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Assessment of growing condition variables on alfalfa productivity

This study was conducted to assess the impact of growing condition variables on alfalfa (Medicago sativa 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...

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Autores principales: Kim, Ji Yung, Han, Kun Jun, Sung, Kyung Il, Kim, Byong Wan, Kim, Moonju
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society of Animal Sciences and Technology 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10640942/
https://www.ncbi.nlm.nih.gov/pubmed/37969334
http://dx.doi.org/10.5187/jast.2023.e14
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author Kim, Ji Yung
Han, Kun Jun
Sung, Kyung Il
Kim, Byong Wan
Kim, Moonju
author_facet Kim, Ji Yung
Han, Kun Jun
Sung, Kyung Il
Kim, Byong Wan
Kim, Moonju
author_sort Kim, Ji Yung
collection PubMed
description This study was conducted to assess the impact of growing condition variables on alfalfa (Medicago sativa 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°C–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.
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spelling pubmed-106409422023-11-15 Assessment of growing condition variables on alfalfa productivity Kim, Ji Yung Han, Kun Jun Sung, Kyung Il Kim, Byong Wan Kim, Moonju J Anim Sci Technol Research Article This study was conducted to assess the impact of growing condition variables on alfalfa (Medicago sativa 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°C–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. Korean Society of Animal Sciences and Technology 2023-09 2023-09-30 /pmc/articles/PMC10640942/ /pubmed/37969334 http://dx.doi.org/10.5187/jast.2023.e14 Text en © Copyright 2023 Korean Society of Animal Science and Technology https://creativecommons.org/licenses/by-nc/4.0/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.
spellingShingle Research Article
Kim, Ji Yung
Han, Kun Jun
Sung, Kyung Il
Kim, Byong Wan
Kim, Moonju
Assessment of growing condition variables on alfalfa productivity
title Assessment of growing condition variables on alfalfa productivity
title_full Assessment of growing condition variables on alfalfa productivity
title_fullStr Assessment of growing condition variables on alfalfa productivity
title_full_unstemmed Assessment of growing condition variables on alfalfa productivity
title_short Assessment of growing condition variables on alfalfa productivity
title_sort assessment of growing condition variables on alfalfa productivity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10640942/
https://www.ncbi.nlm.nih.gov/pubmed/37969334
http://dx.doi.org/10.5187/jast.2023.e14
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