MACSUR Literature Database -- Query Results

Ruete, A., Velarde, A., & Blanco-Penedo, I. (2015). Eco-DREAMS-S: modelling the impact of climate change on milk performance in organic dairy farms. Advances in Animal Biosciences, 6(01), 21–23. Keywords: dairy; organic; THI; heat stress; milk production http://reference.macsur.eu/show.php?record=4679
Bernabucci, U., Biffani, S., Buggiotti, L., Vitali, A., Lacetera, N., & Nardone, A. (2014). The effects of heat stress in Italian Holstein dairy cattle. J. Dairy Sci., 97(1), 471–486. Abstract: The data set for this study comprised 1,488,474 test-day records for milk, fat, and protein yields and fat and protein percentages from 191,012 first-, second-, and third-parity Holstein cows from 484 farms. Data were collected from 2001 through 2007 and merged with meteorological data from 35 weather stations. A linear model (M1) was used to estimate the effects of the temperature-humidity index (THI) on production traits. Least squares means from M1 were used to detect the THI thresholds for milk production in all parities by using a 2-phase linear regression procedure (M2). A multiple-trait repeatability test-model (M3) was used to estimate variance components for all traits and a dummy regression variable (t) was defined to estimate the production decline caused by heat stress. Additionally, the estimated variance components and M3 were used to estimate traditional and heat-tolerance breeding values (estimated breeding values, EBV) for milk yield and protein percentages at parity 1. An analysis of data (M2) indicated that the daily THI at which milk production started to decline for the 3 parities and traits ranged from 65 to 76. These THI values can be achieved with different temperature/humidity combinations with a range of temperatures from 21 to 36°C and relative humidity values from 5 to 95%. The highest negative effect of THI was observed 4 d before test day over the 3 parities for all traits. The negative effect of THI on production traits indicates that first-parity cows are less sensitive to heat stress than multiparous cows. Over the parities, the general additive genetic variance decreased for protein content and increased for milk yield and fat and protein yield. Additive genetic variance for heat tolerance showed an increase from the first to third parity for milk, protein, and fat yield, and for protein percentage. Genetic correlations between general and heat stress effects were all unfavorable (from -0.24 to -0.56). Three EBV per trait were calculated for each cow and bull (traditional EBV, traditional EBV estimated with the inclusion of THI covariate effect, and heat tolerance EBV) and the rankings of EBV for 283 bulls born after 1985 with at least 50 daughters were compared. When THI was included in the model, the ranking for 17 and 32 bulls changed for milk yield and protein percentage, respectively. The heat tolerance genetic component is not negligible, suggesting that heat tolerance selection should be included in the selection objectives. Keywords: Animals; Breeding; Cattle; Dietary Fats/analysis; Dietary Proteins/analysis; Female; Genetic Variation; Heat Stress Disorders/veterinary; Hot Temperature; Humans; Humidity; *Lactation; Linear Models; Milk/chemistry; Parity; Phenotype; Weather; dairy cow; heritability; production trait; temperature-humidity index breaking point http://reference.macsur.eu/show.php?record=4617
Bannink, A., van Lingen, H. J., Ellis, J. L., France, J., & Dijkstra, J. (2016). The contribution of mathematical modeling to understanding dynamic aspects of rumen metabolism. Frontiers in Microbiology, 7, 1820. Abstract: All mechanistic rumen models cover the main drivers of variation in rumen function, which are feed intake, the differences between feedstuffs and feeds in their intrinsic rumen degradation characteristics, and fractional outflow rate of fluid and particulate matter. Dynamic modeling approaches are best suited to the prediction of more nuanced responses in rumen metabolism, and represent the dynamics of the interactions between substrates and micro-organisms and inter-microbial interactions. The concepts of dynamics are discussed for the case of rumen starch digestion as influenced by starch intake rate and frequency of feed intake, and for the case of fermentation of fiber in the large intestine. Adding representations of new functional classes of micro-organisms (i.e., with new characteristics from the perspective of whole rumen function) in rumen models only delivers new insights if complemented by the dynamics of their interactions with other functional classes. Rumen fermentation conditions have to be represented due to their profound impact on the dynamics of substrate degradation and microbial metabolism. Although the importance of rumen pH is generally acknowledged, more emphasis is needed on predicting its variation as well as variation in the processes that underlie rumen fluid dynamics. The rumen wall has an important role in adapting to rapid changes in the rumen environment, clearing of