Soil health spatial-temporal variation influence soil security on Midwestern, U.S. farms

Soil Security

Kristen Veum¹  Matt A. Yost¹  Jeanette Norton²  Grant Cardon³  Maria Bowman⁴  Bradley S. Crookston⁴ 

[1] Corresponding authors.;USDA-Agriculture Research Service, Cropping Systems and Water Quality Research Unit, 269 Agricultural Engineering Bldg., University of Missouri, Columbia, MO 65211, United States;USDA-Economic Research Service 1400 Independence Ave., SW Mail Stop 1800 Washington, DC 20250-0002;Utah State University, 4820 Old Main Hill, Logan UT 84322, United States;

关键词: Soil security;    Soil health;    Indicators;    Variation;   

DOI  :  

来源: DOAJ

【 摘 要 】

Soil security is a multifaceted framework that considers soil as an integral part of addressing societal concerns towards global environmental challenges. Soil health assessments are tools that can be used to integrate knowledge about and social interest in soil resource sustainability. Appropriate interpretation of soil health assessments require robust databases of soil properties and their variation across large regional areas. This analysis explored field-scale spatial and temporal variation in 16 soil health indicators used in common soil health assessments at Soil Health Partnership (SHP) locations throughout the Midwestern U.S. from 2014–2019. Relationships among management, environment, and measured soil properties were examined using various combinations of correlation, principal component analysis (PCA), and multiple regression. Specifically, variability was evaluated using 1) the temporal average of indicator lab test values, 2) the temporal and spatial coefficient of variation (CV), and 3) corn (Zea mays) and soybean (Glycine max) yield variation. Solvita® had the highest spatial and temporal CV, while organic matter (OM), autoclaved citrate extractable protein (ACE), and pH had the lowest spatial and temporal CV values. The PCA analysis identified climate, soil texture, organic C and N pools, and soil water availability as factors that accounted for variation in soil health indicator values. Multiple regression showed that climate variables and field conditions strongly affect corn and soybean yield variation. Solvita, OM, and available water content improved corn and soybean yield variation estimates. These results show that considering spatial and temporal variation when monitoring soil health changes may improve soil health assessment interpretation.

【 授权许可】

Unknown