| description abstract | A study was conducted to test the validity of four different mathematical models imbedded in CropWat software, which is widely used to estimate effective rainfall. The model values were statistically compared with the actual effective rainfall values measured by a field infiltrometer experiment. The study was conducted in a sugarcane land–use system under the tropical climate of Sevanagala, Sri Lanka, during the 2018 to 2019 growing season. The measured effective rainfall in the Sevanagala sugarcane growing area during the 12-month period was 1,068 mm, around 70% of the total rainfall of 1,517 mm. It was also found that 9% of the rainfall received at the top of the canopy was lost due to canopy interception. The results demonstrated the applicability of the mathematical models embedded in CropWat software to estimate the effective rainfall for sugarcane land–use systems in Sevanagala. Furthermore, the empirical regression formula embedded in CropWat software appeared to be the best-fit model for the estimation of effective rainfall because it had the highest index of agreement value, with an accuracy greater than 96%. The current findings can be used to accurately estimate the effective rainfall, which is the actual contribution of rainfall to be used by the sugarcane crop. The results obtains through this process can facilitate managers to prepare plans to reduce irrigation supply without affecting potential crop yields. Furthermore, findings will be useful to technical staff working in the fields of irrigation and water management. The information will be equally helpful to national-level regulatory agencies to prepare feasibility studies and plan future irrigation projects within the country. Irrigation agronomists will also benefit from the paper content because it will help them to handle farm-level operations and irrigation cost-reduction programs. Furthermore, the contents of this article will also be of interest to students and researchers working on hydrological modeling, crop simulation modeling, drought assessment, and empowering sustainable agriculture programs. | |
