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

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 R 2 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.

Detecting dry matter yield trend of whole crop maize considering the climatic factors in the Republic of Korea

“…On the other hand, Peng et al. () reported a decrease in the dry matter yield (DMY) of whole crop maize (WCM) in Republic of Korea, and the sensitivity of the DMY to accumulated heat unit and rainfall has also been indicted (Peng, ). However, studies on WCM were not detailed in terms of how the historical yield trend has been changing in Republic of Korea and decomposing the trend based on period, and yield variation was found to be important.…”

Constructing Italian ryegrass yield prediction model based on climatic data by locations in South Korea

“…However, for the reason that the importance of forage cultivation is not as important as food and cash crops in South Korea, the data acquisition on these ecological factors were not sufficient. Furthermore, as global warming happens, the climate changes with an increasingly unstable fluctuation, and this resulted a more fluctuant crop yield, which is a threat to food and feed supply (Peng et al 2015), and hence, climatic data were used to construct the yield prediction model in this research. Furthermore, as global warming happens, the climate changes with an increasingly unstable fluctuation, and this resulted a more fluctuant crop yield, which is a threat to food and feed supply (Peng et al 2015), and hence, climatic data were used to construct the yield prediction model in this research.…”

“…Meanwhile, climatic data were well recorded over the long term and easily accessible in South Korea (Yun 2003). Furthermore, as global warming happens, the climate changes with an increasingly unstable fluctuation, and this resulted a more fluctuant crop yield, which is a threat to food and feed supply (Peng et al 2015), and hence, climatic data were used to construct the yield prediction model in this research.…”

Climatic suitability mapping and driving factors detection for whole crop maize and sorghum–sudangrass hybrid production in the south area of the Korean Peninsula and Jeju Island

“…After data cleansing, the whole crop maize cultivation data set with a size of 775 during 22 years from 1988 to 2011 (except 1992 and 1997) and the sorghum–sudangrass hybrid cultivation data set with a size of 212 during 17 years from 1988 to 2013 (except 1991–1994, 1997, and 2001–2004) were developed. Referring to the previous research (Hatfield et al, ; Peng et al, ; Takahashi, ), six climatic variables including Seeding–Silking Accumulated Growing Degree Days (SSAGDD, °C–day), Seeding–Harvest Accumulated Growing Degree Days (SHAGDD, °C–day), Seeding–Harvest Mean Temperature (SHMT, °C), Seeding–Harvest Precipitation (SHP, mm), Seeding–Harvest number of Days with Precipitation (SHDP, d), and Seeding–Harvest Duration of Sunshine (SHDS, h) were calculated to each data point in the whole crop maize cultivation data set according the recorded seeding, silking, and harvest dates using the collected basic meteorological data from the Korea meteorological administration. Here, according to Kumudini et al (), the daily GDD was calculated as [(Tmax + Tmin)/2] – 10, when the maximum temperature is above 30°C, Tmax is set as 30℃; while when the minimum temperature is less than 10°C, Tmin is set as 10℃.…”