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A Bayesian belief network framework to predict SOC dynamics of alternative management scenarios
- Dal Ferro, N., Quinn, C., Morari, F.
- Soil & tillage research 2018 v.179 pp. 114-124
- agricultural conservation practice, climate change, cropland, disturbed soils, economic factors, emissions, grasslands, greenhouse gases, mineral soils, models, no-tillage, prediction, soil organic carbon, Italy
- Understanding the key drivers that affect a decline of soil organic carbon (SOC) stock in agricultural areas is of major concern since leading to a decline in service provision from soils and potentially carbon release into the atmosphere. Despite an increasing attention is given to SOC depletion and degradation processes, SOC dynamics are far from being completely understood because they occur in the long term and are the result of a complex interaction between management and pedo-climatic factors. In order to improve our understanding of SOC reduction phenomena in the mineral soils of Veneto region, this study aimed to adopt an innovative probabilistic Bayesian belief network (BBN) framework to model SOC dynamics and identify management scenarios that maximise its accumulation and minimise GHG emissions.Results showed that the constructed BBN framework was able to describe SOC dynamics of the Veneto region, predicting probabilities of general accumulation (11.0%) and depletion (55.0%), similar to those already measured in field studies (15.3% and 50%, respectively). A general enhancement in the SOC content was observed where a minimum soil disturbance was adopted. This outcome suggested that management strategies of conversion from croplands to grasslands, no tillage and conservation agriculture are the most promising management strategies to reverse existing SOC reduction dynamics. Moreover, measures implying SOC stocks were also those providing major benefits in terms of GHGs reduction emissions. Finally, climate change scenarios slightly affected management practice. Advancements in our BBN framework might include more detailed classes at higher resolution as well as any socio-cultural or economic aspect that should improve the evaluation of prediction scenarios.