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Shrestha, S., Ciaian, P., Himics, M., & van Doorslaer, B. (2013). Impacts of climate change on EU agriculture. Review of Agricultural and Applied Economics, 16(2), 24–39.
Abstract: The current paper investigates the medium term economic impact of climate changes on the EU agriculture. The yield change data under climate change scenarios are taken from the BIOMA (Biophysical Models Application) simulation environment. We employ CAPRI modelling framework to identify the EU aggregate economic effects as well as regional impacts. We take into account supply and market price adjustments of the EU agricultural sector as well as technical adaptation of crops to climate change. Overall results indicate an increase in yields and production level in the EU agricultural sector due to the climate change. In general, there are relatively small effects at the EU aggregate. For example, the value of land use and welfare change by approximately between -2% and 0.2%. However, there is a stronger impact at regional level with some stronger effects prevailing particularly in the Central and Northern EU and smaller impacts are observed in Southern Europe. Regional impacts of climate change vary by a factor higher up to 10 relative to the aggregate EU impacts. The price adjustments reduce the response of agricultural sector to climate change in particular with respect to production and income changes. The technical adaption of crops to climate change may result in a change production and land use by a factor between 1.4 and 6 relative to no-adaptation situation.
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Dumont, B., Leemans, V., Ferrandis, S., Bodson, B., Destain, J. - P., & Destain, M. - F. (2014). Assessing the potential of an algorithm based on mean climatic data to predict wheat yield. Precision Agric., 15(3), 255–272.
Abstract: The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days.
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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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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 semi-quantitative 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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Lehtonen, H., Palosuo, T., Korhonen, P., & Liu, X. (2018). Higher Crop Yield Levels in the North Savo Region—Means and Challenges Indicated by Farmers and Their Close Stakeholders. Agriculture, 8(7), 93.
Abstract: The sustainable intensification of farming systems is expected to increase food supply and reduce the negative environmental effects of agriculture. It is also seen as an effective adaptation and mitigation strategy in response to climate change. Our aim is to determine farmers’ and other stakeholders’ views on how higher crop yields can be achieved from their currently low levels. This was investigated in two stakeholder workshops arranged in North Savo, Finland, in 2014 and 2016. The workshop participants, who were organized in discussion groups, considered some agricultural policies to discourage the improvement of crop yields. Policy schemes were seen to support extensification and reduce the motivation for yield improvements. However, the most important means for higher crop yields indicated by workshop participants were improved soil conditions with drainage and liming, in addition to improved crop rotations, better sowing techniques, careful selection of cultivars and forage grass mixtures. Suggested solutions for improving both crop yields and farm income also included optimized use of inputs, focusing production at the most productive fields and actively developed farming skills and knowledge sharing. These latter aspects were more pronounced in 2016, suggesting that farmers’ skills are increasingly being perceived as important.
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