Mapping Topsoil Behavior to Compaction at National Scale from an Analysis of Field Observations

Anne C. Richer- de-Forges, Dominique Arrouays (), Songchao Chen, Zamir Libohova, Dylan E. Beaudette and Hocine Bourennane
Additional contact information
Anne C. Richer- de-Forges: INRAE, Info&Sols, 45075 Orléans, France
Dominique Arrouays: INRAE, Info&Sols, 45075 Orléans, France
Songchao Chen: ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China
Zamir Libohova: USDA-ARS Dale Bumpers Small Farms Research Center, 6883 S. State Hwy. 23, Booneville, AR 72927, USA
Dylan E. Beaudette: USDA-NRCS, Soil and Plant Science Division, 19777 Greenley Rd, Sonora, CA 95370, USA
Hocine Bourennane: INRAE, Info&Sols, 45075 Orléans, France

Land, 2024, vol. 13, issue 7, 1-23

Abstract: Soil compaction is one of the most important and readily mitigated threats to soil health. Digital Soil Mapping (DSM) has emerged as an efficient method to provide broad-scale maps by combining soil information with environmental covariates. Until now, soil information input to DSM has been mainly composed of point-based quantitative measurements of soil properties and/or of soil type/horizon classes derived from laboratory analysis, point observations, or soil maps. In this study, we used field estimates of soil compaction to map soil behavior to compaction at a national scale. The results from a previous study enabled clustering of six different behaviors using the in situ field observations. Mapping potential responses to soil compaction is an effective land management tool for preventing future compaction. Random forest was used to make spatial predictions of soil behavior to compaction over cultivated soils of mainland France (about 210,000 km 2 ). Modeling was performed at 90 m resolution. The map enabled us to spatially identify clusters of possible responses to compaction. Most clusters were consistent with known geographic distributions of some soil types and properties. This consistency was checked by comparing maps with both national and local-scale external sources of soil information. The best spatial predictors were available digital maps of soil properties (clay, silt, sand, organic carbon (SOC) content, and pH), some indicators of soil structural quality using SOC and clay content, and environmental covariates (T °C and relief-related covariates). Predicted maps were interpretable to support management recommendations to mitigate soil compactness at the soil–scape scale. Simple observational field data that are usually collected by soil surveyors, then stored and available in soil databases, provide valuable input data for digital mapping of soil behavior to compaction and assessment of inherent soil sensitivity to compaction.

Keywords: soil; compactness; on-field observations; indicators; sensitivity to compaction; clustering; digital soil mapping; random forest (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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