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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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Shrestha, S., Abdalla, M., Hennessy, T., Forristal, D., & Jones, M. B. (2015). Irish farms under climate change – is there a regional variation on farm responses? J. Agric. Sci., 153(03), 385–398.
Abstract: The current paper aims to determine regional impacts of climate change on Irish farms examining the variation in farm responses. A set of crop growth models were used to determine crop and grass yields under a baseline scenario and a future climate scenario. These crop and grass yields were used along with farm-level data taken from the Irish National Farm Survey in an optimizing farm-level (farm-level linear programming) model, which maximizes farm profits under limiting resources. A change in farm net margins under the climate change scenario compared to the baseline scenario was taken as a measure to determine the effect of climate change on farms. The growth models suggested a decrease in cereal crop yields (up to 9%) but substantial increase in yields of forage maize (up to 97%) and grass (up to 56%) in all regions. Farms in the border, midlands and south-east regions suffered, whereas farms in all other regions generally fared better under the climate change scenario used in the current study. The results suggest that there is a regional variability between farms in their responses to the climate change scenario. Although substituting concentrate feed with grass feeds is the main adaptation on all livestock farms, the extent of such substitution differs between farms in different regions. For example, large dairy farms in the south-east region adopted total substitution of concentrate feed while similar dairy farms in the south-west region opted to replace only 0.30 of concentrate feed. Farms in most of the regions benefitted from increasing stocking rate, except for sheep farms in the border and dairy farms in the south-east regions. The tillage farms in the mid-east region responded to the climate change scenario by shifting arable production to beef production on farms.
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Barnes, A., Shrestha, S., Thomson, S., Toma, L., Mathews, K., & Sutherland, L. A. (2014). Comparing visions for CAP reforms post 2015: Farmer intentions and farm bio-economic modelling. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: This paper illustrates the impacts of two of the potential CAP reform post 2015 scenarios using an optimising farm level model and compares results with farmers’ perception about the policy changes, captured in a farmer intentions survey. The model results suggest that beef farms suffer a loss in farm net margins under fully decoupled (up to -21%) as well as under partially decoupled scenario (up to -19%) compared to current historical single farm payments. The model also shows that farm respond by reducing the number of beef animals on farm by up to 5%. However, under a partial decoupled scenario, beef farms increase calf numbers by 15% to benefit from coupled calf payment. A survey of 1,400 beef producers with respect to their intentions toward 2020 was conducted in the Summer of 2013. A set of hypothetical payment scenarios was used to test self-reported response to a number of scenarios related to expanding and extensifying. These were compared with the modelling results and found a range of responses which could, we argue, be used for future calibration and ‘sense-checking’ of results within future modelling strategies.
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Kipling, R. P., Topp, C. F. E., Bannink, A., Bartley, D. J., Blanco-Penedo, I., Cortignani, R., et al. (2019). To what extent is climate change adaptation a novel challenge for agricultural modellers. Env. Model. Softw., 120, Unsp 104492.
Abstract: Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers’ views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.
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