Identifying Associations Between Soil And Production Variables Using Linear Multiple Regression Models

Authors

  • Hari Dahal Ministry of Agriculture and Cooperatives, Kathmandu
  • JK Routray Rur. and Reg. Dev. Planning, Asian Institute of Technology,

DOI:

https://doi.org/10.3126/aej.v12i0.7560

Keywords:

correlation, crop yield, linear multiple regression, soil factors

Abstract

In agriculture, soil variables are known to be important factors that determine the level of crop productivity in a given situation. To assess which soil variables are important to crop production, soil samples were tested and the test data were correlated with crop yields. A total of six soil variables- soil reaction, organic matter, total nitrogen, available phosphorus, potassium and soil texture were put into Pearson’s correlation with crop yield data. Some of the soil variables were found to be highly correlated. To evaluate the apparent strength of the relationship and to explain the variations on dependent variable (crop yield) multiple regression models were developed. In conclusion, it was found that the most important variables explaining the variations in the yield of paddy were total nitrogen, organic matter and phosphorus.

The Journal of Agriculture and Environment Vol:12, Jun.2011, Page 27-37

DOI: http://dx.doi.org/10.3126/aej.v12i0.7560

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Published

2013-02-05

How to Cite

Dahal, H., & Routray, J. (2013). Identifying Associations Between Soil And Production Variables Using Linear Multiple Regression Models. Journal of Agriculture and Environment, 12, 27–37. https://doi.org/10.3126/aej.v12i0.7560

Issue

Section

Technical Paper