Displays

Agricultural ecosystem models, driven by climate projections and fed with soil information and plausible management scenarios are frequently used tools to predict future developments in agricultural landscapes. On the regional scale, the required soil parameters must be derived from soil maps that are available in different spatial resolutions, ranging from grid cell sizes of 50 m up to 1 km and more. The typical spatial resolution of regional climate projections is currently around 12 km. Given the small-scale heterogeneity in soil properties, using the most accurate soil representation could be important for predictions of crop growth. However, simulations with very highly resolved soil data requires greater computing time and higher effort for data organization and storage. Moreover, the higher resolution may not necessarily lead to better simulations due to redundant information of the land surface and because the impact of climate forcing could dominate over the effect of soil variability. This leads to the question if the use of high-resolution soil data leads to significantly different predictions of future yields and grain protein trends compared to simulations in which soil data is adapted to the resolution of the climate input.

This study investigated the impact of weather and soil input on simulated crop growth in an intensively used agricultural region in Southwest Germany. For all areas classified as ‘arable land’ (CLC10), winter wheat growth was simulated over a 44-year period (2006 to 2050) using weather projections from three regional climate models and soil information at two spatial resolutions. The simulations were performed with the model system Expert-N 5.0, where the crop model Gecros was combined with the Richards equation and the CN turnover module of the model Daisy. Soil hydraulic parameters as well as initial values of soil organic matter pools were estimated from BK50 soil map information on soil texture and soil organic matter content, using pedo-transfer functions and SOM pool fractionation following Bruun and Jensen (2002). The coarser soil map is derived from BK50 soil map (50m x 50m) by selecting only the dominant soil type in a 12km × 12km grid to be representative for that grid cell. The crop model was calibrated with field data of crop phenology, leaf area, biomass, yield and crop nitrogen, which were collected at a research station within the study area between 2009 and 2018.

The predicted increase in temperatures during the growing season correlated with earlier maturity, lower yields and a higher grain protein content. The regional mean values varied by +/- 0.5 t/ha or +/-0.3 percentage points of protein content depending to the climate model used. On the regional scale, the simulated trends remained unchanged using high-resolution or coarse resolution soil data. However, there are strong differences in both the forecasted averages and the distribution of forecasts, as the coarser resolution captures neither the small-scale heterogeneity nor the average of the high-resolution results.

How to cite: Gayler, S., Bajracharya, R., Weber, T., and Streck, T.: Impact of regional climate model input and soil map resolution on projected winter wheat production in SW Germany, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21698, https://doi.org/10.5194/egusphere-egu2020-21698, 2020.

The pressure on soil and water resources to support the demand for crop production calls for effective water management at the regional scale and a need for regional crop models.

In our study, the field-based Aquacrop v.6.1 is modified to a gridded crop model that is run spatially over the main part of Europe at 1-km resolution.

The gridded model simulates spatially distributed soil moisture, crop biomass and yield, given spatial input of meteorological forcings extracted from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and 1-km soil texture information from the Harmonized World Soil Database v1.2 (HWSD v1.2). For the first model evaluation, a hypothetical and uniform crop is implemented, and field management and irrigation practices are not included. We will present preliminary results over Europe by comparing the spatial soil moisture and biomass simulations with remote sensing data.

This work is part of the SHui project, a H2020 project that aims at improving stakeholder decision-making for water scarcity management in European and Chinese cropping systems.

How to cite: de Roos, S., de Lannoy, G., and Raes, D.: From the field-scale Aquacrop model to a regional gridded crop model: initial evaluation over Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5290, https://doi.org/10.5194/egusphere-egu2020-5290, 2020.

Morocco, by its geographical position and its climate, is strongly affected by climate change and presents an ever-increasing vulnerability. In fact, the country's economy, being very dependent on agriculture, would be greatly affected. It is therefore necessary to further develop knowledge about climate change and strengthen forcasting systems for predicting the impacts of climate change.

