The European Forest and Agriculture Optimisation Model -- EUFASOM

Land use is a key factor to social wellbeing and has become a major component in political negotiations. This paper describes the mathematical structure of the European Forest and Agricultural Sector Optimization Model. The model represents simultaneously observed resource and technological heterogeneity, global commodity markets, and multiple environmental qualities. Land scarcity and land competition between traditional agriculture, forests, nature reserves, pastures, and bioenergy plantations is explicitly captured. Environmental change, technological progress, and policies can be investigated in parallel. The model is well-suited to estimate competitive economic potentials of land based mitigation, leakage, and synergies and trade-offs between multiple environmental objectives.Land Use Change Optimization, Resource Scarcity, Market Competition, Welfare Maximization, Bottom-up Partial Equilibrium Analysis, Agricultural Externality Mitigation, Forest Dynamics, Global Change Adaptation, Environmental Policy Simulation, Integrated Assessment, Mathematical Programming, GAMS

The Global Gridded Crop Model Intercomparison phase 1 simulation dataset

The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives. © 2019, The Author(s)

AgroTutor: A Mobile Phone Application Supporting Sustainable Agricultural Intensification

Traditional agricultural extension services rely on extension workers, especially in countries with large agricultural areas. In order to increase adoption of sustainable agriculture, the recommendations given by such services must be adapted to local conditions and be provided in a timely manner. The AgroTutor mobile application was built to provide highly specific and timely agricultural recommendations to farmers across Mexico and complement the work of extension agents. At the same time, AgroTutor provides direct contributions to the United Nations Sustainable Development Goals, either by advancing their implementation or providing local data systems to measure and monitor specific indicators such as the proportion of agricultural area under productive and sustainable agriculture. The application is freely available and allows farmers to geo-locate and register plots and the crops grown there, using the phone’s built-in GPS, or alternatively, on top of very high-resolution imagery. Once a crop and some basic data such as planting date and cultivar type have been registered, the application provides targeted information such as weather, potential and historical yield, financial benchmarking information, data-driven recommendations, and commodity price forecasts. Farmers are also encouraged to contribute in-situ information, e.g., soils, management, and yield data. The information can then be used by crop models, which, in turn, send tailored results back to the farmers. Initial feedback from farmers and extension agents has already improved some of the application’s characteristics. More enhancements are planned for inclusion in the future to increase the application’s function as a decision support tool

Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications

Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark

Calibration and Validation of the EPIC Model for Maize Production in the Eastern Cape, South Africa

Crop models are useful tools to evaluate the effects of agricultural management on ecosystem services. However, before they can be applied with confidence, it is important to calibrate and validate crop models in the region of interest. In this study, the Environmental Policy Integrated Climate (EPIC) model was evaluated for its potential to simulate maize yield using limited data from field trials on two maize cultivars. Two independent fields at the Cradock Research Farm were used, one for calibration and one for validation. Before calibration, mean simulated yield was 8 t ha−1 while mean observed yield was 11.26 t ha−1. Model calibration improved mean simulated yield to 11.23 t ha−1 with a coefficient of determination, (r2) = 0.76 and a model efficiency (NSE) = 0.56. Validation with grain yield was satisfactory with r2 = 0.85 and NSE = 0.61. Calibration of potential heat units (PHUs) and soil-carbon related parameters improved model simulations. Although the study only used grain yield to calibrate and evaluate the model, results show that the calibrated model can provide reasonably accurate simulations. It can be concluded that limited data sets from field trials on maize can be used to calibrate the EPIC model when comprehensive experimental data are not available

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

Paper contact with cynthia festin: [email protected]ïments: C.F. was partly supported by a Research Fellowship of the Center for Advanced Studies of LMU Munich. We thank Joshua Elliott from the Global Gridded Crop Model Intercomparison (GGCMI) project for processing climate input data and the GGCMI and ISI-MIP project teams for providing various input data used in this study.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

