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Introduction: With increasing demand for food and changing environmental conditions, a better understanding of the factors impacting wheat yield is essential for ensuring food security and sustainable agriculture. By analyzing the effect of multiple factors on wheat yield, the presented research provides novel insights into the potential impacts of climate change on wheat production in India. In the present study, datasets consisting of countrywide environmental and agronomic factors and wheat yield were collected. In addition, the study also analyzes the effect of information demand of farmers on production. Methodology: The study employs a regional analysis approach by dividing the country into five zonal clusters: Northern Hills, Central India, Indo-Gangetic Plains, North-Eastern India, and Peninsular India. Correlation and Principal Component Analysis (PCA) were performed to uncover the month-wise key factors affecting wheat yield in each zone. Furthermore, four Machine Learning/Deep Learning-based models, including XGBoost, Multi-layer Perceptron (MLP), Gated Recurrent Unit (GRU), and 1-D Convolutional Neural Network (CNN), were developed to estimate wheat yield. This study estimated partial derivatives for all factors using Newton's Quotient Technique, a numerical method-based approach. Results: The analysis focused on applying this technique to the best-performing wheat yield estimation model, which was the GRU-based model (with RMSE and MAE of 0.60 t/ha and 0.46 t/ha, respectively). Discussion: In the later sections of the article, multiple policy recommendations are communicated based on the extracted insights. The results of the presented research help inform decision-making regarding the development of strategies and policies to mitigate the impacts of climate change on wheat production in India. [ABSTRACT FROM AUTHOR]

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Identification of cropland suitability is obligatory to adapting to the increased food needs driven by population expansion, environmental contamination, and climate change. Given this, the present study was conducted to assess cereal crops' suitability in India's Haryana state by integrating Analytic Hierarchy Process (AHP) and geographic information system (GIS) technique. Multiple factors were considered for this study, such as rainfall, temperature, soil texture, drainage density, pH, organic carbon, electrical conductivity, and slope. The AHP technique was utilized to decide the weights of each individual parameter using experts' opinions. The crop-suitability model was developed using the model builder module in ArcGIS 10.8, and each input parameter was reclassified as per the optimum crop-growth requirement and overlaid utilizing the reclassify tool and weighted overlay analysis. The crop suitability classes were estimated as highly suitable, S1 (6%); moderately suitable, S2 (71%); and marginally suitable, S3 (23%) for the calculated arable land for the wheat crop. Similarly, the crop suitability class of rice S2 (28%); S3 (72%), for sorghum S1 (28%); S2 (71%); S3 (1%), for maize S2 (85%); S3 (15%) and for pearl millet S1 (60%); S2 (40%) were also estimated. The study has observed that, as per the soil physico-chemical characteristics and climate, the area is moderately fertile. Therefore, agricultural production can be improved by cultivating the crop in highly and moderately suitable zones. Diversification of marginally suitable regions for crops other than that for which it is not suitable can be taken up, which will also ensure the income security of marginal farmers. [ABSTRACT FROM AUTHOR]

Yield limitation and widespread sulphur (S) deficiency in pearl-millet-nurturing dryland soils has emerged as a serious threat to crop productivity and quality. Among diverse pathways to tackle moisture and nutrient stress in rainfed ecologies, conservation agriculture (CA) and foliar nutrition have the greatest potential due to their economic and environmentally friendly nature. Therefore, to understand ammonium thiosulphate (ATS)-mediated foliar S nutrition effects on yield, protein content, mineral biofortification, and sulphur economy of rainfed pearl millet under diverse crop establishment systems, a field study was undertaken. The results highlighted that pearl millet grain and protein yield was significantly higher under no-tillage +3 t/ha crop residue mulching (NTCRM) as compared to no-tillage without mulch (NoTill) and conventional tillage (ConvTill), whereas the stover yield under NTCRM and ConvTill remained at par. Likewise, grain and stover yield in foliar S application using ATS 10 mL/L_twice was 19.5% and 13.2% greater over no S application. The sulphur management strategy of foliar-applied ATS 10 mL/L_twice resulted in significant improvement in grain protein content, protein yield, micronutrient fortification, and net returns (₹ 54.6 × 1000) over the control. Overall, ATS-mediated foliar S nutrition can be an alternate pathway to S management in pearl millet for yield enhancement, micronutrient biofortification and grain protein content increase under ConvTill, as well as under the new NTCRM systems. [ABSTRACT FROM AUTHOR]