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Lessire, F.; Hornick, J.L.; Minet, J.; Dufrasne, I. |
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Title |
Rumination time, milk yield, milking frequency of grazing dairy cows milked by a mobile automatic system during mild heat stress |
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Journal Article |
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Year |
2015 |
Publication |
Advances in Animal Biosciences |
Abbreviated Journal  |
Advances in Animal Biosciences |
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Volume |
6 |
Issue |
01 |
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12-14 |
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Keywords |
dairy; heat stress; THI; behaviour; milk yield |
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English |
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2040-4700 |
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LiveM, ft_macsur |
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no |
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Call Number |
MA @ admin @ |
Serial |
4570 |
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Author |
Özkan Gülzari, Ş.; Åby, B.A.; Persson, T.; Höglind, M.; Mittenzwei, K. |
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Title |
Combining models to estimate the impacts of future climate scenarios on feed supply, greenhouse gas emissions and economic performance on dairy farms in Norway |
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Journal Article |
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Year |
2017 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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Volume |
157 |
Issue |
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157-169 |
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Keywords |
Climate change; Dairy farming; Dry matter yield; Economics; Greenhouse gas emission; Modelling |
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Abstract |
• This study combines crop, livestock and economic models.
• Models interaction is through use of relevant input and output variables.
• Future climate change will result in increased grass and wheat dry matter yields.
• Changes in grass, wheat and milk yields in future reduce farm emissions intensity.
• Changes in future dry matter yields and emissions lead to increased profitability.
There is a scientific consensus that the future climate change will affect grass and crop dry matter (DM) yields. Such yield changes may entail alterations to farm management practices to fulfill the feed requirements and reduce the farm greenhouse gas (GHG) emissions from dairy farms. While a large number of studies have focused on the impacts of projected climate change on a single farm output (e.g. GHG emissions or economic performance), several attempts have been made to combine bio-economic systems models with GHG accounting frameworks. In this study, we aimed to determine the physical impacts of future climate scenarios on grass and wheat DM yields, and demonstrate the effects such changes in future feed supply may have on farm GHG emissions and decision-making processes. For this purpose, we combined four models: BASGRA and CSM-CERES-Wheat models for simulating forage grass DM and wheat DM grain yields respectively; HolosNor for estimating the farm GHG emissions; and JORDMOD for calculating the impacts of changes in the climate and management on land use and farm economics. Four locations, with varying climate and soil conditions were included in the study: south-east Norway, south-west Norway, central Norway and northern Norway. Simulations were carried out for baseline (1961–1990) and future (2046–2065) climate conditions (projections based on two global climate models and the Special Report on Emissions Scenarios (SRES) A1B GHG emission scenario), and for production conditions with and without a milk quota. The GHG emissions intensities (kilogram carbon dioxide equivalent: kgCO2e emissions per kg fat and protein corrected milk: FPCM) varied between 0.8 kg and 1.23 kg CO2e (kg FPCM)− 1, with the lowest and highest emissions found in central Norway and south-east Norway, respectively. Emission intensities were generally lower under future compared to baseline conditions due mainly to higher future milk yields and to some extent to higher crop yields. The median seasonal above-ground timothy grass yield varied between 11,000 kg and 16,000 kg DM ha− 1 and was higher in all projected future climate conditions than in the baseline. The spring wheat grain DM yields simulated for the same weather conditions within each climate projection varied between 2200 kg and 6800 kg DM ha− 1. Similarly, the farm profitability as expressed by total national land rents varied between 1900 million Norwegian krone (NOK) for median yields under baseline climate conditions up to 3900 million NOK for median yield under future projected climate conditions. |
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phase 2 |
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CropM, LiveM, TradeM, ft_macsur |
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MA @ admin @ |
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5172 |
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Bertocchi, L.; Vitali, A.; Lacetera, N.; Nardone, A.; Varisco, G.; Bernabucci, U. |
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Title |
Seasonal variations in the composition of Holstein cow’s milk and temperature-humidity index relationship |
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Journal Article |
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Year |
2014 |
Publication |
Animal |
Abbreviated Journal |
Animal |
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Volume |
8 |
Issue |
4 |
Pages |
667-674 |
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Keywords |
Animal Husbandry/*methods; Animals; Cattle/*physiology; Cell Count/veterinary; Dairying; Female; Hot Temperature; Humidity; Italy; Lactation/*physiology; Milk/cytology/*physiology; Retrospective Studies; Seasons |
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Abstract |
A retrospective study on seasonal variations in the characteristics of cow’s milk and temperature-humidity index (THI) relationship was conducted on bulk milk data collected from 2003 to 2009. The THI relationship study was carried out on 508 613 bulk milk data items recorded in 3328 dairy farms form the Lombardy region, Italy. Temperature and relative humidity data from 40 weather stations were used to calculate THI. Milk characteristics data referred to somatic cell count (SCC), total bacterial count (TBC), fat percentage (FA%) and protein percentage (PR%). Annual, seasonal and monthly variations in milk composition were evaluated on 656 064 data items recorded in 3727 dairy farms. The model highlighted a significant association between the year, season and month, and the parameters analysed (SCC, TBC, FA%, PR%). The summer season emerged as the most critical season. Of the summer months, July presented the most critical conditions for TBC, FA% and PR%, (52 054 ± 183 655, 3.73% ± 0.35% and 3.30% ± 0.15%, respectively), and August presented higher values of SCC (369 503 ± 228 377). Each milk record was linked to THI data calculated at the nearest weather station. The analysis demonstrated a positive correlation between THI and SCC and TBC, and indicated a significant change in the slope at 57.3 and 72.8 maximum THI, respectively. The model demonstrated a negative correlation between THI and FA% and PR% and provided breakpoints in the pattern at 50.2 and 65.2 maximum THI, respectively. The results of this study indicate the presence of critical climatic thresholds for bulk tank milk composition in dairy cows. Such indications could facilitate the adoption of heat management strategies, which may ensure the health and production of dairy cows and limit related economic losses. |
