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Author |
Özkan, Ş.; Farquharson, R.J.; Hill, J.; Malcolm, B. |
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Title |
A stochastic analysis of the impact of input parameters on profit of Australian pasture-based dairy farms under variable carbon price scenarios |
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Journal Article |
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Year |
2015 |
Publication |
Environmental Science & Policy |
Abbreviated Journal |
Environmental Science & Policy |
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Volume |
48 |
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Pages |
163-171 |
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Keywords |
carbon tax; operating profit; stochastic dominance; dairy; feeding system; mitigation; cows; systems; efficiency; risk |
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Abstract |
The imposition of a carbon tax in the economy will have indirect impacts on dairy farmers in Australia. Although there is a great deal of information available regarding mitigation strategies both in Australia and internationally, there seems to be a lack of research investigating the variable prices of carbon-based emissions on dairy farm operating profits in Australia. In this study, a stochastic analysis comparing the uncertainty in income in response to different prices on carbon-based emissions was conducted. The impact of variability in pasture consumption and variable prices of concentrates and hay on farm profitability was also investigated. The two different feeding systems examined were a ryegrass pasture-based system (RM) and a complementary forage-based system (CF). Imposing a carbon price ($20-$60) and not changing the systems reduced the farm operating profits by 28.4% and 25.6% in the RM and CF systems, respectively compared to a scenario where no carbon price was imposed. Different farming businesses will respond to variability in the rapidly changing operating environment such as fluctuations in pasture availability, price of purchased feeds and price of milk or carbon emissions differently. Further, in case there is a carbon price imposed for GHG emissions emanated from dairy farming systems, changing from pasture-based to more complex feeding systems incorporating home-grown double crops may reduce the reductions in farm operating profits. There is opportunity for future studies to focus on the impacts of different mitigation strategies and policy applications on farm operating profits. (C) 2015 Elsevier Ltd. All rights reserved. |
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1462-9011 |
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LiveM |
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MA @ admin @ |
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4574 |
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Liu, X.; Lehtonen, H.; Purola, T.; Pavlova, Y.; Rötter, R.; Palosuo, T. |
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Title |
Dynamic economic modelling of crop rotations with farm management practices under future pest pressure |
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Journal Article |
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Year |
2016 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agricultural Systems |
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144 |
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65-76 |
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Keywords |
Farm management; Dynamic optimization; Crop rotation; Risk aversion; Climate change; Prices; climate-change; sequester carbon; changing climate; food security; challenge; Finland; ensembles; systems; europe; tool |
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Abstract |
Agricultural practice is facing multiple challenges under volatile commodity markets, inevitable climate change, mounting pest pressure and various other environment-related constraints. The objective of this research is to present a dynamic optimization model of crop rotations and farm management and show its suitability for economic analysis over a 30 year time period. In this model, we include management practices such as fertilization, fungicide treatment and liming, and apply it in a region in Southwestern Finland. Results show that (i) growing pest pressure favours the cultivation of wheat-oats and wheat-oilseeds combinations, while (ii) market prices largely determine the crops in the rotation plan and the specific management practices adopted. The flexibility of our model can also be utilized in evaluating the value of other management options such as new cultivars under different projections of future climate and market conditions. |
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0308521x |
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CropM, TradeM, ftnotmacsur |
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MA @ admin @ |
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4719 |
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Dono, G.; Raffaele, C.; Luca, G.; Roggero, P.P. |
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Title |
Income Impacts of Climate Change: Irrigated Farming in the Mediterranean and Expected Changes in Probability of Favorable and Adverse Weather Conditions |
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Journal Article |
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2014 |
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German Journal of Agricultural Economics |
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German Journal of Agricultural Economics |
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63 |
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3 |
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177-186 |
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discrete stochastic programming; rdp measures to adapt to climate change; economic impact of climate change; irrigated agriculture and climate change; insurance tools for adaptation to climate change; water markets; risk; variability; management; systems |
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EU rural development policy (RDP) regulation 1305/2013 aims to protect farmers’ incomes from ongoing change of climate variability (CCV), and the increase in frequency of adverse climatic events. An income stabilization tool (IST) is provided to compensate drastic drops in income, including those caused by climatic events. The present study examines some aspect of its application focussing on Mediterranean irrigation area where frequent water shortages may generate significant income reductions in the current climate conditions, and may be further exacerbated by climate change. This enhanced loss of income in the future would occur due to a change in climate variability. This change would appreciably reduce the probability of weather conditions that are favourable for irrigation, but would not significantly increase either the probability of unfavourable weather conditions or the magnitude of their impact. As the IST and other insurance tools that protect against adversity and catastrophic events are only activated under extreme conditions, farmers may not consider them to be suitable in dealing with the new climate regime. This would leave a portion of the financial resources allocated by the RDP unused, resulting in less support for climate change adaptation. |
