland through projects AI-CropPro (decision no. 316172), DivCSA (decision no. 316215), Natural Resources Institute Finland through strategic projects ClimSmartAgri and Boost-IA, German Federal Ministry of Education and Research, ‘Limpopo Living Landscapes’ project within the SPACES program (grant number 01LL1304A), IMPAC^3 project funded by the German Federal Ministry of Education and Research (FKZ 031A351A), MULCLIVAR CGL2012-38923-C02-02 from MINECO and by MACSUR01-UPM from INIA within FACCE-JPI, German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), (2851ERA01J), German Ministry of Education and Research (BMBF), 031B0039C, FACCE-MACSUR project (031A103B) through the metaprogramme on Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA), FACCE-JPI project ClimBar (Academy of Finland decision 284987), JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food, and Forestry Policies, Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat project (BB/P016855/1)., European Project: 613556,EC:FP7:KBBE,FP7-KBBE-2013-7-single-stage,WHEALBI(2014), Georg-August-University = Georg-August-Universität Göttingen, Biotechnology and Biological Sciences Research Council (BBSRC), Università degli Studi di Firenze = University of Florence (UniFI), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Helsingin yliopisto = Helsingfors universitet = University of Helsinki
Subjects: 0106 biological sciences, Mediterranean climate, Atmospheric Science, 010504 meteorology & atmospheric sciences, AIR CO2 ENRICHMENT, Climate change, Crop growth simulation, Agricultural engineering, SIMULATION-MODELS, 01 natural sciences, NITROGEN DYNAMICS, Evapotranspiration, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Precipitation, ATMOSPHERIC CO2, FIELD EXPERIMENT, TEMPERATURE, 1172 Environmental sciences, 0105 earth and related environmental sciences, 2. Zero hunger, Global and Planetary Change, Biomass (ecology), RICE PHENOLOGY, WHEAT GROWTH, Crop growth stimulation, business.industry, Model improvement, Global warming, Uncertainty, Forestry, Agriculture, 15. Life on land, 11831 Plant biology, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Impact, Boreal, 13. Climate action, 415 Other agricultural sciences, Environmental science, business, ELEVATED CO2, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: International audience; Robust projections of climate impact on crop growth and productivity by crop models are key to designing effective adaptations to cope with future climate risk. However, current crop models diverge strongly in their climate impact projections. Previous studies tried to compare or improve crop models regarding the impact of one single climate variable. However, this approach is insufficient, considering that crop growth and yield are affected by the interactive impacts of multiple climate change factors and multiple interrelated biophysical processes. Here, a new comprehensive analysis was conducted to look holistically at the reasons why crop models diverge substantially in climate impact projections and to investigate which biophysical processes and knowledge gaps are key factors affecting this uncertainty and should be given the highest priorities for improvement. First, eight barley models and eight climate projections for the 2050s were applied to investigate the uncertainty from crop model structure in climate impact projections for barley growth and yield at two sites: Jokioinen, Finland (Boreal) and Lleida, Spain (Mediterranean). Sensitivity analyses were then conducted on the responses of major crop processes to major climatic variables including temperature, precipitation, irradiation, and CO2, as well as their interactions, for each of the eight crop models. The results showed that the temperature and CO2 relationships in the models were the major sources of the large discrepancies among the models in climate impact projections. In particular, the impacts of increases in temperature and CO2 on leaf area development were identified as the major causes for the large uncertainty in simulating changes in evapotranspiration, above-ground biomass, and grain yield. Our findings highlight that advancements in understanding the basic processes and thresholds by which climate warming and CO2 increases will affect leaf area development, crop evapotranspiration, photosynthesis, and grain formation in contrasting environments are needed for modeling their impacts.
Published: 2020
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28. Understanding effects of genotype × environment × sowing window interactions for durum wheat in the Mediterranean basin

Author: Alberto Masoni, G. Padovan, Roberto Ferrise, Domenico Ventrella, Mikhail A. Semenov, Camilla Dibari, Marco Bindi, Ignacio J. Lorite, Pierre Martre, Simone Bregaglio, C. Santos, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Rothamsted Research, Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), IFAPA Centro Alameda del Obispo, Instituto Andaluz de Investigación y Formación Agraria y Pesquera (IFAPA), FACCE JPI MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies (D.M. n. 24064/7303/15), Biotechnology and Biological Sciences Research Council, through the Designing Future Wheat (DFW) program (BB/P016855/1), AgriDigit-Agromodelli project (DM n. 36502 of 20/12/2018), funded by the Italian Ministry of Agricultural, Food and Forestry Policies and Tourism, project PR.AVA2018.051 (Innova-Clima) funded by the European Regional Development Fund (FEDER), European Project: 727247,H2020,H2020-SFS-2016-2,SolACE(2017), Università degli Studi di Firenze = University of Florence (UniFI), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Biotechnology and Biological Sciences Research Council (BBSRC), and Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria = Council for Agricultural Research and Economics (CREA)
Subjects: 0106 biological sciences, Mediterranean climate, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Sowing window, Soil Science, Growing season, Biology, 01 natural sciences, Mediterranean Basin, SiriusQuality, Anthesis, Mediterranean environments, Cultivar, SolACEWP1, Durum wheat, 2. Zero hunger, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, Biomass (ecology), Sowing, 04 agricultural and veterinary sciences, 15. Life on land, Agronomy, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Crop simulation model, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: Durum wheat is one of the most important crops in the Mediterranean basin. The choice of the cultivar and the sowing time are key management practices that ensure high yield. Crop simulation models could be used to investigate the genotype × environment × sowing window (G × E×SW) interactions in order to optimize farmers’ actions. The aim of this study was to evaluate the performance of the wheat model SiriusQuality in simulating durum wheat yields in Mediterranean environments and its potential to explore the G × E×SW interactions. SiriusQuality was assessed in multiple growing seasons at seven sites located in Italy, Spain and Morocco, where locally adapted cultivars were grown. The model showed good ability in predicting anthesis and maturity date (Pearson r >0.8), as well as above ground biomass and grain yield (6 % < nRMSE < 18 %). The model was then used to find the optimal 30-day sowing window to maximize grain yields at four sites, two were located in Italy (Florence, Foggia), and the other two were in Spain (Santaella) and Morocco (Sidi El Aydi) respectively. Among the cultivars, on the average between all sowing window, Amilcar had the best performance in Foggia (+33 % compared to the traditional cultivar Simeto) and in Sidi El Aydi (+22 % compared to Karim), Karim in Florence (+19 % compared to Creso) and in Santaella (+6 % compared to Amilcar). Instead Creso and Simeto showed the lowest production at all locations. The results showed that an earlier sowing window compared to the traditional one would have a positive effect on wheat yields in all environments tested, because of increased maximum leaf area index, grain number and size, and grain filling duration. Moreover, with earlier sowing, grain filling coincides with higher soil water availability, reducing the water stress and increasing the accumulation of dry mass in grains. In cooler and wetter locations, cultivars characterized by higher leaf area index and radiation use efficiency had the higher number of grains, while in the hottest and driest locations, short-cycle cultivars with high grain dry matter potential (e.g. through enhanced “stay green” capacity) should be preferred.
Published: 2020
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29. iCROPM 2020: Crop Modeling for the Future

Author: Christophe Pradal, Pierre Martre, Eric Justes, Franck Ewert, Marie Launay, Gerrit Hoogenboom, Senthold Asseng, University of Florida [Gainesville] (UF), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Agroclim (AGROCLIM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), University of Bonn, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Technical University of Munich (TUM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Universität Bonn = University of Bonn, and Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 010504 meteorology & atmospheric sciences, Computer science, 01 natural sciences, Crop, F01 - Culture des plantes, Genetics, Climate change, Model development, Environmental impact assessment, Cropping system, Modélisation environnementale, 0105 earth and related environmental sciences, U10 - Informatique, mathématiques et statistiques, Modélisation des cultures, business.industry, Environmental resource management, 04 agricultural and veterinary sciences, food security, Private sector, simulation, sustainability, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Outreach, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Animal Science and Zoology, business, Agronomy and Crop Science
Abstract: During the past decade, the interest in using crop models for research, education, extension, outreach and in the private sector has rapidly increased. The iCROPM 2020 Symposium entitled ‘Crop Modeling for the Future’, held in February 2020, therefore, provided a great opportunity for over 400 scientists from 50 different countries to exchange information on crop model development, evaluation with experimental data and implementation. A key outcome was the understanding that crop models simulate the dynamics of the soil-plant-atmosphere continuum. Thus, the models can be used for a quantitative and multi-criteria assessment of cropping system functioning, agronomic performance and environmental impact. This should result in more practical applications that can provide actionable information for stakeholders.
Published: 2020
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30. Reuse of process-based models: Automatic transformation into many programming languages and simulation platforms

