Banse, M., Brouwer, F., Palatnik, R. R., & Sinabell, F. (2014). The Economics of European Agriculture under Conditions of Climate Change (Editorial). German Journal of Agricultural Economics, 63(3), 131–132.
Abstract: This Special Issue on “The Economics of European Agriculture under Conditions of Climate Change” brings together a selection of papers that contribute to the understanding of recent developments related to agriculture and climate change in four European coun- tries. The focus of the Special Issue is on quantitative modeling and empirical analyses. The papers presented here not only cover the heterogeneity of agriculture in Europe with case studies from the Mediterranean (Italy), central (Austria) and north-western Europe (Ireland and Scotland) but also give insights into the diversity of quantitative modeling approaches in agriculture.
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Valin, H., Sands, R. D., van der Mensbrugghe, D. and, Nelson, G. C., Ahammad, H., Blanc, E., et al. (2014). The future of food demand: Understanding differences in global economic models. Agric. Econ., 45(1), 51–67.
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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Daccache, A., Ciurana, J. S., Diaz, J. A. R., & Knox, J. W. (2014). Water and energy footprint of irrigated agriculture in the Mediterranean region. Environ. Res. Lett., 9(12), 124014.
Abstract: Irrigated agriculture constitutes the largest consumer of freshwater in the Mediterranean region and provides a major source of income and employment for rural livelihoods. However, increasing droughts and water scarcity have highlighted concerns regarding the environmental sustainability of agriculture in the region. An integrated assessment combining a gridded water balance model with a geodatabase and GIS has been developed and used to assess the water demand and energy footprint of irrigated production in the region. Modelled outputs were linked with crop yield and water resources data to estimate water (m(3) kg(-1)) and energy (CO2 kg(-1)) productivity and identify vulnerable areas or `hotspots’. For a selected key crops in the region, irrigation accounts for 61 km(3) yr(-1) of water abstraction and 1.78 Gt CO2 emissions yr-1, with most emissions from sunflower (73 kg CO2/t) and cotton (60 kg CO2/t) production. Wheat is a major strategic crop in the region and was estimated to have a water productivity of 1000 tMm(-3) and emissions of 31 kg CO2/t. Irrigation modernization would save around 8 km(3) of water but would correspondingly increase CO2 emissions by around +135\%. Shifting from rain-fed to irrigated production would increase irrigation demand to 166 km(3) yr(-1) (+137\%) whilst CO2 emissions would rise by +270\%. The study has major policy implications for understanding the water-energy-food nexus in the region and the trade-offs between strategies to save water, reduce CO2 emissions and/or intensify food production.
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Quain, M. D., Makgopa, M. E., Marquez-Garcia, B., Comadira, G., Fernandez-Garcia, N., Olmos, E., et al. (2014). Ectopic phytocystatin expression leads to enhanced drought stress tolerance in soybean (Glycine max) and Arabidopsis thaliana through effects on strigolactone pathways and can also result in improved seed traits. Plant Biotechnol. J., 12(7), 903–913.
Abstract: Ectopic cystatin expression has long been used in plant pest management, but the cysteine protease, targets of these inhibitors, might also have important functions in the control of plant lifespan and stress tolerance that remain poorly characterized. We therefore characterized the effects of expression of the rice cystatin, oryzacystatin-I (OCI), on the growth, development and stress tolerance of crop (soybean) and model (Arabidopsis thaliana) plants. Ectopic OCI expression in soybean enhanced shoot branching and leaf chlorophyll accumulation at later stages of vegetative development and enhanced seed protein contents and decreased the abundance of mRNAs encoding strigolactone synthesis enzymes. The OCI-expressing A. thaliana showed a slow-growth phenotype, with increased leaf numbers and enhanced shoot branching at flowering. The OCI-dependent inhibition of cysteine proteases enhanced drought tolerance in soybean and A. thaliana, photosynthetic CO2 assimilation being much less sensitive to drought-induced inhibition in the OCI-expressing soybean lines. Ectopic OCI expression or treatment with the cysteine protease inhibitor E64 increased lateral root densities in A. thaliana. E64 treatment also increased lateral root densities in the max2-1 mutants that are defective in strigolactone signalling, but not in the max3-9 mutants that are defective in strigolactone synthesis. Taken together, these data provide evidence that OCI-inhibited cysteine proteases participate in the control of growth and stress tolerance through effects on strigolactones. We conclude that cysteine proteases are important targets for manipulation of plant growth, development and stress tolerance, and also seed quality traits.
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Robinson, S., van Meijl, H., Willenbockel, D., Valin, H., Fujimori, S., Masui, T., et al. (2014). Comparing supply-side specifications in models of global agriculture and the food system. Agric. Econ., 45(1), 21–35.
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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