FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
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Needs on model improvement
Full text linkThe need to answer new scientific questions can be satisfied by an increased knowledge of physiological mechanisms which, in turn, can be used for improving the accuracy of simulations of process-based models. In this context, this report highlights areas that need to be further improved to facilitate the operational use of simulation models. It describes missing approaches within simulation models which, if implemented, would likely improve the representation of the dynamics of processes underlying different compartments of crop and grassland systems (e.g. plant growth and development, yield production, GHG emissions), as well as of the livestock production systems. The following rationale has been used in the organization of this report. We first briefly introduced the need to improve the reliability of existing models. Then, we indicated climate change and its influence on the global carbon balance as the main issue to be addressed by existing crop and grassland (section 2), and livestock (section 3) models. In section 2, among the major aspects that if implemented may reduce the uncertainty inherent to model outputs, we suggested: i) quantifying the effects of climate extremes on biological systems; ii) modelling of multi-species sward; iii) coupling of pest and disease sub-models; iv) improvement of the carry-over effect. In section 3, as the most important aspects to consider in livestock models we indicated: i) impacts and dynamics of pathogens and disease; ii) heat stress effects on livestock; iii) effects on grassland productivity and nutritional values; iv) improvement of GHG emissions dynamics. In Section 4, remarks are made concerning the need to implement the suggested aspects into the existing models
Spatially explicit estimation of climate change related heat stress on the milk production of dairy cows in the United Kingdom
No full textThe impact of climate change on dairy cows' milk production in the UK has been investigatedusing a gridded modelling approach. 12 milk loss calculation methods based on the TemperatureHumidity Index (THI), which accounts for the impact of heat stress, and eleven climate projections(UKCP09) with 25 km spatial resolution and covering the 1950-2100 period were used in thestudy. Half of the investigated methods used daily meteorological data. The other methods usedfiner temporal resolution input data. The number of days when dairy cows are projected to beaffected by heat stress will increase sharply as we approach the end of the century: e.g. InSouthern-England, the number of days of heat stress increases from an annual average of 10(baseline: 1990s) to over 40 per year. The associated milk loss will rise from a 30 kg/cow/yr up to200 kg/cow/yr. In extreme years in the South the annual milk loss may exceed 1000 kg/cow. Bythe end of the century, dairy cattle in large portions of Scotland and Northern Ireland willexperience the same level of heat stress as cattle in Southern-England today. The number of dayswhen daily step methods result in no milk loss while sub-daily time step methods result in nonzeromilk loss increases throughout the century. Consequently, simple methods that use onlydaily average temperature and relative humidity values may underestimate the impact of heatstress in the future
Modelling of carbon cycle in grassland ecosystems of diverse water availability using Biome-BGCMuSo.
Full text linkGrassland ecosystems have an important role in agriculture, and at the same time, are highlysensitive to changes in land use and climate change. Simulation of the biogeochemical cycles ofmanaged grasslands may help in identifying and quantifying the main processes contributing tochanges in their productivity. In our work we used the latest version of Biome-BGCMuSo model,the modified version of the widely used biogeochemical Biome-BGC model, with structuralimprovements to simulate herbaceous ecosystem carbon and water cycles more faithfully.Our sampling areas were in diverse grasslands in the Kiskunság, Hungary. Different soil textureand changing water table level, consequently highly different water conditions are characteristicin these ecosystems, influencing the development and productivity of vegetation, and also thepotential for animal husbandry. Hence, for the meadows and the marshland ecosystems weincluded mowing management in the simulations. In order to compare the ecosystems and studytheir functions we simulated ecosystem variables, such as ecosystem respiration, standing andharvested aboveground biomass etc.We found that ecosystems with higher water availability are more sensitive to changes in waterconditions, and their productivity is more variable between years. By calibration processes usingleaf area and aboveground biomass we aim to further specify our findings.Biome-BGCMuSo is available as a standalone model, but also through virtual laboratoryenvironment and Biome-BGC Projects database (http://ecos.okologia.mta.hu/bbgcdb)developed within the BioVeL project (http://www.biovel.eu). Scientific workflow management,web service and desktop grid technology can support model optimization in the so-called"calibrated runs" within MACSUR
A new version of ORCHIDEE-GM with coupled carbon-nitrogen-phosphorus cycles: parameter calibration and model evaluation.
