The aim of this study was to determine the quality of the water used as irrigation water by the agricultural farmers in the city center of Bingol, to determine the physico-chemical properties of the water used in the region, to study the seasonal changes and to calculate of the irrigation water quality evaluation. Taking into account the seasonal fluctuations of the irrigation water sampling points (O, K, A), pH values 7.63-7.94, EC values 0.14-0.16 dS/m, Na values 0.27-0.32 me/L, K values 0.08 me/L, Ca values 0.75-0.81 me/L, Mg values 0.33-0.38 me/L, SO4 values 0.12-0.13 me/L, bicarbonate 3.16-3.36 me/L, and chlorine values between 0.51-0.55 me/L. Parameters such as SAR, KI, Na%, and PI were calculated to determine the suitability of the existing irrigation water quality in the study area. When the SAR values were examined, it was determined that they were between 0.36-0.42, RSC values were between 1.5-2.5, and IWQI values were between 77.33-78.42. As a result of the quality evaluations made at the irrigation water line located in the center of Bingol province, it was determined that there was no difference in the quality parameters when the seasonal changes in the long-term sampling for five years were taken into consideration.

This study, conducted under semi-arid conditions during the 2022 and 2023 growing seasons, aimed to assess the effects of plant growth-promoting rhizobacteria (PGPR) in combination with different irrigation levels and water qualities on safflower (Carthamus tinctorius L.) quality parameters. The irrigation levels were based on 0% (I0 - rainfed), 25% (I25), 50% (I50), 75% (I75) and 100% (I100) of Class A pan evaporation. Two irrigation water qualities were used: recycled wastewater (RW) and freshwater (FW). The PGPR treatments were applied at four frequencies: R0 (control), R1 (once), R2 (twice) and R3 (three times) starting after sowing at 10-day intervals. Seed protein content ranged from 12.0% to 18.1%, with the highest values under I100-R3 and the lowest under I0-R0. Protein content increased with irrigation and bacterial application frequency. Oil content varied between 25.2% and 38.6%, peaking under full irrigation with triple PGPR application (I100-R3), and was generally higher in RW-irrigated plots. SPAD (Soil Plant Analysis Development) values which are an indication for chlorophyll content in the plant ranged from 45.1 to 76.3, with RW-I100 treatments showing the highest readings. Stomatal conductance values varied between 40.5 and 122.0 mmol/m ² /s¹, increasing with irrigation level. Overall, combining recycled wastewater and PGPR under sufficient irrigation significantly improved safflower’s physiological and biochemical characteristics. The results suggest that this integrated approach enhances oil and protein content while supporting sustainable water use and crop quality improvement in arid and semi-arid regions.

The purpose of this study was to ascertain the fresh herbage yield, fertilizer dosage, and plant characteristics of the Sorghum-Sudangrass hybrid grown in arid and semi-arid regions, as well as their interrelationships. For this reason, data from the Sorghum-Sudangrass hybrid were used to assess the predictive performance of several data mining techniques, including CHAID, CART, MARS, and Bagging MARS. Plant traits were measured in Konya and Sanliurfa during 2021 and 2022. The descriptive statistical values were calculated as follows: plant height 306.27 cm, stem diameter 9.47 mm, fresh herbage yield 10852.51 kg/da, crude protein ratio 9.66%, acid detergent fiber 33.39%, neutral detergent fiber 51.85%, acid detergent lignin 9.76%, dry matter digestibility 62.88%, dry matter intake 2.34%, and relative feed value 114.68 (average values). The predictive capacities of the fitted models were assessed using model fit statistics such as the coefficient of determination (R²), adjusted R², root mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation ratio (SD ratio), and Akaike Information Criterion (AIC). With the lowest values for RMSE, MAPE, SD ratio, and AIC (246, 1.926, 0.085, and 845, respectively), and the highest R² value (0.993) and adjusted R² value (0.989), the MARS algorithm was determined to be the best model for characterizing fresh herbage yield. As a solid alternative to other data mining techniques, the MARS algorithm was shown to be the most appropriate model for forecasting fresh herbage production.

Accurate estimation of reference evapotranspiration (ET ₀ ) and crop coefficients (K _c ) is critical for irrigation planning, particularly in data‐limited regions where agriculture dominates freshwater consumption. Although machine learning (ML) methods have been widely applied to ET ₀ and K _c estimation, most studies address these parameters separately or focus on a single seasonal K _c value rather than stage‐specific coefficients. This study presents an integrated machine learning framework that simultaneously estimates monthly ET ₀ and wheat stage‐specific crop coefficients (K _{c_ini} , K _{c_mid} , K _{c_end} ) using long‐term district‐level meteorological data from Türkiye. A total of 19 algorithms, including linear models, tree‐based ensembles, boosting methods, support vector machines (SVR) and deep learning models, were evaluated using 10‐fold cross‐validation and standard performance metrics. The results showed that linear models provided the most consistent performance for monthly and annual ET ₀ estimation ( R ² ≈0.85–0.87), while nonlinear ensemble models achieved superior accuracy for K _c prediction ( R ² > 0.95). Feature importance analysis identified solar radiation and temperature as dominant ET ₀ drivers, whereas elevation and relative humidity were key controls for stage‐specific K _c . The proposed framework provides a scalable approach for regional agricultural water management and supports model selection under data‐limited conditions.