volatile fatty acids (VFA), and maintaining rumen pH within limits. Dynamics of rumen wall epithelia and their role in VFA absorption needs to be better represented in models that aim to predict rumen responses across nutritional or physiological states. For a detailed prediction of rumen N balance there is merit in a dynamic modeling approach compared to the static approaches adopted in current protein evaluation systems. Improvement is needed on previous attempts to predict rumen VFA profiles, and this should be pursued by introducing factors that relate more to microbial metabolism. For rumen model construction, data on rumen microbiomes are preferably coupled with knowledge consolidated in rumen models instead of relying on correlations with rather general aspects of treatment or animal. This helps to prevent the disregard of basic principles and underlying mechanisms of whole rumen function. Keywords: lactating dairy-cows; milk urea concentration; fatty-acid production; ruminal fermentation; mechanistic model; holstein cows; beef-cattle; stoichiometric parameters; methane production; feeding frequency http://reference.macsur.eu/show.php?record=4932
Pulina, A., Lai, R., Salis, L., Seddaiu, G., Roggero, P. P., & Bellocchi, G. (2018). Modelling pasture production and soil temperature, water and carbon fluxes in Mediterranean grassland systems with the Pasture Simulation Model. Grass Forage Sci., 73(2), 272–283. Abstract: Grasslands play important roles in agricultural production and provide a range of ecosystem services. Modelling can be a valuable adjunct to experimental research in order to improve the knowledge and assess the impact of management practices in grassland systems. In this study, the PaSim model was assessed for its ability to simulate plant biomass production, soil temperature, water content, and total and heterotrophic soil respiration in Mediterranean grasslands. The study site was the extensively managed sheep grazing system at the Berchidda‐Monti Observatory (Sardinia, Italy), from which two data sets were derived for model calibration and validation respectively. A new model parameterization was derived for Mediterranean conditions from a set of eco‐physiological parameters. With the exception of heterotrophic respiration (Rh), for which modelling efficiency (EF) values were negative, the model outputs were in agreement with observations (e.g., EF ranging from ~0.2 for total soil respiration to ~0.7 for soil temperature). These results support the effectiveness of PaSim to simulate C cycle components in Mediterranean grasslands. The study also highlights the need of further model development to provide better representation of the seasonal dynamics of Mediterranean annual species‐rich grasslands and associated peculiar Rh features, for which the modelling is only implicitly being undertaken by the current PaSim release. Keywords: grassland production; Mediterranean pastures; model calibration; PaSim; sheep grazing systems; soil respiration Area: LiveM http://reference.macsur.eu/show.php?record=4973
D’Ottavio, P., Francioni, M., Trozzo, L., Sedic, E., Budimir, K., Avanzolini, P., et al. (2018). Trends and approaches in the analysis of ecosystem services provided by grazing systems: A review. Grass Forage Sci., 73(1), 15–25. Abstract: The ecosystem services (ES) approach is a framework for describing the benefits of nature to human well-being, and this has become a popular instrument for assessment and evaluation of ecosystems and their functions. Grazing lands can provide a wide array of ES that depend on their management practices and intensity. This article reviews the trends and approaches used in the analysis of some relevant ES provided by grazing systems, in line with the framework principles of the Millennium Ecosystem Assessment (MA). The scientific literature provides reports of many studies on ES in general, but the search here focused on grazing systems, which returned only sixty-two papers. This review of published papers highlights that: (i) in some papers, the concept of ES as defined by the MA is misunderstood (e.g., lack of anthropocentric vision); (ii) 34% of the papers dealt only with one ES, which neglects the need for the multisectoral approach suggested by the MA; (iii) few papers included stakeholder involvement to improve local decision-making processes; (iv) cultural ES have been poorly studied despite being considered the most relevant for local and general stakeholders; and (v) stakeholder awareness of well-being as provided by ES in grazing systems can foster both agri-environmental schemes and the willingness to pay for these services. Keywords: climate regulation; food, habitat services; land degradation prevention; moderation of extreme events; natural (landscape) heritage; primary production; regulation of water flows; water quality regulation; Grassland Management; Plant-Communities; Land Degradation; Inner-Mongolia; Trade-Offs; Biodiversity; Provision; Impact; Consequences; Conservation http://reference.macsur.eu/show.php?record=5191

