Search

Sustained irrigation agriculture is critical for food and fiber production to support the growing human population. Increasing salinity is a significant factor in many irrigated lands. Knowledge on the proper time and amount of water of various salinities to be applied for optimum yield of a given crop is important. Because of the numerous variables, computer simulation models would be valuable to partially replace expensive field experiments. Simulated relative yields of corn from the ENVIRO-GRO model were compared with experimentally measured yields which had irrigation water electrical conductivities (ECs) of 1.7, 4.0, 5.0, 8.0, and 10.2 dS [m.sup.-1] and average irrigation intervals of 3.5-, 7-, 14-, and 21-d treatments. The simulations were run for 5 yr although the field experiment was conducted only 1 yr. The simulated root-weighted average matric and osmotic heads prior to irrigation were evaluated during the year. Depending on treatment, yields were depressed because of either osmotic or matric effects or a combination of the two. The agreement between simulated and measured yields was good for all treatments indicating that the model appropriately accounted for both osmotic and matric effects. On the basis of these results, the ENVIRO-GRO model can be used with confidence in simulating the consequences of irrigation management options under saline conditions. The simulated yields were lower the second and subsequent years compared with the first year, emphasizing the fact that results from 1-yr experiments cannot be used reliably for long-term predictions because the results are highly dependent on the soil conditions at the beginning of the growing season.

In the present paper the use of mathematical programming theory is proposed to define optimization criteria for the deficit irrigation of an area. A first application, which was carried out on a sample farm in the Upper Tiber Valley (Central Italy), gave significant results. By using a multiplicative Stewart's formula, it was possible to determine, for various crops, the relationships between crop yield and applied water, which depend on the deterministic component of the process of water exchange soil-crop-atmosphere. The stochastic component of the same process is defined by the hypothesis that the functions are variable from year to year. These relationships were introduced as a constraint in a mathematical programming framework, with the aim of optimizing, in economic terms, the application of available irrigation water taking into account the possibility of varying the crop pattern. Finally, since the optimal solution can be found only on an annual basis, due to the stochastic component of the process, an attempt was made to define a method for determining a single, constant and optimal solution.