FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
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Model intercomparison
Get PDFThis deliverable focuses on some illustrative results obtained with different grassland- specific, grassland adapted crop and dynamic vegetation models selected out of the first list of models compiled in D-L2.1.1 to simulate biomass and flux data from grassland sites in Europe and peri-Mediterranean regions (D-L2.1.1 and D-L2.1.2). Results from uncalibrated simulations were documented in the D-L2.3 report as a blind exercise. Some model improvements are emphasized in this report due to the higher information level of the model calibrations. The complete set of results will include simulations from uncalibrated and calibrated models
CropM: Understanding and Modelling Impacts of Climate Change on Crop Production
Get PDFKey ambition:To developa shared comprehensive information system on the impacts of climate change on European crop production and food securityfirst shared pan-continental assessments and tools(Full) range of important crops and important crop rotationsImproved management and analysis of dataModel improvement (stresses and factors not yet accounted for)Advanced scaling methodsAdvanced link to farm and sector modelsComprehensive uncertainty assessment and reportingTo train integrative crop modelerData ... for better understanding and modelling climate change impactEvaluation of data quality (platinum, gold, silver)Quantify data gaps for modellingEmpirical analysis of crop responses to past climate variability and changeObserved adaptation options and their efficacyEffect of extreme events (past analysis and projections)Climate change scenariosConcept for data management, data journalUncertaintyMethodology & protocols for uncertainty analysisMethodology for standardized model evaluationLocal-scale climate scenarios & uncertainties in climate projectionsBasic methodology for probabilistic assessment of CC impacts using impact response surfacesMethodology for probabilistic evaluation of alternative adaptation options Main aims in MACSUR2:Improve crop model to better capture extremesComplement knowledge from crop models with empirical crop-weather analysisConsider management variables in simulationsFull range of methods for analysing uncertainty in climate impact assessmentsEvaluate potential adaptation optionsContributing to cross-cutting issues and case studies.Further the links with other modelling activitiesLink local to European and global response
Effects of heat stress periods on milk production, milking frequency and rumination time of grazing dairy cows milked by a mobile automatic system in 2013
No full textIn Europe, analysis of meteorological data shows that the average temperature has increased by ~1°C over the past hundred years (IPCC, 2013). Heat stress periods are thus expected to be more frequent even in temperate areas. The use of an automatic milking system (AMS) implies the need to stimulate cows’ traffic to the robot, especially with grazing cows. Describing how heat stress influenced cows’ traffic to the robot is the aim of this study.Grazing dairy cows milked by an automatic system (AMS) experienced heat stress (HS) periods, twice during the summer 2013 in July (J) and August (A). The daily temperature humidity index (THI) during these periods were higher than 75. Each HS period was compared with a “normal period”(N), presenting the same number of cows, similar lactation number, days in milk, distance to come back to the robot and an equal access to water. The first HS period of 5 days with a mean THI of 78.4 was chosen in J, and a second that lasted for 6 days in A with a THI value of 77.3. Heat stress periods were cut off with the same duration of days with no stress (N) and mean THI <70. Milk production, milkings and returns to the robot during HS were compared with N periods.Milkings and visits to AMS were significantly more numerous in HS periods in July (HS: 2.44 vs N: 2.23, 3.97 vs 3.03) but milk production dropped from 20.3 kg to 19.3 kg milk per cow and per day. In August, MY increased slightly during HS. This could be explained by less high ambient temperatures and decreased distance to walk inducing less energy expenditure. The increase in milkings and visits to the robot during HS could be linked to water availability nearby the robot and confirmed previous findings (Lessire et al., 2014)
Methods to limit risks in agriculture in the era of climate change