No full textThe process-based biogeochemical model ORCHIDEE-GM is a version of ORCHIDEE land surface model that includes the grassland management module from PaSim. Accounting for the management practices such as mowing, livestock grazing and fertilizer application on a daily basis, ORCHIDEE-GM proved capable of simulating the dynamics of leaf area index, biomass, and C fluxes of managed grasslands. The previous versions of ORCHIDEE-GM did not include a full nitrogen cycle. The positive effect of nitrogen fertilizers on grassland photosynthesis rates was parameterized with an empirical function calibrated from literature estimate. In this study, ORCHIDEE-GM was merged into the ORCHIDEE-CN-P model, a version with coupled carbon-nitrogen-phosphorus cycles for terrestrial ecosystems. The new ORCHIDEE-GM model is capable to simulate the carbon, nitrogen and phosphorus fluxes among atmosphere, plant, livestock, and soil of managed grasslands. With some of its parameters calibrated, the new model was then evaluated at several grassland sites against eddy covariance fluxes, biometric measurements, and nitrogen-related measurements
Observed Crop-Yield Response Economic and Agro-climatic Factors in Austria - a Spatial Analysis
No full textThe purpose of this study is to investigate empirically the effects of agro-climatic factors, economic incentives and farmers' land-choice on crop yield responses. We focus on soy bean and maize yields observed in Austrian municipalities over the past 14 years.Weather events, economic factors and technology are the driving forces behind the evolution of annual average yields. In the literature crop yields are frequently derived from crop models or from experimental stations or from reports of farmers (e.g. farm accountancy data network FADN). The data we use are obtained from the statistical services. Our approach therefore differs from studies on crop models or contributions based on data of experimental stations, as we focus on economic outcomes (yield of farmers) and explicitly take economic variables (e.g. market prices) into account.The exact relationship between factors affecting crop yields is not yet well understood due to the complexity of the measurement and definition of weather variables as well as the great variety of technological and managerial factors. Many variables influencing crop yield (weather or soil condition) exhibit a pronounced spatial structure, and therefore yields are spatially correlated as well. We account for this potential bias by applying spatial panel models in order to improve the efficiency of the models explaining variation of yields over time and space.Our results show that the effects of weather conditions are statistically significant and economically sizable. However, we can also show that non-spatial models tend to overestimate the parameter estimates of the respective explanatory variables. We conclude that it is advisable to explicitly control for spatial effects in crop yield response studies which are based on observed crop yields
Understanding the potential of existing models to characterize animal health conditions and estimate greenhouse gas emissions
Full text linkThe primary objective of this study was to assess the status and priorities for future development in modelling of the impacts of animal health on greenhouse gas (GHG) emissions. It also aimed to facilitate communication between experimental researchers and modellers by defining a list of parameters that are needed to model livestock health and disease, and the impact of health conditions on GHG emissions. The summary presented here provides a brief overview of ongoing work, which the L2.1/L2.2 partners, with support from the Global Research Alliance Animal Health Network (GRA AHN), is currently developing into a paper for publication in a peer reviewed journal
How is crop growth model calibration performed? Results of a survey.
No full textCrop growth model calibration, or parameter estimation, is a demanding and critical step of a crop modeling project: Projections from a model are determined by the parameter values used in the model, and parameter uncertainty can play a major role in projection uncertainty. Despite its importance little attention has been paid to the calibration approaches and methods used. An open web-based online survey with 39 questions about crop model calibration was conducted in autumn 2016 aiming to record the current practices in crop model calibration across the crop modeling community.The sections of the survey related to the data used for calibration, the parameters calibrated, the calibration method and the software used, as well as estimation of the parameter uncertainty. In addition, there were questions providing background information about the model user and model, time required for calibration and challenges faced. Overall, 211 survey submissions were analyzed to examine the common practices in crop model calibration. The respondents covered both more and less experienced modellers with a wide range of models and calibration approaches used.This talk will present the results of the survey, and what they imply concerning the major choices faced during calibration. An important question asked respondents what they thought was the major challenge of crop model calibration. Only two (out of 211) respondents answered that they saw no major problems. This emphasizes the need for progress in calibration and setting out guidelines of good practices
Comparing annual wheat yield sensitivity to climate at different sites using impact response surfaces.
No full textImpact response surfaces (IRSs) are plots that show the response of a dependent variable (the surface) with respect to two predictor variables. These have been used in recent studies to display wheat yield sensitivity to climate at sites in Europe across an ensemble of crop models. Results focused on period-averaged responses to a wide range of temperature and precipitation perturbations. However, these averaged responses may mask more complex year-to-year sensitivities.In this study we have used the IRS approach to investigate the sensitivity of wheat yields to short-term (inter-annual) climate fluctuations for sites in Finland, Germany and northern Spain. We focus on the baseline period (1981-2100) in order to gain insight into model behaviour in response to present-day seasonal weather variations. We also explore the use of IRS plots to examine regional yield statistics with respect to temperature and precipitation over the same period. Analysis of the IRS patterns allows for a comparison of model behaviour in response to long- and short-term climatic fluctuations as well as a reality check of modelled versus observed yields.Preliminary results indicate some consistency in the association between annual observed yields and temperature and precipitation anomalies compared to long-term responses reported previously. At all sites the relationship between grain yield and climate is positive for precipitation and negative for temperature (except for cool years in Finland). Ongoing analysis is also investigating IRS patterns of inter-annual responses from models participating in an earlier ensemble inter-comparison exercise (Pirttioja et al. 2015) from which co-authors are gratefully acknowledged
Modelling climate change adaptation in European agriculture: Definitions and Current Modelling
Full text linkConfidential content, in preparation for a peer-reviewed publication
Modelling plant disease and pest effects on crop performances.
No full textModelling the effects of plant diseases and pests on crop performance, starting with crop yield, is an important new challenge MACSUR wants to address. We have established a small "Pest and Disease" group within MACSUR, where we address this question, with particular emphasis on wheat and grapevine. In the case of wheat, a reference data set from Denmark is being used as a key reference set for wheat - septoria tritici blotch - leaf rust interaction. In a first step an ensemble of seven wheat growth models of different complexity implement defined mechanisms for damages through pest and diseases using field data of a "pest-free" treatment for crop model calibration and idealised (temporal) patterns of injuries represented by simplified disease progress curves. In a second step field data of non-protected field plots are provided together with disease severity data to test simulations of real disease effects on crop yield loss against observed data. In parallel, we collected information on available data for pest and disease impacts by a questionnaire to evaluate their suitability for crop growth as well as for pest and disease modelling. We shall report our results in this exercise, and outline the approach we envision to (i) continue this work on wheat, and (ii) expand it to other crops such as grapevine