Diane E. Stott

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.

Soil quality assessment is a process for understanding long‐term effects of soil and crop management. Our objective was to assess those effects on Vertisols in Texas after 57 yr. Five fields were sampled in 2006: a managed native pasture (NP), two pastures that were previously cropped and then seeded to Coastal bermudagrass [ Cynodon dactylon (L.) Pers.] for the past 57 (CB) or 31 (CBTL) yr, and two fields that have been continuously cropped (RCTL and RC). The RCTL and CBTL fields have had turkey ( Meleagris gallopavo ) litter amendments. The soil quality assessment was conducted using the Soil Management Assessment Framework, using 10 measured soil attributes. There were significant differences in the overall soil quality ratings, which ranged from 75 to 94% of optimum in the following order: CBTL > NP = RCTL > CB ≈ RC. When separated into index values by sector, physical soil quality ranged from 66 to 84% of optimum, with NP ≈ CBTL > RCTL ≈ CB ≈ RC. All management systems had nearly the same chemical quality rating (94–95%). Focusing only on nutrient attributes, RC, RCTL, and CBTL had significantly higher ratings than the other treatments (99%), with NP being significantly lower (47%). Biological and biochemical soil quality ratings ranged from 44 to 81%, with the NP performing significantly better (81%) and RC significantly lower (44%) than the other treatments. The CBTL treatment exhibited the greatest levels of enzymatic activity and RC the lowest. There were several significant correlations between soil enzymatic activities and other soil parameters. Overall, turkey litter amendments improved soil quality, demonstrating that carefully managed bermudagrass pastures can be more productive than managed native grass pastures, primarily because of improved plant nutrient availability.

The Soil Management Assessment Framework (SMAF) was developed to assess conservation effects on soil, and uses multiple soil quality indicator measurements to compare soil functioning. Our objective was to develop a SMAF‐compatible scoring equation for soil β‐glucosidase (BG) activity using published data sets representing different soils and management. The resulting equation was an S‐shaped curve: y = a /[1 + b exp(− cx )], where x is the measured BG activity (mg p ‐nitrophenol [PNP] released kg ⁻¹ soil h ⁻¹ ), a and b are constants, and c is a factor modified by soil classification, texture, and climate. Data from a study conducted near Mandan, ND were used to test the model for sensitivity to crop management systems. Soil organic C (SOC) content at the site measured 247 to 687 g kg ⁻¹ , while BG activity ranged from 33 to 675 mg kg ⁻¹ h ⁻¹ Using SMAF, SOC indicator scores ranged from 0.25 to 0.73, while BG activity scores varied from 0.17 to 0.93. As the work progressed, it became apparent that when BG activity values were normalized to the SOC content, the resulting ratio could indicate C sequestration trends, with ratios of 10 to 17 g PNP kg ⁻¹ SOC h ⁻¹ reflective of systems in equilibrium. Ratios >17 were mostly from recently altered management systems with SOC contents trending upward, while ratios <10 were generally from soils that were expected to continue to lose soil C. The application of a sensitive C cycling enzyme activity such as BG should improve the SMAF soil quality assessments for soil functions where soil metabolic activity or C‐cycle enzyme activity play a role.

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.