Modeling of Field Traffic Intensity and Soil Compaction Risks in Agricultural Landscapes

Duttmann, Rainer; Augustin, Katja; Brunotte, Joachim; Kuhwald, Michael

doi:10.1007/978-3-030-85682-3_14

Cited by 4 publications

(2 citation statements)

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“…The growing availability of high resolute data from global navigation satellite systems (GNSS) received and recorded by modern farm vehicles enables to precisely predict traffic intensity (e.g., Kroulík et al, 2009;Duttmann et al, 2013) and traffic related compaction effects at field scale (e.g., Duttmann et al, 2014;Augustin et al, 2020;Duttmann et al, 2021).…”

Section: Referencementioning

confidence: 99%

“…) Ledermüller et al (2018) -Mapping soil compaction risk for a feral state (Lower Saxony) in Germany -Application of Lorenz et al (2016) Augustin et al (2020) -Modelling spatial pattern of traffic intensity for an entire crop rotation for one field -Application of FiTraM (Augustin et al, 2019), which incorporates Koolen et al (1992) -Maps of wheel passages, maximum wheel load, maximum mean ground contact pressure for maize harvestDuttmann et al (2021) …”

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Spatio-Temporal High-Resolution Subsoil Compaction Risk Assessment for a 5-Years Crop Rotation at Regional Scale

Kuhwald

Duttmann

2022

Front. Environ. Sci.

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Soil compaction results whenever applied soil stress by machinery exceed the soil strength. Both, soil strength and stress, are spatially and temporally highly variable, depending on the weather situation, the current crop type, and the machinery used. Thus, soil compaction risk is very dynamic, changes from day to day and from field to field. The objective of this study was to analyze the spatio-temporal dynamics of soil compaction risk and to identify hot-spot areas of high soil compaction risk at regional scale. Therefore, we selected a study area (∼2,000 km2) with intensive arable farming in Northern Germany, having a high share of cereals, maize and sugar beets. Sentinel-2 images were used to derive the crop types for a 5-years crop rotation (2016–2020). We calculated the soil compaction risk using an updated version of the SaSCiA-model (Spatially explicit Soil Compaction risk Assessment) for each single day of the period, with a spatial resolution of 20 m. The results showed the dynamic changes of soil compaction risk within a year and throughout the entire crop rotation. The relatively dry years 2016 and 2018–2020 reduced the soil compaction risk even at high wheel loads applied to soil during maize and sugar beet harvest. Contrary, high precipitation in 2017 increased the soil compaction risk considerably. Focusing on the complete 5-year period, 2.7% of the cropland area was identified as hot-spots of soil compaction risk, where the highest soil compaction risk class (“extremely high”) occurred every year. Additionally, 39.8% of the cropland was affected by “extremely high” soil compaction at least in one of the 5 years. Although the soil compaction risk analysis does not provide information on the actual extent of the compacted area, the identification of risk areas within a period may contribute to understand the dynamics of soil compaction risk in crop rotation at regional scale and provide advice to mitigate further soil compaction in areas classified as high risk.

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Section: Referencementioning

confidence: 99%

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confidence: 99%

Spatio-Temporal High-Resolution Subsoil Compaction Risk Assessment for a 5-Years Crop Rotation at Regional Scale

Kuhwald

Duttmann

2022

Front. Environ. Sci.

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How to adequately represent biological processes in modeling multifunctionality of arable soils

Vogel,

Amelung,

Baum

et al. 2024

Biol Fertil Soils

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Essential soil functions such as plant productivity, C storage, nutrient cycling and the storage and purification of water all depend on soil biological processes. Given this insight, it is remarkable that in modeling of these soil functions, the various biological actors usually do not play an explicit role. In this review and perspective paper we analyze the state of the art in modeling these soil functions and how biological processes could more adequately be accounted for. We do this for six different biologically driven processes clusters that are key for understanding soil functions, namely i) turnover of soil organic matter, ii) N cycling, iii) P dynamics, iv) biodegradation of contaminants v) plant disease control and vi) soil structure formation. A major conclusion is that the development of models to predict changes in soil functions at the scale of soil profiles (i.e. pedons) should be better rooted in the underlying biological processes that are known to a large extent. This is prerequisite to arrive at the predictive models that we urgently need under current conditions of Global Change.

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Linking structure and functions in agricultural soils

Vogel,

Weller,

Schlüter

2024

Advances in Agronomy

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Modeling of Field Traffic Intensity and Soil Compaction Risks in Agricultural Landscapes

Cited by 4 publications

References 52 publications

Spatio-Temporal High-Resolution Subsoil Compaction Risk Assessment for a 5-Years Crop Rotation at Regional Scale

Spatio-Temporal High-Resolution Subsoil Compaction Risk Assessment for a 5-Years Crop Rotation at Regional Scale

How to adequately represent biological processes in modeling multifunctionality of arable soils

Linking structure and functions in agricultural soils

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