Towards systematic evaluation of crop model outputs for global land-use models

Land provides vital socioeconomic resources to the society, however at the cost of large environmental degradations. Global integrated models combining high resolution global gridded crop models (GGCMs) and global economic models (GEMs) are increasingly being used to inform sustainable solution for agricultural land-use. However, little effort has yet been done to evaluate and compare the accuracy of GGCM outputs. In addition, GGCM datasets require a large amount of parameters whose values and their variability across space are weakly constrained: increasing the accuracy of such dataset has a very high computing cost. Innovative evaluation methods are required both to ground credibility to the global integrated models, and to allow efficient parameter specification of GGCMs. We propose an evaluation strategy for GGCM datasets in the perspective of use in GEMs, illustrated with preliminary results from a novel dataset (the Hypercube) generated by the EPIC GGCM and used in the GLOBIOM land use GEM to inform on present-day crop yield, water and nutrient input needs for 16 crops x 15 management intensities, at a spatial resolution of 5 arc-minutes. We adopt the following principle: evaluation should provide a transparent diagnosis of model adequacy for its intended use. We briefly describe how the Hypercube data is generated and how it articulates with GLOBIOM in order to transparently identify the performances to be evaluated, as well as the main assumptions and data processing involved. Expected performances include adequately representing the sub-national heterogeneity in crop yield and input needs: i) in space, ii) across crop species, and iii) across management intensities. We will present and discuss measures of these expected performances and weight the relative contribution of crop model, input data and data processing steps in performances. We will also compare obtained yield gaps and main yield-limiting factors against the M3 dataset. Next steps include iterative improvement of parameter assumptions and evaluation of implications of GGCM performances for intended use in the IIASA EPIC-GLOBIOM model cluster. Our approach helps targeting future efforts at improving GGCM accuracy and would achieve highest efficiency if combined with traditional field-scale evaluation and sensitivity analysis

The interdependencies between food and biofuel production in European agriculture - an application of EUFASOM

In the continuous quest to reduce anthropogenic emissions of carbon dioxide, the production and use of organically grown fuels in Europe has increased in importance in the recent past. However, the production of so-called biofuels is a direct competitor of agricultural food production for land, labor, water resources etc. with both land use options influencing each other depending on the respective boundary conditions defined by political regulations and economic considerations. In this study we will explore the economic and technical potentials of biofuels in Europe as well as the interdependencies between these two land use options for different economic incentives for biofuels using the European Forest and Agriculture Sector Optimization Model (EUFASOM). Key data on biodiesel and ethanol production have been gathered and are used for calibration of the model. The simulations extend until the year 2030, for which results are presented. Results indicate that moderate production targets of biofuels lead to an expansion of mainly the biodiesel production while more ambitious targets call for a focus on bioethanol. This has to do with the different levels of production efficiency depending on the production output. Growth of bioethanol feedstock is spread over entire Europe while the production of biodiesel feedstock occurs mainly in Central Europe.biodiesel, bioethanol, Europe, EUFASOM, modeling

Water productivity and footprint of major Brazilian rainfed crops – A spatially explicit analysis of crop management scenarios

Green water is a central resource for global agricultural production. Understanding its role is fundamental to design strategies to increase global food and feed production while avoiding further land conversion, and obtaining more crop per drop. Brazil is a country with high water availability, and a major exporter of agricultural goods and virtual water. We assess here water use and water productivity in Brazil for four major rainfed crops: cotton, maize, soybeans, and wheat. For this, we use the EPIC crop model to perform a spatially explicit assessment of consumptive water use and water productivity under crop management scenarios in Brazil between 1990 and 2013. We investigate four different land-water interactions: (i) water use and productivity for different management scenarios, (ii) the potential of supplemental irrigation for productivity improvement, (iii) changes in green water use throughout the study period, and finally (iv) potential reduction of land and water demand related to agricultural intensification. The results show that, for the studied crops, green water is the main resource for biomass production, and intensification can lead to great improvements in green water productivity. The results also suggest that, despite achieving higher yields, irrigation-based intensification tends to lower overall water productivity, compared to fertilizer-based intensification strategies. This is, however, regionally and crop-specific. Furthermore, due to higher yields and water productivity, producing the same amount of crop output in irrigated or rainfed intensification scenarios would result in the reduction of resource demand, in the order of 34–58 % for cropland, and 29–52 % for water

