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)
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초록
Continuous and tremendous data (canopy temperature and meteorological variables) are necessary to determine Crop Water Stress Index (CWSI). Thisstudy investigated the optimal monitoring time and interval of canopy temperature and meteorological variables (air temperature, relative humidity, solarradiation and wind speed) to determine CWSIs. The Nash-Sutcliffe model efficiency coefficient (NSE) was used to quantitatively describe the accuracyof 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 canopytemperature 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 after2:00 pm. The statistical analysis showed that the data collection at 11:40 am produced the closest CWSI value to the daily average of CWSI, whichindicates that just one time of measurement could be representative throughout the day. Overall, the findings of this study contributes to the economicaland convenient method of quantifying CWSIs and irrigation management.
Abstract
Continuous and tremendous data (canopy temperature and meteorological variables) are necessary to determine Crop Water Stress Index (CWSI). Thisstudy investigated the optimal monitoring time and interval of canopy temperature and meteorological variables (air temperature, relative humidity, solarradiation and wind speed) to determine CWSIs. The Nash-Sutcliffe model efficiency coefficient (NSE) was used to quantitatively describe the accuracyof 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 canopytemperature 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 after2:00 pm. The statistical analysis showed that the data collection at 11:40 am produced the closest CWSI value to the daily average of CWSI, whichindicates that just one time of measurement could be representative throughout the day. Overall, the findings of this study contributes to the economicaland convenient method of quantifying CWSIs and irrigation management.
참고문헌 (14)
- Nurit Agam, "How sensitive is the CWSI to changes in solar radiation?", International Journal of Remote Sensing, 34(17), pp.6109~6120 (2013), Informa UK Limited[DOI]
- Kendall C. DeJonge, "Comparison of canopy temperature-based water stress indices for maize", Agricultural Water Management, 156, pp.51~62 (2015), Elsevier BV[DOI]
- Garcia y Garcia, A., "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, pp.77~87 (2014)[DOI]
- Guisard, Y., "Crop canopy temperature as indicator of water stress: Application to grapevines", Doctoral, pp.2008
- Yeşim Erdem, "Crop water stress index for assessing irrigation scheduling of drip irrigated broccoli (Brassica oleracea L. var. italica)", Agricultural Water Management, 98(1), pp.148~156 (2010), Elsevier BV[DOI]
- R. D. Jackson, "Canopy temperature as a crop water stress indicator", Water Resources Research, 17(4), pp.1133~1138 (1981), American Geophysical Union (AGU)[DOI]
- 김민영, "관개수준별 사과나무의 엽온 및 수분 스트레스 지수 변화 분석", 한국농공학회논문집, 61(5), pp.23~31 (2019), 한국농공학회[DOI]
- L. Li, "Evaluating the Crop Water Stress Index and its correlation with latent heat and CO2 fluxes over winter wheat and maize in the North China plain", Agricultural Water Management, 97(8), pp.1146~1155 (2010), Elsevier BV[DOI]
- J.E. Nash, "River flow forecasting through conceptual models part I — A discussion of principles", Journal of Hydrology, 10(3), pp.282~290 (1970), Elsevier BV[DOI]
- D. N. Moriasi, "Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations", Transactions of the ASABE, 50(3), pp.885~900 (2007), American Society of Agricultural and Biological Engineers (ASABE)[DOI]
- Susan A. O'Shaughnessy, "A crop water stress index and time threshold for automatic irrigation scheduling of grain sorghum", Agricultural Water Management, 107, pp.122~132 (2012), Elsevier BV[DOI]
- Yasin Osroosh, "Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold", Computers and Electronics in Agriculture, 118, pp.193~203 (2015), Elsevier BV[DOI]
- Axel Ritter, "Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments", Journal of Hydrology, 480, pp.33~45 (2013), Elsevier BV[DOI]
- L. Testi, "Crop water stress index is a sensitive water stress indicator in pistachio trees", Irrigation Science, 26(5), pp.395~405 (2008), Springer Science and Business Media LLC[DOI]