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Direct prediction of site-specific lime requirement of arable fields using the base neutralizing capacity and a multi-sensor platform for on-the-go soil mapping
Precision Agriculture ( IF 6.2 ) Pub Date : 2021-07-26 , DOI: 10.1007/s11119-021-09830-x
Sebastian Vogel 1 , Robin Gebbers 1 , Eric Bönecke 2 , Jörg Rühlmann 2 , Charlotte Kling 3 , Eckart Kramer 4 , Ingmar Schröter 4 , Katrin Lück 5 , Golo Philipp 6
Affiliation  

Liming agricultural fields is necessary for counteracting soil acidity and is one of the oldest operations in soil fertility management. However, the best management practice for liming in Germany only insufficiently considers within-field soil variability. Thus, a site-specific variable rate liming strategy was developed and tested on nine agricultural fields in a quaternary landscape of north-east Germany. It is based on the use of a proximal soil sensing module using potentiometric, geoelectric and optical sensors that have been found to be proxies for soil pH, texture and soil organic matter (SOM), which are the most relevant lime requirement (LR) affecting soil parameters. These were compared to laboratory LR analysis of reference soil samples using the soil’s base neutralizing capacity (BNC). Sensor data fusion utilizing stepwise multi-variate linear regression (MLR) analysis was used to predict BNC-based LR (LRBNC) for each field. The MLR models achieved high adjusted R2 values between 0.70 and 0.91 and low RMSE values from 65 to 204 kg CaCO3 ha−1. In comparison to univariate modeling, MLR models improved prediction by 3 to 27% with 9% improvement on average. The relative importance of covariates in the field-specific prediction models were quantified by computing standardized regression coefficients (SRC). The importance of covariates varied between fields, which emphasizes the necessity of a field-specific calibration of proximal sensor data. However, soil pH was the most important parameter for LR determination of the soils studied. Geostatistical semivariance analysis revealed differences between fields in the spatial variability of LRBNC. The sill-to-range ratio (SRR) was used to quantify and compare spatial LRBNC variability of the nine test fields. Finally, high resolution LR maps were generated. The BNC-based LR method also produces negative LR values for soil samples with pH values above which lime is required. Hence, the LR maps additionally provide an estimate on the quantity of chemically acidifying fertilizers that can be applied to obtain an optimal soil pH value.



中文翻译:

使用基础中和能力和多传感器平台直接预测耕地特定地点的石灰需求量,用于移动土壤测绘

石灰农田对于抵消土壤酸度是必要的,是土壤肥力管理中最古老的操作之一。然而,德国石灰的最佳管理实践并未充分考虑田间土壤的变异性。因此,在德国东北部第四纪景观的九个农田上开发并测试了特定地点的可变速率石灰策略。它基于使用电位、地电和光学传感器的近端土壤传感模块的使用,这些传感器已被发现是土壤 pH 值、质地和土壤有机质 (SOM) 的代理,这些是影响最相关的石灰需求 (LR)土壤参数。这些与使用土壤碱中和能力 (BNC) 的参考土壤样品的实验室 LR 分析进行了比较。BNC ) 用于每个字段。MLR 模型实现了介于 0.70 和 0.91 之间的高调整 R 2值和 65 到 204 kg CaCO 3 ha -1 的低 RMSE 值。与单变量建模相比,MLR 模型将预测提高了 3% 到 27%,平均提高了 9%。特定领域预测模型中协变量的相对重要性通过计算标准化回归系数 (SRC) 进行量化。协变量的重要性因场而异,这强调了对近端传感器数据进行场特定校准的必要性。然而,土壤 pH 值是所研究土壤 LR 测定的最重要参数。地统计半方差分析揭示了 LR 空间变异性的领域之间的差异BNC。门槛与范围比 (SRR) 用于量化和比较九个测试场的空间 LR BNC变异性。最后,生成高分辨率 LR 地图。基于 BNC 的 LR 方法也会对 pH 值高于石灰的土壤样品产生负 LR 值。因此,LR 地图还提供了对可用于获得最佳土壤 pH 值的化学酸化肥料数量的估计。

更新日期:2021-07-26
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