Core Ideas: Despite nationwide emphasis on soil health in the USA, current measurements lack consistency. A meta‐analysis showed 8 of 42 soil health indicators reported >20% of time. Only 13 indicators showed short‐term (1–3 yr timescale) responses to cover cropping. Wide variation in soil sampling protocols suggests standardization is needed. Translating soil health research across systems requires a common framework. Despite a nationwide emphasis on improving soil health in the United States, current measurement protocols have little consistency. To survey assessment practices, we conducted a meta‐analysis of cover crop (n = 86) and no‐tillage (n = 106) studies and compiled reported indicators, cropping systems, and soil sampling protocols from each. We then analyzed which indicators significantly responded to cover crop usage after 1 yr and 2 to 3 yr. Our results showed that out of 42 indicators, only 8 were reported in >20% of studies. Thirteen indicators showed >10% relative response after 1 to 3 yr; the remainder lacked either sufficient observations or consistent results. Looking forward, we propose that emphasis should be placed on (i) pursuing dynamic indicators (e.g., aggregate stability), (ii) standardizing sampling protocols, and (iii) developing a common framework for information sharing. These efforts will generate new insight into soil health across systems, ultimately ensuring that soil health science is useful to producers and regulators.

Field studies have been performed for decades to analyze effects of different management practices on agricultural soils and crop yields, but these data have never been combined together in a way that can inform current and future cropland management. Here, we collected, extracted, and integrated a database of soil health measurements conducted in the field from sites across the globe. The database, named SoilHealthDB, currently focuses on four main conservation management methods: cover crops, no-tillage, agro-forestry systems, and organic farming. These studies represent 354 geographic sites (i.e., locations with unique latitudes and longitudes) in 42 countries around the world. The SoilHealthDB includes 42 soil health indicators and 46 background indicators that describe factors such as climate, elevation, and soil type. A primary goal of this effort is to enable the research community to perform comprehensive analyses, e.g., meta-analyses, of soil health changes related to cropland conservation management. The database also provides a common framework for sharing soil health, and the scientific research community is encouraged to contribute their own measurements.

Many clayey soils shrink as they dry, causing a shift of porosity from inside to outside the soil aggregates and leading to the formation of shrinkage cracks and/or surface subsidence. During swelling, shrinkage cracks begin to seal and/or the soil surface rises. Previous models have focused on describing shrinkage at the aggregate level, with little success in predicting soil cracking and subsidence. To remedy this shortcoming, we provide a unified, physically based set of governing equations for these three pore domains (aggregates, cracks, and subsidence) and predict the porosity distribution among domains as a function of soil water content and minimal (up to six) additional parameters. Examples collected from a variety of soils show how these functions describe shrinkage of soil samples in the laboratory; quantify the relationships among soil suction, soil shrinkage, and water content using the same set of parameters; and predict sealing of soil cracks in the field. This approach provides the framework for accurate and unified hydromechanical modeling of swelling soils.

[1] A soil's capillarity, associated with the parameter sorptivity, is a dominant control on infiltration, particularly at the onset of rainfall or irrigation. Many mathematical models used to estimate sorptivity are only valid for dry soils. This paper examines how sorptivity and its capillary component (as wetting front potential) change with initial degree of saturation. We capture these effects with a simple modification to the classic Green-Ampt model of sorptivity. The modified model has practical applications, including (1) accurately describing the relative sorptivity of a soil at various water contents and (2) allowing for quantification of a soil's saturated hydraulic conductivity from sorptivity measurements, given estimates of the soil's characteristic curve and initial water content. The latter application is particularly useful in soils of low permeability, where the time required to estimate hydraulic conductivity through steady-state methods can be impractical.