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Graef, F.; Sieber, S.; Mutabazi, K.; Asch, F.; Biesalski, H.K.; Bitegeko, J.; Bokelmann, W.; Bruentrup, M.; Dietrich, O.; Elly, N.; Fasse, A.; Germer, J.U.; Grote, U.; Herrmann, L.; Herrmann, R.; Hoffmann, H.; Kahimba, F.C.; Kaufmann, B.; Kersebaum, K.-C.; Kilembe, C.; Kimaro, A.; Kinabo, J.; König, B.; König, H.; Lana, M.; Levy, C.; Lyimo-Macha, J.; Makoko, B.; Mazoko, G.; Mbaga, S.H.; Mbogoro, W.; Milling, H.; Mtambo, K.; Mueller, J.; Mueller, C.; Mueller, K.; Nkonja, E.; Reif, C.; Ringler, C.; Ruvuga, S.; Schaefer, M.; Sikira, A.; Silayo, V.; Stahr, K.; Swai, E.; Tumbo, S.; Uckert, G. |
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Title |
Framework for participatory food security research in rural food value chains |
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Journal Article |
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Year |
2014 |
Publication |
Global Food Security |
Abbreviated Journal |
Global Food Security |
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3 |
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1 |
Pages |
8-15 |
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Keywords |
food security; food value chain; action research; tanzania; research framework |
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Abstract |
Enhancing food security for poor and vulnerable people requires adapting rural food systems to various driving factors. Food security-related research should apply participatory action research that considers the entire food value chain to ensure sustained success. This article presents a research framework that focusses on determining, prioritising, testing, adapting and disseminating food securing upgrading strategies across the multiple components of rural food value chains. These include natural resources, Food production, processing, markets, consumption and waste management. Scientists and policy makers jointly use tools developed for assessing potentials for enhancing regional food security at multiple spatial and temporal scales. The research is being conducted in Tanzania as a case study for Sub-Saharan countries and is done in close collaboration with local, regional and national stakeholders, encompassing all activities across all different food sectors. (C) 2014 Elsevier B.V. All rights reserved. |
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2211-9124 |
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CropM, TradeM |
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MA @ admin @ |
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4523 |
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Author |
Bennetzen, E.H.; Smith, P.; Porter, J.R. |
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Title |
Agricultural production and greenhouse gas emissions from world regions—The major trends over 40 years |
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Journal Article |
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Year |
2016 |
Publication |
Global Environmental Change |
Abbreviated Journal |
Glob. Environ. Change |
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Volume |
37 |
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Pages |
43-55 |
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Keywords |
Agriculture; Greenhouse gas intensity; Climate change; Kaya-Porter; identity; Decoupling emissions; Kaya-identity; land-use change; carbon-dioxide emissions; sustainable intensification; livestock production; forest transitions; global agriculture; crop; production; food security; deforestation; mitigation |
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Since 1970, global agricultural production has more than doubled with agriculture and land-use change now responsible for similar to 1/4 of greenhouse gas emissions from human activities. Yet, while greenhouse gas (GHG) emissions per unit of agricultural product have been reduced at a global level, trends in world regions have been quantified less thoroughly. The KPI (Kaya-Porter Identity) is a novel framework for analysing trends in agricultural production and land-use change and related GHG emissions. We apply this to assess trends and differences in nine world regions over the period 1970-2007. We use a deconstructed analysis of emissions from the mix of multiple sources, and show how each is changing in terms of absolute emissions on a per area and per produced unit basis, and how the change of emissions from each source contributes to the change in total emissions over time. The doubling of global agricultural production has mainly been delivered by developing and transitional countries, and this has been mirrored by increased GHG emissions. The decoupling of emissions from production shows vast regional differences. Our estimates show that emissions per unit crop (as kg CO2-equivalents per Giga Joule crop product), in Oceania, have been reduced by 94% from 1093 to 69; in Central & South America by 57% from 849 to 362; in sub-Saharan Africa by 27% from 421 to 309, and in Europe by 56% from 86 to 38. Emissions per unit livestock (as kg CO2-eq. GJ(-1) livestock product) have reduced; in sub-Saharan Africa by 24% from 6001 to 4580; in Central & South America by 61% from 3742 to 1448; in Central & Eastern Asia by 82% from 3,205 to 591, and; in North America by 28% from 878 to 632. In general, intensive and industrialised systems show the lowest emissions per unit of agricultural production. (C) 2016 Elsevier Ltd. All rights reserved. |
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0959-3780 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4758 |
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Kahiluoto, H.; Kaseva, J.; Hakala, K.; Himanen, S.J.; Jauhiainen, L.; Rötter, R.P.; Salo, T.; Trnka, M. |
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Title |
Cultivating resilience by empirically revealing response diversity |
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Journal Article |
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Year |
2014 |
Publication |
Global Environmental Change |
Abbreviated Journal |
Glob. Environ. Change |
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25 |
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186-193 |
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generic approach; climate change; food security; agrifood systems; cultivars; adaptive capacity; climate-change; functional diversity; plant-communities; genetic diversity; biodiversity; ecosystems; management; redundancy; evenness; weather |
