FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
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Impacts of Climate Change on Agricultural Technology Management in the Transylvanian Plain, Romania
No full textThe condition of land degradation in Transylvanian Plain and its effects, being the result of local extreme physical-geographical conditions, susceptible to degradation (evidenced by the erodibility index), which overlap the extreme climatic conditions. Thermal and hydric regime monitoring is necessary in order 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 of ground temperature at 3 depths (10, 30, 50 cm), the humidity at the depth of 10 cm, the air temperature (at 1 m) and precipitations. Climate change in the past few years have significantly altered the climatic indicators of the Transylvanian Plain. Precipitations, although deficient in terms of annual amounts, through their regime, have a negative influence on the plant carpet. Pluvial aggressiveness index reveals, for the research period, a first peak of pluvial aggressiveness during the months of February-April, then in July and in autumn, the months of October-November. This requires special measures for soil conservation, both in autumn and early spring, soil tillage measures being recommended which ensure the presence of plant debris and vegetation in early spring but especially in summer and autumn. Climatic indicators determined for the period 2009-2013 point out, in Transylvanian Plain, a semi-arid and mediterranean climate through the rain factor Lang, respectively semi-arid (in the South), semi-humid (in the North) according to the De Martonne index. This climatic characterization requires special technological measures for soil conservation (green curtains, green manure, no-tillage and minimum tillage with mulch layer). The biologically active temperature recorded in the TP demonstrates the need to renew the division of the crop areas reported in the literature
Understanding Europe’s future ability to feed itself within an uncertain climate change and socio economic scenario space
Get PDFEurope’s ability to feed its population depends on the balance of agricultural productivity (yields and land suitability) and demand which are affected by future climate and socio-economic change (arising from changing food demand; prices; technology change etc). Land use under 2050 climate change and socio-economic scenarios can be rapidly and systematically quantified with a modelling system that has been developed from meta-models of optimal cropping and crop and forest yields derived from the outputs of the previously developed complex models (Audsley et al; 2015). Profitability of each possible land use is modelled for every soil in every grid across the EU. Land use in a grid is then allocated based on profit thresholds set for intensive agriculture extensive agriculture, managed forest and finally unmanaged forest or unmanaged land. The European demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available. The model iterates until demand is satisfied (or cannot be met at any price). Results are presented as contour plots of key variables. For example, given a 40% increase in population from the baseline socio-economic scenario, adapting by increasing crop yields by 40% will leave a 38% probability that the 2050 future climate will be such that we cannot feed ourselves – considering “all” the possible climate scenarios.
The role of uncertainty in assessing agricultural responses to food security and climate change: A Case Study from Norway
Get PDFReport on the analysis of interannual and seasonal variations in productive, reproductive and health data
Get PDFThe work carried out under LiveM, L1.2 and described herein was based on construction and query of large databases which included multiannual productive and health field data. Productive data referred to dairy cows, whereas health data were relative both to dairy cows and pigs. The analysis pointed out significant seasonal variations of parameters under study. In synthesis, summer/hot season was associated with significant worsening of dairy cows milk composition and with significant higher risk of death in pigs. These results may help to predict consequences of climate change in economically important sectors of the livestock industry and also to identify and target adaptation options that are appropriate for specific contexts, and that can contribute to environmental sustainability as well as to economic development
An economist’s wish list for soil and crop modelling
No full textA requirement for successful integration of soil, crop and economic models is a relevant interface of the three. Economic farming models deal with choice of crops, crop management during growing season and stock management after harvest. With detailed daily weather information the state of the soil might be simulated so that a suitable sowing date can be estimated. Moreover with rational beliefs with respect to future crop prices, and with a crop model which responds to management, the management during the growing season might be optimized with respect to choice of cultivar, fertilization and irrigation. So far, as reflected by Müller and Robertson (2014), predictions of future crop yields according to crop models take only to small extent such farmer responses into account, and might therefore overestimate the responses of crop harvests to climate.Comparison of soil, crop and economic simulations with observed weather and crop outcomes might lead to estimation/calibration of unobserved parameters in all models. Such exercises need generic soil, crop and economic models which do not leave modelling outcomes to the crop modeller’s or economist’s discretion
Modeling the effects of Climate Change on dairy farms: an integration of livestock and economic models.