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0002-1121 |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
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4669 |
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Author |
Zimmermann, A.; Webber, H.; Zhao, G.; Ewert, F.; Kros, J.; Wolf, J.; Britz, W.; de Vries, W. |
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Title |
Climate change impacts on crop yields, land use and environment in response to crop sowing dates and thermal time requirements |
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Journal Article |
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Year |
2017 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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157 |
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81-92 |
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Integrated assessment; Crop management; Climate change; Europe; INTEGRATED ASSESSMENT; EUROPEAN AGRICULTURE; FOOD SECURITY; HEAT-STRESS; ADAPTATION; SYSTEMS; TEMPERATURE; SCENARIOS; WHEAT; PRODUCTIVITY; Vries W., 2011, ENVIRONMENTAL POLLUTION, V159, P3254 |
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Impacts of climate change on European agricultural production, land use and the environment depend on its impact on crop yields. However, many impact studies assume that crop management remains unchanged in future scenarios, while farmers may adapt their sowing dates and cultivar thermal time requirements to minimize yield losses or realize yield gains. The main objective of this study was to investigate the sensitivity of climate change impacts on European crop yields, land use, production and environmental variables to adaptations in crops sowing dates and varieties’ thermal time requirements. A crop, economic and environmental model were coupled in an integrated assessment modelling approach for six important crops, for 27 countries of the European Union (EU27) to assess results of three SRES climate change scenarios to 2050. Crop yields under climate change were simulated considering three different management cases; (i) no change in crop management from baseline conditions (NoAd), (ii) adaptation of sowing date and thermal time requirements to give highest yields to 2050 (Opt) and (iii) a more conservative adaptation of sowing date and thermal time requirements (Act). Averaged across EU27, relative changes in water-limited crop yields due to climate change and increased CO2 varied between -6 and + 21% considering NoAd management, whereas impacts with Opt management varied between + 12 and + 53%, and those under Act management between 2 and + 27%. However, relative yield increases under climate change increased to + 17 and + 51% when technology progress was also considered. Importantly, the sensitivity to crop management assumptions of land use, production and environmental impacts were less pronounced than for crop yields due to the influence of corresponding market, farm resource and land allocation adjustments along the model chain acting via economic optimization of yields. We conclude that assumptions about crop sowing dates and thermal time requirements affect impact variables but to a different extent and generally decreasing for variables affected by economic drivers. |
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2017-11-02 |
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0308-521x |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
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5178 |
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Author |
Vilvert, E.; Lana, M.; Zander, P.; Sieber, S. |
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Title |
Multi-model approach for assessing the sunflower food value chain in Tanzania |
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Journal Article |
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Year |
2018 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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159 |
Issue |
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103-110 |
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Sunflower; Food value chain; Modelling; Tanzania; Food security; Systems Simulation; Crop Model; Agricultural Systems; Farming Systems; Yield Response; Land-Use; Water; Aquacrop; Security; Stics |
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Sunflower is one of the major oilseeds produced in Tanzania, but due to insufficient domestic production more than half of the country’s demand is imported. The improvement of the sunflower food value chain (FVC) understanding is important to ensure an increase in the production, availability, and quality of edible oil. In order to analyse causes and propose solutions to increase the production of sunflower oil, a conceptual framework that proposes the combined use of different models to provide insights about the sunflower FVC was developed. This research focus on the identification of agricultural models that can provide a better understanding of the sunflower FVC in Tanzania, especially within the context of food security improvement. A FVC scheme was designed considering the main steps of sunflower production. Thereafter, relevant models were selected and placed along each step of the FVC. As result, the sunflower FVC model in Tanzania is organized in five steps, namely (1) natural resources; (2) crop production; (3) oil processing; (4) trade; and (5) consumption. Step 1 uses environmental indicators to analyse soil parameters on soil-water models (SWAT, LPJmL, APSIM or CroSyst), with outputs providing data for step 2 of the FVC. In the production step, data from step 1, together with other inputs, is used to run crop models (DSSAT, HERMES, MONICA, STICS, EPIC or AquaCrop) that analyse the impact on sunflower yields. Thereafter, outputs from crop models serve as input for bio-economic farm models (FSSIM or MODAM) to estimate production costs and farm income by optimizing resource allocation planning for step 2. In addition, outputs from crop models are used as inputs for macro-economic models (GTAP, MAGNET or MagPie) by adjusting supply functions and environmental impacts within steps 3, 4, and 5. These models simulate supply and demand, including the processing of products to determine prices and trade volumes at market equilibrium. In turn, these data is used by bio-economic farm models to assess sunflower returns for different farm types and agro-environmental conditions. Due to the large variety of models, it is possible to assess significant parts of the FVC, reducing the need to make assumptions, while improving the understanding of the FVC. |
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2018-01-25 |
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0308-521x |
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CropM, TradeM, ft_macsur |
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Call Number |
MA @ admin @ |
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5187 |
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