Abstract: Assessments of climate change impacts on agricultural markets and land-use patterns rely on quantification of climate change impacts on the spatial patterns of land productivity. We supply a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models. Aggregation in space and time leads to information losses that can determine climate change impacts on agricultural markets and land-use patterns because often aggregation is across steep gradients from low to high impacts or from increases to decreases. The four climate change impact scenarios supplied here were designed to represent the most significant impacts (high emission scenario only, assumed ineffectiveness of carbon dioxide fertilization on agricultural yields, no adjustments in management) but are consistent with the assumption that changes in agricultural practices are covered in the economic models. Globally, production of individual crops decrease by 10-38% under these climate change scenarios, with large uncertainties in spatial patterns that are determined by both the uncertainty in climate projections and the choice of impact model. This uncertainty in climate impact on crop productivity needs to be considered by economic assessments of climate change.

Abstract: In order to feed the world we need innovative thinking on how to increase agricultural production whilst also mitigating climate change. Agriculture and land-use change are responsible for approximately one-third of total anthropogenic greenhouse gas (GHG) emissions but hold potential for climate change mitigation but are only tangentially included in UNFCCC mitigation policies. To get a full estimate of GHG emissions from agricultural crop production both energy-based emissions and land-based emissions need to be accounted for. Furthermore, the major mitigation potential is likely to be indirect reduction of emissions i.e. reducing emissions per unit of agricultural product rather than the absolute emissions per se. Hence the system productivity must be included in the same analysis. This paper presents the Kaya-Porter identity, derived from the Maya identity, as a new way to calculate GHG emissions from agricultural crop production by deconstructing emissions into five elements; the GHG intensity of the energy used for production (kg CO2-eq./MJ), energy intensity of the production (MJ/kg dry matter), areal productivity (kg dry matter/ha), areal land-based GHG emissions (CO2-eq./ha) and area (ha). These separate elements in the identity can be targeted in emissions reduction and mitigation policies and are useful to analyse past and current trends in emissions and to explore future scenarios. Using the Kaya-Porter identity we have performed a case study on Danish crop production and find emissions to have been reduced by 12% from 1992 to 2008, whilst yields per unit area have remained constant. Both land-based emissions and energy-based emissions have decreased, mainly due to a 41% reduction in nitrogen fertilizer use. The initial identity based analysis for crop production presented here needs to be extended to include livestock to reflect the entire agricultural production and food demand sectors, thereby permitting analysis of the trade-offs between animal and plant food production, human dietary preferences and population and resulting GHG emissions. (C) 2012 Elsevier B.V. All rights reserved.

Abstract: The Transylvanian Plain (TP) is an important agricultural production area of Romania that is included among the areas with the lowest potential of adapting to climate changes in Europe. Thermal and hydric regime monitoring is necessary to identify and implement measures of adaptation to the impacts of climate change. Soil moisture and temperature regimes were evaluated using a set of 20 data logging stations positioned throughout the plain. Each station stores electronic data regarding ground temperature at 3 depths (10, 30, 50 cm), humidity at a depth of 10 cm, air temperature (at 1 m) and precipitation. For agricultural crops, the periods of drought and extreme temperatures require specific measures of adaptation to climate changes. During the growing season of crops in the spring (April – October) in the south-eastern, southern, and eastern escarpments, precipitation decreased by 43.8 mm, the air temperature increased by 0.37 degrees C, and the ground temperature increased by 1.91 degrees C at a depth of 10 cm, 2.22 degrees C at a depth of 20 cm and 2.43 degrees C at a depth of 30 cm compared with values recorded for the northern, north-western or western escarpments. Water requirements were ensured within an optimal time frame for 58.8-62.1% of the spring row crop growth period, with irrigation being necessary to guarantee the optimum production potential. The biologically active temperature recorded in the TP demonstrates the need to renew the division of the crop areas reported in the literature.