Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa

Abstract

Abstract. Dynamic global vegetation models (DGVMs) are important tools for modelling impacts of global change on ecosystem services. However, most models do not take full account of human land management and land use and land cover changes (LULCCs). We integrated croplands and pasture and their management and natural vegetation recovery and succession following cropland abandonment into the LPJ-GUESS DGVM. The revised model was applied to Africa as a case study to investigate the implications of accounting for land use on net ecosystem carbon balance (NECB) and the skill of the model in describing agricultural production and reproducing trends and patterns in vegetation structure and function. The seasonality of modelled monthly fraction of absorbed photosynthetically active radiation (FPAR) was shown to agree well with satellite-inferred normalised difference vegetation index (NDVI). In regions with a large proportion of cropland, the managed land addition improved the FPAR vs. NDVI fit significantly. Modelled 1991-1995 average yields for the seven most important African crops, representing potential optimal yields limited only by climate forcings, were generally higher than reported FAO yields by a factor of 2-6, similar to previous yield gap estimates. Modelled inter-annual yield variations during 1971-2005 generally agreed well with FAO statistics, especially in regions with pronounced climate seasonality. Modelled land-atmosphere carbon fluxes for Africa associated with land use change (0.07 PgC yr −1 release to the atmosphere for the 1980s) agreed well with previous estimates. Cropland management options (residue removal, grass as cover crop) were shown to be important to the landatmosphere carbon flux for the 20th century.

Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework

“…PLUM can reproduce global historic agricultural land use change , and results have demonstrated that agricultural land use is highly sensitive to uncertainties in crop yield growth rates . To estimate the temporal trends and changes in spatial patterns of crop yields in response to climate change, country-level cereal yields were derived from the global dynamic vegetation model LPJ-GUESS (Lindeskog et al, 2013;Smith et al, 2001). LPJ-GUESS accounts for the effects of temperature, precipitation and atmospheric CO 2 concentrations on crop yields and the productivity of natural vegetation.…”

“…The direction of socio-economic drivers is referred to as deep uncertainties, which are addressed through the use of scenarios (van Vuuren et al, 2008). Cropland projections at the high end of the projected uncertainty range for global cropland would have profound consequences for, for example, global carbon and nitrogen fluxes, the global water balance, biodiversity, and other ecosystem services (Lindeskog et al, 2013;Pereira et al, 2012;Zaehle et al, 2007). Hence, quantifying and understanding the inherent uncertainties in the drivers of LULCC has important consequences for policy responses to support sustainable development.…”

“…The RCP-SSP scenario framework was used since it is the most recent scenario approach for global environmental change research O'Neill et al, 2016;van Vuuren et al, 2011van Vuuren et al, , 2014. We apply these scenarios to a global-scale, socio-economic model of agricultural land use change (the Parsimonious Land Use Model, PLUM; Engström et al, 2016) in combination with crop yield time series derived from the dynamic global vegetation model, LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator; Lindeskog et al, 2013;Smith et al, 2001). PLUM has been benchmarked against different models and scenario studies in a land use model intercomparison exercise Prestele et al, 2016) that has demonstrated its consistency in comparison with other global cropland simulations.…”

Modelling feedbacks between human and natural processes in the land system

“…Agricultural and pastoral systems are 10 represented as a prescribed fractional cover of area under human land use per grid cell. Four crop functional types modelled on winter wheat, spring wheat, rice, and maize were used to simulate croplands , with representation of sowing, harvesting, irrigation, fertilisation, tillage, and the use of cover crops (Lindeskog et al, 2013;Olin et al, 2015;Pugh et al, 2015). Pastures are represented by competing C3 and C4 grass, with 50% of the above-ground biomass removed annually to 15 represent the effects of grazing (Lindeskog et al, 2013).…”

“…Four crop functional types modelled on winter wheat, spring wheat, rice, and maize were used to simulate croplands , with representation of sowing, harvesting, irrigation, fertilisation, tillage, and the use of cover crops (Lindeskog et al, 2013;Olin et al, 2015;Pugh et al, 2015). Pastures are represented by competing C3 and C4 grass, with 50% of the above-ground biomass removed annually to 15 represent the effects of grazing (Lindeskog et al, 2013). A general circulation model (GCM) emulator, IMOGEN, provides feedback between the carbon cycle and climate to be investigated without the computational demands of running a full Earth System Model (Huntingford et al, 2010).…”