Climate change is expected to have a major effect on crop production in sub‐Saharan Africa. Crop models can help to guide crop management under future climate. The objective of the study was to investigate the possible effects of climate change on Ethiopian barley (Hordeum vulgare L.) production using the Decision Support System for Agrotechnology Transfer (DSSAT)‐Crop Environment Resource Synthesis (CERES)‐Barley model. The study included field data of two barley cultivars (Traveller and EH‐1493) and four climate study areas in Ethiopia over 5 yr. Climate change scenarios were set up over 60 yr using representative concentration pathways (RCP; RCP4.5 and RCP8.5) and five global climate models (GCM). The model results indicated that the prediction of days to anthesis and maturity, as well as final grain yield, was highly accurate for cultivar Traveller with normalized RMSE (nRMSE) of 2, 1, and 12%, respectively, and for cultivar EH‐1493 with nRMSE of 2, 4, and 11%. A consistent increase in average temperature up to 5 °C and a mixed pattern of rainfall (‐61 to +86%) were projected. Yield simulations showed a potential reduction in yield up to 98% for cultivar Traveller and 63% for cultivar EH‐1493 in the future. Within a sensitivity analysis, different sowing dates, sowing densities, and fertilizer rates were tested as potential adaptation approaches to climate change. The negative effects of climate change could be mitigated by early sowing, with an increased sowing density of 25% and fertilizer rate of 50% more than what is recommended. Overall, the results indicated the ability of the CERES‐Barley model to evaluate climate change effects and adaptation options on rainfed barley production in Ethiopia.

Crop models are valuable tools for simulating and assessing genotype‐by‐environment interactions. In most studies, these models are parameterized based on crop data from a few sites and years, which often limits their applicability to a broader geographic context. Therefore, we utilize countrywide multi‐environment variety trial data in this study to implement a genotype‐specific model parameterization for winter rye (Secale cereale L.) in Germany. We use the Crop and Environment REsource Synthesis (CERES) model originally used for wheat available in the decision support system for agrotechnology transfer (DSSAT) framework and adapt and evaluate it for rye. Calibration and evaluation involved a comprehensive agronomic trial datasets for the rye cultivar Palazzo, encompassing 194 site‐years of experiments covering various cereal production regions in Germany. The parameterization followed a structured approach, encompassing phenology, growth, and yield‐specific coefficients. The parameterized CSM‐CERES‐Rye (where CSM is cropping system model) demonstrated reasonable accuracy in simulating critical crop parameters, including aboveground biomass, leaf area index, tiller, grain number, unit seed weight, and grain yield. The model is available for diverse model‐based assessments of rye cultivation, including evaluating crop management, analyzing crop rotations, and assessing rye's suitability across varied environments, making it valuable for sustainable agriculture and decision‐making.

Multi‐modeling (MM) approaches allow increasing modeling accuracy through a combination of different modeling structures for the simulation of plant growth and yield. The Decision Support System for Agrotechnology Transfer (DSSAT) 4.7 modeling platform currently includes three different wheat (Triticum aestivum L.) models (CERES, N‐Wheat, and Cropsim). However, the main obstacle for using an MM approach is the calibration procedure. Calibration is time consuming and complex, especially if the user is not familiar with all three models. It results in a subjective calibration optimum and might discriminate models if the user is less trained. To avoid these conflicts, an automated calibration program which optimizes cultivar coefficients based on the root means square error (RMSE) of time‐series data was developed to ensure objective calibration results across three different wheat models and to highlight the potential of MM approaches for decision support in the future. Model calibration was performed on a 4‐yr nitrogen wheat fertilizer trial (0–240 kg ha⁻¹ N) in southwest Germany. The evaluation mean showed satisfying results for the calibration (d‐index = .93) and evaluation dataset (d‐index = .81). By comparing different years, the MM approach improved modeling accuracy in most cases. Especially in the drought season of 2018, the MM approach revealed higher modeling accuracy for yield (d‐index = .61) in contrast to a single simulation of CERES (d‐index = .34) and Cropsim (d‐index = .39). This demonstrated the advantage of an MM approach as different modeling structures could compensate for errors that occur in single modeling approaches.

Autonomous selective spraying could be a way for agriculture to reduce production costs, save resources, protect the environment and help to fulfill specific pesticide regulations. The objective of this paper was to investigate the use of a low-cost sonar sensor for autonomous selective spraying of single plants. For this, a belt driven autonomous robot was used with an attached 3-axes frame with three degrees of freedom. In the tool center point (TCP) of the 3-axes frame, a sonar sensor and a spray valve were attached to create a point cloud representation of the surface, detect plants in the area and perform selective spraying. The autonomous robot was tested on replicates of artificial crop plants. The location of each plant was identified out of the acquired point cloud with the help of Euclidian clustering. The gained plant positions were spatially transformed from the coordinates of the sonar sensor to the valve location to determine the exact irrigation points. The results showed that the robot was able to automatically detect the position of each plant with an accuracy of 2.7 cm and could spray on these selected points. This selective spraying reduced the used liquid by 72%, when comparing it to a conventional spraying method in the same conditions.

Cropping system models (CSMs) are valuable tools for analyzing genotype, environment, and management (G × E × M) interactions in crop production. To apply a CSM in a new region with specific soils, climate, and cultivars, proper calibration and evaluation are required. However, calibration methods vary widely, often depending on modelers' expertise and approach. This study compares three calibration strategies for the DSSAT‐Nwheat model using two datasets: one including yield components (1000‐kernel mass, ears per m ² , grain number per m ² ) alongside phenology and grain yield, and another excluding yield components. The datasets cover ∼100 site‐years of winter wheat ( Triticum aestivum ) data from German pre‐registration trials and field experiments. The calibration approaches were (1) stepwise calibration of phenology, biomass, and yield, (2) simultaneous calibration of multiple genetic coefficients, and (3) a hybrid approach combining elements of both. The Time‐Series cultivar coefficient estimator tool was used for implementation. Including yield component data improved model accuracy, reducing root mean square error (RMSE) by up to 10% for key variables such as phenology (3.4–5.5 days). Future wheat yield projections under selected climate scenarios varied by strategy and dataset, ranging from 6376 to 7473 kg ha ⁻¹ in fertile, wet soils and 6108 to 6757 kg ha ⁻¹ in poorer, dry soils. These results highlight the impact of calibration strategy and dataset choice on model performance. Transparent calibration practices are essential for improving CSM reliability in regional agricultural analysis under diverse environmental conditions.