Author: Ioannis N. Athanasiadis, Christophe Pradal, Cyrille Ahmed Midingoyi, Frédérick Garcia, Cheryl Porter, Peter J. Thorburn, Pierre Martre, Helene Raynal, Gerrit Hoogenboom, Marcello Donatelli, Davide Fumagalli, Dean Holzworth, Andreas Enders, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Wageningen University and Research [Wageningen] (WUR), Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, European Commission - Joint Research Centre [Ispra] (JRC), JRC Institute for Environment and Sustainability (IES), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), University of Florida [Gainesville] (UF), AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016), European Project: 817617,H2020,IPM Decisions(2019), European Project: 810775,DRAGON, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria = Council for Agricultural Research and Economics (CREA), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Source code, 010504 meteorology & atmospheric sciences, Java, Computer science, media_common.quotation_subject, Plant Science, Reuse, computer.software_genre, 01 natural sciences, Biochemistry, Genetics and Molecular Biology (miscellaneous), Laboratory of Geo-information Science and Remote Sensing, F01 - Culture des plantes, Laboratorium voor Geo-informatiekunde en Remote Sensing, Crop model, 0105 earth and related environmental sciences, media_common, Reusability, computer.programming_language, business.industry, Programming language, U10 - Informatique, mathématiques et statistiques, Modélisation des cultures, model reuse, source transformation, Modèle de simulation, 04 agricultural and veterinary sciences, Modular design, Python (programming language), PE&RC, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, software reuse, transpiler, Modeling and Simulation, 040103 agronomy & agriculture, Programming paradigm, 0401 agriculture, forestry, and fisheries, Software design, business, Agronomy and Crop Science, computer
Abstract: The diversity of plant and crop process-based modelling platforms in terms of implementation language, software design and architectural constraints limits the reusability of the model components outside the platform in which they were originally developed, making model reuse a persistent issue. To facilitate the intercomparison and improvement of process-based models and the exchange of model components, several groups in the field joined to create the Agricultural Model Exchange Initiative (AMEI). Agricultural Model Exchange Initiative proposes a centralized framework for exchanging and reusing model components. It provides a modular and declarative approach to describe the specification of unit models and their composition. A model algorithm is associated with each model specification, which implements its mathematical behaviour. This paper focuses on the expression of the model algorithm independently of the platform specificities, and how the model algorithm can be seamlessly integrated into different platforms. We define CyML, a Cython-derived language with minimum specifications to implement model component algorithms. We also propose CyMLT, an extensible source-to-source transformation system that transforms CyML source code into different target languages such as Fortran, C#, C++, Java and Python, and into different programming paradigms. CyMLT is also able to generate model components to target modelling platforms such as DSSAT, BioMA, Record, SIMPLACE and OpenAlea. We demonstrate our reuse approach with a simple unit model and the capacity to extend CyMLT with other languages and platforms. The approach we present here will help to improve the reproducibility, exchange and reuse of process-based models.
Published: 2020
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31. Pan-European multi-crop model ensemble simulations of wheat and grain maize under climate change scenarios

Author: Giacomo Trombi, Heidi Webber, C. Gabaldón-Leal, Diane Cooke, Jon I. Lizaso, Loic Manceau, Thomas Gaiser, Pierre Martre, Jørgen E. Olesen, Tommaso Stella, Alex C. Ruane, Alfredo Rodríguez, Stefan Fronzek, Claas Nendel, Frank Ewert, Roberto Ferrise, Mohamed Jabloun, Margarita Ruiz-Ramos, Mikhail A. Semenov, Marco Moriondo, Marco Bindi, Ignacio J. Lorite, Nándor Fodor, Kurt Christian Kersebaum, Pierre Stratonovitch, and Brian Collins
Subjects: 0303 health sciences, Climate change, Representative Concentration Pathways, 04 agricultural and veterinary sciences, PE&RC, Atmospheric sciences, Crop, Set (abstract data type), Data set, 03 medical and health sciences, Plant Production Systems, Pan european, Plantaardige Productiesystemen, General Circulation Model, 040103 agronomy & agriculture, Life Science, 0401 agriculture, forestry, and fisheries, Environmental science, Baseline (configuration management), 030304 developmental biology
Abstract: The simulated data set described in this paper was created by an ensemble of nine different crop models: HERMES (HE), Simplace (L5), SiriusQuality (SQ), MONICA (MO), Sirius2014 (S2), FASSET (FA), 4M (4M), SSM (SS), DSSAT-CSM IXIM (IX). Simulations were performed for grain maize (six models) and winter wheat (eight models) under diverse conditions over agriculturally relevant areas in the EU-27 at a 25 x 25 km spatial resolution. Simulations were drawn from combinations of three representative concentration pathways and climate outputs from five general circulation models for time periods 2040-2069 and 2070-2099. Historical climate data was the basis for simulation years 1980-2010 and considered as a baseline. Simulation results from 1980-2010 and 2040-2069 were used to analyze crop responses to changing climatic variables and their diverging sensitivities to these variables. This data paper describes the creation, motivation and format of the simulation results to enable others to use the data set.
Published: 2020
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32. Crop simulation model inter-comparison and improvement

