Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
권호 표지
DOI QR코드

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 (Department of Animal Life Science, Kangwon National University) ;
  • Kim, Moon-ju (Department of Statistics, Kangwon National University) ;
  • Kim, Young-ju (Department of Statistics, Kangwon National University) ;
  • Jo, Mu-hwan (Foundation for the Rural Youth) ;
  • Nejad, Jalil Ghassemi (Department of Animal Life Science, Kangwon National University) ;
  • Lee, Bae-hun (Department of Animal Life Science, Kangwon National University) ;
  • Ji, Do-hyeon (Department of Animal Life Science, Kangwon National University) ;
  • Kim, Ji-yung (Department of Animal Life Science, Kangwon National University) ;
  • Oh, Seung-min (Department of Animal Life Science, Kangwon National University) ;
  • Kim, Byong-wan (Department of Animal Life Science, Kangwon National University) ;
  • Kim, Kyung-dae (Gangwondo Agricultural Research and Extension Services) ;
  • So, Min-jeong (National Institute of Animal Science, RDA) ;
  • Park, Hyung-soo (National Institute of Animal Science, RDA) ;
  • Sung, Kyung-il (Department of Animal Life Science, Kangwon National University)
  • Received : 2015.06.17
  • Accepted : 2015.09.03
  • Published : 2015.09.30
Download PDF
⟨ Previous Next ⟩

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.

본 연구는 탐색적 자료분석을 이용하여 사일리지용 옥수수(whole crop maize, WCM) 연도별 건물수량 변동의 유의성과 이와 관련 있는 기후요인의 유의성을 확인 하는데 목적이 있다. WCM의 원자료는 30년간의 농협중앙회 수입적응시험 심의결과(7개도 124품종 1,027점)였으며, 이 중 미비하거나 중복된 자료는 삭제 또는 수정하여 최종적으로 7개 도에서 124품종 993점을 이용하였다. WCM의 상대숙도와 출사일수를 기준으로 분류하면 조생종(25품종 200점), 중생종(40품종 409점), 만생종(27품종 234점)및 기타(32품종 150점)였다. 기상자료를 이용하여 기후요인을 측정하기 위한 6개의 기상변수를 생성하였다. 건물수량 및 기후요인을 탐색하기 위해서 기술통계량 및 정규성검정을 실시하였으며, 통계분석은 SPSS 21.0을 이용하였다. 연도별 평균 건물수량을 상위와 하위집단으로 분류하여 비교한 결과 상위와 하위집단 간에는 건물수량의 유의적 차이가 있었다(p<0.05). 두 집단간 차이의 원인을 구명하기 위하여 기후관련 요인들을 분석한 결과 파종수확적산생육온도일수(파종~수확 생육온도일수의 합), 파종수확일조시간(파종일부터 수확일까지의 일조시간의 합) 및 파종수확강수량(파종일부터 수확일까지 일강수량의 합)에서 집단간 유의적 차이가 나타났다(p<0.05). 반면 파종수확강수일수(파종에서 수확까지 강수일수)는 건물수량에 영향을 미치지 않았다(p>0.05). 이상의 결과에서 여러 기상변수 중 파종에서 수확까지의 적산온도, 일조시간 및 강수량이 WCM의 건물수량과 연관이 있는 것으로 나타났다. 그러나 세 변수 간의 기여도($R^2$)는 비교할 수가 없어 추후 회귀분석을 이용하여 WCM의 건물수량에 미치는 각 변수의 기여도 및 건물수량 예측모형을 구축할 필요가 있다.

Keywords

References

  1. Baier, W., 1977: Crop-weather models and their use in yield assessments. WMO Technical Note no. 151, World Meteorological Organization, Geneva, 48pp.
  2. Hamilton, J. D., 1994: Time series analysis. Princeton University Press, 43-59.
  3. 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
  4. SPSS 21.0. Released 2012: IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.
  5. 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)
  6. 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
  7. 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).
  8. 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
  9. 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)
  10. Mann, P. S. 2009: Introductory statistics (6th ed.). JOHN WILEY&SONS, 377-394.
  11. 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
  12. Robertson, K. R. 1974: The genera of Rosaceae in the southeastern United States. Journal of the Arnold Arboretum 55, 303-332.
  13. 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
  14. 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)
  15. 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)
  16. 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.
  17. Takahashi, S. 2002: A model predicting forage maize growth based on temperature and solar radiation. Grassland Science, 48(1), 43-49.
  18. 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.
  19. http://agron-www.agron.iastate.edu/Courses/agron212/Calculations/GDD.htm (2015.04.08)

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