Search

Treatment of subclinical mastitis (SCM) during lactation is rarely recommended due to concerns related to both antimicrobial usage and the costs associated with milk discard. Nisin is a naturally produced antimicrobial peptide with a gram-positive spectrum that, when given to dairy cows, does not require milk discard. We evaluated the economic impact of the treatment of SCM during early lactation using a nisin-based intramammary treatment under different scenarios that included various treatment costs, milk prices, and cure rates. We stochastically simulated the dynamics of SCM detected during the first week of lactation. The net economic impact was expressed in US dollars per case. The probabilities of an event and their related costs were estimated using a model that was based on pathogen-specific assumptions selected from peer-reviewed articles. Nisin cure rates were based on results of pivotal studies included in the US Food and Drug Administration (FDA) approval submission. Based on our model, the average cost of a case of intramammary infection (i.e., only true-positive cases) in early lactation was $170 (90% = $148–$187), whereas the cost of a clinical mastitis case was $521 (90% range = $435–$581). Both estimates varied with etiology, parity, and stage of lactation. When comparing the net cost of SCM cases (i.e., CMT-positive tests) detected during the first week of lactation, nisin treatment generated an average positive economic impact of $19 per CMT-positive case. The use of nisin to treat SCM was beneficial 93% of the time. Based on the sensitivity analysis, treatment would result in an economically beneficial outcome for 95% and 73% of multiparous and primiparous cows, respectively. At the herd level, use of intramammary nisin to treat SCM in cows in early lactation was economically beneficial in most tested scenarios. However, the economic impact was highly influenced by factors such as rate of bacteriological cure, cost of treatment, and parity of the affected animal. These factors should be considered when deciding to use nisin as a treatment for SCM. [ABSTRACT FROM AUTHOR]

Simple Summary: This study presents a user-friendly model for assessing the value of dairy bulls in artificial insemination (AI) companies. The model, a Markov Chain model, considers important factors for replacement decisions, such as a bull's age, expected semen production, and predicted genetic merit. Data from a leading AI company were used to apply this model, which calculates a bull's net present value based on various costs and revenues. The model also calculates the value of a bull compared to a potential young replacement. Findings showed that nearly half of the bulls were recommended for replacement due to a negative value comparison. The value of a bull was primarily influenced by market distribution and pricing, as well as the interaction of semen production with genetic merit. Deciding when to replace dairy bulls presents a complex challenge for artificial insemination (AI) companies. These decisions encompass multiple factors, including a bull's age, predicted semen production, and estimated genetic merit. This study's purpose was to provide a practical, objective tool to assist in these decisions. We utilized a Markov Chain model to calculate the economic valuation of dairy bulls, incorporating key factors such as housing costs, collection and marketing expenses, and the bull's probable tenure in the herd. Data from a leading AI company were used to establish baseline values. The model further compared a bull's net present value to that of a potential young replacement, establishing a relative valuation (BullVal$). The range of BullVal$ observed spanned from −USD 316,748 to USD 497,710. Interestingly, the model recommended culling for 49% of the bulls based on negative BullVal$. It was found that a bull's net present value was primarily influenced by market allocation and pricing, coupled with the interaction of semen production and genetic merit. This study offers a robust, data-driven model to guide bull replacement decisions in AI companies. Key determinants of a bull's valuation included market dynamics, semen production rates, and genetic merit. [ABSTRACT FROM AUTHOR]

An artificial insemination (AI) company seeks to allocate semen units globally by balancing perceived demand with uncertain product supply, in what is an arduous subjective process. This study aimed to objectivize this process by providing a user-friendly linear programming model to allocate bulls' semen units to regions for the next trimester sales period based on maximum revenue, and to describe the features and outcomes of this model when applied to a sample bull herd and global demand scenario reflective of a leading AI company. The objective function of maximizing revenue was calculated by summing the product of units allocated by bull and region with purchase prices assigned by bull and region. Constraints considered were regional demand for overall units, regional preferences for specific genetic traits, bulls' production capacity, and percentage of bulls' units allocated to a single region. A sensitivity analysis was performed to identify the effects of variables and constraints on total revenue. Production, sales, and bull demographic data from 2018 to 2021 from a leading AI company were used to establish base values and build a sample herd of 61 bulls and 5 global regions. The case study provided a maximum revenue of $8,287,197 in semen sales per trimester, with 634,700 units allocated. Of the 61 bulls in the case study, 9 were not allocated to any region. The most limiting constraint was regional demand, which resulted in a surplus of 274,564 units not allocated. A sensitivity analysis confirmed this finding, with the largest shadow prices assigned to regional demands, and indicated that a single unit increase in regional demand would add up to $14.84 in total revenue. [ABSTRACT FROM AUTHOR]

