集约化生产条件下稻田土壤机械压实预测模型构建与验证

传统的土壤压实风险评估方法是基于土壤的先期固结压力理论，以机械的接地压力与土壤先期固结压力间关系作为判断依据，缺少针对集约化稻作“湿耕烂种”等生产场景中由定量机械压实造成的土壤结构破坏程度的评价方法和依据。为研究适合中国稻作特色，可以定量预测机械压实危害程度的压实容重预测模型，该研究基于土壤的回弹指数和压缩指数推导出土壤压实容重预测模型，以适用于集约化生产条件下稻田土壤机械压实预测。采用调控原状土含水率的单轴压缩试验法分别构建了土壤初始容重、初始含水率与弹性压缩模量、塑性压缩模量和先期固结压力之间的传递函数，然后基于典型机型的田间原位平板下陷试验验证所建模型的可靠性和实用性。结果表明，基于单轴压缩试验法构建的各传递函数拟合决定系数大于0.95。将各传递函数模型所得的弹性压缩模量、塑性压缩模量和先期固结压力输入土壤压实容重模型预测的压实后的土壤容重与实测值的相对误差小于5%。可见，该研究设计的土壤压实预测模型能够准确量化受机械压实情况下土壤容重的变化量，而土壤传递函数法能为构建和应用区域性农业土壤的压实模型提供便利。研究可为集约化生产条件下稻作“湿耕烂种”等生产场景中由定量机械压实造成的土壤结构破坏程度评价提供可靠方法和依据。

Establishment and verification of soil mechanical compaction prediction model in paddy field under intensive production conditions

Soil compaction risk can be normally assessed, in terms of the relationship between the wheel contacting pressure and the soil pre-consolidation stress. It is still lacking in the evaluation of the damage degree of soil structure that is caused by quantitative mechanical compaction in production scenarios, such as wet tillage and rot under intensive rice farming. In this study, a prediction model of soil compacted bulk density was derived using soil rebound and compression index in paddy fields under intensive production conditions. The improved model was verified by the laboratory uniaxial compression test and in situ flat subsidence test of undamaged soil in paddy fields. An indoor uniaxial compression test was also carried out under the undisturbed soil with different moisture content (15%, 20%, 25%, 30%, and 35%). Among them, there was no change in the bulk density of the undisturbed soil. The transfer functions were constructed for the soil initial bulk density (ρ), initial moisture content (w), elastic compression modulus (Es), plastic compression modulus (Ec), and soil pre-consolidation stress (σpc) after the uniaxial compression test. The coefficient of determination was greater than 0.95 after fitting the test data with each transfer function. The results show that the improved model was operable and accurate using the transfer function. At the same time, the Es presented a significant negative correlation with the ρ, whereas, there was a significant positive correlation with the w. The Es reached the maximum when ρ=1.1 g/cm3 and w=35%. The minimum was obtained, when ρ=1.8 g/cm3 and w=15%. The Es value ranged from 0-0.25 cm/kPa. The Ec presented a significant negative correlation with the ρ, and there was a quadratic polynomial relationship with the w. The Ec reached the maximum when ρ=1.1 g/cm3 and w=25%. The minimum Ec was obtained, when ρ=1.8 g/cm3 and w=15%, where the value ranged from 0-0.8 cm/kPa. There was a significant positive correlation between the σpc and ρ, whereas, a significant negative correlation was found between the σpc and w. The σpc reached the maximum when ρ=1.8 g/cm3 and w=15%. The minimum was obtained, when ρ=1.1 g/cm3 and w=35%. The value ranged from 20-160 kPa. The earth pressure was determined as 30, 60, 90, and 120 kPa in the in-situ flat subsidence test, according to the several types of harvesters. The soil bulk density in the uncompacted area was taken as the initial bulk density, while, the measured bulk density of the soil in the compacted area was as the measured bulk density. The ρ, w, elastic compression modulus, plastic compression modulus, and pre-consolidation pressure from the uncompacted region were then input into the prediction model of soil compacted bulk density to obtain the predicted bulk density. Finally, the measured bulk density was compared with the predicted. In-situ plate sinkage test showed that there was less than 5% error between the measured and the predicted using the transfer function-derived soil elastic compression and plastic compression modulus. At the same time, it was found that the wheel contacting pressure was greater than the soil pre-consolidation stress under the large w and the small ρ. There was a risk of soil compaction, even with the small contacting pressure of the wheel in the harvester. Therefore, a reasonable time and machine type can greatly contribute to the implementation of field tillage. Consequently, the prediction model of soil compaction can be expected to accurately quantify the soil bulk density under mechanical compaction. The finding can provide a strong reference for a better prediction model in regional agricultural usage.