Environmental characterization for defining the target population of environments (TPE) is critical to improve the efficiency of breeding programs in crops, such as sorghum (Sorghum bicolorL.). The aim of this study was to characterize the spatial and temporal variation for a TPE for sorghum within the United States. APSIM-sorghum, included in the Agricultural Production Systems sIMulator software platform, was used to quantify water-deficit and heat patterns for 15 sites in the sorghum belt. Historical weather data (∼35 years) was used to identify water (WSP) and heat (HSP) stress patterns to develop water–heat clusters. Four WSPs were identified with large differences in the timing of onset, intensity, and duration of the stress. In the western region of Kansas, Oklahoma, and Texas, the most frequent WSP (∼35%) was stress during grain filling with late recovery. For northeast Kansas, WSP frequencies were more evenly distributed, suggesting large temporal variation. Three HSPs were defined, with the low HSP being most frequent (∼68%). Field data from Kansas State University sorghum hybrid yield performance trials (2006–2013 period, 6 hybrids, 10 sites, 46 site × year combinations) were classified into the previously defined WSP and HSP clusters. As the intensity of the environmental stress increased, there was a clear reduction on grain yield. Both simulated and observed yield data showed similar yield trends when the level of heat or water stressed increased. Field yield data clearly separated contrasting clusters for both water and heat patterns (with vs. without stress). Thus, the patterns were regrouped into four categories, which account for the observed genotype by environment interaction (GxE) and can be applied in a breeding program. A better definition of TPE to improve predictability of GxE could accelerate genetic gains and help bridge the gap between breeders, agronomists, and farmers.

Investigating soil and climate variability is critical to defining environments for field crops, understanding yield-limiting factors, and contributing to the sustainability and resilience of agro-ecosystems. Following this rationale, the aim of this study was to develop a soil–climate characterization to describe environmental constraints in the Senegal summer-crops region. For the soil database, 825 soil samples were collected characterizing pH, electrical conductivity (EC), phosphorus (P), potassium (K), cation exchange capacity (CEC), and total carbon (C) and nitrogen (N). For the climate, monthly temperature, precipitation, and evapotranspiration layers were retrieved from WorldClim 2.1, CHIRPS and TERRACLIMATE. The same analysis was applied individually to both databases. Briefly, a principal component analysis (PCA) was executed to summarize the spatial variability. The outcomes from the PCA were subjected to a spatial fuzzy c-means algorithm, delineating five soil and three climate homogeneous areas, accounting for 73% of the soil and 88% of the climate variation. To our knowledge, no previous studies were done with large soil databases since availability field data is often limited. The use of soil and climate data allowed the characterization of different areas and their main drivers. The use of this classification will assist in developing strategic planning for future land use and capability classifications.

Optimizing planting date by maturity group (PD × MG) is critical to increase productivity and reduce production risks. Understanding the effect of management, not only under current, but also future weather conditions, is even more relevant for developing effective mitigation strategies. This paper provides an analysis of the optimum combinations of soybean PD × MG management in the central-eastern region of Kansas (United States) for both current and future weather conditions. Three geographical clusters illustrating the main environmental and management characteristics were defined within the central-eastern region of Kansas. The Agricultural Production Systems Simulator platform was employed to explore PD × MG combinations (PD from mid-April to mid-July; MG from III to VI) comparing current (2011–2021) and future (2042–2052) weather conditions. Overall, early planting dates produce greater yields, but reduce their stability over time (with a 15% increase in yield variation relative to late planting) across the clusters. Late planting dates resulted in a reduction close to 27% for soybean yields relative to those obtained by planting at early dates under current weather conditions. Furthermore, longer maturity groups (IV, V, and VI) resulted in a reduced yield penalty when planting time was delayed under the current weather conditions. However, this combination did not always represent the strategy that maximized yields.

Estimating pasture biomass has emerged as a promising avenue to assist farmers in identifying the best cutting times for maximizing biomass yield using satellite data. This study aims to develop an innovative framework integrating field and satellite data to estimate aboveground biomass in alfalfa (Medicago sativa L.) at farm scale. For this purpose, samples were collected throughout the 2022 growing season on different mowing dates at three fields in Kansas, USA. The satellite data employed comprised four sources: Sentinel-2, PlanetScope, Planet Fusion, and Biomass Proxy. A grid of hyperparameters was created to establish different combinations and select the best coefficients. The permutation feature importance technique revealed that the Planet’s PlanetScope near-infrared (NIR) band and the Biomass Proxy product were the predictive features with the highest contribution to the biomass prediction model’s. A Bayesian Additive Regression Tree (BART) was applied to explore its ability to build a predictive model. Its performance was assessed via statistical metrics (r²: 0.61; RMSE: 0.29 kg.m⁻²). Additionally, uncertainty quantifications were proposed with this framework to assess the range of error in the predictions. In conclusion, this integration in a nonparametric approach achieved a useful predicting tool with the potential to optimize farmers’ management decisions.

Previous literature documented an imbalance for sorghum [ Sorghum bicolor (L.) Moench] between source (leaves) and sink (grains), favoring the source. Therefore, reducing leaf number, anticipating maturity, and placing the dry‐down with more favorable environment might be advantageous for producers to fit another crop in the rotation. The aims of this study were to (1) evaluate via in‐silico the effects of leaf removal during the grain filling and (2) explore those impacts using a field dataset for sorghum yield. For the first objective, the APSIM (Agricultural Production Systems Simulator) sorghum model was tested with four hybrids across 12 locations in the United States (2015–2023) resulting in an RRMSE (relative root mean squared error) of 25% for yield. As a second step, an APSIM defoliation module was developed using field data of one site‐year, demonstrating an RRMSE of 17% for yield. As a last step, the model was used to simulate the effect of sequential defoliations on yield, across 38 years of weather data (1984–2022), without showing any yield penalties when removing up to four leaves after flowering. Leaf area removal after flowering indicated a positive imbalance in source:sink ratio (i.e., source excess). For the second objective, a field dataset from 21 sorghum hybrids with different attainable leaf numbers and cycle duration did not result in significant yield differences. Early maturity hybrids with fewer leaves give farmers the opportunity to intensify crop sequences. Less focus in sorghum improvement for early relative to late maturing hybrids has been reported; therefore, there is still ample room for future yield gains.