76 resultados para Dairy Production

em CentAUR: Central Archive University of Reading - UK


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Organic farming aims to create an integrated, humane, environmentally and economically sustainable agricultural system. For organic dairy systems, the fulfilment of these aims requires the understanding and integration of a number of systems components including land use (mixed or dairy only) and stocking rate; grassland and forage production, including quantity and quality; potential milk yield and milk quality; animal nutrition (largely farm based) and health; environmental sustainability such as farm nutrient balance; the financial status of the farm, including enterprise performance, fixed costs and labour use, and farm income and profit; and finally the policy environment in which organic dairy systems operate. This review discusses worldwide research undertaken into each of these key components of organic dairy production systems. As converting organic dairy systems are often considerably different to established organic systems, both the converting and developed organic dairy system are discussed in this paper.

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A Participatory Rural Appraisal (PRA) was conducted in dairy farms of the North West Province of Cameroon. The aim of the PRA was to have a better understanding of the prevailing dairy systems, identify problems, and set priorities for research and development that can contribute to improved systems of production. A multidisciplinary team of researchers and extension agents was constituted. It was made up of scientists of the following fields: cattle management, forage science, agro economy, veterinary, dairy technology, nutrition and extension. The research team visited farmers' groups and divided itself into subgroups for farm and village walks during which direct observations were also noted. The extension agent of the locality, key informants, gave additional information overlooked by farmers. Interviews were also carried out with other stakeholders of the dairy sector. The research team met the day following the visit to agree on a common report. Results show that five small scale dairy production systems are found in the region: transhumance, improved extensive, semi intensive, zero grazing and peri-urban. Agriculture is well integrated to dairying. Main constraints include in order of importance: poor marketing opportunities and long distances to market, limited grazing land and poor supplementation strategies, poor reproductive management and poor calving interval, inadequate knowledge in processing, hygiene and milk preservation, and limited health control. In market oriented farms, reproduction and feeding were the most important constraints. Main factors influencing dairy production are: milk collection, fresh milk price, consumer demand, genotype and management. These results suggest that much can be done to improve production by extending improved packages to dairy farmers.

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Fundamental nutrition seeks to describe the complex biochemical reactions involved in assimilation and processing of nutrients by various tissues and organs, and to quantify nutrient movement (flux) through those processes. Over the last 25 yr, considerable progress has been made in increasing our understanding of metabolism in dairy cattle. Major advances have been made at all levels of biological organization, including the whole animal, organ systems, tissues, cells, and molecules. At the whole-animal level, progress has been made in delineating metabolism during late pregnancy and the transition to lactation, as well as in whole-body use of energy-yielding substrates and amino acids for growth in young calves. An explosion of research using multicatheterization techniques has led to better quantitative descriptions of nutrient use by tissues of the portal-drained viscera (digestive tract, pancreas, and associated adipose tissues) and liver. Isolated tissue preparations have provided important information on the interrelationships among glucose, fatty acid, and amino acid metabolism in liver, adipose tissue, and mammary gland, as well as the regulation of these pathways during different physiological states. Finally, the last 25 yr has witnessed the birth of "molecular biology" approaches to understanding fundamental nutrition. Although measurements of mRNA abundance for proteins of interest already have provided new insights into regulation of metabolism, the next 25 yr will likely see remarkable advances as these techniques continue to be applied to problems of dairy cattle biology. Integration of the "omics" technologies (functional genomics, proteomics, and metabolomics) with measurements of tissue metabolism obtained by other methods is a particularly exciting prospect for the future. The result should be improved animal health and well being, more efficient dairy production, and better models to predict nutritional requirements and provide rations to meet those requirements.

