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Leclère, D., Jayet, P. - A., & de Noblet-Ducoudré, N. (2013). Farm-level Autonomous Adaptation of European Agricultural Supply to Climate Change. Ecol. Econ., 87, 1–14.
Abstract: The impact of climate change on European agriculture is subject to a significant uncertainty, which reflects the intertwined nature of agriculture. This issue involves a large number of processes, ranging from field to global scales, which have not been fully integrated yet. In this study, we intend to help bridging this gap by quantifying the effect of farm-scale autonomous adaptations in response to changes in climate. To do so, we use a modelling framework coupling the STICS generic crop model to the AROPAj microeconomic model of European agricultural supply. This study provides a first estimate of the role of such adaptations, consistent at the European scale while detailed across European regions. Farm-scale autonomous adaptations significantly alter the impact of climate change over Europe, by widely alleviating negative impacts on crop yields and gross margins. They significantly increase European production levels. However, they also have an important and heterogeneous impact on irrigation water withdrawals, which exacerbate the differences in ambient atmospheric carbon dioxide concentrations among climate change scenarios. (c) 2012 Elsevier B.V. All rights reserved.
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Lotze-Campen, H., von Lampe, M., Kyle, P., Fujimori, S., Havlik, P., van Meijl, H., et al. (2014). Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison. Agric. Econ., 45(1), 103–116.
Abstract: Integrated Assessment studies have shown that meeting ambitious greenhouse gas mitigation targets will require substantial amounts of bioenergy as part of the future energy mix. In the course of the Agricultural Model Intercomparison and Improvement Project (AgMIP), five global agro-economic models were used to analyze a future scenario with global demand for ligno-cellulosic bioenergy rising to about 100 ExaJoule in 2050. From this exercise a tentative conclusion can be drawn that ambitious climate change mitigation need not drive up global food prices much, if the extra land required for bioenergy production is accessible or if the feedstock, for example, from forests, does not directly compete for agricultural land. Agricultural price effects across models by the year 2050 from high bioenergy demand in an ambitious mitigation scenario appear to be much smaller (+5% average across models) than from direct climate impacts on crop yields in a high-emission scenario (+25% average across models). However, potential future scarcities of water and nutrients, policy-induced restrictions on agricultural land expansion, as well as potential welfare losses have not been specifically looked at in this exercise.
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Malone, R. W., Kersebaum, K. C., Kaspar, T. C., Ma, L., Jaynes, D. B., & Gillette, K. (2017). Winter rye as a cover crop reduces nitrate loss to subsurface drainage as simulated by HERMES. Agric. Water Manage., 184, 156–169.
Abstract: HERMES is a widely used agricultural system model; however, it has never been tested for simulating N loss to subsurface drainage. Here, we integrated a simple drain flbw component into HERMES. We then compared the predictions to four years of data (2002-2005) from central Iowa fields in corn-oybean with winter rye as a cover crop (CC) and without winter rye (NCC). We also compared the HERMES predictions to the more complex Root Zone Water Quality Model (RZWQM) predictions for the same dataset. The average annual observed and simulated N loss to drain flow were 43.8 and 44.4 kg N/ha (NCC) and 17.6 and 18.9 kg N/ha (CC). The slightly over predicted N loss for CC was because of over predicted nitrate concentration, which may be partly caused by slightly under predicted average annual rye shoot N (observed and simulated values were 47.8 and 46.0 kg N/ha). Also, recent research from the site suggests that the soil field capacity may be greater in CC while we used the same soil parameters for both treatments. A local sensitivity analysis suggests that increased field capacity affects HERMES simulations, which includes reduced drain flow nitrate concentrations, increased denitrification, and reduced drain flow volume. HERMES-simulated cumulative monthly drain flow and annual drain flow were reasonable compared to field data and HERMES performance was comparable to other published drainage model tests. Unlike the RZWQM simulations, however, the modified HERMES did riot accurately simulate the year to year variability in nitrate concentration difference between NCC and CC, possibly due in part to the lack of partial mixing and displacement of the soil solution. The results suggest that 1) the relatively simple model HERMES is a promising tool to estimate annual N loss to drain flow under corn-soybean rotations with winter rye as a cover crop and 2) soil field capacity is a critical parameter to investigate to more thoroughly understand and appropriately model denitrification and N losses to subsurface drainage. Published by Elsevier B.V.
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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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Molina-Herrera, S., Haas, E., Grote, R., Kiese, R., Klatt, S., Kraus, D., et al. (2017). Importance of soil NO emissions for the total atmospheric NOX budget of Saxony, Germany. Atm. Environ., 152, 61–76.
Abstract: Soils are a significant source for the secondary greenhouse gas NO and assumed to be a significant source of tropospheric NOx in rural areas. Here we tested the LandscapeDNDC model for its capability to simulate magnitudes and dynamics of soil NO emissions for 22 sites differing in land use (arable, grassland and forest) and edaphic as well as climatic conditions. Overall, LandscapeDNDC simulated mean soil NO emissions agreed well with observations (r(2) = 0.82). However, simulated day to day variations of NO did only agree weakly with high temporal resolution measurements, though agreement between simulations and measurements significantly increased if data were aggregated to weekly, monthly and seasonal time scales. The model reproduced NO emissions from high and low emitting sites, and responded to fertilization (mineral and organic) events with pulse emissions. After evaluation, we linked the LandscapeDNDC model to a GIS database holding spatially explicit data on climate, land use, soil and management to quantify the contribution of soil biogenic NO emissions to the total NOx budget for the State of Saxony, Germany. Our calculations show that soils of both agricultural and forest systems are significant sources and contribute to about 8% (uncertainty range: 6 -13%) to the total annual tropospheric NO, budget for Saxony. However, the contributions of soil NO emission to total tropospheric NO, showed a high spatial variability and in some rural regions such as the Ore Mts., simulated soil NO emissions were by far more important than anthropogenic sources. (C) 2016 Elsevier Ltd. All rights reserved.
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