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Our objective is to provide an optimistic strategy for reversing soil degradation by increasing public and private research efforts to understand the role of soil biology, particularly microbiology, on the health of our world's soils. We begin by defining soil quality/soil health (which we consider to be interchangeable terms), characterizing healthy soil resources, and relating the significance of soil health to agroecosystems and their functions. We examine how soil biology influences soil health and how biological properties and processes contribute to sustainability of agriculture and ecosystem services. We continue by examining what can be done to manipulate soil biology to: (i) increase nutrient availability for production of high yielding, high quality crops; (ii) protect crops from pests, pathogens, weeds; and (iii) manage other factors limiting production, provision of ecosystem services, and resilience to stresses like droughts. Next we look to the future by asking what needs to be known about soil biology that is not currently recognized or fully understood and how these needs could be addressed using emerging research tools. We conclude, based on our perceptions of how new knowledge regarding soil biology will help make agriculture more sustainable and productive, by recommending research emphases that should receive first priority through enhanced public and private research in order to reverse the trajectory toward global soil degradation.

Recent breakthroughs in remote‐sensing technology have led to the development of high spectral resolution imaging sensors for observation of earth surface features. This research was conducted to evaluate the effects of organic matter content and composition on narrow‐band soil reflectance across the visible and reflective infrared spectral ranges. Organic matter from four Indiana agricultural soils, ranging in organic C content from 0.99 to 1.72%, was extracted, fractionated, and purified. Six components of each soil were isolated and prepared for spectral analysis. Reflectance was measured in 210 narrow (10‐nm) bands in the 400‐ to 2500‐nm wavelength range. Statistical analysis of reflectance values indicated the potential of high dimensional reflectance data in specific visible, near‐infrared, and middle‐infrared bands to provide information about soil organic C content, but not organic matter composition. Although reflectance in the visible bands (425–695 nm) had the highest correlation (r = −0.991 or better) with organic C content among the soils having the same parent material, these bands also responded significantly to Fe‐ and Mn‐oxide content. For soils formed on different parent materials, five long, middle‐infrared bands (1955–1965, 2215, 2265, 2285–2295, and 2315–2495 nm) gave the best correlation (r = −0.964 or better) with organic C content. Several wavebands were identified in which the soils were separable, but the reflectance response was dominated by soil factors other than organic matter content, indicating that choice of wavebands should not be based on spectral curve separability alone.

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Assessment tools are needed to evaluate agronomic management effects on critical soil functions such as carbon sequestration, nutrient cycling and water partitioning. These tools need to be flexible in terms of selection of soil functions to be assessed and indicators to be measured to ensure that assessments are appropriate for the management goals. The soil management assessment framework (SMAF) is being developed to meet this need. The SMAF uses soil physical, chemical and biological indicator data to assess management effects on soil function using a three-step process for (1) indicator selection, (2) indicator interpretation and (3) integration into an index. While SMAF is functional in its present format, it is intended to be malleable so that user needs can be met. Development of additional indicator interpretation scoring curves is one way that this framework can be expanded. Scoring curve development is a multi-step process of identifying an indicator, determining the nature of the relationship of the indicator to a soil function, programming an algorithm and/or logic statements describing that relationship and validating the resulting scoring curve. This paper describes the steps involved in developing an SMAF scoring curve. Scoring curves for interpreting water-filled pore space (WFPS) and Mehlich extractable potassium (K) were developed using the described protocol. This protocol will assist users of the SMAF in understanding how the existing scoring curves were developed and others interested in developing scoring curves for indicators that are not in the current version.