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Zhao, G., Hoffmann, H., Van Bussel, L., Enders, A., Specka, X., Sosa, C., et al. (2014). Weather data aggregation’s effects on simulation of cropping systems: a model, production system and crop comparison. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Interactions of climate, soil and management practices in cropping systems can be simulated at different scales to provide information for decision making. Low resolution simulation need less effort, but important details could be lost through data aggregation effects (DAEs). This paper aims to provide a general method to assess the DAEs on weather data and the simulation of cropping systems, and further investigate how the DAEs vary with changing crop models, crops, variables and production systems. A 30-year continuous cropping system was simulated for winter wheat and silage maize and potential, water-limited and water-nitrogen-limited production situations. Climate data of 1 km resolution and aggregations to resolutions of 10 to 100 km was used as input for the simulations. The data aggregation narrowed the variation of weather data and DAEs increased with increasingly coarser spatial resolution, causing the loss of hot spots in simulated results. Spatial patterns were similar across different resolutions. Consistent with DAEs on weather data, the DAEs on simulated yield (0 to 1.2 t ha-1 for winter wheat and 0 to 1.7 t ha-1 for silage maize), evapotranspiration (3 to 45 mm yr-1 for winter wheat and 4 to 40 mm yr-1 for silage maize), and water use efficiency (0.02 to 0.25 kg m-3 for winter wheat and 0.04 to 0.4 kg m-3 for silage maize), increased with coarser spatial resolution. Thus, if spatial information is needed for local management decisions, higher resolution is needed to adequately capture the spatial heterogeneity or hot spots in the region.
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Havlik, P., Leclere, D., Valin, H., Herrero, M., Schmid, E., & Obersteiner, M. (2014). Effects of climate change on feed availability and the implications for the livestock sector. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Global mean surface temperature is projected to rise by 0.4-2.6°C until 2050, and the contrast in precipitations between wet and dry regions and wet and dry seasons will also increase according to the IPCC 5th Assessment Report (2013). The climate change will impact livestock in many ways going from heat stress through livestock diseases to feed quality and availability (Thornton et al., 2009). Recently, projected climate change impacts on crop and grassland productivity became available with high spatial resolution at global scale through the AgMIP and ISI-MIP projects. The objective of this paper is to investigate how climate change impacts on crops and grassland will influence livestock production globally and its distribution across regions. This analysis is carried out using the global partial equilibrium agricultural and forestry sector model GLOBIOM (Havlík et al., 2013). The model represents agricultural production at a spatial resolution going down to 5 x 5 minutes of arc. Crop and grassland productivities are estimated by means of biophysical process based models (EPIC and CENTURY) at this resolution for current and future climate. Livestock representation follows a simplified version of the Seré and Steinfeld (1996) production system classification. This approach recognizes differences in feed base and productivities between grazing and mixed crop-livestock production systems across different agro-ecological zones (arid, humid, temperate/highlands). Our study highlights that the differential impacts of climate change on crop and grassland productivity will influence the relative competitiveness of different livestock production systems. Maintaining livestock production in some regions will depend on their capacity to adapt. Institutional and physical infrastructure will be needed to facilitate these transformations.
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Van den Pol-van Dasselaar, A., Evers, A., & De Haan, M. (2014). Modelling emissions of greenhouse gases from dairy farms in the Netherlands using DairyWise. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: The DairyWise model (Schils et al., 2007) is an empirical model that simulates technical, environmental, and financial processes on a dairy farm. The central component is the FeedSupply model that balances the herd requirements, as generated by the DairyHerd model, and the supply of home-grown feeds, as generated by the crop models for grassland and silage maize. The GrassGrowth model predicts the daily rate of DM accumulation of grass, including several feed quality parameters. Depending on (daily) grazing, the amount of grass silage is calculated which also leads to the purchase (or sale) of roughage. The final output is a farm plan describing cattle performance, crop yield, grazing, feeding, and nutrient flows and the consequences on the environment and economy. The capabilities of DairyWise will be illustrated at the MACSUR meeting in Sassari with results of dairy farming in the Netherlands: farm characteristics, economics, NPK balances and greenhouse gas emissions.
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Bojar, W., Knopik, L., & Żarski, J. (2014). Integrated assessment of business crop productivity and profitability for use in food supply forecasting. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Climate change suggests long periods without rainfall will occur in the future quite often. Previous approach on dependence crop-yields from size of rain confirms the existence of a statistically significant relation. We built a model describing the amount of precipitation and taking into account periods of drought, using a mixture of gamma distribution and one point-distribution. Parameter estimators were constructed from rainfall data using the method of maximum likelihood. Long series of days or decades of drought allow to determine the probabilities of adverse developments in agriculture as the basis for forecasting crop yields in the future (years 2030, 2050). Forecasted yields can be used for assessment of productivity and profitability of some selected crops in Kujavian-Pomeranian region. Assumptions and parameters of large-scale spatial economic models will be applied to build up relevant solutions. Calculated with this approach output could be useful to expect decrease in agricultural output in the region. It will enable to shape effective agricultural policy to know how to balance food supply and demand through appropriate managing with stored food raw material and/or import/export policies. Used precipitation-yields dependencies method let verify earlier used methodology through comparison of obtained solutions concerning forecasted yields and closed to it uncertainty analysis.This work was co-financed by NCBiR, Contract no. FACCE JPI/04/2012 – P100 PARTNER
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Brouwer, F., & Sinabell, F. (2014). TradeM planning session of pilot studies. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: TradeM will organise a session to plan for the three regional pilot studies. Focus will be on the expected outcomes until early 2015 (e.g. progress in farm modelling, and other scientific advancements – uncertainty, model integration). In addition to the planning of the regional pilot studies for the remaining year in MACSUR, we will also elaborate proposals for the years 2015-2017. Moreover, the session will enable research groups to present and discuss their plans for cross-theme investigations.
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