Bio-based fertilizers: A practical approach towards circular economy.

Bacteria are ubiquitous in the bovine uterus after parturition, but 50 years ago, cows tolerated these bacteria and few animals developed uterine disease. Now, up to 40% of dairy cattle develop postpartum uterine disease. Uterine disease causes infertility by compromising the function of not only the endometrium but also the ovary. Animals defend themselves against pathogens using tolerance and resistance mechanisms. Tolerance is the ability to limit the disease severity induced by a given pathogen burden. Resistance is the ability to limit the pathogen burden and is usually the function of immunity. Endometrial cells contribute to tolerance and have roles in innate immunity and the inflammatory response to pathogens. However, failures in endometrial tolerance and the character of the inflammatory response shape postpartum uterine disease. We propose that uterine health is more dependent on the ability of the endometrium to tolerate pathogens than the ability to resist invading bacteria.

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Tolerance and Innate Immunity Shape the Development of Postpartum Uterine Disease and the Impact of Endometritis in Dairy Cattle

Genomic selection is revolutionizing plant breeding and therefore methods that improve prediction accuracy are useful. For this reason, active research is being conducted to build and test methods from other areas and adapt them to the context of genomic selection. In this paper we explore the novel deep learning (DL) methodology in the context of genomic selection. We compared DL methods with densely connected network architecture to one of the most often used genome-enabled prediction models: Genomic Best Linear Unbiased Prediction (GBLUP). We used nine published real genomic data sets to compare a fraction of all possible deep learning models to obtain a "meta picture" of the performance of DL methods with densely connected network architecture. In general, the best predictions were obtained with the GBLUP model when genotype×environment interaction (G×E) was taken into account (8 out of 9 data sets); when the interactions were ignored, the DL method was better than the GBLUP in terms of prediction accuracy in 6 out of the 9 data sets. For this reason, we believe that DL should be added to the data science toolkit of scientists working on animal and plant breeding. This study corroborates the view that there are no universally best prediction machines.

Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture

In the next two decades, the world population will increase significantly; the majority in the developing countries located in the tropics of Africa, Asia, Latin America, and the Caribbean. To feed such a population, it is necessary to increase the availability of food, particularly high-value animal protein foods produced locally, namely meat and dairy products. Dairy production in tropical regions has a lot of growth potential, but also poses a series of problems, particularly as dairy production systems were developed in temperate countries and in most cases are difficult to implement in the tropics. Drawbacks include hot weather and heat stress, the lack of availability of adequate feeds, poor infrastructure, and cold chain and the competition with cheap imports from temperate countries. This position paper reviews the major drawbacks in dairy production for the five major dairy species: cattle, water buffalo, sheep, goat, and camel, as well as the future trends in research and development. It also concerns the major trends in reproduction and production systems and health issues as well as environmental concerns, particularly those related to greenhouse gas emissions. Tropical Animal Health and Production now launches a topical collection on Tropical Dairy Science. We aim to publish interesting and significant papers in tropical dairy science. On behalf of the editorial board of the Tropical Animal Health and Production, we would like to invite all authors working in this field to submit their works on this topic to this topical collection in our journal.

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Dairy science and health in the tropics: challenges and opportunities for the next decades.

The ability of dairy farmers to keep their cows for longer could positively enhance the economic performance of the farms, reduce the environmental footprint of the milk industry, and overall help in justifying a sustainable use of animals for food production. However, there is little published on the current status of cow longevity and we hypothesized that a reason may be a lack of standardization and an over narrow focus of the longevity measure itself. The objectives of this critical literature review were: (1) to review metrics used to measure dairy cow longevity; (2) to describe the status of longevity in high milk-producing countries. Current metrics are limited to either the length of time the animal remains in the herd or if it is alive at a given time. To overcome such a limitation, dairy cow longevity should be defined as an animal having an early age at first calving and a long productive life spent in profitable milk production. Combining age at first calving, length of productive life, and margin over all costs would provide a more comprehensive evaluation of longevity by covering both early life conditions and the length of time the animal remains in the herd once it starts to contribute to the farm revenues, as well as the overall animal health and quality of life. This review confirms that dairy cow longevity has decreased in most high milk-producing countries over time and its relationship with milk yield is not straight forward. Increasing cow longevity by reducing involuntary culling would cut health costs, increase cow lifetime profitability, improve animal welfare, and could contribute towards a more sustainable dairy industry while optimizing dairy farmers' efficiency in the overall use of resources available.

https://www.mdpi.com/2076-2615/11/3/808/pdf

Keeping Dairy Cows for Longer: A Critical Literature Review on Dairy Cow Longevity in High Milk-Producing Countries

https://cronfa.swan.ac.uk/Record/cronfa39071/Download/0039071-14032018142942.pdf

Invited review: Learning from the future-A vision for dairy farms and cows in 2067.

Milk and dairy products provide highly sustainable concentrations of essential amino acids and other required nutrients for humans; however, amount of milk currently produced per dairy cow globally is inadequate to meet future needs. Higher performing dairy cows and herds produce more milk with less environmental impact per kg than lower performing cows and herds. In 2018, 15.4% of the world's dairy cows produced 45.4% of the world's dairy cow milk, reflecting the global contribution of high-performing cows and herds. In high-performing herds, genomic evaluations are utilized for multiple trait selection, welfare is monitored by remote sensing, rations are formulated at micronutrient levels, health care is focused on prevention and reproduction is managed with precision. Higher performing herds require more inputs and generate more waste products per cow, thus innovations in environmental management on such farms are essential for lowering environmental impacts. Our focus is to provide perspectives on technologies and practices that contribute most to sustainable production of milk from high-performing dairy cows and herds.

https://cronfa.swan.ac.uk/Record/cronfa57314/Download/57314__20404__9ff843b10de9464ba429d3702d339d4b.pdf

G.A. Jones

Papers

Invited review: Learning from the future-A vision for dairy farms and cows in 2067.

Review: Perspective on high-performing dairy cows and herds