Impact of Yield Risk on Cotton Cultivation Decisions: A Study from Rajkot District, Gujarat, India
Darshan Muliya *
Post Graduate Institute of Agri-Business Management, Junagadh Agricultural University, Junagadh – 362001, India.
Chirag Bharodia
Post Graduate Institute of Agri-Business Management, Junagadh Agricultural University, Junagadh – 362001, India.
Deven J. Patel
Post Graduate Institute of Agri-Business Management, Junagadh Agricultural University, Junagadh – 362001, India.
*Author to whom correspondence should be addressed.
Abstract
Cotton remains a cornerstone of agricultural economies across the globe, particularly in countries like India, where it plays a vital role in rural livelihoods, industrial supply chains, and export earnings. As a cash crop with significant economic importance, the decisions of farmers make makes regarding cotton cultivation, especially the allocation of land or acreage, are influenced by a complex interplay of market signals, policy interventions, climatic conditions, and resource availability. The study aimed to analyse the acreage response of cotton cultivation in Rajkot district using the Nerlovian adjustment lag model, with the objective of identifying key factors influencing farmers’ decisions on area allocation. A quantitative research design was employed using 15 years of time-series data, and the model incorporated variables such as competing crop area, past prices, past yields, price risk, yield risk, and rainfall. Results showed that the model had high explanatory power with an R² of 0.8520, explaining 85.2% of the variation in cotton acreage. The intercept (523,203.16) was statistically significant at the 5% level (p = 0.016). The area under groundnut exerted a negative effect (-0.39152, p = 0.12), suggesting competition with cotton. Past price (-3.76806, p = 0.18), past yield (-12.71739, p = 0.30), price risk (33.9989, p = 0.43), and rainfall (-8.78208, p = 0.71) were all found to be statistically non-significant. In contrast, yield risk emerged as the most influential factor with a large negative coefficient (-62,980.84) and was statistically significant at the 5% level (p = 0.03), indicating that higher yield variability strongly discouraged farmers from allocating more land to cotton cultivation. The study concluded that while most explanatory variables did not significantly impact cotton acreage, yield risk played a critical role in influencing farmers’ decisions, highlighting the need for improved agronomic practices, technological support, and effective risk management measures to reduce yield uncertainty and stabilise cotton production in the region.
Keywords: Cotton, acreage response, nerlovian adjustment lag model, Rajkot, cash crop