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), and multiple 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.