North American animal breeding and production: meeting the needs of a changing landscape
Bibliographic record
Abstract
The North American livestock sector is economically mature; much of the increased demand for livestock products is driven by modest increases in human population and substitution of one species for another in response to commodity prices and consumer trends. Depending on the price of production inputs and farm gate prices, the population of national herds can be cyclical. Major weather events or disease outbreaks can cause subregional fluctuations in population sizes. Accordingly, exportation of animal products is in part based upon the cyclical nature of markets. The vast majority of livestock entering the food chain do so through highly efficient production, processing and retailing systems, although the ‘slow food’ movement and niche markets are gaining traction and developing at relatively fast rates (primarily due to their small initial size). While these niche markets provide interesting opportunities for livestock producers, or producer groups, their robustness to changes in price and product supply is unknown. Due to the mature market structure for livestock products, producers focus attention on approaches that can improve their production efficiency. It is recognized that early adoption of new technology can improve profitability. This drive for efficient animal production has had the net result of decreasing the number of livestock. Regionally from the 1970s to the present the inventory of dairy cattle has decreased while total milk production has increased largely due to genetic improvement. This same trend has been observed with beef cattle for both Canada and the USA, which experienced a 13% and 32% decrease in cow numbers, with little change in total production levels over recent decades. The increased cow productivity has been made possible by selection for increased weaning and yearling weights, which have been translated into increased dressed steer weights. Similar types of production efficiency gains have also been achieved with traits with lower heritability. For example, during the last decade the number of piglets, born per litter has increased by about 10% for the region. While breeding programmes have increased productivity, there has been a contraction in the number of breeders across species. That said, across ruminants and purebred swine, there are still substantial numbers of breeders controlling livestock breeding programmes. As might be expected with a wide range of actors with varying resources, all breeders do not act in unison when implementing selection programmes. As a result, there have always been breeders who tend to be counter-cyclical in their breeding decisions. In general, the rational behaviour of breeders has served the industry well by providing a pool of genetic resources to be drawn upon if current popular trends result in negative impacts on productivity or marketability of their stock. Furthermore, and as a failsafe, both countries have developed substantial cryopreserved germplasm collections, which can be utilized to increase genetic variability and to facilitate adaptation to shifting market demands or environments. Across livestock species, breeders have adapted breeding technologies. In several instances, consortiums of public and private sector stakeholders have been developed to facilitate the access to and implementation of various tools to accelerate genetic gains. The estimation of breeding values for a wide variety of traits is common place across all species. Breed associations have tended to work on a contractual basis with the public sector to develop breeding value estimates for new traits. Larger breed associations and corporate breeding firms have also been among the first to genotype animals and to use this information in developing genomic breeding values that combine molecular information and quantitative data. Smaller associations have started to genotype animals and develop training datasets so that molecular breeding values can be developed. Even though breeders may be armed with access to such technologies, it is a private business level decision to use such tools and the amount of selection pressure to apply. A number of important factors will impact breeding and selection programmes. The trend towards higher meat quality will likely continue to influence breeding decisions across species. But in addition, and particularly for ruminants, there is a growing awareness of a need to better match genotypes to the diverse production environments found in North America. For example, it has been discussed in several fora, the need to optimize cow size, especially in the more arid and subtropical environments. Such a driver will force breeders to explore alternatives – to increase selection pressure on traits for adaptability (e.g. high altitude) while scaling back selection intensity on other traits in an effort to better balance livestock to their environment. There is now interest in increasing productivity and profitability through selecting for better animal health and decreased residual feed intake, both efforts contribute to environmental sustainability by reducing the environmental footprint of livestock production. Certainly, genomic tools will have a role to play in developing solutions for the above issues. To better identify optimal production and breeding strategies, reinitiating deterministic modelling approaches would provide effective insights for breeders to base the direction of their selection programmes and the economic impact of such firm level decisions. Generally, climate change will have a larger negative impact on the US livestock sector when compared to Canada. The breeding and animal science community has started to address those potential stressors. For example, research has been initiated into the role of heat-shock proteins (Collier et al. 2008, J. Dairy Sci. 91, 445), SNPs associated with embryonic survival (Cochran et al. 2013, BMC Genetics 14, 49) and the ‘slick-haired’ gene in cattle (Olson et al. 2003, J. Anim. Sci. 81, 80) – all of which improve our understanding of and ability to manipulate populations for greater climate adaptability. In addition, given the relatively long time frame (2050) before the projected full impact of climate change, as identified by the Intergovernmental Panel on Climate Change, modifications in animal genetic composition can be made to meet this challenge. The potential for diseases increases as climate change progresses. Inquiry into the genetic basis of disease resistance is under investigation in North America and globally. Additional efforts among the monogastric species selection have begun to maintain performance levels while altering diets and implementing production systems which are perceived as more animal welfare friendly. North America has a broad array of genetic resources which have been used to develop populations capable of performing in diverse production settings. For example, of the top 10 international (or transboundary) cattle breeds identified by FAO, all are found in North America along with active breeder associations. The underlying success in using these resources has been the willingness of producers/breeders to incorporate new technologies that aid selection and to develop and evaluate new breed types, composites or synthetics to match their performance under different agro-ecosystems and to meet specific market niches. As a result, North American genetics have been widely exported through individual breeders or corporate breeding firms. As a 2009 report found, over 50% of the globally exported bovine semen was from North America (Gollin et al. 2009, Livestock Sci. 120, 248). Particularly, among the monogastric species, we note with interest that while developing countries have imported intensively bred and produced livestock, it has been the subject of criticism by some. But these imported populations and associated production systems may be solely responsible for the increased monogastric per animal productivity observed in the least developed countries during the last decade (based upon FAOSTAT). The use of productive genetic resources has been called for by the ‘High Level Panel of Experts on Food Security and Nutrition’ convened by FAO. While the panel recognizes the need for genetic resources exchange, as does the CBD, neither has acknowledged that mechanisms are already in place in the livestock sector to facilitate genetic resource exchange. Principally, as livestock and their genetic resources are private property (affirmed in the Interlaken Declaration), owners/breeders have and continue to practice genetic resource exchange, which benefits the global community. Close association between the property rights of breeders and their selection decisions has contributed to the genetic progress achieved during the past 20–30 years and suggests Darwin's observation about livestock breeders, and particularly in the North American context, is still relevant today: ‘breeders habitually speak of an animal's organization as something plastic, which they model as they please’ – long live the breeder.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".