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Tao, F., Roetter, R. P., Palosuo, T., Hernandez Diaz-Ambrona, C. G., Ines Minguez, M., Semenov, M. A., et al. (2018). Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Glob. Chang. Biol., 24(3), 1291–1307.
Abstract: Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.
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Hutchings, N. J., Özkan Gülzari, Ş., de Haan, M., & Sandars, D. (2018). How do farm models compare when estimating greenhouse gas emissions from dairy cattle production. Animal, 12(10), 2171–2180.
Abstract: The European Union Effort Sharing Regulation (ESR) will require a 30% reduction in greenhouse gas (GHG) emissions by 2030 compared with 2005 from the sectors not included in the European Emissions Trading Scheme, including agriculture. This will require the estimation of current and future emissions from agriculture, including dairy cattle production systems. Using a farm-scale model as part of a Tier 3 method for farm to national scales provides a more holistic and informative approach than IPCC (2006) Tier 2 but requires independent quality control. Comparing the results of using models to simulate a range of scenarios that explore an appropriate range of biophysical and management situations can support this process by providing a framework for placing model results in context. To assess the variation between models and the process of understanding differences, estimates of GHG emissions from four farm-scale models (DailyWise, FarmAC, HolosNor and SFARMMOD) were calculated for eight dairy farming scenarios within a factorial design consisting of two climates (cool/dry and warm/wet) x two soil types (sandy and clayey) x two feeding systems (grass only and grass/maize). The milk yield per cow, follower cow ratio, manure management system, nitrogen (N) fertilisation and land area were standardised for all scenarios in order to associate the differences in the results with the model structure and function. Potential yield and application of available N in fertiliser and manure were specified separately for grass and maize. Significant differences between models were found in GHG emissions at the farm-scale and for most contributory sources, although there was no difference in the ranking of source magnitudes. The farm-scale GHG emissions, averaged over the four models, was 10.6 t carbon dioxide equivalents (CO(2)e)/ha per year, with a range of 1.9 t CO(2)e/ha per year. Even though key production characteristics were specified in the scenarios, there were still significant differences between models in the annual milk production per ha and the amounts of N fertiliser and concentrate feed imported. This was because the models differed in their description of biophysical responses and feedback mechanisms, and in the extent to which management functions were internalised. We conclude that comparing the results of different farm-scale models when applied to a range of scenarios would build confidence in their use in achieving ESR targets, justifying further investment in the development of a wider range of scenarios and software tools.
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Sakschewski, B., von Bloh, W., Huber, V., Müller, C., & Bondeau, A. (2014). Feeding 10 billion people under climate change: How large is the production gap of current agricultural systems. Ecol. Model., 288, 103–111.
Abstract: The human population is projected to reach more than 10 billion in the year 2100. Together with changing consumption pattern, population growth will lead to increasing food demand. The question arises whether or not the Earth is capable of fulfilling this demand. In this study, we approach this question by estimating the carrying capacity of current agricultural systems (K-C), which does not measure the maximum number of people the Earth is likely to feed in the future, but rather allows for an indirect assessment of the increases in agricultural productivity required to meet demands. We project agricultural food production under progressing climate change using the state-of-the-art dynamic global vegetation model LPJmL, and input data of 3 climate models. For 1990 to 2100 the worldwide annual caloric yield of the most important 11 crop types is simulated. Model runs with and without elevated atmospheric CO2 concentrations are performed in order to investigate CO2 fertilization effects. Country-specific per-capita caloric demands fixed at current levels and changing demands based on future GDP projections are considered to assess the role of future dietary shifts. Our results indicate that current population projections may considerably exceed the maximum number of people that can be fed globally if climate change is not accompanied by significant changes in land use, agricultural efficiencies and/or consumption pathways. We estimate the gap between projected population size and K-C to reach 2 to 6.8 billion people by 2100. We also present possible caloric self-supply changes between 2000 and 2100 for all countries included in this study. The results show that predominantly developing countries in tropical and subtropical regions will experience vast decreases of self-supply. Therefore, this study is important for planning future large-scale agricultural management, as well as the critical assessment of population projections, which should take food-mediated climate change feedbacks into account
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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., Höhn, J. G., & Fronzek, S. (2012). Projections of climate change impacts on crop production: A global and a Nordic perspective. Acta Agriculturae Scandinavica, Section A – Animal Science, 62(4), 166–180.
Abstract: Global climate is changing and food production is very sensitive to weather and climate variations. Global assessments of climate change impacts on food production have been made since the early 1990s, initially with little attention to the uncertainties involved. Although there has been abundant analysis of uncertainties in future greenhouse gas emissions and their impacts on the climate system, uncertainties related to the way climate change projections are scaled down as appropriate for different analyses and in modelling crop responses to climate change, have been neglected. This review paper mainly addresses uncertainties in crop impact modelling and possibilities to reduce them. We specifically aim to (i) show ranges of projected climate change-induced impacts on crop yields, (ii) give recommendations on use of emission scenarios, climate models, regionalization and ensemble crop model simulations for different purposes and (iii) discuss improvements and a few known unknowns’ affecting crop impact projections.
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