No full textNowadays, you can forecast that in twenty-first century a probability of drought risk occurrence, a one of the threatening a type of risk in agriculture, will reach a level between 66 and 90 per cent [IPCC 2001].The beginning of the twenty-first century is a time to seek new methods of risk management in agriculture. This is confirmed by the reports and surveys carried out in many research centres, as well as commissioned by public authorities [Xu et al. 2008]. Currently, you can observe the growing importance of the issue of risk in agriculture due to the worsening climate change, changes in the Common Agricultural Policy, the progressive liberalization of food trade on a global scale (less market intervention, increased price volatility and fluctuations in food supply and demand) and associated with those phenomena increase market risk [Jerzak 2008]. Demographic boom, growth in epidemics and diseases or changes in models of consumer behaviour as a result of today's food trends healthy diet have an impact on food security. It is of interest to large research teams in Europe, just as the above risk factors affect the imbalance of global supply and demand for food in the long term. The Stern [Stern 2006] and report the Foundation for the Development of Polish Agriculture - FDPA) [Report FDPA 2008] and the communications of the European Commission show that in agriculture a lack of system solutions for the management of various risks and set of management instruments it is inadequate to the current situation of the sector.Analyzing historical data, one can conclude that in Poland more often we have to deal with losses caused by deficiency of precipitation than the excess [Mizak et al. 2013]. Droughts in Poland are most common when during the growing season flows very warm and dry air. In 2008, the area of arable land, determined in accordance with the applicable System Monitoring Agricultural Drought criterion of a 20 percent reduction in crop yields covered more than 8.1 million hectares, which accounted for 54% of arable land in Poland [Mizak et al. 2011]. Appropriate agricultural policy and trade policy should ensure sufficient food for the rapidly growing global population under mentioned above extreme natural events circumstances.Research centers in many EU countries and beyond should create appropriate models, tools and techniques in order to solve signaled above specific problems at farms, regions, countries and groups of countries in order to reduce the risks associated with food production [Bojar et al . 2012]. Such models were created as part of the research carried out in the Kujawy & Pomorze region where their results show the possibility of predicting the effects of climate change in the long term [Bojar et al., 2013, Żarski et al. 2014, Bojar at al., 2013].In particular, the series established the likelihood of a lack of rain in the forecast for the years 2030 and 2050 at a certain level and so the series 7, 8, 9 and 10 decades without rain likely to occur by 2030 amounts to 0.302, 0.109, 0.032 and 0.009, while for the year 2050 decades for a series of 7, 8, 9 and 10 respectively 0,543, 0,222, 0,070 and 0,019. It follows that, for a series of seven and eight decades without rain probability of such unfavorable phenomena is highest. Then established the relationship that the lack of rainfall will decrease yields of cereals in total, winter wheat, spring barley and potatoes. It results in the decline in land productivity in the years 2030 and 2050 will amount to cereals in total, winter wheat, spring barley and potatoes in the range of the maximum and minimum respectively 2.51 t/ha -3.67 t/ha, 3.10 t/ha- 4.10 t/ha, 1.63 t/ha - 3.33 t/ha and 15.30 t/ha- 21.00 t/ha [Bojar et al. 2013].The above-described conditions of risk of conducting agricultural activities indicate the need to develop methods of mitigating their negative effects.Mitigation of production and business risks in agriculture can be reached as follows:- advancement models for defining dependencies between yields and whether in long-term to forecasts negative effects in farming productivity and profitability and this way minimize production and business risks,- advancement of system of crop insurance,- improvement of the infrastructure of small retention and simulation of the impact of various forms of cooperation of agricultural producers to increase the efficiency of their operations (joint purchasing of inputs, selling of agricultural products and/or use of machinery [Bojar 2008], work specialization versus production specialization [Bojar W., Drelichowski L., 1994.], common trainings, advertisements [Bojar, Kinder 2008, etc.]. Own preliminary research findings confirmed that approximately one third of the respondents jointly purchases and sales their products and forms of farmer cooperation with a joint market activities (transaction) in the Kujavian & Pomeranian region.For more detail and more precise explanation of dependency between yield and rainfalls some efforts will be focused on mathematical models describing agriculture and climate change problems that can be encountered in risk and safety analysis. We need to describe the uncertainties from incomplete knowledge, imperfect models or measurement errors.Because yields of crops depend strongly on rainfall there will be considered different models of rainfall. You will attempt of the generalization of model mixture the gamma distribution and a single point at zero distribution. This approach will be a continuation of the work that has been sent to print. To extend this application it could be performed calculations for the empirical data coming from the Kujavian & Pomeranian region for different crops.This work was co-financed by NCBiR, Contract no. FACCE JPI/04/2012 - P100 PARTNE
Sensitivity and uncertainty analysis of grassland models in Europe and Israel
