Comparison of direct and indirect soil organic carbon prediction at farm field scale

A - Papers appearing in refereed journals

Segura, C., Neal, A. L., Castro-Sardina, L., Harris, P., Rivero, M. J., Cardenas, L. M. and Irisarri, G. 2024. Comparison of direct and indirect soil organic carbon prediction at farm field scale. Journal of Environmental Management. 365 (August), p. 121573. https://doi.org/10.1016/j.jenvman.2024.121573

AuthorsSegura, C., Neal, A. L., Castro-Sardina, L., Harris, P., Rivero, M. J., Cardenas, L. M. and Irisarri, G.
Abstract

To advance sustainable and resilient agricultural management policies, especially during land use changes, it is imperative to monitor, report, and verify soil organic carbon (SOC) content rigorously to inform its stock. However, conventional methods often entail challenging, time-consuming, and costly direct soil measurements. Integrating data from long-term experiments (LTEs) with freely available remote sensing (RS) techniques presents exciting prospects for assessing SOC temporal and spatial change. The objective of this study was to develop a low-cost, field-based statistical model that could be used as a decision-making aid to understand the temporal and spatial variation of SOC content in temperate farmland under different land use and management. A ten-year dataset from the North Wyke Farm Platform, a 20-field, LTE system established in southwestern England in 2010, was used as a case study in conjunction with an RS dataset. Linear, additive and mixed regression models were compared for predicting SOC content based upon combinations of environmental variables that are freely accessible (termed open) and those that require direct measurement or farmer questionnaires (termed closed). These included an RS-derived Ecosystem Services Provision Index (ESPI), topography (slope, aspect), weather (temperature, precipitation), soil (soil units, total nitrogen [TN], pH), and field management practices. Additive models (specifically Generalised Additive Models (GAMs)) were found to be the most effective at predicting space-time SOC variability. When the combined open and closed factors (excluding TN) were considered, significant predictors of SOC were: management related to ploughing being the most important predictor, soil unit (class), aspect, and temperature (GAM fit with a normalised RMSE = 9.1%, equivalent to 0.4% of SOC content). The relative strength of the best-fitting GAM with open data only, which included ESPI, aspect, and slope (normalised RMSE = 13.0%, equivalent to 0.6% of SOC content), suggested that this more practical and cost-effective model enables sufficiently accurate prediction of SOC.

KeywordsGrazing grasslands; Arable Land; Land use change; Ecosystem Services Provision Index; Remote sensing; Topography; Temperate climate; Open data; Farm management
Year of Publication2024
JournalJournal of Environmental Management
Journal citation365 (August), p. 121573
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jenvman.2024.121573
Web address (URL)https://www.sciencedirect.com/science/article/pii/S0301479724015597
Related Output
Has metadatahttps://doi.org/10.23637/rothamsted.98y60
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Natural Environment Research Council
Funder project or codeS2N - Soil to Nutrition - Work package 2 (WP2) - Adaptive management systems for improved efficiency and nutritional quality
S2N - Soil to Nutrition - Work package 1 (WP1) - Optimising nutrient flows and pools in the soil-plant-biota system
Growing Health [ISP]
Resilient Farming Futures
The North Wyke Farm Platform- National Capability [2023-28]
AgZero+
Growing Health (WP2) - bio-inspired solutions for healthier agroecosystems: Understanding soil environments
Growing Health (WP3) - bio-inspired solutions for healthier agroecosystems: Discovery landscapes
Output statusPublished
Publication dates
Online26 Jun 2024
Publication process dates
Accepted19 Jun 2024
ISSN0301-4797
PublisherElsevier

Permalink - https://repository.rothamsted.ac.uk/item/98yqq/comparison-of-direct-and-indirect-soil-organic-carbon-prediction-at-farm-field-scale

Restricted files

Publisher's version

Under embargo indefinitely

Accepted author manuscript

Under embargo indefinitely

Supplemental file

Under embargo indefinitely

42 total views
1 total downloads
1 views this month
0 downloads this month