Towards Systematic Evaluation of Crop Model Outputs for Global Land-use Models

Land provides vital socioeconomic resources to the society; however, at the cost of large environmental degradation (Verburg et al., 2013). At the crossroads of these dimensions, agriculture becomes increasingly interconnected to various natural and human systems across various scales. In order to inform the design of policies to navigate land use towards a more sustainable operating space, comprehensive global assessment models are increasingly being used. They rely partly on the loose coupling of biophysical crop models to global economic models, via one-way exchange of output variables (Rosenzweig et al. 2013). Accuracy of variables exchanged strongly influences the outcomes assessed at various scales, and its improvement is likely to require iterative improvements. Yet there has been little effort to document, evaluate and compare these exchange variables across models (Mueller & Robertson et al. 2014). We here present a novel dataset (the Hypercube) generated by the Environmental Policy Integrated Model (EPIC) crop model and providing the Global Biosphere Management Model (GLOBIOM) with high-resolution information at global scale on the yield, water, and nutrient needs of 16 crops for 15 different combinations of management. We present the dataset and its links to the EPIC and GLOBIOM model, and the rationale for developing a systematic evaluation of the data, before illustrating them with preliminary results

Modelling crop yield, soil organic C and P under variable long-term fertilizer management in China

Phosphorus (P) is a major limiting nutrient for plant growth. P, as a nonrenewable resource and the controlling factor of aquatic entrophication, is critical for food security and human future, and concerns sustainable resource use and environmental impacts. It is thus essential to find an integrated and effective approach to optimize phosphorus fertilizer application in the agro-ecosystem while maintaining crop yield and minimizing environmental risk. Crop P models have been used to simulate plant-soil interactions but are rarely validated with scattered long-term fertilizer control field experiments. We employed a process-based model named Environmental Policy Integrated Climate model (EPIC) to simulate grain yield, soil organic carbon (SOC) and soil available P based upon 8 field experiments in China with 11 years dataset, representing the typical Chinese soil types and agro-ecosystems of different regions. 4 treatments, including N, P, and K fertilizer (NPK), no fertilizer (CK), N and K fertilizer (NK) and N, P, K and manure (NPKM) were measured and modelled. A series of sensitivity tests were conducted to analyze the sensitivity of grain yields and soil available P to sequential fertilizer rates in typical humid, normal and drought years. Our results indicated that the EPIC model showed a significant agreement for simulating grain yields with R2=0.72, index of agreement (d)=0.87, modeling efficiency (EF)=0.68, p<0.01 and SOC with R2=0.70, d=0.86, EF=0.59, and p<0.01. EPIC can well simulate soil available P moderately and capture the temporal changes in soil P reservoirs. Both of Crop yields and soil available were found more sensitive to the fertilizer P rates in humid than drought year and soil available P is closely linked to concentrated rainfall. This study concludes that EPIC model has great potential to simulate the P cycle in croplands in China and can explore the optimum management practices

Towards an assessment of adaptive capacity of the European agricultural sector to droughts

Analyses of climate change vulnerability and risk have been steadily evolving, and have moved from an impact-focused towards a more risk-based approach. In the risk and vulnerability communities, the relevance of resilience and adaptive capacity (AC) are increasingly emphasized. Another emerging analytical framework is the idea of assessing AC and resilience in terms of the Sustainable Livelihoods Approach (SLA), which studies welfare as a function of multiple forms of assets (‘capital’) that systems and agents may utilize to both recover as well as increase resilience in the future. We assess a new method for assessing AC at a sectoral level and operationalize AC measurement based on an SLA to assess the ability of the European agricultural sector to adapt to extreme droughts. We create a set of indicators which highlight areas of high or low AC, forecast to estimated times the world will reach 2° of warming using Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP) scenarios to drive AC indicator projections based on a fixed effects model. We find that based on this approach, Central and Northern Europe rank higher in overall capacity than countries on the periphery, and projections to 2 °C do not change results to a large degree. We critically reflect on the use of this approach and suggest possible use cases for results in larger studies of sectoral vulnerability, and highlight key data gaps and the need for a stronger empirical basis for selection of indicators, which constrain our ability to assess AC

Simulated impact of paleoclimate change on Fremont Native American maize farming in Utah, 850–1449 CE, using crop and climate models

