مجله تحقیقات آب و خاک ایران
سال پنجاه و چهارم شماره 6 (پیاپی 90، شهریور 1402)

The present research performed to estimate soil texture using visible near-infrared spectrometry in Semirom, Isfahan. A total number of 200 soil samples (0-10 cm) were collected from the Semirom area (51º 17' - 52º 3' E; 30º 42' - 31º 51' N), Isfahan. The samples were air dried and passed through a 2 mm sieve, and soil particles percentage was determined in the laboratory using hydrometry method. Reflectance spectra of all samples were measured using an ASD field spectrometer. Different pre-processing methods i.e., First Derivatives and Savitzky-Golay Filter, Multiplicative Scatter Correction and Standard Normal Variable were applied and performed on spectral data. The Partial Least Squares Regression, Support Vector Machine Regression and Artificial Neural Network models were used to estimate soil texture. The best result was obtained for Silt estimation, with excellent values of RPD >2, R2 =0.98 and RMSE=1.08 using Artificial Neural Network model with MSC pre-processing technique. The results indicated the desirable capability of Artificial Neural Network model with MSC and SNV pre-processing techniques in estimating the Clay (RPD >2, R2=0.94 and RMSE=1.21) and Sand (RPD >2, R2=0.84 and RMSE=6.24) contents of the soils, respectively. In general, based on the results of this study, VNIR spectroscopy was successful in estimating soil particles percentage and showed its potential for substituting laboratory analyses.

The purpose of this study was to assess irrigation management and determine water productivity in olive orchards managed by local farmers in the country. To achieve this, the amount of water provided by gardeners and the yield of 102 olive orchards that underwent irrigation using various methods, surface and drip irrigation, were measured across the provinces of Qazvin, Fars, Zanjan, Gilan, Golestan, and Semnan. This data was collected during two consecutive years, 2018 and 2019. Measured values were compared with NETWAT-estimated net irrigation requirements as well as Penman-Monteith-derived values. Based on the results, it can be concluded that the average volume of irrigation water, yield, irrigation water productivity, and applied water productivity in the selected provinces are significantly different at a probability level of 1%. According to the study, the volume of irrigation water used in olive orchards varied from 2848 to 11463 m3/ha, with a weighted average of 6011 m3/ha. The average yield of olives in the production poles of this product has varied from 1500 to 11000 kg/ha over the two-year period, and its weighted average has been 4867 kg/ha. It is also worth mentioning that the water productivity ranged from 0.2 to 2.40 and its weighted average was 0.95 kg/m3. Furthermore, the applied water productivity in the selected provinces ranged from 0.18 to 1.45 and its weighted average was 0.63 kg/m3. Upon evaluating the efficiency of irrigation in the olive orchards, it was observed that the quantity of water delivered was approximately 27% and 17% less in comparison to the irrigation requirement calculated using recent 10-year meteorological data and NETWAT, respectively. In other word, forced deficit irrigation has taken place in the olive orchards