We are developing a real-time, data-integrated, data-driven, continuous decision-making engine, The Dairy Brain, by applying precision farming, big data analytics, and the Internet of Things. This is a transdisciplinary research and extension project that engages multidisciplinary scientists, dairy farmers, and industry professionals. Dairy farms have embraced large and diverse technological innovations such as sensors and robotic systems, and procured vast amounts of constant data streams, but they have not been able to integrate all this information effectively to improve whole-farm decision making. Consequently, the effects of all this new smart dairy farming are not being fully realized. It is imperative to develop a system that can collect, integrate, manage, and analyze on- and off-farm data in real time for practical and relevant actions. We are using the state-of-the-art database management system from the University of Wisconsin-Madison Center for High Throughput Computing to develop our Agricultural Data Hub that connects and analyzes cow and herd data on a permanent basis. This involves cleaning and normalizing the data as well as allowing data retrieval on demand. We illustrate our Dairy Brain concept with 3 practical applications: (1) nutritional grouping that provides a more accurate diet to lactating cows by automatically allocating cows to pens according to their nutritional requirements aggregating and analyzing data streams from management, feed, Dairy Herd Improvement (DHI), and milking parlor records; (2) early risk detection of clinical mastitis (CM) that identifies first-lactation cows under risk of developing CM by analyzing integrated data from genetic, management, and DHI records; and (3) predicting CM onset that recognizes cows at higher risk of contracting CM, by continuously integrating and analyzing data from management and the milking parlor. We demonstrate with these applications that it is possible to develop integrated continuous decision-support tools that could potentially reduce diet costs by $99/cow per yr and that it is possible to provide a new dimension for monitoring health events by identifying cows at higher risk of CM and by detecting 90% of CM cases a few milkings before disease onset. We are securely advancing toward our overarching goal of developing our Dairy Brain. This is an ongoing innovative project that is anticipated to transform how dairy farms operate. [ABSTRACT FROM AUTHOR]

The objective of the study was to evaluate the associations of the variable intensity in culling of dairy cows and culling due to the movement disorders, mammary gland diseases, long calving interval, low fertility and postpartum complications with production, reproduction and economic parameters on 60 commercial dairy herds. The data encompassed 34 632 cow records from the Czech Republic 12 regions and were collected during a 1-yr period (2012). The milk yield during the production period was analysed relative to the reproduction and economic parameters. The main reasons for culling cows were fertility problems and movement disorders, followed by the low milk yield, mammary gland diseases, and postpartum complications. The analysis of fertility showed that the herds with the longest calving intervals (≥ 410 d) and the highest culling due to fertility (≥ 25% of the total) achieved the lowest milk yields. The average diff erence between the highest and lowest calving interval (≤ 389 d) groups was 721 kg/cow per yr. The lowest reported profitability of costs was for the longest calving interval and the highest postpartum complications groups. Although the reproductive performance directly affects the dairy farm profitability, the dairy cows' production potential should be considered when making culling decisions in order to achieve the most profitable management strategy. [ABSTRACT FROM AUTHOR]

Th e study evaluates the associations of the farm herd size (HERD), the milk yield (MY, kg/cow per year), and cows per worker (CW) with the production, reproduction, and economic traits in 60 commercial dairy herds (34 633 cows) in the Czech Republic. Each parameter was split into 3 groups. The largest herds (≥ 750 cows) had the highest profitability of costs without subsidies (-3.8 ± 4.3%), the lowest cost for roughages (1.2 CZK/L milk), and the lowest cost for breeding operations (0.17 CZK/L milk), but the highest costs for the cereal grains and concentrates (2.4 CZK/L milk). Herds with the lowest MY (≤ 7 499) had the lowest profitability of costs without subsidies (-15.8 ± 3.93%), the highest total costs (9.27 CZK/L milk), the highest labour costs (1.6 CZK/L milk), and the highest cow depreciation costs (0.97 CZK/L milk). Herds with the lowest CW (≤ 39) had the highest mean labour costs (1.51 CZK/L milk) and the highest costs for breeding operations (0.22 CZK/L milk). Herds with the highest CW (≥ 60) and the lowest CW (≤ 39) also diff ered in the total feed costs (4.1 vs. 3.73 CZK/L milk) and the costs of cereals and concentrates (2.34 vs. 1.96 CZK/L milk). The increasing herd size tended to be accompanied by higher milk yields and the overall efficiency. [ABSTRACT FROM AUTHOR]

Simple Summary: In order to capture the complexity and dynamics of bovine mastitis, which has multiple effects, such as milk losses, increased risk of culling, or a higher likelihood of reproductive failure, a simulation model was developed. The economic evaluation of the mastitis control was performed considering different interventions combining a basic control plan with either segregation or culling of chronically infected cows. Changes in transmission probability, milk price, and strategies efficacy were evaluated. Several economic parameters in the model could be adjusted to represent farm-specific situations. This is a flexible tool that may support the decision-making of producers and veterinarians. The economic evaluation of mastitis control is challenging. The objective of this study was to perform the economic evaluation of mastitis control, under different intervention scenarios, quantifying the total cost of mastitis caused by S. aureus in Holstein cows in Argentina. A model was set for a dairy herd of Holstein cows endemically infected with S. aureus. A basic mastitis control plan including proper milking procedures, milking machine test, dry cow therapy, and treatment for clinical mastitis, was compared against other more complex and costly interventions, such as segregation and culling of chronically infected cows. Sensitivity analysis was performed by modifying the intramammary infection transition probabilities, economic parameters, and efficacy of treatment strategies. The basic mastitis control plan showed a median total cost of USD88.6/cow per year, which was close to the infected cows culling scenarios outputs. However, the segregation scenario was the most efficient, in which the total cost was reduced by about 50%. Such cost was more sensitive to probabilities and efficacy than the economic parameters. The model is flexible and can be customized by producers and veterinarians according to different control and herd settings. [ABSTRACT FROM AUTHOR]

