Arjun Nayak,
K.S. Tailor,
- Research scholar, Department of Statistics, M. K. Bhavnagar University, Bhavnagar- 364002, Gujarat, India
- Assistant Professor, Department of Statistics, M. K. Bhavnagar University, Bhavnagar- 364002, Gujarat, India
Abstract
Bhavnagar district is one of the prominent onion-growing areas in the Saurashtra region of Gujarat, encompassing key talukas such as Mahuva, Talaja, Ghogha, Jesar, and Palitana. Onion cultivation in the district is carried out across three distinct seasons: rabi, kharif, and late kharif with harvesting periods extending from April to May for the rabi crop and from October to March for the kharif and late kharif crops. The productivity of onion crops is strongly influenced by soil characteristics, particularly pH balance and nutrient availability. This study examines the effects of soil pH and NPK (Nitrogen, Phosphorus, and Potassium) levels on onion yield in the Bhavnagar district of Gujarat, a major onion-producing region. A multivariate adaptive regression splines (MARS) modeling approach was employed to capture nonlinear relationships and complex interactions between soil parameters and crop yield. The MARS model effectively identified key spline functions representing critical thresholds in pH and nutrient levels that significantly impact yield. The findings provide valuable insights for site-specific soil management and precision agriculture practices, enabling farmers to optimize fertilizer application and soil conditioning strategies. This data-driven approach supports sustainable agricultural planning, improved resource-use efficiency, and enhanced crop productivity, offering a practical decision-support framework for policymakers, agronomists, and extension services involved in onion cultivation in semi-arid regions.
Keywords: Statistical Analysis, Soil Parameters, Onion crop, Regression, MARS model
[This article belongs to Research & Reviews : Journal of Agricultural Science and Technology ]
Arjun Nayak, K.S. Tailor. A Multivariate Adaptive Regression Splines Based Study of Soil Parameters and Their Impact on Onion Yield in Bhavnagar District. Research & Reviews : Journal of Agricultural Science and Technology. 2026; 15(01):53-64.
Arjun Nayak, K.S. Tailor. A Multivariate Adaptive Regression Splines Based Study of Soil Parameters and Their Impact on Onion Yield in Bhavnagar District. Research & Reviews : Journal of Agricultural Science and Technology. 2026; 15(01):53-64. Available from: https://journals.stmjournals.com/rrjoast/article=2026/view=242187
References
- Singh GP, Meena ML, Pankaj TR. Effect of different levels of nitrogen, phosphorus and potassium on growth and bulb yield of onion. The Pharma Innovation Journal. 2021;10(10):1504-7.
- KABIR MR. EFFECT OF DIFFERENT SOURCES OF NITROGEN ON GROWTH AND YIELD OF TOMATO (Solanum lycopersicum L.)(Doctoral dissertation, DEPARTMENT OF SOIL SCIENCE, SHER-E-BANGLA AGRICULTURAL UNIVERSITY, SHER-E-BANGLA NAGAR, DHAKA).
- Garai S, Paul RK, Yeasin M, Paul AK. CEEMDAN-based hybrid machine learning models for time series forecasting using MARS algorithm and PSO-optimization. Neural Processing Letters. 2024 Mar 6;56(2):92.
- Mirani A, Memon MS, Chohan R, Wagan AA, Qabulio M. Machine learning in agriculture: A review. LUME. 2021 Jan;10:5.
- Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference and prediction. New York: Springer-Verlag; 2001.
- Celik S, Boydak E. Description of the relationships between different plant characteristics in soybean using multivariate adaptive regression splines (MARS) algorithm. JAPS: Journal of Animal & Plant Sciences. 2020 Apr 1;30(2).
- Friedman JH. Multivariate adaptive regression splines. The annals of statistics. 1991 Mar;19(1):1-67.
- Sephton P. Forecasting recessions: can we do better on MARS. Federal Reserve Bank of St. Louis Review. 2001 Mar 1;83(March/April 2001).
- Kornacki J, Cwik J. Statistical Learning Systems (in Polish) WNT. Warsaw, Poland. 2005.
- Willmott CJ, Matsuura K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research. 2005 Dec 19;30(1):79-82.
- Liddle AD, Davies AH. Pelvic congestion syndrome: chronic pelvic pain caused by ovarian and internal iliac varices. Phlebology. 2007 Jun 1;22(3):100-4.
- Takma Ç, Atıl H, Aksakal V. Comparison of multiple linear regression and artificial neural network models goodness of fit to lactation milk yields.
- Eyduran E, Akin M, Eyduran SP. Application of multivariate adaptive regression splines through R software. Ankara Turkey: Nobel Academic Publishing. 2019.
- Zabihi M, Pourghasemi HR, Pourtaghi ZS, Behzadfar M. GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environmental Earth Sciences. 2016 Apr;75(8):665.
- Milborrow S. Notes on the earth package. Retrieved October. 2014 Jan 28;31:2017.
- R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2014. Available from: http://www.R-project.org
| Volume | 15 |
| Issue | 01 |
| Received | 06/01/2026 |
| Accepted | 24/01/2026 |
| Published | 30/04/2026 |
| Publication Time | 114 Days |
Login
PlumX Metrics
