L’augmentation de la concentration atmosphérique de CO 2 et des températures ainsi que la modification des régimes hydriques constituent des déterminants majeurs du fonctionnement et de la productivité des agroécosystèmes. Si les effets de chacun de ces facteurs ont été largement étudiés individuellement, leur action conjointe reste encore mal comprise, alors même que ces facteurs interagissent étroitement dans les conditions climatiques futures. Cet article propose une synthèse des connaissances actuelles sur les impacts combinés de l’élévation de CO 2 , des températures et du déficit hydrique sur les plantes cultivées, avec un accent particulier sur le blé. Après un état des lieux des dispositifs expérimentaux mobilisés pour étudier ces interactions, nous analysons les réponses écophysiologiques et agronomiques des plantes à différentes combinaisons de facteurs climatiques, en distinguant les effets propres, les interactions et les mécanismes de compensation. Les résultats issus de synthèses et méta-analyses récentes mettent en évidence une forte variabilité des réponses, dépendante des espèces, des génotypes, des stades phénologiques et des modalités expérimentales. Les résultats expérimentaux, tous dispositifs confondus, montrent notamment que l’effet fertilisant du CO 2 ne compense pas de manière générale les impacts négatifs des stress hydriques et thermiques, en particulier lorsque ceux-ci sont combinés. Enfin, l’article discute des implications de ces résultats pour la modélisation des cultures et l’anticipation des trajectoires de productivité des agroécosystèmes dans un contexte de changement climatique, en soulignant la nécessité d’intégrer explicitement les interactions entre facteurs climatiques, processus biologiques et variabilité génétique.

Rising atmospheric CO2 concentrations and temperatures, as well as changes in water regimes, are major determinants of the functioning and productivity of agroecosystems. Although the individual effects of each of these factors have been extensively studied, their combined action remains poorly understood, despite the fact that these factors interact closely to shape future climate conditions. This article provides a synthesis of current knowledge on the combined impacts of rising CO2, temperatures and water deficiency on crop plants, with a particular focus on wheat. Following a review of the experimental setups used to study these interactions, we analyse the ecophysiological and agronomic responses of plants to different combinations of climatic factors, distinguishing between specific effects, interactions and compensatory mechanisms. Results from recent syntheses and meta-analyses highlight a high degree of variability in responses, depending on species, genotypes, phenological stages and experimental conditions. Experimental results, across all experimental setups, show in particular that the fertilising effect of CO2 does not generally compensate for the negative impacts of water and heat stress, particularly when these are combined. Finally, this article discusses the implications of these results for crop modelling and the prediction of agroecosystem productivity trajectories in a climate change context, emphasising the need to explicitly integrate interactions between climatic factors, biological processes and genetic variability.

Context: Cultural diversification is presented as an effective method to increase the resilience of agrosystems in the face of climate change and the need to reduce reliance on artificial inputs. In particular, there is a growing interest in understanding how diversified cultures can help in reducing pesticide use. Objective: In the case of intercropping, specific mechanisms have been identified which impact disease development: dilution and barrier effects as well as changes in microclimate in the canopy, but they are hardly characterised. Methods: We propose a process-based modelling approach to study a virtual wheat/pea intercrop submitted to a brown rust epidemic, coupling two previously validated models. We thus deciphered in silico the effect of dilution, barrier and microclimate mechanisms and their sensitivity to changes in spatial arrangement of the field. Results and conclusions: We found that in 93% of cases, intercropping reduced disease, by up to half with 60% wheat. Moreover, intercropping had overall beneficial effects in terms of disease control by protecting the photosynthetic capacity of wheat for 37 days longer on average. Furthermore, barrier and dilution effects counteracted the adverse microclimate throughout the crop cycle. Significance: Given the complex interactions between spatial arrangement and interannual variability, we argue that modelling is a valuable tool to run multiple simulations, identify scenarios most conducive to effective disease protection, and provide a reflexion on further research directions.

