Advances in Crop Cutting Experiments for Yield Estimation: Insights from Gram Cultivation in North-West Rajasthan: A Review

In the arid expanses of North-West Rajasthan, gram (chickpea) sustains smallholder farmers amid harsh climates and poor soils. Accurate yield estimation, critical for planning and insurance, has historically relied on Crop Cutting Experiments (CCE), but traditional methods are labour-intensive and error-prone. This article explores recent advances in CCE, focusing on gram cultivation in districts like Bikaner and Jodhpur. Innovations such as satellite imagery, machine learning, and drone-based monitoring enhance precision, achieving up to 90% accuracy in yield predictions. Frontline demonstrations show yield increases of 18–35% through improved varieties and practices. By integrating real-time data collection and AI-driven models, these methods reduce bias and expedite results, vital for timely interventions under schemes like Pradhan Mantri Fasal Bima Yojana. We review 20 studies, analyse yield data from 2020–2024 showing steady growth despite climatic challenges, and discuss implications for sustainable agriculture. Enhanced CCE empowers farmers with reliable forecasts, improving food security and economic stability in a region where agriculture is a gamble against nature. Suggestions include policy support for tech adoption and farmer training to bridge digital divides, ensuring these advances translate into tangible benefits for Rajasthan’s pulse growers.