Abstract. A future of increasing atmospheric carbon dioxide concentrations, changing climate, growing human populations, and shifting socioeconomic conditions means that the global agricultural system will need to adapt in order to feed the world. These changes will affect not only agricultural land but terrestrial ecosystems in general. Here, we use the coupled land use and vegetation model LandSyMM (Land System Modular Model) to quantify future land use change (LUC) and resulting impacts on ecosystem service indicators relating to carbon sequestration, runoff, biodiversity, and nitrogen pollution. We additionally hold certain variables, such as climate or land use, constant to assess the relative contribution of different drivers to the projected impacts. Some ecosystem services depend critically on land use and management: for example, carbon storage, the gain in which is more than 2.5 times higher in a low-LUC scenario (Shared Socioeconomic Pathway 4 and Representative Concentration Pathway 6.0; SSP4-60) than a high-LUC one with the same carbon dioxide and climate trajectory (SSP3-60). Other trends are mostly dominated by the direct effects of climate change and carbon dioxide increase. For example, in those two scenarios, extreme high monthly runoff increases across 54 % and 53 % of land, respectively, with a mean increase of 23 % in both. Scenarios in which climate change mitigation is more difficult (SSPs 3 and 5) have the strongest impacts on ecosystem service indicators, such as a loss of 13 %–19 % of land in biodiversity hotspots and a 28 % increase in nitrogen pollution. Evaluating a suite of ecosystem service indicators across scenarios enables the identification of tradeoffs and co-benefits associated with different climate change mitigation and adaptation strategies and socioeconomic developments.
Abstract. Biochar application in croplands aims to sequester carbon and improve soil quality, but its impact on soil organic carbon (SOC) dynamics is not represented in most land models used for assessing land-based climate mitigation, therefore we are unable to quantify the effect of biochar applications under different climate conditions or land management. To fill this gap, here we implemented a submodel to represent biochar into a microbial decomposition model named MIMICS (MIcrobial-MIneral Carbon Stabilization). We first calibrate MIMICS with new representations of density-dependent microbial turnover rate, adsorption of available organic carbon on mineral soil particles, and soil moisture effects on decomposition using global field measured cropland SOC at 58 sites. The calibration of MIMICS leads to an increase in explained spatial variation of SOC from 38 % in the default version to 47 %–52 % in the updated model with new representations. We further integrate biochar in MIMICS resolving its effect on microbial decomposition and SOC sorption/desorption and optimize two biochar-related parameters in these processes using 134 paired SOC measurements with and without biochar addition. The MIMICS-biochar version can generally reproduce the short-term (≤ 6 yr) and long-term (8 yr) SOC changes after adding biochar (mean addition rate: 25.6 t ha-1) (R2 = 0.65 and 0.84) with a low root mean square error (RMSE = 3.61 and 3.31 g kg-1). Our study incorporates sorption and soil moisture processes into MIMICS and extends its capacity to simulate biochar decomposition, providing a useful tool to couple with dynamic land models to evaluate the effectiveness of biochar applications on removing CO2 from the atmosphere.
Abstract Climate change, growing populations and economic shocks are adding pressure on the global agricultural system’s ability to feed the world. In addition to curbing the emissions from fossil fuel use, land-based actions are seen as essential in the effort to mitigate climate change, but these tend to reduce areas available for food production, thereby further increasing this pressure. The actors of the food system have the capacity to respond and adapt to changes in climate, and thereby reduce the negative consequences, while potentially creating additional challenges, including further greenhouse gas emissions. The food system actors may respond autonomously based on economic drivers and other factors to adapt to climate change, whereas policy measures are usually needed for mitigation actions to be implemented. Much research and policy focus has been given to land-based climate change mitigation, but far less emphasis has to date been given to the understanding of adaptation, or the interaction between adaptation and mitigation in the land use and food system. Here, we present an approach to better understand and plan these interactions through modelling. Climate change adaptation and mitigation strategies and the impacts on the global food system and socio-economic development can be simulated over long-term predictions, thanks to the new combination of multiple models into the Land System Modular Model (LandSyMM). LandSyMM takes into account the impacts in changes in climate (i.e. temperature, precipitation, atmospheric greenhouse gas concentrations) and land management on crop yields with its implications for land allocation, food security and trade. This new coupled model integrates, over fine spatial scale, the interactions between commodities consumption, land use management, vegetation and climate into a worldwide dynamic economic system. This study offers an outline description of the LandSyMM as well as the perspectives of uses for climate adaptation assessment.
