Abstract

Soil acidification is caused by natural paedogenetic processes and anthropogenic impacts but can be counteracted by regular lime application. Although sensors and applicators for variable-rate liming (VRL) exist, there are no established strategies for using these tools or helping to implement VRL in practice. Therefore, this study aimed to provide guidelines for site-specific liming based on proximal soil sensing. First, high-resolution soil maps of the liming-relevant indicators (pH, soil texture and soil organic matter content) were generated using on-the-go sensors. The soil acidity was predicted by two ion-selective antimony electrodes (RMSEpH: 0.37); the soil texture was predicted by a combination of apparent electrical resistivity measurements and natural soil-borne gamma emissions (RMSEclay: 0.046 kg kg−1); and the soil organic matter (SOM) status was predicted by a combination of red (660 nm) and near-infrared (NIR, 970 nm) optical reflection measurements (RMSESOM: 6.4 g kg−1). Second, to address the high within-field soil variability (pH varied by 2.9 units, clay content by 0.44 kg kg−1 and SOM by 5.5 g kg−1), a well-established empirical lime recommendation algorithm that represents the best management practices for liming in Germany was adapted, and the lime requirements (LRs) were determined. The generated workflow was applied to a 25.6 ha test field in north-eastern Germany, and the variable LR was compared to the conventional uniform LR. The comparison showed that under the uniform liming approach, 63% of the field would be over-fertilized by approximately 12 t of lime, 6% would receive approximately 6 t too little lime and 31% would still be adequately limed.

Highlights

  • The productivity of agricultural soils is highly controlled by their acidity and buffering capacity

  • The spatial correlation structure of the sensor data on the test field can be best characterized by circular (γ, elevation, pH, electrical conductivity (ECa)), exponential (Red, IR) and Gaussian (ERa) models

  • The present study presents a developed procedure that allows the easy and semi-automated generation of topsoil pH, texture and soil organic matter maps based on proximal soil sensing

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Summary

Introduction

The productivity of agricultural soils is highly controlled by their acidity and buffering capacity. (v) Soil structure, porosity and aggregate stability (aeration, water availability, root growth) (Fiedler and Bergmann 1955; Hartge 1959; Schachtschabel and Hartge 1958), and (vi) Water infiltration, water storage and soil erosion (Ahn et al 2013; Cuisinier et al 2011; Horsnell 1984). For these reasons, farmers strive to obtain and maintain an optimal soil pH to improve crop growth in their fields (Tunney et al 2010). In soils that do not contain geogenic carbonates, farmers need to apply lime to their fields to maintain soil fertility

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