Get PDFGrassland models are valuable tools to test hypotheses on grassland ecosystem functioning. In the frame of FACCE MACSUR LiveM, a model intercomparison was conducted using a dataset from an observational and experimental network of nine multi-year grassland sites spread across Europe (France, Italy, Germany, Switzerland, The Netherlands, and United Kingdom) and Israel, and a suite of nine models to understand grassland functioning in the region. Grassland-specific approaches were compared to approaches mainly conceived to simulate crops and plant functional types. Model evaluation against actual measurements was performed before and after model calibration. The calibrated models were used to analyze their sensitivity to independent variations of temperature, precipitation and [CO2]. The results show to which extent calibration can accommodate model discrepancies. The sensitivity of simulated gross primary production to [CO2] and temperature is an important outcome, considering the fundamental effect of rising temperature and [CO2] on the C cycling of terrestrial ecosystems in the Euro-Mediterranean region. Overall, alternative models exhibit a different sensitivity to climate change factors, with different performances over different conditions. Explained by the basic processes of each model and also induced by different calibration methods, this difference is indicative that more models can be complementary and deliver greater insights than if they were applied individually
Operational database for storing and extracting data
Get PDFThis deliverable lays out the work as done as part of MACSUR CropM on data, with the focus on improving data management and have shared data curation for future use. The issue was tackled with help from the MACSUR central hub coordination in the form of Jason Jargenson from University of Reading. The data management as proposed and implemented in this deliverable is very much a bottom up process, in which partners in a meeting in Spring 2013 in Aarhus investigated the best way forward for data management across activities in CropM.As a follow up to this, the work was mainly divided in three parts: 1. The Open Data Journal for Agricultural Research, mainly focused on long term data archival and citation of data sets, as input and outputs to the modelling work, as part of MACSUR, lead by Wageningen UR 2. The Geonetwork data catalog hosted at Aarhus Universitet, that allows for operational access and storage of data sets as part of the ongoing work, also for restricted access of the consortium, and as a first step to visualization, lead by Aarhus Universitet. 3. The work on rating data sets, that provides a tool for improving data set access in an early phase for connecting them to models, lead by Reading University. At the end of the deliverable some next steps are giving for data activities in the context of AgMIP and beyond
Climate-dependent yields
Get PDFIn this report we summarize the contributions made by four groups to the subject of climate dependent yields. The first is by Waldemar Bojar, Leszek Knopik, Jacek Żarski, Cezary Sławiński, Piotr Baranowski and Wojciech Żarski on the subject of “the impact of extreme climate changes on the forecasted agriculture production”. It presents general characteristics of resources and outputs of agriculture in the Kujawsko-Pomorskie (K&P) and Lubelskie regions, based on statistical databases and the literature review. In this study, some statistically significant dependencies between the climatic parameters and yields of selected important crops in the abovementioned regions were worked out on the basis of empirical survey conducted in the University of Technology and Life Sciences and Institute of Agrophysics in Lublin. Efforts were taken to make integrated assessments of forecasted agricultural outputs influenced by climate extreme phenomena on the basis of the found dependencies’ yields – precipitation and the data coming from wide area model regional outputs such as prices, areas of farmland and yields. The second contribution is by Bojar W., Knopik L. and Żarski J. on the subject of “integrated assessment of business crop productivity and profitability to use in food supply forecasting”. It examines the proposals to build a model describing the amount of precipitation and taking into account periods without rain. This model is based on a mixture of gamma distribution and one point-distribution. The third contribution is by Iddo Kan on the Vegetative Agricultural Land Use Economic (VALUE) model. It discusses the sub-task with respect to crops of statistically estimating with statistical methods predictions of expected crop-yield contingent on climate, soil and production cost for use in existing trade models, or refined versions thereof, and how VALUE can contribute to this sub-task. The fourth contribution was made by Christoph Muller and Richard D. Robertson on the subject of “projecting future crop productivity for global economic modelling”. It supplies a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models
Integrated Assessment of Climate Change Mitigation and Adaptation Impacts at Landscape Level in the Austrian Mostviertel Region