Abstract Crop models are essential for predicting climate change impacts on agriculture, yet their validation under multi-stress conditions remains limited. This study evaluated two widely-used wheat models, APSIM and STICS, using data from three Free-Air CO 2 Enrichment (FACE) experiments (USA, Germany, Australia) combining elevated CO 2 (eCO 2 ), water deficit, and warming. Environmental characterisation using simulation-based stress indices revealed that intended “controls” frequently experienced hidden heat and water stress, meaning models were calibrated on crops already undergoing physiological adjustments. Evaluation of simulated yield and components revealed a clear hierarchy in prediction errors (RRMSE): unlimited conditions (3–9%) < single stress (4–27%, with a need to improve response to heat stress) < combined stress (17–123%). Elevated CO 2 generally increased prediction uncertainty for crops experiencing water stress. Our results suggest that current stress functions from the models fail to capture the synergistic coupling between drought and heat stress. This highlights the urgent need for more mechanistic modelling to improve the reliability of climate change impact assessments.

Crop models need to be regularly upgraded with parametrization for new cultivars but this requires calibration, which is a major challenge. With winter wheat cultivar Rubisko as a case study, we propose to apply a calibration protocol to estimate the parameters of this new cultivar with multi-trials experimental data. We tested the calibration protocol in different conditions including or not LAI and/or biomass experimental data and we found that the resulting LAI and biomass dynamics strongly diverge. Several key findings emerge from this study: (1) RUE parameters should be excluded from the calibration process, as their critical role in biomass dynamics causes the optimization algorithm to treat them as adjustment parameters, resulting in unrealistic values for multiple parameters; (2) either LAI or biomass variables alone are sufficient for calibration, enabling experimental efforts to focus on one variable rather than both; and (3) the use of a synthetic dataset has facilitated the identification of the optimal type and timing of data collection needed to parameterize a new variety in the model. Moreover, the proposed methodology offers extrapolatable solutions applicable to other contexts (e.g., different models or datasets) and provides guidance on acquiring the most effective dataset for optimal calibration. The unbalanced structure of our dataset also highlighted the need to mobilize other calibration criteria (weighted RMSE) and alternative solutions to bridge the gap between quantitative metrics and empirical visual assessments. ### Competing Interest Statement The authors have declared no competing interest.

Future crop production will depend on plant plasticity in response to increases in atmospheric CO2, mean temperature, heatwave and drought events. The present review intends to highlight the impact of interactions between high CO2 levels, warming and water deficit in existing published experimental data in the case of wheat. To do so, we identified experiments quantifying the effects of such interactions on traits related to crop productivity and water use. We used the collected data to estimate plasticity indices assessing compensation and interaction between elevated CO2 and adverse climatic conditions, bringing a new perspective on the matter. In the studied data, even though there is an important variability, we found that crop productivity tends to decrease despite the positive effects of the rise in CO2 concentration. Conversely, with elevated CO2, water consumption tends to decrease despite the warmer conditions. We hypothesized that the positive effect of CO2 on crop productivity is greater under drought conditions, which is confirmed in 54% of the experiments. This review highlights the need to acquire further experimental data under possible future conditions to calibrate and validate crop models: their range of validity requires more thorough testing under the wide range of projected environmental conditions. ### Competing Interest Statement The authors have declared no competing interest.

This chapter offers a general presentation of the STICS soil-crop model. Through numerous illustrations dealing notably with genetic x environment x management interactions, it presents an overview of the wide domain of validity of the model, along with its performances, demonstrating its potential for a wide range of agronomic and environmental applications. Since the beginning of its story in the early 90’s, STICS has been able to remain a generic and robust model that allows to simulate the functioning of agro-ecosystems, both in temperate and tropical environments. As highlighted in this chapter, and particularly in the last two sections, many efforts were, and are still, put to keep the model up-to-date and in a permanent state of evolution towards better representation of cropping systems, allowing to analyze novel research and applied questions.

