Lake, I. R., Jones, N. R., Agnew, M., Goodess, C. M., Giorgi, F., Hamaoui-Laguel, L., et al. (2017). Climate change and future pollen allergy in Europe. Environ Health Perspect, 125(3), 385–391.
Abstract: BACKGROUND: Globally, pollen allergy is a major public health problem, but a fundamental unknown is the likely impact of climate change. To our knowledge, this is the first study to quantify the consequences of climate change upon pollen allergy in humans. OBJECTIVES: We produced quantitative estimates of the potential impact of climate change upon pollen allergy in humans, focusing upon common ragweed (Ambrosia artemisiifolia) in Europe. METHODS: A process-based model estimated the change in ragweed’s range under climate change. A second model simulated current and future ragweed pollen levels. These findings were translated into health burdens using a dose-response curve generated from a systematic review and from current and future population data. Models considered two different suites of regional climate/pollen models, two greenhouse gas emissions scenarios [Representative Concentration Pathways (RCPs) 4.5 and 8.5], and three different plant invasion scenarios. RESULTS: Our primary estimates indicated that sensitization to ragweed will more than double in Europe, from 33 to 77 million people, by 2041-2060. According to our projections, sensitization will increase in countries with an existing ragweed problem (e.g., Hungary, the Balkans), but the greatest proportional increases will occur where sensitization is uncommon (e.g., Germany, Poland, France). Higher pollen concentrations and a longer pollen season may also increase the severity of symptoms. Our model projections were driven predominantly by changes in climate (66%) but were also influenced by current trends in the spread of this invasive plant species. Assumptions about the rate at which ragweed spreads throughout Europe had a large influence upon the results. CONCLUSIONS: Our quantitative estimates indicate that ragweed pollen allergy will become a common health problem across Europe, expanding into areas where it is currently uncommon. Control of ragweed spread may be an important adaptation strategy in response to climate change. Citation: Lake IR, Jones NR, Agnew M, Goodess CM, Giorgi F, Hamaoui-Laguel L, Semenov MA, Solomon F, Storkey J, Vautard R, Epstein MM. 2017. Climate change and future pollen allergy in Europe. Environ Health Perspect 125:385-391; http://dx.doi.org/10.1289/EHP173.
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Semenov, M. A., Mitchell, R. A. C., Whitmore, A. P., Hawkesford, M. J., Parry, M. A. J., & Shewry, P. R. (2012). Shortcomings in wheat yield predictions. Nat. Clim. Change, 2(6), 380–382.
Abstract: Predictions of a 40–140% increase in wheat yield by 2050, reported in the UK Climate Change Risk Assessment, are based on a simplistic approach that ignores key factors affecting yields and hence are seriously misleading.
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Dumont, B., Vancutsem, F., Seutin, B., Bodson, B., Destain, J. - P., & Destain, M. - F. (2012). Simulation de la croissance du blé à l’aide de modèles écophysiologiques: Synthèse bibliographique des méthodes, potentialités et limitations. Biotechnologie, Agronomie, Société et Environnement, 163, 376–386.
Abstract: Crop models describe the growth and development of a crop interacting with its surrounding agro-environmental conditions (soil, climate and the close conditions of the plant). However, the implementation of such models remains difficult because of the high number of explanatory variables and parameters. It often happens that important discrepancies appear between measured and simulated values. This article aims to highlight the different sources of uncertainty related to the use of crop models, as well as the actual methods that allow a compensation for or, at least, a consideration of these sources of error during analysis of the model results. This article presents a literature review, which firstly synthesises the general mathematical structure of crop models. The main criteria for evaluating crop models are then described. Finally, several methods used for improving models are given. Parameter estimation methods, including frequentist and Bayesian approaches, are presented and data assimilation methods are reviewed.
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Del Prado, A., Crosson, P., Olesen, J. E., & Rotz, C. A. (2013). Whole-farm models to quantify greenhouse gas emissions and their potential use for linking climate change mitigation and adaptation in temperate grassland ruminant-based farming systems. Animal, 7 Suppl 2, 373–385.
Abstract: The farm level is the most appropriate scale for evaluating options for mitigating greenhouse gas (GHG) emissions, because the farm represents the unit at which management decisions in livestock production are made. To date, a number of whole farm modelling approaches have been developed to quantify GHG emissions and explore climate change mitigation strategies for livestock systems. This paper analyses the limitations and strengths of the different existing approaches for modelling GHG mitigation by considering basic model structures, approaches for simulating GHG emissions from various farm components and the sensitivity of GHG outputs and mitigation measures to different approaches. Potential challenges for linking existing models with the simulation of impacts and adaptation measures under climate change are explored along with a brief discussion of the effects on other ecosystem services.
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Foyer, C. H., Siddique, K. H. M., Tai, A. P. K., Anders, S., Fodor, N., Wong, F. - L., et al. (2019). Modelling predicts that soybean is poised to dominate crop production across Africa. Plant Cell Environ., 42(1), 373–385.
Abstract: The superior agronomic and human nutritional properties of grain legumes (pulses) make them an ideal foundation for future sustainable agriculture. Legume-based farming is particularly important in Africa, where small-scale agricultural systems dominate the food production landscape. Legumes provide an inexpensive source of protein and nutrients to African households as well as natural fertilization for the soil. Although the consumption of traditionally grown legumes has started to decline, the production of soybeans (Glycine max Merr.) is spreading fast, especially across southern Africa. Predictions of future land-use allocation and production show that the soybean is poised to dominate future production across Africa. Land use models project an expansion of harvest area, whereas crop models project possible yield increases. Moreover, a seed change in farming strategy is underway. This is being driven largely by the combined cash crop value of products such as oils and the high nutritional benefits of soybean as an animal feed. Intensification of soybean production has the potential to reduce the dependence of Africa on soybean imports. However, a successful “soybean bonanza” across Africa necessitates an intensive research, development, extension, and policy agenda to ensure that soybean genetic improvements and production technology meet future demands for sustainable production.
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