Modeling of agricultural soil compaction using discrete Bayesian networks
The intensive use of machinery in the agricultural area can have serious effects on the soil quality causing its compaction and changes in the related vegetation growth. In fact, consequent phenomena such as water stagnation due to lack of water infiltration, insects proliferation and vegetable pests attack might be observed. Accordingly, the soil compaction causes and diagnosis should be deeply studied. In this paper, based on Bayesian networks, a decision support system suitable for soil compaction risk assessment was built. A discrete Bayesian network was calibrated, starting from raw measured data, to discover the relationships between different variables characterizing the agricultural soil and the experimental conditions. These variables include humidity, the tractor’s weight, the number of passages and the wheels inflation pressure. The compaction phenomenon is studied through analyzing the sensitivity of the soil’s resistance to penetration and its bulk density to the above-cited factors. The developed Bayesian network is proven to be useful as a decision-making tool helping the users to perform both causal and diagnostic analysis on soil state.