essay on livestock farming

The future of livestock farming

Farming animals for food raises complex questions. Livestock’s versatility makes them central to the survival of millions of people in marginal areas.

A cow lazily swishes its tail at a persistent buzzing, but the drone maintains its station hovering above the herd. The images it collects are analyzed with data from the animals and an array of sensors around the farm. A few miles away, the farmer acts on the information and decides to move the herd. Virtual gates open in an invisible fence and the drone emits a signal that stirs the animals into movement. Such futuristic cattle farming is not so far away. Farming animals for food raises complex questions. Livestock’s versatility makes them central to the survival of millions of people in marginal areas. Meat and dairy are excellent sources of protein, vitamins, and minerals, and when managed correctly livestock contribute to important ecosystem functions such as soil fertility. But there are concerns over the industry’s sustainability. Meat is a relatively inefficient way to produce calories. Livestock use up about 40% of global arable land to deliver 20% of human calorific intake: the ratio is 12 calories of chicken for every 100 calories of grain; beef is 3 to 100. However, while livestock consume around one third of all cereal production, 86% of their plant diet comes from grass, leaves, and other foods humans cannot eat. In this way, it’s argued, livestock positively contribute to food security by making the inedible, edible. As the debate continues, so does demand. Over the last thirty years, meat and dairy consumption has tripled in low and middle-income countries, largely driven by rising prosperity and urbanization. This growth is on top of already enormous demand in developed nations: the average American consumes 222lbs of meat per year. With global demand forecast to increase a further 80% by 2030, this could place a severe strain on our ability to feed a growing population with earth’s limited agricultural land. The world’s 1.4 billion cattle, plus billions of pigs and chickens, already occupy two billion hectares of grasslands, of which some 700 million hectares could arguably be used more effectively to grow crops that can be eaten directly by humans. One potential solution, unless we all become vegetarians, is to make farmed animals more productive. The average farm animals may not be meeting their genetic potential when it comes to production; but techniques and technologies are being developed and deployed to close that gap and keep meat on the menu. Farmers have always striven for efficiency. For millennia they have selectively bred animals to increase their inherent resilience and productivity: in the USA, dairy cows produce four times more milk than 75 years ago. With genome sequencing, artificial insemination, and embryo transfer, science could soon bring some animals to peak productivity. Supporting this is better nutrition, improving an animal’s conversion of feed into protein. Adding natural enzymes and organic acids increases the digestibility of feeds, enabling animals to draw more nutrition from a greater variety of poorer plants. It also supports a healthier gut making them less susceptible to disease. A growing understanding of animals’ precise nutritional needs is producing feeds tailored to optimize their energy, protein, and vitamins while improving overall wellbeing—better yields and healthier herds. At the heart of most people’s vision of future farming is technology, and the drones, sensors, and wearables of precision farming all contribute to greater efficiency. Drones are increasingly used to monitor the health and productivity of both animals and the land they graze. Able to operate over vast swathes of difficult terrain, a drone fitted with infrared sensors and multi-spectrum, high-definition cameras can send real-time images of herds and flocks. This helps farmers to quickly and easily find lost animals, identify newborns, and diagnose sickness in herds and individual animals. Equally, drones show the condition of pasture, informing decisions on moving animals for food, water, or safety. It may even be possible to teach livestock to follow a drone like a high-tech, long-distance sheepdog. Drones will be just one of many digital inputs feeding information back to the farmer. 3D cameras at water troughs can accurately assess an animal’s weight and carcass grade for optimum yield, while also identifying possible illness. Thermal imaging cameras in the cow shed can detect the inflammatory condition mastitis that reduces milk production, and camera systems in chicken sheds can monitor thousands of individual birds to spot the behavior changes associated with many poultry problems. Smart collars and wearables could one day monitor everything from fertility to health, with E-tags clipped to the ear constantly measuring body temperature while Bluetooth-enabled sweat strips send reports on sodium, potassium, and glucose levels. Even a cow’s breath can be analyzed for signs of nutritional problems. Armed with the ubiquitous smartphone, a farmer can use apps for on-the-spot diagnoses such as detecting metabolic diseases in cows and pigs from just a few snapshots. Livestock farmers have been early adopters of robotics, and rapid advances are being made in everything from automatic feeders to herder bots. This technology is more than labor saving: automated milking robots enable cows to be milked according to their individual biorhythms, improving their health and yield. At the same time, robots are capturing vast amounts of information. All this digital data will synchronize with farm management software to provide the farmer with an overview of the health of a whole herd as well as specific actions for individual animals. An extension of this is cybernetic grazing that uses GPS and animal-mounted collars to measure the height of grass and move the herd to fresh pastures by opening and closing virtual fences defined by stimuli based on sight, sound, or shock. Not all improvements are high-tech. Silvopastoral systems, where animals graze among shrubs and trees with edible leaves or fruits, produce more milk and meat as well as being better for the animals and environment. In Colombia, planting the shrub Leucaena with pasture grass increased protein by 64%, while elsewhere it is credited with higher milk production. One of the most radical possibilities for meeting our future needs is cellular agriculture – growing animal-based protein products from cells instead of animals. Growing meat in factories resembling breweries would cut out the need for feed, water, and medicines while freeing up valuable agricultural land. The science and the economics are still being worked out, but it could make a valuable contribution to meeting the challenge, since it seems that the desire for meat growing, not going away. We asked some big questions about living a better life. Discover more about how we can overcome the world’s biggest challenges at natgeo.com/questionsforabetterlife