Data integration is one of the biggest challenges the dairy industry faces nowadays due to increased number of technologies and data overflow at the farm. Here we review the current situation of precision dairy farming technologies that use integrated data and its application on the management decision process at the dairy farm. The most common data connections were those from activity monitors, dairy herd improvement records, herd management, and milking recordings. Algorithms used can be defined in general as artificial intelligence and machine learning approaches. Most of the 22 revised papers, research or review, demonstrated that applying different algorithms to integrated data provides additional and complementary insights to improve decision-making tools and therefore enhance economic, management and animal welfare, and hence the sustainability of dairy farms. All revised studies acknowledge the importance of live data integration to develop relevant decision support tools to improve decision making. [ABSTRACT FROM AUTHOR]

Dairy farmers face increasing pressure to decrease environmental impact while remaining economically viable. Adaptation of farm management practices in response to seasonal climate forecasts may be one means of achieving these objectives. This paper describes the interactive and iterative process by which farmers, researchers, extension agents, regulatory agencies, and other stakeholders collaborated to create, calibrate, and validate the Dynamic North Florida Dairy Farm model (DyNoFlo), a whole-farm decision support system to decrease nitrogen leaching while maintaining profitability under variable climate conditions. Participatory modeling may enhance the creation of adoptable and adaptable user-friendly models that include environmental, economic and biophysical components. By providing farmers, policy makers, and other stakeholders with a more holistic view of current practices, common ground among them was more easily identified and collaboration was fostered. Farmer values included willingness to be good environmental stewards when they are profitable. The participatory research and development process enhanced understanding of and potential adaptation to seasonal climate variability conditioned to the El Niño Southern Oscillation (ENSO) phases in light of increasing environmental regulations and economic challenges. Adoption of the collaboratively-developed DyNoFlo is expected to be higher than usual because stakeholders feel greater ownership of the final product. [ABSTRACT FROM AUTHOR]

The article focuses on the increase in soybean production in Paraguay. According to the article, the increase in production makes the country as the fourth largest soybean exporter in the world. As stated, differences in mean precipitation among El Niño Southern Oscillation (ENSO) phases were investigated under the context of crop development phases. Influences of ENSO phases on mean precipitation during planting and blooming, blooming and seed podding and from young pods to physiological maturity were explored through tests of difference in the central tendency. Moreover, mean precipitation between seed podding and crop maturity was found to be significantly lower during La Niña years than during El Niño years in one of the locations studied.

Abstract: Predictability of seasonal climate variations associated with ENSO suggests a potential to reduce farm risk by tailoring agricultural management strategies to mitigate the impacts of adverse conditions or to take advantage of favorable conditions. Federal farm policies may enhance or limit the usefulness of this climate information. A representative peanut–cotton–corn non-irrigated North Florida farm was used to estimate the value of the ENSO-based climate information and examine impacts of farm programs under uncertain conditions of climate, prices, and risk aversion levels. Yields from crop model simulations and historical series of prices were used to generate stochastic distributions that were fed into a whole farm model, first, to optimize crop selection and planting dates, and then, to simulate uncertain outcomes under risk aversion, with and without the use of climate information, and with and without the inclusion of farm programs. Results suggest that seasonal climate forecasts have higher value for more risk averse farmers when La Niña or El Niño ENSO phases are forecast. Highly risk averse farmers could benefit from the forecast by taking advantage of potential favorable conditions (offensive responses). The inclusion of Commodity Loan Programs (CLP) and Crop Insurance Programs (CIP) decreased the overall value of the forecast information even to negative levels. However, more risk averse farmers could still benefit moderately from El Niño and marginally from La Niña forecasts when they participate in CLP and CIP. [Copyright &y& Elsevier]

Evidence of increasing nitrogen levels in the Suwannee River Basin in North Florida demands a collaborative effort to find creative ways to reduce N pollution. This study explores the perspectives, perceptions, and attitudes of dairy farmers regarding adoption of climate forecasts as a potential way to mitigate the problem. These farmers are heavily scrutinized because of their nitrogen emissions. By contrasting scientists' pre-conceived attitudes about the usefulness of ENSO-based forecasts with dairy farmers' perceptions, gathered in a participatory and consensual manner, valuable lessons were learned. A deeper understanding of the day to day realities of dairy farming systems help researchers pinpoint management adaptations that are not only useful, but feasible, in light of improved seasonal climate forecasts. Furthermore, dairy farmers' perceptions regarding the use of seasonal climate information to mitigate the nitrate problem are critical for designing future dairy systems. [ABSTRACT FROM AUTHOR]