Author: Frank Ewert, Pierre Martre, and Senthold Asseng
Subjects: Agricultural engineering, Crop simulation model, Mathematics
Published: 2019
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33. Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Author: Mónica Espadafor, Bing Liu, Frank Ewert, Mukhtar Ahmed, Liujun Xiao, Thilo Streck, Senthold Asseng, Soora Naresh Kumar, Gerrit Hoogenboom, John R. Porter, Sara Minoli, Margarita Garcia-Vila, Joost Wolf, Juraj Balkovic, Alex C. Ruane, Giacomo De Sanctis, Pierre Martre, Roberto C. Izaurralde, Marijn van der Velde, Dominique Ripoche, Roberto Ferrise, Davide Cammarano, Fulu Tao, Bruno Basso, Christoph Müller, Heidi Webber, Yujing Gao, Andrea Maiorano, Christian Klein, Ann-Kristin Koehler, Andrew J. Challinor, Reimund P. Rötter, Garry O'Leary, Manuel Montesino San Martin, Eckart Priesack, Peter J. Thorburn, Heidi Horan, Kurt Christian Kersebaum, Iwan Supit, Zhigan Zhao, Taru Palosuo, Belay T. Kassie, Christian Biernath, Pramod K. Aggarwal, Katharina Waha, Sebastian Gayler, Daniel Wallach, Yan Zhu, Marco Bindi, Zhao Zhang, Claas Nendel, Enli Wang, Curtis D. Jones, Ehsan Eyshi Rezaei, Mikhail A. Semenov, Claudio O. Stöckle, Benjamin Dumont, National Science Foundation (US), National Natural Science Foundation of China, International Food Policy Research Institute (US), CGIAR (France), Institut National de la Recherche Agronomique (France), Federal Ministry of Education and Research (Germany), Biotechnology and Biological Sciences Research Council (UK), China Scholarship Council, Department of Agriculture and Water Resources (Australia), Ministero delle Politiche Agricole Alimentari e Forestali, Gorgan University, Victoria State Government, National Institute of Food and Agriculture (US), Federal Ministry of Food and Agriculture (Germany), German Research Foundation, Academy of Finland, LabEx Agro, Natural Resources Institute Finland, National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), University of Copenhagen = Københavns Universitet (KU), Lincoln University, University of Leeds, CGIAR-ESSP Program on Climate Change,Agriculture and Food Security, International Center for Tropical Agriculture, Potsdam Institute for Climate Impact Research (PIK), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), International Maize and Wheat Improvement Centre [Inde] (CIMMYT), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Biological Systems Engineering, Washington State University (WSU), Department of Agronomy, University of El-Tarf, International Institute for Applied Systems Analysis, Ecosystem Services and Management Program, affiliation inconnue, Comenius University in Bratislava, Department of Earth and Environmental Sciences [East Lansing], Michigan State University [East Lansing], Michigan State University System-Michigan State University System, W. K. Kellogg Biological Station (KBS), Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Department of Agrifood Production and Environmental Sciences (DISPAA), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), The James Hutton Institute, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Université de Liège, University of Córdoba, Department of Crop Sciences, Georg-August-University [Göttingen], Institute of Soil Science and Land Evaluation, University of Hohenheim, 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), Food Systems Institute [Gainesville] (UF|IFAS), Department of Geographical Sciences, College Park, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, European Food Safety Authority (EFSA), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Agriculture Victoria Research, Institute for Natural Resources, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen - Georg-August-Universität Göttingen, Rothamsted Research, Wageningen University and Research Centre (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), European Commission - Joint Research Centre [Ispra] (JRC), 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, Plant Production Systems, Wageningen University and Research [Wageningen] (WUR), State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University (BNU), Department of Agronomy and Biotechnology, China Agricultural University (CAU), Agricultural Model Intercomparison and Improvement Project (AgMIP), and Biotechnology and Biological Sciences Research Council (BBSRC), Grant/Award Number: BB/, P016855/1
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0106 biological sciences, 010504 meteorology & atmospheric sciences, Food prices, Water en Voedsel, Climate change, Atmospheric sciences, 010603 evolutionary biology, 01 natural sciences, Model ensemble, Extreme low yields, model ensemble, 1.5°C warming, Temperate climate, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Environmental Chemistry, 0105 earth and related environmental sciences, General Environmental Science, 2. Zero hunger, Global and Planetary Change, WIMEK, Water and Food, Food security, Ecology, business.industry, wheat production, Crop yield, Global warming, food security, 15. Life on land, PE&RC, climate change, Plant Production Systems, 13. Climate action, Agriculture, Plantaardige Productiesystemen, extreme low yields, 8. Economic growth, Environmental science, Wheat production, Water Systems and Global Change, business, Cropping
Abstract: Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming, We thank the Agricultural Model Intercomparison and Improvement Project (AgMIP) for support. B.L., L.X., and Y.Z. were supported by the National Science Foundation for Distinguished Young Scholars (31725020), the National Natural Science Foundation of China (31801260, 51711520319, and 31611130182), the Natural Science Foundation of Jiangsu province (BK20180523), the 111 Project (B16026), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). S.A. and B.K. received support from the International Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), and the CGIAR Research Program on Wheat. P.M, D.R., and D.W. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). F.T. and Z.Z. were supported by the National Natural Science Foundation of China (41571088, 41571493, 31761143006, and 31561143003). R.R. acknowledges support from the German Federal Ministry for Research and Education (BMBF) through project “Limpopo Living Landscapes” project (SPACES program; grant number 01LL1304A). Rothamsted Research receives grant‐aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat project [BB/P016855/1]. L.X. and Y.G. acknowledge support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan Univ. of Agric. Sci. & Natur. Resour for his support. K.C.K. and C.N. received support from the German Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through BMBF. G.J.O. acknowledges support from the Victorian Department of Economic Development, Jobs, Transport and Resources, the Australian Department of Agriculture and Water Resources. P.K.A. was supported by the multiple donors contributing to the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). B.B. received financial support from USDA NIFA‐Water Cap Award 2015‐68007‐23133. F.E. acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of Food and Agriculture (2815ERA01J) and from the German Science Foundation (project EW 119/5‐1). J.R.P. acknowledges the support of the Labex Agro (Agropolis no. 1501‐003). La. T.P. and F.T. received financial support from the Academy of Finland through the project PLUMES (decision nos. 277403 and 292836) and from Natural Resources Institute Finland through the project ClimSmartAgri.
Published: 2019
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34. An AgMIP framework for improved agricultural representation in IAMs

Author: Alex C, Ruane, Cynthia, Rosenzweig, Senthold, Asseng, Kenneth J, Boote, Joshua, Elliott, Frank, Ewert, James W, Jones, Pierre, Martre, Sonali P, McDermid, Christoph, Müller, Abigail, Snyder, and Peter J, Thorburn
Subjects: Article
Abstract: Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.
Published: 2019

35. Estimation of Plant and Canopy Architectural Traits Using the Digital Plant Phenotyping Platform

Author: Pierre Martre, Samuel Buis, Shouyang Liu, Mariem Abichou, Frédéric Baret, and Bruno Andrieu
Subjects: 0106 biological sciences, 2. Zero hunger, Canopy, Physiology, Plant Science, Green area index, Plant phenotyping, 01 natural sciences, Crop, Degree day, Time course, Genetics, Phyllochron, Biological system, Architectural model, SolACEWP2, 010606 plant biology & botany, Mathematics
Abstract: The extraction of desirable heritable traits for crop improvement from high-throughput phenotyping (HTP) observations remains challenging. We developed a modeling workflow named “Digital Plant Phenotyping Platform” (D3P), to access crop architectural traits from HTP observations. D3P couples the Architectural model of DEvelopment based on L-systems (ADEL) wheat (Triticum aestivum) model (ADEL-Wheat), which describes the time course of the three-dimensional architecture of wheat crops, with simulators of images acquired with HTP sensors. We demonstrated that a sequential assimilation of the green fraction derived from Red–Green–Blue images of the crop into D3P provides accurate estimates of five key parameters (phyllochron, lamina length of the first leaf, rate of elongation of leaf lamina, number of green leaves at the start of leaf senescence, and minimum number of green leaves) of the ADEL-Wheat model that drive the time course of green area index and the number of axes with more than three leaves at the end of the tillering period. However, leaf and tiller orientation and inclination characteristics were poorly estimated. D3P was also used to optimize the observational configuration. The results, obtained from in silico experiments conducted on wheat crops at several vegetative stages, showed that the accessible traits could be estimated accurately with observations made at 0° and 60° zenith view inclination with a temporal frequency of 100 °Cd (degree day). This illustrates the potential of the proposed holistic approach that integrates all the available information into a consistent system for interpretation. The potential benefits and limitations of the approach are further discussed.
Published: 2019
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36. Plant and crop simulation models: powerful tools to link physiology, genetics, and phenomics

Author: Bertrand Muller, Pierre Martre, 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), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)
Subjects: 0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Genotype, Physiology, Plant Science, Computational biology, 01 natural sciences, 03 medical and health sciences, Phenomics, Crop simulation models, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Link (knot theory), Genetic Association Studies, 030304 developmental biology, 2. Zero hunger, 0303 health sciences, Model improvement, Models, Theoretical, Plants, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Genotype to phenotype prediction, Phenotype, 13. Climate action, Environmental science, Crop simulation model, 010606 plant biology & botany
Abstract: International audience; Plant and crop simulation models are powerful tools for predicting the impact of climate change, innovative crop management practices, and new trait- or gene-based breeding technologies on the production of crops and agricultural systems. In this special issue, we gather a collection of review, opinion, and primary research papers that represent the current status and future of crop and plant simulation models. The models presented in these papers have been informed and improved by new hypotheses and concepts from (eco)physiology, fueled by phenomics data, and used to bridge the gap between genes/genotypes and phenotypes.
Published: 2019
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37. Model-Driven Multidisciplinary Global Research to Meet Future Needs: The Case for 'Improving Radiation Use Efficiency to Increase Yield'

