FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
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Regional Pilot Case Study: Mostviertal – AT, upcoming project phase
Get PDFThe presentation indicates our plans for activities in the Mostviertel region in the next project phase
Projected climate change impact on wheat and maize in Italy
No full textAgriculture is one of the most important sectors for global economy. Its high vulnerability to climate conditions cause a serious concern for the consequence determined by the incoming climate changes. The increase in temperature and decrease in rainfall, projected for the next decades in the Mediterranean Basin, may cause a significant impact on crop development and production. In this contest, the assessment of the climate change impacts on crop growth and yield is necessary in order to identify the crops and areas more vulnerable and suggest adaptation strategies to cope with climate change. The use of crop simulation models, such as those implemented in DSSAT-CSM (Decision Support System for Agrotechnology Transfer - Cropping System Model) software, version 4.5., is the most common approach for the assessment of climate change impacts on crop development and yields. These models are often used at field scale. However, recent studies have been carried out at both regional and continental scale. In this work, CSM-CERES-Wheat and CSM-CERES-Maize crop models, parameterized at Italian scale for different varieties of durum wheat, common wheat and maize, were applied to assess climate change impacts on crop phenology and productivity. Dynamically downscaled climate data, using by the Regional Climate Model COSMO-CLM, and RCP 4.5 and 8.5 scenarios were used for impact assessment. Moreover, some adaptation strategies were evaluated. Results, analyzed at regional level, will be discussed
TradeM synergies with AGMIP
No full textThe AgMIP network started activities on intercomparison of global economic modelling at a time when MACSUR was not yet established. The achievements made so far are highly relevant for TradeM and several partners (Wageningen University, IIASA, PIK, University Bonn) are in both networks. The MOU between MACSUR and AGMIP established formal links between the two projects and TradeM is activley working on establishing further collaboration. Preparations are underway to bring together researchers of both networks in a joint workshop to be held in Austria, September 2014. The topic will be on issues related to linking local and regional models with global ones. TradeM will actively contribute to the workshop and will host a one-day side-event
Implementing agricultural land-use in the CARAIB dynamic vegetation model
No full textCARAIB (Dury et al., 2011) is a state-of-the-art dynamic vegetation model with various modules dealing with (i) soil hydrology, (ii) photosynthesis/stomatal regulation, (iii) carbon allocation and biomass growth, (iv) litter/soil carbon dynamics, (v) vegetation cover dynamics, (vi) seed dispersal, and (vii) vegetation fires. Climate and atmospheric CO2 are the primary inputs. The model calculates all major water and CO2/carbon fluxes and pools. It can be run with plant functional types or species (up to 100 different species) at various spatial scales, from the municipality to country or continental levels. Within the VOTES project (Fontaine et al., 2013), the model has been improved to include crops and meadows, and some modules have been written to translate model outputs into quantitative indicators of ecosystem services (e.g., evaluate crop yield from net primary productivity or calculate soil erosion from runoff, slope, grown species and various soil attributes). The model was run over an area covering four municipalities in central Belgium, where land-use is dominated by crops, meadows, housing and some forests and was introduced in the model at the land parcel level. Simulations were also performed for the future. In these simulations, CARAIB was combined with the Aporia Agent-Based Model, to project land-use changes up to 2050. This approach is currently extended within the MASC project (funded by Belgian Science Policy, BELSPO) to the whole Belgian territory (at 1 km2) and to Western Europe (at 20 km x 20 km)
Using indicators to inform agricultural decision making
No full textMost farmers carry out several types of activity of their land (different crops or livestock) and use a wide range of agricultural techniques. They often need to address one of the following questions. How would the economic returns from my various activities be affected by using production practices which have different effects on soil conservation or degradation? How would the economic returns from these activities change, if the product price and/or subsidies structure and/or input costs changed? ManPrAs is a tool for Agricultural Management Practices Assessment developed. It is a method, to assess the sustainability of different agricultural practices by combining their soil conservation index (SCI) with their economic results (Gross Margin-GM). It also simulates the impact of alternative crops and management techniques on soil degradation, farm profitability and other socio-economic aspects. ManPrAs is strongly user-orientated and is a powerful simulation tool for farmers and stakeholders involved in land management
Weather data aggregation’s effects on simulation of cropping systems: a model, production system and crop comparison
No full textInteractions 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
CLIMSAVE interactive platform for climate change impacts in Europe
No full textDescribe the CLIMSAVE Integrated Assessment Platform showing the scope of models, inputs and outputs available. Present the results from applying the IAP for the six scenarios on the regional case study regions. Describe the new aims of the follow-on IMPRESSIONS project
Integrated assessment of business crop productivity and profitability for use in food supply forecasting
Get PDFClimate 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 PARTNE
Within-season predictions of durum wheat yield over the Mediterranean Basin
Get PDFCrop yield is the result of the interactions between weather in the incoming season and how farmers decide to manage and protect their crops. According to Jones et al. (2000), uncertainties in the weather of the forthcoming season leads farmers to lose some productivity by taking management decisions based on their own experience of the climate or by adopting conservative strategies aimed at reducing the risks. Accordingly, predicting crop yield in advance, in response to different managements, environments and weathers would assist farm-management decisions(Lawless and Semenov, 2005). Following the approach described by Semenov and Doblas-Reyes (2007), this study aimed at assessing the utility of different seasonal forecasting methodologies in predicting durum wheat yield at 10 different sites across the Mediterranean Basin. The crop model, SiriusQuality (Martre et al., 2006), was used to compute wheat yield over a 10-years period. First, the model was run with a set of observed weather data to calculate the reference yield distributions. Then, starting from 1st January, yield predictions were produced at a monthly time-step using seasonal forecasts. The results were compared with the reference yields to assess the efficacy of the forecasting methodologies to estimate within-season yields. The results indicate that durum wheat phenology and yield can be accurately predicted under Mediterranean conditions well before crop maturity, although some differences between the sites and the forecasting methodologies were revealed. Useful information can be thus provided for helping farmers to reduce negative impacts or take advantage from favorable conditions
Relationships between temperature humidity index, mortality, milk yield and composition in Italian dairy cows
Get PDFThe aim of this presentation is to illustrate the activities performed by the LiveM-Task L1.2. group based at the University of Tuscia, Viterbo, Italy. Three different pluriannual databases were built to perform retrospective studies aimed at establishing the relationships between temperature humidity index (THI) and parameters of interest for dairy cow farms. The THI combines temperature and humidity in a single value and has been widely used to quantify heat stress in farm animals. The first database was built to assess the relationships between THI and mortality over a 6 yr period (2002-2007); the second one was a 7 yr database (2001-2007) which was built to establish the relationships between THI and milk yield; the last database included THI, milk somatic cell counts, total bacterial counts, fat and protein percentages data collected over a 7 yr period (2003-2009). The analysis of the three databases provided several equations which demonstrated and quantified an increase of mortality, reduction of milk yield and a worsening of milk quality in hot environment. Results of these analyzes authorized speculations about risks for dairy cows and their productivity in a warming planet. Furthermore, the same results are being utilized by economists also working within MACSUR at the University of Tuscia for an integrated study aimed at establishing the economic impact of climate change in the dairy sector. Combining this information with climate change regional scenarios might permit prediction of the impact of global warming and identification of adaptation measures that are appropriate for specific contexts