Crop models need to be regularly updated with parameterizations for new cultivars, but this requires calibration, which is a major challenge. Using the winter wheat cultivar Rubisko as a case study, we applied for the first time on experimental data a new calibration protocol to estimate the parameters of the STICS crop model for this new cultivar with multi-trial experimental data. We tested the calibration protocol in different conditions, with or without LAI and/or biomass experimental data, and we found that the resulting LAI and biomass dynamics strongly diverged. This study contributes to provide guidance to modelers for the calibration of a new cultivar in a crop model by focusing on the selection of variables and parameters to estimate as well as criteria for evaluating calibration strategies. With an application to winter wheat for the STICS crop model, this study has shown that the choice of calibration steps has a major impact on simulated outputs, but with a strong dependence on the structure of the experimental dataset. Firstly, this paper provides a methodology for the selection of calibration variables and associated parameters based on three criteria: 1) the relevance of the values of the estimated parameters, 2) the bias part of the mean square error, and 3) the analysis of the residuals. Secondly, by applying this methodology, we have shown that calibration based on LAI measurements is the most robust in the case of sparse observed data at the end of the cycle. Based on these results, we recommend caution when including parameters related to radiation-use efficiency; in particular, they should not be calibrated together with parameters related to leaf growth on biomass data alone. This study has enabled an appropriate calibration strategy to be defined, which will allow more modern French wheat cultivars to be parameterized in the STICS crop model.

ABSTRACT Fusarium head blight (FHB) is a devastating fungal disease affecting cereals, caused by Fusarium species that can produce harmful mycotoxins. Fusarium species coexist within the same ecological niche during infection, with their population dynamics and associated mycotoxin patterns strongly influenced by the environment. This study provides a comprehensive investigation of the ecophysiological responses of the major Fusarium species causing FHB under varying abiotic factors. We assessed growth and mycotoxin production of different isolates of Fusarium avenaceum, Fusarium graminearum , Fusarium langsethiae, Fusarium poae, and Fusarium tricinctum under 24 combinations of temperature (θ = 15, 20, 25, 30°C) and water activity levels ( a w = 0.99, 0.98, 0.97, 0.96, 0.95, 0.94). Our findings indicated that θ, a w , and their interaction have a main significant impact on species behavior. Thanks to innovative statistical approaches using fungal growth data from optical density measurements and mycotoxin quantification, we demonstrated significant inter- and intra-specific differences in environmental responses. Growth and mycotoxin production of F. graminearum and F. avenaceum appeared favored under high temperature (≥25°C) and high water activity (≥0.97), whereas lower a w levels (≥0.95) were also conducive for F. poae and F. tricinctum . A specific and unique behavior of F. langsethiae to lowest temperatures (≤20°C) was highlighted. Understanding the ecophysiological requirements of Fusarium species is crucial in the context of climate change, which is expected to worsen disease outbreaks. This study provides valuable knowledge for improving the reliability and robustness of FHB prediction models and anticipating the associated mycotoxin risk. IMPORTANCE Fusarium species pose a significant threat to major cereal crops, particularly wheat, by reducing yields and producing mycotoxins that are harmful to animals and humans. The prevalence of each Fusarium species is strongly influenced by environmental conditions, and climate changes have already been reported as responsible for shifts in pathogen populations, leading to changes in mycotoxin patterns. This study revealed distinct ecophysiological behaviors, including growth and mycotoxin production, of the five major Fusarium species infecting small grain cereals when exposed to varying temperature and water activity conditions. Our findings provide a valuable foundation for a deeper understanding of mycotoxin risk and for developing more effective mitigation strategies in the near future.

Remote sensing based on the reflectance of light at certain wavelengths enables the calculation of various vegetation indices (VIs) as proxies for agronomic variables. However, drone-mounted sensors have a limited number of bands, so the wavelengths defining VIs often have to be modified in line with sensor characteristics. This article addresses the problem of such wavelength shift based on experimental agronomic measurements and on reflectances acquired by both multispectral spectrophotometers and drone-mounted sensors. We demonstrate that wavelength shift can significantly affect VIs, particularly those using the red-edge band, compared to a multispectral reference. In the worst cases, the drone's VI was not even correlated with its multispectral target. We therefore propose a calibration method using a "virtual drone" simulated from a complete dataset obtained by multispectral measurements in order to use sensors with a limited number of bands. Virtual drones can guide the choice of drone sensors, depending on the features to estimate, or facilitate the intercalibration of sensors for comparisons of the results of the literature studies. This study aims at providing the agronomist community with a method for intercomparing VIs acquired by drones.