Storylines of weather-induced crop failure events under climate change

Unfavourable weather is a common cause for crop failures all over the world. Whilst extreme weather conditions may cause extreme impacts, crop failure commonly is induced by the occurrence of multiple and combined anomalous meteorological drivers. For these cases, the explanation of conditions leading to crop failure is complex, as the links connecting weather and crop yield can be multiple and non-linear. Furthermore, climate change is likely to perturb the meteorological conditions, possibly altering the occurrences of crop failures or leading to unprecedented drivers of extreme impacts. The goal of this study is to identify important meteorological drivers that cause crop failures and to explore changes in crop failures due to global warming. For that, we focus on a historical failure event, the extreme low soybean production during the 2012 season in the midwestern US. We first train a random forest model to identify the most relevant meteorological drivers of historical crop failures and to predict crop failure probabilities. Second, we explore the influence of global warming on crop failures and on the structure of compound drivers. We use large ensembles from the EC-Earth global climate model, corresponding to present-day, pre-industrial +2 and 3° C warming, respectively, to isolate the global warming component. Finally, we explore the meteorological conditions inductive for the 2012 crop failure and construct analogues of these failure conditions in future climate settings. We find that crop failures in the midwestern US are linked to low precipitation levels, and high temperature and diurnal temperature range (DTR) levels during July and August. Results suggest soybean failures are likely to increase with climate change. With more frequent warm years due to global warming, the joint hot-dry conditions leading to crop failures become mostly dependent on precipitation levels, reducing the importance of the relative compound contribution. While event analogues of the 2012 season are rare and not expected to increase, impact analogues show a significant increase in occurrence frequency under global warming, but for different combinations of the meteorological drivers than experienced in 2012. This has implications for assessment of the drivers of extreme impact events

EnerGEO biomass pilot

In the framework of the EU FP7 project EnerGEO (Earth Observation for Monitoring and Assessment of the Environmental Impact of Energy Use) sustainable energy potentials for forest and agricultural areas were estimated by applying three different model approaches. Firstly, the Biosphere Energy Transfer Hydrology (BETHY/DLR) model was applied to assess agricultural and forest biomass increases on a regional scale with the extension to grassland. Secondly, the EPIC (Environmental Policy Integrated Climate) – a cropping systems simulation model – was used to estimate grain yields on a global scale and thirdly the Global Forest Model (G4M) was used to estimate global woody biomass harvests and stock. The general objective of the biomass pilot is to implement the observational capacity for using biomass as an important current and future energy resource. The scope of this work was to generate biomass energy potentials for locations on the globe and to validate these data. Therefore, the biomass pilot was focused to use historical and actual remote sensing data as input data for the models. For validation purposes, forest biomass maps for 1987 and 2002 for Germany (Bundeswaldinventur (BWI-2)) and 2001 and 2008 for Austria (Austrian Forest Inventory (AFI)) were prepared as reference. The output of BETHY/DLR, EPIC and G4M was used as input for the energy scenario-models REMIX (Renewable Energy Mix for Sustainable Electricity Supply in Europe, developed and operated by DLR-TT) , TASES (Time And Space resloved Energy Simulation, developed and operated by Research-Studio, Salzburg) and BeWhere (a techno-economic model developed by IIASA and Lud university and operated by IIASA). The EPIC modelling results for agricultural areas are input to TASES and REMIX. G4M also provided input data for TASES on a global scale starting with the year 2000 and ending in 2050 with 10 years steps. The main conclusions from the Biomass Pilot are: 1) It is possible to calculate biomass energy potentials for wood and agricultural crops by applying BETHY/DLR, EPIC or G4M models for Europe (1x1 km2) and the globe (0.5° x 0.5°). 2) The outcomes of biomass energy models are sensitive to input data by 40% or more. This is a consequence of biological sensitiveness to factors that determine growth such as weather, soil, species and cultivation. Collecting more and better input data is therefore essential. 3) Intensive effort was put on validation activities for all three models as well as a model intercomparison. For agricultural and forested areas all models showed significant linear relationship with reference data (R2 up to 0.95). 4) Remote sensing data can be used for generating some input data for biomass potential modelling such as weather and land use data 5) Remote sensing data have to be further developed before a differentiation can be made between different species and crops or biomass stacks can be modelled

Global Homogeneous Response Units

The concept of homogenous response units (HRU) was designed as a general concept for the delineation of basic spatial units. Only those characteristics of landscape, which are relatively stable over time (even under climate change) and largely unsusceptible to anthropogenic influence, were selected. The HRU can be seen as a basic spatial framework for the implementation of climate change and land management alternative scenarios into global modeling and therefore is a basic input for delineation of landscape units. HRUs are defined based on classifications of altitude (five classes: 1 (0 - 300m), 2 (300 - 600m), 3 (600 - 1100m), 4 (1100 - 2500m), 5 (> 2500m)), slope (seven classes(degrees): 1 (0 - 3), 2 (3 - 6), 3 (6 - 10), 4 (10 - 15), 5 (15 - 30), 6 (30 - 50), 7 (> 50)) and soil composition (five classes: 1 (sandy), 2 (loamy), 3 (clay), 4 (stony), 5 (peat)). e.g. HRU111 refers to Altitude class 1: 0-300m; Slope class 1: 0-3 degrees; and Soil class 1: sandy. Areas of non-soil are assigned 88. HRUs have a spatial resolution of approximately 10 km**2