Title: Analysis of influencing factors of grain yield based on multiple linear regression

Authors: Victor Chang; Qianwen Xu

Addresses: School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK ' International Business School Suzhou, Xi'an Jiaotong-Liverpool University, Suzhou, China

Abstract: Food security is a strategic issue affecting economic development and social stability and agriculture has always been at the forefront of national economic development. As a large agricultural country and a country with a large population, the production of grain is of great importance to China. Therefore, in order to ensure national food security and assist the food administrative department in making scientific and effective decisions, it is significant to study the law of variance in grain production and make accurate forecasting of its development trend. This paper constructs the stepwise regression model and principal component regression to analyse the influencing factors of grain yield respectively and compares these two models in terms of their accuracy in prediction. After conducting the two regressions, this paper concludes that the two models both explain the variance in grain yield ideally, but from the aspect of accuracy in prediction, the principal component regression is more effective than stepwise linear regression.

Keywords: grain yield; influencing factors; prediction; stepwise regression model; principal component regression.

DOI: 10.1504/IJBSR.2021.10028503

International Journal of Business and Systems Research, 2021 Vol.15 No.3, pp.337 - 355

Received: 02 Aug 2019
Accepted: 08 Nov 2019
Published online: 12 May 2021 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article