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Detecting the Climate Factors related to Dry Matter Yield of Whole Crop Maize

사일리지용 옥수수의 건물수량에 영향을 미치는 기후요인 탐색

  • Received : 2015.06.17
  • Accepted : 2015.09.03
  • Published : 2015.09.30

Abstract

The purpose of this research is to identify the significance of climate factors related to the significance of change of dry matter yield (DMY) of whole crop maize (WCM) by year through the exploratory data analysis. The data (124 varieties; n=993 in 7 provinces) was prepared after deletion and modification of the insufficient and repetitive data from the results (124 varieties; n=1027 in 7 provinces) of import adaptation experiment done by National Agricultural Cooperation Federation. WCM was classified into early-maturity (25 varieties, n=200), mid-maturity (40 varieties, n=409), late-maturity (27 varieties, n=234) and others (32 varieties, n=150) based on relative maturity and days to silking. For determining climate factors, 6 weather variables were generated using weather data. For detecting DMY and climate factors, SPSS21.0 was used for operating descriptive statistics and Shapiro-Wilk test. Mean DMY by year was classified into upper and lower groups, and a statistically significant difference in DMY was found between two groups (p<0.05). To find the reasons of significant difference between two groups, after statistics analysis of the climate variables, it was found that Seeding-Harvesting Accumulated Growing Degree Days (SHAGDD), Seeding-Harvesting Precipitation (SHP) and Seeding-Harvesting Hour of sunshine (SHH) were significantly different between two groups (p<0.05), whereas Seeding-Harvesting number of Days with Precipitation (SHDP) had no significant effects on DMY (p>0.05). These results indicate that the SHAGDD, SHP and SHH are related to DMY of WCM, but the comparison of R2 among three variables (SHAGDD, SHP and SHH) couldn't be obtained which is needed to be done by regression analysis as well as the prediction model of DMY in the future study.

Keywords

Whole crop maize;Dry matter yield;Climate factors;Exploratory data analysis

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Cited by

  1. Constructing Italian ryegrass yield prediction model based on climatic data by locations in South Korea vol.63, pp.3, 2017, https://doi.org/10.1111/grs.12163

Acknowledgement

Supported by : 농촌진흥청, 농림수산식품기술기획평가원