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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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Abstract |
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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Valin, H.; Sands, R.D.; van der Mensbrugghe, D. and; Nelson, G.C.; Ahammad, H.; Blanc, E.; Bodirsky; Benjamin; Fujimori, S.; Hasegawa, T.; Havlik, P.; and Heyhoe, E.; Kyle, P.; Mason-D’Croz, D.; Paltsev; Sergey; Rolinski, S.; Tabeau, A.; van Meijl, H. and; von Lampe, M.; Willenbockel, D. |
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Title |
The future of food demand: Understanding differences in global economic models |
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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 |
Pages |
51-67 |
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Keywords |
world food demand; socioeconomic pathways; climate change; computable general equilibrium; partial equilibrium; systems |
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Abstract |
Understanding the capacity of agricultural systems to feed the world population under climate change requires projecting future food demand. This article reviews demand modeling approaches from 10 global economic models participating in the Agricultural Model Intercomparison and Improvement Project (AgMIP). We compare food demand projections in 2050 for various regions and agricultural products under harmonized scenarios of socioeconomic development, climate change, and bioenergy expansion. In the reference scenario (SSP2), food demand increases by 59-98% between 2005 and 2050, slightly higher than the most recent FAO projection of 54% from 2005/2007. The range of results is large, in particular for animal calories (between 61% and 144%), caused by differences in demand systems specifications, and in income and price elasticities. The results are more sensitive to socioeconomic assumptions than to climate change or bioenergy scenarios. When considering a world with higher population and lower economic growth (SSP3), consumption per capita drops on average by 9\% for crops and 18% for livestock. The maximum effect of climate change on calorie availability is -6% at the global level, and the effect of biofuel production on calorie availability is even smaller. |
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0169-5150 |
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TradeM, ft_macsur |
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MA @ admin @ |
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4752 |
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Tao, F.; Zhang, Z.; Zhang, S.; Rötter, R.P. |
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Title |
Variability in crop yields associated with climate anomalies in China over the past three decades |
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Journal Article |
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Year |
2016 |
Publication |
Regional Environmental Change |
Abbreviated Journal |
Reg Environ Change |
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16 |
Issue |
6 |
Pages |
1715-1723 |
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Adaptation; Climate change; Climate extremes; Drought; Impacts and vulnerability |
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We used simple and explicit methods, as well as improved datasets for climate, crop phenology and yields, to address the association between variability in crop yields and climate anomalies in China from 1980 to 2008. We identified the most favourable and unfavourable climate conditions and the optimum temperatures for crop productivity in different regions of China. We found that the simultaneous occurrence of high temperatures, low precipitation and high solar radiation was unfavourable for wheat, maize and soybean productivity in large portions of northern, northwestern and northeastern China; this was because of droughts induced by warming or an increase in solar radiation. These climate anomalies could cause yield losses of up to 50 % for wheat, maize and soybeans in the arid and semi-arid regions of China. High precipitation and low solar radiation were unfavourable for crop productivity throughout southeastern China and could cause yield losses of approximately 20 % for rice and 50 % for wheat and maize. High temperatures were unfavourable for rice productivity in southwestern China because they induced heat stress, which could cause rice yield losses of approximately 20 %. In contrast, high temperatures and low precipitation were favourable for rice productivity in northeastern and eastern China. We found that the optimum temperatures for high yields were crop specific and had an explicit spatial pattern. These findings improve our understanding of the impacts of extreme climate events on agricultural production in different regions of China. |
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2016-06-20 |
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1436-3798 1436-378x |
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CropM, ft_macsur |
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MA @ admin @ |
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4757 |
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Zhao, G.; Hoffmann, H.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.L.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.G.; Nendel, C.; Kiese, R.; Eckersten, H.; Haas, E.; Vanuytrecht, E.; Wang, E.; Kuhnert, M.; Trombi, G.; Moriondo, M.; Bindi, M.; Lewan, E.; Bach, M.; Kersebaum, K.C.; Rotter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables |
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Journal Article |
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2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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65 |
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141-157 |
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Keywords |
crop model; model comparison; spatial resolution; data aggregation; spatial heterogeneity; scaling; climate-change scenarios; sub-saharan africa; winter-wheat; spatial-resolution; yield response; input data; systems simulation; large-scale; soil data; part i |
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We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area. |
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0936-577x |
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CropM, ft_macsur |
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MA @ admin @ |
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4754 |
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Dumont, B.; Basso, B.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Assessing and modeling economic and environmental impact of wheat nitrogen management in Belgium |
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Journal Article |
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2016 |
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Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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79 |
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184-196 |
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Tactical nitrogen management; Climatic variability; Probability risk; assessment; LARS-WG; Crop model; STICS; stics crop model; generic model; simulation; yield; water; soil; fertilizer; behavior; climate; maize |
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Future progress in wheat yield will rely on identifying genotypes & management practices better adapted to the fluctuating environment Nitrogen (N) fertilization is probably the most important practice impacting crop growth. However, the adverse environmental impacts of inappropriate N management (e.g., lixiviation) must be considered in the decision-making process. A formal decisional algorithm was developed to tactically optimize the economic & environmental N fertilization in wheat. Climatic uncertainty analysis was performed using stochastic weather time-series (LARS-WG). Crop growth was simulated using STICS model. Experiments were conducted to support the algorithm recommendations: winter wheat was sown between 2008 & 2014 in a classic loamy soil of the Hesbaye Region, Belgium (temperate climate). Results indicated that, most of the time, the third N fertilization applied at flag-leaf stage by farmers could be reduced. Environmental decision criterion is most of the time the limiting factor in comparison to the revenues expected by farmers. (C) 2016 Elsevier Ltd. All rights reserved. |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4749 |
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