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Nelson, G. C., van der Mensbrugghe, D., Ahammad, H., Blanc, E., Calvin, K., Hasegawa, T., et al. (2014). Agriculture and climate change in global scenarios: why don’t the models agree. Agric. Econ., 45(1), 85.
Abstract: Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs involves direct use of weather inputs (temperature, solar radiation available to the plant, and precipitation). Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes such as prices, production, and trade arising from differences in model inputs and model specification. This article presents climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By harmonizing key drivers that include climate change effects, differences in model outcomes were reduced. The particular choice of climate change drivers for this comparison activity results in large and negative productivity effects. All models respond with higher prices. Producer behavior differs by model with some emphasizing area response and others yield response. Demand response is least important. The differences reflect both differences in model specification and perspectives on the future. The results from this study highlight the need to more fully compare the deep model parameters, to generate a call for a combination of econometric and validation studies to narrow the degree of uncertainty and variability in these parameters and to move to Monte Carlo type simulations to better map the contours of economic uncertainty.
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Rötter, R. P., Palosuo, T., Kersebaum, K. C., Angulo, C., Bindi, M., Ewert, F., et al. (2012). Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models. Field Crops Research, 133, 23–36.
Abstract: In this study, the performance of nine widely used and accessible crop growth simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSET, HERMES, MONICA, STICS and WOFOST) was compared during 44 growing seasons of spring barley (Hordeum vulgare L) at seven sites in Northern and Central Europe. The aims of this model comparison were to examine how different process-based crop models perform at multiple sites across Europe when applied with minimal information for model calibration of spring barley at field scale, whether individual models perform better than the multi-model mean, and what the uncertainty ranges are in simulated grain yields. The reasons for differences among the models and how results for barley compare to winter wheat are discussed. Regarding yield estimation, best performing based on the root mean square error (RMSE) were models HERMES, MONICA and WOFOST with lowest values of 1124, 1282 and 1325 (kg ha(-1)), respectively. Applying the index of agreement (IA), models WOFOST, DAISY and HERMES scored best having highest values (0.632, 0.631 and 0.585, respectively). Most models systematically underestimated yields, whereby CROPSYST showed the highest deviation as indicated by the mean bias error (MBE) (-1159 kg ha(-1)). While the wide range of simulated yields across all sites and years shows the high uncertainties in model estimates with only restricted calibration, mean predictions from the nine models agreed well with observations. Results of this paper also show that models that were more accurate in predicting phenology were not necessarily the ones better estimating grain yields. Total above-ground biomass estimates often did not follow the patterns of grain yield estimates and, thus, harvest indices were also different. Estimates of soil moisture dynamics varied greatly. In comparison, even though the growing cycle for winter wheat is several months longer than for spring barley, using RMSE and IA as indicators, models performed slightly, but not significantly, better in predicting wheat yields. Errors in reproducing crop phenology were similar, which in conjunction with the shorter growth cycle of barley has higher effects on accuracy in yield prediction. (C) 2012 Elsevier B.V. All rights reserved.
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Perego, A., Sanna, M., Giussani, A., Chiodini, M. E., Fumagalli, M., Pilu, S. R., et al. (2014). Designing a high-yielding maize ideotype for a changing climate in Lombardy plain (northern Italy). Science of The Total Environment, 499, 497–509.
Abstract: The expected climate change will affect the maize yields in view of air temperature increase and scarce water availability. The application of biophysical models offers the chance to design a drought-resistant ideotype and to assist plant breeders and agronomists in the assessment of its suitability in future scenarios. The aim of the present work was to perform a model-based estimation of the yields of two hybrids, current vs ideotype, under future climate scenarios (2030-2060 and 2070-2100) in Lombardy (northern Italy), testing two options of irrigation (small amount at fixed dates vs optimal water supply), nitrogen (N) fertilization (300 vs 400 kg N ha(-1)), and crop cycle durations (current vs extended). For the designing of the ideotype we set several parameters of the ARMOSA process-based crop model: the root elongation rate and maximum depth, stomatal resistance, four stage-specific crop coefficients for the actual transpiration estimation, and drought tolerance factor. The work findings indicated that the current hybrid ensures good production only with high irrigation amount (245-565 mm y(-1)). With respect to the current hybrid, the ideotype will require less irrigation water (-13%, p<0.01) and it resulted in significantly higher yield under water stress condition (+15%, p<0.01) and optimal water supply (+2%, p<0.05). The elongated cycle has a positive effect on yield under any combination of options. Moreover, higher yields projected for the ideotype implicate more crop residues to be incorporated into the soil, which are positively correlated with the SOC sequestration and negatively with N leaching. The crop N uptake is expected to be adequate in view of higher rate of soil mineralization; the N fertilization rate of 400 kg N ha(-1) will involve significant increasing of grain yield, and it is expected to involve a higher rate of SOC sequestration.
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Reidsma, P., Wolf, J., Kanellopoulos, A., Schaap, B. F., Mandryk, M., Verhagen, J., et al. (2015). Climate change impact and adaptation research requires integrated assessment and farming systems analysis: a case study in the Netherlands. Environ. Res. Lett., 10(4), 045004.
Abstract: Rather than on crop modelling only, climate change impact assessments in agriculture need to be based on integrated assessment and farming systems analysis, and account for adaptation at different levels. With a case study for Flevoland, the Netherlands, we illustrate that (1) crop models cannot account for all relevant climate change impacts and adaptation options, and (2) changes in technology, policy and prices have had and are likely to have larger impacts on farms than climate change. While crop modelling indicates positive impacts of climate change on yields of major crops in 2050, a semiquantitative and participatory method assessing impacts of extreme events shows that there are nevertheless several climate risks. A range of adaptation measures are, however, available to reduce possible negative effects at crop level. In addition, at farm level farmers can change cropping patterns, and adjust inputs and outputs. Also farm structural change will influence impacts and adaptation. While the 5th IPCC report is more negative regarding impacts of climate change on agriculture compared to the previous report, also for temperate regions, our results show that when putting climate change in context of other drivers, and when explicitly accounting for adaptation at crop and farm level, impacts may be less negative in some regions and opportunities are revealed. These results refer to a temperate region, but an integrated assessment may also change perspectives on climate change for other parts of the world.
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Porter, J. R., & Christensen, S. (2013). Deconstructing crop processes and models via identities. Plant Cell and Environment, 36(11), 1919–1925.
Abstract: This paper is part review and part opinion piece; it has three parts of increasing novelty and speculation in approach. The first presents an overview of how some of the major crop simulation models approach the issue of simulating the responses of crops to changing climatic and weather variables, mainly atmospheric CO2 concentration and increased and/or varying temperatures. It illustrates an important principle in models of a single cause having alternative effects and vice versa. The second part suggests some features, mostly missing in current crop models, that need to be included in the future, focussing on extreme events such as high temperature or extreme drought. The final opinion part is speculative but novel. It describes an approach to deconstruct resource use efficiencies into their constituent identities or elements based on the Kaya-Porter identity, each of which can be examined for responses to climate and climatic change. We give no promise that the final part is correct’, but we hope it can be a stimulation to thought, hypothesis and experiment, and perhaps a new modelling approach.
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