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• SCIENCE AND TECHNOLOGY
• URBAN AGRICULTURE

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Livestock Farming – Definition, Benefits and Types

What is livestock farming.

Livestock farming is simply the management and breeding of domestic, livestock or farm animals for the purpose of obtaining their meat and products (milk, eggs, leather, etc.). It can also be described as the economic activity that involves raising domestic animals for human consumption and obtaining meat, milk, wool, fur, and honey among others.

Livestock farming is one of the oldest economic activities of man started by early men. It guarantees food supply, hides, skins, bones, milk and other animal products without going to the forest to hunt. Livestock farming includes the breeding of cattle , sheep, pigs , goats , poultry , rabbits , snails , fish, and honeybees .

Read: Farm Animals | Definition, Examples & Characteristics

What are the Importance and Benefits of Livestock Farming?

• Livestock farming is a vital activity in the development of humanity and continues to occupy a prominent place among the primary activities of the world economy.
• It generates high-quality food products such as meat, egg, milk, cheese, etc.
• Other local economic sectors benefit directly or indirectly from its activity: food processing industries, handicrafts, tourism and hospitality.
• It is one of the few human-productive economic activities that are truly sustainable.
• It generates employment opportunities and serves as a source of income
• It can also serve as a hobby for some people.
• It can help a country to generate foreign exchange earnings through the export of livestock products. This will further strengthen the local currency value.
• Bigger animals such as cattle, horses and donkeys can be used for some special farm operations such as plowing, harrowing and even beasts of burden.

Read: A-Z List of All the Animals in the World

Types of Livestock Farming

There are different types of livestock farming systems that are differentiated by the production processes that take place in each of them.

1. Intensive Livestock Farming

Intensive livestock farming is one in which the animals are housed with adequate temperatures, feed and health care necessary for the production of animals to be healthy and faster. In this system, the selection of breeds is made for different types of production. It is both capital and labor-intensive.

2. Semi-intensive Livestock Farming

Semi-intensive livestock farming is one in which the animals are housed and fed, but are allowed to graze or move around the farm to scavenge within an enclosed area within the farm area.

3. Extensive Livestock Farming

Extensive livestock farming is one that is carried out on large areas of land, such as meadows, pastures or mountains so that animals graze and take advantage of the natural resources of various areas. It is usually carried out with animals that are adapted to the type of field to which they are intended to take. This system promotes the conservation of the ecosystem.

4. Nomadic Livestock Farming

Nomadic livestock farming is characterized by the grazing of animals like cattle on a large expanse of land so that they have a natural diet. In this system, the animals are taken to different lands to eat various foods and resources. This type of livestock farming is known as nomadic or semi-nomadic. It is typical of people who live in arid areas where cultivation is difficult to carry out, as in some territories in Africa and Asia.

5. Transhumant Livestock Farming

Transhumant livestock farming is one in which the animals are moved to areas whose fields have food, depending on the season of the year. This livestock farming system is very advantageous because it increases the fertility of the soils thanks to the manure of the cattle. The animals feed on various grasses and vegetables and contribute to the dispersal of seeds, among others. At the moment, the transhumant cattle ranch is little practiced. However, it is still carried out in various areas of Africa.

6. Organic Livestock Farming

Organic farming is a livestock production system with the aim of obtaining the highest quality food without using synthetic chemicals such as pesticides, chemical fertilizers, etc. In addition, animals need a large space and feed on natural products.

Livestock account represents all types of animals like cattle, buffaloes, sheep, goats, pigs, horses, etc. and they are raised mainly for meat, milk or wool production. Livestock farming is associated with the production of meat, milk and eggs from domesticated animals.