Phenology is a key adaptive trait of organisms, shaping biotic interactions in response to the environment. It has emerged as a critical topic with implications for societal and economic concerns due to the effects of climate change on species' phenological patterns. Fungi play essential roles in ecosystems, and plant pathogenic fungi have significant impacts on global food security. However, the phenology of plant pathogenic fungi, which form a huge and diverse clade of organisms, has received limited attention in the literature. This diversity may have limited the use of a common language for comparisons and the integration of phenological data for these taxonomic groups. Here, we delve into the concept of 'phenology' as applied to plant pathogenic fungi and explore the potential drivers of their phenology, including environmental factors and the host plant. We present the PhenoFun scale, a phenological scoring system suitable for use with all fungi and fungus-like plant pathogens. It offers a standardised and common tool for scientists studying the presence, absence, or predominance of a particular phase, the speed of phenological phase succession, and the synchronism shift between pathogenic fungi and their host plants, across a wide range of environments and ecosystems. The application of the concept of 'phenology' to plant pathogenic fungi and the use of a phenological scoring system involves focusing on the interacting processes between the pathogenic fungi, their hosts, and their biological, physical, and chemical environment, occurring during the life cycle of the pathogen. The goal is to deconstruct the processes involved according to a pattern orchestrated by the fungus's phenology. Such an approach will improve our understanding of the ecology and evolution of such organisms, help to understand and anticipate plant disease epidemics and their future evolution, and make it possible to optimise management models, and to encourage the adoption of cropping practices designed from this phenological perspective.

Climate change threatens food security by affecting the productivity of major cereal crops. To date, agroclimatic risk projections through indicators have focused on expected hazards exposure during the crop’s current vulnerable seasons, without considering the non-stationarity of their phenology under evolving climatic conditions. We propose a new method for spatially classifying agroclimatic risks for wheat, combining high-resolution climatic data with a wheat’s phenological model. The method is implemented for French wheat involving three GCM-RCM model pairs and two emission scenarios. We found that the precocity of phenological stages allows wheat to avoid periods of water deficit in the near future. Nevertheless, in the coming decades the emergence of heat stress and increasing water deficit will deteriorate wheat cultivation over the French territory. Projections show the appearance of combined risks of heat and water deficit up to 4 years per decade under the RCP 8.5 scenario. The proposed method provides a deep level of information that enables regional adaptation strategies: the nature of the risk, its temporal and spatial occurrence, and its potential combination with other risks. It’s a first step towards identifying potential sites for breeding crop varieties to increase the resilience of agricultural systems.

Starting from grain yield, quality and resistance against multiple diseases, the characterization of the cultivar's behavior increased in recent decades. Needs in quantitative assessments of a larger range of criteria has greatly evolved towards yield stability in a large range of fluctuating environments. Using a large dataset crossing cultivars and environments, we thus explored the relationships between yield and Healthy Area Duration (HAD), as affected by genotype, environment and septoria caused by Zygmoseptoria tritici. A set of indexes was then proposed to properly profile cultivar's behavior. A curvilinear relationship relating HAD to potential yield was first parameterized. It allows quantifying HAD efficiency. Susceptibility (HAD loss) was differentiated from total tolerance (the ratio between yield loss and HAD loss). Finally the specific tolerance, i.e. not due to HAD level, was quantified. Correlations between indexes pointed out that no trade-off was shown between total tolerance and actual or potential yield as well as disease susceptibility. These correlations partially depended on the nitrogen status of crops, underlining other G×E interactions indexes may trap. Finally, as HAD efficiency appeared more highly linked to actual yield than potential yield we proposed an alternative set on indexes based on Healthy Area Absorption (HAA) that accounted for meteorological variability. Interestingly, these last indexes were insensitive to nitrogen nutrition as well as to cultivar susceptibility to Z. tritici. The developed indexes allowed profiling the cultivars' behavior under a common range of environments. HAA-based indexes open the way to a useful global characterization of cultivars by breeders. Moreover, HAA can be assessed using high-throughput phenotyping tools. A thorough evaluation of this last point needs to be done.

