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Evaluating high-throughput soil profile information is essential in safflower precision agriculture, as it facilitates efficient resource management and design of an experiment that promotes sustainable production. We collected soil from representative target environments (TE) of safflower cultivation and evaluated 14 soil physio-chemical features for constructing fine-resolution maps. The robustness, versatility, and predictive ability of two statistical learning models in correctly classifying the soil profile to clusters were tested. Calcium, sand, soil organic carbon, phosphorous, potassium, and sodium were found to be most influential in classifying the representative TE. Random Forest model was found to be the best performing with average prediction accuracy above 85% in all test settings which reached 100% in some. The optimal training population size for prediction was found to be 70–80%. The spatial distribution of sodium in Delhi was found to be aligned with the low yield of safflower emphasizing the importance of fine-resolution soil mapping to design a field experiment and optimize the nutrient supply. Fine-resolution mapping not only enhance soil management strategies but also support government initiatives such as soil health cards, delineation of cultivable land, and risk assessments in crop-growing areas. [ABSTRACT FROM AUTHOR]

Variation in crop performance is directly affected by the environment in which the plant grows. Analyses and estimation of genotype x environment interactions (GxE) have the potential to provide information about the characteristics of genotypes, identify elite genotypes and suitable environmental conditions, establish breeding objectives, and make recommendations for crop management practices. For the past half century, a variety of statistical models have been used for estimating GxE in plant breeding field experiments to facilitate the allocation of superior genotypes to the target population of environments. The most commonly used models are described in this review starting with linear regression and ANOVA models. We then describe modifications in the form of multiplicative models, models that can accommodate external variables, and mixed effect models. Quantification of differential effects of segments of a genome across environments is shown by exploiting marker x environment (MxE) interac-tions. We close with a brief overview of some nonparametric concepts that aim to understand genotypic stability. [ABSTRACT FROM AUTHOR]