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Özkan, Ş., Farquharson, R. J., Hill, J., & Malcolm, B. (2015). A stochastic analysis of the impact of input parameters on profit of Australian pasture-based dairy farms under variable carbon price scenarios. Environmental Science & Policy, 48, 163–171.
Abstract: The imposition of a carbon tax in the economy will have indirect impacts on dairy farmers in Australia. Although there is a great deal of information available regarding mitigation strategies both in Australia and internationally, there seems to be a lack of research investigating the variable prices of carbon-based emissions on dairy farm operating profits in Australia. In this study, a stochastic analysis comparing the uncertainty in income in response to different prices on carbon-based emissions was conducted. The impact of variability in pasture consumption and variable prices of concentrates and hay on farm profitability was also investigated. The two different feeding systems examined were a ryegrass pasture-based system (RM) and a complementary forage-based system (CF). Imposing a carbon price ($20-$60) and not changing the systems reduced the farm operating profits by 28.4% and 25.6% in the RM and CF systems, respectively compared to a scenario where no carbon price was imposed. Different farming businesses will respond to variability in the rapidly changing operating environment such as fluctuations in pasture availability, price of purchased feeds and price of milk or carbon emissions differently. Further, in case there is a carbon price imposed for GHG emissions emanated from dairy farming systems, changing from pasture-based to more complex feeding systems incorporating home-grown double crops may reduce the reductions in farm operating profits. There is opportunity for future studies to focus on the impacts of different mitigation strategies and policy applications on farm operating profits. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords: carbon tax; operating profit; stochastic dominance; dairy; feeding system; mitigation; cows; systems; efficiency; risk
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Hidy, D., Barcza, Z., Haszpra, L., Churkina, G., Pintér, K., & Nagy, Z. (2012). Development of the Biome-BGC model for simulation of managed herbaceous ecosystems. Ecol. Model., 226, 99–119.
Abstract: Apart from measurements, numerical models are the most convenient instruments to analyze the carbon and water balance of terrestrial ecosystems and their interactions with changing environmental conditions. The process-based Biome-BGC model is widely used to simulate the storage and flux of water, carbon, and nitrogen within the vegetation, litter, and soil of unmanaged terrestrial ecosystems. Considering herbaceous vegetation related simulations with Biome-BGC, soil moisture and growing season control on ecosystem functioning is inaccurate due to the simple soil hydrology and plant phenology representation within the model. Consequently, Biome-BGC has limited applicability in herbaceous ecosystems because (1) they are usually managed; (2) they are sensitive to soil processes, most of all hydrology; and (3) their carbon balance is closely connected with the growing season length. Our aim was to improve the applicability of Biome-BGC for managed herbaceous ecosystems by implementing several new modules, including management. A new index (heatsum growing season index) was defined to accurately estimate the first and the final days of the growing season. Instead of a simple bucket soil sub-model, a multilayer soil sub-model was implemented, which can handle the processes of runoff, diffusion and percolation. A new module was implemented to simulate the ecophysiological effect of drought stress on plant mortality. Mowing and grazing modules were integrated in order to quantify the functioning of managed ecosystems. After modifications, the Biome-BGC model was calibrated and validated using eddy covariance-based measurement data collected in Hungarian managed grassland ecosystems. Model calibration was performed based on the Bayes theorem. As a result of these developments and calibration, the performance of the model was substantially improved. Comparison with measurement-based estimate showed that the start and the end of the growing season are now predicted with an average accuracy of 5 and 4 days instead of 46 and 85 days as in the original model. Regarding the different sites and modeled fluxes (gross primary production, total ecosystem respiration, evapotranspiration), relative errors were between 18-60% using the original model and 10-18% using the developed model; squares of the correlation coefficients were between 0.02-0.49 using the original model and 0.50-0.81 using the developed model. (c) 2011 Elsevier B.V. All rights reserved.
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Sandor, R., Ehrhardt, F., Grace, P., Recous, S., Smith, P., Snow, V., et al. (2020). Ensemble modelling of carbon fluxes in grasslands and croplands. Field Crops Research, 252, 107791.