Abstract: Dairies are challenged to comply with stricter environmental regulations and remain economically viable. This paper studies the potential use of ENSO-based climate forecasts to reduce N leaching without reducing profits on North Florida dairies. A model was created to perform the analyses, the Dynamic North Florida Dairy Farm Model (DyNoFlo). DyNoFlo is an integrated dynamic model that incorporates Markov-chain simulation of cow flows and crop model simulations for historical climatic years (El Niño, La Niña, and Neutral years). It also includes optimization of managerial options. It responds to dairy-specific environment (climate and soils) and management (livestock management, waste management, crop systems management). The DyNoFlo model was designed to be a tool for producers, regulatory agencies, and extension services in addition to a research tool. Analyzing a typical North Florida dairy farm, it was found that N leaching was highest during winter El Niño years. Sandy soils were substantially more prone to leach N, and perennial grasses were better to prevent N leaching. The typical farm could decrease N leaching up to 23% in an El Niño year and still maintain profit by adjusting protein in the diet, confinement time for milking cows, and combining perennial grasses and forages in pastures and sprayfields. Application of the DyNoFlo model to small, medium, and large dairy farms showed that they could decrease their N leaching by 9, 20, and 25%, respectively, without reducing profits by varying management strategies according to ENSO phases. [Copyright &y& Elsevier]

Abstract: This paper describes the computer implementation of the Dynamic North Florida Dairy farm model (DyNoFlo Dairy). The DyNoFlo Dairy is a decision support system that integrates nutrient budgeting, crop, and optimization models created to assess nitrogen (N) leaching from North Florida dairy farm systems and the economic impacts resulting from reducing it under different climatic conditions. The decision support system, based on Excel® and Visual Basic® software, responds to dairy-specific environmental (climate and soils) and managerial characteristics (livestock management, waste management, crop systems management) and can be used to study the economic and ecologic sustainability of these systems. The DyNoFlo Dairy model is a dynamic adaptation of the framework “balance” of nutrients in dairy farms, commonly used in Florida. The DyNoFlo Dairy model incorporates Markov-chain probabilistic simulation of cow-flows and crop simulation for historical climatic years El Niño southern oscillation (ENSO), automated optimization of managerial options, participatory modeling, and user friendliness. This paper discusses the model components and its computer implementation in a user-friendly application. The model was parameterized for conditions found in North Florida dairy farm systems. It is intended to be a tool for producers, regulatory agencies, and extension services, and because of that, participatory and interdisciplinary work was pursued during model creation, calibration, and validation. A case study for a synthesized North Florida dairy farm using the DyNoFlo Dairy model found substantial differences in the N leaching for different ENSO phases and other managerial factors; and the possibility of decreasing N leaching up to 25% while still maintaining profitability levels. [Copyright &y& Elsevier]

Simple Summary: Twinning in dairy cattle is not desirable due to negative effects on both cows that calve twins and calves born as twins that result in economic losses to dairy farms. Although a twin pregnancy could bring additional income from extra calves and it could shorten gestation length, twinning compromises milk production, increases the incidence of dystocia and perinatal mortality, decreases calf birth weight, increases the incidence of metabolic diseases, decreases fertility, increases the incidence of freemartinism, increases overall culling risks, and shortens the productive lifespan of cows. Based on a summary of economic analyses from several studies, the estimated losses due to twinning range between $59 to $161 per twin pregnancy. When twins are diagnosed early during gestation, management options might include doing nothing, terminating the pregnancy, or attempting manual embryo reduction. Based on a recent economic analysis of these options, attempting manual embryo reduction decreased the economic losses of a twin pregnancy by $23 to $45. Twinning in Holstein dairy cows has increased over time concurrent with increased milk production. Twinning in dairy cattle is not desirable due to the negative effects on both cows that calve twins and calves born as twins that result in economic losses to dairy farms. Although a twin pregnancy could bring additional income from extra calves and shorten gestation length, twinning compromises milk production, increases the incidence of dystocia and perinatal mortality, decreases calf birth weight, increases the incidence of metabolic diseases, decreases fertility, increases the incidence of freemartinism, increases overall culling risks, and shortens the productive lifespan of cows. Based on a summary of economic analyses from several studies, the estimated losses due to twinning range between $59 to $161 per twin pregnancy. Most twinning in dairy cows is dizygotic and directly related to the incidence of double ovulations, and economic losses are greater for unilateral than for bilateral twins. Hormonal manipulation before artificial insemination that allows for timed artificial insemination is a primary strategy for decreasing twinning in dairy cows before it occurs by decreasing the incidence of double ovulation thereby decreasing conception of dizygotic twins and the associated negative economic consequences. When twins are diagnosed early during gestation, management options might include doing nothing, terminating the pregnancy, or attempting manual embryo reduction. Based on a recent economic analysis of these options, attempting manual embryo reduction decreased the economic losses of a twin pregnancy by $23 to $45. [ABSTRACT FROM AUTHOR]

