Modern plant breeding optimizes varieties for sole cropping, creating trait mismatches that constrain the diversification essential for climate resilience. We propose Smart Systems Breeding, reframing environmental variability as strategic information for precision variety deployment. This approach integrates farmer networks, environmental monitoring and modelling to accelerate breeding for locally adapted intercropping, crop mixtures and agroforestry that supports sustainable agriculture.

Soil salinization poses a significant threat to ecosystems and food security, and an operational microwave remote-sensing framework for salinity monitoring is therefore needed. Microwave remote sensing affords penetration through vegetation and sensitivity to soil dielectric properties. However, a transferable, practical framework for salinity retrieval has not yet been established, largely attributable to the fact that existing soil dielectric models seldom provide a generalizable, high-precision description to serve as the mechanistic basis over 1-6 GHz, covering the commonly used L-band and C-band frequencies for microwave observations. We prepared six diverse types of soil samples using a desalination-drying preparation and systematically configuring gradients of soil salinity and soil moisture, and measured the Soil Complex Permittivity (SCP) using the coaxial probe method. Based on the measured data, this study develops a microphysical-electromagnetic coupling SCP model framework. (1) The broadening parameter beta in the classical Cole-Cole model is expressed as a function of concentration c, yielding a dynamic Cole-Cole model for saline solutions. (2) The Poisson equation is coupled with the Bikerman's theory to form the Poisson-Bikerman model, describing microscale ionic distribution within the bound-water phase. (3) The Poisson-Bikerman and dynamic Cole-Cole models with electric-field suppression are integrated into the soil volumetric four-component scheme and optimized at both L- and C-bands. On an independent testing set, the proposed SCP model attains R-2 = 0.955, Normalized Root Mean Square Error (NRMSE sigma, normalized by standard deviation sigma) = 0.213 for the real part epsilon(soil)', and R-2 = 0.960, NRMSE sigma = 0.200 for the imaginary part epsilon(soil)'', with per-sample median R-2 values for epsilon(soil)' and epsilon(soil)'' generally around 0.95 upon generalization to 1-6 GHz; sensitivity analysis of the key parameter specific surface area further demonstrated stability of the SCP model. The results show that salinity predominantly affects epsilon(soil)'', L-band is more sensitive to salinity whereas C-band is more stable; and once the soil solution approaches the solubility limit with salt precipitation, further increases in soil salinity have little discernible effect on SCP. Moreover, the proposed SCP model outperforms existing saline-soil dielectric models on the laboratory dataset, and has the potential to be extended to higher-clay soils (approximate to 40%) and to varying ionic compositions and temperature regimes. Scenariobased Synthetic Aperture Radar (SAR) simulations further show that the added value of the proposed formulation is most evident for L-band satellite observations under low-SM, typical surface roughness, and saline conditions, where the importance of explicit bound-water modeling is more clearly manifested. In addition, SCPdriven forward simulations of C-band SAR backscattering coefficients, evaluated against Sentinel-1 observations, provide further support for the physical feasibility of the proposed SCP model for microwave remote-sensing applications.

Exposure to extreme high temperatures is a major constraint on global crop productivity, yet most large-scale assessments rely on fixed temperature thresholds that overlook regional variation in genetics, environment and management. Consequently, the temperature thresholds at which heat exposure begins to cause substantial yield loss and their spatial variability remain unclear. Here we compiled subnational yield census over Northern Hemisphere (20° N-55° N) and analysed the extreme degree days (EDDs) to estimate a data-driven critical threshold (EDDthreshold). Our findings reveal EDDthreshold for maize and soybean are 34.8 ± 4.0 °C and 33.7 ± 3.9 °C, respectively. In contrast, state-of-the-art crop models significantly underestimated EDDthreshold and its spatial variations, leading to overestimated extreme heat exposure, partially explaining their underestimate in yield loss during extreme heat events. We estimate that without adaptations, growing-season extreme heat exposure could increase by 2.4%-16.1% for maize and 4.9%-16.0% for soybean by the end of the century, and sowing-date adjustment alone cannot fully offset the projected increase in extreme heat exposure.