Author: Brian L. Beres, Pierre Martre, P. Stephen Baenziger, Hans-Joachim Braun, Jacques Le Gouis, Jérôme Salse, M. Fernanda Dreccer, Matthew P. Reynolds, Frank Ewert, Senthold Asseng, Peter Langridge, University of Florida [Gainesville] (UF), 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), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Agriculture and Agri-Food [Ottawa] (AAFC), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), University of Adelaide, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), University of Nebraska System, and Agriculture and Agri-Food (AAFC)
Subjects: 2. Zero hunger, 0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Yield (finance), 04 agricultural and veterinary sciences, Agricultural engineering, 15. Life on land, Biology, 01 natural sciences, Multidisciplinary approach, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: International audience; Improving wheat (Triticum aestivum L.) yields to meet the projected demand for food in the future requires the talents of diverse scientists. In this article, we present the rationale for crop-model-driven, trait-focused collaborative research emphasizing radiation use efficiency (RUE). Improving RUE is one of the promising avenues to substantially increase potential yields. A collaboration of crop modelers, physiologists, geneticists, plant breeders, and system agronomists is proposed to efficiently create genetic improvements that, when taken to the field, will have regional and global yield impacts. The effort will require a large, internationally integrated science project and related infrastructure such as was recently developed in the Wheat Initiative. The Wheat Initiative is an international consortium of scientists and public and industry funders to sustainably improve global wheat production. The proposed integrated research is applicable to explore other traits and trait combinations and could become a model for yield improvements for major food crops.
Published: 2019
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38. Using crop growth model stress covariates and AMMI decomposition to better predict genotype-by-environment interactions

Author: Pierre Martre, F.A. van Eeuwijk, Gaëtan Touzy, Agathe Mini, Renaud Rincent, J. Le Gouis, Matthieu Bogard, Behnam Ababaei, Marcos Malosetti, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Biometris, Wageningen University and Research [Wageningen] (WUR), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Groupe Limagrain, ARVALIS - Institut du végétal [Paris], Genetics and Genomics in Cereals, BIOGEMMA, French National Research National Agency under Investment for the Future (BreedWheat Project ANR-10-BTBR-03), FranceAgriMer., French National Research National Agency under the Investment for the Future Phenome project (ANR-11-INBS-12), European Regional Development Fund (AV0011535), AgreenSkills + fellowship Grant Agreement No. FP7- 609398, ANR-10-BTBR-0003,BREEDWHEAT,Développer de nouvelles variétés de blé pour une agriculture durable(2010), ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), European Project: 609398,EC:FP7:PEOPLE,FP7-PEOPLE-2013-COFUND,AGREENSKILLSPLUS(2014), and 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)
Subjects: Crops, Agricultural, 0106 biological sciences, Mixed model, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Genotype, Context (language use), 01 natural sciences, Wiskundige en Statistische Methoden - Biometris, Similarity (network science), Covariate, Statistics, Genetics, Decomposition (computer science), Life Science, SolACEWP4, Mathematical and Statistical Methods - Biometris, Triticum, 2. Zero hunger, Models, Statistical, Models, Genetic, biology, food and beverages, Regression analysis, Ammi, General Medicine, 15. Life on land, Covariance, biology.organism_classification, PE&RC, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Phenotype, 13. Climate action, Gene-Environment Interaction, Agronomy and Crop Science, 010606 plant biology & botany, Biotechnology
Abstract: Key message: We propose new methods to predict genotype × environment interaction by selecting relevant environmental covariates and using an AMMI decomposition of the interaction. Abstract: Farmers are asked to produce more efficiently and to reduce their inputs in the context of climate change. They have to face more and more limiting factors that can combine in numerous stress scenarios. One solution to this challenge is to develop varieties adapted to specific environmental stress scenarios. For this, plant breeders can use genomic predictions coupled with environmental characterization to identify promising combinations of genes in relation to stress covariates. One way to do it is to take into account the genetic similarity between varieties and the similarity between environments within a mixed model framework. Molecular markers and environmental covariates (EC) can be used to estimate relevant covariance matrices. In the present study, based on a multi-environment trial of 220 European elite winter bread wheat (Triticum aestivum L.) varieties phenotyped in 42 environments, we compared reference regression models potentially including ECs, and proposed alternative models to increase prediction accuracy. We showed that selecting a subset of ECs, and estimating covariance matrices using an AMMI decomposition to benefit from the information brought by the phenotypic records of the training set are promising approaches to better predict genotype-by-environment interactions (G × E). We found that using a different kinship for the main genetic effect and the G × E effect increased prediction accuracy. Our study also demonstrates that integrative stress indexes simulated by crop growth models are more efficient to capture G × E than climatic covariates.
Published: 2019
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39. Experimental and modeling evidence of carbon limitation of leaf appearance rate for spring and winter wheat

Author: Boris Parent, Steven M. Driever, Pierre Martre, Maeva Baumont, Hamish E. Brown, Loic Manceau, Bertrand Muller, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Plant & Food Research, Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University and Research [Wageningen] (WUR), French National Research Agency (ANR) through the ERANET + project MODCARBOSTRESS, Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI, grant agreement no. 618105), ANR-14-JFAC-0003,MODCARBOSTRESS,Improving models and plant phenotyping pipelines for a smart agriculture under abiotic stress combination and elevated CO2(2014), European Project: 618105,EC:FP7:KBBE,FP7-ERANET-2013-RTD,FACCE ERA NET PLUS(2013), and 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)
Subjects: 0106 biological sciences, 0301 basic medicine, Physiology, [SDV]Life Sciences [q-bio], Winter wheat, Irradiance, Plant Science, Atmospheric sciences, 01 natural sciences, modèle de croissance, quotient photothermique, SiriusQuality, wheat, Phyllochron, Triticum, 2. Zero hunger, photoperiodism, allocation de carbone, PE&RC, Plant development, [SDE]Environmental Sciences, Centre for Crop Systems Analysis, écophysiologie végétale, Crop Physiology, croissance foliaire, chemistry.chemical_element, photoperiod, Models, Biological, 03 medical and health sciences, taux d'apparition des feuilles, développement de la plante, température, crop model, leaf appearance rate, photothermal quotient, Sowing, temperature, photopériode, Carbon, Plant Leaves, 030104 developmental biology, chemistry, 13. Climate action, phyllochron, Environmental science, daylength, Crop model, Daylength, Leaf appearance rate, Photoperiod, Photothermal quotient, Temperature, Wheat, 010606 plant biology & botany, Field conditions
Abstract: International audience; Accurate predictions of the timing of physiological stages and the development rate are crucial for predicting crop performance under field conditions. Plant development is controlled by the leaf appearance rate (LAR) and our understanding of how LAR responds to environmental factors is still limited. Here, we tested the hypothesis that carbon availability may account for the effects of irradiance, photoperiod, atmospheric CO2 concentration, and ontogeny on LAR. We conducted three experiments in growth chambers to quantify and disentangle these effects for both winter and spring wheat cultivars. Variations of LAR observed between environmental scenarios were well explained by the supply/demand ratio for carbon, quantified using the photothermal quotient. We therefore developed an ecophysiological model based on the photothermal quotient that accounts for the effects of temperature, irradiance, photoperiod, and ontogeny on LAR. Comparisons of observed leaf stages and LAR with simulations from our model, from a linear thermal-time model, and from a segmented linear thermal-time model corrected for sowing date showed that our model can simulate the observed changes in LAR in the field with the lowest error. Our findings demonstrate that a hypothesis-driven approach that incorporates more physiology in specific processes of crop models can increase their predictive power under variable environments.
Published: 2019
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40. Testing a crop model with extreme low yields from historical district records