The agriculture in Morocco is largely dominated by rainfed crops and therefore dependent on pluviometry.According to the report of the Moroccan Minister of Agriculture (Agriculture En Chiffres version 2019), about 59% of arable land is devoted to cereals, 16% to plantation crops (olives, almonds, citrus, grapes, dates), 5% to forage, 3% to vegetables, 5% to other crops (sugar beets, sugar cane, cotton and oilseeds), and 12% is fallow. In this project we are going to focus on cereals, olives, potatoes and sugar beets. Regarding the climate, Morocco is characterized by a wide variety of topographies ranging from mountains to plains, oasis and Saharan dunes. For this reason, the country experiences diverse climatic conditions with large spatial and intra- and inter-annual variability of precipitation. Morocco faces irregular rain patterns, cold spells and heat waves increasingly resulting in droughts, which significantly affects agriculture.

Our research, funded by a bilateral project of Wallonie-Bruxelles International, aims to study the response of Moroccan agriculture to climate change, using the dynamic vegetation model CARAIB (CARbon Assimilation In the Biosphere) developed within the Unit for Modelling of Climate and Biogeochemical Cycles (UMCCB) of the University of Liège. This spatial model includes crops and natural vegetation and may react dynamically to land use changes. Originally constructed to study vegetation dynamics and carbon cycle, it includes coupled hydrological, biogeochemical, biogeographical and fire modules. These modules respectively describe the exchange of water between the atmosphere, the soil and the vegetation, the photosynthetic production and the evolution of carbon stocks and fluxes in this vegetation-soil system. The biogeographical module describes, for natural vegetation, the establishment, growth, competition, mortality, and regeneration of plant species, as well as the occurrence and propagation of fires. For crops, a specific module describes basic management (sowing, harvest, rotation) and phenological phases.   

Model simulations are performed across north-west Morocco, where the crops activities are important, by using different input data. The timeline of simulations is divided in two periods: past (from 1901 to 2018) and future (from 2019 to 2100). For the past period, we are using high resolution (30 arc sec) gridded climate data derived from WorldClim (climatology) and interpolated anomalies from Climate Research Unit CRU (trend and variability).  For the future period, we use interpolated and bias-corrected fields from a regional climate model (ALADIN-Climate) from the Med-CORDEX initiative run at a spatial resolution of 12 km and for three different Representative Concentration Pathway scenarios (RCP2.6, RCP4.5 and RCP8.5).

How to cite: Loudiyi, I., Jacquemin, I., Tychon, B., Francois, L., Lahlou, M., Wellens, J., and Balaghi, R.: Simulating and analysing climate change impacts on crop yields in Morocco using the CARAIB dynamic vegetation model driven by Med-CORDEX projections, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11350, https://doi.org/10.5194/egusphere-egu2020-11350, 2020.

Future climate projections indicate a northward shift of suitable agricultural areas with a potential intensification of cropping systems in northern Europe, and a decrease in crop productivity in southern Europe mainly due to extreme temperatures and reduction in precipitation and water availability. Furthermore, the uncertainty in projecting climate change impacts on agricultural productivity at the regional scale affects the choice of appropriate adaptation strategies.

The main objective of this study is to assess the potential risk for cereal production and food security in the Euro-Mediterranean area and North Africa due to climate change, integrating multiple factors. Simulations were carried out using the CSM-CERES-Wheat and CSM-CERES-Maize crop models implemented in the DSSAT (Decision Support System for Agrotechnology Transfer) software. A spatially distributed routine integrating DSSAT with large geodatasets characterizing the environment (climate and edaphic) conditions and management options (e.g. agronomic practices, irrigation, fertilization) was applied to perform the simulation of grain yield for durum wheat, common wheat, and maize in each grid cell. An ensemble of climate projections from ISIMIP (Inter-Sectoral Impact Model Intercomparison Project) were used as input to the crop models. The uncertainty and model agreement of projected changes in crop yield, under current and future CO₂ values (according to RCP8.5) were evaluated and new potential and high risk areas for cereal production were identified. Moreover, socio-economic indicators were considered to evaluate the exposure and adaptive capacity of the system and estimate the potential risk for cereal production. The result is a four dimensions single map that combine the selected variable and indices. Results show a potential decrease in cereal production per capita in the west Mediterranean area, North Africa and Turkey. However, the potential risk differ across these regions, according with the adaptive capacity of each area. Results also show a potential increase in cereal production per capita in north eastern Europe. Overall, there is high agreement across models.

How to cite: Mereu, V., Costa Saura, J. M., Trabucco, A., and Spano, D.: Assessment of cereal production and food security under climate change in the Euro-Mediterranean Region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21722, https://doi.org/10.5194/egusphere-egu2020-21722, 2020.

Climate change uncertainty largely complicates adaptation and risk management evaluation at the regional level, therefore new approaches for managing this uncertainty are still being developed. In this study three crop models (DNDC, WOFOST and DSSAT) were used to explore the utility of impact response surfaces (IRS) and adaptation response surfaces (ARS) methodologies (Pirttioja et al., 2015; Ruiz-Ramos et al., 2018).

To build IRS, the sensitivity of modelled yield to systematic increments of changes in temperature (-1 to +6°C) and precipitation (-30 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather. Four levels of CO2 (360, 447, 522 and 601 ppm) representing future conditions until 2070 were considered. In turn, to build ARS, adaptation options were: shortening or extending the crop cycle of the standard cultivar, sowing earlier or later than the standard date and additional irrigation. Preliminary data indicate that yields are declining with higher temperatures and decreased precipitation. Yield is more sensitive to changes in baseline temperature values and much less sensitive to changes in baseline precipitation values for arable fields in Finland, while for arable fields in Germany, ARS indicates yield sensitivity at a similar level for both variables. Also, our data suggests that some adaptation options provides increase of the yield up to 1500 kg/ha, which suggest that ARSs may be valuable tool for planning an effective adaptation treatments. This research shows how to analyze and assess the impact of adaptation strategies in the context of the high level of regional uncertainty in relation to future climate conditions. Developed methodology can be applied to other climatic zones to help in planning adaptation and mitigation strategies.

This study has been partly financed from the funds of the Polish National Centre for Research and Development in frame of the project: MSINiN, contract number: BIOSTRATEG3/343547/8/NCBR/2017

How to cite: Krzyszczak, J., Baranowski, P., and Zubik, M.: Climate change impact evaluation in various regions in Europe on the base of ensemble modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18052, https://doi.org/10.5194/egusphere-egu2020-18052, 2020.

Climate change impacts on agriculture are subject to large uncertainties from a variety of sources. One of the most important sources of uncertainty is the uncertainty in the realization of climate change itself. In the absence of clear climate mitigation strategies and substantial uncertainties on population growth, economic development, technology and lifestyles, a very broad set of greenhouse gas emission scenarios has been developed to inform climate modeling. Climate models often differ in the spatial patterns of projected changes in particular with respect to changes in precipitation. The Coupled Model Intercomparison Project (CMIP5, CMIP6) provides a broad range of future climate change projections.

Crop models are often applied at selected sites or with global coverage, as in the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Global crop model applications have been shown to have some skill, but also add additional uncertainty, given that many processes cannot be calibrated properly for the lack of suitable reference data and because management information is largely absent (Müller et al., 2017).

However, already the computational power required to compute the comprehensive set of climate projections prohibits such applications. Instead, typically, small and largely random selections of climate scenarios are used to project impacts, such as agricultural crop yields. McSweeney and Jones (2016) find that a selection of 5 climate models as often applied, is insufficient to cover the range of projections in all regions.

Here we present initial results of a comprehensive global climate impact assessment for crop yields that explores the full range of the CMIP6 climate projection archive. For this, we use a set of 9 global gridded crop model emulators (Franke et al., 2019b) that were trained on a very large systematic input sensitivity analysis with up to 1404 global-coverage, 31-year simulation data sets per crop and crop model (Franke et al., 2019a). The training domain includes variations in atmospheric carbon dioxide (CO2) concentrations (4 levels from 360 ppm to 810 ppm), air temperature (7 levels from -1 to +6°C), water supply (8 levels from -50 to +30% and full irrigation), nitrogen fertilization (3 levels from 10 to 200 kgN/ha) and adaptation (2 levels: none and regained growing seasons) and thus represents an unprecedented rich data base for emulator training. The emulators, in form of grid-cell specific regression models with 27 coefficients, are computationally light-weight and can thus be applied to the full CMIP6 data archive.

We here present first results from this analysis, breaking down the different sources of uncertainty (emission concentration pathways, climate model, crop model). Results will help to interpret crop model simulations in general: the unstructured reduction of the uncertainty space from selecting a small number of climate scenarios by e.g. first availability and/or individual crop models has so far hampered to quantify the uncertainty in crop model projections.

Franke (2019a) Geoscientific Model Development Discuss, 2019:1-30.

Franke (2019b)  Geoscientific Model Development, submitted

McSweeney & Jones, (2016) Climate Services, 1:24-29.

Müller (2017) Geoscientific Model Development, 10:1403-1422.

How to cite: Müller, C. and the AgMIP GGCMI team: Comprehensive global climate impact assessment for crop yields, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20676, https://doi.org/10.5194/egusphere-egu2020-20676, 2020.

Climate change impact on crop production depends on the cultivated crop and its position within crop rotations and on site conditions, e.g. soils and hydrology, buffering adverse weather situations. We present a regional study across the federal state of Brandenburg/Germany based on gridded climate data and a digital soil map using the HERMES-to-Go model. The aim was to investigate defined crop rotations and common agricultural practices under current and future climate conditions regarding productivity and environmental effects. Two contrasting GCMs (HAD and MPI) were used to generate climate input for modelling for the RCPs 2.6 and 8.5.

5 different types of crop production were simulated by defining crop rotations over 4-5 years for soil quality rating groups. While one rotation is comprised by the most common crops, another rotation modifies the first one by introducing a legume followed by a more demanding crop. The third rotation intends to produce higher value crops, e.g. potatoes than the first one, while the fourth rotation has its focus on fodder grass and cereal production. Building on this the fifth rotation replaces the fodder grass by alfalfa. All rotations are simulated in shifted phases to ensure that each crop is simulated for each year.

Sowing, harvest and nitrogen fertilization were derived by algorithms based on soil and climate information to allow self-adaptation to changing climate conditions. The crop rotations are simulated under rainfed and irrigated conditions and with and without the implementation of cover crops to prevent winter fallow.

We used the digital soil map 1:300.000 for Brandenburg with 99 soil map units. Within the soil map unit, up to three dominant soil types were considered to achieve at least 65% coverage. 276 soil types are defined by their soil profiles including soil organic matter content and texture down to 2 meters. Groundwater levels are estimated using the depth of reduction horizons as constant values over the year, to consider capillary rise depending on soil texture and distance between the root zone and the groundwater table.

In total each climate scenario contains about 148.000 simulations of 30 years. Beside crop yields we analyse the outputs for trends in soil organic matter, groundwater recharge, nitrogen leaching and the effect on water and nitrogen management using algorithms for automatic management.

Results indicate that spring crops were more negatively affected by climate change than winter crops especially on soils with low water holding capacity. However, few areas with more loamy soils and potential contribution of capillary rise from a shallow groundwater even benefited from climate change. Irrigation in most cases improved crop yield especially for spring crops. However, further analysis is required to assess if irrigation gains an economic benefit for all crop rotations. Nitrogen leaching can be reduced by implementing winter cover crops. Soil organic matter is assessed to decline for most sites and rotations. Only the rotations with multiyear grass or alfalfa can keep the level, but not on all sites.

How to cite: Kersebaum, K.-C., Schulz, S., and Wallor, E.: Site specific impacts of climate change on crop rotations and their management in Brandenburg/Germany, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11783, https://doi.org/10.5194/egusphere-egu2020-11783, 2020.

On the background of increasing welfare and continued population growth, there is an ever-increasing pressure on land and other natural resources in many parts of the world. The situation is, however, particularly severe in the drylands of Sub-Saharan Africa. Southern African landscapes, composed of arable lands, tree orchards and rangelands, provide a range of important ecosystem functions. These functions are increasingly threatened by land use changes through competing claims on land by agriculture, tourism, mining and other sectors, and by environmental change, namely climate change and soil degradation. Among others, climate models project that drought risk in the region will increase considerably. Based on comprehensive data sets originating from previous groundwork by several collaborative projects on the functioning of these ecosystems, a number of biophysical and bio-economic models have been developed and evaluated. In the framework of the South African Limpopo Landscapes network (SALLnet) we have now refined and tailored these models for combined use for the assessment of changes in multiple functions of the prevailing agroecosystems when affected by alternative climate and land management scenarios - from field to regional scale. We apply vegetation models (such as aDGVM), crop models (such as APSIM) and integrative farm level models (e.g. agent-based) for different farming systems in conjunction with geo-referenced databases. Model outputs are combined to assess the impact of management x environment interactions on various ecosystem functions. Of special interest in our study are the ecosystem services related to the provision of food, feed and fuel, soil and water conservation, as well as recycling and restoring carbon and nutrients in soil. To illustrate how the combination of various modelling components can work in assessing management intervention effects under different environmental conditions on landscape level ecosystem services, a case study was defined in Limpopo province, South Africa. We investigated effects of current management practices and an intensification scenario over a longer period of years on soil organic carbon change under rangeland and arable land, potential erosion, productive water use, biomass production, monthly feed gaps, and rangeland habitat quality. Tentative results showed that sustainable intensification closed the livestock feed gap, but further reduced soil organic carbon. More generally, coupling the output of vegetation and crop models regionally calibrated with sound ground/ experimental data appears promising to provide meaningful insights into the highly complex interconnections of different ecosystem services at a landscape level.

How to cite: Roetter, R., Nelson, W., Isselstein, J., Scheiter, S., Pfeiffer, M., Hoffmann, M., Ayisi, K., Linstädter, A., Behn, K., Westphal, C., Grass, I., Feil, J.-H., Odhiambo, J., Taylor, P., Twine, W., Merante, P., Bracho Mujica, G., Bringhenti, T., Lamega, S., Yazdan Bakhsh, S., Krieger, W., Linden, V., Weier, S., and Erasmus, B.: Modelling impacts of climate change and alternative management interventions on the multi-functionality of agricultural landscapes in southern Africa, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21649, https://doi.org/10.5194/egusphere-egu2020-21649, 2020.

Despite the consensus among researchers that future diets should include more plant-based proteins, animal-based foodstuffs are unlikely to disappear completely from our diet. Natural grasslands yield a notable part of the world`s animal protein production, but little is known about the sustainable potential of different areas, and thus the level of meat production that could be achieved globally by grazing. Whilst heavy stocking densities and overgrazing occurs in many regions, there still remain areas that have the potential to increase grazing from a carrying capacity perspective. This study aims to estimate the aboveground biomass that is sustainably available for grazers on the grasslands and savannas based on the MODIS Net Primary Production (NPP) approach at the global scale. We then use this information to calculate reasonable livestock-carrying capacities, using slopes, forest cover densities, proper use factors, and animal forage requirements as restrictions. The use of remote sensing to assess carrying capacities is still in its infancy, and this study represents the first global application of this novel approach. In addition, this study provides a methodology for examining the spatial and temporal variability of carrying capacities between seasons and years. Here we define the regional upper limits for pasture-based animal production, identify where future production could sustainably concentrate, and quantify the amount of protein intake that can be fulfilled by grazing animals.

How to cite: Piipponen, J., Rizayeva, A., de Leeuw, J., Jalava, M., and Kummu, M.: Livestock carrying capacity: assessment of world`s grasslands based on MODIS data products. , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8065, https://doi.org/10.5194/egusphere-egu2020-8065, 2020.

How to cite: Andrijevic, M., van Maanen, N., Schleussner, C.-F., and Rosa, L.: Closing the Global Irrigation Yield Gap alongside SSPs, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21304, https://doi.org/10.5194/egusphere-egu2020-21304, 2020.

In order to limit global warming to 2°C, a variety of mitigation measures are needed, including those that result in net negative emissions. Soil carbon sequestration (SCS) through changed land management practices has the potential to help meet this need, but it requires further study to represent a viable policy option. Rice cultivation plays a major role in South Asian agriculture, accounting for almost 40% of the crop’s harvested area worldwide. Its greenhouse gas (GHG) profile means it contributes disproportionately more than other crops to the region’s emissions. Adapting rice system management for SCS may therefore represent a compelling mitigation opportunity for the agricultural sectors of South Asian countries. This study uses a process-based modelling approach to compare the performance of two models, ECOSSE and DAYCENT, in assessing the mitigation potential of increasing soil organic carbon (SOC) stocks on a Bangladeshi test site under rice cultivation. A previous study using DAYCENT showed an increase in SOC stock as well as an overall GHG emissions reduction for several management practices relative to the baseline scenario. ECOSSE, calibrated to the same measurements, also showed an increase in SOC and net emissions reduction relative to the baseline. However, the models differed significantly in the extent of mitigation predicted as well as the GHG emissions profile. Given these differences, further analysis is needed to reduce error and uncertainty in these models. The results of this study form a basis for spatial model approaches to assess the mitigation potential of rice production in Bangladesh.

How to cite: Walton, J., Kuhnert, M., Begum, K., Abdul Kader, M., Dondini, M., Hillier, J., Wollenberg, L., and Smith, P.: Mitigation potential for increasing soil organic carbon of rice fields in Bangladesh – a case study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9762, https://doi.org/10.5194/egusphere-egu2020-9762, 2020.

Conservation agriculture (CA) is a farming system that promotes maintenance of (1) minimum soil disturbance avoiding soil inversion (i.e. no-tillage or minimum tillage), (2) a permanent soil cover with crop residues and/or cover crops, and (3) diversification of plant species. The adoption of CA is promoted by FAO as a response to sustainable land management, environmental protection and climate change adaptation and mitigation. According to FAO, implementation of CA in Europe would reduce emissions by about 200 Mt CO₂ per year. The carbon credit system (1 credit = 1t CO₂ reduced) allows the compensation of the release of GHG generated by industries by means of funding emission reduction projects. Despite its potential for emission reduction, agricultural systems, however, are nearly beyond of carbon market.

The objectives of this study were (1) to assess the potential of CA for soil organic carbon (SOC) sequestration for the current climate conditions and for a future climate scenario; (2) to estimates carbon balance and possibility to obtain carbon credits in Southern Finland.

Five cropping systems were simulated by using the ARMOSA process-based crop model: conventional systems under ploughing with monoculture and residues removed (Conv–R) or residues retained (Conv+R); no-tillage; CA and CA with a cover crop, Italian ryegrass (CA+CC). In Conv–R, Conv+R and NT, the simulated monocultures were spring barley. In CA and CA+CC crop rotations were spring barley - oilseed rape - oats - spring wheat. Simulations were carried out for the current (1998-2017) and future climatic scenarios (period 2020-2040, scenario Representative Concentration Pathway 6.0).

We evaluated carbon balance by using SALM method (Verified Carbon Standard, VM0017), which is a method to quantify in terms of carbon credits the Sustainable Agricultural Land Management projects. The method takes into account the dynamics of carbon stored in soil and the direct emission of N₂O due to use of fertilizers (organic and mineral) and CO₂ emission due to chemical fertilizer production, the amount of fuel used in tillage and other field operations. For estimation, we used the value of carbon credit of 21€.

Under current climate conditions, Conv–R and Conv+R emitted totally about 4.7 and 2.0 t CO₂e ha^-1yr^-1, respectively, mainly due to SOC loss (1 and 0.34 t ha^-1 yr^-1, respectively). No-tillage emitted 0.4 t CO₂e ha^-1yr^-1, mainly, due to N₂O from fertilizers and chemical fertilizer production. In contrast, CA and CA+CC allowed to SOC sequestration of 0.315 and 0.650 t ha^-1yr^-1, resulting in emissions reduction of 0.420 and 1.62 t CO₂e ha^-1 yr^-1, respectively. By adopting CA and CA+CC in Finland, there is a potential to obtain 2.5 and 3.7 carbon credits with the value of 52 and 77 € ha^-1yr^-1 respect to baseline (Conv+R).

Under future climate scenario (+0.6 °C; –120 mm y^-1), SOC decline for conventional systems will be more pronounced compared to that under actual climate, and SOC sequestration will be possible to accomplish only for CA+CC.

How to cite: Valkama, E. and Acutis, M.: Modelling of soil organic carbon changes and carbon balance under Conservation Agriculture and conventional cropping systems in Southern Finland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4934, https://doi.org/10.5194/egusphere-egu2020-4934, 2020.

Biochar additions to agricultural fields could greatly increase the carbon sink potential of sugarcane plantations, turning abundant crop residues into highly recalcitrant forms. Biochar not only stores carbon but the production process is energy positive. Gradual improvement to soil cation exchange capacity and bulk density may benefit nutrient and water retention, potentially mitigating some effects of climate change.