Robust quantification of crop status in real-time is essential for agile decision-making. While use of unmanned aerial vehicle (UAV) data appears promising in this vein, the contribution and transferability of various features (e.g. vegetation indices, plant height and texture features) in crop above ground biomass (AGB) prediction remain poorly understood. Here, our objectives were to (1) evaluate the performance of various machine learning (ML) algorithms in the synthesis of multiple features, (2) elicit the contribution of various UAV features, (3) assess the transferability of features across growth stages and sites. Four field experiments, incorporating several water and nitrogen treatments across two sites, were assembled for use in AGB prognostics. We invoked four ML algorithms-Random forest (RF), Lasso regression (LR), K-nearest neighbors (KNN) and a stacked ensemble integrating the three methods (SML)-to predict wheat AGB using multiple UAV data and phenological information. Additionally, interpretable ML techniques were employed to elucidate the influence of UAV features on AGB prediction across growth stages. Our results showed that all algorithms exhibited robust performance in predicting wheat biomass, with RMSE values of 1.64, 1.71, 1.71, and 1.57 Mg ha-1 for RF, LR, KNN, and SML, respectively. RF predominantly relied on plant height features, LR leveraged vegetation indices, and KNN prioritized texture features, while SML synthesized the advantages of multiple ML algorithms. Fusion of multiple datasets amplified model prognostic capacity and scalability, with R2 and rRMSE of 0.92 and 22 % when using data from external sites. Features pertaining to vegetation indices and plant height during vegetative growth and around flowering had seminal contributions of model predictions. Texture features significantly reduced the saturation effect during the reproductive stage but diminished the model's transferability during the vegetative stage. Complementarity among data types enhanced effectiveness of ensemble machine learning, which leverages strengths of diverse data to improve the accuracy and robustness of AGB predictions. Future studies could combine multiple sources of remote sensing, such as LiDAR and thermal infrared alongside system modeling, to improve ML accuracy and generalization capability.

Context: The global shift toward sustainable agriculture has increased interest in using process-based models to design and optimize intercropping systems. However, these models differ fundamentally in how they represent trade-offs (competition for light, water, and nutrients) and synergies (facilitation, complementarity) in resource sharing between species. This conceptual variation creates uncertainty in model selection and application, particularly since most models were originally developed for monoculture and subsequently adapted for intercropping. Objective: In this study, we describe how current crop models represent interspecies resource-sharing mechanisms, analyse their structural differences, and synthesize findings from existing validation studies to understand how their structural differences affect model performance. The models examined include APSIM (APSIM-Canopy, APSIM-Micromet, APSIM-Strip, APSIM-Alternating, APSIM-APSwim, APSIM-SoilArbitrator), DayCent, DSSAT-Mixed, DSSAT-MPI, LandscapeDNDC, LUCIA, MONICA, SIMPLACE Lintul5-Intercrop, STICS-Big-Leaf, STICS-Multilayer, and WaNuLCAS. Methods: Through the Agricultural Model Intercomparison and Improvement Project (AgMIP) platform, we engaged with model developers and expert users to collect detailed information on how crop models represent intercropping systems using structured interviews and questionnaires. We then developed a framework that groups models by their core conceptual approaches to simulating resource sharing. Finally, we synthesize findings from existing quantitative validation studies to connect conceptual intercomparison to predictive performance across different intercrop characteristics and environments. Results and Conclusions: Our analysis identifies six distinct conceptual approaches for simulating light sharing and four for belowground resource (water and nutrient) competition. Intercrop models show greater structural divergence in canopy than in belowground representation. Furthermore, competitive trade-offs (light, water, and nutrients) are widely represented while facilitative and other complex processes like Na-fixation, plasticity (shoot and root), microclimate effects or hydraulic lift are often simplified or omitted. Our analysis of model validation studies reveals a critical trade-off: structurally complex models often perform well in simulating intercropping when calibration is done on sole crops, whereas simpler models require extensive intercrop-specific calibration to achieve better prediction performance. This distinction is vital for model application in data-scarce environments, as more complex architectures can leverage existing sole-crop data to effectively simulate intercrop systems. The classification of the structural resource capture differences in combination with the evaluated model performance analysis allowed us to devise an evidence-based model selection criteria framework useful also for for non-specialist, while the unique detailed description provided are highly valuable for the model research community. Significance: This study establishes a conceptual framework that provides the necessary foundation for a meaningful quantitative intercomparison of intercrop models, as structural understanding enables the interpretation of numerical differences in model outputs. It also guides hypothesis testing, model choice, priorities in model development and improvements.