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In the past decade, a number of mechanistic, dynamic simulation models of several components of the dairy production system have become available. However their use has been limited due to the detailed technical knowledge and special software required to run them, and the lack of compatibility between models in predicting various metabolic processes in the animal. The first objective of the current study was to integrate the dynamic models of [Brit. J. Nutr. 72 (1994) 679] on rumen function, [J. Anim. Sci. 79 (2001) 1584] on methane production, [J. Anim. Sci. 80 (2002) 2481 on N partition, and a new model of P partition. The second objective was to construct a decision support system to analyse nutrient partition between animal and environment. The integrated model combines key environmental pollutants such as N, P and methane within a nutrient-based feed evaluation system. The model was run under different scenarios and the sensitivity of various parameters analysed. A comparison of predictions from the integrated model with the original simulation models showed an improvement in N excretion since the integrated model uses the dynamic model of [Brit. J. Nutr. 72 (1994) 6791 to predict microbial N, which was not represented in detail in the original model. The integrated model can be used to investigate the degree to which production and environmental objectives are antagonistic, and it may help to explain and understand the complex mechanisms involved at the ruminal and metabolic levels. A part of the integrated model outputs were the forms of N and P in excreta and methane, which can be used as indices of environmental pollution. (C) 2004 Elsevier B.V All rights reserved.

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Accurate knowledge of lactation curves has an important relevance to management and research of dairy production systems. A number of equations have been proposed to describe the lactation curve, the most widely applied being the gamma equation. The objective of this work was to compare and evaluate candidate functions for their predictive ability in describing lactation curves from central Mexican dairy cows reared under 2 contrasting management systems. Five equations were considered: Gaines ( exponential decay), Wood ( gamma equation), Rook ( Michaelis-Menten x exponential), and 2 more mechanistic ones (Dijkstra and Pollott). A database consisting of 701 and 1283 records of cows in small-scale and intensive systems, respectively, was used in the analysis. Before analysis, the database was divided into 6 groups representing first, second, and third and higher parity cows in both systems. In all cases except second and above parity cows in small-scale systems, all models improved on the Gaines equation. The Wood equation explained much of the variation, but its parameters do not have direct biological interpretation. Although the Rook equation fitted the data well, some of the parameter estimates were not significant. The Dijkstra equation consistently gave better predictions, and its parameters were usually statistically significant and lend themselves to physiological interpretation. As such, the differences between systems and parity could be explained due to variations in theoretical initial milk production at parturition, specific rates of secretory cell proliferation and death, and rate of decay, all of which are parameters in the model. The Pollott equation, although containing the most biology, was found to be over-parameterized and resulted in nonsignificant parameter estimates. For central Mexican dairy cows, the Dijkstra equation was the best option to use in describing the lactation curve.

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There is increasing concern that the intensification of dairy production reduces the concentrations of nutritionally desirable compounds in milk. This study therefore compared important quality parameters (protein and fatty acid profiles; α-tocopherol and carotenoid concentrations) in milk from four dairy systems with contrasting production intensities (in terms of feeding regimens and milking systems). The concentrations of several nutritionally desirable compounds (β-lactoglobulin, omega-3 fatty acids, omega-3/omega-6 ratio, conjugated linoleic acid c9t11, and/or carotenoids) decreased with increasing feeding intensity (organic outdoor ≥ conventional outdoor ≥ conventional indoors). Milking system intensification (use of robotic milking parlors) had a more limited effect on milk composition, but increased mastitis incidence. Multivariate analyses indicated that differences in milk quality were mainly linked to contrasting feeding regimens and that milking system and breed choice also contributed to differences in milk composition between production systems.

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Routine milk recording data, often covering many years, are available for approximately half the dairy herds of England and Wales. In addition to milk yield and quality, these data include production events that can be used to derive objective Key Performance Indicators (KPI) describing a herd's fertility and production. Recent developments in information systems give veterinarians and other technical advisers access to these KPIs on-line. In addition to reviewing individual herd performance, advisers can establish local benchmark groups to demonstrate the relative performance of similar herds in the vicinity. The use of existing milk recording data places no additional demands on farmer's time or resources. These developments could also readily be exploited by universities to introduce veterinary undergraduates to the realities of commercial dairy production.