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Intensified climate and market turbulence requires resilience to a multitude of changes. Diversity reduces the sensitivity to disturbance and fosters the capacity to adapt to various future scenarios. What really matters is diversity of responses. Despite appeals to manage resilience, conceptual developments have not yet yielded a break-through in empirical applications. Here, we present an approach to empirically reveal the ‘response diversity’: the factors of change that are critical to a system are identified, and the response diversity is determined based on the documented component responses to these factors. We illustrate this approach and its added value using an example of securing food supply in the face of climate variability and change. This example demonstrates that quantifying response diversity allows for a new perspective: despite continued increase in cultivar diversity of barley, the diversity in responses to weather declined during the last decade in the regions where most of the barley is grown in Finland. This was due to greater homogeneity in responses among new cultivars than among older ones. Such a decline in the response diversity indicates increased vulnerability and reduced resilience. The assessment serves adaptive management in the face of both ecological and socioeconomic drivers. Supplier diversity in the food retail industry in order to secure affordable food in spite of global price volatility could represent another application. The approach is, indeed, applicable to any system for which it is possible to adopt empirical information regarding the response by its components to the critical factors of variability and change. Targeting diversification in response to critical change brings efficiency into diversity. We propose the generic procedure that is demonstrated in this study as a means to efficiently enhance resilience at multiple levels of agrifood systems and beyond. (C) 2014 The Authors. Published by Elsevier Ltd. |
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0959-3780 |
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CropM |
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MA @ admin @ |
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4525 |
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Nendel, C.; Wieland, R.; Mirschel, W.; Specka, X.; Guddat, C.; Kersebaum, K.C. |
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Title |
Simulating regional winter wheat yields using input data of different spatial resolution |
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Journal Article |
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Year |
2013 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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145 |
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67-77 |
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Keywords |
monica; agro-ecosystem model; dynamic modelling; scaling; input data; climate-change; crop yield; nitrogen dynamics; food security; mineral nitrogen; soil-moisture; scaling-up; model; maize; water |
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The success of using agro-ecosystem models for the high-resolution simulation of agricultural yields for larger areas is often hampered by a lack of input data. We investigated the effect of different spatially resolved soil and weather data used as input for the MONICA model on its ability to reproduce winter wheat yields in the Federal State of Thuringia, Germany (16,172 km(2)). The combination of one representative soil and one weather station was insufficient to reproduce the observed mean yield of 6.66 +/- 0.87 t ha(-1) for the federal state. Use of a 100 m x 100 m grid of soil and relief information combined with just one representative weather station yielded a good estimator (7.01 +/- 1.47 t ha(-1)). The soil and relief data grid used in combination with weather information from 14 weather stations in a nearest neighbour approach produced even better results (6.60 +/- 1.37 t ha(-1)); the same grid used with 39 additional rain gauges and an interpolation algorithm that included an altitude correction of temperature data slightly overpredicted the observed mean (7.36 +/- 1.17 t ha(-1)). It was concluded that the apparent success of the first two high-resolution approaches over the latter was based on two effects that cancelled each other out: the calibration of MONICA to match high-yield experimental data and the growth-defining and -limiting effect of weather data that is not representative for large parts of the region. At the county and farm level the MONICA model failed to reproduce the 1992-2010 time series of yields, which is partly explained by the fact that many growth-reducing factors were not considered in the model. (C) 2013 Elsevier B.V. All rights reserved. |
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0378-4290 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4498 |
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Kipling, R.P.; Bannink, A.; Bellocchi, G.; Dalgaard, T.; Fox, N.J.; Hutchings, N.J.; Kjeldsen, C.; Lacetera, N.; Sinabell, F.; Topp, C.F.E.; van Oijen, M.; Virkajärvi, P.; Scollan, N.D. |
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Modeling European ruminant production systems: Facing the challenges of climate change |
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Journal Article |
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2016 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agricultural Systems |
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147 |
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24-37 |
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Keywords |
Food security; Livestock systems; Modeling; Pastoral systems; Policy support; Ruminants |
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Abstract |
Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensi- fication of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a prior- ity. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are ap- plied to develop ‘smart’ empirical modeling, may overcome the trade-off between complexity and usability. De- veloping the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems re- lated to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks |
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0308521x |
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Review |
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LiveM, ft_macsur |
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MA @ admin @ |
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4734 |
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