Exploring the relationship between rainfall and crop yield and best practices adoption using participatory approach

Abstract

Crop yield is a standard measurement for the amount of agricultural production. Sustainable agriculture demands an increase in crop yield. This study deals with rainfed agriculture; hence, precipitation becomes the driving factor for crop yield. Heat maps are used to examine the rainfall and crop yield correlations. ML is an essential tool in decision-making, and many ML algorithms are available for prediction. This study uses the ML algorithms to predict whether the crop yield will increase with increased rainfall. Logistic regression, Decision tree classifier, Random Forest classifier, and XGBoost classifier are the algorithms chosen for analysis. Altogether this region consists of forty crops but focuses on five predominant annual crops. Implementation-based results are the universal goal of every research which society needs. The chances of implementation are associated with two major components: the reliability of the results and society's willingness. Analysis of these components needs ground truthing and Participatory Rural Appraisal, respectively. Farmers and villagers filled out a questionnaire about the details required for this study. The survey was an active approach to collecting necessary information from the participants. The survey showed positive results among one hundred and fifty samples from six blocks. Finally, cashew nut, sugarcane, and turmeric showed good dependency on the precipitation, and around 88% of villagers are ready to implement the results derived from ML algorithms.

Keywords

• Precipitation
• ground truthing
• participatory approach
• implementetion of results

References

1. Klompenburga TV, Kassahuna A, and Catalb C, “Crop yield prediction using machine learning: A systematic literature review,” Computers and Electronics in Agriculture, vol. 177, 2020.
2. Xu X et al., “Design of an integrated climatic assessment indicator (ICAI) for wheat production: a case study in Jiangsu Province, China,” Ecological Indicators, vol. 101, pp. 943–953, 2019.
3. Pradyumn Kumar et al., “Assessment of crop loss caused by Chilo partellus in maize,” Indian Journal of Agricultural Sciences, vol. 91, no. 2, pp. 218–221, 2023.
4. Steve Oberlin, “Machine Learning, Cognition and Big Data,” CA Technology Exchange, United States., 2012.
5. Niveditha Nath, “From Pilgrim Landscape to Pilgrim Road: Tracing the transformation of the Char Dham Yatra in Colonial Garhwal,” Journal for the study of religion, nature and culture, pp. 419–437, 2018.
6. Ramos PJ, Prieto FA, Montoya EC, and Oliveros CE, “Automatic fruit count on coffee branches using computer vision,” Computer Electronics and Agriculture, vol. 137, pp. 9–22, 2017.
7. Beulah R, “A survey on different data mining techniques for crop yield prediction,” International Journal of Computer Science Engineering, vol. 7, no. 1, pp. 738–744, 2019.
8. Weigend A, “An overfitting and the effective number of hidden units. Lawrence Erlbaum Associates,” Hillsdale, pp. 335–342, 1993.

9. Chandra G, “Participatory Rural Appraisal Issues and Tools for Social Science Research in Inland Fisheries,” Central Inland Fisheries Research Institute. Bulletin, vol. 163, pp. 286–302, 2010.
10. Lilian A Omondi, “Learning together: Participatory rural appraisal for coproduction of climate change knowledge,” Action Research, vol. 21, no. 2, 2020.
11. Rudi Saprudin Darwis, Risna Resnawaty, and Eva Nuriyah, “Increasing the Sensitivity of Local Leadership in Citarum River Management through Participatory Rural Appraisal (PRA) techniques in Rancamanyar Village,” Kumawula Journal of Community Service, vol. 3, no. 1, pp. 48–59, 2020.
12. Pankaj Kumar, Dheeraj Kumar, Sachin Kumar, Jitendra Kumar, Kiran Pal, and Nikhil Jadhav, “Historical Perspective of Watershed Management in India: A Participatory Rural Appraisal (PRA) based Assessment,” Asian Journal of Agricultural Extension, Economics & Sociology, vol. 40, no. 10, pp. 406–418, 2022.
13. Alejo LA and Alejandro AS, “Validating CHIRPS ability to estimate rainfall amount and detect rainfall occurrences in the Philippines.,” Theoritical Applications of Climatology, vol. 145, pp. 967–997, 2021.
14. Jainendra Singh, “Big Data Analytic abd Mining with Machine Learning Algorithm,” International Journal of Information and Computation Technology, vol. 4, no. 1, pp. 33–40, 2014.
15. Nusinovici S et al., “Logistic regression was as good as machine learning for predicting major chronic diseases,” Journal of Clinical Epidemiology, vol. 122, pp. 56–69, 2020.
16. Jijo BT and Abdulazeez AM, “Classification Based on Decision Tree Algorithm for Machine Learning,” Journal of Applied Science and Technology Trends, vol. 2, no. 1, pp. 20–28, 2021.
17. Peter DC and Arandejelovic O, “Precision medicine in digital pathology via image analysis and machine learning,” Artificial Intelligence and Deep Learning in Pathology, pp. 149–173, 2021.
18. Jerome H Friedman, “Greedy Function Approximation: A Gradient Boosting Machine,” Annals of statistics, pp. 1189–1232, 2001.
19. Ramraj S, Nishant U, Sunil R, and Shatadeep B, “Experimenting XGBoost algorithm for prediction and classification of different datasets,” International journal of control theory and applications, vol. 9, pp. 615–662, 2016.
20. Muralidhara MB et al., “Survey, collection and characterization of Indian Avocado (Persea Americana) germplasm for morphological characters,” Indian Journal of Agricultural Sciences, vol. 93, no. 2, pp. 139–144, 2023.
21. Katiha P, Vass KK, Sharma AP, and Bhaumik U, “Issues and Tools for Social Science Research in Inland Fisheries,” Central Inland Fisheries Research Institute. Bulletin, vol. 163, 2010.
22. Alpaydin E, Introduction to Machine Learning, 2nd Edition. MIT Press, Cambridge, MA, 2010.