Keywords:-

Keywords: Agricultural productivity, Maharashtra agriculture, Crop yield analysis, Correlation analysis, Linear regression, ANOVA, t-test, Time series analysis

Article Content:-

Abstract

This study presents a comprehensive statistical analysis of agricultural productivity in Maharashtra, focusing on five major crops Rice, Wheat, Soyabean, Sugarcane, and Cotton using district-level data from the ICRISAT dataset (1966–2015) . The objective is to evaluate the interrelationship between cultivated area, total production, and yield using descriptive statistics, correlation analysis, linear regression, ANOVA, and t-tests . The findings reveal that area and production are strongly correlated (r ≈ 0.9–1.0), indicating that as cultivation area increases, production also rises proportionally, consistent with earlier findings . However, the correlation between area and yield is weak (r ≈ ±0.3), suggesting that yield is influenced by non-land factors such as irrigation, technology, and rainfall . Regression results show that Wheat (R² = 0.38) has the strongest dependency between area and yield, while Rice and Cotton show weak relationships, aligning with prior econometric applications in agriculture . The t-test between Rice and Wheat yields (p = 0.63) indicates no significant difference in mean productivity. Time series analysis further demonstrates steady yield improvement in Wheat and Sugarcane over five decades . The study concludes that Maharashtra’s agricultural growth depends more on improved resource efficiency and technology adoption than on area expansion.

References:-

References

ICRISAT. (2020). District-Level Database for India (1966–2015). International Crops Research Institute for the Semi-Arid Tropics. Hyderabad, India.

Directorate of Economics and Statistics, Government of Maharashtra. (2024). Agricultural Statistical Information System (MAHADES Portal). Retrieved from https://mahades.maharashtra.gov.in

Ministry of Agriculture and Farmers Welfare. (2024). Agricultural Statistics at a Glance 2024. Government of India, New Delhi.

NITI Aayog. (2022). Agriculture and Farmer Productivity Report. Government of India, New Delhi.

Food and Agriculture Organization of the United Nations (FAO). (2023). FAOSTAT: Global Agricultural Database. Retrieved from https://www.fao.org/faostat

Gujarati, D. N., & Porter, D. C. (2020). Basic Econometrics (6th ed.). New York, NY: McGraw-Hill Education.

Draper, N. R., & Smith, H. (2014). Applied Regression Analysis (3rd ed.). Hoboken, NJ: John Wiley & Sons.

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regression Analysis (5th ed.). Hoboken, NJ: Wiley.

Gujrati, N. D. (2013). Econometric Applications in Agriculture. New Delhi, India: Oxford University Press.

Government of India. (2023). Handbook of Agricultural Statistics 2023. Directorate of Economics and Statistics, Ministry of Agriculture.

Deshmukh, A., & Kale, S. (2021). Statistical analysis of crop productivity trends in Maharashtra using regression and correlation models. Indian Journal of Agricultural Economics, 76(2), 245–258.

Patil, R., & Kharat, S. (2020). Impact of technological interventions on crop yield in semi-arid regions of India. Journal of Statistics and Agricultural Research, 8(3), 67–75.

Rao, C. H. (2019). Agricultural Growth and Productivity in India: Policy Perspectives. New Delhi, India: Academic Foundation.

Panse, V. G., & Sukhatme, P. V. (1985). Statistical Methods for Agricultural Workers (Revised ed.). New Delhi, India: Indian Council of Agricultural Research (ICAR).

Kumar, S., & Sharma, A. (2022). Time series analysis of major crop yields in India using ARIMA models. International Journal of Applied Statistics and Data Science, 5(1), 31–44.

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Kamthe, R., Lembhe, A., Dange, A., Akolkar, D., & Dokrimare, S. (2026). Comparative Statistical Analysis of Major Crop Productivity in Maharashtra. International Journal Of Mathematics And Computer Research, 14(03), 180-186. https://doi.org/10.47191/ijmcr/v14iSPC3.35