Browse > Article
http://dx.doi.org/10.5389/KSAE.2019.61.6.073

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))
Publication Information
Journal of The Korean Society of Agricultural Engineers / v.61, no.6, 2019 , pp. 73-79 More about this Journal
Abstract
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.
Keywords
Crop water stress index (CWSI); canopy temperature; optimal monitoring time;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 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.   DOI
2 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.   DOI
3 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.   DOI
4 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.   DOI
5 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.   DOI
6 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.   DOI
7 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.   DOI
8 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.   DOI
9 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.   DOI
10 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.   DOI
11 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.   DOI
12 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.
13 Guisard, Y., 2008. Crop canopy temperature as indicator of water stress: Application to grapevines. Doctoral thesis, Charles Sturt University, Australia.
14 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.   DOI