Crop and soil organic matter simulation models – A brief review of their basic features and application in sub-Saharan Africa

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

The Impact of Treatment of Organic Manures on Future Soil Carbon Sequestration Under Different Tillage Systems in Pakistan

Funds provided by Higher Education Commission (HEC) of Pakistan for carrying out this Ph.D. research work under “Indigenous 5000 Fellowship Program” and “International Research Support Initiative Program” are highly acknowledged.Peer reviewedPublisher PD

Simulating microbial degradation of organic matter in a simple porous system using the 3-D diffusion-based model MOSAIC

This paper deals with the simulation of microbial degradation of organic matter in soil within the pore space at a microscopic scale. Pore space was analysed with micro-computed tomography and described using a sphere network coming from a geometrical modelling algorithm. The biological model was improved regarding previous work in order to include the transformation of dissolved organic compounds and diffusion processes. We tested our model using experimental results of a simple substrate decomposition experiment (fructose) within a simple medium (sand) in the presence of different bacterial strains. Separate incubations were carried out in microcosms using five different bacterial communities at two different water potentials of −10 and −100 cm of water. We calibrated the biological parameters by means of experimental data obtained at high water content, and we tested the model without changing any parameters at low water content. Same as for the experimental data, our simulation results showed that the decrease in water content caused a decrease of mineralization rate. The model was able to simulate the decrease of connectivity between substrate and microorganism due the decrease of water content

How do agroforest trees affect the supply of regulating ecosystem services?

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What is the potential for biogas digesters to improve soil carbon sequestration in Sub-Saharan Africa? Comparison with other uses of organic residues

Acknowledgments We are very grateful to the UK Department for International Development (DFID) New and Emerging Technologies Research Call for funding this work. PS is a Royal Society-Wolfson Research Merit Award holder.Peer reviewedPostprin

Evaluation of Parametric Limitations in Simulating Greenhouse Gas Fluxes from Irish Arable Soils Using Three Process-Based Models

The senior author gratefully acknowledges the funding by the Science, Technology, Research and Innovation for the Environment (STRIVE) Programme of the Irish Government under the National Development Plan 2007-2013 and the Department of the Environment, Heritage and Local Government. The authors would like to thanks Phillip O’Brien (EPA) for extending technical and relevant support; Mike Williams, Mike Jones and Matt Saunders (TCD), Komsan Rueangritsarakul and Mohamed Helmy (UCD) for supplying experimental data for modelling work; as well as Tom Bolger and Tommy Gallagher (UCD) for providing administrative support.Peer reviewe

Using EcoYieldSAFE to compare soil carbon dynamics under future climate in two contrasting agroforestry systems

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MODELING REGIONAL ALTERNATIVE MANAGEMENT SCENARIOS WITH FUTURE CLIMATIC CHANGE INFLUENCE ACCOUNTING

The methodology of construction of the alternative agricultural production scenarios at regional level includes profitability and feasibility analysis based on assessment the effect of global climate change on productivity parameters for the main agricultural crops, cost efficiency of crop growing and cattle breeding. To propose links between economic adaptation to climate change and carbon (organic C) stock management in agricultural ecosystems for use in developing long-term adoption strategies at regional level, the regional economic-mathematical model was elaborated. It allows us to unify soil C driving variables and human environment factors.Regional Economic-Mathematical Model, Regional Alternative Management Scenarios, Climate change, Linear Programming Task, Model of humus balance, European Russia, Crop Production/Industries, Environmental Economics and Policy, Livestock Production/Industries, Research Methods/ Statistical Methods,

Direct soil moisture controls of future global soil carbon changes: An important source of uncertainty

The nature of the climate–carbon cycle feedback depends critically on the response of soil carbon to climate, including changes in moisture. However, soil moisture–carbon feedback responses have not been investigated thoroughly. Uncertainty in the response of soil carbon to soil moisture changes could arise from uncertainty in the relationship between soil moisture and heterotrophic respiration. We used twelve soil moisture–respiration functions (SMRFs) with a soil carbon model (RothC) and data from a coupled climate–carbon cycle general circulation model to investigate the impact of direct heterotrophic respiration dependence on soil moisture on the climate carbon cycle feedback. Global changes in soil moisture acted to oppose temperature‐driven decreases in soil carbon and hence tended to increase soil carbon storage. We found considerable uncertainty in soil carbon changes due to the response of soil respiration to soil moisture. The use of different SMRFs resulted in both large losses and small gains in future global soil carbon stocks, whether considering all climate forcings or only moisture changes. Regionally, the greatest range in soil carbon changes across SMRFs was found where the largest soil carbon changes occurred. Further research is needed to constrain the soil moisture–respiration relationship and thus reduce uncertainty in climate–carbon cycle feedbacks. There may also be considerable uncertainty in the regional responses of soil carbon to soil moisture changes since climate model predictions of regional soil moisture changes are less coherent than temperature changes

Evaluation of the ECOSSE model to predict heterotrophic soil respiration by direct measurements

Acknowledgements This work contributes to the ELUM (Ecosystem Land Use Modelling & Soil Carbon GHG Flux Trial) project, which was commissioned and funded by the Energy Technologies Institute (ETI), and to Carbo-BioCrop (http://www.carbobiocrop.ac.uk; a NERC funded project; NE/H010742/1), UKERC Phase II and III (NERC; NE/H013237/1), MAGLUE (http://www.maglue.ac.uk; an EPSRC funded project; EP/M013200/1) and as part of the Seventh Framework For Research Programme of the EU, within the EUROCHAR project (N 265179) and EXPEER within WU FP7-Infrastructures. We acknowledge the use of the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu). We thank two anonymous reviewers and Dr William van Dijk for their valuable suggestions.Peer reviewedPostprin