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8 thoughts on “Livestock Farming – Definition, Benefits and Types”

Pls where can I gain practical experience on animal husbandry that you can recommend in any part of South Western part of Nigeria? I am seriously interested in this. Thanks

I have an A-z guide send me a mail on : [email protected]

Send a mail to [email protected] for practical guide

Im writing for an opinion from someone that is educated enough to know that a human being is not defined as livestock. Some leaders in Hawaiis political world, with high ranking status has an ignorant definition as far as Humans, being considered as livestock even those that are buried in the grave (hawaiian word Kupuna Iwi). Uncontiously, these so called want to be Hawaiian people are not considered to be indigenous as I am, but instead in my eyes are just another ignorant person that wants authority from very well educated Hawaiian lady as me. As my kupuna would say in Hawaii Nei – ” No be disrespectful…respect others”.

for those livestock that are housed and fed by the farmer,are they be outside the farm in some cases?

Thanks for your advice

You’re welcome

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Livestock farmers’ working conditions in agroecological farming systems. A review

• Review Article
• Open access
• Published: 17 March 2021
• Volume 41 , article number  22 , ( 2021 )

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• Julie Duval   ORCID: orcid.org/0000-0002-7798-1842 1 ,
• Sylvie Cournut 1 &
• Nathalie Hostiou 1  

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A Correction to this article was published on 08 July 2021

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The livestock farming sector is under stress as fewer and fewer people are willing or able to become livestock farmers. Contributing to the decline in attractiveness of the profession are, among other factors, agricultural crises, higher consumer expectations, and difficult working conditions. Agroecology is a sustainable solution that can maintain livestock production and provide positive contributions to society without negatively affecting the environment. Moreover, in its search for social sustainability, agroecological farming could offer better working conditions to farmers and thus contribute to a sustainable future for the livestock farming sector. Here, we review research on livestock farmers’ working conditions in agroecological farming systems. This paper aims to give a comprehensive overview of the available research findings and the dimensions used to describe farmers’ working conditions. The major findings are the following: (i) relatively little published research is available; (ii) it is difficult to compare findings across studies as different dimensions are used to study working conditions and, in certain cases, detailed descriptions of the farming systems are not provided; (iii) certain dimensions were rarely addressed, such as farmers’ health, or work organization; and (iv) in general, farmers’ work is addressed as a component of environmental and economic analyses of the performance of agroecological livestock farming systems, using most often indicators on labor productivity and/or efficiency. Comprehensive multidimensional approaches to study working conditions are lacking, as are studies on the interactions and trade-offs between dimensions (e.g., workload, fulfillment, work organization). To study livestock farmers’ working conditions in agroecological farming systems, we recommend to use a comprehensive approach assessing different dimensions contributing to working conditions, combined with the description of farmers’ activities and work environment.

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1. Introduction

2. Methodology

2.1 Search criteria for the database query

2.2 selection procedure of relevant papers, 2.3 analysis of the selected papers.

3. A scarcity of studies specifically addressing farmers’ work and working conditions in agroecological livestock farming systems

4. Dimensions which are not addressed in the literature

5. A need to get closer to the reality of farmers’ working situations and activities

6. Contrasting impacts on workload, work organization, and complexity of agroecological systems

7. Skills, knowledge, norms, and values at the center of agroecological transitions

8. A broader andmultidimensional framework to study livestock farmers’ working conditions

9. Conclusions

Acknowledgements and Declarations

1 Introduction

The negative impacts of livestock production on the environment (e.g., loss of biodiversity, contribution to greenhouse gas emissions, and water pollution) are widely recognized. However, the livestock sector also performs important positive functions for societies by contributing, for example, to agricultural economies, livelihoods, and human health (Steinfeld et al. 2006 ; Ryschawy et al. 2015 ). The livestock production sector is furthermore expected to grow to meet increasing global demand for food of animal origin (Steinfeld et al. 2006 ; Searchinger et al. 2018 ).

With a generation of farmers soon to retire, the farming sector is facing an additional challenge, namely, how to ensure that a new generation of farmers will step into their shoes. In Europe, just 7.5% of farmers are under 35 years of age, while 30% are over 65 (Council of the European Union 2014 ). A major obstacle to ensuring continuity is the perceived lack of attractiveness of farming in general and livestock farming in particular, as a profession (Servière et al. 2019b ; Hostiou et al. 2020 ). The working conditions of livestock farmers are even more challenging than those of crop farmers; livestock farmers are known to have long working days, are concerned with work-related health problems (Hostiou et al. 2020 ), and must deal with the constraint posed by animals needing daily care and monitoring which cannot be postponed. Livestock farmers also have expressed the desire for working conditions that would enable them to achieve a better work-family balance and obtain greater financial rewards for their work (Servière et al. 2019b ).

