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Soil water content characteristics included field capacity (FC) and permanent wilting point (PWP) are key soil indicators to study permeability, soil water retention capacity, drainage, irrigation, plant water stress and solute movement. It is necessary to use an appropriate model for predicting these characteristics because of the hardness of their measurement. The present research shows a comparison of intelligent models included ANN, ANFIS, SVM and SVM optimised by firefly and particle swarm meta-heuristic algorithms (SVM-FFA and SVM-PSA) to predict soil water content at −33 and −1500 kPa matric potentials in agricultural soils. Results showed that the SVM-PSA and SVM-FFA hybrid models presented the best performance rather than the others, with predictor variables included geometric mean diameter (dg) of soil particles, bulk density, organic carbon, NDVI and NSMI and the values of R2 and NRMSE in validation dataset, 0.94, 0.081, 0.96 and 0.077 for FC and PWP in SVM-PSA, respectively, and 0.96, 0.079, 0.97 and 0.075 for FC and PWP in SVM-FFA model. Therefore, these intelligent machines are preferable for accurate prediction and could be used to predict soil water content of soils with different soil textures in other regions of Iran or even the world. [ABSTRACT FROM AUTHOR]

The shortage of elements in the soil and water available to the plant are some of the most common environmental stresses in Iran which faces drought every year. The purpose of this study was to study the effect of pitting and organic fertilizers on the growth and physiological characteristics of vetiver grass in drought stress conditions. After cultivating the seedlings in the pots in the greenhouse and their growth for three months, the seedlings were transformed to the field and the treatments were applied to them for six months. This experiment was conducted as a factorial experiment based on randomized complete block design in the field. The treatments applied to the seedlings were cultivation with pitting and without pitting, three levels of fertilizer (0, 150, and 300 kg fertilizer per hectare), and two levels of irrigation determined based on field capacity (FC) (FC and 1/4FC). After harvest, the plants were transferred to the laboratory for measurements. The results showed that by applying pitting and fertilizers, shoot height changed from 9.8 cm to 16.68 cm, root length from 18.81 cm to 25.55 cm, potassium (K) from 25.95 mg/kg to 34.30 mg/kg, calcium (Ca) from 1.20 mg/kg in pitting treatment to 2.27 mg/kg in non-pitting treatment, phenol from 0.22 mg/g DW to 0.83 mg/g DW and phosphorus (P) from 2.99 mg/kg to 11.95 mg/kg. The results indicated the interactive effects of organic fertilizer and pitting due to improvement in soil physicochemical conditions and increase in growth and nutrient uptake of vetiver grass. [ABSTRACT FROM AUTHOR]

BACKGROUND: Despite mechanization development, leafy vegetable cultivation (LVC), as a labor-intensive activity in both developed and developing countries, still suffers from heavy physical activities. OBJECTIVE: The present study evaluated the human physiological strains of LVC's workers to identify relationships among contributing factors affecting human physiological strains. METHODS: Thirty male workers were included in this study. Working heart rate (HR) was measured using a heart rate sensor during various operations. The time taken to treat a known area was measured using a stopwatch to calculate work speed (or field capacity (FC)) for each operation. Pearson correlation coefficient and linear regression were used to investigate the relationships among HR, heart rate ratio, FC and mechanization status (MS), and human energy expenditure rate and total energy expenditure per unit area. RESULTS: The highest HR was at seedbed preparing (120.1 beats/min) and lowest at manual harvesting (87.8 beats/min). Manual hoe-used operations (seedbed preparing, manure application and irrigating) were demonstrated as the critical operations concerning physiological strains. The operations performed by machine power corresponded to a high FC. CONCLUSIONS: Variables influencing the area treating speed (i.e. MS and FC) are negatively linked to the human energy consumed per unit area and variable changed in time unit (i.e. HR) was positively linked to the human energy expenditure speed. [ABSTRACT FROM AUTHOR]

Thirteen pedotransfer functions (PTFs), namely Rosetta PTF, Brakensiek, Rawls, British Soil Survey Topsoil, British Soil Survey Subsoil, Mayr-Jarvis, Campbell, EPIC, Manrique, Baumer, Rawls–Brakensiek, Vereecken, and Hutson were evaluated for accuracy in predicting the soil moisture contents at field capacity (FC) and wilting point (WP), of fine-textured soils of the Zagros mountain region of Iran. PTFs were developed using the laboratory measurements made on soil moisture at FC and WP, particle-size distribution, bulk density, and organic matter content. PTFs were evaluated on the basis of mean-squared deviation (MSD) between the observed and predicted values. Results agreed with the concept that the PTFs developed on soils of similar properties to the ones under study generally perform better than the others. In the case of the Zagros mountain soils, the “British Soil Survey” and “Brakensiek” PTFs were found to be the best methods. Since the soils under study had a wide range of organic matter contents (0.2–5.5%), the better performance of these PTFs may also be explained by the fact that they happen to be the only ones that require organic matter content as input. Rosetta, a software package that involves an artificial neural network approach, was of intermediate value in estimating soil moistures of the soils in question. This was attributed to the fact that the texture and the bulk density of the Zagros soils were not in the range of those used to develop Rosetta. [Copyright &y& Elsevier]