Author: Senthold Asseng, Jose Rafael Guarin, Pierre Martre, Nikolay Bliznyuk, University of Florida [Gainesville] (UF), 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), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)
Subjects: 0106 biological sciences, Soil Science, 01 natural sciences, Crop, Wheat stress, Extreme weather, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Precipitation, Crop model, 2. Zero hunger, food and beverages, Model testing, Food security, 04 agricultural and veterinary sciences, Extreme events, 15. Life on land, Historical district yield records, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, 13. Climate action, Excess water, Climatology, Soil water, Frost, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Grain yield, Environmental science, Crop simulation model, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: International audience; Extreme weather events across the world cause large variations in daily seasonal temperature and precipitation, potentially reducing grain yield and negatively affecting global food security. Thus, it is important to assess if crop growth models can simulate the yield losses caused by these extreme weather events. We tested if the DSSAT-NWheat crop simulation model, which has been successfully validated with many controlled field-experimental data across the world, can reproduce historical extreme low-yielding years at three global locations in the USA, France, and Australia. The crop model reproduced extreme low yields in some of the driest and hottest years at all three locations. However, the model failed to simulate some of the observed extreme low-yielding years. Also, the model simulated extreme low yields in some years that were not extreme low yielding in the observed records. Surprisingly, some of the driest and hottest years did not show up as observed extreme low-yielding years in the district records. This discrepancy could be explained by reporting yields, or in this case “not reporting extreme low yields” in district records, as indicated by large drops in harvested areas in extreme dry and hot seasons. Other discrepancies exist because crop models do not often consider many factors under farmer-field conditions that lead to extreme low yields in district records, including frost, hail and lodging, pests and diseases, and excess water. Historical district yield records are also limited for model testing due to unknown and changing aggregation across a district, possible omission of extreme low-yielding fields in some years, and unknown spatial and temporal varying cultivars and crop management, particularly in initial soil water conditions. In conclusion, we do not recommend using historical district yield records for model testing of extreme low yields.
Published: 2020
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41. Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Author: Xenia Specka, Mikhail A. Semenov, Carlos Gregorio Hernández Díaz-Ambrona, Frank Ewert, Roberto Ferrise, Margarita Ruiz-Ramos, Fulu Tao, Pierre Martre, Alan H. Schulman, Holger Hoffmann, Marco Bindi, Jukka Höhn, Thomas Gaiser, Anaëlle Dambreville, Claas Nendel, Taru Palosuo, Davide Cammarano, Lucía Rodríguez, M. Ines Minguez, Kurt Christian Kersebaum, Reimund P. Rötter, Tapio Salo, Natural Resources Institute Finland (LUKE), Georg-August-University [Göttingen], Centre for Biodiversity and Sustainable Land Use (CBL), University of Madrid, Biotechnology and Biological Sciences Research Council, Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, Leibniz Association, Crop Science Group, INRES, Rheinische Friedrich-Wilhelms-Universität Bonn, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Department of Agri-Food Production and Environmental Sciences, University delgi Studi di Firenze, The James Hutton Institute, University of Helsinki, FACCE-JPI, the FACCE-MACSUR, Grant/Award Number: 031A103B, Finland Ministry of Agriculture and Forestry, FACCE-MACSUR, Academy of Finland, the NORFASYS, Grant/Award Number: 268277, 292944, PLUMES, Grant/Award Number: 277403, 292836, German Federal Ministry of Education and Research, Grant/Award Number: 01LL1304A, 031A351A, Spanish Ministry of Economy, Industry and Competitiveness, the MULCLIVAR, Grant/Award Number: CGL2012-38923- C02-02, German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), Grant/Award Number: 2851ERA01J, German Ministry of Education and Research BMBF), Grant/Award Number: 031B0039C, French National Institute for Agricultural Research, Grant/Award Number: 031A103B, Italian Ministry for Agricultural, Food, and Forestry Policies, Biotechnology and Biological Sciences Research Council (BBSRC), Grant/Award Number: BB/P016855/1, European Project: 613556,EC:FP7:KBBE,FP7-KBBE-2013-7-single-stage,WHEALBI(2014), Georg-August-University = Georg-August-Universität Göttingen, Biotechnology and Biological Sciences Research Council (BBSRC), 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), Università degli Studi di Firenze = University of Florence (UniFI), and Helsingin yliopisto = Helsingfors universitet = University of Helsinki
Subjects: Mediterranean climate, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Crops, Agricultural, Time Factors, 010504 meteorology & atmospheric sciences, Yield (finance), Climate Change, Climate change, Super-ensemble, 01 natural sciences, Models, Biological, Crop, Barley, Environmental Chemistry, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Geología, Finland, 0105 earth and related environmental sciences, General Environmental Science, Global and Planetary Change, Ecology, Impact assessment, Arctic Regions, Mediterranean Region, Agricultura, Simulation modeling, Probabilistic logic, Uncertainty, 04 agricultural and veterinary sciences, Europe, Impact, Boreal, 13. Climate action, Spain, Climatology, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Forecasting
Abstract: International audience; Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was −4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981–2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.
Published: 2018
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42. The Hot Serial Cereal Experiment for modeling wheat response to temperature: Field experiments and AgMIP-Wheat multi-model simulations

Author: Roberto C. Izaurralde, Jakarat Anothai, Andrew J. Challinor, Reimund P. Rötter, Jørgen E. Olesen, Curtis D. Jones, Bing Liu, T. Palosuo, Peter J. Thorburn, Kurt Christian Kersebaum, Mohamed Jabloun, Iwan Supit, Frank Ewert, Mikhail A. Semenov, Margarita Garcia-Vila, Claudio O. Stöckle, Benjamin Dumont, Belay T. Kassie, Jordi Doltra, Christian Biernath, Dominique Ripoche, Giacomo De Sanctis, Enli Wang, Elias Fereres, Bruce A. Kimball, Thilo Streck, Gerard W. Wall, L. A. Hunt, Pierre Stratonovitch, Fulu Tao, Jeffrey W. White, Christoph Müller, Bruno Basso, Senthold Asseng, Katharina Waha, Ann-Kristin Koehler, Andrea Maiorano, Ehsan Eyshi Rezaei, Eckart Priesack, Soora Naresh Kumar, Claas Nendel, Gerrit Hoogenboom, Davide Cammarano, David B. Lobell, Joost Wolf, Pierre Martre, Pramod K. Aggarwal, Garry O'Leary, Zhigan Zhao, Michael J. Ottman, Sebastian Gayler, Yan Zhu, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Agricultural Research Service / US Arid Land Agricultural Research Center, United States Department of Agriculture, The School of Plant Sciences, University of Arizona, 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), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Consultative Group on International Agricultural Research (CGIAR), AgWeatherNet Program, Washington State University (WSU), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, German Research Center for Environmental Health - Helmholtz Center München (GmbH), University of Leeds, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Catabrian Agricultural Research and Training Center (CIFA), Universidad de Córdoba [Cordoba], Instituto de Agricultura Sostenible - Institute for Sustainable Agriculture (IAS CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, 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], Leibniz Association, Potsdam Institute for Climate Impact Research (PIK), Indian Agricultural Research Institute (IARI), 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 Environmental Earth System Science and Center on Food Security and the Environment, Stanford University, Landscape & Water Sciences, Department of Environment of Victoria, Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Computational and Systems Biology Department, Rothamsted Research, Biological Systems Engineering, PPS and WSG &CALM, Wageningen University and Research [Wageningen] (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), College of Agronomy and Biotechnology, and Southwest University
Subjects: Simulations, 0106 biological sciences, Irrigation, 010504 meteorology & atmospheric sciences, Water en Voedsel, Wheat, Field experimental data, Heat stress, Crop model simulations, AgMIP, Hot Serial Cereal, 01 natural sciences, donnée expérimentale, Crop, blé, température, Life Science, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Relative humidity, Cultivar, 0105 earth and related environmental sciences, 2. Zero hunger, WIMEK, Water and Food, Vegetal Biology, Global warming, Sowing, essai en plein champ, food and beverages, série climatique, Modélisation et simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Agronomy, Plant Production Systems, 13. Climate action, Plantaardige Productiesystemen, Modeling and Simulation, Frost, Weather data, Environmental science, Water Systems and Global Change, stress hydrique, Biologie végétale, modèle de production, 010606 plant biology & botany
Abstract: The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models. Data access via doi 10.7910/DVN/M9ZT0F
Published: 2018
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43. Maize yields over Europe may increase in spite of climate change, with an appropriate use of the genetic variability of flowering time