Data pervades the dairy farming industry. However, specific data streams are most often ad-hoc and poorly linked to each other and to decision making processes. It is imperative to develop a system that can collect, integrate, manage, and analyze on- and off-farm data in real-time for practical and relevant actions. Hence, we are developing a real-time, data-integrated, datadriven, continuous decision-making engine: The Dairy Brain by applying Precision Farming, Big Data analytics, and the Internet of Things. This is a transdisciplinary research and extension project that engages multi-disciplinary scientists, dairy farmers, and industry professionals. We have a four-part strategy: (1) Create a Coordinated Innovation Network (CIN) to shape data service development; (2) Create a prototype Agricultural Data Hub (AgDH) to gather/disseminate multiple data streams relevant to dairy operations; (3) Build the Dairy Brain - a suite of analytical modules that leverages the AgDH to provide insight to the management of dairy operations and serve as an exemplar of an ecosystem of connected services; and (4) Design and execute an innovative Extension program. We will illustrate our Dairy Brain concept with a few practical applications. Tomorrow's dairy industry will be built on the effective capture and integration of more data streams, not fewer. This is a critical moment to develop the structures that can move the industry towards modernized data exchange. We are confident the Dairy Brain concept could shift the paradigm of how dairy farms operate. [ABSTRACT FROM AUTHOR]

Pakistan is ranked fourth among all the milk-producing countries across the globe. Despite this, livestock farm families are often located far from potential locations for dairy production. This gap exists due to a lack of information regarding recommended dairy production practices. In this modern era, the provision of information necessary for enhanced dairy production could be achieved through the effective use of electronic media. The present study was confined to the district of Faisalabad due to our limited time and economic resources. We aimed to identify farmers' perceptions of the use of electronic media as a source of reliable information. A total of 165 dairy farmers participated in this research survey after being selected using a simple random sampling technique. The findings of the present research study indicate that a lack of education followed by male-female influences the adoption of electronic media as a potential information tool. The use of mobile phones for obtaining necessary information, on the other hand, was ranked the highest among dairy farmers. It is recommended that dairy farmers' training includes the effective use of electronic media so that they may equip themselves with advanced dairy production information. [ABSTRACT FROM AUTHOR]

We are developing a Dairy Brain by applying precision dairy farming, big data analytics, artificial intelligence, and the internet of things with the aim to accomplish a near real-time, data-integrated, datadriven, continuous decision-making engine through which management decisions can be better informed by integrated data streams to improve the economics of the farm and to positively benefit the individual animal and the overall farm through descriptive, predictive, and prescriptive analytics. Within this general concept, we have developed a machine learning hybrid K-medoids, random forest and support vector regression (K-R-S) approach for predicting the lactation curves of individual primiparous cows within a targeted environment using monthly milk production data from their dams and paternal siblings for 6,400 calves born in Wisconsin farms in year 2016. Our K-R-S hybrid approach outperformed the mean of paternal siblings in predicting first lactation test-day milk yield of primiparous cows 74.2% of the predictions. The algorithm has the ability to predict all data points required to construct primiparous cows' full lactation curves even before they have started their productive life. Our approach allows the genotype-by-environment interactions to be portrayed in the prediction algorithm. Our current model uses only test-day (monthly) production data, but the artificial intelligence architecture is prepared to accommodate more frequent farm records (e.g., daily milk records from milking parlor) or other phenotype measures such as reproductive or health parameters for better characterization of the animals and their production environment towards improved prediction accuracy. We envision the prediction architecture will serve as an engine of farm-specific continuous prediction relaying on a constant flux of integrated data from different farm data sources following the Dairy Brain notion of data integration, analytics, and decision making. [ABSTRACT FROM AUTHOR]

Each cow in a group has different nutritional requirements even if the group is formed by cows of similar age, number of lactations, and lactation stage. Common dairy farm management setup does not support formulating a diet that accurately matches individual nutritional requirements for each cow; therefore, a proportion of cows in the group will be overfed and another proportion underfed. Overfeeding and underfeeding cows increases the risk of metabolic diseases, decreases milk production, and increases nutrient waste. Consequently, profitability of dairy farms and the environment are negatively affected. Nutritional grouping is a management strategy that aims to allocate lactating cows homogeneously according to their nutritional requirements. Groups of cows with more uniform nutritional requirements facilitates the formulation of more accurate diets for the group. Current availability of large data streams on dairy farms facilitates the design of algorithms to implement nutritional grouping. Our review summarizes important factors to consider when grouping cows, describes nutritional grouping approaches, and summarizes benefits of implementing nutritional grouping in dairy farms. [ABSTRACT FROM AUTHOR]

