The world's population will reach 10.4 billion in 2067, with 81% residing in Africa or Asia. Arable land available for food production will decrease to 0.15 ha per person. Temperature will increase ...in tropical and temperate zones, especially in the Northern Hemisphere, and this will push growing seasons and dairy farming away from arid areas and into more northern latitudes. Dairy consumption will increase because it provides essential nutrients more efficiently than many other agricultural systems. Dairy farming will become modernized in developing countries and milk production per cow will increase, doubling in countries with advanced dairying systems. Profitability of dairy farms will be the key to their sustainability. Genetic improvements will include emphasis on the coding genome and associated noncoding epigenome of cattle, and on microbiomes of dairy cattle and farmsteads. Farm sizes will increase and there will be greater lateral integration of housing and management of dairy cattle of different ages and production stages. Integrated sensors, robotics, and automation will replace much of the manual labor on farms. Managing the epigenome and microbiome will become part of routine herd management. Innovations in dairy facilities will improve the health of cows and permit expression of natural behaviors. Herds will be viewed as superorganisms, and studies of herds as observational units will lead to improvements in productivity, health, and well-being of dairy cattle, and improve the agroecology and sustainability of dairy farms. Dairy farmers in 2067 will meet the world's needs for essential nutrients by adopting technologies and practices that provide improved cow health and longevity, profitable dairy farms, and sustainable agriculture.
The global dairy industry needs to reappraise the systems of milk production that are operated at farm level with specific focus on enhancing technical efficiency and competitiveness of the sector. ...The objective of this study was to quantify the factors associated with costs of production, profitability, and pasture use, and the effects of pasture use on financial performance of dairy farms using an internationally recognized representative database over an 8-yr period (2008 to 2015) on pasture-based systems. To examine the associated effects of several farm system and management variables on specific performance measures, a series of multiple regression models were developed. Factors evaluated included pasture use kg of dry matter/ha and stocking rate (livestock units/ha), grazing season length, breeding season length, milk recording, herd size, dairy farm size (ha), farmer age, discussion group membership, proportion of purchased feed, protein %, fat %, kg of milk fat and protein per cow, kg of milk fat and protein per hectare, and capital investment in machinery, livestock, and buildings. Multiple regression analysis demonstrated costs of production per hectare differed by year, geographical location, soil type, level of pasture use, proportion of purchased feed, protein %, kg of fat and protein per cow, dairy farm size, breeding season length, and capital investment in machinery, livestock, and buildings per cow. The results of the analysis revealed that farm net profit per hectare was associated with pasture use per hectare, year, location, soil type, grazing season length, proportion of purchased feed, protein %, kg of fat and protein per cow, dairy farm size, and capital investment in machinery and buildings per cow. Pasture use per hectare was associated with year, location, soil type, stocking rate, dairy farm size, fat %, protein %, kg of fat and protein per cow, farmer age, capital investment in machinery and buildings per cow, breeding season length, and discussion group membership. On average, over the 8-yr period, each additional tonne of pasture dry matter used increased gross profit by €278 and net profit by €173 on dairy farms. Conversely, a 10% increase in the proportion of purchased feed in the diet resulted in a reduction in net profit per hectare by €97 and net profit by €207 per tonne of fat and protein. Results from this study, albeit in a quota limited environment, have demonstrated that the profitability of pasture-based dairy systems is significantly associated with the proportion of pasture used at the farm level, being cognizant of the levels of purchased feed.