Get PDFClimate Change (CC) may increase the incidence of heat stress on dairy cattle, sharpening the reduction of daily milk production and the drop of its quality (decrease of percentage fat and protein content and increase in somatic cells counts). It may also increase the rate of annual mortality of livestock. On the other side, CC itself and these effects can also change nutritional requirements of the herd. This paper discusses the approach by means of which all these aspects were integrated into a model of Discrete Stochastic programming (DSP), representing the decision making and the related income results in the short term in dairy farms. The functional relationships between conditions of temperature and humidity and the productive performances of dairy cattle are reconstructed on the basis of specific studies conducted in the Po Valley. The same for mortality rate. The effects of production performances on nutritional requirements of the herd are estimated with the mathematical model CNCPS, which also takes into account the influence of climatic conditions. These parameters are then used as inputs for the DSP model, whose structure is based on the assumption that the breeder considers them in planning land use of the farm for the production of forage and grain crops. The model also simulates the amount and composition of purchased feed for completely satisfying nutritional requirements of the herd. The comparison between the results of the DSP model, with productive performances and feed purchasing under current and future climate, indicates the possible management and income effects of CC
Inter-comparison of statistical models for projecting winter oilseed rape yield in Europe under climate change
Get PDFWhile intercomparison of process-based crop models for projections under climate change is being intensively studied at European as well as at the global scale, little effort has been made for comparing statistical models. In this study, several regression techniques (ordinary least squares, stepwise, shrinkage methods, principle components and partial least squares) were combined with different types of climate input variables (with different temporal resolution) in order to define a large range of statistical models. Each model was fitted to winter oilseed rape data collected in 689, 325 and 173 field experiments carried out in Denmark, Germany, and Czech Republic, respectively. The fitted models were then used to predict yield of winter oilseed rape in the field experiments during more than 20 years, up to 2013. Interpretability of the estimated climate variable effects and accuracy of yield predictions were both analysed. Results suggest that recent statistical methods (e.g., shrinkage methods) may have considerable capabilities to complement traditional statistical methods in yield prediction. The selection of the most influential variables was strongly influenced by the statistical method used to analyse the data. Among the most recent statistical methods, the uncertainties in projecting yield of winter oilseed rape under climate change were mainly due to residual errors and uncertainty in estimated parameter values, and not to model choice
Review of regional scale models in the EU and methods commonly used when modelling outcomes of the implementation of the climate change mitigation policies
No full textManagement of Nitrogen (N) losses and the related greenhouse gas emissions is one of the most important environmental issues related to agriculture. This report shows examples of an integrated model tool, developed to quantify the N‐dynamics at the complex interface between agriculture and the environment, and quantify effects of different management practices. Based on results from the EU funded research projects NitroEurope (www.NitroEurope.eu) and MEAscope (www.MEA‐scope.org), examples from the quantification of farm N‐losses in European agricultural landscapes are demonstrated. Applications of the dynamic whole farm model FASSET (www.FASSET.dk), and the Farm‐N tool (www.farm‐N.dk/FarmNTool) to calculate farm N balances, and distribute the surplus N between different types of N‐losses (volatilisation, denitrification, leaching), and the related greenhouse gas emissions, show significant variation between landscapes and management practices. Moreover, significant effects of the nonlinearities, appearing when integrating over time, and scaling up from farm to landscape, are demonstrated. Finally, perspectives for stakeholder involvement is included and general recommendations for landscape level management of farm related nitrogen and greenhouse gas fluxes are made, and discussed in relation to ongoing research in the European research projects
Yield gap analysis of cereals in Europe supported by local knowledge
No full textThe increasing demand for food requires a sustainable intensification of crop production in underperforming areas. Many global and local studies have addressed yield gaps, i.e. the difference between potential or water-limited yields and actual yields. Global studies generally rely on generic models combined with a grid-based approach. Although using a consistent method, it has been shown they are not suitable for local yield gap assessment. Local studies generally exploit knowledge of location-specific conditions and management, but are less comparable across locations due to different methods. To overcome these inconsistencies, the Global Yield Gap Atlas (GYGA, www.yieldgap.org) proposes a consistent bottom-up approach to estimate yield gaps. This paper outlines the implementation of GYGA for estimating yield gaps of cereals across Europe. For each country, climate zones are identified which represent the major growing areas. Within these climate zones, weather stations are selected with >=15 years of daily data. For dominant soil types within a buffer zone around the weather stations, the potential and water-limited yields are simulated with a crop model, using local knowledge on management. Actual yields are derived from sub-national statistics. Yield gaps are scaled up from buffer zones to climate zones and countries. We will present the first results for selected regions in Europe, and discuss methodological issues on location specific weather and upscaling from weather station buffer zones to climate zones and countries. Furthermore we will look ahead at the implementation of the yield gap cross cutting activity (XC9) in MACSUR-2