<p>Forest fires in some regions have intensified over recent decades due to climate change. This trend threatens ecosystems (habitat and biodiversity loss), human health (particulate-matter pollution, smoke), property (burned urban areas, burned forestry yields, monetary loss), and potentially climate mitigation goals (rising carbon dioxide levels, possibly decreased land carbon sink).</p><p>Here, we investigate whether forest management can reduce future impacts of forest fire and help to control fire regimes in the future. We are using the process-based dynamic global vegetation model LPJ-GUESS with the fire module SIMFIRE-BLAZE to explore this question. The analyzed treatments compare a non-managed stand with stands receiving thinning, prescribed burning, or both. We focus on two regions: The Iberian Peninsula (due to its long history of burning) and Eastern Europe (which may become more fire-prone in the future). Results are compared between CMIP6 scenarios of low-intensity vs. high-intensity climate change (RCPs 2.6 and 8.5, respectively).</p><p>The results show that prescribed fire raises the amount of burned area but possibly not the property risk because fire line intensities are mitigated; thinning can reduce the amount of prescribed fire required. Thinning reduces fire emissions whereas prescribed burning is the other way around, which could contribute to health and climate risks caused by particulate-matter-pollution. Managements do not seem to have effects on the carbon balance according to end of the century carbon pools, which implies that they do not actively help achieve climate mitigation goals.</p>
This dataset consists of a netCDF file with a number of layers at half-degree global resolution. Each layer is a binary map representing whether each gridcell is included (1 if yes, 0 if no) in one or more input datasets used in the ISIMIP3 Agriculture (GGCMI phase 3) model runs. Individual-dataset masks: has_soil indicates inclusion in the ISIMIP3 soil input dataset (Volkholz & Müller, 2020) (ignoring "gravel," which has some missing cells). has_cropcals indicates inclusion in the Jägermeyr et al. (publication in prep.) crop calendar dataset (ignoring second-season rice, which is not grown in all gridcells). has_lu indicates inclusion in all 15 area maps in the historical land use area dataset prepared for ISIMIP3 (landuse-totals_histsoc_annual_1850_2014.nc). has_crops indicates inclusion in all 15 area maps in the historical 15-crop dataset prepared for ISIMIP3 (landuse-15crops_histsoc_annual_1850_2014.nc). Note that there are two gridcells that are missing from this has_fertilizer indicates inclusion in every fertilizer_application_histsoc*.nc file in the fertilizer and manure dataset prepared by Heinke et al. (2021) for GGCMI3. has_all is a composite mask indicating inclusion in all of the above. Also included are a figure showing the masks and the MATLAB script used to generate the data and figure.
Abstract Global food prices are rising rapidly in response to a dramatic increase in global energy prices and the Ukraine-Russia war, causing severe impacts on the world’s poorest people. The FAO Food Price Index increased by 23% from May 2021 to May 2022 and the Cereals Price Index increased by 30%. Sanctions or blockades that restrict exports from Russia or Ukraine – two of the world’s most important food-exporting countries – are exacerbated by energy price rises which further increase food prices through higher costs for agricultural inputs, such as fertiliser. We consider the role of increasing agricultural input costs and the curtailment of exports from Russia and Ukraine to quantify the potential outcomes on human health and the environment. We show that the combination of agricultural inputs costs and food export restrictions could increase food costs by 60-100% in 2023 from 2021 levels, leading to the deaths of 400 thousand to 1 million additional people in 2023 and undernourishment of 60-110 million people. Furthermore, a reduction in land use intensification, arising from higher input costs, leads to agricultural land expansion, especially in the tropics, with potential consequences for deforestation and carbon and biodiversity loss. We find that the impact of agricultural input costs on food prices is larger than the effect of the curtailment of exports from Russia and Ukraine. Restoring food trade from Ukraine and Russia alone would be insufficient to substantially reduce the magnitude of the current food insecurity problem. We contend that the immediacy of the food export problems associated with the Ukraine-Russia war has diverted attention away from the principal causes of current global food insecurity, which may hinder the quest to find solutions.
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