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Improved nutrient utilization efficiency is strongly related to enhanced economic performance and reduced environmental footprint of dairy farms. Pasture-based systems are widely used for dairy production in certain areas of the world, but prediction equations of fresh grass nutritive value (nutrient digestibility and energy concentrations) are limited. Equations to predict digestible energy (DE) and metabolizable energy (ME) used for grazing cattle have been either developed with cattle fed conserved forage and concentrate diets or sheep fed previously frozen grass, and the majority of them require measurements less commonly available to producers, such as nutrient digestibility. The aim of the present study was therefore to develop prediction equations more suitable to grazing cattle for nutrient digestibility and energy concentrations, which are routinely available at farm level by using grass nutrient contents as predictors. A study with 33 nonpregnant, nonlactating cows fed solely fresh-cut grass at maintenance energy level for 50 wk was carried out over 3 consecutive grazing seasons. Freshly harvested grass of 3 cuts (primary growth and first and second regrowth), 9 fertilizer input levels, and contrasting stage of maturity (3 to 9 wk after harvest) was used, thus ensuring a wide representation of nutritional quality. As a result, a large variation existed in digestibility of dry matter (0.642-0.900) and digestible organic matter in dry matter (0.636-0.851) and in concentrations of DE (11.8-16.7 MJ/kg of dry matter) and ME (9.0-14.1 MJ/kg of dry matter). Nutrient digestibilities and DE and ME concentrations were negatively related to grass neutral detergent fiber (NDF) and acid detergent fiber (ADF) contents but positively related to nitrogen (N), gross energy, and ether extract (EE) contents. For each predicted variable (nutrient digestibilities or energy concentrations), different combinations of predictors (grass chemical composition) were found to be significant and increase the explained variation. For example, relatively higher R(2) values were found for prediction of N digestibility using N and EE as predictors; gross-energy digestibility using EE, NDF, ADF, and ash; NDF, ADF, and organic matter digestibilities using N, water-soluble carbohydrates, EE, and NDF; digestible organic matter in dry matter using water-soluble carbohydrates, EE, NDF, and ADF; DE concentration using gross energy, EE, NDF, ADF, and ash; and ME concentration using N, EE, ADF, and ash. Equations presented may allow a relatively quick and easy prediction of grass quality and, hence, better grazing utilization on commercial and research farms, where nutrient composition falls within the range assessed in the current study.

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This work aimed to test if milk preserved with bronopol can be reliably used for fatty acid determination. Dairy production and milk quality are often monitored regularly to assess performance and contribute to selection indices. With evidence that fat composition can be influenced by selective breeding, there might be an interest in using samples collected in routine testing to evaluate individual cow fatty acid profiles, contributing to breeding indices. However, most recording services use a preservative such as bronopol and there is no published record if this influences subsequent fatty acid analysis. This study used milk from an oil seed supplementation trial, generating a wide range of milk fatty acid profiles, to test if the concentration of 31 individual fatty acids determined by GC were influenced by bronopol. Provided preserved samples are subsequently frozen, milk treated with bronopol can reliably be used to evaluate fatty acid composition in most cases; however bronopol might influence a few long-chain fatty acids present in relatively low concentrations. This is one small step towards simplifying milk compositional analysis but it could ultimately streamline the inclusion of milk fat quality into breeding indices, either with a view to 'healthier' milk or potentially reducing methane output and the environmental impact of dairy production.

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Forty-multiparous Holstein cows were used in a 16-wk continuous design study to determine the effects of either selenium (Se) source, selenized yeast (SY) (derived from a specific strain of Saccharomyces cerevisiae CNCM I-3060 Sel-Plex®) or sodium selenite (SS), or inclusion rate of SY on Se concentration and speciation in blood, milk and cheese. Cows received ad libitum a TMR with 1:1 forage:concentrate ratio on a dry matter (DM) basis. There were four diets (T1-T4) which differed only in either source or dose of Se additive. Estimated total dietary Se for T1 (no supplement), T2 (SS), T3 (SY) and T4 (SY) was 0.16, 0.30, 0.30 and 0.45 mg/kg DM, respectively. Blood and milk samples were taken at 28 day intervals and at each time point there were positive linear effects of SY on Se concentration in blood and milk. At day 112 blood and milk Se values for T1-T4 were 177, 208, 248, 279 ± 6.6 and 24, 38, 57, 72 ± 3.7 ng/g fresh material, respectively and indicate improved uptake and incorporation of Se from SY. While selenocysteine (SeCys) was the main selenised amino acid in blood its concentration was not markedly affected by treatment, but the proportion of total Se as selenomethionine (SeMet) increased with increasing inclusion rate of SY. In milk, there were no marked treatment effects on SeCys content, but Se source had a marked effect on the proportion of total Se as SeMet. At day 112 replacing SS (T2) with SY (T3) increased the SeMet concentration of milk from 36 to 111 ng Se/g and its concentration increased further to 157 ng Se/g as the inclusion rate of SY increased further (T4) to provide 0.45 mg Se/kg TMR. Neither Se source nor inclusion rate effected the keeping quality of milk. At day 112, milk from T1, T2, and T3 was made into a hard cheese and Se source had a marked effect on total Se and the proportion of total Se comprised as either SeMet or SeCys. Replacing SS (T2) with SY (T3) increased total Se, SeMet and SeCys content from 180 to 340 ng Se/g, 57 to 153 ng Se/g and 52 to 92 ng Se/g, respectively. Key words: dairy cow, milk and cheese, selenomethionine, selenocysteine, milk keeping quality