Dairy small ruminants account for approximately 21% of all sheep and goats in the world, produce around 3.5% of the world's milk, and are mainly located in subtropical-temperate areas of Asia, ...Europe, and Africa. Dairy sheep are concentrated around the Mediterranean and Black Sea regions, where their dairy products are typical ingredients of the human diet. Dairy goats are concentrated in low-income, food-deficit countries of the Indian subcontinent, where their products are a key food source, but are also present in high-income, technologically developed countries. This review evaluates the status of the dairy sheep and goat sectors in the world, with special focus on the commercially and technically developed industries in France, Greece, Italy, and Spain (FGIS). Dairy small ruminants account for a minor part of the total agricultural output in France, Italy, and Spain (0.9 to 1.8%) and a larger part in Greece (8.8%). In FGIS, the dairy sheep industry is based on local breeds and crossbreeds raised under semi-intensive and intensive systems and is concentrated in a few regions in these countries. Average flock size varies from small to medium (140 to 333 ewes/farm), and milk yield from low to medium (85 to 216 L/ewe), showing substantial room for improvement. Most sheep milk is sold to industries and processed into traditional cheese types, many of which are Protected Denomination of Origin (PDO) cheeses for gourmet and export markets (e.g., Pecorino, Manchego, and Roquefort). By comparing break-even milk price among FGIS countries, we observed the following: (1) most Greek and French dairy sheep farms were unprofitable, with the exception of the intensive Chios farms of Greece; (2) milk price was aligned with cost of production in Italy; and (3) profitable farms coexisted with unprofitable farms in Spain. In FGIS, dairy goat production is based on local breeds raised under more extensive systems than sheep. Compared with sheep, average dairy goat herds are smaller (36 to 190 does/farm) but milk yield is greater (153 to 589 L/doe), showing room for improvement. Goat milk is mainly processed on-farm into dairy products for national markets, but some PDO goat milk cheeses (e.g., Murcia al Vino) are exported. Processed goat milk is sold for local human consumption or dehydrated for export. Mixed sheep-goat (e.g., Feta) and cow-sheep-goat milk cheeses are common in many countries. Strategies to improve the dairy sheep and goat sectors in these 4 countries are proposed and discussed.
Over the past 100 yr, the range of traits considered for genetic selection in dairy cattle populations has progressed to meet the demands of both industry and society. At the turn of the 20th ...century, dairy farmers were interested in increasing milk production; however, a systematic strategy for selection was not available. Organized milk performance recording took shape, followed quickly by conformation scoring. Methodological advances in both genetic theory and statistics around the middle of the century, together with technological innovations in computing, paved the way for powerful multitrait analyses. As more sophisticated analytical techniques for traits were developed and incorporated into selection programs, production began to increase rapidly, and the wheels of genetic progress began to turn. By the end of the century, the focus of selection had moved away from being purely production oriented toward a more balanced breeding goal. This shift occurred partly due to increasing health and fertility issues and partly due to societal pressure and welfare concerns. Traits encompassing longevity, fertility, calving, health, and workability have now been integrated into selection indices. Current research focuses on fitness, health, welfare, milk quality, and environmental sustainability, underlying the concentrated emphasis on a more comprehensive breeding goal. In the future, on-farm sensors, data loggers, precision measurement techniques, and other technological aids will provide even more data for use in selection, and the difficulty will lie not in measuring phenotypes but rather in choosing which traits to select for.
An online survey to identify producer precision dairy farming technology perception was distributed in March 2013 through web links sent to dairy producers through written publications and e-mail. ...Responses were collected in May 2013 and 109 surveys were used in statistical analysis. Producers were asked to select parameters monitored by technologies on their farm from a predetermined list and 68.8% of respondents indicated technology use on their dairies (31.2% of producers not using technologies). Daily milk yield (52.3%), cow activity (41.3%), and mastitis (25.7%) were selected most frequently. Producers were also asked to score the same list of parameters on usefulness using a 5-point scale (1=not useful and 5=useful). Producers indicated (mean ± SE) mastitis (4.77±0.47), standing estrus (4.75±0.55), and daily milk yield (4.72±0.62) to be most useful. Producers were asked to score considerations taken before deciding to purchase a precision dairy farming technology from a predetermined list (1=not important and 5=important). Producers indicated benefit-to-cost ratio (4.57±0.66), total investment cost (4.28±0.83), and simplicity and ease of use (4.26±0.75) to be most important when deciding whether to implement a technology. Producers were categorized based on technology use (using technology vs. not using technology) and differed significantly across technology usefulness scores, daily milk yield (using technologies: 4.83±0.07 vs. not using technologies: 4.50±0.10), and standing estrus (using technologies: 4.68±0.06 vs. not using technologies: 4.91±0.09). The same categories were used to evaluate technology use effect on prepurchase technology selection criteria and availability of local support (using technologies: 4.25±0.11 vs. not using technologies: 3.82±0.16) differed significantly. Producer perception of technology remains relatively unknown to manufacturers. Using this data, technology manufacturers may better design and market technologies to producer need.
