# **3. Comparison of Methods**

Given the complexity of soil and the varying needs of different crops and agricultural systems, assessing the overall quality of soil at an objective level can be difficult in itself. Comparing categorical subjective questions to the array of objective measurements and evaluating how well those subjective data reflect the true soil quality is even more challenging. To simplify the process, we first analyze the ability of subjective questions to predict soil carbon levels, a proxy for overall soil health. We attempt to explain which respondent and plot characteristics improve the ability of subjective questions to accurately (or relatively more accurately) assess soil quality. Subsequently, in order to incorporate more of the rich laboratory data and better capture the complex nature of the soil, we construct two variations of soil quality indices. Basic OLS regression is then used to identify which subjective questions, if any, significantly predict changes in the soil quality indicators. Finally, to reinforce the value of plot-level spectral analysis, the spectral results are briefly compared with publicly available geospatial data. All analyses are conducted using top soil (0-20 cm depth) measurements unless otherwise specified.<sup>5</sup>

# _3.1 Carbon as a proxy for overall soil quality_

Carbon content is often considered to be the best single indicator of soil quality (IIASA/FAO, 2012). Higher levels of organic carbon indicate greater soil fertility and more optimal soil structure (IIASA/FAO, 2012). Carbon is also highly correlated with other key properties such as total nitrogen (with rho of 0.974 in this data set). Do farmer assessments of overall soil quality reflect carbon levels? Descriptive analysis reveals little relation between organic carbon content (percent) and the respondent’s assessment of the soil as _poor_ , _fair_ , or _good_ . As seen above, 42 percent, 53 percent, and 5 percent of the household respondents classified the status of their soil as _good_ , _fair_ , and _poor_ , respectively. T-test results provide weak evidence of distinction between organic (or acidified) carbon content.<sup>_6_</sup> In plots with reportedly _good_ or _fair_ soils, there is a greater organic carbon content than in plots reported with _poor_ soils (difference is statistically significant at the 10 percent level). There is no significant difference in organic carbon content on plots with _good_ and _fair_ soils. The significant difference in organic carbon content on _good_ and _poor_ soils (3.36 percent and 3.10 percent, respectively) is consistent with other

> 5 The regression analysis found in Section 3.2 was also conducted using sub-soil results. For brevity, the results are not reported here. The findings using sub-soils are largely consistent with those using top soils, however subjective indicators appear to be a slightly better predictor of top soil soil quality indices. Sub soil results available from the authors.

> 6 No significant difference is found between total carbon content in plots reported as good, fair, and poor. However, correlation between total carbon and acidified carbon among top soil samples in the LASER data is very high (0.9851).

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