The Fremont were members of an expansive maize-based Ancestral Puebloan (AP) cultural complex who disappeared from Utah between the 12th and 13th centuries CE. This period brackets that of a climatic transition in the Southwest from the warm, dry Medieval Climate Anomaly (MCA, ca. 850–1350 CE) to the cool, hydro-climatically variable Little Ice Age (LIA, ca. 1350–1850 CE). We simulated maize (Zea mays) crop productivity for Fremont AP archaeological sites in Utah between 850 and 1449 CE using a process-based crop model driven by climatologies from a statistically downscaled a climate model. We compared the model-simulated crop yields to time-series of archaeological site occupations given by spatially discrete, chronologically summed probability distributions (SPDs) of radiocarbon-dated Fremont artifacts. We found that the anomalous abandonment of different sites throughout Utah may be explained by site-specific combinations of reduced mean yield due to volatile year-to-year yields caused by increasing temperature variability, increasing hydro-climatic variability, and loss of soil quality, which depended on crop management strategy. In other words, we model the elimination of the Fremont AP ecological niche by exogenous influences of temperature and precipitation variability at the MCA-LIA transition and endogenous degradation of soil from organic carbon and nitrogen loss. Our method has broad applicability to contexts of low-technology, dryland farming human-environmental interactions

Uncertainty in soil data can outweigh climate impact signals in crop yield simulations

Global gridded crop models (GGCMs) are increasingly used for agro-environmental assessments and estimates of climate change impacts on food production. Recently, the influence of climate data and weather variability on GGCM outcomes has come under detailed scrutiny, unlike the influence of soil data. Here we compare yield variability caused by the soil type selected for GGCM simulations to weather-induced yield variability. Without fertilizer application, soil-type-related yield variability generally outweighs the simulated inter-annual variability in yield due to weather. Increasing applications of fertilizer and irrigation reduce this variability until it is practically negligible. Importantly, estimated climate change effects on yield can be either negative or positive depending on the chosen soil type. Soils thus have the capacity to either buffer or amplify these impacts. Our findings call for improvements in soil data available for crop modelling and more explicit accounting for soil variability in GGCM simulations

Impacts and Uncertainties of +2°C of Climate Change and Soil Degradation on European Crop Calorie Supply

Even if global warming is kept below +2°C, European agriculture will be significantly impacted. Soil degradation may amplify these impacts substantially and thus hamper crop production further. We quantify biophysical consequences and bracket uncertainty of +2°C warming on calories supply from ten major crops and vulnerability to soil degradation in Europe using crop modelling. The Environmental Policy Integrated Climate (EPIC) model together with regional climate projections from the European branch of the Coordinated Regional Downscaling Experiment (EURO-CORDEX) were used for this purpose. A robustly positive calorie yield change was estimated for the EU Member States except for some regions in Southern and South-Eastern Europe. The mean impacts range from +30 Gcal ha–1 in the north, through +25 and +20 Gcal ha–1 in Western and Eastern Europe, respectively, to +10 Gcal ha–1 in the south if soil degradation and heat impacts are not accounted for. Elevated CO2 and increased temperature are the dominant drivers of the simulated yield changes in high-input agricultural systems. The growth stimulus due to elevated CO2 may offset potentially negative yield impacts of temperature increase by +2°C in most of Europe. Soil degradation causes a calorie vulnerability ranging from 0 to 80 Gcal ha–1 due to insufficient compensation for nutrient depletion and this might undermine climate benefits in many regions, if not prevented by adaptation measures, especially in Eastern and North-Eastern Europe. Uncertainties due to future potentials for crop intensification are about two to fifty times higher than climate change impacts

Increasing crop production in Russia and Ukraine—regional and global impacts from intensification and recultivation

Russia and Ukraine are countries with relatively large untapped agricultural potentials, both in terms of abandoned agricultural land and substantial yield gaps. Here we present a comprehensive assessment of Russian and Ukrainian crop production potentials and we analyze possible impacts of their future utilization, on a regional as well as global scale. To this end, the total amount of available abandoned land and potential yields in Russia and Ukraine are estimated and explicitly implemented in an economic agricultural sector model. We find that cereal (barley, corn, and wheat) production in Russia and Ukraine could increase by up to 64% in 2030 to 267 million tons, compared to a baseline scenario. Oilseeds (rapeseed, soybean, and sunflower) production could increase by 84% to 50 million tons, respectively. In comparison to the baseline, common net exports of Ukraine and Russia could increase by up to 86.3 million tons of cereals and 18.9 million tons of oilseeds in 2030, representing 4% and 3.6% of the global production of these crops, respectively. Furthermore, we find that production potentials due to intensification are ten times larger than potentials due to recultivation of abandoned land. Consequently, we also find stronger impacts from intensification at the global scale. A utilization of crop production potentials in Russia and Ukraine could globally save up to 21 million hectares of cropland and reduce average global crop prices by more than 3%