SoilManageR—An R Package for Deriving Soil Management Indicators to Harmonise Agricultural Practice Assessments

Abstract

Understanding the effects of agricultural soil management on the soil system and its functions is crucial to ensure the sustainable use of soil. Due to the countless ways in which soil can be managed, it is not an easy task to compare soil management practices across different locations and over time. One approach to making soil management comparable is the use of numerical soil management indicators. However, due to the lack of standardisation of soil management data and indicators, the comparability of results across studies remains limited. To address these shortcomings, we developed SoilManageR, an accessible R package. The first version of SoilManageR calculates numerical soil management indicators for carbon (C) input, tillage intensity, soil cover duration, nitrogen (N) fertilisation, equivalent livestock units per area, and plant diversity. In this paper, we present the functionality of SoilManageR and demonstrate its capabilities with three case studies. The cases were selected to compare soil management across space, time and context, as well as to relate soil management to soil quality. For this, we calculated soil management indicators for 16 experimental treatments from six agricultural long-term experiments and for 18 farmers' fields in Switzerland. We found that experimental treatments were representative of the management of the farmers' fields in terms of tillage intensity and soil cover, but that farmers' fields tended to exhibit higher livestock integration, leading to higher C and N inputs through organic amendments. We related soil management indicators to selected soil quality indicators in experimental treatments and showed that tillage intensity is the most important management driver of earthworm biomass, whereas C and N inputs were the best predictors of the organic carbon content of the topsoil. Finally, we applied SoilManageR to three sites of the Swiss Soil Monitoring Network and identified significant reductions of N inputs across time in two sites. We demonstrate that SoilManageR is a versatile tool for quantifying multiple aspects of soil management intensity, which can be useful to analyse how policy changes affect soil management. Additionally, SoilManageR can be used to assess soil management impacts on soil quality and provide guidance based on these insights.