The challenge is thus to find sustainable solutions that would allow livestock production and its positive contributions to society to be maintained without negatively impacting the environment (Steinfeld et al. 2006 ), and possibly contributing to its protection (Stoate et al. 2009 ). Agroecology as a science is expected to draw on ecological concepts and principles to contribute to the design, development, and management of sustainable food systems (Gliessman 2007 ). Animals in farming systems are of interest as they can provide positive contributions to agroecosystems, e.g., by contributing to nutrient and energy cycles or more broadly by shaping the vegetation of ecosystems (Gliessman 2007 ; Dumont et al., 2013 ). In terms of farm work, agroecological farming offers people the promise of an opportunity to obtain honorable and fulfilling employment (Gliessman 2007 ). Reducing working time can be a reason for livestock farmers to adopt certain agroecological farming systems (Lusson and Coquil 2016 ). However, in other cases, agroecological practices can prove to be more time-consuming, leading to situations in which farmers abandon these practices (Aubron et al. 2016 ). Agroecology can increase demand for labor and creates employment (Van Der Ploeg et al. 2019 ). However, an improvement in farmers’ working conditions is not guaranteed unless this is considered a starting point in the design of farming systems (Béguin and Pueyo 2011 ).

In general, a combination of different indicators related to environmental, productive, and socio-economic performance are used to assess the sustainability of agroecological farming systems. A recent study showed that compared to other pillars, the social pillar of sustainability is less taken into account in the existing models used to study livestock production systems, and “job quality” and working conditions were rarely considered (van der Linden et al. 2020 ). Studying working conditions is complex as these conditions are themselves determined by multiple dimensions (e.g., work environment, nature of the job, balance between work and personal life, professional relations, health) (e.g., Kling-Eveillard et al. 2012 ; Dumont and Baret 2017 ; Servière et al. 2019b ). Different indicators are used to analyze working conditions, for example, work duration, work organization (Cournut et al. 2018a ), and labor productivity (Aubron et al. 2016 ). To understand farmers’ working conditions, one must consider not only quantifiable dimensions (e.g., the length of working days) but also dimensions that can explain how working conditions are experienced by workers (e.g., by understanding farmer’s reasons for acting) (Kling-Eveillard et al. 2012 ). Moreover, the interactions of the different dimensions determining working conditions need to be understood (Martel and Dupuis 2006 ).

Given the need to replace older farmers on the verge of retirement, the difficult work conditions of livestock farming, and the higher labor requirements of certain agroecological farming practices, the working conditions of livestock farmers and farm workers in agroecological farming systems need to be examined. Based on an exploratory review of the literature, this article therefore aims to describe what is currently known about livestock farmers’ working conditions in agroecological farming systems and to analyze the dimensions used to describe these working conditions. We first describe how, through a search of the scientific literature, data on working conditions in agroecological livestock farms were obtained. We then describe the extent of existing knowledge of farmers’ working conditions in agroecological livestock farms and the main dimensions used to study these (Fig.  1 ).

Livestock farmers at work in different situations and performing a diversity of tasks (photos by Jean-Yves Pailleux)

2 Methodology

Using a topic search, the Web of Science (all databases) was interrogated between the beginning of January and on May 15, 2020. Agroecology at the farm level can be defined as a set of agricultural practices that privilege the biological interactions of an ecosystem with the aim to use them in the most optimal way (Journal Officiel, 2015 ). A review of ecological farming approaches by Rega et al. ( 2018 ) shows that no general agreement exists regarding how to define agroecological farming practices and systems. Farming practices considered to be agroecological, such as using organic manure and crop rotations, also are used in organic, low-input, and integrated farming systems. The different systems represent a continuum rather than strictly delimited types (Rega et al. 2018 ). Moreover, agroecological principles can be applied in various ways, giving rise to a diversity of farm systems. The following search criteria were used to search journal articles: livestock AND (work OR work conditions OR labo$r conditions OR labo$r) AND (agroecology OR organic farming OR crop-livestock OR low-input). The classification of farming systems used as search criteria were derived from the review from Rega et al. ( 2018 ), and “crop-livestock” was used to specify integrated farming systems. We chose to use a generic search (such as “working conditions”) rather than specific dimensions (e.g., “health”) as one of the objectives of this paper was to understand how papers flagged as concerning livestock farmers’ working conditions use dimensions and indicators to study these.

The timespan chosen was 1990 to the present since scientific interest in the topic of farmers’ work and working conditions started to emerge in the early 1990s in the livestock farming systems research community (Dedieu and Servière 2012 ). In addition, we interrogated the database of the journal, Agroecology and Sustainable Food Systems , as we expected that it could contain relevant papers based on the topics it addresses. Moreover, we consulted the “Scientific event and literature monitoring newsletters” of the International Association on Work in Agriculture (IAWA, https://www.workinagriculture.com/ ). In addition, papers of interest were obtained by scrutinizing reference lists of the papers retained from the search. We considered journal articles written in English or French, from across the world. The language criterion led to the exclusion of two papers.

