Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

We investigated the perceived effects of salinity on farming practices, income, and challenges for crop production in Blang Nibong village in North Aceh, Indonesia. We surveyed 120 smallholder farmers chosen in consultation with local leaders considering their agricultural activities and salinity susceptibility. Farmers’ perceptions of major crop production constraints (e.g., salinity) and potential adaptation strategies were assessed using open and closed questions. The study revealed that farmers in the study region primarily grew rain-fed rice using traditional monoculture. Salinity was identified as the primary crop production constraint by all respondents, resulting in plant mortality, decreased soil health and water quality, limited plant growth, and low yields. Additionally, salinity has reduced the arable area (>0.5 ha), resulting in lower total production. The implications of the salinity were further corroborated by the low farmers’ income. In fact, farming activities are not contributing positively to farmers’ income as the results revealed off-farm activities (77%) as the main source of income. Based on the farmer’s current activities to overcome salinity problems on their farms, they were clustered into adaptive and non-adaptive farmers. The non-adaptive group prefers to convert their land to pasture (81%), whereas the adaptive group prefers to improve the irrigation system (77%).

Due to global climate change, droughts are likely to become more frequent and more severe in many regions such as in South Africa. In Limpopo, observed high climate variability and projected future climate change will likely increase future maize production risks. This paper evaluates drought patterns in Limpopo at two representative sites. We studied how drought patterns are projected to change under future climatic conditions as an important step in identifying adaptation measures (e.g., breeding maize ideotypes resilient to future conditions). Thirty-year time horizons were analyzed, considering three emission scenarios and five global climate models. We applied the WOFOST crop model to simulate maize crop growth and yield formation over South Africa’s summer season. We considered three different crop emergence dates. Drought indices indicated that mainly in the scenario SSP5-8.5 (2051–2080), Univen and Syferkuil will experience worsened drought conditions (DC) in the future. Maize yield tends to decline and future changes in the emergence date seem to impact yield significantly. A possible alternative is to delay sowing date to November or December to reduce the potential yield losses. The grain filling period tends to decrease in the future, and a decrease in the duration of the growth cycle is very likely. Combinations of changed sowing time with more drought tolerant maize cultivars having a longer post-anthesis phase will likely reduce the potential negative impact of climate change on maize.

Sorghum holds the potential for enhancing food security, yet the impact of the interplay of water stress and salinity on its growth and productivity remains unclear. To address this, we studied how drought and salinity affect physiological traits, water use, biomass, and yield in different tropical sorghum varieties, utilizing a functional phenotyping platform, Plantarray. Cultivars (Kuali, Numbu, Samurai2) were grown under moderate and high salinity, with drought exposure at booting stage. Results showed that Samurai2 had the most significant transpiration reduction under moderate and high salt (36% and 48%) versus Kuali (22% and 42%) and Numbu (19% and 16%). Numbu reduced canopy conductance (25% and 15%) the most compared to Samurai2 (22% and 33%) and Kuali (8% and 35%). In the drought*salinity treatment, transpiration reduction was substantial for Kuali (54% and 57%), Samurai2 (45% and 60%), and Numbu (29% and 26%). Kuali reduced canopy conductance (36% and 53%) more than Numbu (36% and 25%) and Samurai2 (33% and 49%). Biomass, grain yield, and a-100 grain weight declined in all cultivars under both salinity and drought*salinity, and Samurai2 was most significantly affected. WUE_biomass significantly increased under drought*salinity. Samurai2 showed reduced WUE_grain under drought*salinity, unlike Kuali and Numbu, suggesting complex interactions between water limitation and salinity in tropical sorghum.

Grain production must increase by 60% in the next four decades to keep up with the expected population growth and food demand. A significant part of this increase must come from the improvement of staple crop grain yield potential. Crop growth simulation models combined with field experiments and crop physiology are powerful tools to quantify the impact of traits and trait combinations on grain yield potential which helps to guide breeding towards the most effective traits and trait combinations for future wheat crosses. The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models.