Pearl Millet Yield Prediction: A Comparative Analysis for Forecasting Models

Nikita Dhankar, Srikanta Routroy () and Satyendra Kr. Sharma ()
Additional contact information
Nikita Dhankar: Amity Global Business School
Srikanta Routroy: BITS Pilani, Pilani Campus, Department of Mechanical Engineering & Joint Faculty, School of Interdisciplinary Research and Entrepreneurship (SIRE)
Satyendra Kr. Sharma: BITS Pilani, Pilani Campus, Department of Management

Chapter 7 in Decision Sciences for Quality and Productivity Improvement, 2026, pp 173-191 from Springer

Abstract: Abstract The crop yield estimation is an important aspect in agri-supply chain and forms the basis of decision-making for its different functional areas. The crop yield has a direct impact on farmer income, logistics planning and its infrastructure development. In the current study, pearl millet yield in Indian context is predicted considering available secondary data (rainfall, year, temperature, soil moisture, and pressure) for six districts (Nagaur, Jodhpur, Alwar, Barmer, Churu, and Jaipur) of Rajasthan from 1966 to 2020. The pearl millet yield is predicted using various predictive analytics tools such as multivariate adaptive regression splines (MARS), random forest (RF), multiple regression, TreeNet, and classification and regression tree (CART) in Minitab version 21. The root mean square error (RMSE), correlation factor (R), and adjusted R-squared were the three accuracy measures utilized to validate the models. All models were statistically tested using out-of-bag or with fivefold cross-validation. The results show that fairly accurate prediction was obtained The results of the study indicate that the TreeNet model outperformed other predictive models, achieving an R-squared value of 74.59%, demonstrating its superior capability in predicting pearl millet yield. Temperature has the highest relative importance based on the predicted model, followed by pressure, rainfall, and soil moisture. The present work will be a valuable addition to the pearl millet yield for casting literature in specific.

Keywords: Pearl millet; Machine learning algorithms; Yield prediction; Multiple regression; CART; Random forest; MARS; TreeNet (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-95-7545-9_7

Ordering information: This item can be ordered from
http://www.springer.com/9789819575459

DOI: 10.1007/978-981-95-7545-9_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().