| Records |
| Author |
Mitter, H.; Sinabell, F.; E., S. |
| Title |
Assessing climate change and policy impacts on protein crop production in Austria |
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Conference Article |
| Year |
2013 |
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Pages |
527-535 |
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TradeM |
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Impacts World 2013, International Conference on Climate Change Effects, Potsdam, Germany, 2013-05-27 to 2013-05-30 |
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| Call Number |
MA @ admin @ |
Serial |
2647 |
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| Author |
Bojar, W.; Dzieza, G.; Sikora, M.; Spiewak, J.; Wyszkowska, Z.; Januszewski, A.; Zóltowski, M. |
| Title |
Wybrane metody ograniczania dzialania czynników ryzyka w rolnictwie w swietle wspólczesnych wyzwan (Selected methods of limiting of risk factors in agriculture in a view of contemporary challenges) Y ograniczania dzialania czynników ryzyka w rolnictwie |
Type |
Journal Article |
| Year |
2014 |
Publication |
Roczniki naukowe ekonomii rolnictwa i rozwoju obszarów wiejskich |
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| Volume |
101(4) |
Issue |
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Pages |
7-18 |
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TradeM |
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no |
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MA @ admin @ |
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2076 |
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| Author |
Schmid, E. |
| Title |
Integrated land use modelling — a course for doctoral students |
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Report |
| Year |
2017 |
Publication |
FACCE MACSUR Reports |
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| Volume |
10 |
Issue |
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Pages |
T4.1-4.2-XC4.3-4.4-D |
| Keywords |
TradeM |
| Abstract |
The course on “Integrated land use modelling” took place at BOKU Vienna between 24. – 28. April 2017. It was a five-days course capturing many aspects in quantitative integrated land use modelling using GAMS (see course outline). 10 students have participated the course coming from several countries. Students finishing the course have received 3 ECTS points. The course was offered by BOKU and the Doctoral Certificate Program in Agricultural Economics (https://www.agraroekonomik.de/index.html ). |
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no |
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MA @ admin @ |
Serial |
5036 |
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Robinson, S.; van Meijl, H.; Willenbockel, D.; Valin, H.; Fujimori, S.; Masui, T.; Sands, R.; Wise, M.; Calvin, K.; Havlik, P.; Mason d’Croz, D.; Tabeau, A.; Kavallari, A.; Schmitz, C.; Dietrich, J.P.; von Lampe, M. |
| Title |
Comparing supply-side specifications in models of global agriculture and the food system |
Type |
Journal Article |
| Year |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
| Volume |
45 |
Issue |
1 |
Pages |
21-35 |
| Keywords |
global agricultural models; global food system scenario analysis; general equilibrium; partial equilibrium; growth; trade |
| Abstract |
This article compares the theoretical and functional specification of production in partial equilibrium (PE) and computable general equilibrium (CGE) models of the global agricultural and food system included in the AgMIP model comparison study. The two model families differ in their scopepartial versus economy-wideand in how they represent technology and the behavior of supply and demand in markets. The CGE models are deep structural models in that they explicitly solve the maximization problem of consumers and producers, assuming utility maximization and profit maximization with production/cost functions that include all factor inputs. The PE models divide into two groups on the supply side: (1) shallow structural models, which essentially specify area/yield supply functions with no explicit maximization behavior, and (2) deep structural models that provide a detailed activity-analysis specification of technology and explicit optimizing behavior by producers. While the models vary in their specifications of technology, both within and between the PE and CGE families, we consider two stylized theoretical models to compare the behavior of crop yields and supply functions in CGE models with their behavior in shallow structural PE models. We find that the theoretical responsiveness of supply to changes in prices can be similar, depending on parameter choices that define the behavior of implicit supply functions over the domain of applicability defined by the common scenarios used in the AgMIP comparisons. In practice, however, the applied models are more complex and differ in their empirical sensitivity to variations in specificationcomparability of results given parameter choices is an empirical question. To illustrate the issues, sensitivity analysis is done with one global CGE model, MAGNET, to indicate how the results vary with different specification of technical change, and how they compare with the results from PE models. |
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English |
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0169-5150 |
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| Notes |
CropM, TradeM, ft_macsur |
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no |
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MA @ admin @ |
Serial |
4735 |
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Reidsma, P.; Bakker, M.M.; Kanellopoulos, A.; Alam, S.J.; Paas, W.; Kros, J.; de Vries, W. |
| Title |
Sustainable agricultural development in a rural area in the Netherlands? Assessing impacts of climate and socio-economic change at farm and landscape level |
Type |
Journal Article |
| Year |
2015 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agricultural Systems |
| Volume |
141 |
Issue |
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Pages |
160-173 |
| Keywords |
Integrated assessment; Global change; Sustainability; Agriculture; Farm; structural change; Spatially explicit; Climate smart agriculture; affecting land-use; integrated assessment; multiobjective optimization; analytical framework; trade-offs; systems; uncertainties; policies; future; adaptation |
| Abstract |
Changes in climate, technology, policy and prices affect agricultural and rural development. To evaluate whether this development is sustainable, impacts of these multiple drivers need to be assessed for multiple indicators. In a case study area in the Netherlands, a bio-economic farm model, an agent-based land-use change model, and a regional emission model have been used to simulate rural development under two plausible global change scenarios at both farm and landscape level. Results show that in this area, climate change will have mainly negative economic impacts (dairy gross margin, arable gross margin, economic efficiency, milk production) in the warmer and drier W+ scenario, while impacts are slightly positive in the G scenario with moderate climate change. Dairy farmers are worse off than arable farmers in both scenarios. Conversely, when the W+ scenario is embedded in the socio-economic Global Economy (GE) scenario, changes in technology, prices, and policy are projected to have a positive economic impact, more than offsetting the negative climate impacts. Important is, however, that environmental impacts (global warming, terrestrial and aquatic eutrophication) are largely negative and social impacts (farm size, number of farms, nature area, odour) are mixed. In the G scenario combined with the socio-economic Regional Communities (RC) scenario the average dairy gross margin in particular is negatively affected. Social impacts are similarly mixed as in the GE scenario, while environmental impacts are less severe. Our results suggest that integrated assessments at farm and landscape level can be used to guide decision-makers in spatial planning policies and climate change adaptation. As there will always be trade-offs between economic, social, and environmental impacts stakeholders need to interact and decide upon most important directions for policies. This implies a choice between production and income on the one hand and social and environmental services on the other hand |
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2016-06-01 |
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English |
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0308-521x |
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| Notes |
CropM |
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no |
| Call Number |
MA @ admin @ |
Serial |
4742 |
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