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Lessire, F. |
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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 |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-37 |
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In 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). No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2152 |
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Cammarano, D.; Rivington, M.; Matthews, K.; B,; Bellocchi, G. |
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Estimates of crop responses to climate change with quantified ranges of uncertainty |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C4.1.3 |
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In estimating responses of crops to future climate realisations, it is necessary to understand and differentiate between the sources of uncertainty in climate models and how these lead to errors in estimating the past climate and biases in future projections, and how these affect crop model estimates. This paper investigates the complexities in using climate model projections representing different spatial scales within climate change impacts and adaptation studies. This is illustrated by simulating spring barley with three crop models run using site-specific observed, original (50•50 km) and bias corrected downscaled (site-specific) hindcast (1960-1990) weather data from the HadRM3 Regional Climate Model (RCM). Original and bias corrected downscaled weather data were evaluated against the observed data. The comparisons made between the crop models were in the light of lessons learned from this data evaluation. Though the bias correction downscaling method improved the match between observed and hindcast data, this did not always translate into better matching of crop models estimates. At four sites the original HadRM3 data produced near identical mean simulated yield values as from the observed weather data, despite differences in the weather data, giving a situation of ‘right results for the wrong reasons’. This was likely due to compensating errors in the input weather data and non-linearity in crop models processes, making interpretation of results problematic. Overall, bias correction downscaling improved the quality of simulated outputs. Understanding how biases in climate data manifest themselves in crop models gives greater confidence in the utility of the estimates produced using downscaled future climate projections. The results indicate implications on how future projections of climate change impacts are interpreted. Fundamentally, considerable care is required in determining the impact weather data sources have in climate change impact and adaptation studies, whether from individual models or ensembles. No Label |
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2098 |
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Banse, M. |
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What drives meat consumption? Combining cross-country analysis with an applied trade model |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-3 |
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In a cross country analysis using national data for both OECD and developing countries, we estimate a regression model with different coefficients for different drivers for per capita meat consumption. The model contains data from approximately 125 countries (depending on the variables included) on meat consumption and production, relative size of agricultural area and pasture and meadows, PPP adjusted consumer prices for meat (and for food as control variable), PPP adjusted GNI per capita, HDI, degree of urbanisation, religion and geographical/cultural belonging.A regression analysis has been conducted, using OLS with data from 2011 and an aggregation of all meat types as the dependent variable. In the results all of the mentioned variables have a significant impact on meat consumption.Based on a first scenario analysis which has been presented on a TradeM Workshop of MACSUR in September 2014, this paper will extend the approach of an estimated cross-country analysis to improve the demand elasticities in the MAGNET model for meat and meat products. Further other demand determining factors of meat consumption, e.g. behavioural change towards less meat consumption (vegetarian or vegan) derived from the regression analysis will be fed into the MAGNET model. This extended approach will help to analyse the resulting market effects of a changing demand pattern for meat. MAGNET will provide insights in consequences on supply and international trade for meat and meat products.The aim of this combined approach is to further explore the relationship between production and consumption, and to what extent the one is driving the other. Based on the application of the panel data method for a detailed demand analysis with the combination of the feedback from the supply and trade side based on the MAGNET model we will be able to provide a tool which is able to address the important questions of demand responses under different adaptation or mitigation strategies towards clime change, such as tax measures like fat taxes. This extended tool also contributes to an improved decision making process of policy makers under different options to respond to climate change issues – not only with regard to the supply side of agricultural production but also to the consumption side. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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2118 |
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Scollan, N.; Bannink, A.; Kipling, R.; Saetnan, E.; Van Middelkoop, J. |
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Livestock and feed production, especially dairy and beef |
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2015 |
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FACCE MACSUR Reports |
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6 |
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Sp6-3 |
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Improving health and welfare is an important adaptation and mitigation strategyDeveloping process based modelling, responsive to adaptationLinks to climate and land use change modelling are essential Livestock systems likely to be hit hardest by climate changeNeed to develop animal health models that respond to adaptation by farmersBringing together direct and indirect impacts of climate change vitalAdaptation and mitigation need to be considered and modelled togetherLinking models across scales is important to support policy decisionsLearning between sectors carries potential for novel solutions and methodological advancesEffective communication of outcomes to stakeholders (how?) No Label |
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Brussels |
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Climate-change impacts on farming systems in the next decades: Why worry when you have CAP? A FACCE MACSUR workshop for policymakers |
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MA @ admin @ |
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2084 |
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Pirttioja, N. |
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A crop model ensemble analysis of wheat yield sensitivity to changes in temperature and precipitation across a European transect |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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5 |
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Sp5-46 |
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Impact response surfaces (IRSs) were constructed to depict the sensitivity of modelled spring and winter wheat yields to systematic changes in baseline temperature (between -2°C and +9°C) and precipitation (-50 to +50%) as simulated by a 26-member ensemble of process-based crop simulation models. The study was conducted across a latitudinal transect for sites in Finland, Germany and Spain.In spite of large differences in simulated yield responses to both baseline and changed climate between models, sites, crops and years, several common messages emerged. Ensemble average yields decline with warming (3-7% per 1°C) and decreased precipitation (3-9% per 10% decrease), but benefit from increased precipitation (0-8% per 10% increase). Yields are more sensitive to temperature than precipitation changes at the Finnish site while sensitivities are mixed at the other sites. Inter-model variability is highest for baseline climate at the Spanish site but is affected little by changed climate. Model responses diverge most under warming at the Finnish and German sites for winter wheat. The IRS pattern of yield reliability tracks average yield levels.Optimal temperatures for present-day cultivars are below the baseline at the German and Spanish sites suggesting that adoption of cultivars with higher temperature requirements might already be advantageous, and increasingly so at all sites under future warming.The study was conducted in the CropM component of the FACCE-JPI/MACSUR project. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2161 |
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