Input use efficiency management for Paddy Production Systems in India: a machine learning approach

Bhoi P. B., Wali V. S., Swain D. K., Sharma K., Bhoi A. K., Bacco M., Barsocchi P.

Paddy  Stochastic frontier  Machine learning  K-nearest neighbour  Support vector machine  Random forest  Agriculture 

This research illustrates the technical efficiency of the pan-India paddy cultivation status obtained through a stochastic frontier approach. The results suggest that the mean technical efficiency varies from 0.64 in Gujarat to 0.95 in Odisha. Inputs like human labor, mechanical labor, fertilizer, irrigation and insecticide were found to determine the yield in paddy cultivation across India (except for Chhattisgarh). Inefficiency in the paddy production in Punjab, Bihar, West Bengal, Andhra Pradesh, Tamil Nadu, Kerala, Assam, Gujarat and Odisha in 2016-2017 was caused by technical inefficiency due to poor input management, as suggested by the significant ?2U and ?2v values of the stochastic frontier model. In addition, most of the farm groups in the study operated in the high-efficiency group (80-90% technical efficiency). No specific pattern of input use can be visualized through descriptive measures to give any specific policy implication. Thus, machine learning algorithms based on the input parameters were tested on the data in order to predict the farmers' efficiency class for individual states. The highest mean accuracy of 0.80 for the models of all of the states was achieved in random forest models. Among the various states of India, the best random forest prediction model based on accuracy was fitted to the input data of Bihar (0.91), followed by Uttar Pradesh (0.89), Andhra Pradesh (0.88), Assam (0.88) and West Bengal (0.86). Thus, the study provides a technique for the classification and prediction of a farmer's efficiency group from the levels of input use in paddy cultivation for each state in the study. The study uses the DES input dataset to classify and predict the efficiency group of the farmer, as other machine learning models in agriculture have used mostly satellite, spectral imaging and soil property data to detect disease, weeds and crops.

Source: Agriculture (Basel) 11 (2021). doi:10.3390/agriculture11090837

Publisher: Molecular Diversity Preservation International, Basel

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