Author: Mikhail A. Semenov, Sébastien Lacube, Pierre Martre, François Tardieu, Boris Parent, Claude Welcker, Margot Leclere, 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), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Biotechnology and Biological Sciences Research Council, Agence Nationale de la Recherche projects ANR-10-BTBR-01 (Amaizing), ANR-11-INBS-0012 (Phenome), strategic funding from the Biotechnology and Biological Sciences Research Council., ANR-10-BTBR-0001,AMAIZING,Développer de nouvelles variétés de maïs pour une agriculture durable: une approche intégrée de la génomique à la sélection(2010), ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), European Project: 244374,EC:FP7:KBBE,FP7-KBBE-2009-3,DROPS(2010), and Biotechnology and Biological Sciences Research Council (BBSRC)
Subjects: Crops, Agricultural, 0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Yield, Time Factors, 010504 meteorology & atmospheric sciences, Management practices, Climate change, Flowers, Biology, Zea mays, 01 natural sciences, Crop, Effects of global warming, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, management practices, Genetic variability, Duration (project management), Baseline (configuration management), Fowering time, 0105 earth and related environmental sciences, 2. Zero hunger, Multidisciplinary, Agricultural Sciences, Crop yield, Modeling, Sowing, Agriculture, modeling, flowering time, Biological Sciences, Adaptation, Physiological, Europe, climate change, Agronomy, 13. Climate action, 010606 plant biology & botany
Abstract: Significance The consequences of climate change on European maize yields may become positive if farmers in 2050 use the decision rules they currently follow for adapting plant cycle duration and sowing dates to the diversity of environmental conditions. Experiments and simulations show that the current genetic variability of flowering time allows identifying a cycle duration that maximizes yield at every maize field in Europe. The assumption that farmers use this optimal cycle length in each site was supported by comparison with historical data. Simulated European production for 2050 was stable under the hypotheses of unchanged practices but was increased if farmers continued adopting the decision rules they currently use for adjusting sowing date and crop cycle duration to local environment., Projections based on invariant genotypes and agronomic practices indicate that climate change will largely decrease crop yields. The comparatively few studies considering farmers’ adaptation result in a diversity of impacts depending on their assumptions. We combined experiments and process-based modeling for analyzing the consequences of climate change on European maize yields if farmers made the best use of the current genetic variability of cycle duration, based on practices they currently use. We first showed that the genetic variability of maize flowering time is sufficient for identifying a cycle duration that maximizes yield in a range of European climatic conditions. This was observed in six field experiments with a panel of 121 accessions and extended to 59 European sites over 36 years with a crop model. The assumption that farmers use optimal cycle duration and sowing date was supported by comparison with historical data. Simulations were then carried out for 2050 with 3 million combinations of crop cycle durations, climate scenarios, management practices, and modeling hypotheses. Simulated grain production over Europe in 2050 was stable (−1 to +1%) compared with the 1975–2010 baseline period under the hypotheses of unchanged cycle duration, whereas it was increased (+4–7%) when crop cycle duration and sowing dates were optimized in each local environment. The combined effects of climate change and farmer adaptation reduced the yield gradient between south and north of Europe and increased European maize production if farmers continued to make the best use of the genetic variability of crop cycle duration.
Published: 2018
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44. Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions

Author: Marco Bindi, Behnam Ababaei, Senthold Asseng, Matthew P. Reynolds, Ehsan Eyshi Rezaei, Douglas J. Hunsaker, Heidi Webber, Bruce A. Kimball, Roberto Ferrise, Pierre Martre, Kurt Christian Kersebaum, Paul J. Pinter, Arne M. Ratjen, Adam Luig, Jerry L. Hatfield, Mikhail A. Semenov, Gerard W. Wall, Pierre Stratonovitch, Jordi Doltra, Steven W. Leavitt, Jeffrey W. White, Belay T. Kassie, Frank Ewert, Jørgen E. Olesen, Robert L. LaMorte, Henning Kage, Rheinische Friedrich-Wilhelms-Universität Bonn, USDA-ARS : Agricultural Research Service, 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), Center for Development Research (ZEF), European Biological Control Laboratory, USDA-ARS (EBCL - USDA ARS), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), 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), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), DISPAA, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Catabrian Agricultural Research and Training Center (CIFA), Institute of Crop Science and Plant Breeding, Christian-Albrechts University of Kiel, Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Department of Agroecology, Aarhus University [Aarhus], Computational and Systems Biology Department, Rothamsted Research, Laboratory of Tree-Ring Research, University of Arizona, German Science Foundation (project EW 119/5-1), FACCE JPI MACSUR project (2812ERA115), Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), CGIAR Research Program on Wheat, FACCE JPI MACSUR project (031A103B), metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) INRA, JPI FACCE MACSUR, JPI FACCE MACSUR2, and German Science Foundation (project KA 3046/8-1)
Subjects: Canopy, 010504 meteorology & atmospheric sciences, STOMATAL CONDUCTANCE, AIR CO2 ENRICHMENT, Energy balance, Soil Science, Climate change, klim, Canopy temperature, Atmospheric sciences, 01 natural sciences, Heat stress, climate change impact assessment, SPRING WHEAT, Atmospheric instability, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, FARMING SYSTEMS, Robustness (economics), Heat and drought interactions, Crop model improvement, WHEAT EVAPOTRANSPIRATION, 0105 earth and related environmental sciences, 2. Zero hunger, CLIMATE-CHANGE, MULTIMODEL ENSEMBLES, Simulation modeling, Climate change impact assessments, 04 agricultural and veterinary sciences, 15. Life on land, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, ELEVATED CARBON-DIOXIDE, Stability conditions, Agronomy, heat and drought interaction, 13. Climate action, Soil water, Wheat, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, WATER-USE EFFICIENCY, DROUGHT-STRESS, Agronomy and Crop Science
Abstract: International audience; Despite widespread application in studying climate change impacts, most crop models ignore complex interactions among air temperature, crop and soil water status, CO2 concentration and atmospheric conditions that influence crop canopy temperature. The current study extended previous studies by evaluating Tc simulations from nine crop models at six locations across environmental and production conditions. Each crop model implemented one of an empirical (EMP), an energy balance assuming neutral stability (EBN) or an energy balance correcting for atmospheric stability conditions (EBSC) approach to simulate Tc. Model performance in predicting Tc was evaluated for two experiments in continental North America with various water, nitrogen and CO2 treatments. An empirical model fit to one dataset had the best performance, followed by the EBSC models. Stability conditions explained much of the differences between modeling approaches. More accurate simulation of heat stress will likely require use of energy balance approaches that consider atmospheric stability conditions.
Published: 2018
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45. Wheat response to a wide range of temperatures, as determined from the Hot Serial Cereal (HSC) Experiment

Author: Gerard W. Wall, Pierre Martre, Michael J. Ottman, Jeffrey W. White, Bruce A. Kimball, Agricultural Research Service / US Arid Land Agricultural Research Center, United States Department of Agriculture, University of Arizona, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)
Subjects: 0106 biological sciences, 0301 basic medicine, Range (biology), 01 natural sciences, modèle de croissance, Crop, 03 medical and health sciences, date de plantation, blé, Yield (wine), température, Planting date, Climate change, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, triticum aestivum, Agricultural productivity, changement climatique, Vegetal Biology, Phenology, Global warming, Temperature, Sowing, food and beverages, Wheat, Infrared warming, 030104 developmental biology, Agronomy, Air temperature, Soil water, rendement agricole, Environmental science, écophysiologie végétale, Biologie végétale, 010606 plant biology & botany
Abstract: Data access via doi 10.7910/DVN/AEFLHB.; Temperatures are warming on a global scale, a phenomenon that likely will affect future crop productivity. Crop growth models are useful tools to predict the likely effects of these global changes on agricultural productivity and to develop strategies to maximize the benefits and minimize the detriments of such changes. However, few such models have been tested at the higher temperatures expected in the future. Therefore, a “Hot Serial Cereal” experiment was conducted on wheat (Triticum aestivum L.), the world’s foremost food and feed crop, in order to obtain a dataset appropriate for testing the high temperature performance of wheat growth models. The wheat (Cereal) was planted serially (Serial) about every six weeks for over two years at Maricopa, Arizona, USA, which experiences the whole range of temperatures at which plants grow on Earth. In addition, on six planting dates infrared heaters in a T-FACE (temperature free-air controlled enhancement) system (Hot) were deployed over one-third of the plots to warm the wheat by additional target 1.5°C during daytime and 3.0°C at night. Achieved average degrees of warming were 1.3 and 2.7°C for day and night. Overall, a dataset covering 27 differently treated wheat crops with three replicates each was obtained covering an air temperature range from -2 to 42°C. Herein, the management, soils, weather, physiology, phenology, growth, yield, quality, and other data are presented.
Published: 2018
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46. A Model of Leaf Coordination to Scale-Up Leaf Expansion from the Organ to the Canopy