To identify relevant papers for this review, the titles and abstracts of papers were screened. Our main criteria for inclusion of papers in the review was that the study’s main focus was on livestock farmers’ work and working conditions in an agroecological farming system. For this purpose, a database was created in order to compare papers by listing the type of production system studied, the methodology, the dimensions and indicators used by the authors to assess farmers’ work and working conditions, the geographic region, and the scientific discipline.

Criteria to reject papers were related to the origin of the obtained data and the amount of detail provided. We only retained papers that based their results on empirical data obtained from commercial livestock farms. The search result contained several conference papers, but when these proved to contain insufficient details to be analyzed, they were also set aside. In addition, when the type of production (crop or livestock) was unspecified, making it impossible to relate the results to animal production systems papers, these papers were rejected for further analysis.

Papers from production systems other than livestock (horticulture and arable farming systems) were used: (i) when no references were available on livestock farming systems, to study certain dimensions of farmers’ working conditions; (ii) for comparative purpose: and/or (iii) for an illustrative purpose.

The working dimensions studied in the individual papers with a focus on livestock farmers’ working conditions were listed and are presented in the results section. A comparative analysis was conducted to show whether or not dimensions were studied across papers and in which farming context. To identify dimensions not studied in agroecological livestock farming systems, results from other production systems applying agroecological practices (Hall and Mogyorody 2007 ; Navarrete et al. 2015 ; Finley et al. 2017 ; Baer-Nawrocka and Błocisz 2018 ) and literature on working conditions in farms in general were used (Martel and Dupuis 2006 ; Servière et al. 2019a ).

As a complement, a separate list was made of papers obtained through the query that did not focus on farmers’ working conditions, but which analyzed agroecological livestock farming systems’ performance in general and studied social performance related to farm work by using indicators such as labor productivity or labor requirements. They were listed separately because, in line with Jansen ( 2000 ), it was considered that the sole use of these types of indicators provides an incomplete picture of farmers’ working conditions. The aim was to obtain an overview on the indicators used in these cases and discuss the value of these approaches compared to those in the papers identified as focusing on working conditions.

3 A scarcity of studies specifically addressing farmers’ work and working conditions in agroecological livestock farming systems

Few papers focused specifically on the working conditions of livestock farmers and farm workers in agroecological farming systems. Using our search strategy, we identified nine papers matching our criteria (Table 1 ). Across these papers, different animal production systems and agroecological farming systems (organic n  = 4, crop-livestock n  = 3, and low-input n  = 2) were studied. In some articles, comparisons with non-agroecological systems were included ( n  = 6). Different livestock production systems have their own constraints affecting farmers’ working conditions; for example, seasonal lambing periods result in workload peaks at certain periods during the year, and dairy systems have the constraint of daily milking activities. Therefore, comparisons with conventional farming systems of the same production system are probably more informative to understand the impact of the adoption of agroecological farming practices than comparisons across production systems. Moreover, some activities, such as direct-sales activities, have a strong impact on farmers’ work and working conditions (Darduin et al. 2015 ) and can be found across production systems.

Given the limited number of relevant papers, it is unsurprising that we could not observe geographical differences. The high number of papers from France might be explained by the relative importance of the scientific community within French national research institutes for agriculture, food, and environment working in the field of farm work, as identified by Malanski et al. ( 2019 ). The only topic that this scientific community is not studying is that of “occupational health” (Malanski et al. 2019 ).

From the nine papers, we identified 11 categories of dimensions that were used to study farmers’ working conditions in agroecological farming systems. These are dimensions that are not specific to agroecological farming systems but can be found in agricultural contexts in general. Not all dimensions contributing to farmers’ working conditions were addressed across the different papers (Table 2 ). The dimensions most frequently studied in the papers were “workload” and “knowledge, skills, experience, resources and/or tools used,” followed by “work organization.” Sometimes, specific dimensions of work were studied in specific farming systems; for example, farmers’ use of resources considered a reflection of operational and cognitive changes made by farmers to transition to self-sufficient crop-dairy farming systems (Coquil et al. 2013 , 2014 ) or the impact of agroecological farming practices on labor productivity in suckler ewe farms with different types of feed systems (Aubron et al. 2016 ). In addition, some papers addressed a specific activity, like Darduin et al. ( 2015 ) describing the impact of direct-sales of farm products on the work of organic broiler farmers in France. Others studied overall farm work (Cournut et al. 2018b ).

