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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]

Exploring linkages between agriculture, nutrition and human health has become an emerging field of research. Literature shows us both positive and negative externalities of agriculture on nutrition and health. Hence, the first step towards exploring agriculture–nutrition linkages would be ‘establishing’ the relationship between agriculture and nutrition, and thereby human health. This study, while doesn’t explicitly model and estimate complex linkages, attempts to study the direction, strength and magnitude of agriculture on nutritional status in rural India. Using a panel data covering 1991–2012, the study explores the linkages at two stages. Analysis indicates a ‘positive causality’ of agriculture on human nutrition and the ordinary least squares estimates provide evidence for it. Fixed effects estimates confirm the ‘positive causality’ after controlling for the effects of several factors. The study concludes that agriculture has a significant positive influence on nutrition, and thereby on health. [ABSTRACT FROM AUTHOR]