|GONZALEZ DUGO, M.|
Submitted to: Congreso Nacional De Riegos
Publication Type: Proceedings
Publication Acceptance Date: 1/31/2002
Publication Date: 1/31/2002
Citation: Gonzalez Dugo, M.P., Bryant, R., Moran, M.S. 2002. Uso de la variabilidad termica en la determinacion del estres hidrico en cultivos. XVIII Congreso Nacional de Riegos, June 20-24, 2002, Helva Espana. 2002 Unpaginated CD ROM.
Interpretive Summary: For efficient crop irrigation, it is necessary to know the water needs of the crop throughout the growing season. Results from studies conducted over the past twenty years have shown that measurements of plant temperature, relative to air temperature, could be used to assess crop water status. This approach, often termed the Crop Water Stress Index (CWSI), has been adopted by several commercial companies for crop irrigation scheduling. In this study, we compared the CWSI with a simpler, temperature-based approach that relies solely on the distribution of crop temperatures and does not require measurements of air temperature and other meteorological data. This alternative approach was termed the Histogram-Derived Crop Water Stress Index (HCWSI). Good agreement was found with CWSI for cotton and alfalfa crops in Central Arizona. With some refinement, this HCWSI could produce useful crop management information with a minimum investment of time and money.
Technical Abstract: Infrared thermometry has been commonly applied to monitor plant water stress, based on the relation between crop temperature and crop transpiration rate. Remote measurements of surface temperature avoid the disadvantages of other point measurement methods. Several indices to assess the water content and the health of the plant have been developed combining meteorological data with remotely sensed information. Two of these indices, the crop water stress index (CWSI) and the water deficit (WDI), are compared in this work with two new approaches based on crop temperature variability: the histogram-derived crop water index (HCWSI) and the standard deviation of thermal data (STD). The good agreement found with accepted methods, especially with the latter, is a promising result that, with further refinement, may produce management information with minimum input data and time of processing.