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A Bayesian modeling framework for estimating equilibrium soil organic C sequestration in agroforestry systems

Menichetti, Lorenzo and Kätterer, Thomas and Bolinder, Martin (2020). A Bayesian modeling framework for estimating equilibrium soil organic C sequestration in agroforestry systems. Agriculture, Ecosystems and Environment. 303 , 107118
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Abstract

Agroforestry is a form of productive land management that combines trees or bushes with annual crops or pasture, and it can bring benefits in terms of food security and increased carbon (C) sequestration compared with conventional agriculture. But agroforestry as a structured form of agronomic management is relatively new compared with well-established and widespread agronomic systems. Consequently, there is a lack of data and few models of soil organic carbon (SOC) have been developed specifically for agroforestry systems. Also, agroforestry SOC sequestration data measured in field experiments are often reported only as average linear sequestration rates over the study period. This approach, equivalent to zero-order kinetics, makes it difficult to compare results since, in reality, SOC sequestration rates are variable over time and change depending on the duration of measurements. Sequestration rates are also strongly dependent on former C stocks in the soil, further hampering comparisons between agroforestry systems established on different former land uses.To describe the SOC stocks variation over time, researchers often employ models considering at least first order kinetics. This approach can take care of the two above mentioned issues, considering both the variation of the sequestration over time and the effect of previous land use. However, the variability of agroforestry systems makes applying these models more challenging compared to simpler agricultural systems. To deal with this problem we propose to use detailed uncertainty estimation methods, based on stochastic calibrations that can deal with broad probability distributions.To do so, we adapted a first-order compartmental SOC model to agroforestry systems. It was calibrated within a Bayesian framework on global agroforestry data. Compared to linear coefficients, the model (ICBMAgroforestry) estimates equilibrium SOC stocks of different agroforestry systems probabilistically and is providing uncertainty bounds. These values are independent of initial land use and time duration of the experiments. ICBMAgroforestry can be used for rapid assessment and comparison of the maximum potential SOC stocks for different agroforestry systems and climatic zones. In this study, we could use our approach to estimate the global maximum C that can be sequestered by agroforestry systems at equilibrium, which ranged between 156 and 263 Mg C ha(-1) on average, above but comparable with similar estimates for simpler agricultural systems.

Authors/Creators:Menichetti, Lorenzo and Kätterer, Thomas and Bolinder, Martin
Title:A Bayesian modeling framework for estimating equilibrium soil organic C sequestration in agroforestry systems
Year of publishing :2020
Volume:303
Article number:107118
Number of Pages:11
ISSN:0167-8809
Language:English
Publication Type:Journal article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 106 Biological Sciences (Medical to be 3 and Agricultural to be 4) > Ecology
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
Keywords:Agroforestry, First-order kinetic, SOC equilibrium, SOC modeling, Uncertainty, Bayesian statistics
URN:NBN:urn:nbn:se:slu:epsilon-p-108032
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-108032
Additional ID:
Type of IDID
DOI10.1016/j.agee.2020.107118
Web of Science (WoS)000569778400005
ID Code:17786
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Ecology
(S) > Dept. of Ecology
Deposited By: SLUpub Connector
Deposited On:13 Oct 2020 07:00
Metadata Last Modified:13 Oct 2020 07:01

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