Next Article in Journal
Context Specificity and Time Dependency in Classifying Sub-Saharan Africa Dairy Cattle Farmers for Targeted Extension Farm Advice: The Case of Uganda
Previous Article in Journal
Productivity and Efficiency in European Milk Production: Can We Observe the Effects of Abolishing Milk Quotas?
Previous Article in Special Issue
Improved Rice Technology Adoption: The Role of Spatially-Dependent Risk Preference
Article

Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach

1
Department of Economics and Sociology, Punjab Agricultural University, Ludhiana 141004, Punjab, India
2
Indian Institute of Millets Research, Hyderabad 500030, Telangana, India
3
Department of Agricultural Statistics, Faculty of Agricultural Sciences, Siksha ‘O’ Anusandhan University, Bhubaneswar 751003, Odisha, India
4
Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar 737136, Sikkim, India
5
Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
*
Authors to whom correspondence should be addressed.
Academic Editors: Vítor João Pereira Domingues Martinho, Paulo Reis Mourão and Nikolaos Georgantzis
Received: 27 July 2021 / Revised: 27 August 2021 / Accepted: 28 August 2021 / Published: 31 August 2021
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 σ2U and σ2v 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. View Full-Text
Keywords: paddy; stochastic frontier; machine learning; k-nearest neighbour (KNN); support vector machine (SVM); random forest (RF) paddy; stochastic frontier; machine learning; k-nearest neighbour (KNN); support vector machine (SVM); random forest (RF)
Show Figures

Figure 1

MDPI and ACS Style

Bhoi, P.B.; Wali, V.S.; Swain, D.K.; Sharma, K.; Bhoi, A.K.; Bacco, M.; Barsocchi, P. Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach. Agriculture 2021, 11, 837. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11090837

AMA Style

Bhoi PB, Wali VS, Swain DK, Sharma K, Bhoi AK, Bacco M, Barsocchi P. Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach. Agriculture. 2021; 11(9):837. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11090837

Chicago/Turabian Style

Bhoi, Priya B., Veeresh S. Wali, Deepak K. Swain, Kalpana Sharma, Akash K. Bhoi, Manlio Bacco, and Paolo Barsocchi. 2021. "Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach" Agriculture 11, no. 9: 837. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11090837

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop