BG3.21

The majority of food production is based on agricultural practices developed for the stable Holocene climatic conditions, which now are under risk for rapid change due to climate change. Although various studies have assessed the potential changes in climatic conditions and their projected impacts on yields globally, there is no clear understanding on the climatic niche of the current food production. Nor, which areas are under risk of falling outside this niche.

In this study we aim first at defining the novel concept Safe Climatic Space (SCS) by using a combination of three key climatic parameters. SCS is defined here as the climate conditions to which current food production systems (here crop production and livestock production separately) are accustomed to, an analogue to Safe Operating Space (SOS) concepts such as Planetary Boundaries and human climate niche. We use a combination of selected key climatic factors to define the SCS through the Holdridge Life Zone (HLZ) concept. It allows us to first define the SCS based on three climatic factors (annual precipitation, biotemperature and aridity) and to identify which food production areas would stay within it under changed future climate conditions. 

We show that a rapid and unhalted growth of GHG emissions (SSP5-8.5) could force 31% (25-37% with 5th-95th percentile confidence interval) of global food crop production and 34% (26-43%) of livestock production beyond the SCS by 2081-2100. Our results underpin the importance of committing to a low emission scenario (SSP1-2.6), whereupon the extent of food production facing unprecedented conditions would be a fraction: 8% (4-10%) for crop production and 4% (2-8%) for livestock production. The most vulnerable areas are the ones at risk of leaving SCS with low resilience to cope with the change, particularly South and Southeast Asia and Africa’s Sudano-Sahelian Zone. 

Our findings reinforce the existing research in suggesting that climate change forces humanity into a new era of reduced validity of past experiences and dramatically increased uncertainties. Future solutions should be concentrated on actions that would both mitigate climate change as well as increase resilience in food systems and societies, increase the food production sustainability that respects key planetary boundaries, adapt to climate change by, for example, crop migration and foster local livelihoods especially in the most critical areas.

How to cite: Kummu, M., Heino, M., Taka, M., Varis, O., and Viviroli, D.: One-third of global food production at risk for unprecedented climate conditions due to climate change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11923, https://doi.org/10.5194/egusphere-egu21-11923, 2021.

Climate change poses increasing risks to global food security with more severe heat stress, water scarcity, and flooding. As one of the major adaptation measures, adjusting crop calendars could be a feasible and effective solution to avoid adverse effects on crop yield potentials in a changing climate by allowing crops to grow in more favorable weather conditions. Previous single-crop and single-objective studies on the optimization of crop planting dates lack comprehensive consideration of multi-crop rotation systems, especially rice-based cropping systems with short growing season intervals in Asian tropical monsoon regions. This study seeks to better understand potentials and limitations of adjusting crop calendars for climate change adaptation of double-rice and rice-wheat rotation systems, with a particular focus on the following questions: (1) Is it possible to avoid yield loss of rice and wheat through adjusting crop calendars in the study area? (2) How will fallow period between crop growing seasons change in the future? (3) What are relationships between crop yield improvement, irrigation water requirement, and heat stress mitigation in the study area?

To address these questions, we calibrated a spatial implementation of the Environmental Policy Integrated Climate (EPIC) agronomic model to estimate annual potential yields, irrigation water requirement, and heat stress days of irrigated double-rice and rice-wheat cropping systems in Bangladesh, India, and Myanmar (the BIM countries), and adjusted crop calendars (a) by single-objective optimization with maximum yield and (b) multi-objective optimization with least irrigation water requirement, minimum heat stress days, and highest potential yield under climate change.

Our results indicate that most yield loss in rice and wheat could be avoided through shifting planting dates while considering effects of elevated atmospheric CO₂ concentration on biomass assimilation and transpiration. The model indicates that fallow periods between kharif-rice harvest dates and rabi-rice planting dates in double-rice systems are likely to become longer due to shorter growing season duration meanwhile fallow periods between kharif-rice harvest dates and rabi-wheat planting dates in rice-wheat systems are likely to become shorter due to advanced planting dates of rabi wheat, which implies that double-rice systems in the BIM countries will have more flexibility to cope with smaller time windows for crop growth and development in the future. Moreover, nearly half of the study area has the potential to increase yield by more than 10% through changing crop calendars compared to the basic scenario with non-adjusted crop calendars under RCP8.5 in 2080s, but 59% of these areas would face contradictions in obtaining crop yield improvement, saving irrigation water, and mitigating heat stress in the future. We found those areas suitable for adopting shifting planting dates as one of adaptation strategies from the perspective of climate conditions, such as Punjab state in India and Rangpur in Bangladesh, are also the areas with shortened growing season intervals, which requires great efforts to achieve the adaptation objectives under climate change. Thus, the trade-off among climate change adaptation, ecological sustainability, and farmer decision making should be carefully considered for local governments when promoting adjustment of crop calendars in rice-based multiple cropping systems.

How to cite: Wang, X., Folberth, C., Wang, S., Skalsky, R., and Juraj, B.: Potentials and limitations of climate change adaptation with crop calendar optimization in rice-based multiple cropping systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2888, https://doi.org/10.5194/egusphere-egu21-2888, 2021.

Food security, in Morocco as in many parts of the world, depends heavily on cereal production which fluctuates relying on weather conditions. In fact, Morocco has a production system for cereals which is dominated by rainfed. It is therefore necessary to further develop knowledge about climate change and strengthen forecasting systems for predicting the impacts of climate change.

Our research, funded by a bilateral project of Wallonie-Bruxelles International, aims to study the response of cereal production 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. For crops, a specific module describes basic management parameters (sowing, harvest, rotation) and phenological phases.

The simulations are performed across all Morocco using different input data. The three main cereal crops simulated include soft wheat, durum wheat and barley, they are grown in all provinces and all agro-ecological zones. Regarding climatic inputs, we’re using two sets of data: the first one is interpolated and bias-corrected fields from the climate model HadGEM2-AO for the historical period (1990-2005), in addition to three different Representative Concentration Pathway scenarios (RCP2.6, RCP4.5 and RCP8.5) from 2005 to 2100. The second one is high resolution (30 arc sec) gridded climate data derived from WorldClim combined with interpolated anomalies from CRU (Climatic Research Unit) over the historical period 1990 to 2018.

After obtaining preliminary results for the past period, and in order to improve the prediction using the field data which are the observed yields, we performed a sensitivity analysis. We used the One-at-a-time (OAT) approach by moving one input variable, keeping others at their baseline (nominal) values, then, returning the variable to its nominal value, then repeating for each of the other inputs in the same way. Sensitivity may then be measured by monitoring changes in the output, using linear regression. The inputs studied are the initial value of carbon pool, leaf C/N ratio, water stress, sowing date, GDD harvest, stomatal conductance parameters, specific leaf area, and rooting depth.

How to cite: Loudiyi, I., Jacqemin, I., Tychon, B., François, L., Lahlou, M., Wellens, J., and Balaghi, R.: Cereal yield forecasting in Morocco using the CARAIB dynamic vegetation model driven by HadGEM2-AO projections, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10042, https://doi.org/10.5194/egusphere-egu21-10042, 2021.

Anthropogenic greenhouse gases emissions are the main driving force of climate change. They need to be strongly reduced during the next Century until carbon neutrality in order to keep the international 2°C objective of the Paris Agreement on Climate. The “4per1000” initiative was launched in 2015 as a climate mitigation option, with an aspiration to increase global soil organic carbon (SOC) stocks by 4‰ per year to compensate for the anthropogenic emissions of carbon dioxide in the atmosphere. The “4per1000” is not applicable everywhere, hence a full compensation of anthropogenic emissions is unlikely. Nevertheless, where possible, it has been identified as an interesting approach to mitigate climate change and, at the same time, ensure food security through improved soil fertilization. To reach such an objective one must either reduce carbon outputs (e.g. erosion and respiration) or increase the inputs of biomass to the soil.

Here, we use a multi-modelling approach to study the challenges of SOC storage potential through increased organic inputs in agricultural sites. The aim is to respond to the following question: “What is the amount of carbon inputs that needs to be brought to soils as a means to increase SOC stocks by 4‰ per year?” This scientific question belongs to the family of inverse problems and is addressed by using a multi-modelling approach, to improve the predictions and associated uncertainties of model outputs.

The amount of required carbon inputs to reach the 4per1000 is estimated over 30 years of simulations with five different models (Century, RothC, ICBM, AMG and Millennial) and is compared to more than 15 long-term arable experiments of organic matter addition in Europe. This allows estimating the feasibility of a 4per1000 objective in temperate, north-temperate and Mediterranean regions with different treatments of organic matter inputs. As a final step, we evaluate the sensitivity of the predicted carbon inputs requirement to future projections of climate change.

The 4per1000 initiative is an interesting approach to contribute for the mitigation of climate change through agriculture. Here, we will present preliminary results of a multi-modelling analysis showing that the necessary inputs to reach the 4per1000 target are realistic for some experimental conditions, but might be too high to be implemented at a larger scale.

How to cite: Bruni, E.: How much should we increase carbon inputs to the soil to reach a 4per1000 objective of soil organic carbon storage in European agricultural sites: a multi-modelling approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1666, https://doi.org/10.5194/egusphere-egu21-1666, 2021.

Enhanced weathering through basalt application on agricultural land represents a proposed strategy for the removal of carbon dioxide from the atmosphere. Co-benefits related to soil health, resilience and crop yield make basalt excellently suited as a sustainably technology for GHG mitigation in agriculture. It has been shown that enhanced weathering is principally feasible on a global scale, but it remains unclear whether it can be implemented on a local level. With this in mind, we estimate the potential for CO₂ removal through a case study for Austria. Scenarios are estimated for three different particle size distributions (< 100 µm, < 10 µm and < 1 µm). We find that transport related emissions may cancel out any drawdown if grain sizes (< 100 μm) are used. However, under optimal transport conditions the large-scale application of particles with a diameter < 10 μm may remove about 2% of Austria's annual Greenhouse gas emissions while at the same time supplying important plant nutrients. We discuss challenges towards this goal, including the enormous amounts of basalt needed and the energy requirement related to grinding, as well as uncertainties related to actual field weathering rates. Those uncertainties hinder the precise quantification of CO₂ drawdown as of now. While enhanced weathering remains a promising path for climate change mitigation, further research at laboratory and field scale is required to put this technology to optimal use. 

How to cite: Rinder, T. and von Hagke, C.: The potential of carbon dioxide removal through enhanced weathering of basalt on agricultural land in Austria, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2277, https://doi.org/10.5194/egusphere-egu21-2277, 2021.

Increasing soil carbon stocks has been identified as a major climate change mitigation solution. As a consequence, an objective of 4/1000 yearly increment in soil carbon stocks has been proposed at the COP21.  Sustainable agriculture provides several solutions to meet this objective and among those solutions, the implementation of cover crops has been identified as most efficient. Currently, a comprehensive modeling tool that takes into account the major bio-geophysical processes with associated uncertainties, while assimilating frequent high-resolution observations at large scale could allow accounting for the effect of cover crops on the carbon budget in a realistic way. In this study, we quantify the components of the carbon budget at high resolution and we analyse the effect of cover crops. Computations are based on the newly developed AgriCarbon-EO tool which assimilates full resolution (10-20m) Sentinel-2 optical data into a radiative transfer model (PROSAIL), and a crop model (SAFYE-CO2). The assimilation scheme is based on a Bayesian approach which provides the retrieved biogeophysical variables with their associated uncertainties. Uncertainties are essential when determining the carbon stocks. For instance, the future European Common Agricultural Practice (CAP) may take into consideration the uncertainty of the determination of the soil carbon stocks changes in the evaluation of the subsidies. The main inputs of the computations are weather data, soil texture maps, crop maps and surface reflectances. The Sentinel-2 Leaf Area Index (LAI) are obtained from those Sentinel-2 surface reflectance by inverting the PROSAIL model. These are then assimilated into the SAFY_CO2 model to determine the carbon budget components. To validate our approach, we implemented the AgriCarbon-EO tool over a set of plots in south-western France over which we dispose of biomass measurements for cover crops in wheat/cover crop/maize rotations for 2017-2018 and 2019-2020 agricultural seasons. Also, the CO2 fluxes are validated against eddy covariance flux measurements in the same context. Our study shows that the cover crops allow on average 250gC/m² of organic carbon with a high spatial heterogeneity. This has important implications regarding the dynamic of carbon storage in agronomic soils and demonstrates the importance of high-resolution agronomic modeling.

How to cite: Wijmer, T., Al Bitar, A., Fieuzal, R., Arnaud, L., Pique, G., and Ceschia, E.: Assessing the impact of cover crop as a GHG mitigation solution at intra-field scale using the AgriCarbon-EO tool, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15272, https://doi.org/10.5194/egusphere-egu21-15272, 2021.

In southern Africa, sustainable intensification (SI) of low input farming is promoted as a key strategy to improve the livelihoods and food security of smallholder farmers. It has been argued, however, that due to the severity and frequency of droughts, irrigation is a prerequisite for sustainable yield improvement and stability, and less crop failures. Restricted access to water for such farmers in the study region necessitates the investigation of alternative adaptive management options suited to smallholder systems. Using the Limpopo province South Africa as a case study, we use a combination of survey data (140 households) and detailed quantitative agronomic measurements and observations (116 georeferenced on-farm plots) to understand yield limitations in maize-based smallholder systems. Data was collected from five villages in the Mopani district representing a distinct climate gradient. Agronomic measurements included soil characteristics such as CN ratio, texture, rooting depth and management aspects such as weed type and soil cover, as well as maize planting density, biomass and yield. Combined insights from the interviews and detailed on-farm observations were used to benchmark the agro-ecosystem model APSIM, which was then setup for different technology levels. These were defined through combinations of advanced crop and soil management practices plus the status quo as observed through the ground-truthing campaign with no irrigation, zero to low fertilisation, little weeding, no pest management, and low planting density. Advanced practices involved higher input levels including irrigation and fertiliser, as well as management aspects such as increased planting density and intense weeding.

Survey results showed that farmers adjusted sowing time and planting density according to rainfall availability and perceived risk. Overall, input intensity levels were low (fertiliser and density) and all villages expressed similar challenges to adapt to climate variability. It appeared most farmers lacked knowledge about drought avoidance measures, and only very few had access to water for crop irrigation.

Our simulation results showed that irrigation could increase maize grain yields by around two tons ha^-1 over a three-year average for a moderately wet site under current management practices. For the driest site, this led to an increase of just over one ton ha^-1. If irrigation is applied it necessitates an increase in biotic stress management, as failing to do so can compromise potential yield gains. Higher labour input, increased input costs and possibly associated increased economic risks make such intensification strategies unattractive for some farmers depending on their age and household economic security.

For this case study, we outlined and implemented a novel method of linking survey and agro-ecosystem modelling data to assess ex-ante potential impacts of SI in smallholder cropping systems vulnerable to climate-induced risk.

How to cite: Nelson, W., Hoffmann, M., May, C., Mashao, F., Ayisi, K., and Rötter, R.: Constraints and options to sustainably intensifying smallholder maize farming systems in southern Africa, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15043, https://doi.org/10.5194/egusphere-egu21-15043, 2021.

In the semi-arid Seewinkel region in Austria, competing demands exist for land and water such as from agriculture, nature protection, tourism and settlements. In addition, water quality problems are prevalent due to nitrate leaching in groundwater in the region. Climate change likely will amplify existing resource demands and environmental impacts, imposing considerable challenges for adapting and regulating agriculture in the Seewinkel. Hence, compromises between competing policy objectives are needed.
The aim of this presentation is to assess efficient land and water management strategies considering several economic and agro-ecological policy objectives in the Seewinkel region in context of climate scenarios. A multi-objective optimization experiment was performed with an integrated modelling framework to compute agro-economic-ecological Pareto frontiers. The frontiers combine levels of (i) net benefits from agricultural production, (ii) groundwater extraction for agricultural irrigation, (iii) nitrate leaching from agricultural production, and (iv) topsoil organic carbon stocks. 30 stochastic realizations of three climate scenarios are considered for a future period of 31 years: WET, SIMILAR and DRY, which mainly differ regarding annual precipitation volumes.
Model results show that a 1% (20%) reduction of agricultural net benefits can lower groundwater extraction by 11-83% (61-100%) and nitrate leaching by 18-19% (49-53%) as well as increase topsoil organic carbon sequestration by 1% (5%) depending on the climate scenario. However, substantial changes in land use and management would be required. For instance, less groundwater extraction by 11-83% requires a 6-21% reduction of irrigated cropland, a 21-33% reduction of highly fertilized cropland, a 10-24% increase of grassland, and a 23-52% increase of abandoned land depending on the climate scenario. Less nitrate leaching by 18-19% (or higher topsoil organic carbon stocks by 1%) require that highly fertilized cropland decreases by 9-13% (4-7%), abandoned land increases by 5-9% (19-49%) and grassland either declines by 3% (14%) or increases by up to 5% (32%) depending on the climate scenario. In general, the share of grassland increases in the wetter climate scenario.
Overall, the analysis reveals that especially groundwater extraction and nitrate leaching can be reduced substantially for fairly small reduction in agricultural net benefits in all climate scenarios. 50% of maximum modelled improvements of agro-ecological objectives can be already achieved at 1-15% reductions of agricultural net benefit depending on climate scenarios. Thus, respective land use policies would allow considerable improvements of the agro-ecological performance at relatively low costs. However, improving the agro-ecological performance beyond a particular level can quickly lead to high reductions of agricultural net benefits, as depicted by the non-linear form of the Pareto frontiers. This is mainly related to large declines of cropland and increases in grassland or abandoned land. Furthermore, the results indicate that water management policies are less costly than climate change mitigation policies, at least in the Seewinkel region.

How to cite: Karner, K., Mitter, H., and Schmid, E.: Analyzing competing land use policy objectives under climate change – a regional case study from Austria, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8155, https://doi.org/10.5194/egusphere-egu21-8155, 2021.

Bees provide essential pollination services to natural ecosystems and agricultural crops. However, managed and wild (unmanaged) bee populations are in decline worldwide. In order to better manage and restore bee populations, long-term monitoring programs are required. Direct bee monitoring is costly, labor intensive, and requires high expertise. Therefore, cost-effective indicators for bee diversity and community composition are essential.
Here we propose to test the cost-efficacy of novel aerial techniques along with classical ground methods to collect biotic and a-biotic indicators of bee diversity and community composition. We will couple classical ecological monitoring approach with advanced photogrammetric tools, in order to develop a multi-scale and multi-temporal platform for monitoring bees. To this end, we formed a complementary, interdisciplinary research group of a pollination ecologist, soil chemists, environmental engineer, geomatics engineer, and topography surveyot. The study will include field work in two complimentary study systems in central Israel, light sandy vs heavy vertisol soils. In each study system we will concurrently conduct bee, flower, bee nesting substrates and soil surveys using classical tools/approaches, as well as apply advanced photogrammetric tools, based on RGB images, with thermal, multispectral data. The indicative ability for bee diversity and community composition of the different biotic and a-biotic measures collected, will be tested using advances statistical tools. Our findings may be instructive to other insects and plant groups, thus provide a novel generic approach towards the ecological monitoring of terrestrial systems.

How to cite: Di Pietra, V., Dabove, P., Mandelik, Y., Mishael, Y., Levy, K., and Dor, M.: Monitoring Bee Diversity in Natural Systems – Novel Aerial and Classical Ground Methods to Evaluate Biotic and Abiotic Indicators, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2178, https://doi.org/10.5194/egusphere-egu21-2178, 2021.

The Global Gridded Crop Model Intercomparison was founded in 2012 as a joint activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP) and the InterSectoral Model Intercomparison Project (ISIMIP). Over these 10 years, GGCMI has attracted contributions from many international crop modeling groups and has generated large global agricultural data sets in different model simulation phases. Input data comprise gridded management data for agricultural systems that can be used in combination with climate data that are provided by ISIMIP. Annual output data include crop yields and other variables of plants and soil status for irrigated and purely rainfed production systems for different field crops at 0.5 degree spatial resolution, covering the whole land surface, where crop production is feasible. All data are made publicly available. While Phase 1 of GGCMI was focused on the historical period^[1,2], aiming at model evaluation^[3], Phase 2 generated an unprecedented large data set of systematic disturbances along the CO2 (C), Temperature (T), Water (W) and Nitrogen (N) dimension^[4]. A major outcome of Phase 2 is a very large set of emulators^[5] that allows for lightweight, flexible and comprehensive crop yield projections and analyses. With analyses of Phase 2 still forming, Phase 3 was started in collaboration with ISIMIP’s Phase 3, providing new future projections for a range of CMIP6 climate change projections and different management scenarios. Crop models do not only provide outputs on crop yields but also on various processes, such as evapotranspiration, leaf area index, phenology and soil dynamics that allow for broader analyses. GGCMI is a collaborative effort and always open to new contributors. Given the amount and complexity of in- and output data, we welcome proposals for new studies and data analyses. In this presentation we’re providing an overview of the GGCMI activities and exemplify possible entry points for collaboration.

^{[1] http://dx.doi.org/10.5194/gmd-8-261-2015}

^{[2] http://dx.doi.org/10.1038/s41597-019-0023-8}

^{[3] http://dx.doi.org/10.5194/gmd-10-1403-2017}

^{[4] http://dx.doi.org/10.5194/gmd-13-2315-2020}

^{[5] http://dx.doi.org/10.5194/gmd-13-3995-2020}

How to cite: Müller, C., Jägermeyr, J., and GGCMI team, T.: The Global Gridded Crop Model Intercomparison – an AgMIP activity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14299, https://doi.org/10.5194/egusphere-egu21-14299, 2021.