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1751-7311 |
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LiveM, ft_macsur |
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MA @ admin @ |
Serial |
4618 |
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Author |
Hutchings, N.J.; Özkan Gülzari, Ş.; de Haan, M.; Sandars, D. |
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Title |
How do farm models compare when estimating greenhouse gas emissions from dairy cattle production |
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Journal Article |
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Year |
2018 |
Publication |
Animal |
Abbreviated Journal |
Animal |
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Volume |
12 |
Issue |
10 |
Pages |
2171-2180 |
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Keywords |
dairy cattle; farm-scale; model; greenhouse gas; Future Climate Scenarios; Systems-Analysis; Milk-Production; Crop; Production; Mitigation; Intensity; Impacts |
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Abstract |
The European Union Effort Sharing Regulation (ESR) will require a 30% reduction in greenhouse gas (GHG) emissions by 2030 compared with 2005 from the sectors not included in the European Emissions Trading Scheme, including agriculture. This will require the estimation of current and future emissions from agriculture, including dairy cattle production systems. Using a farm-scale model as part of a Tier 3 method for farm to national scales provides a more holistic and informative approach than IPCC (2006) Tier 2 but requires independent quality control. Comparing the results of using models to simulate a range of scenarios that explore an appropriate range of biophysical and management situations can support this process by providing a framework for placing model results in context. To assess the variation between models and the process of understanding differences, estimates of GHG emissions from four farm-scale models (DailyWise, FarmAC, HolosNor and SFARMMOD) were calculated for eight dairy farming scenarios within a factorial design consisting of two climates (cool/dry and warm/wet) x two soil types (sandy and clayey) x two feeding systems (grass only and grass/maize). The milk yield per cow, follower cow ratio, manure management system, nitrogen (N) fertilisation and land area were standardised for all scenarios in order to associate the differences in the results with the model structure and function. Potential yield and application of available N in fertiliser and manure were specified separately for grass and maize. Significant differences between models were found in GHG emissions at the farm-scale and for most contributory sources, although there was no difference in the ranking of source magnitudes. The farm-scale GHG emissions, averaged over the four models, was 10.6 t carbon dioxide equivalents (CO(2)e)/ha per year, with a range of 1.9 t CO(2)e/ha per year. Even though key production characteristics were specified in the scenarios, there were still significant differences between models in the annual milk production per ha and the amounts of N fertiliser and concentrate feed imported. This was because the models differed in their description of biophysical responses and feedback mechanisms, and in the extent to which management functions were internalised. We conclude that comparing the results of different farm-scale models when applied to a range of scenarios would build confidence in their use in achieving ESR targets, justifying further investment in the development of a wider range of scenarios and software tools. |
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2019-01-07 |
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1751-7311 |
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TradeM, ft_macsur |
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Call Number |
MA @ admin @ |
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5212 |
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Author |
Kässi, P.; Känkänen, H.; Niskanen, O.; Lehtonen, H.; Höglind, M. |
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Title |
Farm level approach to manage grass yield variation under climate change in Finland and north-western Russia |
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Journal Article |
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Year |
2015 |
Publication |
Biosystems Engineering |
Abbreviated Journal |
Biosystems Engineering |
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Volume |
140 |
Issue |
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Pages |
11-22 |
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Keywords |
silage grass; risk management; dairy farms; buffer storage; agricultural economics; grassland modelling; dairy-cows; impact; security; timothy; harvest; future; growth; norway; europe; time |
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Cattle feeding in Northern Europe is based on grass silage, but grass growth is highly dependent on weather conditions. If ensuring sufficient silage availability in every situation is prioritised, the lowest expected yield level determines the cultivated area in farmers’ decision-making. One way to manage the variation in grass yield is to increase grass production and silage storage capacity so that they exceed the annual consumption at the farm. The cost of risk management in the current and the projected future climate was calculated taking into account grassland yield and yield variability for three study areas under current and mid-21st century climate conditions. The dataset on simulated future grass yields used as input for the risk management calculations were taken from a previously published simulation study. Strategies investigated included using up to 60% more silage grass area than needed in a year with average grass yields, and storing silage for up to 6 months more than consumed in a year (buffer storage). According to the results, utilising an excess silage grass area of 20% and a silage buffer storage capacity of 6 months were the most economic ways of managing drought risk in both the baseline climate and the projected climate of 2046-2065. It was found that the silage yield risk due to drought is likely to decrease in all studied locations, but the drought risk and costs implied still remain significant. (C) 2015 IAgrE. Published by Elsevier Ltd. All rights reserved. |
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1537-5110 |
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TradeM |
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MA @ admin @ |
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4671 |
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