This paper studies the selection of optimal crop insurance under climate variability and fluctuating market prices. A model was designed to minimize farmers’ expected losses (including insurance costs) while using the conditional-value-at-risk measure to acquire the risk-aversion level. The application of the model was illustrated by studying a farm with two crops (cotton and peanut) in Jackson County, Florida. The climate variability was caused by ENSO phenomenon. Crop-insurance contracts with minimized losses were 75% actual production history (APH) during El Niño and neutral years and 65% APH during La Niña years for peanut and 75% APH in all ENSO phases for cotton. In addition, risk-averse farmers could select 75% APH for peanut during La Niña years as a means of attaining less expected loss. [ABSTRACT FROM AUTHOR]

Different classification machine learning techniques (Naïve Bayes, Random Forest and Extreme Gradient Boosting) were evaluated to identify cows positive for clinical mastitis (CM) during their first lactation (1st lactation) and to daily predict the onset of CM (continuous). Integrated data from different software were used to feed the algorithms. In both cases, the best predictions were obtained with the Random Forest algorithm. The algorithms correctly classified 71% and 85% of the CM cows for the 1st lactation and continuous models, respectively. Both analyses had the same accuracy of 72%. Results showed that it is feasible to integrate different data streams to develop predictive and prescriptive decision support tools. Having two different algorithms working concomitantly, one for predicting the imminent risk and the other one for the overall risk during the first lactation, could help in the short, mid-, and long-term decision-making process. [ABSTRACT FROM AUTHOR]

A letter to the editor and a response to the article "Rosuvastatin reduces nonalcoholic fatty liver disease in patients with chronic hepatitis C treated with a-interferon and ribavirin," by M. Malaguarnera and colleagues are presented.

The objective of this study was to develop a model application to systematize nutritional grouping (NG) management in commercial dairy farms. The model has 4 sub-sections: (1) real-time data stream integration, (2) calculation of nutritional parameters, (3) grouping algorithm, and (4) output reports. A simulation study on a commercial Wisconsin dairy farm was used to evaluate our NG model. On this dairy farm, lactating cows (n = 2,374 ± 185) are regrouped weekly in 14 pens according to their parity and lactation stage, for which 9 diets are provided. Diets are seldom reformulated and nutritional requirements are not factored to allocate cows to pens. The same 14 pens were used to simulate the implementation of NG using our model, closely following the current farm criteria but also including predicted nutritional requirements (net energy for lactation and metabolizable protein; NE L and MP) and milk yield in an attempt to generate more homogeneous groups of cows for improved diet accuracy. The goal of the simulation study was to implement a continuous weekly system for cows' pen allocation and diet formulation. The predicted MP and NE L requirements from the NG were used to formulate the diets using commercial diet formulation software and the same feed ingredients, feed prices, and other criteria as the current farm diets. Diet MP and NE L densities were adjusted to the nutritional group requirements. Results from the simulation study indicated that the NG model facilitates the implementation of an NG strategy and improves diet accuracy. The theoretical diet cost and predicted nitrogen supply with NG decreased for low-nutritional-requirement groups and increased for high-nutritional-requirement groups compared with current farm groups. The overall average N supply in diets for NG management was 15.14 g/cow per day less than the current farm grouping management. The average diet cost was $3,250/cow per year for current farm management and $3,219/cow per year for NG, which resulted in a theoretical $31/cow per year diet cost savings. [ABSTRACT FROM AUTHOR]

The common practice on most commercial dairy farms is to inseminate all cows that are eligible for breeding, while ignoring (or absorbing) the costs associated with semen and labor directed toward low-fertility cows that are unlikely to conceive. Modern analytical methods, such as machine learning algorithms, can be applied to cow-specific explanatory variables for the purpose of computing probabilities of success or failure associated with upcoming insemination events. Lift chart analysis can identify subsets of high fertility cows that are likely to conceive and are therefore appropriate targets for insemination (e.g., with conventional artificial insemination semen or expensive sex-enhanced semen), as well as subsets of low-fertility cows that are unlikely to conceive and should therefore be passed over at that point in time. Although such a strategy might be economically viable, the management, environmental, and financial conditions on one farm might differ widely from conditions on the next, and hence the reproductive management recommendations derived from such a tool may be suboptimal for specific farms. When coupled with cost-sensitive evaluation of misclassified and correctly classified insemination events, the strategy can be a potentially powerful tool for optimizing the reproductive management of individual farms. In the present study, lift chart analysis and cost-sensitive evaluation were applied to a data set consisting of 54,806 insemination events of primiparous Holstein cows on 26 Wisconsin farms, as well as a data set with 17,197 insemination events of primiparous Holstein cows on 3 Wisconsin farms, where the latter had more detailed information regarding health events of individual cows. In the first data set, the gains in profit associated with limiting inseminations to subsets of 79 to 97% of the most fertile eligible cows ranged from $0.44 to $2.18 per eligible cow in a monthly breeding period, depending on days in milk at breeding and milk yield relative to contemporaries. In the second data set, the strategy of inseminating only a subset consisting of 59% of the most fertile cows conferred a gain in profit of $5.21 per eligible cow in a monthly breeding period. These results suggest that, when used with a powerful classification algorithm, lift chart analysis and cost-sensitive evaluation of correctly classified and misclassified insemination events can enhance the performance and profitability of reproductive management programs on commercial dairy farms. [ABSTRACT FROM AUTHOR]