Author: Pierre Martre, Anaëlle Dambreville, 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), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and European Project: 613556,EC:FP7:KBBE,FP7-KBBE-2013-7-single-stage,WHEALBI(2014)
Subjects: 0106 biological sciences, Canopy, Light, Physiology, Range (biology), Nitrogen, Climate change, Plant Science, Biology, 01 natural sciences, Models, Biological, Genetics, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Computer Simulation, Plant breeding, Leaf area index, Triticum, 2. Zero hunger, Leaf expansion, Plant Stems, Sowing, Water, 04 agricultural and veterinary sciences, Organ Size, Articles, 15. Life on land, Plant Leaves, Agronomy, Organ Specificity, SCALE-UP, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Seasons, 010606 plant biology & botany
Abstract: International audience; Process-based crop growth models are popular tools with which to analyze and understand the impact of crop management, genotype-by-environment interactions, or climate change. The ability to predict leaf area development is critical to predict crop growth, particularly under conditions of limited resources. Here, we aimed at deciphering growth coordination rules between wheat (Triticum aestivum) plant organs (i.e. between leaves within a stem, between laminae and sheaths, and between the mainstem and axillary tillers) to model the dynamics of canopy development. We found a unique relationship between laminae area and leaf rank for the mainstem and its tillers, which was robust across a range of sowing dates and plant densities. Robust relationships between laminae and sheath areas also were found, highlighting the tight control of organ growth within and between phytomers. These relationships identified at the phytomer scale were used to develop a simulation model of leaf area dynamics at the canopy level that was integrated in the wheat model SiriusQuality. The model was then evaluated using several independent experiments. The model accurately predicts leaf area dynamics under different scenarios of nitrogen and water limitations. It accounted for 85%, 64%, and 73% of the variability of the surface area of leaf cohorts, total leaf area index, and total green area index, respectively. The process-based model of the dynamics of leaf area described here is a key element to quantify the value of candidate traits for use in plant breeding and to project the impact of climate change on wheat growth.
Published: 2018
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47. Multimodel ensembles improve predictions of crop–environment–management interactions

Author: Giacomo De Sanctis, Taru Palosuo, Davide Cammarano, Frank Ewert, Soora Naresh Kumar, Roberto C. Izaurralde, Gerrit Hoogenboom, Elias Fereres, Bing Liu, Thilo Streck, Mikhail A. Semenov, Ehsan Eyshi Rezaei, Peter J. Thorburn, Claudio O. Stöckle, Benjamin Dumont, Andrea Maiorano, Eckart Priesack, Iwan Supit, Heidi Horan, Kurt Christian Kersebaum, Pierre Martre, Margarita Garcia-Vila, Dominique Ripoche, Pierre Stratonovitch, Yujing Gao, Zhao Zhang, Ann-Kristin Koehler, Curtis D Jones, Fulu Tao, Claas Nendel, Christoph Müller, Andrew J. Challinor, Reimund P. Rötter, Mukhtar Ahmed, Senthold Asseng, Christine Girousse, Bruno Basso, Christian Klein, Pramod K. Aggarwal, Joost Wolf, Glenn J. Fitzgerald, Martin K. van Ittersum, Garry O'Leary, Belay T. Kassie, Christian Biernath, Sebastian Gayler, Daniel Wallach, Sara Minoli, 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, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Nanjing Agricultural 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), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Plant Production Systems Group, Wageningen University and Research [Wageningen] (WUR), Agriculture and Food Security (CCAFS), Biological Systems Engineering, Washington State University (WSU), Department of Agronomy, University of El-Tarf, Department of Earth and Environmental Sciences [East Lansing], Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Plant Pathology, The James Hutton Institute, University of Leeds, Consultative Group on International Agricultural Research (CGIAR), GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Department Terra & AgroBioChem, Gembloux Agro‐Bio Tech, Université de Liège, Center for Development Research, University of Córdoba, Agriculture Victoria Research, University of Melbourne, Institute of Soil Science and Land Evaluation, University of Hohenheim, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), University of Florida [Gainesville] (UF), 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, Institute of landscape systems analysis, School of Earth and Environment, European Food Safety Authority (EFSA), Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), University Medical Center Göttingen (UMG), Computational and Systems Biology Department, Rothamsted Research, Water & Food and Water Systems & Global Change Group, Wageningen University, Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Plant Production Systems, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University (BNU), European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Université de Toulouse (UT)-Université de Toulouse (UT), 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), Nanjing Agricultural University (NAU), Universidad de Córdoba = University of Córdoba [Córdoba], Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), and Chinese Academy of Sciences [Beijing] (CAS)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 010504 meteorology & atmospheric sciences, Mean squared error, Climate Change, [SDE.MCG]Environmental Sciences/Global Changes, Inverse, Water en Voedsel, Monotonic function, multi-model ensemble, Environment, High skill, 01 natural sciences, crop models, ensemble mean, Statistics, Environmental Chemistry, Triticum, 0105 earth and related environmental sciences, General Environmental Science, Mathematics, 2. Zero hunger, ensemble median, Global and Planetary Change, WIMEK, Water and Food, Ecology, Ensemble average, Simulation modeling, Agriculture, 04 agricultural and veterinary sciences, Variance (accounting), prediction, Models, Theoretical, PE&RC, Plant Production Systems, multimodel ensemble, 13. Climate action, Plantaardige Productiesystemen, climate change impact, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Water Systems and Global Change, Environment management
Abstract: International audience; A recent innovation in assessment of climate change impact on agricultural production has been to use crop multi model ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e‐mean) and median (e‐median) often seem to predict quite well. However few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e‐mean and e‐median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e‐mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2‐6 models if best‐fit models are added first. Our theoretical results describe the ensemble using four parameters; average bias, model effect variance, environment effect variance and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e‐mean will always be smaller than MSEP averaged over models, and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e‐mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e‐mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.
Published: 2018
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48. Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions

Author: Enli Wang, Pierre Martre, Zhigan Zhao, Frank Ewert, Andrea Maiorano, Reimund P. Rötter, Bruce A. Kimball, Michael J. Ottman, Gerard W. Wall, Jeffrey W. White, Matthew P. Reynolds, Phillip D. Alderman, Pramod K. Aggarwal, Jakarat Anothai, Bruno Basso, Christian Biernath, Davide Cammarano, Andrew J. Challinor, Giacomo De Sanctis, Jordi Doltra, Elias Fereres, Margarita Garcia-Vila, Sebastian Gayler, Gerrit Hoogenboom, Leslie A. Hunt, Roberto C. Izaurralde, Mohamed Jabloun, Curtis D. Jones, Kurt C. Kersebaum, Ann-Kristin Koehler, Leilei Liu, Christoph Müller, Soora Naresh Kumar, Claas Nendel, Garry O’Leary, Jørgen E. Olesen, Taru Palosuo, Eckart Priesack, Ehsan Eyshi Rezaei, Dominique Ripoche, Alex C. Ruane, Mikhail A. Semenov, Iurii Shcherbak, Claudio Stöckle, Pierre Stratonovitch, Thilo Streck, Iwan Supit, Fulu Tao, Peter Thorburn, Katharina Waha, Daniel Wallach, Zhimin Wang, Joost Wolf, Yan Zhu, and Senthold Asseng
Subjects: Plant Science
Published: 2017
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49. The uncertainty of crop yield projections is reduced by improved temperature response functions

