4.7 Article

A calculator to quantify cover crop effects on soil health and productivity

期刊

SOIL & TILLAGE RESEARCH
卷 199, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.still.2020.104575

关键词

Conservation agriculture; Soil quality; Meta-Analysis

资金

  1. U.S. Department of Agriculture NRCS Conservation Innovation Grant [69-3A75-14-260]
  2. Virginia Agricultural Experiment Station
  3. Hatch Program of the National Institute of Food and Agriculture, U.S. Department of Agriculture
  4. US Department of Energy, Office of Science, Biological and Environmental Research as part of the Terrestrial Ecosystem Sciences Program

向作者/读者索取更多资源

Many producers use cover crops as a means to increase soil health and agricultural productivity, yet benefits of this practice vary depending on environmental and management conditions. In an effort to objectively evaluate how cover crops affect soil properties and crop production across climates and systems, we compiled data from 269 studies that compared cover crop treatments versus no cover crop controls. We then used t-tests and unbalanced analysis of variation tests to evaluate cover crop-related effects on 38 indicators of soil health and productivity. The t-test analysis indicated that cover cropping caused significant changes in 28 of 38 indicators, with differences seen for all physical parameters and most indicators associated with biological and environmental measurements. The unbalanced analysis of variation test allowed us to identify a hierarchy of most to least important environment and management factors for each indicator. Using this hierarchy, we developed a calculator that allows users to evaluate how cover crop usage affects 13 key indicators, including cash crop yield, weed pressure, soil aggregate stability, soil organic carbon, soil nitrogen, and infiltration rates. The calculator requires only four inputs - climatic region, soil texture group, cash crop rotation, and cover crop type - and as output provides mean percent change for each indicator based on the selected factors. The analyses produced in this study provide new insight into specific soil health and productivity responses to cover cropping, and the corresponding web-based calculator will help to ensure that soil health measurements from the field and laboratory are useful to farmers, planners, and regulators. Further, as more data becomes integrated into the calculator, results will continue to improve in accuracy and realism, ultimately helping to make soil health evaluation a practical outcome for more producers.

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