Johne’s Disease-Dairy, Spring 2010, Vol. 1, no.4

Animal science newsletter about Johne's Disease in cattle. Statistics, treatments, information about the disease itself and more.

Johne’s Disease-Dairy, Winter 2009, Vol. 1, no.3

Animal science newsletter about Johne's Disease in cattle. Statistics, treatments, information about the disease itself and more.

Johne’s Disease-Dairy, Fall 2011, Vol. 4, no.1

Animal science newsletter about Johne's Disease in cattle. Statistics, treatments, information about the disease itself and more.

Johne’s Disease-Dairy, Spring 2011, Vol. 3, no.3

Animal science newsletter about Johne's Disease in cattle. Statistics, treatments, information about the disease itself and more.

Johne’s Disease-Dairy, Summer 2010, Vol. 2, no.1

Animal science newsletter about Johne's Disease in cattle. Statistics, treatments, information about the disease itself and more.

Johne’s Disease-Dairy, Summer 2011, Vol. 3, no.4

Animal science newsletter about Johne's Disease in cattle. Statistics, treatments, information about the disease itself and more.

Johne’s Disease-Dairy, Winter 2011, Vol. 2, no.3

Animal science newsletter about Johne's Disease in cattle. Statistics, treatments, information about the disease itself and more.

Johne’s Disease-Dairy, Winter 2012, Vol.42, no.2

Animal science newsletter about Johne's Disease in cattle. Statistics, treatments, information about the disease itself and more.

Little Dairy on the Prairie: From Butter-makin' Women to High-tech Agriculture, July 10, 2008

In 2001, extensive archaeological excavations were conducted at the Oneida Cheese Factory in Jones County. The county is a microcosm of larger dairying trends found throughout northeast Iowa, the state's premier dairy-producing region, Jones County moved from homemade cheese and butter production by farm women, to the industrialization of the dairy farm and opening of cheese factories and butter creameries.A number of innovations affected the industry around the turn-of-the-twentieth century, including reliable butterfat testing, the introduction of ensilage (silos) that created year round milk production, and consolidation of the many local creameries into larger creamery organizations, such as the Diamond Creamery run by Henry D. Sherman of Jones County. Iowa's dairy industry of today looks very different from its heritage: consolidation and competition have drastically reduced the number of cows, dairy farms, and processing plants. In recent years, northeast Iowa has become the center of a movement to revitalize Iowa's dairy industry, particularly through the use of value-added strategies, such as niche markets and large regional co-operatives: the lessons from Iowa's dairying legacy are resurfacing as a solution to modern agricultural challenges.

Review of greenhouse gas emissions from the storage and land application of farm dairy effluent

The amounts of farm dairy effluent stored in ponds and irrigated to land have steadily increased with the steady growth of New Zealand's dairy industry. About 80% of dairy farms now operate with effluent storage ponds allowing deferred irrigation. These storage and irrigation practices cause emissions of greenhouse gases (GHG) and ammonia. The current knowledge of the processes causing these emissions and the amounts emitted is reviewed here. Methane emissions from ponds are the largest contributor to the total GHG emissions from effluent in managed manure systems in New Zealand. Nitrous oxide emissions from anaerobic ponds are negligible, while ammonia emissions vary widely between different studies, probably because they depend strongly on pH and manure composition. The second-largest contribution to GHG emissions from farm dairy effluent comes from nitrous oxide emissions from land application. Ammonia emissions from land application of effluent in New Zealand were found to be less than those reported elsewhere from the application of slurries. Recent studies have suggested that New Zealand's current GHG inventory method to estimate methane emissions from effluent ponds should be revised. The increasing importance of emissions from ponds, while being a challenge for the inventory, also provides an opportunity to achieve mitigation of emissions due to the confined location of where these emissions occur. © 2015 © 2015 The Royal Society of New Zealand.

got your dairy today? : 10 tips to help you eat and drink more fat-free or low-fat dairy foods

The Dairy Group includes milk, yogurt, cheese, and fortified soymilk. They provide calcium, vitamin D, potassium, protein, and other nutrients needed for good health throughout life. Choices should be lowfat or fat-free—to cut calories and saturated fat. How much is needed? Older children, teens, and adults need 3 cups* a day, while children 4 to 8 years old need 2½ cups, and children 2 to 3 years old need 2 cups.

