AbstractResults from the study of a differential psychro method for evaluating irrigation needs conducted in a greenhouse support previous laboratory results that the psychrometric potential obtained (∆t) can be related to atmospheric conditions and to the moisture characteristics of the matrix. Weather and plant, or plant-soil parameters, combined effectively to predict ∆t in these investigations. From coffee and from bean plants growing in pots, the actual evapotranspiration rates (Eta), transpiration rates (Tsp), and soil moisture content (0) of the plant pots were averaged for various short time intervals, simultaneously with values of ∆t by the technique described previously by Capiel. Also determined were the corresponding mean values of weather indices, including air temperature (Ta) vapor pressure deficit (ed), net solar radiation (Rn), and potential evapotranspiration (Etp). It was found by simple regression analysis that, generally, the individual plant, soil, and weather parameters correlated poorly with ∆t. The linear regression of ∆t on ed with coffee plants was found to exhibit the highest simple account (r2 = 0.61) for the fluctuations in ∆t. This is similar to the simple influence of ed in the previously reported laboratory study, by which the moisture characteristics of a gypsum block were also involved in the prediction of ∆t. A step-wise multiple regression of ∆t on several combinations of plant, soil and weather indices appeared to be profitable on improving the prediction of ∆t. This was especially helpful when evaluating ∆t in the bean plants. These exhibited much more intensive transpiration rates than coffee under comparable environmental conditions. The model equation ∆t = f(Eta, Rn y ed) accounted for 91.7 percent of the fluctuations in ∆t, and all the three parameters were highly significant in their effects. This prediction even excelled ∆t = f(Eta, Etp) in which both indices portray the actual and the potential water needs of crops, respectively. Multiple regression did not work as well in coffee as in bean plants. However, in ∆t = f(Tsp, Tsp2, Rn, ed), the linear and quadratic factors accounted for 76.4 percent of the fluctuations in ∆t, and all the four parameters were highly significant. It was interesting to note that by incorporating the quadratic term Tsp2 beside the linear (Tsp), both became highly significant. In other words, by providing a moderating adjustment to the linear increase of transpiration as evaporative demands increased (Rn, ed), ∆t could be more "effectively" predicted. It is thus suggested that intrinsic plant characteristics can also be related to the prediction of ∆t by multiple regression analysis as found with coffee plants.
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