Simple Summary: A better understanding of the current challenges and opportunities regarding data management and data governance in the dairy industry is key to design and define effective data utilization. Thus, a survey was conducted to understand the attitudes of farmers and non-farmers. Respondents strongly agreed that data sharing is a valuable enterprise. They recognized that raw data collected at the farm should be the property of the farmer, and that incentives could motivate farmers to continue, or increase, their data sharing, but most of them were unfamiliar with data collection protocols. Although most farmers are already sharing data, most of them have not signed a data share agreement and feel they do not have data control, once their data are accessed by others. Most respondents exhibited concern about critical data issues, such as ownership, confidentiality, security, lack of integration, and even lack of awareness of the importance of data integration. Farmers indicated that they would be encouraged to adopt a new technology if it is easy to implement and has the potential to improve herd or farm management and profit, whereas they would be discouraged if the technology is expensive, difficult to use, or they do not have clear information about its use. A survey to explore the challenges and opportunities for dairy farm data management and governance was completed by 73 farmers and 96 non-farmers. Although 91% of them find data sharing beneficial, 69% are unfamiliar with data collection protocols and standards, and 66% of farmers feel powerless over their data chain of custody. Although 58% of farmers share data, only 19% of them recall having signed a data share agreement. Fifty-two percent of respondents agree that data collected on farm belongs only to the farmer, with 25% of farmers believing intellectual property products are being developed with their data, and 90% of all said companies should pay farmers when making money from their data. Farmers and non-farmers are somewhat concerned about data ownership, security, and confidentiality, but non-farmers were more concerned about data collection standards and lack of integration. Sixty-two percent of farmers integrate data from different sources. Farmers' most used technologies are milk composition (67%) and early disease detection (56%); most desired technologies are body condition score (56%) and automatic milking systems (46%); most abandoned technologies are temperature and activity sensors (14%) and automatic sorting gates (13%). A better understanding of these issues is paramount for the industry's long-term sustainability. [ABSTRACT FROM AUTHOR]

Abstract: The Livestock Gross Margin Insurance for Dairy Cattle is a federally reinsured insurance program that enables US dairy producers to establish minimum levels of milk income net of feed cost. Given the structure of this program there are an infinite number of possible contract designs based on the choice of deductible level and proportion of production insured. Adding to this complexity, producers vary in their risk preferences, which affect the incentive to insure their margin. It is unclear as to how producers may adopt this program for revenue risk management. This paper investigates the interplay between producer risk preferences, contract design and the subsidization of premium in determining program coverage. We undertook this analysis within an expected utility framework. Optimal contracts under different rates of constant relative rate of risk aversion and subsidies were analyzed using a nonlinear optimization model. We found that total optimal coverage increased significantly with the level of risk of aversion at lower deductibles but as deductible level increased, the level of risk aversion had a lesser impact on total optimal coverages. As expected, at the same deductible and risk aversion levels, inclusion of a premium subsidy increased the total optimal coverage. [Copyright &y& Elsevier]

Simple Summary: There are no existing laws that regulate data usage in the dairy industry in the U.S. Therefore, challenges regarding data management, availability, use, and security are a growing concern. These concerns are especially important in the light of the growing dependency and monetization of such data. Although some codes of practices have been published, these are voluntary, which makes them legally vulnerable and without guarantees under the law. Thus, in this paper, we propose creating a Farmers Bill of Rights to standardize data governance and data regulations and to promote an equitable relationship of dairy data governance between farmers, industry professionals, and companies. Data governance is a growing concern in the dairy farm industry because of the lack of legal regulation. In this commentary paper, we discuss the status quo of the available legislation and codes, as well as some possible solutions. To our knowledge, there are currently four codes of practice that address agriculture data worldwide, and their objectives are similar: (1) raise awareness of diverse data challenges such as data sharing and data privacy, (2) provide data security, and (3) illustrate the importance of the transparency of terms and conditions of data sharing contracts. However, all these codes are voluntary, which limits their adoption. We propose a Farmers Bill of Rights for the dairy data ecosystem to address some key components around data ownership and transparency in data sharing. Our hope is to start the discussion to create a balanced environment to promote equity within the data economy, encourage proper data stewardship, and to foster trust and harmony between the industry companies and the farmers when it comes to sharing data. [ABSTRACT FROM AUTHOR]