Ruete, A., Velarde, A., & Blanco-Penedo, I. (2015). Eco-DREAMS-S: modelling the impact of climate change on milk performance in organic dairy farms. Advances in Animal Biosciences, 6(01), 21–23.

Bernabucci, U., Biffani, S., Buggiotti, L., Vitali, A., Lacetera, N., & Nardone, A. (2014). The effects of heat stress in Italian Holstein dairy cattle. J. Dairy Sci., 97(1), 471–486.

Abstract: The data set for this study comprised 1,488,474 test-day records for milk, fat, and protein yields and fat and protein percentages from 191,012 first-, second-, and third-parity Holstein cows from 484 farms. Data were collected from 2001 through 2007 and merged with meteorological data from 35 weather stations. A linear model (M1) was used to estimate the effects of the temperature-humidity index (THI) on production traits. Least squares means from M1 were used to detect the THI thresholds for milk production in all parities by using a 2-phase linear regression procedure (M2). A multiple-trait repeatability test-model (M3) was used to estimate variance components for all traits and a dummy regression variable (t) was defined to estimate the production decline caused by heat stress. Additionally, the estimated variance components and M3 were used to estimate traditional and heat-tolerance breeding values (estimated breeding values, EBV) for milk yield and protein percentages at parity 1. An analysis of data (M2) indicated that the daily THI at which milk production started to decline for the 3 parities and traits ranged from 65 to 76. These THI values can be achieved with different temperature/humidity combinations with a range of temperatures from 21 to 36°C and relative humidity values from 5 to 95%. The highest negative effect of THI was observed 4 d before test day over the 3 parities for all traits. The negative effect of THI on production traits indicates that first-parity cows are less sensitive to heat stress than multiparous cows. Over the parities, the general additive genetic variance decreased for protein content and increased for milk yield and fat and protein yield. Additive genetic variance for heat tolerance showed an increase from the first to third parity for milk, protein, and fat yield, and for protein percentage. Genetic correlations between general and heat stress effects were all unfavorable (from -0.24 to -0.56). Three EBV per trait were calculated for each cow and bull (traditional EBV, traditional EBV estimated with the inclusion of THI covariate effect, and heat tolerance EBV) and the rankings of EBV for 283 bulls born after 1985 with at least 50 daughters were compared. When THI was included in the model, the ranking for 17 and 32 bulls changed for milk yield and protein percentage, respectively. The heat tolerance genetic component is not negligible, suggesting that heat tolerance selection should be included in the selection objectives.

http://reference.macsur.eu/show.php?record=4617

Bannink, A., van Lingen, H. J., Ellis, J. L., France, J., & Dijkstra, J. (2016). The contribution of mathematical modeling to understanding dynamic aspects of rumen metabolism. Frontiers in Microbiology, 7, 1820.

Abstract: All mechanistic rumen models cover the main drivers of variation in rumen function, which are feed intake, the differences between feedstuffs and feeds in their intrinsic rumen degradation characteristics, and fractional outflow rate of fluid and particulate matter. Dynamic modeling approaches are best suited to the prediction of more nuanced responses in rumen metabolism, and represent the dynamics of the interactions between substrates and micro-organisms and inter-microbial interactions. The concepts of dynamics are discussed for the case of rumen starch digestion as influenced by starch intake rate and frequency of feed intake, and for the case of fermentation of fiber in the large intestine. Adding representations of new functional classes of micro-organisms (i.e., with new characteristics from the perspective of whole rumen function) in rumen models only delivers new insights if complemented by the dynamics of their interactions with other functional classes. Rumen fermentation conditions have to be represented due to their profound impact on the dynamics of substrate degradation and microbial metabolism. Although the importance of rumen pH is generally acknowledged, more emphasis is needed on predicting its variation as well as variation in the processes that underlie rumen fluid dynamics. The rumen wall has an important role in adapting to rapid changes in the rumen environment, clearing of volatile fatty acids (VFA), and maintaining rumen pH within limits. Dynamics of rumen wall epithelia and their role in VFA absorption needs to be better represented in models that aim to predict rumen responses across nutritional or physiological states. For a detailed prediction of rumen N balance there is merit in a dynamic modeling approach compared to the static approaches adopted in current protein evaluation systems. Improvement is needed on previous attempts to predict rumen VFA profiles, and this should be pursued by introducing factors that relate more to microbial metabolism. For rumen model construction, data on rumen microbiomes are preferably coupled with knowledge consolidated in rumen models instead of relying on correlations with rather general aspects of treatment or animal. This helps to prevent the disregard of basic principles and underlying mechanisms of whole rumen function.

Keywords: lactating dairy-cows; milk urea concentration; fatty-acid production; ruminal fermentation; mechanistic model; holstein cows; beef-cattle; stoichiometric parameters; methane production; feeding frequency

http://reference.macsur.eu/show.php?record=4932

Pulina, A., Lai, R., Salis, L., Seddaiu, G., Roggero, P. P., & Bellocchi, G. (2018). Modelling pasture production and soil temperature, water and carbon fluxes in Mediterranean grassland systems with the Pasture Simulation Model. Grass Forage Sci., 73(2), 272–283.

D’Ottavio, P., Francioni, M., Trozzo, L., Sedic, E., Budimir, K., Avanzolini, P., et al. (2018). Trends and approaches in the analysis of ecosystem services provided by grazing systems: A review. Grass Forage Sci., 73(1), 15–25.