|Evett, Steven - Steve|
Submitted to: International Conference on Water Resources Engineering Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 6/7/1995
Publication Date: N/A
Interpretive Summary: Water resources are becoming more valuable at the same time that farmers are paying more for power to pump water for irrigation. However, these are not the only reasons that farmers seek to manage irrigation water applications more efficiently. The need to keep expensive fertilizers and other applied chemicals within the root zone as well as to avoid deep percolation of these chemicals to the water table also motivates efficient irrigation. In the past, calculation of irrigation water amounts has been based on a simple method involving a crop coefficient number multiplied by a potential water use figure calculated from weather data. The crop coefficient varies over the season and several seasons' data are combined to come up with a general crop coefficient curve that, unfortunately, may still not be correct for any given year, and is only useful for the region in which it was developed. We used a physically based computer model of crop water use and compared its estimates with those from the crop coefficient method and with values of crop water use measured directly in the field for three years of winter wheat at Bushland, TX. On a daily basis the comparisons showed that the physical model was more accurate than the crop coefficient method but when five day cumulative crop water use data were compared the crop coefficient method was more accurate. These results indicate that crop coefficients produce a less biased result than the model. The physical model may still be more useful when frequent irrigations are needed or when predictions of water use must be made in regions for which no crop coefficients are available.
Technical Abstract: Daily evapotranspiration (ET) estimates from reference ET (ETR) values multiplied by a crop coefficient (KC) have been the standard method for irrigation scheduling purposes for many years. However, curves of KC vs. cumulative growing degree days (CGDD) or days after planting are averages of data from several years and the KC value from such a curve may vary considerably from the KC value for any one year. Mechanistic models may b more accurate but also require more data about the crop. Typically, a mechanistic model will require information about the leaf area index (LAI) and rooting depth on a daily basis to provide good ET estimates. We compared ET estimated using crop coefficients developed at our location for winter wheat (Triticum aestivum L.) with ET estimated by the mechanistic model ENWATBAL and ET measured by weighing lysimeters for three years of winter wheat grown on the southern high plains. Values of LAI were measured periodically in the field and a spline fit interpolation was used to describe the evolution of LAI on a daily basis throughout each year. In addition, a general curve of LAI vs. CGDD was developed from the data from all three years and used to parameterize ENWATBAL. For all years the ENWATBAL model using field measured LAI data gave better estimates of daily and cumulative ET than those derived from KC and ETR. For two of three years the ENWATBAL model using the general LAI vs. CGDD curve predicted ET better than KC and ETR. However, when five day cumulative ET values were compared the KC and ETR method gave more accurate estimates that ENWATBAL. For multiple day forecasting and irrigation scheduling the KC and ETR method is preferable to ENWATBAL.