An identified agronomic interpretation for potassium permanganate oxidizable carbon

Authors: SVEDIN, JEFFREY - University Of Missouri; Veum, Kristen; Ransom, Curtis; Kitchen, Newell; ANDERSON, STEPHEN - University Of Missouri

Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/15/2022
Publication Date: 11/23/2022
Citation: Svedin, J., Veum, K.S., Ransom, C.J., Kitchen, N.R., Anderson, S.H. 2022. An identified agronomic interpretation for potassium permanganate oxidizable carbon. Soil Science Society of America Journal. 87(2):291-308. https://doi.org/10.1002/saj2.20499.
DOI: https://doi.org/10.1002/saj2.20499

Interpretive Summary: Linking soil health measurements to agronomic outcomes is essential for widespread adoption of soil health assessments by producers. This research investigated the relationships between soil health indicators and corn productivity to develop interpretive benchmarks for soil health indicators in Missouri. Corn grain yield, soil health, and soil fertility data was collected from 446 monitoring sites in 84 commercial production fields. Soil and weather information was also collected to account for site-specific conditions. Models were developed using traditional statistical approaches and advanced machine learning approaches. Model performance was significantly better using machine learning algorithms over the traditional approaches. Rainfall and a soil health indicator known as active carbon were identified as top factors governing grain productivity in each modelling approach. In addition, a threshold level of active carbon was identified for optimal corn grain productivity. These results provide a production-oriented framework for interpretation of a soil health indicator, benefitting scientists and producers seeking to optimize agricultural productivity and maintain soil health.

Technical Abstract: The absence of clear empirical relationships between soil health and agronomic outcomes remains an obstacle to widespread adoption of soil health assessments in row crop systems. The objectives of this research were 1) determine whether soil health indicators are connected to corn (Zea mays L.) productivity, and 2) establish interpretive benchmarks for soil health indicators in Missouri. The objectives were accomplished by collecting corn grain yield at 446 monitoring sites (37 m2) in 84 commercial production fields in 2018-2020. Soil health and soil fertility samples were collected prior to planting at each site. This data, along with site-specific soil and weather data, were modeled using traditional stepwise regression and nonparametric random forest (RF) and conditional inference forest (CIF) approaches. Root-mean-square-errors were similar (1.4-1.5 Mg ha-1) with distinct R2 improvements over stepwise regression for both CIF (R2 = 0.45) and RF (R2 = 0.46) algorithms. Only seasonal rainfall and potassium permanganate oxidizable carbon (POXC) were included as top factors governing grain productivity in each model approach, thus demonstrating a regionally robust empirical relationship between POXC and grain productivity. Partial dependency analysis and two decision tree approaches identified 415 mg POXC kg-1 as a threshold for optimal grain productivity, providing a framework for regional interpretation of on-farm soil health assessments. Little evidence was found connecting grain productivity with autoclaved citrate extractable protein and soil respiration. These findings underscore the power of POXC as an emerging SH indicator to assess and quantify soil management effects on grain productivity.