بررسی نیاز آبی پنبه در سطوح احتمالاتی مختلف در نیمه جنوبی استان فارس

Background and Aim: Cotton is one of the most important industrial crops globally, and Fars province ranks first in Iran in terms of cultivation area and yield. Plant water requirement, defined as the total water needed throughout the growing season, is typically estimated using indirect methods. Recently, the NIAZAB irrigation requirement system, developed by the Soil and Water Research Institute of the Ministry of Agricultural Jihad, was designed to determine the water requirements of all horticultural and agricultural crops, including cotton. In the NIAZAB System, the annual water requirement of each crop is calculated for individual statistical years, and long-term requirements are estimated at a 50% probability level using historical meteorological data. However, in many practical situations, it is necessary to determine crop water requirements at different probability levels to support irrigation planning under variable climatic conditions. The objective of this study was to determine the water requirement of cotton in Fars province at 10%, 50%, and 90% probability levels using NIAZAB System data.
 
Method: The study focused on key cotton-growing counties, including Darab, Lar, Zarrin-Dasht, Fasa, Estahban, Jahrom, Mohr, Sarvestan, Lamerd, Shiraz, Khonj, and Gerash. Counties Estahban, Sarvestan, Khonj, and Gerash were excluded due to incomplete data. Annual cotton water requirement data up to the end of 1401 (2022) were extracted from the NIAZAB System. Outliers identified in Lar and Zarrin-Dasht were removed prior to analysis. Using EasyFit software, 65 statistical distributions were fitted to the data, and water requirements were estimated at 10%, 50%, and 90% probability levels. The best-fitting distribution for each county was determined using Kolmogorov–Smirnov, Anderson–Darling, and Chi-square goodness-of-fit tests.
 
Results: Among all tested distributions, the Weibull and Generalized Extreme Value distributions were identified as the most appropriate for modelling cotton water requirement data in the different counties. The ratio of the reported long-term water requirement in the NIAZAB System to the water requirement at the 10% probability level ranged from 1.06 to 1.23, with an average of 1.12, indicating that long-term estimates exceed the low-probability requirement. Conversely, the ratio to the 90% probability level ranged from 0.81 to 0.95, with an average of 0.91, showing that reported long-term requirements are generally lower than high-probability estimates. For the 50% probability level, ratios ranged between 0.98 and 1.03, with an average of 1.00, indicating that long-term NIAZAB estimates closely align with median water requirements.
 
Conclusion: Overall, the NIAZAB System provides reliable estimates of cotton water requirement at the 50% probability level across Fars province. However, adjustments are recommended when considering low (10%) or high (90%) probability levels to optimise irrigation planning under variable climatic conditions. This study provides a robust statistical framework for determining probability-based water requirements, supporting efficient and sustainable water management in cotton cultivation.