Abstract: Climate change has impacts on agricultural yields, which could alter cropland requirements and hence deforestation rates. Thus, land-use responses to climate change might influence terrestrial carbon stocks. Moreover, climate change could alter the carbon storage capacity of the terrestrial biosphere and hence the land-based mitigation potential. We use a global spatially explicit economic land-use optimization model to (a) estimate the mitigation potential of a climate policy that provides economic incentives for carbon stock conservation and enhancement, (b) simulate land-use and carbon cycle responses to moderate climate change (RCP2.6), and (c) investigate the combined effects throughout the 21st century. The climate policy immediately stops deforestation and strongly increases afforestation, resulting in a global mitigation potential of 191 GtC in 2100. Climate change increases terrestrial carbon stocks not only directly through enhanced carbon sequestration (62 GtC by 2100) but also indirectly through less deforestation due to higher crop yields (16 GtC by 2100). However, such beneficial climate impacts increase the potential of the climate policy only marginally, as the potential is already large under static climatic conditions. In the broader picture, this study highlights the importance of land-use dynamics for modeling carbon cycle responses to climate change in integrated assessment modeling.

Abstract: Climate change poses fundamental challenges on agriculture. It triggers autonomous adaptation responses of famers and thereby impacts the success of climate change mitigation. Integrated modelling frameworks (IMF) on land use serve as decision support instruments under such conditions by considering climate signals and accounting for combined mitigation and adaptation policies. We apply an IMF at the farm level in two contrasting grassland and cropland dominated landscapes in Austria to analyze climate change impacts on land use as well as impacts from mitigation and adaptation policies on the abiotic and biotic environment and the landscape. Results show that the impacts on farm gross margins and the abiotic and biotic environment are substantial either directly from climate change (e.g. changing erosion levels) or triggered via adaptation responses (i.e. land use and management change). Average gross margins increase between 1% and 12% depending on the case study landscape, the climate change scenario, and the policy scenario. With respect to biodiversity indicators, land use changes in the adaptation scenario decrease plant species diversity on farmland by 13% on average and losses are up to 80% for some farms. These changes are driven by policies in the adaptation scenario as responses on climate change in the absence of policies are modest with minor impacts on biodiversity. Results indicate the effectiveness of climate change adaptation in increasing farm incomes and the need to coordinate mitigation and adaptation policies to manage environmental outcomes. The IMF turns out to be effective in revealing heterogeneity of climate change impacts among farms and regions and linkages among adaptation and mitigation policies. No Label

Abstract: Climate variability (CV) normally influences production and farm management, and climate change (CC) has precisely the effect of changing this variability. Thus, models that estimate the economic impact of CC, integrating with climatic models, agronomic, and livestock, must represent the implications of this variability on farm management. This study describes an economic model based on Discrete Stochastic Programming (DSP) which assesses the impact of CC on milk production in the Grana Padano area. The model is based on 23 farm typologies from FADN that represent 856 farms in Piacenza and Cremona, two of the most important provinces for Grana Padano production. The results of the model were projected at the regional scale. The climate scenarios, current and future, are generated with a Regional Atmospheric Modeling System. The forage production under these scenarios is estimated with the EPIC agronomic model. Estimates on milk production and livestock mortality are based on studies conducted in the Po valley. The nutritional needs of the cattle are estimated with the CNCPS model. Probability distribution functions (PDF) express the relations between the CV and the productive variables under both climate scenarios. These PDFs represent the expectations of farmers on the productive-climate variability in the DSP model, which is PMP calibrated based on land distribution observed in a reference year. Comparing the model results in the two scenarios indicates the effects of CC, given the opportunity to adapt the use of resources and techniques of cultivation. The structure of the model, and its economic results are presented and discussed, along with the strengths and weaknesses of this approach. No Label