Sai Prabhakar Balantrapu

Background: India’s minimum support price (MSP) regime creates an administered floor for wholesale (mandi) prices, making accurate price forecasting the analytical foundation for farmer revenue-risk assessment, government procurement planning and the design of price- or revenue-based crop insurance instruments. Wheat and maize present a sharp institutional contrast: wheat is procurement-binding through the Food Corporation of India, while maize receives little procurement support and its price is left largely to private trade. This study benchmarks machine-learning and classical time-series forecasters for the monthly modal mandi price of both crops across a structurally distinct, post-pandemic hold-out period and defines, in measurable terms, what it means for such a series to be MSP-anchored. Methods: This analysis was carried out at Sri Sathya Sai Institute of Higher Learning (SSSIHL), Prasanthi Nilayam, Andhra Pradesh, India, during 2026. Daily AGMARKNET records for wheat and maize (April 2008-March 2025) were aggregated into 204 monthly All-India modal-price observations per crop-the mean of daily modal prices-with the corresponding MSP reported for reference. Eight algorithms-ridge regression, support vector regression, random forest, extremely randomised trees, gradient boosting, XGBoost, ARIMA and SARIMA-together with a naïve persistence benchmark, were trained on 177 months (April 2008-December 2022) and evaluated out-of-sample on 27 held-out months (January 2023-March 2025) using the mean absolute error, root mean squared error and mean absolute percentage error (MAPE). Result: Ridge regression on lagged prices and a month indicator dominated every fitted ensemble and, with ARIMA and SARIMA evaluated as static multi-step forecasts, every time-series competitor for both crops, achieving a MAPE of 2.75 per cent for wheat (mean absolute error ₹ 68.97/quintal) and 2.59 per cent for maize (₹ 55.97/quintal). Tree-based learners and support vector regression deteriorated sharply on the trending test window; SARIMA was competitive only for maize. Against naïve persistence, however, ridge’s edge was modest for wheat and mixed for maize-persistence matched ridge on MAE and MAPE and narrowly beat it on RMSE-indicating ridge’s advantage lies chiefly over the more flexible learners. This is attributed to the near-unit autocorrelation of MSP-anchored series and the inability of tree ensembles to extrapolate beyond their training range, consistent with, though not proving, the institutional asymmetry between procurement-stabilised wheat and procurement-absent maize. These forecasts form the price engine for companion volume-forecasting and procurement-liability studies within the broader actuarial data-science revenue-protection framework.

This chapter delves into the intersection of agriculture and the burgeoning realm of the Metaverse, exploring how this digital landscape is revolutionizing crop revenue generation and protection. Beginning with an introduction to the concept of the Metaverse and its implications, it navigates through the intricate components of crop revenue within this virtual space. The integration of crop revenue components with the Metaverse is meticulously examined, showcasing the emergence of virtual farming, innovative educational platforms, and dynamic marketplaces. However, alongside the opportunities, the chapter scrutinizes the challenges posed by the Metaverse in the realm of crop revenue framework. Strategies to mitigate these challenges are proposed, emphasizing the importance of adaptation and innovation. Moreover, the chapter highlights the crucial aspect of crop revenue protection within the Metaverse, emphasizing the need for effective crop insurance mechanisms. It scrutinizes the challenges inherent in this domain and suggests strategies to address them while also delving into the ethical considerations associated with utilizing 44 the Metaverse for crop revenue protection. In conclusion, the chapter advocates for proactive engagement with the Metaverse, presenting a compelling case for its integration into agricultural practices to secure future crop revenue and foster sustainable agricultural development.

Agriculture remains a critical pillar of the Indian economy, employing 42% of the labour force and representing 18% of the nation's Gross Domestic Product (GDP). Although it holds great value, the sector grapples with countless hurdles within the supply chain, involving both production and market flow. Weather variability and the market conditions cause price volatility which is a source of uncertainty to both farmers and policymakers. The authors in this chapter analyse major Indian crops - Wheat and Maize using price prediction models using machine learning approaches such as Linear Regression, Decision Tree, Random Forest, XGBoost and K-Nearest Neighbours. The chapter establishes that the Random Forest algorithm outperforms the other ML models in predicting crop prices. These findings offer valuable insights for policymakers, enabling the development of more effective, data-driven price stabilization strategies and risk reduction measures to support India's agricultural economy.