Journal of Animal Science and Technology
Korean Society of Animal Science and Technology
Article

Assessment of growing condition variables on alfalfa productivity

Ji Yung Kim1, Kun Jun Han3, Kyung Il Sung1, Byong Wan Kim1, Moonju Kim2,*
1Department of Animal Life Science, Kangwon National University, Chuncheon 24341, Korea.
2Institute of Animal Life Science, Kangwon National University, Chuncheon 24341, Korea.
3School of Plant, Environmental and Soil Sciences, Louisiana State University, Baton Rouge 70803, United States.
*Corresponding Author: Moonju Kim, Institute of Animal Life Science, Kangwon National University, Chuncheon 24341, Korea, Republic of. Phone: +82-33-250-8635. E-mail: lunardevil@kangwon.ac.kr.

© Copyright 2023 Korean Society of Animal Science and Technology. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: May 11, 2022; Revised: Sep 07, 2022; Accepted: Jan 28, 2023

Published Online: Feb 09, 2023

Abstract

This study was conducted to assess the impact of growing condition variables on alfalfa (<italic>Medicago sativa</italic> L.) productivity. A total of 197 alfalfa yield results were acquired from the alfalfa field trials conducted by the South Korean National Agricultural Cooperative Federation or Rural Development Administration between 1983 and 2008. The corresponding climate and soil data were collected from the database of the Korean Meteorological Administration. Twenty-three growing condition variables were developed as explaining variables for alfalfa forage biomass production. Among them, twelve variables were chosen based on the significance of the partial-correlation coefficients or potential agricultural values. The selected partial correlation coefficients between the variables and alfalfa forage biomass ranged from -0.021 to 0.696. The influence of the selected twelve variables on yearly alfalfa production was summarized into three dominant factors through factor analysis. Along with the accumulated temperature variables, the loading scores of the daily mean temperature higher than 25°C were over 0.88 in factor 1. The sunshine duration at temperature between 0 ~ 25°C was 0.939 in factor 2. Precipitation days were 0.82, which was the greatest in factor 3. Stepwise regression applied with the three dominant factors resulted in the coefficients of factors 1, 2, and 3 for 0.633, 0.485, and 0.115, respectively, and the R-square of the model was 0.602. The environmental conditions limiting alfalfa growth, such as daily temperature higher than 25 °C or daily mean temperature affected annual alfalfa production most substantially among the growing condition variables. Therefore, future cultivar selection should consider the capability of alfalfa to be tolerant to extreme summer weather along with biomass production potential.

Keywords: Forage; Biomass; Weather; Factor Analysis