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Most soil function indicators could be predicted using a single quantitative visual observation (Table 4.3), which could be explained by the fact that both visual soil evaluation and most soil function indicators were quantified based on soil physical properties. Especially the quantified soil function indicators plant available water, water storage capacity, modelled yield gaps, modelled relative oxygen and drought stress are mainly affected by soil texture and soil structure (Vereecken et al., 2010).

Nitrate and phosphate concentrations in ground- and drain water were also strongly correlated with quantitative visual observations. This was unexpected, because nitrate and phosphate concentrations were likely not only dependent on soil physical properties, but also on soil chemical properties, climatic conditions and management factors (Freese et al., 1992; Lipiec and Stępniewski, 1995; Oenema et al., 2010, 2004;

Schoumans et al., 2013). A reason could be that there are many internal correlations between soil physical, chemical, and biological aspects (Karlen et al., 2001; Pulido Moncada et al., 2014; Sonneveld et al., 2014), and land management and weather patterns (Figure 4.1).

Table 4.6. Pearson correlation coefficients (r) between soil functions, only presented when significant at P=0.05. Bolt font: not optimal combination (trade-off) between soil function indicators. Note that values were only shown below the diagonal, to avoid repetition. Plant available water (cm3 cm-3 ) Water storage capacity (cm3 cm-3 ) Yield gap 2016 (%) Yield gap 2001 (%) Yield gap 2003 (%) Nitrate concentration (mg L-1) Phosphate concentration (mg L-1) Relative oxygen stress 2001 (%)

Relative drought stress 2003 (%) Plant available water (cm3 cm-3) Waterstorage capacity (cm3 cm-3) Yield gap 2016 (%) -0.53 Yield gap 2001 (%) -0.60.95 Yield gap 2003 (%) -0.45 0.930.94 Nitrate concentration (mg L-1)

-0.52 0.64 -0.57 Phosphate concentration (mg L-1 )

-0.62 Relative oxygen stress 2001 (%) -0.60.910.960.89-0.67 Relative drought stress 2003 (%) -0.50.680.560.77 0.5

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Chapter 4

Previous studies reported contrasting results about the association between measured crop yields and visual evaluation of soil structure (e.g. Ball et al., 2007; Guimarães et al., 2013) with. For example, visual soil evaluation of soil structure (VESS) significantly correlated with crop yield (Mueller et al., 2013), while other studies found insignificant correlations between VESS and crop yield (Giarola et al., 2013 and Mueller et al., 2009).

In these studies it is likely that land management and climatic conditions affected measured crop yields, while VESS focusses on soil physical properties (Mueller et al., 2013, Figure 4.1). VESS may be therefore not a good predictor for measured crop yield, especially when the limiting factors are others than soil physical factors (e.g. above-ground factors or soil chemical factors). Instead, a broad set of visual observations likely better represent soil function indicators, as was found in the present study.

Besides using a broad set of visual observations to assess soil functions, including clay content could improve the assessment of soil functions. For some soil function indicators in the present study (water storage capacity, yield gap 2001, nitrate and phosphate concentrations in drain- or groundwater) the association with a quantitative visual observation became stronger when an interaction with clay content was included (Table 4.4) than when those interactions were not included (Table 4.3). Previous studies showed that texture influenced the VSE scores.

Nevertheless, in the present study Ramsey’s RESET test indicated that including interactions and/or quadratic terms (indicating nonlinear relationships) would not improve the correlation between a set of visual observations and soil function indicators. The reason could be that we did not observe the full range of possible quantitative visual soil observations and soil function indicators, and that the effect of other (linear) terms overruled the effect of interactions and/or quadratic terms. To conclude, it was found that soil function indicators can be best predicted based on a combination of visual observations and clay content.

4.4.2 Relation between multiple quantitative visual observations and indicators for soil functions

The stepwise linear regression models resulted in a better prediction of soil function indicators (i.e. high adjusted R², Table 4.5) than when using a single quantitative visual observation (Table 4.3) or when adding an interaction term with clay content to a single quantitative visual observation (Table 4.4). Modelled yield gaps in a wet year (2001) and a dry year (2003) were associated with the same quantitative visual observations, which were soil structure (fraction largest soil structural elements), soil compaction, root depth (85% of roots) and A horizon depth (Table 4.5). It should be noted that these modelled yield gaps were mainly the result of a combination of modelled plant oxygen and drought stress, and not of nutrient limitations, and therefore it was found that visual soil physical properties correlated with modelled yield gaps. As for modelled yield gaps in a wet and a dry year, the crop sensitivity to

oxygen and drought stress is affected by similar soil properties. Under wet conditions, oxygen stress for crop growth is mainly governed by the absence of air filled soil pores, which is affected by texture, soil compaction, soil structure and the presence of continuous soil pores (Håkansson and Lipiec, 2000). Whereas in dry conditions, drought stress is mainly governed by the absence of plant available water, deep groundwater tables and a limited capillary rise of water to the root zone (Kroes, 2018), which is affected by texture, soil compaction and soil structure. Also, deep rooting plants are generally less sensitive to drought than shallow rooting plants. Nevertheless it depends on the water and nutrient availability in deeper soil layers whether plants can actually grow and increase biomass in dry periods, or whether they just remain green without growing (Kemp and Culvenor, 1994). Unexpectedly, although the yield gap in 2003 could be predicted, the drought stress in the dry year (2003) could not be predicted using (a set of) quantitative visual observations. The best model that associated with drought stress was based on maximum rooting depth and an interaction with clay content (Table 4.4). The prediction of drought stress in SWAP might improve if a dynamic root model is included (Kroes, 2018).

It turned out that for each quantified soil function indicator the selected set of quantitative visual observations was different (Table 4.5). This suggests that not all quantitative visual observations need to be assessed when assessing a specific soil function, which reduces assessment time in the field. In practice, farmers often ask which visual soil property to improve to improve crop growth. Another common question is how much a soil property should change to reach a satisfying soil functioning level. The stepwise linear regression modelling results suggest that a soil function can be changed by just changing one of the quantitative visual observations (Table 4.5). However, there are many associations between (visual) soil properties (e.g.

Ball et al., 2017; Sonneveld et al., 2014). It is likely that by changing one of the soil properties other soil properties change as well, and therefore it cannot be easily predicted how a change in one soil property will affect a soil function of interest.

Instead, after implementing a soil management strategy, stepwise linear regression models could be used to monitor how visual properties change, and how the soil functions change. This only holds for visual observations that fall in similar ranges as the data used to develop the stepwise linear regression models (Table A.4.9 and A.4.10).