Soil mechanical resistance is high in the site of experiment due to high soil specific surface and cohesion between primary particles. Soil mechanical resistance influences wheat yield by affecting root growth in this area. Correlations show lowering mechanical resistance by intensified tillage improves plant density and head number and hence straw and grain yield. Moldboard tillage (MT) reduced soil mechanical resistance that caused to increase the root growth, water and nutrient uptake, wheat head number per square meter, and yields. Therefore managements as tillage and irrigation may influence root density, wheat nutrient uptake and yield. This may reduce fertilizer requirements and costs. Soil bulk density affected wheat yield through affecting soil mechanical resistance.
The main objective of this study was to explore the capability of particle swarm optimization (PSO), genetic algorithm (GA), andmultiple regression (MR) in estimation of soil mechanical resistance. The results obtained from PSO model are in agreement with the experimental results. The comparisons between three models mentioned above indicate PSO clearly outperforms GA and MR model. This was evidenced by statistical performance criteria used for evaluating the models. The PSO model produced high R2 and VAF values and low RMSE and MAPE values.