Author: Alex C. Ruane, Peter J. Thorburn, Mikhail A. Semenov, Joost Wolf, Claudio O. Stöckle, Pramod K. Aggarwal, Gerard W. Wall, Margarita Garcia-Vila, Matthew P. Reynolds, Eckart Priesack, Jørgen E. Olesen, Enli Wang, Bruce A. Kimball, Jordi Doltra, Iurii Shcherbak, Ehsan Eyshi Rezaei, Jeffrey W. White, Leilei Liu, L. A. Hunt, Senthold Asseng, Frank Ewert, Yan Zhu, Fulu Tao, Christoph Müller, Daniel Wallach, Christian Biernath, Davide Cammarano, Mohamed Jabloun, Zhigan Zhao, Michael J. Ottman, Pierre Martre, Sebastian Gayler, Garry O'Leary, Zhimin Wang, Jakarat Anothai, Elias Fereres, Claas Nendel, Bruno Basso, Thilo Streck, Curtis D. Jones, Andrea Maiorano, Phillip D. Alderman, Andrew J. Challinor, Reimund P. Rötter, Taru Palosuo, Iwan Supit, Katharina Waha, Giacomo De Sanctis, Kurt Christian Kersebaum, Soora Naresh Kumar, Gerrit Hoogenboom, Dominique Ripoche, Pierre Stratonovitch, Ann-Kristin Koehler, Roberto C. Izaurralde, Commonwealth Scientific and Industrial Research Organisation (Australia), Chinese Academy of Sciences, China Scholarship Council, Ministry of Education of the People's Republic of China, Institut National de la Recherche Agronomique (France), European Commission, International Food Policy Research Institute (US), CGIAR (France), Department of Agriculture (US), Federal Ministry of Education and Research (Germany), Deutsche Gesellschaft für Internationale Zusammenarbeit, Danish Council for Strategic Research, Federal Ministry of Food and Agriculture (Germany), Finnish Ministry of Agriculture and Forestry, National Natural Science Foundation of China, Helmholtz Association, Grains Research and Development Corporation (Australia), Texas AgriLife Research, Texas A&M University, National Institute of Food and Agriculture (US), CSIRO, 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), College of Agronomy and Biotechnology, Southwest University, Institute of Crop Science and Resource Conservation, Division of Plant Nutrition-University of Bonn, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Department of Crop Sciences, University of Goettingen, Centre of Biodiversity and Sustainable Land Use (CBL), United States Department of Agriculture - Agricultural Research Service, The School of Plant Sciences, University of Arizona, Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Washington State University (WSU), Department of Earth and Environmental Sciences and W.K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Agricultural and Biological Engineering Department, Purdue University [West Lafayette], Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Cantabrian Agricultural Research and Training Centre, Dep. Agronomia, University of Córdoba [Córdoba], Spanish National Research Council (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, 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&M AgriLife Research and Extension Center, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], National Engineering and Technology Center for Information Agriculture, Nanjing Agricutural University, Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Economic Development, Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Natural Resources Institute Finland, Institute of Crop Science and Resource Conservation (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, Biological Systems Engineering, University of Wisconsin-Madison, PPS and WSG &CALM, Wageningen University and Research Center (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Agriculture and Food, Universidad de La Rioja (UR), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, China Agricultural University, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), China Agricultural University (CAU), Institute of Crop Science and Resource Conservation [Bonn], Georg-August-University [Göttingen], Arid-Land Agricultural Research Center, Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), 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), University of Leeds, European Food Safety Authority (EFSA), Catabrian Agricultural Research and Training Center (CIFA), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), AgWeatherNet Program, Texas A and M AgriLife Research, Jiangsu Collaborative Innovation Center for Modern Crop Production, Landscape and Water Sciences, Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Wageningen University and Research [Wageningen] (WUR), Chinese Academy of Agricultural Sciences (CAAS), 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, European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), 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 ), Leibniz Centre for Agricultural Landscape Research, International Maize and Wheat Improvement Center ( CIMMYT ), CGIAR Research Program on Climate Change, Agriculture and Food Security ( CCAFS ), Washington State University ( WSU ), Michigan State University, Institute of Biochemical Plant Pathology ( BIOP ), Institute for Climate and Atmospheric Science, School of Earth and Environment, European Food Safety Authority, Spanish National Research Council ( CSIC ), Texas A and M University ( TAMU ), Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Department of Economic Development, Jobs, Transport and Resources ( DEDJTR ), University of Bonn (Rheinische Friedrich-Wilhelms), UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Wisconsin-Madison [Madison], Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Universidad de La Rioja ( UR ), University of Bonn-Division of Plant Nutrition, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), USDA-ARS : Agricultural Research Service, Consultative Group on International Agricultural Research [CGIAR]-Consultative Group on International Agricultural Research [CGIAR], Natural resources institute Finland, Georg-August-University = Georg-August-Universität Göttingen, Universidad de Córdoba = University of Córdoba [Córdoba], Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), and Université de Toulouse (UT)-Université de Toulouse (UT)
Subjects: Crops, Agricultural, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Yield (finance), Water en Voedsel, Growing season, Climate change, klim, Plant Science, Agricultural engineering, Models, Biological, 01 natural sciences, [SHS]Humanities and Social Sciences, Crop, Life Science, Computer Simulation, Productivity, 0105 earth and related environmental sciences, 2. Zero hunger, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, WIMEK, Water and Food, Food security, business.industry, Crop yield, Temperature, Agriculture, 04 agricultural and veterinary sciences, 15. Life on land, Climate Resilience, Agronomy, Klimaatbestendigheid, 13. Climate action, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Water Systems and Global Change, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business
Abstract: Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections., E.W. acknowledges support from the CSIRO project ‘Enhanced modelling of genotype by environment interactions’ and the project ‘Advancing crop yield while reducing the use of water and nitrogen’ jointly funded by CSIRO and the Chinese Academy of Sciences (CAS). Z.Z. received a scholarship from the China Scholarship Council through the CSIRO and the Chinese Ministry of Education PhD Research Program. P.M., A.M. and D.R. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). A.M. received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills fellowship under grant agreement No. PCOFUND-GA-2010-267196. S.A. and D.C. acknowledge support provided by the International Food Policy Research Institute (IFPRI), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat and the Wheat Initiative. C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905 L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Federal Ministry of Economic Cooperation and Development (Project: PARI). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through the National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM-Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and PD.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O'L. was funded through the Australian Grains Research and Development Corporation and the Department of Economic Development, Jobs, Transport and Resources Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. B.B. was funded by USDA-NIFA Grant No: 2015-68007-23133.
Published: 2017
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50. Nitrogen nutrition index predicted by a crop model improves the genomic prediction of grain number for a bread wheat core collection

Author: Pierre Martre, Sylvie Huet, Karine Chenu, Arnaud Gauffreteau, David Gouache, Delphine Ly, Jacques Bordes, Renaud Rincent, Gilles Charmet, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Queensland Alliance for Agriculture and Food Innovation (QAAFI), University of Queensland [Brisbane], Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de la Recherche Agronomique (INRA), Terres Inovia, 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), Enterprise Competitiveness Fund Project 'Semences de Demain', INRA metaprogram SELGEN, Auvergne Region, Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), QAAFI, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA)
Subjects: 0106 biological sciences, 0301 basic medicine, Nitrogen stress, prédiction génétique, Soil Science, Agricultural engineering, Biology, 01 natural sciences, Crop, 03 medical and health sciences, blé, wheat, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Plant breeding, Gene–environment interaction, Crop model, Environment characterization, 2. Zero hunger, rendement en grain, Genomic prediction, business.industry, Genotype-by-environment interactions, fungi, Crop growth, Grain number, food and beverages, genotype environment interaction, Biotechnology, interaction génotype environnement, 030104 developmental biology, stress environnemental, business, index de nutrition azotée, Agronomy and Crop Science, Genomic selection, modèle de production, 010606 plant biology & botany
Abstract: In plant breeding, one of the major challenges of genomic selection is to account for genotype-by-environment (G × E) interactions, and more specifically how varieties are adapted to various environments. Crop growth models (CGM) were developed to model the response of plants to environmental conditions. They can be used to characterize eco-physiological stresses in relation to crop growth and developmental stages, and thereby help to dissect G × E interactions. Our study aims at demonstrating how environment characterization using crop models can be integrated to improve both the understanding and the genomic predictions of G × E interactions. We evaluated the usefulness of using CGM to characterize environments by comparing basic and CGM-based stress indicators, to assess how much of the G × E interaction can be explained and whether gains in prediction accuracy can be made. We carried out a case study in wheat (Triticum aestivum) to model nitrogen stress in a CGM in 12 environments defined by year × location × nitrogen treatment. Interactions between 194 varieties of a core collection and these 12 different nitrogen conditions were examined by analyzing grain number. We showed that (i) CGM based indicators captured the G × E interactions better than basic indicators and that (ii) genomic predictions were slightly improved by modeling the genomic interaction with the crop model based characterization of nitrogen stress. A framework was proposed to integrate crop model environment characterization into genomic predictions. We describe how this characterization promises to improve the prediction accuracy of adaptation to environmental stresses.
Published: 2017
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