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A model of sugarcane digestion was applied to indicate the suitability of various locally available supplements for enhancing milk production of Indian crossbred dairy cattle. Milk production was calculated according to simulated energy, lipogenic, glucogenic and aminogenic substrate availability. The model identified the most limiting substrate for milk production from different sugarcane-based diets. For sugarcane tops/urea fed alone, milk production was most limited by amino acid followed by long chain fatty acid availability. Among the protein-rich oil cake supplements at 100, 200 and 300 g supplement/kg total DM, cottonseed oil cake proved superior with a milk yield of 5.5, 7.3 and 8.3 kg/day, respectively. This was followed by mustard oil cake with 5.1, 6.5 and 7.6 kg/day, respectively. In the case of a protein-rich supplement (fish meal), milk yield was limited to 6.6 kg/day due to a shortage of long chain fatty acids. However, at 300 g of supplementation, energy became limiting, with a milk yield of 6.7 kg/day. Supplementation with rice bran and rice polishings at 100, 200 and 300 g restricted milk yield to 4.3, 4.9 and 5.5 and 4.5, 5.3 and 6.1 kg/day, respectively, and amino acids became the factor limiting milk production. The diet comprising basal sugarcane tops supplemented by leguminous fodder, dry fodder (e.g. rice or wheat straw) and concentrates at levels of 100, 200 and 300 g supplements/kg total diet DM proved to be the most balanced with a milk yield of 5.1, 6.7 and 9.0 kg/day, respectively.

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The objectives were to compare the chemical composition, nutritive value, feed intake, milk production and composition, and presence in milk of transgenic DNA and the encoded protein Cry1Ab when corn silages containing 2 transgenes (2GM: herbicide tolerance: mepsps and insect resistance: cry1Ab) were fed as part of a standard total mixed ration (TMR) compared with a near isogenic corn silage ( C) to 8 multiparous lactating Holstein dairy cows in a single reversal design study. Cows were fed a TMR ration ad libitum and milked twice daily. Diets contained [ dry matter (DM) basis] 45% corn silage, 10% alfalfa hay, and 45% concentrate (1.66 Mcal of net energy for lactation/kg of DM, 15.8% crude protein, 35% neutral detergent fiber, and 4.1% fat). Each period was 28-d long. During the last 4 d of each period, feed intake and milk production data were recorded and milk samples taken for compositional analysis, including the presence of transgenic DNA and Cry1Ab protein. There was no significant difference in the chemical composition between C and 2GM silages, and both were within the expected range (37.6% DM, 1.51 Mcal of net energy for lactation/kg, 8.6% crude protein, 40% neutral detergent fiber, 19.6% acid detergent fiber, pH 3.76, and 62% in vitro DM digestibility). Cows fed the 2GM silage produced milk with slightly higher protein (3.09 vs. 3.00%), lactose ( 4.83 vs. 4.72%) and solids-not-fat (8.60 vs. 8.40%) compared with C. However, the yield (kg/d) of milk (36.5), 3.5% fat-corrected milk (34.4), fat (1.151), protein (1.106), lactose (1.738), and solids-not-fat ( 3.094), somatic cell count (log(10): 2.11), change in body weight (+ 7.8 kg), and condition score (+ 0.09) were not affected by type of silage, indicating no overall production difference. All milk samples were negative for the presence of transgenic DNA from either trait or the Cry1Ab protein. Results indicate that the 2GM silage modified with 2 transgenes did not affect nutrient composition of the silages and had no effect on animal performance and milk composition. No transgenic DNA and Cry1Ab protein were detected in milk.

