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In order to investigate the effect of treated municipal wastewater on soil chemical properties and heavy metal uptake by sorghum an experiment was carried out in the Institute of Agriculture at Zabol University, Iran 2007 in a randomized complete block design with four replications. The treatments were managed for irrigation; with well water during entire period of growing season as control (T1); wastewater during the first half of growing season (T2); wastewater during the second half of growing season (T3); wastewater and well water alternately (T4) and wastewater during entire period of growing season (T5). The results have indicated that irrigation with wastewater lead to significant increase in N, P, K, Ca, Na, Mg, SAR, EC, O.C% than control treatment (p ≤ 0.05). In addition, the increases of Zn, Fe, Mo in soil, and Cu, Fe in sorghum plant were statistically significant in comparison with control treatment (p≤0.01). The highest transfer coefficient was observed for Mo and Pb (1.28, 1.02), and the lowest for Cu (0.077) which highlight the high absorption power of sorghum toward these elements.

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Field experiment was conducted at Kerman Agricultural and Natural Resources Research Centre (Iran) during [2008][2009] to evaluate the effect of bio-fertilizer, phosphorus and foliar application of micronutrients on dry matter accumulation, yield, and phosphorus and zinc concentration of maize (Zea mays L.). A split plot experiment based on randomized complete blocks design (RCBD) with four replications was followed in the study. The micronutrients foliar application in two levels (foliar application and non foliar application) were the main plots, and four levels of phosphate (T 1 : 0 (no fertilizer), T 2 : 100 kg ha -1 P 2 O 5 , T 3 : 100g bio-phosphate, T 4 : 100g bio-phosphate with 50 kg ha -1 P 2 O 5 ) as the sub plots. Results showed that micronutrients foliar application and biological and chemical phosphorus fertilizers had a significant influence on dry matter accumulation. The maximum dry matter accumulation was obtained by applying 50 kg/ha P 2 O 5 plus bio-fertilizer. Grain yield, 1000-seed weight and protein content of grain were significantly affected by micronutrients and phosphorus fertilizers treatments. Micronutrients foliar application and phosphorus fertilizers interaction had no significantly effect on grain yield, 1000-seed weight and grain protein content. Grain phosphorus and zinc concentration where significantly increased by application of micronutrients and phosphorus fertilizers.

The effect of primary-treated municipal wastewater (TMWW) on the leaf area index (LAI) and quality of maize (Zea mays) was studied in comparison to the clean irrigation water (control). The experiment was based on a randomized block design with four replicates, and it was conducted in a field experiment in Aligoudarz (Iran). Irrigation was applied with five different methods as treatments: T1: irrigation with clean water during whole growing period (control); T2: 75% clean water and 25% TMWW; T3: 50% clean water and 50% TMWW; T4: 25% clean water and 75% TMWW; T5: irrigation with TMWW during whole growing period. Results showed that irrigation with TMWW had a significant positive impact on all characters compared with the control. Maximum LAI was yielded on the 80th day after emergence in T4. Use of TMWW increased seed oil to 5.85%, which was 29.2% more than that in the control. Maximum values for percentage of protein, total dry matter and phosphorus concentration were obtained in T5. Maximum zinc concentration (15.93 mg kg(-1)) was obtained in T4; it was 8% more than the control. According to the results there was no significant difference in treatment T4 and T5.

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method. Robust and popular random Forest (RF), cubist (CB) along with random forest-ordinary kriging (RF-OK), and cubist-ordinary kriging (CB-OK) hybrid ML models were applied to the prediction of SOCS. Ten-fold CV was implemented for modeling performance and uncertainty map. According to data analysis, the maximum, minimum, and average values of SOCS are 44.50, 10.50, and 20.50 (ton. ha −1 ) at the surface depth (0-30 cm), respectively. In general, normalized and standardized height covariates had a higher effect related to other predictors. On the other hand, two remote sensing (RS) indices, including salinity ratio (salinity) and GNDVI index, had a better impact on SOCS variability. The external validation of model performance indicated that RF-OK with (R 2 = 0.75, RMSE = 6.33 ton. ha −1 ) with the high and low uncertainty range (3.33-9.50 ton. ha −1 ) was the outperformed ML model in compare with other models as RF (R 2 = 0.65, RMSE = 7.38 ton. ha −1 ), CB-OK (R 2 = 0.56, RMSE = 9.22 ton. ha −1 ), and CB (R 2 = 0.33, RMSE = 10.42 ton. ha −1 ). In general, the hybrid models improved the accuracy of RF and CB with increased 0.11 until 0.23 of R 2 , and 1.05 to 1.2 (ton. ha −1 ) decreased RMSE of model's prediction. Hence, we conclude that the topographic attributes (especially normalized and standardized height) were the most critical factors in controlling surface SOCS in arid rangelands when combining with robust RF ML model, and optimized soil sampling methods like RF-cLHS can prepare acceptable soil properties maps.