Over the past fifty years, tomato has become one of the most extensively cultivated horticultural crops in the Mediterranean region. Climate projections for Italy indicate that temperature increases and rainfall changes will cause a 15% yield reduction in processing tomatoes, requiring an additional 85-110 mm of irrigation and 20-30 kg N ha-1 to partially offset negative impacts. Mediterranean agriculture is particularly threatened by projected climate changes in temperature and precipitation patterns. Region-specific crop models, validated against local field data, are therefore critical tools for assessing yield risks and identifying effective agronomic adaptations. Conventional process-based crop models often rely on fixed transplanting or sowing dates and harvesting dates, which fail to reflect spatiotemporal variability in management practices. Such assumptions can lead to systematic biases in regional simulations and environmental assessments. Yet phenological observations (e.g., flowering, fruit set, harvest dates) are essential for parameterizing crop models, available data typically represent point locations or experimental stations rather than the field to regional scale resolution needed for spatially explicit modelling. Sentinel-2’s high temporal frequency and spatial resolution allow tracking of within-season crop development at field scale. This study aims to: (a) compare model performance using broad agricultural land masks versus pixel-level tomato identification; and (b) evaluate whether incorporating satellite-observed canopy development dynamics (greenness trajectories, growth stage timing) reduces uncertainty in simulated crop growth, water use, and nitrogen cycling processes including nitrate leaching risk.We propose a simulation framework that combines the process-based model MONICA (Model for Nitrogen and Carbon dynamics in Agro-ecosystems) with earth observation data for processing tomatoes in the Emilia Romagna region, a major tomato production area in Italy. MONICA was calibrated and validated using four years field trials and two years on-farm data from 49 fields. We integrated two remote sensing inputs: (i) field scale processed tomato masks, and (ii) dynamic transplant and harvest dates extracted from Sentinel-2 EVI time series (validated against on-farm data, R²=0.90). We conducted regional simulations (2007-2023) comparing four model set-ups: fixed transplant and harvest dates with basic cropland mask, fixed dates with tomato masks, dynamic dates with tomato masks, and modified dynamic dates with tomato masks for sensitivity tests on transplanting date.Our research results indicate that employing specific tomato field maps combined with dynamically determined growing periods significantly improved yield simulation accuracy compared to basic cropland mask (reducing RMSE by 24%) and specific maps without consideration of remotely sensed growing season dynamics (reducing RMSE by 10%). Incorporating remote sensing data and tomato maps into the MONICA crop model also improved the model’s ability to capture yield anomalies as an indicator of its sensitivity to climatic signals, with a 24% reduction in RMSE. Integrating remote sensing-derived growing periods into crop models resulted in a wider range of simulated values, enhancing the model’s capacity to simulate nitrate leaching under real-world conditions.This study demonstrates that using remote sensing data to inform crop models significantly enhances the understanding of dynamic growth patterns, thereby supporting regional yield estimation and nitrate leaching simulations, while providing crucial insights for agricultural resource management.

Crop models increasingly project that irrigation can offset agricultural losses from climate change, informing major adaptation investments worldwide. Yet these models may systematically overestimate irrigation’s protective capacity because the representation of how irrigation alters crop thermal environments remains incomplete in most large-scale assessments and assumes static efficiency under changing atmospheric conditions. More realistic assessments are urgently needed to avoid underestimating future water demands and regional adaptation failures.