4 Dimensions which are not addressed in the literature

Different dimensions known to contribute to working conditions were not addressed in the papers obtained through our query. Workforce composition and distribution of work among farm workers can be impacted by the adoption of agroecological practices. For example, it is recognized that organic farming systems require more labor (Finley et al. 2017 ; Baer-Nawrocka and Błocisz 2018 ). Task distribution and/or specialization of farm work among farm workers might also be impacted in more agroecological diversified systems (Navarrete et al. 2015 ). These dimensions were not addressed except in the papers by Cournut et al. ( 2018b ) and Hostiou ( 2013 ), but these papers do not allow a comparison of results with non-agroecological livestock systems, nor do they provide detailed information on farmers’ activities. Hall and Mogyorody ( 2007 ) studied whether the distribution of tasks between men and woman was different in organic farming systems compared to conventional systems. They concluded that organic farming provided a window for women to be more involved in farming activities and decision-making, but the issue was complex and involved gender, farmers’ ideologies, farm structure, labor intensity, and the level of experience and knowledge of individuals (Hall and Mogyorody 2007 ). In the nine papers identified, no clear identification of, or specific analyses distinguishing between, the working conditions of self-employed farmers and employees were made. Hostiou ( 2013 ) was the one exception, as the paper considered all farm workers (including voluntary workers) to analyze work organization and flexibility in organic suckler sheep farms and showed that in some cases, specific tasks can be assigned to employees. As agroecology stimulates employment, attention needs to be paid to the working conditions of all types of farm workers. For example, wage levels, participation in decision-making processes, and the distribution of work (of physical and tedious tasks) of employees should be analyzed (Timmermann and Félix 2015 ). Income, social security, access to health insurance in cases of health problems, and the level of work insecurity are also relevant for farm owners (Dumont and Baret 2017 ). Dumont and Baret ( 2017 ) also identified the “leeway and control level” dimension, under which we find, for example, the feeling of being able to innovate and to be autonomous in decision-making processes.

In contrast with the abundant literature available on workers’ health in agriculture (Malanski et al. 2019 ), only one paper related to the health dimension in agroecological livestock systems was obtained from the query. It studied the prevalence of antibiotic resistant bacteria among broilers and humans living around and/or working on organic broiler farms. No differences in prevalence were detected between organic and non-organic broiler farms (Huijbers et al. 2015 ). This paper referred to a very specific health risk; no studies were identified on unpleasant tasks, physical or physiological issues related to work (Dumont and Baret 2017 ). The scant amount of available literature including health when studying working conditions is regrettable, as improving human health is known to be an important motivation of farmers to convert to organic farming (Rigby et al. 2001 ). More generally, farming can be a profession that is physically and psychologically difficult. The result obtained might be explained by the query used since more specific keywords such as “injury,” “occupational health,” and “occupational exposure” are most often used by authors studying health at work in agriculture (Malanski et al. 2019 ). We chose to use more generic terms as one of the objectives of this paper was to understand how working conditions are considered in the available scientific literature by describing and comparing the dimensions and indicators used across studies.

5 A need to get closer to the reality of farmers’ working situations and activities

The search string used identified numerous papers studying the sustainability of farming systems from different continents. The search string selected these papers since they addressed indicators related to work to study either a social or economic dimension of sustainability. Most often, the use of one indicator, such as labor costs, labor productivity, or labor efficiency, was involved (e.g., Kumar et al. 2012 ; Toro-Mujica et al. 2012 ; Veysset et al. 2014 ; Stark et al. 2016 ). This thus provides a very narrow view of the working conditions of farm workers despite the fact that in 2000, Jansen already argued for a wider definition of labor, including qualitative changes in labor. Few exceptions were found. Monzote et al. ( 2012 ) identified different social indicators, including working conditions, stability of labor, equity of income distribution, and women’s involvement in farming activities, to compare the sustainability of three mixed crop-livestock systems. The small-scale mixed farming system scored better than the specialized medium-size dairy farm system. Another example is the review by Bokkers and de Boer ( 2009 ) on the economic, ecological, and social performances of conventional and organic broiler production in the Netherlands. They considered working conditions as a factor contributing to social dimensions alongside animal welfare, food safety, and product quality. The indicators of working conditions used were working hours, number of physical and psychological complaints related to work, and the effect of barn conditions on health, but no comparative studies between organic and conventional farms were found. In addition, labor productivity indicators allow the conclusion that working conditions of farmers are different, but do not allow an assessment of whether or not the conditions are improved.

Farming activities and by consequence working conditions on an extensive grazing-based cattle farm are different than on an integrated crop-livestock-forestry farm, but both might be considered agroecological. The question which then arises concerns how agroecological farming systems may be compared when studying farmers’ working conditions. A similar question arises concerning the comparison with the so-called conventional systems which also represent a diversity of systems, with some systems that can be close to forms of agroecological systems. Not all authors describe on what ground they claim that they are studying agroecological farming systems. Aubron et al. ( 2016 ) provided the indicators used to assess the level of agroecology of the farm systems, namely, the farms’ feed systems (contribution of grazing activities and level of feed autonomy) and the use of local feed resources. Coquil et al. ( 2014 ) used the indicator of imported nitrogen units per hectare per year to quantify a certain type of autonomy of crop-livestock dairy systems. It is clear that for comparison purposes, it is necessary to describe the level of agroecology of the systems under study.

