Better understanding genotype by environment interaction (GxE) can help breeding for better adapted varieties. Envirotyping for environmental water status was applied to assist interpretation of GxE interactions for wheat yield in multi-environment trials conducted in drought-prone Australian environments. Genotypes from a multi-reference parent nested association mapping (MR-NAM) population were tested in 10 trials across the Australian wheatbelt. Genotype yield and phenology were measured in all trials, while traits associated with the stay-green phenotype were assessed for a subset of 5 trials. Envirotyping was conducted by characterizing water stress experienced by genotypes at each trial using crop modelling. Envirotyping facilitated the understanding of GxE interactions by explaining 75, 67, and 66% of the genotypic variance for yield in severe water-limited (ET3), mild terminal water-stress (ET2), and water-sufficient (ET1) environments, respectively. Yield and stay-green were negatively correlated with flowering time in most trials. However, when focusing on genotypes flowering at similar times within a trial, no significant correlation was found between yield and flowering. Importantly stay-green traits remained significantly correlated with yield. Stay-green traits such as delayed onset of senescence and slower senescence rate benefited yield by 0.2 to 1.1 t/ha across environments, highlighting the breeding potential for stay-green traits in both water-sufficient and water-limited environments. Hence, sustaining green leaf area during grain filling helped to enhance yield. Envirotyping to better understand GxE interactions for yield, coupled with screening for traits exhibiting superior adaptive mechanisms, are powerful assets in assisting plant breeders to select more effectively drought adapted genotypes.

Adopting early-maturing maize varieties can substantially increase yield and yield stability in suitable environments. Actionable recommendations that specify where early-maturing varieties can be suitably applied are lacking across low-income countries. We found for maize in Malawi that varieties with longer maturity duration provide on average the highest yield. However, if water stress occurs, we found that its timing determines which seed variety performs best. If water stress conditions are confined to the late season, early-maturing varieties escape drought and perform better than medium- and late-maturing varieties. Instead, if water stress conditions start already from mid-season, early-maturing varieties perform worst. Our results demonstrate that the typical seasonal timing of water stress can serve as a suitable criterion for recommending where to adopt early-maturing varieties. Finally, we propose an integrated research framework that complements our econometric analysis and allows to derive actionable variety suitability recommendations at the country level.

Integrating weather forecasts into decision support systems empowers farmers to optimise irrigation schedules, thereby boosting crop yields and conserving water. However, inaccurate forecasts can jeopardise productivity and irrigation efficiency. This study combines a crop model with a stochastic pseudo-weather forecast algorithm to: (1) determine the reliability needed in a weather forecast algorithm for effective irrigation management; and (2) assess the impact of weather forecast reliability on the productivity and environmental footprint of various maize cropping systems across diverse climates. It employs the Next Generation of Agricultural Production Systems sIMulator (APSIM NextGen) to simulate maize growth at eleven locations representing diverse climates globally. Various planting schedules, soil types, irrigation systems, and nitrogen availability levels were considered to examine the effects of perfect and imperfect weather forecasts. The findings underscore the potential of integrating weather forecasts into irrigation management for enhanced productivity and sustainability. High-confidence forecasts and longer lead times increase yields (up to 11 %) and improve sustainability outcomes, particularly in wetter climates and for conditions with low nitrogen availability. Conversely, when the accuracy of forecasts is low, forecast-driven irrigation management may lead to yield reductions compared to a baseline system, especially in drier climates (up to 26 % reduction), necessitating tailored management strategies. Soil type and farmer's risk tolerance further influence the effectiveness of forecast-driven irrigation management, emphasising the need for context-specific approaches. By understanding and leveraging the interconnected impacts of weather forecasts on yield, water use efficiency, nitrogen loss, and greenhouse gas emissions, farmers can optimise productivity while minimising environmental impacts.

Context: Wheat crops are highly sensitive to elevated temperatures and experience significant yield losses when short periods of heat occur at sensitive developmental phases. Objective: This research aimed at quantifying wheat responses of grain yield and yield components to heat indicators in fluctuating field conditions. Methods: The impacts of high temperature on yield and its components were assessed for 20-35 wheat lines in irrigated multi-environment trials over three years. Genotypes were cultivated using a novel photoperiod-extension method (PEM) adjacent to some conventional yield plots with different sowing dates. In the PEM, either single stems or plant quadrates were tagged at specific growth stages and hand-harvested at maturity, while conventional plots were mechanically harvested at maturity. The impact of heatwaves was estimated for events occurring at different developmental stages and for different temperature thresholds (26-35 • C). Results: The strongest correlation between heat and grain number was observed between 300 and 200 ○ Cd before flowering for a threshold temperature of 28 • C. For each hot hour (T > 28 • C) during this period, wheat genotypes lost on average an extra 0.25 grain at the spike level, and 281 grains m − 2 at the canopy level in conventional plots. For individual grain weight, correlations were statistically the closest for threshold temperatures above 32 • C post-flowering. In the tested environments, grain number was most sensitive to heat between 300 and 200 • Cd before flowering. Each post-flowering hour with T>32 • C (between 0 and 500 • Cd after flowering) reduced individual grain weight by an average of 0.26 mg at the spike level (PEM spike harvest) and grain yield by 2.44 g m − 2 at the canopy level (conventional plot harvest). Impacts of heatwaves were clearest when measured at the organ level (i.e. spikes) and for material with synchronised phenology. In addition, results suggest that heat impacts can also be quantified more reliably using finer time units (i.e. hot hours rather than days). Conclusions: In the studied well-watered conditions, natural heatwaves strongly impacted grain number for temperatures above 28 • C and individual grain weight for temperatures above 32 • C. Reductions in grain number and individual grain weight were strongly associated with accumulated hot hours that occurred during 200-300 • Cd before and 0-500 • Cd after flowering, respectively. Implications: The findings from this study will assist improvement for crop modelling in response to heatwaves, development of relevant phenotyping methods and selection of cultivars with better adaptation to warmer environments.