Predicting crop yield response to irrigation level is increasingly important to
optimize irrigation under limited available water and for enhancing
sustainability and profitable production. This study was carried out to evaluate
the performance of
CropWat model in predicting deficit irrigation effect on
cotton crop, and to explore some alternatives for cotton irrigation. Crop yield
and water use data were collected from a 3-yr (2007-2009) field experiment to
assess the response of drip-irrigated cotton to deficit irrigation (DI).
This study was carried out to compare the performance of the FAO
AquaCrop and CropWat models in simulating the effects of deficit irrigation on
cotton crop. The models were calibrated using data from the 2007 growing
season of a field study conduc
ted to assess deficit irrigation effects on cotton,
whereas the models were validated by comparing their outputs for yield and
water use (ETc) with the measured values of the two variables in the 2008 and
2009. The relationship between measured and predicted values of yield and
ETc revealed that the AquaCrop was better than CropWat in predicting water
stress impact on yield and ETc. The linear regression equation for AquaCrop
had a small intercept and its slope was very close to unity. The index of
agreement (d) was close to one for both models, except its value for ETc in the
2009 year. Both models could reproduce the general trend of the changes in soil
water content in the different irrigation levels. Accordingly, the use of
AquaCrop instead of CropWat should be encouraged for management and
planning of irrigation, since it is a practitioner type model keeps a good balance
between output accuracy and simplicity.