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Grass-based diets are of increasing social-economic importance in dairy cattle farming, but their low supply of glucogenic nutrients may limit the production of milk. Current evaluation systems that assess the energy supply and requirements are based on metabolisable energy (ME) or net energy (NE). These systems do not consider the characteristics of the energy delivering nutrients. In contrast, mechanistic models take into account the site of digestion, the type of nutrient absorbed and the type of nutrient required for production of milk constituents, and may therefore give a better prediction of supply and requirement of nutrients. The objective of the present study is to compare the ability of three energy evaluation systems, viz. the Dutch NE system, the agricultural and food research council (AFRC) ME system, and the feed into milk (FIM) ME system, and of a mechanistic model based on Dijkstra et al. [Simulation of digestion in cattle fed sugar cane: prediction of nutrient supply for milk production with locally available supplements. J. Agric. Sci., Cambridge 127, 247-60] and Mills et al. [A mechanistic model of whole-tract digestion and methanogenesis in the lactating dairy cow: model development, evaluation and application. J. Anim. Sci. 79, 1584-97] to predict the feed value of grass-based diets for milk production. The dataset for evaluation consists of 41 treatments of grass-based diets (at least 0.75 g ryegrass/g diet on DM basis). For each model, the predicted energy or nutrient supply, based on observed intake, was compared with predicted requirement based on observed performance. Assessment of the error of energy or nutrient supply relative to requirement is made by calculation of mean square prediction error (MSPE) and by concordance correlation coefficient (CCC). All energy evaluation systems predicted energy requirement to be lower (6-11%) than energy supply. The root MSPE (expressed as a proportion of the supply) was lowest for the mechanistic model (0.061), followed by the Dutch NE system (0.082), FIM ME system (0.097) and AFRCME system(0.118). For the energy evaluation systems, the error due to overall bias of prediction dominated the MSPE, whereas for the mechanistic model, proportionally 0.76 of MSPE was due to random variation. CCC analysis confirmed the higher accuracy and precision of the mechanistic model compared with energy evaluation systems. The error of prediction was positively related to grass protein content for the Dutch NE system, and was also positively related to grass DMI level for all models. In conclusion, current energy evaluation systems overestimate energy supply relative to energy requirement on grass-based diets for dairy cattle. The mechanistic model predicted glucogenic nutrients to limit performance of dairy cattle on grass-based diets, and proved to be more accurate and precise than the energy systems. The mechanistic model could be improved by allowing glucose maintenance and utilization requirements parameters to be variable. (C) 2007 Elsevier B.V. All rights reserved.

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Genetic parameters and breeding values for dairy cow fertility were estimated from 62 443 lactation records. Two-trait analysis of fertility and milk yield was investigated as a method to estimate fertility breeding values when culling or selection based on milk yield in early lactation determines presence or absence of fertility observations in later lactations. Fertility traits were calving interval, intervals from calving to first service, calving to conception and first to last service, conception success to first service and number of services per conception. Milk production traits were 305-day milk, fat and protein yield. For fertility traits, range of estimates of heritability (h(2)) was 0.012 to 0.028 and of permanent environmental variance (c(2)) was 0.016 to 0.032. Genetic correlations (r(g)) among fertility traits were generally high ( > 0.70). Genetic correlations of fertility with milk production traits were unfavourable (range -0.11 to 0.46). Single and two-trait analyses of fertility were compared using the same data set. The estimates of h(2) and c(2) were similar for two types of analyses. However, there were differences between estimated breeding values and rankings for the same trait from single versus multi-trait analyses. The range for rank correlation was 0.69-0.83 for all animals in the pedigree and 0.89-0.96 for sires with more than 25 daughters. As single-trait method is biased due to selection on milk yield, a multi-trait evaluation of fertility with milk yield is recommended. (C) 2002 Elsevier Science B.V. All rights reserved.