Immune function, gene expression, blood indices and performance in transition dairy cows affected by diet and inflammation

The transition period is associated with the peak incidence of production problems, metabolic disorders and infectious diseases in dairy cows (Drackley, 1999). During this time the cow’s immune system seems to be weakened; it is apparent that metabolic challenges associated with the onset of lactation are factors capable of affecting immune function. However, the reasons for this state are not entirely clear (Goff, 2006). The negative energy balance associated with parturition leads to extensive mobilization of fatty acids stored in adipose tissue, thus, causing marked elevations in blood non-esterified fatty acids (NEFA) and B-hydroxybutyrate (BHBA) concentrations (Drackley et al., 2001). Prepartal level of dietary energy can potentially affect adipose tissue deposition and, thus, the amount of NEFA released into blood and available for metabolism in liver (Drackley et al., 2005). The current feeding practices for pregnant non-lactating cows has been called into question because increasing amounts of moderate-to-high energy diets (i.e. those more similar to lactation diets in the content of energy) during the last 3 wk postpartum have largely failed to overcome peripartal health problems, excessive body condition loss after calving, or declining fertility (Beever, 2006). Current prepartal feeding practices can lead to elevated intakes of energy, which can increase fat deposition in the viscera and upon parturition lead to compromised liver metabolism (Beever, 2006, Drackley et al., 2005). Our general hypothesis was that overfeeding dietary energy during the dry period, accompanied by the metabolic challenges associated with the onset of lactation would render the cow’s immune function less responsive early postpartum. The chapters in this dissertation evaluated neutrophil function, metabolic and inflammation indices and gene expression affected by the plane of dietary energy prepartum and an early post-partum inflammatory challenge in dairy cows. The diet effect in this experiment was transcendental during the transition period and potentially during the entire lactation. Changes in energy balance were observed and provided a good model to study the challenges associated with the onset of lactation. Overall the LPS model provided a consistent response representing an inflammation incident; however the changes in metabolic indices were sudden and hard to detect in most of the cases during the days following the challenge. In general overfeeding dietary energy during the dry period resulted in a less responsive immune function during the early postpartum. In other words, controlling the dietary energy prepartum has more benefits for the dairy cow during transition.

Logoplaste-What are the opportunities in the European dairy industry?

Field lab: Entrepreneurial and innovative ventures

THE PROSPECT OF BIOGAS AMONG SMALL-HOLDER DAIRY GOAT FARMERS IN THE ULUGURU MOUNTAINS, TANZANIA

Biogas can be a clean cooking alternative where biomass is the dominant source of cooking energy and where feedstock for anaerobic digestion is available. By substituting woody biomass for energy, biogas may reduce local deforestation. Tanzania has more than 15.6 million goats. Dairy goats of different breeds are found in the mid- to high altitudes of the country. Population density has made firewood increasingly scarce and there are few energy alternatives in mountainous areas such as in the Uluguru Mountains. In Mgeta ward, Morogoro region, introduction of Norwegian dairy goats in the 1980s has improved livelihoods in the area. In this study, goat manure was assessed as feedstock for biogas and as fertilizer. Field work among small-holder dairy goat farmers in Mgeta was conducted to measure daily manure production, and to provide a basic model for prediction of the quantity of droppings which may be collected by farmers. Biogas and fertilizer potential from goat manure was compared to cow and pig manure. Buswell’s formula was used to calculate approximate methane yield. The results show that goat manure from Mgeta can yield 167 l∙kg Volatile Solids-1 (VS). Compared with other substrates approximate methane yield can be ranked as pig > guatemala grass > cow > goat. The average goat of 25 kg in Mgeta leaves 61 kg Total Solids (TS) droppings per year. It was estimated that 15 goats capita-1 would be required to meet the total cooking energy needs of small-holder households in the study location. N:P:K content in goat manure (TS) is 2:1:1, similar to cow and pig manure. Goat droppings had to be macerated to reduce particle size for anaerobic digestion. Biogas from dairy goats could be combined with the year-round irrigated horticulture production in Mgeta. Vegetable gardens in the slope below the digesters could be fertilized by gravitation with the NH4+-rich bioslurry, to save labour and increase yields.

Management interventions in dairy herds: exploring within herd uncertainty using an integrated Bayesian model

Knowledge of the efficacy of an intervention for disease control on an individual farm is essential to make good decisions on preventive healthcare, but the uncertainty in outcome associated with undertaking a specific control strategy has rarely been considered in veterinary medicine. The purpose of this research was to explore the uncertainty in change in disease incidence and financial benefit that could occur on different farms, when two effective farm management interventions are undertaken. Bovine mastitis was used as an example disease and the research was conducted using data from an intervention study as prior information within an integrated Bayesian simulation model. Predictions were made of the reduction in clinical mastitis within 30 days of calving on 52 farms, attributable to the application of two herd interventions previously reported as effective; rotation of dry cow pasture and differential dry cow therapy. Results indicated that there were important degrees of uncertainty in the predicted reduction in clinical mastitis for individual farms when either intervention was undertaken; the magnitude of the 95% credible intervals for reduced clinical mastitis incidence were substantial and of clinical relevance. The large uncertainty associated with the predicted reduction in clinical mastitis attributable to the interventions resulted in important variability in possible financial outcomes for each farm. The uncertainty in outcome associated with farm control measures illustrates the difficulty facing a veterinary clinician when making an on-farm decision and highlights the importance of iterative herd health procedures (continual evaluation, reassessment and adjusted interventions) to optimise health in an individual herd.