- Volume 17 Issue 3
DOI QR Code
Detecting the Climate Factors related to Dry Matter Yield of Whole Crop Maize
사일리지용 옥수수의 건물수량에 영향을 미치는 기후요인 탐색
- Peng, Jing-lun ;
- Kim, Moon-ju ;
- Kim, Young-ju ;
- Jo, Mu-hwan ;
- Nejad, Jalil Ghassemi ;
- Lee, Bae-hun ;
- Ji, Do-hyeon ;
- Kim, Ji-yung ;
- Oh, Seung-min ;
- Kim, Byong-wan ;
- Kim, Kyung-dae ;
- So, Min-jeong ;
- Park, Hyung-soo ;
- Sung, Kyung-il
- Received : 2015.06.17
- Accepted : 2015.09.03
- Published : 2015.09.30
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.
Whole crop maize;Dry matter yield;Climate factors;Exploratory data analysis
- Baier, W., 1977: Crop-weather models and their use in yield assessments. WMO Technical Note no. 151, World Meteorological Organization, Geneva, 48pp.
- Hamilton, J. D., 1994: Time series analysis. Princeton University Press, 43-59.
- Hatfield, J. L., K. J. Booteb, B. A. Kimballc, L. H. Ziskad, R. C. Izaurraldee, D. Ortf, A. M. Thomsong, and D. Wolfeh, 2011: Climate impacts on agriculture: implications for crop production. Agronomy Journal 103(2), 351-370. https://doi.org/10.2134/agronj2010.0303
- SPSS 21.0. Released 2012: IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.
- Kim, M. J., K. I. Sung, B. W. Kim, J. L. Peng, D. H. Ji, B. H. Lee, E. J. Kim, M. H. Jo, Y. C. Lim, and G. D. Kim, 2014a: Study of dry matter yield prediction of Italian ryegrass (IRG) using climatic factors and soil factors. Proceedings of 2014 Annual Congress of Korean Society of Animal Sciences and Technology, Hongcheon, Kangowndo, Korean Society of Animal Sciences and Technology, Vol. II, 204pp. (in Korean)
- Kim, M. J., K. I. Sung, and Y. J. Kim, 2014b: Analysis of climate effects on Italian ryegrass yield via structural equation model. The Korean Journal of Applied Statistics 27(7), 1187-1196 (in Korean with English abstract). doi: 10.5351/KJAS.2014.27.7.1187 https://doi.org/10.5351/KJAS.2014.27.7.1187
- Kim, K. D. 2012: Soil climate maps and suitability classes for forage production in Gangwon Province using soil and climate digital database. Ph.D. thesis, Kangwon National University. Chuncheon. Korea. (in Korean with English abstract).
- Kim, K. D., K. I. Sung, Y. S. Jung, H. I. Lee, E. J. Kim, J. G. Nejad, M. H. Jo, and Y. C. Lim, 2012: Suitability classes for Italian ryegrass (Lolium multiflorum Lam.) using soil and climate digital database in Gangwon Province. Journal of the Korean Society of Grassland and Forage Science 32(4), 437-446 (in Korean with English abstract). doi: 10.5333/KGFS.2012.32.4.437 https://doi.org/10.5333/KGFS.2012.32.4.437
- Kim, K. D., K. I. Sung, J. H. Joo, B. W. Kim, J. L. Peng, B. H. Lee, J. G. Nejad, M. H. Jo, and Y. C. Lim. 2013: Suitability classes for whole crop barley (Hordeum vulgare var. hexastichon(L.) Asch.) using soil and climate digital database in Gangwon Province. Journal of Agricultural, Life and Environmental Sciences 25(3), 26-31. (in Korean with English abstract)
- Mann, P. S. 2009: Introductory statistics (6th ed.). JOHN WILEY&SONS, 377-394.
- Ottman, M. J. and L. F. Welch, 1988: Supplemental radiation effects on senescence, plant nutrients, and yield of fieldgrown corn. Agronomy Journal 80, 619-626. https://doi.org/10.2134/agronj1988.00021962008000040015x
- Robertson, K. R. 1974: The genera of Rosaceae in the southeastern United States. Journal of the Arnold Arboretum 55, 303-332.
- Shapiro, S. S. and M. B. Wilk, 1965: An analysis of variance test for normality (complete samples). Biometrika 52(3-4), 591-611. https://doi.org/10.1093/biomet/52.3-4.591
- Sung, K. I., J. G. Nejad, E. J. Kim, M. H. Cho, S. H. Yoon, and G. D. Kim, 2011: Dry matter yield changes of whole crop maize in Suwon and Pyeongchang areas. Proceedings of 2011 Annual Congress of Korean Society of Animal Sciences and Technology, Miryang, Gyeongsangnam-do, Korean Society of Animal Sciences and Technology, Vol. II, 163pp. (in Korean)
- Sung, K. I., J. G. Nejad, B. W. Kim, J. L. Peng, D. H. Ji, B. H. Lee, E. J. Kim, M. H. Jo, Y. C. Lim, G. D. Kim, and M. J. Kim, 2014: Study of dry matter yield prediction of Italian ryegrass (IRG) in relation to climatic factors. Proceedings of 2014 Annual Congress of Korean Society of Animal Sciences and Technology, Hongcheon, Kangowndo, Korean Society of Animal Sciences and Technology, Vol. II, 203pp. (in Korean)
- Sung, K. I., Y. S. Jung, K. D. Kim, H. I. Lee, E. J. Kim, J. G. Nejad, M. H. Jo, and Y. C. Lim, 2013: Suitability classes for green barley(whole crop barley, silage barley) using soil and climate digital database in Gangwon Province. Proceeding of 11th World Conference on Animal Production, Beijing, China, World Association of Animal Production, 258 pp.
- Takahashi, S. 2002: A model predicting forage maize growth based on temperature and solar radiation. Grassland Science, 48(1), 43-49.
- Thomas, R. K., M. M. Jerry, and C. P. Thomas, 2009: Global climate change impacts in the United States. Cambridge University Press, 71-78 pp.
- http://agron-www.agron.iastate.edu/Courses/agron212/Calculations/GDD.htm (2015.04.08)
- 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
Supported by : 농촌진흥청, 농림수산식품기술기획평가원