Accurate crop growth monitoring is essential for effective regional agricultural management. This study investigates how the use of growing-period data and crop-specific field masks derived from remote sensing improves the accuracy of processing-tomato growth simulations. This study extracted transplanting and harvesting dates from Sentinel-2 enhanced vegetation index (EVI) time series data, demonstrating high consistency with field observations (R2 = 0.90; RMSE = 30.36 and 6.39 days, respectively). However, we observed systematic bias in transplanting dates derived from remote sensing data. For this reason, we applied a combined smoothing and interpolation approach prior to threshold detection to improve phenology retrieval; this substantially reduced transplanting date root mean square error (RMSE) from 30.36 to 27.89 days. We then calibrated and validated MONICA, a process-based crop model, using field experiments (four growing seasons) and on-farm data (49 fields - two growing seasons). Subsequently, we employed the model for eight provinces in the Emilia-Romagna region of Northern Italy to simulate crop yield under various combinations of basic cropland mask, and processingtomato mask, with and without consideration of remotely sensed growing dynamics. Results showed that employing specific tomato field maps combined with dynamic growing periods significantly improved yield simulation accuracy compared to basic cropland mask (reducing RMSE by 24%) and specific maps without consideration of remotely sensed growing season dynamics (reducing RMSE by 10%). Incorporating sensing data and tomato maps into MONICA also improved the model's ability to capture yield anomalies as an indicator of its sensitivity to climatic signals, with a 24% reduction in RMSE. Integrating remote sensing-derived growing periods into crop models resulted in a wider range of simulation values, enhancing the model's capacity to simulate nitrate leaching under real-world conditions. This study demonstrates that using remote sensing data to inform crop models significantly enhances scholarly understanding of dynamic growth patterns, supporting regional yield estimation and nitrate leaching simulations, while providing crucial insights for agricultural resource management.

Cultivar evolution through plant breeding is a cornerstone of contemporary food security, but the extent to which genetic adaptation to climatic variability and shocks contributes to yield gains is not well known. Here, we compile 48,797 cultivar-site-year observations from 2001 to 2020, covering the four prominent maize production regions in China with differing shifts in climatic conditions. The data shows that cultivar evolution underlies long-term yield gains, with productivity increasing by 0.3-2.8 Mg ha-1 per decade. Yields in Northeast China (NEC) and North China (NC) are most vulnerable to heat stress during July and August, whereas high or insufficient precipitation during the growing season is a foremost constraint to yield gains in Southwest China (SWC) and Northwest China (NWC), respectively. Cultivar evolution has significant impacts on yield sensitivity to climate, with genotypic sensitivities to heat stress amplifying in NEC and diminishing over time in NC, respectively. In contrast, yield sensitivity to precipitation increases in SWC and NWC as a result of breeding. These results underscore the importance of breeding climate-resilient cultivars that account for contextualised in situ environmental constraints and climatic adversities in obtaining high yield.

Breeding programs prioritize the average performance of a genotype across environments and may overlook promising candidates for specific environments. To address this challenge, we propose a genomic prediction framework to select high-yielding genotypes tailored to individual environments. We compiled winter wheat grain yield data from 13,285 genotypes—6,766 lines and 6,519 hybrids—evaluated in yield plots at 31 central european sites from 2010 to 2022. With integrated genomic data, we show that only as the size of the training dataset increase, convolutional neural networks benchmark competitive to superior compared with traditional genomic best linear unbiased predictions (GBLUP) in predicting average genotype performance of lines. We then extend our prediction models to account for genotype times environment (G × E) interactions by incorporating information about the growth environment. We observe a 23

Climate change increasingly affects agricultural systems in Central Europe, necessitating the development of robust forecasting models for drought, heat, and fire events (DHF). These hazards pose significant threats to crop production and require proactive measures to enhance resilience and adaptation.This research project is dedicated to constructing a thorough framework for forecasting DHF events in Central Europe. It integrates an agro-ecosystem model aimed at examining how crops respond, particularly when it comes to water availability. The focus of this research extends to the region's awareness to climate-related threats and the robustness of its agricultural systems.We utilize the MONICA (Model for Nitrogen and Carbon in Agriculture) crop model to simulate crop growth and response across a spectrum of environmental conditions. The MONICA model is designed to represent the complexity of crop development, considering factors such as soil properties and weather variations. MONICA model has the capacity to explore various scenarios, including heat stress and drought sensitivity, providing a comprehensive view of how crops respond to these challenges. The used data includes high-resolution meteorological (1km resolution, daily), topographic, historical crop records and soil information for whole Germany. The dataset covers the past two decades, encompassing vital information such as crop yield records.By sensitivity analysiswe systematically identified key parameters influencing simulated crop yield and above ground biomass, particularly in the context of drought and heat stress. These insights are invaluable for advancing our understanding of how crops respond to environmental stressors.Moving forward, our focus shifts to the calibration and optimization routines to quantify specific parameter sets for individual NUTS-3 regions within Germany.In the poster presentation, we look forward to sharing the newest findings from our ongoing research on advanced calibration tools and yield simulations conducted over Germany. Simulation results are compared to observed yield data, providing valuable insights into the effectiveness and real-world applicability of the modelling approaches.