Moreover, a comparison of the different situations is difficult as the papers identified did not provide detailed insight into the exact content of farmers’ work, the conditions under which they work, or the difficulties they face. In addition, agroecological farming practices need to be described to be able to explain their impact on farmers’ working conditions. For example, labor requirements can depend on the degree of specialization of a farm, farm size, type of production system, crop choices, on-farm processing and direct-sales activities, and the level of experimentation on the farm (Jansen 2000 ; Bendahan et al. 2018 ). Bendahan et al. ( 2018 ) quantified the expected additional labor needed for the adoption of crop-livestock-forestry practices in three different types of livestock systems. They showed increased labor requirements in all systems, ranging from an additional 21 to 80% depending on the system. In another example, the evolution of different crop-livestock farming systems in Guadeloupe showed that labor productivity (added value/work day) depended on the type of crops produced and the level of mechanization (Stark et al. 2016 ). Such detailed data are not always available and make it difficult to compare farms and systems.

6 Contrasting impacts on workload, work organization, and complexity of agroecological systems

Concerning the different dimensions determining working conditions, we observed positive as well as negative impacts of agroecological practices and/or farming systems across and sometimes within different dimensions (Table 3 ). For example, the amount of work in agroecological livestock farming systems increased in some examples, but also was found to decrease in certain cases. Cournut et al. ( 2018b ) showed that the time spent at work can be highly variable across organic livestock farms. Sometimes, this can be explained by specific choices made, such as direct-sales of farm products, or farmers’ objectives of having free time and holidays. Whether the amount of work was acceptable to the farmers and/or farm workers was not always evaluated. This information is relevant to assess whether the working conditions can be regarded as sustainable (Navarrete et al. 2015 ). Moreover, how the workload is experienced is not necessarily only related to the amount of work but also to the distribution of work over time and how it is balanced with free time (Cournut et al. 2018b ) and family life, for example (Servière et al. 2019a ; Hostiou et al. 2020 ).

The choices farmers make related to their work organization (e.g., through the delegation of certain tasks, simplification of herd management practices or choices in work distribution over the year) can have an important impact on the daily workload and consequently the time they have available for unexpected tasks or free time (Hostiou 2013 ). Work organization might be an important determinant of working conditions. Work organization in agroecological farming systems was considered to be more complex than in non-agroecological systems in some cases (Bendahan et al. 2018 ) and less complex in others (Lusson and Coquil 2016 ). The level of experienced complexity might be dependent on the initial level of complexity of the farm system before the transition. For example, in extensive cattle systems that were appreciated by farmers for their flexibility, the transition to crop-livestock-forestry systems that require certain activities to be implemented during a specific time window was difficult. Moreover, the overall organization of farm work changes when adopting such systems. It involves more than introducing new components and their related activities; it requires rethinking all of the interactions within the farm system (Bendahan et al. 2018 ) and possible competition between farming activities as shown in different crop systems (Dupré et al. 2017 ; Delecourt et al. 2019 ). Therefore, although certain activities such as marketing farm products might not be specific to agroecological farming systems, they still have important implications for farmers’ work (Darduin et al. 2015 ). Moreover, as shown by Dupré et al. ( 2017 ) in the context of market gardening, the chosen marketing route affects crop choices and the corresponding workload, skills, and knowledge necessary on the farm and the perceived complexity in the planning of farming activities. It also raises the question whether the complexity perceived is temporary or related to the transition and whether this will diminish by obtaining certain skills, knowledge, and experience. Furthermore, farmers do not necessarily perceive complexity as a source of discomfort as shown in diversified vegetable farming systems. On the contrary, it can be a source of pleasure (Navarrete et al. 2015 ). However, in certain cases, perceived complexity was a barrier to the adoption of agroecological practices (Lusson and Coquil 2016 ). In general, to better understand how livestock farmers manage their transition, it would be interesting to understand how different dimensions contributing to working conditions interact, the trade-offs farmers make, and whether these are evolving over time and why.

