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Gutierrez, L., Piras, F., & Roggero, P. P. (2015). A global vector autoregression model for the analysis of wheat export prices. American Journal of Agricultural Economics, 97(5), 1494–1511.
Abstract: Food commodity price fluctuations have an important impact on poverty and food insecurity across the world. Conventional models have not provided a complete picture of recent price spikes in agricultural commodity markets, and there is an urgent need for appropriate policy responses. Perhaps new approaches are needed to better understand international spill-overs, the feedback between the real and the financial sectors, as well as the link between food and energy prices. In this article, we present the results from a new worldwide dynamic model that provides the short and long-run impulse responses of the international wheat price to various real and financial shocks.
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Strauss, F., Moltchanova, E., & Schmid, E. (2013). Spatially explicit modeling of long-term drought impacts on crop production in Austria. American Journal of Climate Change, 2(3), 1–11.
Abstract: Droughts have serious and widespread impacts on crop production with substantial economic losses. The frequency and severity of drought events may increase in the future due to climate change. We have developed three meteorological drought scenarios for Austria in the period 2008-2040. The scenarios are defined based on a dry day index which is combined with bootstrapping from an observed daily weather dataset of the period 1975-2007. The severity of long-term drought scenarios is characterized by lower annual and seasonal precipitation amounts as well as more sig- nificant temperature increases compared to the observations. The long-term impacts of the drought scenarios on Aus- trian crop production have been analyzed with the biophysical process model EPIC (Environmental Policy Integrated Climate). Our simulation outputs show that—for areas with historical mean annual precipitation sums below 850 mm— already slight increases in dryness result in significantly lower crop yields i.e. depending on the drought severity, be- tween 0.6% and 0.9% decreases in mean annual dry matter crop yields per 1.0% decrease in mean annual precipitation sums. The EPIC results of more severe droughts show that spring and summer precipitation may become a limiting factor in crop production even in regions with historical abundant precipitation.
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Hutchings, N. J., Özkan Gülzari, Ş., de Haan, M., & Sandars, D. (2018). How do farm models compare when estimating greenhouse gas emissions from dairy cattle production. Animal, 12(10), 2171–2180.
Abstract: The European Union Effort Sharing Regulation (ESR) will require a 30% reduction in greenhouse gas (GHG) emissions by 2030 compared with 2005 from the sectors not included in the European Emissions Trading Scheme, including agriculture. This will require the estimation of current and future emissions from agriculture, including dairy cattle production systems. Using a farm-scale model as part of a Tier 3 method for farm to national scales provides a more holistic and informative approach than IPCC (2006) Tier 2 but requires independent quality control. Comparing the results of using models to simulate a range of scenarios that explore an appropriate range of biophysical and management situations can support this process by providing a framework for placing model results in context. To assess the variation between models and the process of understanding differences, estimates of GHG emissions from four farm-scale models (DailyWise, FarmAC, HolosNor and SFARMMOD) were calculated for eight dairy farming scenarios within a factorial design consisting of two climates (cool/dry and warm/wet) x two soil types (sandy and clayey) x two feeding systems (grass only and grass/maize). The milk yield per cow, follower cow ratio, manure management system, nitrogen (N) fertilisation and land area were standardised for all scenarios in order to associate the differences in the results with the model structure and function. Potential yield and application of available N in fertiliser and manure were specified separately for grass and maize. Significant differences between models were found in GHG emissions at the farm-scale and for most contributory sources, although there was no difference in the ranking of source magnitudes. The farm-scale GHG emissions, averaged over the four models, was 10.6 t carbon dioxide equivalents (CO(2)e)/ha per year, with a range of 1.9 t CO(2)e/ha per year. Even though key production characteristics were specified in the scenarios, there were still significant differences between models in the annual milk production per ha and the amounts of N fertiliser and concentrate feed imported. This was because the models differed in their description of biophysical responses and feedback mechanisms, and in the extent to which management functions were internalised. We conclude that comparing the results of different farm-scale models when applied to a range of scenarios would build confidence in their use in achieving ESR targets, justifying further investment in the development of a wider range of scenarios and software tools.
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Müller, C. (2013). African lessons on climate change risks for agriculture. Ann. Rev. Nutr., 33(1), 395–411.
Abstract: Climate change impact assessments on agriculture are subject to large uncertainties, as demonstrated in the present review of recent studies for Africa. There are multiple reasons for differences in projections, including uncertainties in greenhouse gas emissions and patterns of climate change; assumptions on future management, aggregation, and spatial extent; and methodological differences. Still, all projections agree that climate change poses a significant risk to African agriculture. Most projections also see the possibility of increasing agricultural production under climate change, especially if suitable adaptation measures are assumed. Climate change is not the only projected pressure on African agriculture, which struggles to meet demand today and may need to feed an additional one billion individuals by 2050. Development strategies are urgently needed, but they will need to consider future climate change and its inherent uncertainties. Science needs to show how existing synergies between climate change adaptation and development can be exploited.
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Park, S. K., Sungmin, O., & Cassardo, C. (2017). Soil temperature response in Korea to a changing climate using a land surface model. Asia-Pacific Journal of Atmospheric Sciences, 53(4), 457–470.
Abstract: The land surface processes play an important role in weather and climate systems through its regulation of radiation, heat, water and momentum fluxes. Soil temperature (ST) is one of the most important parameters in the land surface processes; however, there are few extensive measurements of ST with a long time series in the world. According to the CLImatology of Parameters at the Surface (CLIPS) methodology, the output of a trusted Soil-Vegetation- Atmosphere Transfer (SVAT) scheme can be utilized instead of observations to investigate the regional climate of interest. In this study, ST in South Korea is estimated in a view of future climate using the output from a trusted SVAT scheme – the University of TOrino model of land Process Interaction with Atmosphere (UTOPIA), which is driven by a regional climate model. Here characteristic changes in ST are analyzed under the IPCC A2 future climate for 2046-2055 and 2091-2100, and are compared with those under the reference climate for 1996-2005. The UTOPIA results were validated using the observed ST in the reference climate, and the model proved to produce reasonable ST in South Korea. The UTOPIA simulations indicate that ST increases due to environmental change, especially in air temperature (AT), in the future climate. The increment of ST is proportional to that of AT except for winter. In wintertime, the ST variations are different from region to region mainly due to variations in snow cover, which keeps ST from significant changes by the climate change.
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