Bodemgeschiktheidskaarten voor landbouw in de provincie Noord-Brabant : een toepassing van Waterwijzer Landbouw

Mulder, Martin; Walvoort, D.J.J.; Brouwer, Fokke; Tol-Leenders, Dorothee van; Verzandvoort, S.J.E.

doi:10.18174/576638

Cited by 2 publications

(2 citation statements)

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“…For example, spatial information of soil properties can be used to evaluate soil health on Dutch agricultural fields using tools such as the Open Soil Index (OSI; Ros et al, 2022;Ros, 2023) and Soil Indicators for Agriculture (BLN 2.0; Ros et al, 2023). In addition, data of basic soil properties serve as inputs for modelling agricultural suitability (Mulder et al, 2022), crop precision agriculture (Been et al, 2023) and Soil-Water-Atmosphere-Plant interactions (SWAP; van Dam et al, 1997;Kroes et al, 2017), the latter of which is the underlying model for the Watervision Agriculture and Nature initiative (Hack-ten Broeke et al, 2019). Furthermore, spatial soil property data is also helpful for the Hydrological Instrumentations of the Netherlands (NHI, 2023) and the Delta Program 2024 (Delta Programme, 2023).…”

Section: Soil Surveys and Maps In The Netherlandsmentioning

confidence: 99%

“…Information on soil texture and soil organic matter (SOM) are necessary for greenhouse gas reporting of the Land Use, Land Use Change and Forestry (LULUCF) sector for the United Nations Framework Convention on Climate Change and the Dutch LULUCF submission under the Kyoto Protocol (KP-LULUCF; Arets et al, 2020). Data of basic soil properties serve as inputs for modelling agricultural suitability (Mulder et al, 2022), crop precision agriculture (Been et al, 2023) and Soil-Water-Atmosphere-Plant interactions (SWAP; van Dam et al, 1997;Kroes et al, 2017) Soil maps can also be used to identify and prioritize threats to soil health, as reviewed for the Netherlands by Römkens & Oenema (2004) and Hack-ten Broeke et al (2009). Specific threats to soil health in the Netherlands include soil compaction (van den Akker & Hoogland, 2011;van den Akker et al, 2012), subsidence of peat due to oxidation and compaction (Brouwer et al, 2018;van Asselen et al, 2018), subsidence of young clay soils due to ripening on reclaimed land (Brouwer et al, 2018), and soil erosion (Hessel et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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BIS-4D: a high-resolution soil modelling and mapping platform for the Netherlands in 3D space and time

Helfenstein

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Section: Soil Surveys and Maps In The Netherlandsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BIS-4D: a high-resolution soil modelling and mapping platform for the Netherlands in 3D space and time

Helfenstein

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BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands

Helfenstein,

Mulder,

Hack-ten Broeke

et al. 2024

Earth Syst. Sci. Data

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Abstract. In response to the growing societal awareness of the critical role of healthy soils, there has been an increasing demand for accurate and high-resolution soil information to inform national policies and support sustainable land management decisions. Despite advancements in digital soil mapping and initiatives like GlobalSoilMap, quantifying soil variability and its uncertainty across space, depth and time remains a challenge. Therefore, maps of key soil properties are often still missing on a national scale, which is also the case in the Netherlands. To meet this challenge and fill this data gap, we introduce BIS-4D, a high-resolution soil modeling and mapping platform for the Netherlands. BIS-4D delivers maps of soil texture (clay, silt and sand content), bulk density, pH, total nitrogen, oxalate-extractable phosphorus, cation exchange capacity and their uncertainties at 25 m resolution between 0 and 2 m depth in 3D space. Additionally, it provides maps of soil organic matter and its uncertainty in 3D space and time between 1953 and 2023 at the same resolution and depth range. The statistical model uses machine learning informed by soil observations amounting to between 3815 and 855 950, depending on the soil property, and 366 environmental covariates. We assess the accuracy of mean and median predictions using design-based statistical inference of a probability sample and location-grouped 10-fold cross validation (CV) and prediction uncertainty using the prediction interval coverage probability. We found that the accuracy of clay, sand and pH maps was the highest, with the model efficiency coefficient (MEC) ranging between 0.6 and 0.92 depending on depth. Silt, bulk density, soil organic matter, total nitrogen and cation exchange capacity (MEC of 0.27 to 0.78), and especially oxalate-extractable phosphorus (MEC of −0.11 to 0.38) were more difficult to predict. One of the main limitations of BIS-4D is that prediction maps cannot be used to quantify the uncertainty in spatial aggregates. We provide an example of good practice to help users decide whether BIS-4D is suitable for their intended purpose. An overview of all maps and their uncertainties can be found in the Supplement. Openly available code and input data enhance reproducibility and help with future updates. BIS-4D prediction maps can be readily downloaded at https://doi.org/10.4121/0c934ac6-2e95-4422-8360-d3a802766c71 (Helfenstein et al., 2024a). BIS-4D fills the previous data gap of the national-scale GlobalSoilMap product in the Netherlands and will hopefully facilitate the inclusion of soil spatial variability as a routine and integral part of decision support systems.

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Bodemgeschiktheidskaarten voor landbouw in de provincie Noord-Brabant : een toepassing van Waterwijzer Landbouw

Cited by 2 publications

References 1 publication

BIS-4D: a high-resolution soil modelling and mapping platform for the Netherlands in 3D space and time

BIS-4D: a high-resolution soil modelling and mapping platform for the Netherlands in 3D space and time

BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands

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