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Martre, P., Wallach, D., Asseng, S., Ewert, F., Jones, J. W., Rötter, R. P., et al. (2015). Multimodel ensembles of wheat growth: many models are better than one. Glob. Chang. Biol., 21(2), 911–925.
Abstract: Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.
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Nendel, C., Kersebaum, K. C., Mirschel, W., & Wenkel, K. O. (2014). Testing farm management options as climate change adaptation strategies using the MONICA model. European Journal of Agronomy, 52, 47–56.
Abstract: Adaptation of agriculture to climate change will be driven at the farm level in first place. The MONICA model was employed in four different modelling exercises for demonstration and testing different management options for farmers in Germany to adjust their production system. 30-Year simulations were run for the periods 1996-2025 and 2056-2085 using future climate data generated by a statistical method on the basis of measured data from 1961 to 2000 and the A1B scenario of the IPCC (2007a). Crop rotation designs that are expected to become possible in the future due to a prolonged vegetation period and at the same time shortened cereal growth period were tested for their likely success. The model suggested that a spring barley succeeding a winter barley may be successfully grown in the second half of the century, allowing for a larger yields by intensification of the cropping cycle. Growing a winter wheat after a sugar beet may lead to future problems as late sowing makes the winter wheat grow into periods prone to drought. Irrigation is projected to considerably improve and stabilise the yields of late cereals and of shallow rooting crops (maize and pea) on sandy soils in the continental climate part of Germany, but not in the humid West. Nitrogen fertiliser management needs to be adjusted to increasing or decreasing yield expectations and for decreasing soil moisture. On soils containing sufficient amounts of Moisture and soil organic matter, enhanced mineralisation is expected to compensate for a greater N demand. (C) 2012 Elsevier B.V. All rights reserved.
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Kersebaum, K. C., Boote, K. J., Jorgenson, J. S., Nendel, C., Bindi, M., Frühauf, C., et al. (2015). Analysis and classification of data sets for calibration and validation of agro-ecosystem models. Env. Model. Softw., 72, 402–417.
Abstract: Experimental field data are used at different levels of complexity to calibrate, validate and improve agroecosystem models to enhance their reliability for regional impact assessment. A methodological framework and software are presented to evaluate and classify data sets into four classes regarding their suitability for different modelling purposes. Weighting of inputs and variables for testing was set from the aspect of crop modelling. The software allows users to adjust weights according to their specific requirements. Background information is given for the variables with respect to their relevance for modelling and possible uncertainties. Examples are given for data sets of the different classes. The framework helps to assemble high quality data bases, to select data from data bases according to modellers requirements and gives guidelines to experimentalists for experimental design and decide on the most effective measurements to improve the usefulness of their data for modelling, statistical analysis and data assimilation. (C) 2015 Elsevier Ltd. All rights reserved.
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Gutzler, C., Helming, K., Balla, D., Dannowski, R., Deumlich, D., Glemnitz, M., et al. (2015). Agricultural land use changes – a scenario-based sustainability impact assessment for Brandenburg, Germany. Ecological Indicators, 48, 505–517.
Abstract: Decisions for agricultural management are taken at farm scale. However, such decisions may well impact upon regional sustainability. Two of the likely agricultural management responses to future challenges are extended use of irrigation and increased production of energy crops. The drivers for these are high commodity prices and subsidy policies for renewable energy. However, the impacts of these responses upon regional sustainability are unknown. Thus, we conducted integrated impact assessments for agricultural intensification scenarios in the federal state of Brandenburg, Germany, for 2025. One Irrigation scenario and one Energy scenario were contrasted with the Business As Usual (BAU) scenario. We applied nine indicators to analyze the economic, social and environmental effects at the regional, in this case district scale, which is the smallest administrative unit in Brandenburg. Assessment results were discussed in a stakeholder workshop involving 16 experts from the state government. The simulated area shares of silage maize for fodder and energy were 29%, 37% and 49% for the BAU, Irrigation, and Energy scenarios, respectively. The Energy scenario increased bio-electricity production to 41% of the demand of Brandenburg, and it resulted in CO2 savings of up to 3.5 million tons. However, it resulted in loss of biodiversity, loss of landscape scenery, increased soil erosion risk, and increased area demand for water protection requirements. The Irrigation scenario led to yield increases of 7% (rapeseed), 18% (wheat, sugar beet), and 40% (maize) compared to the BAU scenario. It also reduced the year-to-year yield variability. Water demand for irrigation was found to be in conflict with other water uses for two of the 14 districts. Spatial differentiation of scenario impacts showed that districts with medium to low yield potentials were more affected by negative impacts than districts with high yield potentials. In this first comprehensive sustainability impact assessment of agricultural intensification scenarios at regional level, we showed that a considerable potential for agricultural intensification exists. The intensification is accompanied by adverse environmental and socio-economic impacts. The novelty lies in the multiscale integration of comprehensive, agricultural management simulations with regional level impact assessment, which was achieved with the adequate use of indicators. It provided relevant evidence for policy decision making. Stakeholders appreciated the integrative approach of the assessment, which substantiated ongoing discussions among the government bodies. The assessment approach and the Brandenburg case study may stay exemplary for other regions in the world where similar economic and policy driving forces are likely to lead to agricultural intensification. (C) 2014 The Authors. Published by Elsevier Ltd.
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Asseng, S., Ewert, F., Martre, P., Rötter, R. P., Lobell, D. B., Cammarano, D., et al. (2014). Rising temperatures reduce global wheat production. Nat. Clim. Change, 5(2), 143–147.
Abstract: Crop models are essential tools for assessing the threat of climate change to local and global food production1. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature2. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time.
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