Relatively little is known about the kinetics of biochar carbon decay since accumulation over decades to centuries is not directly observed. Modelling decay based on known biotic and abiotic factors in soil and climate requires knowledge of biochar sub-pools, specifically their size and rate constants.

Here we have used accelerated chemical ageing as a proxy for oxidative ageing in soils. The resulting partitioning of biochar recalcitrance with mean residence time of up to 10,000 years allows extraction of decay parameters without resorting to extrapolation from short-term study. We compared carbon accumulation using 1, 2 and 3 biochar pools based on differently adapted versions of the RothC soil carbon model.

Results from sensitivity analyses will be presented in terms of biochar type, model structure and climate.  These will be illustrated in the context of the sugarcane system of Sao Paulo, Brazil, under current and potential future climate.

How to cite: Allen, N., Borges, B., and Sohi, S.: Modelling the potential for soil carbon storage using biochar- a case study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1141, https://doi.org/10.5194/egusphere-egu2020-1141, 2020.

The impacts of climate change on natural systems and biodiversity are known and already visible in some regions. With regard to agronomic systems, the effects of climate change have also been widely studied. However, some processes are still poorly understood, such as the links between pollinators and climate change or land use change. The feedbacks between different systems under climate change and land use change are still very little explored and require a multidisciplinary approach. It is within this framework that the MAPPY project fits.

The overall objective of the MAPPY project, funded by the AXIS program of JPI-Climate, is to study quantitatively feedback processes linking pollinators, plant diversity and crop yields in the context of climate and land use changes. A set of complementary models will be assembled, iteratively, to capture the dynamics of this complex system at regional level. Dynamic vegetation models and species distribution models will be used to assess the impacts of future climate change. Then, an agent-based model will be used to derive detailed land use and land cover change scenarios for the future at the scale of studied regions. The results of this combination of models will make it possible to assess the potential impacts on pollinator communities, which will make it possible to refine crop models. Finally, the socio-economic impacts of these forecasts will be assessed.

Several case study regions are defined in Europe. The entire study will be undertaken with local stakeholders who will identify the most relevant topics to be addressed. Indeed, stakeholders are asking more and more questions about climate change impact on crop yields, fruit crop damage, pollinator decline. Therefore, they will help us select the results that will be useful to them. Finally, a web platform will be developed with online tools allowing exploration of project results. The platform will be designed by involving stakeholders from the start of the project.

How to cite: Antoine, M. and the Maurine Antoine(1): MAPPY : Multisectoral Analysis of climate and land use change impacts on Pollinators, Plant diversity and crops Yields, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21788, https://doi.org/10.5194/egusphere-egu2020-21788, 2020.

Increasing competing demands for land, water and energy along with increasing word population call for strategies to minimise environmental impacts while producing adequate food for 9 billion people. Studies have highlighted trade-offs between yields, biodiversity and socioeconomic goals in alternative land management solutions that share or spare agricultural land, pointing out the necessity of demand-side adjustments to meet environmental and food security goals. On the contrary, research has demonstrated that agricultural intensification through sparing and sharing agricultural land at global scales has the capacity to close yield gaps, reduce land requirements and increase biodiversity. Here we address the fundamental question: Would agricultural systems produce adequate food under a land sharing and targeted sparing scenario at lower financial costs? Optimal allocation of agricultural production, based on biophysical constraints, enables increased efficiencies and thus, we hypothesize that production is going to be less costly at global scales. To address this question, a cost engineering method is employed using crop modelling and inventory data on 16 crops to assess financial implications of sharing and sparing production scenarios. Preliminary findings demonstrate that at national scales, where there are potentials for greater and more efficient food production, there is larger spatial aggregation of production systems and thus higher costs that relate to large inputs of nutrients required to close yield gaps. Further forthcoming research will allow the identification of financial balances at global scales and enable the present study to confirm that current production volumes can be maintained at lower financial and environmental costs.

How to cite: Vittis, Y. and Obersteiner, M.: Global Agricultural Costing and Investment model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22455, https://doi.org/10.5194/egusphere-egu2020-22455, 2020.