Context: Given the negative impacts of climate change on crop production, it is vital to implement efficient adaptation and mitigation strategies. The diversification of cropping systems, particularly through intercropping combined with shifts in sowing times, could have the potential to offset such negative impacts. Yet, both experimental data and simulation studies are scarce to elucidate the intercropping performance under future climate change conditions, particularly for evaluating its potential to offset climate impacts on crop and protein yields in German wheat-based systems. Objective: This study aimed to simulate the grain yield and grain protein performance of winter wheat-soybean relay-row intercropping across Germany under future climate conditions, comparing it to sole cropping systems. Methods: We employed the MONICA agroecosystem model and its intercropping module to simulate the performance of an innovative winter wheat-soybean relay intercropping system. This was in combination with a wide range of shifts in sowing dates, and we compared it against standard sole cropping under low and high emission scenarios across Germany. Results: The model projected a 15% higher sole wheat yield under the future (2031-2060) high emission scenario than that of the historical period (1981-2020), while sole soybean yield increased by 8% in the same case. Although the simulation of winter wheat-soybean relay intercropping across Germany indicated a 9 % yield penalty compared to sole cropping in the future, with a transgressive overyielding index of 0.91, intercropping emerged as particularly advantageous in terms of land-use efficiency and protein production. It saved 17 % of land compared to sole cropping, thus produced equal amounts of grain yield, and produced 16% more protein than sole cropping in the high emission scenario. On top of that, shifting the sowing dates of the component crops to earlier times was found to substantially enhance the advantages of intercropping, resulting in a maximum of 44 % higher total yield production, and 47 % higher protein production than sole wheat without shifting sowing date in the future projection window. Conclusion: Our findings highlight the grain yield and protein production potential of intercropping versus sole cropping under futuristic high emission scenarios (RCP 8.5), and underscoring its potential to create a win-win situation of increased crop diversity and productivity. The results affirm the crucial importance of selecting optimal sowing dates for the component crops in intercropping, to maximize production and ensure resilience in the face of a changing climate.

Process-based models help disentangle management effects from climate, soil and genetics influences on crop growth and development. However, model parameter sensitivity varies under different environmental and management conditions, posing challenges for model application. We conducted a global sensitivity analysis to identify key parameters of STICS model influencing winter wheat growth and yield under diverse nitrogen and water stress scenarios in the Huanghuaihai Farming Region (HFR) of China. HFR is China’s largest winter wheat planting region that contributes about 13 % of global wheat production. Our results revealed that parameters such as nitrogen critical dilution curve (bdil and adil) and leaf lifespan (durvieF) are highly sensitive to nitrogen stress. Similarly, the coefficient for water requirements (kmax) critically affects the responses of winter wheat to water stress. These parameters should therefore be calibrated under their respective stress conditions. Maximum temperature strongly influenced the sensitivity of tmaxremp, while precipitation shaped the model’s response to water stress. Additionally, soil properties (e.g., finert, pH and HMINF) played critical roles in mediating nitrogen-water stress effects. Parameter sensitivity varied across growth stages, for example, stlevamf exhibited high sensitivity (sensitivity index achieved 0.4) during jointing but showed negligible effects at other stages. After calibration and validation, STICS effectively simulated winter wheat under various nitrogen and water management with validation set rRMSE of 21 %, 8 % and 10 % for LAI, biomass and yield, respectively. These findings provide critical insights for improving STICS model accuracy in simulating winter wheat under various nitrogen-water management in the HFR of China, similar methods could be used in many other agroecological regions.

