Crop and soil organic matter simulation models – A brief review of their basic features and application in sub-Saharan Africa
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Nyawira, S.S. (2019). Crop and soil organic matter simulation models – A brief review of their basic features and application in sub-Saharan Africa. Research Brief. International Center for Tropical Agriculture, Nairobi, Kenya. 10 p.
Permanent link to cite or share this item: https://hdl.handle.net/10568/107769
Over the past decades, numerous crop-soil models have been developed to represent dynamic processes in cropland systems, including soil organic carbon (SOC) dynamics (Campbell and Paustian, 2015). These models use mathematical equations that determine carbon allocation in the vegetation and biomass and soils to represent biogeochemical processes, such as photosynthesis, respiration and decomposition. Furthermore, a range of crop management practices are represented in most of the models, enabling an assessment of their impacts on SOC in agricultural systems. Although models were initially developed for research purposes, they are increasingly becoming important in many aspects of environmental policies (Manlay et al., 2007). Extensively tested models provide effective tools that can be used in identifying sustainable land management practices across different agroecological conditions. Compared to field experiments, which are time and resource consuming, models are more effective for making predictions and understanding crop and SOC dynamics on large scales and different time scales. However, the choice of the model depends on the ability of the model to simulate key processes in the region of interest. We conducted a survey to identify the features of the commonly used crop-soil models in order to inform the choices for application in sub-Saharan Africa. The survey was administered online to the model developers. In addition, we also conducted a literature search to assess the usage of the different models in different parts of sub-Saharan. In this brief, we provide a basic summary of the information from the survey and literature review.
CGIAR Author ORCID iDs
Sylvia Sarah Nyawirahttps://orcid.org/0000-0003-4913-1389