Bridging the gap between the available and required soil data for regional land use analysis

Abstract

The United Nations pledged to achieve the Sustainable Development Goals by 2030. Regional land use analyses (RLUA) have an essential contribution to achieving these goals. To better meet the needs for achieving sustainable development, RLUA became more quantitative and more interdisciplinary over recent decades. This change resulted in an increased use of quantitative simulation models, which changed the type and nature of input data as well. Soil data are one of the input data RLUA require. Available soil data often do not meet the soil data requirements anymore, due to the change in RLUA. Therefore, a gap exists between the available and required soil data. This thesis aims to find possible solutions to bridge this gap. In Chapter 2, different soil datasets are compared to identify the gap and to analyse the effect of using different soil datasets as input for a regional land use analysis (RLUA). Main challenges with soil data in RLUA are: i) understanding the assumptions in soil datasets, ii) creating soil datasets that meet the requirements for regional land use analysis, iii) not only rely on available soil data but also collect new soil data and iv) validate soil datasets. Chapter 2 demonstrated differences between soil datasets, which had significant effect on the results of RLUA. Three potential solutions on bridging the gap between the available and required soil data are given in Chapter 3, 4 and 5. A literature study showed that RLUA hardly combine available and newly collected soil data. Chapter 3 analyses what complementary data RLUA require by combining available soil data and newly collected soil data. Two case studies were carried out to illustrate how a combination can enrich the soil data for RLUA. Predicting soil properties, in particular soil organic matter, using newly collected soil data often result in soil maps of poor quality. The digital soil mapping techniques that are currently being used for predicting soil properties make dominantly use of statistical models, while much knowledge on the mechanistic processes that influence a soil property are available. To improve the prediction of soil organic matter, a mechanistic model for digital soil mapping (DSM) is developed and the potential of mechanistic digital soil mapping is explored in Chapter 4. Mechanistic digital soil mapping predicts soil properties by values that typically stay within realistic boundaries. Complex soil mapping techniques are increasingly being used to better meet the data requirements, because the use of quantitative simulation models in RLUA increased over recent decades. Chapter 5 analyses whether the required soil data can be obtained more targeted to RLUA. Three case studies were carried out to illustrate that the complexity of quantitative simulation models can differ from the complexity required by the RLUA. Therefore, the spatial variation at which the soil properties are provided need to be in line with the spatial variation at which the RLUA operate. In the synthesis (Chapter 6), the research findings, the implementation of the research findings, the hypothesis and future perspectives are discussed and recommendations towards the soil science community and the people involved in RLUA are provided. In this chapter, the ARDAIG approach is introduced, which aims to be an approach that helps obtaining the required soil data for RLUA more targeted. If the soil science community and the people involved in RLUA will implement the presented recommendations, there are opportunities to make soil science contribute more efficiently in RLUA.