An Approach for Simulating Soil Loss from an Agro-Ecosystem Using Multi-Agent Simulation: A Case Study for Semi-Arid Ghana

Abstract

Soil loss is not limited to change from forest or woodland to other land uses/covers. It may occur when there is agricultural land-use/cover modification or conversion. Soil loss may influence loss of carbon from the soil, hence implication on greenhouse gas emission. Changing land use could be considered actually or potentially successful in adapting to climate change, or may be considered maladaptation if it creates environmental degradation. In semi-arid northern Ghana, changing agricultural practices have been identified amongst other climate variability and climate change adaptation measures. Similarly, some of the policies aimed at improving farm household resilience toward climate change impact might necessitate land use change. The heterogeneity of farm household (agents) cannot be ignored when addressing land use/cover change issues, especially when livelihood is dependent on land. This paper therefore presents an approach for simulating soil loss from an agro-ecosystem using multi-agent simulation (MAS). We adapted a universal soil loss equation as a soil loss sub-model in the Vea-LUDAS model (a MAS model). Furthermore, for a 20-year simulation period, we presented the impact of agricultural land-use adaptation OPEN ACCESSLand 2015, 4 608 strategy (maize cultivation credit i.e., maize credit scenario) on soil loss and compared it with the baseline scenario i.e., business-as-usual. Adoption of maize as influenced by maize cultivation credit significantly influenced agricultural land-use change in the study area. Although there was no significant difference in the soil loss under the tested scenarios, the incorporation of human decision-making in a temporal manner allowed us to view patterns that cannot be seen in single step modeling. The study shows that opening up cropland on soil with a high erosion risk has implications for soil loss. Hence, effective measures should be put in place to prevent the opening up of lands that have high erosion risk.

Behavioral models of climate change adaptation and mitigation in land‐based sectors

“…Though less common, a number of models consider competition between agriculture and other land uses such as forestry 53,68,74,75 or urban development 85 . Most agricultural models allow decision‐making on the basis of direct climatic impacts, but several (especially cross‐sectoral models) simulate responses to taxes or subsidies designed to encourage adaptation or mitigation 49,59,66,67,71,74,79,80 . These decisions tend not to be subject to social pressures, except in studies of communal or marginal agriculture (e.g., Ref ) or of water resource management 36,54,60,84 …”

“…Where subsistence land uses are considered, they are usually treated as closely related to social structure, highlighting interactions between climate change, social change, and land management decisions 56–58 . These interactions are complex and explorations of them have been limited, however, with some models of marginal agriculture dealing only with individual behavior 37,49,69 …”

“…This is regularly acknowledged in modeling papers, but is also used to excuse a subsequent neglect of institutional actors and effects. In some cases, it is explicitly argued that scenarios of institutional interventions are more transparent and appropriate than attempts to model institutional behavior (e.g., Ref ), and this certainly seems a logical minimum requirement for models intended to evaluate policy options 49,53,59,63,66–68,79,99 …”

“…Social and behavioral factors of the kinds discussed above have generally acknowledged importance in climate change adaptation and mitigation, and this is reflected by the inclusion of at least one of these factors in the majority of relevant models. However, while these factors often inform the decisions made by model agents, their exact roles and importance varies widely and often without detailed justification, except where determined by data availability (e.g., Refs , , and ). Furthermore, despite the explicitly behavioral focus of models included in this review, the majority assume some form of economic rationality as the basis for land management decisions.…”

“…85 Most agricultural models allow decision-making on the basis of direct climatic impacts, but several (especially cross-sectoral models) simulate responses to taxes or subsidies designed to encourage adaptation or mitigation. 49,59,66,67,71,74,79,80 These decisions tend not to be subject to social pressures, except in studies of communal or marginal agriculture (e.g., Ref 57) or of water resource management. 36,54,60,84 Forestry Generally, models of the forestry sector are not as numerous as models of agriculture, and this imbalance is also present in models that consider climate change and, especially, human behavior.…”