Abstract: Croplands and grasslands are agricultural systems that contribute to land–atmosphere exchanges of carbon (C). We evaluated and compared gross primary production (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE) of CO2, and two derived outputs – C use efficiency (CUE=-NEE/GPP) and C emission intensity (IntC= -NEE/Offtake [grazed or harvested biomass]). The outputs came from 23 models (11 crop-specific, eight grassland-specific, and four models covering both systems) at three cropping sites over several rotations with spring and winter cereals, soybean and rapeseed in Canada, France and India, and two temperate permanent grasslands in France and the United Kingdom. The models were run independently over multi-year simulation periods in five stages (S), either blind with no calibration and initialization data (S1), using historical management and climate for initialization (S2), calibrated against plant data (S3), plant and soil data together (S4), or with the addition of C and N fluxes (S5). Here, we provide a framework to address methodological uncertainties and contextualize results. Most of the models overestimated or underestimated the C fluxes observed during the growing seasons (or the whole years for grasslands), with substantial differences between models. For each simulated variable, changes in the multi-model median (MMM) from S1 to S5 was used as a descriptor of the ensemble performance. Overall, the greatest improvements (MMM approaching the mean of observations) were achieved at S3 or higher calibration stages. For instance, grassland GPP MMM was equal to 1632 g C m−2 yr-1 (S5) while the observed mean was equal to 1763 m-2 yr-1 (average for two sites). Nash-Sutcliffe modelling efficiency coefficients indicated that MMM outperformed individual models in 92.3 % of cases. Our study suggests a cautious use of large-scale, multi-model ensembles to estimate C fluxes in agricultural sites if some site-specific plant and soil observations are available for model calibration. The further development of crop/grassland ensemble modelling will hinge upon the interpretation of results in light of the way models represent the processes underlying C fluxes in complex agricultural systems (grassland and crop rotations including fallow periods).
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Leclère, D., Jayet, P. - A., & de Noblet-Ducoudré, N. (2013). Farm-level Autonomous Adaptation of European Agricultural Supply to Climate Change. Ecol. Econ., 87, 1–14.
Abstract: The impact of climate change on European agriculture is subject to a significant uncertainty, which reflects the intertwined nature of agriculture. This issue involves a large number of processes, ranging from field to global scales, which have not been fully integrated yet. In this study, we intend to help bridging this gap by quantifying the effect of farm-scale autonomous adaptations in response to changes in climate. To do so, we use a modelling framework coupling the STICS generic crop model to the AROPAj microeconomic model of European agricultural supply. This study provides a first estimate of the role of such adaptations, consistent at the European scale while detailed across European regions. Farm-scale autonomous adaptations significantly alter the impact of climate change over Europe, by widely alleviating negative impacts on crop yields and gross margins. They significantly increase European production levels. However, they also have an important and heterogeneous impact on irrigation water withdrawals, which exacerbate the differences in ambient atmospheric carbon dioxide concentrations among climate change scenarios. (c) 2012 Elsevier B.V. All rights reserved.
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Schmitz, C., Lotze-Campen, H., Gerten, D., Dietrich, J. P., Bodirsky, B., Biewald, A., et al. (2013). Blue water scarcity and the economic impacts of future agricultural trade and demand. Water Resource Research, 49(6), 3601–3617.
Abstract: An increasing demand for agricultural goods affects the pressure on global water resources over the coming decades. In order to quantify these effects, we have developed a new agroeconomic water scarcity indicator, considering explicitly economic processes in the agricultural system. The indicator is based on the water shadow price generated by an economic land use model linked to a global vegetation-hydrology model. Irrigation efficiency is implemented as a dynamic input depending on the level of economic development. We are able to simulate the heterogeneous distribution of water supply and agricultural water demand for irrigation through the spatially explicit representation of agricultural production. This allows in identifying regional hot spots of blue water scarcity and explicit shadow prices for water. We generate scenarios based on moderate policies regarding future trade liberalization and the control of livestock-based consumption, dependent on different population and gross domestic product (GDP) projections. Results indicate increased water scarcity in the future, especially in South Asia, the Middle East, and north Africa. In general, water shadow prices decrease with increasing liberalization, foremost in South Asia, Southeast Asia, and the Middle East. Policies to reduce livestock consumption in developed countries not only lower the domestic pressure on water but also alleviate water scarcity to a large extent in developing countries. It is shown that one of the two policy options would be insufficient for most regions to retain water scarcity in 2045 on levels comparable to 2005.
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