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.
Considerable progress has been made in understanding the protein and amino acid (AA) nutrition of dairy cows. The chemistry of feed crude protein (CP) appears to be well understood, as is the ...mechanism of ruminal protein degradation by rumen bacteria and protozoa. It has been shown that ammonia released from AA degradation in the rumen is used for bacterial protein formation and that urea can be a useful N supplement when lower protein diets are fed. It is now well documented that adequate rumen ammonia levels must be maintained for maximal synthesis of microbial protein and that a deficiency of rumen-degradable protein can decrease microbial protein synthesis, fiber digestibility, and feed intake. Rumen-synthesized microbial protein accounts for most of the CP flowing to the small intestine and is considered a high-quality protein for dairy cows because of apparent high digestibility and good AA composition. Much attention has been given to evaluating different methods to quantify ruminal protein degradation and escape and for measuring ruminal outflows of microbial protein and rumen-undegraded feed protein. The methods and accompanying results are used to determine the nutritional value of protein supplements and to develop nutritional models and evaluate their predictive ability. Lysine, methionine, and histidine have been identified most often as the most-limiting amino acids, with rumen-protected forms of lysine and methionine available for ration supplementation. Guidelines for protein feeding have evolved from simple feeding standards for dietary CP to more complex nutrition models that are designed to predict supplies and requirements for rumen ammonia and peptides and intestinally absorbable AA. The industry awaits more robust and mechanistic models for predicting supplies and requirements of rumen-available N and absorbed AA. Such models will be useful in allowing for feeding lower protein diets and increased efficiency of microbial protein synthesis.
Automatic milking systems (AMS) have the potential to increase dairy farm productivity and profitability; however, adoption rates, particularly in pasture-based systems, have been lower than ...expected. The objectives of this study were to compare the physical and economic performance of pasture-based AMS with conventional milking systems (CMS) and to identify gaps for improving AMS productivity and profitability. We used data from 14 AMS and 100 CMS located in the main Australian dairy regions and collected over 3 yr (2015–2016, 2016–2017, 2017–2018). Farms within similar regions and herd sizes were compared. Results showed that all the main physical performance indicators evaluated such as milk production per cow, milk production per hectare, pasture grazed per hectare, or milk solids per full-time equivalent were similar between systems. The AMS farms had higher overhead costs such as depreciation and repairs and maintenance; however, no differences in total labor costs were observed between systems. Profitability, measured as earnings before interest and tax, operating profit margin, and return on total assets, was not significantly different between AMS and CMS. Opportunities for improving pasture utilization, labor efficiency, and robot utilization in AMS farms were identified. Improving efficiency in these areas could improve productivity and profitability of these systems, and therefore increase the interest of this technology.
The adoption rate of sensors on dairy farms varies widely. Whereas some sensors are hardly adopted, others are adopted by many farmers. A potential rational explanation for the difference in adoption ...may be the expected future technological progress in the sensor technology and expected future improved decision support possibilities. For some sensors not much progress can be expected because the technology has already made enormous progress in recent years, whereas for sensors that have only recently been introduced on the market, much progress can be expected. The adoption of sensors may thus be partly explained by uncertainty about the investment decision, in which uncertainty lays in the future performance of the sensors and uncertainty about whether improved informed decision support will become available. The overall aim was to offer a plausible example of why a sensor may not be adopted now. To explain this, the role of uncertainty about technological progress in the investment decision was illustrated for highly adopted sensors (automated estrus detection) and hardly adopted sensors (automated body condition score). This theoretical illustration uses the real options theory, which accounts for the role of uncertainty in the timing of investment decisions. A discrete event model, simulating a farm of 100 dairy cows, was developed to estimate the net present value (NPV) of investing now and investing in 5 yr in both sensor systems. The results show that investing now in automated estrus detection resulted in a higher NPV than investing 5 yr from now, whereas for the automated body condition score postponing the investment resulted in a higher NPV compared with investing now. These results are in line with the observation that farmers postpone investments in sensors. Also, the current high adoption of automated estrus detection sensors can be explained because the NPV of investing now is higher than the NPV of investing in 5 yr. The results confirm that uncertainty about future sensor performance and uncertainty about whether improved decision support will become available play a role in investment decisions.
PDF
