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Sensitivity and variability of soil health indicators in a California cropping system
Author(s) -
Lazicki Patricia,
Mazza Rodrigues Jorge L.,
Geisseler Daniel
Publication year - 2021
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.1002/saj2.20278
Subject(s) - environmental science , cropping , mediterranean climate , crop rotation , agriculture , cropping system , sampling (signal processing) , agronomy , crop , biology , ecology , filter (signal processing) , computer science , computer vision
Abstract An indicator that is used to monitor whether a management practice is improving soil health must be sensitive to management changes. However, it should not be overly influenced by variations in sampling time or location, previous crop, or annual differences in weather or operations timing. In this study, we assessed the sensitivity and variability of several soil health indicators in long‐term plots under typical farming practices in a Mediterranean climate. These plots have been conventionally or organically farmed in a corn ( Zea mays L.)–processing tomato ( Solanum lycopersicum L.) rotation for 25 yr. We sampled in both crop phases prior to planting and midseason for two consecutive years, analyzing subsamples taken from three adjacent locations per plot. Management was the most significant factor differentiating most indicators, particularly indicators of biological processes and C accumulation. Whereas management differences were consistent across sampling times, average indicator values for a management system often varied significantly between dates and years. Crop phases, conversely, were usually similar. Accounting for soil texture increased management sensitivity for aggregate stability and most C accumulation indicators. Sensitive indicators such as mineral N, particulate organic matter C, and mineralizable C had greater subsample variability than indicators measuring large, stable pools, such as total C. Our results show that indicators relating to organic C and biological processes most strongly differentiated the two systems, and underline the importance of using consistent sampling dates. They also suggest that an indicator dataset including both stable and sensitive indicators may be the most reliable to interpret.