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Waha, K.; Müller, C.; Bondeau, A.; Dietrich, J.P.; Kurukulasuriya, P.; Heinke, J.; Lotze-Campen, H. |
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Title |
Adaptation to climate change through the choice of cropping system and sowing date in sub-Saharan Africa |
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Journal Article |
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Year |
2013 |
Publication |
Global Environmental Change |
Abbreviated Journal |
Glob. Environ. Change |
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Volume |
23 |
Issue |
1 |
Pages |
130-143 |
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Keywords |
multiple cropping; sequential cropping systems; crop modelling; agricultural management; adaptation options; global vegetation model; future food-production; rainy-season; west-africa; agriculture; yield; maize; soil; variability; heat |
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Abstract |
Multiple cropping systems provide more harvest security for farmers, allow for crop intensification and furthermore influence ground cover, soil erosion, albedo, soil chemical properties, pest infestation and the carbon sequestration potential. We identify the traditional sequential cropping systems in ten sub-Saharan African countries from a survey dataset of more than 8600 households. We find that at least one sequential cropping system is traditionally used in 35% of all administrative units in the dataset, mainly including maize or groundnuts. We compare six different management scenarios and test their susceptibility as adaptation measure to climate change using the dynamic global vegetation model for managed land LPJmL. Aggregated mean crop yields in sub-Saharan Africa decrease by 6-24% due to climate change depending on the climate scenario and the management strategy. As an exception, some traditional sequential cropping systems in Kenya and South Africa gain by at least 25%. The crop yield decrease is typically weakest in sequential cropping systems and if farmers adapt the sowing date to changing climatic conditions. Crop calorific yields in single cropping systems only reach 40-55% of crop calorific yields obtained in sequential cropping systems at the end of the 21st century. The farmers’ choice of adequate crops, cropping systems and sowing dates can be an important adaptation strategy to climate change and these management options should be considered in climate change impact studies on agriculture. (C) 2012 Elsevier Ltd. All rights reserved. |
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2016-10-31 |
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0959-3780 |
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CropM |
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Call Number |
MA @ admin @ |
Serial |
4823 |
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Vosough Ahmadi, B.; Shrestha, S.; Thomson, S.G.; Barnes, A.P.; Stott, A.W. |
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Title |
Impacts of greening measures and flat rate regional payments of the Common Agricultural Policy on Scottish beef and sheep farms |
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Journal Article |
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Year |
2015 |
Publication |
Journal of Agricultural Science |
Abbreviated Journal |
J. Agric. Sci. |
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Volume |
153 |
Issue |
04 |
Pages |
676-688 |
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Keywords |
CAP reform; models; level; water; Agriculture |
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Abstract |
The latest Common Agricultural Policy (CAP) reforms could bring substantial changes to Scottish farming communities. Two major components of this reform package, an introduction of environmental measures into the Pillar 1 payments and a move away from historical farm payments towards regionalized area payments, would have a significant effect on altering existing support structures for Scottish farmers, as it would for similar farm types elsewhere in Europe where historic payments are used. An optimizing farm-level model was developed to explore how Scottish beef and sheep farms might be affected by the greening and flat rate payments under the current CAP reforms. Nine different types of beef and sheep farms were identified and detailed biophysical and financial farm-level data for these farm types were used to parameterize the model. Results showed that the greening measures of the CAP did not have much impact on net margins of most of the beef and sheep farm businesses, except for ‘Beef Finisher’ farm types where the net margins decreased by 3%. However, all farm types were better off adopting the greening measures than not qualifying for the greening payments through non-compliance with the measures. The move to regionalized farm payments increased the negative financial impact of greening on most of the farms but it was still substantially lower than the financial sacrifice of not adopting greening measures. Results of maximizing farm net margin, under a hypothetical assumption of excluding farm payments, showed that in most of the mixed (sheep and cattle) and beef suckler cattle farms the optimum stock numbers predicted by the model were lower than actual figures on farm. When the regionalized support payments were allocated to each farm, the proportion of the mixed farms that would increase their stock numbers increased whereas this proportion decreased for beef suckler farms and no impact was predicted in sheep farms. Also under the regionalized support payments, improvements in profitability were found in mixed farms and sheep farms. Some of the specialized beef suckler farms also returned a profit when CAP support was added. |
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0021-8596 1469-5146 |
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LiveM |
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MA @ admin @ |
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4654 |
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Author |
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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Title |
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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Year |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
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45 |
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1 |
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3-3 |
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Keywords |
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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Abstract |
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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0169-5150 |
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TradeM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4822 |
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Author |
Vitali, A.; Felici, A.; Esposito, S.; Bernabucci, U.; Bertocchi, L.; Maresca, C.; Nardone, A.; Lacetera, N. |
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Title |
The effect of heat waves on dairy cow mortality |
Type |
Journal Article |
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Year |
2015 |
Publication |
Journal of Dairy Science |
Abbreviated Journal |
J. Dairy Sci. |
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Volume |
98 |
Issue |
7 |
Pages |
4572-4579 |
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Keywords |
Animal Welfare; Animals; Cattle; Cross-Over Studies; Female; Heat Stress Disorders/*mortality; *Hot Temperature; Italy/epidemiology; Logistic Models; *Movement; Retrospective Studies; Seasons; dairy cow; global warming; heat wave; mortality; welfare |
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Abstract |
This study investigated the mortality of dairy cows during heat waves. Mortality data (46,610 cases) referred to dairy cows older than 24 mo that died on a farm from all causes from May 1 to September 30 during a 6-yr period (2002-2007). Weather data were obtained from 12 weather stations located in different areas of Italy. Heat waves were defined for each weather station as a period of at least 3 consecutive days, from May 1 to September 30 (2002-2007), when the daily maximum temperature exceeded the 90th percentile of the reference distribution (1971-2000). Summer days were classified as days in heat wave (HW) or not in heat wave (nHW). Days in HW were numbered to evaluate the relationship between mortality and length of the wave. Finally, the first 3 nHW days after the end of a heat wave were also considered to account for potential prolonged effects. The mortality risk was evaluated using a case-crossover design. A conditional logistic regression model was used to calculate odds ratio and 95% confidence interval for mortality recorded in HW compared with that recorded in nHW days pooled and stratified by duration of exposure, age of cows, and month of occurrence. Dairy cows mortality was greater during HW compared with nHW days. Furthermore, compared with nHW days, the risk of mortality continued to be higher during the 3 d after the end of HW. Mortality increased with the length of the HW. Considering deaths stratified by age, cows up to 28 mo were not affected by HW, whereas all the other age categories of older cows (29-60, 61-96, and >96 mo) showed a greater mortality when exposed to HW. The risk of death during HW was higher in early summer months. In particular, the highest risk of mortality was observed during June HW. Present results strongly support the implementation of adaptation strategies which may limit heat stress-related impairment of animal welfare and economic losses in dairy cow farm during HW. |
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2016-06-01 |
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0022-0302 |
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LiveM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4744 |
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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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Keywords |
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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Abstract |
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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