The process to obtain soil data

The process on how to obtain soil data for RLUA differed between the three case studies. The input data of all three case studies was the same, but the aim and location of the case studies differed. To illustrate the process on how to obtain soil data for RLUA we illustrate the diagram of Hoosbeek and Bryant (1992), which was slightly adapted by Bouma and Hoosbeek (1996) (Fig.5.5). To obtain soil data for a RLUA, different models can be used. These models differ in degree of complexity and degree of computation. The degree of complexity ranges from empirical to mechanistic and the degree of computation ranges from qualitative to quantitative.

Figure 5.5. To obtain the required soil data, different models can be used. These models can be classified based on hierarchical scale level, degree of computation and degree of complexity.

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The spatial scale ranges between molecular and world. Different knowledge levels can be attributed to different models. K1 includes user expertise, K2 includes expert knowledge, K3 includes generalized holistic models, K4 includes complex holistic models and K5 includes complex models for parts of the system to be studied. To run the analyses with the DSSAT model, all three case studies required data at K5 level and at plot scale. However, the k-level and scale hierarchy at which the analyses were done can change from the k-level and scale hierarchy at which the study operates. For the analyses of case study 1, K5 level was required, but the study operates at K2 or K3 level. Case study 2 operates at K3 level and case study 3 operates at K5 level. This study illustrated that RLUA still operate at different k-levels, while the soil data that are collected are often processed to serve studies at K5 level. Available soil data that are not transformed only serve K1, K2 and in some cases K3 levels. The number of studies that operate at K4 or K5 level increased over recent decades, but this does not mean that soil data should only be transformed or processed to serve studies at K5 level.

Each method on how to obtain soil data for RLUA has its strengths, weaknesses, opportunities and threats. However, one soil data analysis can be more efficient than the other. For example, in a study of Rodríguez Martín et al. (2016) new soil data were collected using an intensive sampling scheme, while for the study area high quality auxiliary data were available and could be used for a more efficient sampling scheme. To obtain soil data for RLUA, it is important to define the modelling approach first (Hoosbeek and Bryant, 1992). Defining the modelling approach can help specifying the required soil data. Many RLUA do not define the modelling approach. When the modelling approach is not clearly defined, soil data analyses are often highly simplified, e.g., selecting only the most dominant soil type per mapping unit, or the soil data analyses are highly complex, e.g., providing three-dimensional soil properties that keep correlations between soil properties and variation over depth (e.g., Angelini et al., 2016). The highly simplified soil data analyses often require more detail on the spatial variation, while the highly complex soil data analyses often bring confusions about the use of the dataset for RLUA. The spatial variation of the input data should match with the spatial variation that is required by

133 the RLUA (Fig. 5.5). The case studies required quantitative soil data for the quantitative, empirical DSSAT model (Bouma, 1997), but the spatial variation of the soil input data and required data differed, which made the soil data analysis differ.

Nowadays, it is often assumed that spatially continuous data are required. However, many simulation models were not developed for two or three-dimensional purposes and therefore require not necessarily spatially continuous soil data (e.g., DSSAT, WOFOST, Nutmon) (Bouman et al., 1996). However, RLUA require increasingly spatially exhaustive results. Instead of providing spatially continuous soil data, the map on water-limited maize yields can be created after the model run for the point observations (Case study 2).

5.7 Conclusions

For RLUA that use crop-growth simulation models it is important to consider variation over depth, obtaining soil data at the spatial variation that is in line with the required spatial variation and obtaining functional soil data (e.g., complex soil properties). However, there is not a single solution to the question ‘how to obtain soil data for regional land use analyses?’. Studies need to define the modelling approach before they start obtaining the required soil data. After defining the modelling approach, soil data can be obtained more targeted to the aim of the RLUA. The complexity and computation of the mapping technique need to be in line with the study. The complexity and computation of the study can differ from the quantitative simulation model. In the end, ‘smart’ analyses are required to obtain the required for RLUA. These analyses make efficient use of available soil data, project resources, auxiliary data and mapping tools and techniques and pedological knowledge.

Chapter 6

Synthesis

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6.1 Introduction

The growing demand for quantitative soil profile data at detailed scale widens the gap between the required and available soil data for regional land use analyses (RLUA). For about 70% of the global surface there are no soil maps at a scale larger than 1:1million available (Nachtergaele and Van Ranst, 2003) and the soil data that are available, often do not meet the data requirements. Complementary soil data are required to narrow the gap between the required and available soil data for RLUA.

However, which complementary soil data to obtain differs per RLUA. For example, in some RLUA complementary soil data consist of collecting more data on the spatial variation (Chapter 5), while in other RLUA complementary soil data consist of collecting soil data on different land use and management (Chapter 3). Different solutions are provided in this thesis to obtain complementary soil data. These solutions aim to bridge the gap between the available and required soil data for RLUA.

This synthesis assesses and discusses how the gap between the required and available soil data for RLUA can be bridged. In section 6.2 the research findings provide the lessons learnt and answers the sub-questions of this thesis: (i) does it matter which available soil data are used for RLUA (section 6.2.1), (ii) what complementary data are needed to meet the required soil data demand for RLUA (section 6.2.2), and (iii) how to obtain the required soil data for RLUA in an effective manner (section 6.2.3)? Implications of the research findings are discussed in section 6.3. In section 6.4 the aim of this study and the hypothesis are discussed. This section 6.4 provides a flowchart that helps obtaining the best soil data for RLUA and recommendations to the soil science community and the community that works with RLUA. Section 6.5 focusses on future perspectives of soil data for RLUA.

6.2 Research findings