Adaptation of cropping management strategies is necessary to ensure the sustainability of our agriculture, which is facing threats arising from climate change. A methodology is proposed to find out and compare the most promising adaptation strategies in this context considering both biotic and abiotic stresses. A set of pre-selected strategies were evaluated based on economic, plant health and environmental criteria. A dedicated workflow combining the STICS crop model, epidemiological models and multi-criteria analysis was designed, implemented and tested for a wheat production situation. Flexible by design, this methodology can consider different criteria weights to be used as an exchange support with stakeholders.

This article comments on: Pélissier R, Buendia L, Brousse A, Temple C, Ballini E, Fort F, Violle C, Morel JB. 2021. Plant neighbour-modulated susceptibility to pathogens in intraspecific mixtures. Journal of Experimental Botany 72, 6570–6580.

Crop fungal diseases threaten food security in the dual context of a growing global population and a warming climate. Leaf rust is one of the most important wheat diseases which can result in yield losses of more than 40 %. When considering these crucial questions, innovative approaches to crop cultivation are clearly required. One essential prerequisite before the development of adaptive strategies to climate change, is to understand and forecast the potential impact of this change on fungal diseases, based on the use of modelling approaches. However, numerous epidemiological models are available; they vary considerably in terms of their complexity, and are based on hypotheses that oversimplify factors that influence the prediction of epidemics. During this study, we implemented six combinations of leaf wetness duration and infection efficiency models to simulate the future evolution of leaf rust of wheat, and compared the resulting trends. Daily and seasonal climatic indicators were inferred from the simulated infection efficiencies, from 1950 to 2100, with two contrasted Representative Concentration Pathways, RCP 4.5 and RCP 8.5, at three sites representative of traditional French wheat production areas. The inferred indicators characterize the intensity and frequency of leaf rust infection, the length and calendar positioning of the longest sequences without infection, and the relevant microclimate. Their absolute values varied considerably depending on the model combinations used, even more than between the present and future climatic periods or RCP scenarios. However, the same trends were observed in the future, with climate change being a significant explanatory variable of the evolution of the six climatic indicators simulated. The results of combining these models showed that the climatic risk of both the frequency and intensity of leaf rust infection would increase during the autumn and winter seasons, and a distinct drop should be expected during the summer, enabling a longer risk-free period. Some important common trends were thus highlighted, reinforcing confidence in the robustness of the results. These findings should be taken into account when designing adaptive strategies that will sustain production under future abiotic stresses while minimizing sanitary risks.

Yellow rust is a devastating wheat disease. Since 2000, Puccinia striiformis f. sp. tritici strains PstS1 and PstS2 have become adapted to high temperatures and have spread worldwide. By 2011, Warrior strains had invaded both warm and cold areas of Europe. This study questioned whether thermal aptitude promoted the spread of Warrior strains, similar to PstS1/PstS2, by comparing infection efficiency (IE) at five temperatures and latent period (LP) under warm and cold regimes for Warrior isolates and pre‐2011 reference strains on two susceptible wheat varieties. The Warrior isolates showed a range of IE and LP responses to temperature that was intermediate between the northern reference isolates adapted to cold conditions and both the southern and invasive PstS2 isolates adapted to warm conditions. Warrior isolates had the highest IE under optimal temperatures of 10 and 15 °C, and displayed reduced infectivity under the warmest (20 °C) and coldest (5 °C) temperatures. Warrior strains acted as thermal generalists and the reference isolates acted as specialists. An IE thermal response was used to simulate the development of each isolate under future climate scenarios in a temperate and Mediterranean region. Isolates had the same ranking for yearly IE over the three 30‐year periods (1971–2000, 2021–2050, 2071–2100) and both locations, with a slight infection increase in the future. However, in the future IEs increased in earlier months. The thermal generalist profile of Warrior isolates for IE was confirmed, with an intermediate capacity to tolerate warming climate, whereas the southern isolates are better adapted to warm conditions, but do not have the virulences necessary to develop on current varieties.