DOI QR코드

DOI QR Code

작물 수분 스트레스 지수 산정을 위한 최적의 관측 간격과 시간에 대한 통계적 분석

Statistical Analysis of Determining Optimal Monitoring Time Schedule for Crop Water Stress Index (CWSI)

  • Choi, Yonghun (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) ;
  • Kim, Minyoung (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) ;
  • Oh, Woohyun (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) ;
  • Cho, Junggun (Department of Horticultural Crop Research, National Institute of Horticultural and Herbal Sciences (NIHHS), Rural Development Administration (RDA)) ;
  • Yun, Seokkyu (Department of Horticultural Crop Research, National Institute of Horticultural and Herbal Sciences (NIHHS), Rural Development Administration (RDA)) ;
  • Lee, Sangbong (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) ;
  • Kim, Youngjin (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) ;
  • Jeon, Jonggil (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA))
  • 투고 : 2019.09.19
  • 심사 : 2019.10.28
  • 발행 : 2019.11.30

초록

Continuous and tremendous data (canopy temperature and meteorological variables) are necessary to determine Crop Water Stress Index (CWSI). This study investigated the optimal monitoring time and interval of canopy temperature and meteorological variables (air temperature, relative humidity, solar radiation and wind speed) to determine CWSIs. The Nash-Sutcliffe model efficiency coefficient (NSE) was used to quantitatively describe the accuracy of sampling method depending upon various time intervals (t=5, 10, 15, 20, 30 and 60 minutes) and CWSIs per every minute were used as a reference. The NSE coefficient of wind speed was 0.516 at the sampling time of 60 minutes, while the ones of other meteorological variables and canopy temperature were greater than 0.8. The pattern of daily CWSIs increased from 8:00 am, reached the maximum value at 12:00 pm, then decreased after 2:00 pm. The statistical analysis showed that the data collection at 11:40 am produced the closest CWSI value to the daily average of CWSI, which indicates that just one time of measurement could be representative throughout the day. Overall, the findings of this study contributes to the economical and convenient method of quantifying CWSIs and irrigation management.

키워드

참고문헌

  1. Agam, N., Y. Cohen, V. Alchanatis, and A. Ben-Gal, 2013. How sensitive is the CWSI to changes in solar radiatoin?. International Journal of Remote Sensing 34(17): 6109-6120. doi:10.1080/01431161.2013.793873.
  2. DeJonge, K. C., S. Taghvaeian, T. J. Trout, and L. H. Comas, 2015. Comparison of canopy temperature-based water stress indices for maize, Agricultural Water Management 156: 51-62. doi:10.1016/j.agwat.2015.03.023.
  3. Garcia y Garcia, A., M. A. Abritta, C. M. T. Soler, and A. Green, 2014. Water and heat stress: The effect on the growth and yield of maize and the impacts on irrgiation water. WIT Transactions on Ecology and The Environment 185: 77-87. doi:10.2495/SI140081.
  4. Guisard, Y., 2008. Crop canopy temperature as indicator of water stress: Application to grapevines. Doctoral thesis, Charles Sturt University, Australia.
  5. Erdem, Y., L. Arin, T. Erdem, S. Polat, M. Deveci, H. Okursoy, and H. T. Gultas, 2010. Crop water stress index for assessing irrigation scheduling of drip irrigated broccoli (Brassica oleracea L. var. italica). Agricultural Water Mangement 98(1): 148-156. doi:10.1016/j.agwat.2010.08.013.
  6. Jackson, R. D., S. B. Idso, R. J. Reginato, and P. J. Pinter Jr., 1981. Canopy temperature as a crop water stress indicator. Water Resources Research 17(4): 1133-1138. doi:10.1029/WR017i004p01133.
  7. Kim, M., Y. Choi, J. Cho, S. Yun, J. Park, Y. Kim, J. Jeon, and S. Lee, 2019. Response of crop water stress index (CWSI) and canopy temperature of apple tree to irrigation treatment schemes. Journal of the Korean Society of Agricultural Engineers 61(5): 23-31. (in Korea). doi:10.5389/KSAE.2019.61.5.023.
  8. Li, L., D. C. Nielsen, Q. Yu, L. Ma, and L. R. Ahuja, 2010. Evaluating the crop water stress index and its correlation with latent heat and $CO_2$ fluxes over winter wheat and maize in the North China plain. Agricultural Water Management 97(8): 1146-1155. doi:10.1016/j.agwat.2008.09.015.
  9. Nash, J. E., and J. V. Sutcliffe, 1970. River flow forecasting through conceptual models part 1 - A discussion of principles. Journal of Hydrology 10(3): 282-290. doi:10.1016/0022-1694(70)90255-6.
  10. Moriasi, D. N., J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, and T. L. Veith, 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE 50(3): 885-900. doi:10.13031/2013.23153.
  11. O'Shaughnessy, S., S. R. Evett, P. D. Colaizzi, and T. A. Howell, 2012. A crop water stress index and time threshold for automatic irrigation scheduling of grain sorghum. Agricultural Water Management 107: 122-132. doi:10.1016/j.agwat.2012.01.018.
  12. Osroosh, Y., R. T. Peters, C. S. Campbell, and Q. Zhang, 2015. Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold. Computers and Electronics in Agriculture 118: 193-203. doi:10.1016/j.compag.2015.09.006.
  13. Ritter, A., and R. Munoz-Carpena, 2013. Performance evaluation of hydrological models: statistical significance for reducing subjectivity in goodness-of-fit assessments. Journal of Hydrology 480(1): 33-45. doi:10.1016/j.jhydrol.2012.12.004.
  14. Testi, L., D. A. Goldhamer, F. Iniesta, and M. Salinas, 2008. Crop water stress index is a sensitive water stress indicator in pistachio trees. Irrigation Science 26(5): 395-405. doi:10.1007/s00271-008-0104-5.