A Differential Psychro Test of Plant-Soil-Weather Relationships and Water Use

Abstract

Results 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 (Et_a), 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 (T_a) vapor pressure deficit (e_d), net solar radiation (R_n), and potential evapotranspiration (Et_p).  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(Et_a, R_n y e_d) 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(Et_a, Et_p) 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, R_n, e_d), 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 (R_n, e_d), ∆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.