JWSS - Journal of Water and Soil Science

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Organic matters are the most important factors that affect soil compactability and physical characteristics. In order to study the effect of pistachio waste on physical characteristics of two soils, a factorial experiment was conducted in a completely randomized design with three replications in a greenhouse. The treatments included pistachio waste at 4 levels (0, 3, 6 and 9 w/w %) and two types of soil texture (silty clay loam and sand).The results showed that the bulk density of sandy soil was decreased at high levels of waste application before compaction but had no significant effect on the bulk density of clay soil. The penetration resistance of both soil types was decreased by pistachio waste application. Soil water holding capacity increased and moisture curves shifted up for higher levels of organic matter application, while compaction curve reciprocally shifted into the lower levels by incorporation of wastes into the soils. At higher levels of organic matters, maximum bulk density was decreased and critical moisture was increased specially in fine texture soil. After compaction, the application of pistachio waste significantly reduced penetration resistance in silty clay loam soil relative to control but in sandy soil its effect on penetration resistance was only significant at maximum level (9 %).

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Field capacity and permasent wilting point are the most important parameters in designing and programming irrigation, whose measurements are troublesome and time-consuming. But these parameters could be estimated by easy data characteristics such as soil texture, organic matter and gypsum, using Pedotransfer Functions (PTFs) with high precision. In order to estimate soil moisture at FC and PWP by easy data characteristic, using neural network (ROSETA) and regression models ,20 soil samples with 6 replications were collected from around Bardsir area in Kerman province and the charactersifics including, bulk density, clay, sand, silt, FC, PWP,T.N.V and organic matter were determined for each sample. The results showed that progress in neural network from a low level up to higher level needs new inputs (charactersifics), but without any considerable increase in the precision of prediction. Also, regression analysis for estimation of linear models to predict FC and PWP showed that PWP has a significant positive correlation with clay, and FC significantly correlated with sand, silt and clay. Therefore, two prediction models were constructed for FC and PWP with (R2= 69.2) and (R2= 76.6), respectively.