Get PDFClimate change poses fundamental challenges on agriculture. It triggers autonomous adaptation responses of famers and thereby impacts the success of climate change mitigation. Integrated modelling frameworks (IMF) on land use serve as decision support instruments under such conditions by considering climate signals and accounting for combined mitigation and adaptation policies. We apply an IMF at the farm level in two contrasting grassland and cropland dominated landscapes in Austria to analyze climate change impacts on land use as well as impacts from mitigation and adaptation policies on the abiotic and biotic environment and the landscape. Results show that the impacts on farm gross margins and the abiotic and biotic environment are substantial either directly from climate change (e.g. changing erosion levels) or triggered via adaptation responses (i.e. land use and management change). Average gross margins increase between 1% and 12% depending on the case study landscape, the climate change scenario, and the policy scenario. With respect to biodiversity indicators, land use changes in the adaptation scenario decrease plant species diversity on farmland by 13% on average and losses are up to 80% for some farms. These changes are driven by policies in the adaptation scenario as responses on climate change in the absence of policies are modest with minor impacts on biodiversity. Results indicate the effectiveness of climate change adaptation in increasing farm incomes and the need to coordinate mitigation and adaptation policies to manage environmental outcomes. The IMF turns out to be effective in revealing heterogeneity of climate change impacts among farms and regions and linkages among adaptation and mitigation policies
The effect of combination of drought and heat stresses on plant transpiration and photosynthesis
No full textExpected increasing intensity and frequency of droughts with climate changes is often accompanied by increased air temperature resulting in decreased stability of crop yields. Owing to the complex nonlinear interactions between a plant and its environment, it is difficult to evaluate the effect of multi-stress on plant functioning.The main aim of presented research was to analyse spring wheat response to combination of two abiotic stresses: drought and heat.The growth chamber experiment with controlled environment was conducted on spring wheat growing in cylindrical soil columns. Four treatments were compared: control with optimum soil moisture and air temperature (C), heat wave (HW) – as C but with temperature elevated up to 34°C for four days at flowering, drought (D) with soil water content decreasing from initially optimum level to water deficit (pF> 3.4) at flowering, drought and heat wave (DHW) - the combination of two stresses .The results indicated different course of leaf transpiration and photosynthesis rates in analysed treatments in response to soil water content . HW treatment during period of increased temperature were characterised by significantly increased average transpiration as compared to all other treatments. However photosynthesis rate in this treatment were slightly lower than in control plants. Comparison of D and DHW treatments shows similarities in the trends of transpiration increase with increasing soil moisture with some offset to lower soil moisture in DHW resulting from higher evapotranspiration. Photosynthesis rate showed relatively large variation characterised by steeper increase with increasing soil water content in D as compared to DHW
Determining the variability in optimal sowing date of spring cereals in South Eastern Norway
No full textSpring cereals are important agricultural crops in Northern Europe. The short growing season in this region necessitates early sowing. The earliest possible date is often determined by the soil water content, which usually decreases during and after snowmelt at rates varying with the weather and the soil characteristics. Tillage and sowing operations on soils with too high a water content can lead to soil compaction, increased soil erosion, and losses of nutrients and soil organic matter. Rainfall intensity also affects crop emergence, through its potentially negative effects on surface capping. The objective of this study was to determine the earliest possible sowing date of spring cereals for representative soil and climate scenarios in southeastern Norway. Criteria were set for pre-sowing tillage operations and sowing, based on the water content in differ soil layers and the incidence of rainfall. To determine the day of the year when these criteria were first met, the soil water content during the spring was simulated with the soil module in DSSAT v4.5. These simulations were performed for contrasting soil types and climate scenarios representing the period 1961-90 and 2046-65 respectively. For each combination of soil and climate, one hundred simulations with individual weather data were performed. The results provide information about the timing and variability of the optimal planting date for the current and projected climate in South Eastern Norway