Droughts pose a substantial threat to various sectors, including agriculture, human water supply but also natural ecosystems. While various studies have been conducted for drought evaluation, the majority of them have focused on a particular drought type. This may lead to a lack of comprehensive understanding of the features and progression of droughts among different drought types through time. For example, for water resources management and planning purposes, it is critical to understand the changes and temporal development of drought signals from abnormal meteorological conditions to soil moisture, groundwater levels, and streamflow. Within the OUTLAST project, which aims at developing an operational, multi-sectoral global drought hazard forecasting system, we develop a near real-time drought hazard monitoring and forecasting system which, for the first time, includes tailored indicators for various sectors, including water supply, riverine and non-agricultural land ecosystems, as well as rainfed and irrigated agriculture. In this context, the primary objectives of this study are to 1) develop different drought hazard indicators (DHI) to monitor and forecast the drought across different sectors; and 2) assess the spread and propagation of droughts across different sectors and regions at a global scale. For this purpose, DHIs were computed for a 40-year reference period (1981 to 2020) using ERA5 as meteorological forcing data to drive the DHIs using the global hydrological model (WaterGAP) and the global crop water model (GCWM). These DHIs cover meteorological (SPEI and SPI), hydrological (empirical percentiles and relative deviations of soil moisture and streamflow), as well as agricultural droughts (crop-specific DHIs for rainfed and irrigated croplands). In this project, we focus on the period 2011 to 2015, with 2012 being a year in which droughts had major impacts on various regions and sectors. The study investigates drought propagation from meteorological drought, extending to rainfed agriculture due to soil moisture deficiency, over streamflow, and eventually reaching irrigated agriculture. In doing so, region-specific features and the dependency of drought propagation on the magnitude of the drought are highlighted. Finally, as monitoring and projecting drought characteristics are important for comprehending drought-related issues, our multi-sectoral drought hazard forecasting system enables us to evaluate the state of drought propagation at a global scale.

Crop diversification can buffer climate extremes and support biodiversity and has been reported to meet both production and sustainability needs. In Germany, winter wheat is the primary crop, often grown in relatively simple rotations with other cereals, rapeseed, and maize. Intercropping practices such as winter wheat-soybean relay intercropping (RC) with high spatial-temporal differentiation have the potential to enhance sustainability. However, the establishment of soybeans into a winter cereal remains a challenge due to high competition for water indicating the need for supplementary irrigation for successful RC in dry regions. This study presents an irrigation scheduling framework for winter wheat-soybean RC in temperate regions. We calibrated and validated the process-based agroecosystem model MONICA using three-year field data from 2020 to 2023. The model accurately reproduced aboveground biomass (R2 = 0.88, n = 66) and grain yield for both crops in RC (R2 = 0.70, n = 16), with root mean square errors of 2091 kg ha-1 (biomass) and 372 kg ha-1 (grain yield). The validated model was used to test the effect of irrigation timing, dosage, and strategy on RC yield performance across two soil types (594 simulations in total). A sensitivity test of 20 mm irrigation increments across phenological stages showed that soybean grain yield was most responsive to irrigation at the first pod stage, resulting in intercropped soybean yield increases up to 47 % (+249 kg ha-1) compared to rain-fed. Yield gains from irrigation increment of 20 mm peaked at moderate application rates of 80 mm in sandy and 120 mm in loamy soils, then declined. Contrasting soil texture simulations indicated that loamy soils required higher irrigation volumes but achieved greater land-use efficiency. Land equivalent ratio (LER) reached 1.21 with cumulative seasonal total irrigation of 140 mm, while sandy soils benefited from low-volume, high-frequency irrigation (LER = 1.11 with 140 mm), eliminating 64 % of drought-induced yield loss versus 48 % in loamy soils. These results highlight the importance of tailored irrigation strategies based on soil texture and crop phenology, providing a practical foundation for supplementary irrigation in cereal-legume RC systems and supporting crop diversification in temperate climates.