• Continuous integration of dairy farm data helps to improve decision making and farm management. • Automating dairy farm data collection, decoding, cleaning, homogenization, and integration can facilitate dairy farm analytics. • Engineering techniques to dairy data management increase the development, applicability, and distribution of new research knowledge. Modern dairy farms generate vast amounts of data, with different sections of the operation having the ability to produce its own uniquely structured data stream depending on the specific hardware and software used. As a result of this heterogeneity, these streams are difficult to link to each other, thus it is rarely done. This creates an opportunity to add value to the data by integrating and homogenizing data from the different sources, with the end result of enriching analyses and helping to improve farm management decisions. Within a proposed project, a two component modular system is being developed. One component collects, cleans, and integrates data from on-farm systems into a centralized hub (AgDH). This system provides data to a framework designed to deploy and operationalize existing research-derived analytical tools and provide access to these tools and data via a user interface. The AgDH follows five steps to ingest different data streams available at the dairy farm: 1) transporting raw data into a centralized system; 2) decoding and storing data in a database; 3) cleaning data to ensure its validity; 4) homogenization of data by extracting the common features among the different software and farms; and 5) integration of data from the different systems. Each of these steps is crucial to make data available from different sources in a consistent manner, ease algorithmic development and its implementation, and facilitate the deployment of new tools that utilize the integrated data. In order to automate the process and make the data continuously available an open source workflow management platform was used. Both historical and current data can be made available to authenticated users via an application programming interface hosted through a web service. This framework needs to be designated to be flexible and able to adapt quickly to the changes and new technologies that are continuously being developed in the dairy industry. The integration and accessibility of data can facilitate a wide range of descriptive, predictive, and prescriptive analytics that can be developed and deployed directly on farms to increase animal performance, efficiency, health and welfare, profit margins, and decrease the environmental impact of dairy farming. [ABSTRACT FROM AUTHOR]

Simple Summary: Consumers are increasingly concerned about the sustainable production of food, leading producers and scientists to evaluate farming practices that preserve environmental resources, provide adequate production, and are economically viable. However, there are challenges to synthesize these results and apply them on-farm in a holistic nature. Simulation modeling of farm systems, such as the dairy system, can allow producers, industry members, and policy makers to prioritize interventions that improve sustainable outcomes. We introduce the Animal Module of the Ruminant Farm Systems (RuFaS) model—a whole farm dairy system model—and describe its use to assess the environmental impact of improved feed efficiency in dairy cows. By decreasing the amount of feed intake required to produce the same amount of milk, the RuFaS model provides estimates of the reduction in feed use, enteric methane, and manure production. Dairy production is an important source of nutrients in the global food supply, but environmental impacts are increasingly a concern of consumers, scientists, and policy-makers. Many decisions must be integrated to support sustainable production—which can be achieved using a simulation model. We provide an example of the Ruminant Farm Systems (RuFaS) model to assess changes in the dairy system related to altered animal feed efficiency. RuFaS is a whole-system farm simulation model that simulates the individual animal life cycle, production, and environmental impacts. We added a stochastic animal-level parameter to represent individual animal feed efficiency as a result of reduced residual feed intake and compared High (intake = 94% of expected) and Very High (intake = 88% of expected) efficiency levels with a Baseline scenario (intake = 100% of expected). As expected, the simulated total feed intake was reduced by 6 and 12% for the High and Very High efficiency scenarios, and the expected impact of these improved efficiencies on the greenhouse gas emissions from enteric methane and manure storage was a decrease of 4.6 and 9.3%, respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: Cows are social animals, therefore understanding the ways that they interact can help improve their management and welfare. We used social network analysis (SNA) to data on voluntary cow movement through a sort gate in an automatic milking system to identify pairs of cows that repeatedly passed through a sort gate in close succession (affinity pairs). Results from this exploratory study showed that when cows were separated from their affinity-pair cow the day-day variability in milk production increased by a factor of 3, a possible indicator of stress. The results of this exploratory study suggest that SNA could be used as a tool to better understand the social dynamics of dairy cows and inform group and regrouping process to produce positive outcomes. We have applied social network analysis (SNA) to data on voluntary cow movement through a sort gate in an automatic milking system to identify pairs of cows that repeatedly passed through a sort gate in close succession (affinity pairs). The SNA was applied to social groups defined by four pens on a dairy farm, each served by an automatic milking system (AMS). Each pen was equipped with an automatic sorting gate that identified when cows voluntarily moved from the resting area to either milking or feeding areas. The aim of this study was two-fold: to determine if SNA could identify affinity pairs and to determine if milk production was affected when affinity pairs where broken. Cow traffic and milking performance data from a commercial guided-flow AMS dairy farm were used. Average number of milked cows was 214 ± 34, distributed in four AMS over 1 year. The SNA was able to identify clear affinity pairs and showed when these pairings were formed and broken as cows entered and left the social group (pen). The trend in all four pens was toward higher-than-expected milk production during periods of affinity. Moreover, we found that when affinities were broken (separation of cow pairs) the day-to-day variability in milk production was three times higher than for cows in an affinity pair. The results of this exploratory study suggest that SNA could be potentially used as a tool to reduce milk yield variation and better understand the social dynamics of dairy cows supporting management and welfare decisions. [ABSTRACT FROM AUTHOR]