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Wallach, D.; Rivington, M. |
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A framework structure to integrate improved methods for uncertainty evaluation, and protocols for methods application |
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2014 |
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FACCE MACSUR Reports |
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3 |
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D-C4.1.2 |
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CropM |
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MA @ admin @ |
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2078 |
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von Lampe, M.; Willenbockel, D.; Ahammad, H.; Blanc, E.; Cai, Y.; Calvin, K.; Fujimori, S.; Hasegawa, T.; Havlik, P.; Heyhoe, E.; Kyle, P.; Lotze-Campen, H.; Mason, d’C., Daniel; Nelson, G.C.; Sands, R.D.; Schmitz, C.; Tabeau, A.; Valin, H.; van der Mensbrugghe, D.; van Meijl, H. |
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Why do global long-term scenarios for agriculture differ? An overview of the AgMIP Global Economic Model Intercomparison |
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Journal Article |
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2014 |
Publication |
Agricultural Economics |
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Agric. Econ. |
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45 |
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1 |
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3-3 |
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Computable general equilibrium; Partial equilibrium; Meta-analysis; Socioeconomic pathway; Climate change; Bioenergy; Land use; Model; intercomparison; land-use change; food demand; crop productivity; climate-change; future |
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Recent studies assessing plausible futures for agricultural markets and global food security have had contradictory outcomes. To advance our understanding of the sources of the differences, 10 global economic models that produce long-term scenarios were asked to compare a reference scenario with alternate socioeconomic, climate change, and bioenergy scenarios using a common set of key drivers. Several key conclusions emerge from this exercise: First, for a comparison of scenario results to be meaningful, a careful analysis of the interpretation of the relevant model variables is essential. For instance, the use of real world commodity prices differs widely across models, and comparing the prices without accounting for their different meanings can lead to misleading results. Second, results suggest that, once some key assumptions are harmonized, the variability in general trends across models declines but remains important. For example, given the common assumptions of the reference scenario, models show average annual rates of changes of real global producer prices for agricultural products on average ranging between -0.4% and +0.7% between the 2005 base year and 2050. This compares to an average decline of real agricultural prices of 4% p.a. between the 1960s and the 2000s. Several other common trends are shown, for example, relating to key global growth areas for agricultural production and consumption. Third, differences in basic model parameters such as income and price elasticities, sometimes hidden in the way market behavior is modeled, result in significant differences in the details. Fourth, the analysis shows that agro-economic modelers aiming to inform the agricultural and development policy debate require better data and analysis on both economic behavior and biophysical drivers. More interdisciplinary modeling efforts are required to cross-fertilize analyses at different scales. |
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2016-10-31 |
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English |
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0169-5150 |
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TradeM, ft_macsur |
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MA @ admin @ |
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4822 |
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Köchy, M. |
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The FACCE MACSUR Mid-Term Scientific Conference: ‘Achievements, Activities, Advancement’ |
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2014 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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3 |
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M-H3.5 |
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Hub |
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The mid-term meeting was held in Sassari, Sardinia, 1-4 April 2014. The meeting was attended by 120 researchers and stakeholders from 16 countries (Fig. 1). After a day of looking back on the achievements during the first two years and presenting results to stakeholders, researchers focused on fine-tuning the planning of remaining work for the project till May 2015 and preparations for a follow-up project (MACSUR2) till May 2017. On an excursion, scientists and stakeholders visited farms in the Oristano region, one of the regional case studies of MACSUR. The meeting was a unique opportunity in this pan-European project for discussing in person common issues with and among stakeholders of different regions and how to approach the impact of climate change to producing food in Europe in a world with a growing population. A report in La Nueva Sardegna highlighted the conference. Excursion: dairy sheep farm “Su Pranu” (Siamanna), dairy cattle farm “Sardo Farm” (Arborea), Arborea Cooperative Recordings of the presentations are available on YouTube: https://www.youtube.com/channel/UCrjoXlUIJNBW8cWOgh0_g The presentations are available on the conference website: http://ocs.macsur.eu/index.php/Hub/Mid-term/schedConf/presentations Short papers derived from the presentations are available on the conference website and in FACCE MACSUR Reports vol 5. The food consumed during lunches at the conference originated mostly from the Oristano region. Remaining food in good condition was donated to a charity organisation for needy people. Fig. 1. Number of participants per country. |
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2267 |
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Kässi, P.; Niskanen, O.; Känkänen, H. |
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Farm level approach to manage grass yield variation in changing climate in Jokioinen and St. Petersburg |
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2014 |
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FACCE MACSUR Reports |
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3 |
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Sp3-11 |
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Cattle’s feeding is based on grass silage in Northern Europe, but grass growth is highly dependent on weather conditions. In farms decision making, grass area is usually determined by the variation of yield. To be adequate in every situation, the lowest expected yield level determines the cultivated area. Other way to manage the grass yield risk is to increase silage storage capacity over annual consumption. Variation of grass yield in climate data from years 1961-1990 was compared with 15 different climate scenario models simulating years 2046-2065. A model was developed for evaluating the inadequacy risk in terms of cultivated area and storing capacity. The cost of risk is presented and discussed.In northern Europe a typical farm has storage for roughage consumption of almost one year. In addition, there can be a buffer storage. The extra storage is to be used before and during the harvest season. New harvest will be fed to animals only after the buffer empty. Shortage in the buffer storage is possible to be filled, when the yield exceeds the target level. For risk management, two alternative mechanisms are given: forage buffer and possibility to alter the field area.According to our results, there are no significant adverse effects in the cost of risk and implied farm profitability due to climate change. Selecting the risk management scenario of 30 % grass yield risk turned out to be the least cost solution. No Label |
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MA @ admin @ |
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2228 |
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Lehtonen, H.S.; Kässi, P.; Korhonen, P.; Niskanen, O.; Rötter, R.; Palosuo, T.; Liu, X.; Purola, T. |
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Specific problems and solutions in climate change adaptation in North Savo region |
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2014 |
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FACCE MACSUR Reports |
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3 |
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Sp3-10 |
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Crop production for feed dominates land use in North Savo in eastern Finland. The value of dairy and beef production is appr. 70 % of the total value of agricultural production of the region. In climate change adaptation research we are especially interested in dairy and meat sectors, which are directly dependent on the development of productivity of crop production. Climate change implies changes in cereals and forage crop yields and nutritive quality. There are most likely increasing problems and risks related to overwintering and growing periods. Grass silage is mainly self-produced on farms and most often there is no market for silage. Silage production and use are vulnerable to changes in local climate, because lost yield cannot be easily replaced from market. Risks and costs due to increasing inter-annual yield volatility can be reduced by good management practices, such as crop rotation, plant protection, soil improvements and better crop protection against plant diseases.However the profitability of such measures is dependent on market and policy conditions. Nevertheless new cultivars and species, as well as various options for production and risk management, are most likely needed in future climate. Some adaptations may have multiple benefits which however may realize only in medium or long run. It is important to safeguard the most important and obviously needed adaptations, and identify market and socio-economic conditions which inhibit farmers from necessary adaptations and lead to reduced productivity and increased production costs. No Label |
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MA @ admin @ |
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2227 |
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