Climate change poses significant challenges for sugar beet cultivation in Iran, where this industrial crop accounts for over 50 % of national sugar production but relies heavily on scarce water resources. This study evaluated the potential of autumn sowing as an adaptation strategy compared to conventional spring sowing under baseline (1980-2010) and future climate projections (2040-2070, RCP4.5 and RCP8.5) across 21 diverse agricultural locations in Iran. The SUCROS crop growth model was modified to simulate sugar beet response to frost damage and applied to assess yields under full and supplementary irrigation regimes. Results showed that spring-sown sugar beet failed under all supplementary irrigation scenarios, requiring full irrigation for feasibility. Autumn-sown sugar beet yields averaged 23.25 t ha(-1) under supplementary irrigation compared to 89.85 t ha(-1) under full irrigation at baseline. Climate change projections indicated autumn-sown yields would increase by 21.87 % (RCP4.5) and 27.80 % (RCP8.5) compared to baseline, with significant spatial variability across locations. Frost events during autumn-sown growing seasons declined substantially under future scenarios (63 % fewer in RCP4.5 and 76 % fewer in RCP8.5), with frost intensity decreasing by 4.21 % on average. Southern regions exhibited no frost events, while northeastern locations experienced the most severe frost damage. Supplementary irrigation at mid-growth stage produced the highest autumn-sown yields across most locations. These findings demonstrate that autumn sowing offers a practical adaptation strategy for sugar beet cultivation in Iran under climate change, with reduced frost risk and improved water use efficiency, particularly in warmer regions and under supplementary irrigation regimes.

This paper describes the dataset that was used to test the reliability of eight crop models in simulating growth and yield of canola in response to sowing dates, nitrogen inputs and climate variability across five countries. The dataset includes four spring cultivars and three winter cultivars across six sites, which represents a diverse range of canola production areas around the world. Model calibration and validation were conducted in the framework of the Agricultural Model Intercomparison and Improvement Project for canola (AgMIP-Canola). Field experimental datasets include site characterization, soil profile characterization, initial soil conditions (soil water and mineral nitrogen contents), in-season and end-season crop measurements (phenology, LAI, biomass, and nitrogen content in leaves, stems and pods, some with seed oil content), and daily weather data. Simulation datasets include the simulation results generated by ten individual model frameworks (eight crop models, APSIM and DSSAT respectively by two groups) for the experimental periods, and scenario simulations using 30 years historical weather data (1981 – 2010) together with a full multi-factorial combinations of temperature (-3, 0, +3, +6, +9 oC), rainfall (-25%, -10%, 0, +10%, +25%), CO2 concentrations (360, 450, 540, 630, 720 ppm) and nitrogen input rates (0, +25%, +50%, +100%, +150%).

Ensuring crop yield stability is crucial for food security in Africa, where agriculture faces increasing food demand amid considerable vulnerabilities. Remote sensing and reanalyzed data products offer the potential for capturing crop growth dynamics and understanding their drivers. However, the impacts of cropland masks on relative yield anomalies (RYA) and the contributions of variables across Africa and crops remain unclear. This study explores the explanatory power of air and land surface temperatures (AT and LST), precipitation, evapotranspiration, and soil moisture on maize, millet, and sorghum RYA in Africa for 2001–2020 under seven cropland masks with distinct configurations for temporal, crop type, and water supply systems. Results indicate that (a) North Africa was particularly affected by soil moisture variation and evapotranspiration, West Africa was strongly impacted by precipitation, Central and East Africa were highly influenced by mean AT and total precipitation, and South Africa was mainly affected by high LST, mean evapotranspiration, and precipitation variation. (b) Interactions between precipitation and LST improved the explanatory power of the multiple stepwise regression model from 67% to 73%, while that of the random forest model considering complex variable interactions reached 83%. (c) Variables with high contributions were less impacted by the choice of masks. Mask configurations with broader crop coverage compensated for the limitations of temporally static masks, while crop type identification enhanced explanatory power when using year‐specific and crop‐specific maps. Future research should integrate process‐based crop models to better understand the mechanisms behind the diverse drivers of yield at the regional scale in Africa.

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 (< 0.4 t ha− 1) was observed with increases in K and FE alone. Utilizing field-scale input data showed the greatest yield improvement per unit increment in RUE. Resolution of water related inputs (soil characteristics and precipitation) had a notably higher impact on simulated yield than of temperature. However, it did not alter the effects of high-yielding traits on yield. Simulated yields were only slightly affected by data aggregation for the different trait combinations. Warm-dry conditions diminished the benefits of high-yielding traits, suggesting that benefits from high-yielding traits depend on environments. The current findings emphasize the critical role of input data resolution and source in quantifying a large-scale impact of high-yielding traits.