7 Skills, knowledge, norms, and values at the center of agroecological transitions

There is a general consensus concerning the fact that the acquisition of new skills, experience, and informal and/or formal knowledge is necessary to adopt agroecological practices and/or stimulates the adoption of agroecological practices (Table 3 ). Change can be a source of uncertainty for some (Lusson and Coquil 2016 ) or a contribution to the challenging nature of work, which can be a source of pleasure for others (Navarrete et al. 2015 ). We did not identify papers specifically focusing on how information about aspects of the farming systems related to work and work organization is used by livestock farmers when transitioning or experimenting with agroecological farming. Delecourt et al. ( 2019 ) have shown that farmers’ work-related information needs evolve when transitioning toward more sustainable cropping practices. This is probably no different for livestock farmers. Coquil et al. ( 2013 ) showed that dairy cattle farmers use new resources (e.g., as animal and plant observation methods) when piloting the evolution of their farming system toward self-sufficient crop-livestock systems. Authors also agreed that the adoption of agroecological farming practices allowed farmers to work in a system that is more in line with their personal beliefs and motivations (Table 3 ). Although this sometimes necessitated changing professional norms, a process that is not always easy for farmers (Coquil et al. 2014 ), it can contribute to the positive appreciation farmers have of their work.

8 A broader and multidimensional framework to study livestock farmers’ working conditions

In agriculture in general, few studies exist which focus on working conditions using a multidimensional approach (Malanski et al. 2019 ). Dumont and Baret ( 2017 ) proposed a multidimensional framework to compare vegetable farmers’ working conditions in conventional, organic, and agroecological systems. They showed that farmers’ working and employment conditions were not per se better in agroecological systems and that farmers make concessions between the economic, social and ecological aspects of their enterprise. To our knowledge, such a multidimensional approach has not yet been used to study livestock farmers’ working conditions. Bouttes et al. ( 2020 ) analyzed the evolution, during the conversion to organic farming, of dairy farmers’ satisfaction levels concerning their work conditions, economic, agronomic, livestock-related, and social issues. To study dairy farmers’ satisfaction regarding their working conditions after conversion to organic farming, they used multiple dimensions: workload, the perceived difficulty of seasonal and year-round tasks, and the free time available to cope with unexpected events or non-professional activities. However, the results were aggregated, making it impossible to understand which dimensions contributed to the overall score and how. Even though the majority of the farmers interviewed were satisfied with their working conditions after the conversion to organic farming, the results showed that not all farmers’ working conditions had evolved positively, and not all farmers were satisfied with their current working conditions (Bouttes et al. 2020 ).

It would be interesting to develop such a framework for livestock farming systems as it would make it possible to add specific dimensions, such as human-animal relationships, and address additional implications on working conditions of working with animals that contribute to the difficulty of attracting a new generation of livestock farmers. For example, farm workers’ working conditions can impact their views of, and measures taken to favor, animal welfare (Anneberg and Sandøe 2019 ). Porcher ( 2011 ) showed that in industrialized farming systems, farm workers can be forced into working conditions that are incompatible with their wish to “raise animals” due to the strong rationalization of farm work in terms of economic and technical performances. Consequently, this leads to situations of “suffering” in both animals and farm workers. Specific constraints, such as the need of a daily presence related to raising animals or tasks that cannot be postponed (e.g., milking), also need to be taken into account as they can interfere with certain agroecological practices that need to occur at a specific moment in time or that require more time (e.g., observation of animals needed to be able to use alternative medicine).

In this paper, the choice was made to focus on only one aspect of social sustainability, namely, the work and working conditions of farmers and farm workers. However, when considering agroecology as a way to contribute to social equity, we can argue that dimensions related to job security, social benefits, income, and political experience at work should also be included to evaluate farmers’ and farm workers’ working conditions. Although agroecology offers the promise of better working conditions, there are examples in agroecological farming systems showing that when farm owners have to find a balance between economic and social performances, the latter are not always a priority (Shreck et al. 2006 ; Dumont and Baret 2017 ). Moreover, it also would be interesting to combine this with studies of the social sustainability of agroecological farming systems at a territorial level. Examples include studying the impact of changes in working conditions on the quality of rural life, employment, and landscapes and in maintaining local culture and know-how. These factors are known to contribute to how farmers themselves appreciate their profession (Servière et al. 2019a ). They also should include social interactions with neighbors, colleagues, and persons outside the farming community who can provide support to farmers in their daily work (e.g., by sharing work or risks or providing emotional support) (Bouttes et al. 2020 ).

The study of working conditions in agroecological farming systems therefore requires, as previously mentioned, not only a multidimensional but also an interdisciplinary approach combining scientific disciplines that are able to describe, understand, and evaluate livestock farmers’ working conditions, farming activities, and agroecological farming systems. Combining systemic approaches of work and activities (Coquil et al. 2018 ) with livestock farming systems approaches.

9 Conclusions

It is difficult to compare the literature available as there are few studies on livestock farmers’ working conditions in agroecological farming systems, and those that do exist focus on different geographic regions, animal production systems, and agroecological systems. To allow comparisons between production contexts, sufficient data should be provided on farm structure, farming activities, and the agroecological practices adopted. Different agroecological practices have specific and sometimes contrasting impacts on the different dimensions that affect farmers’ working conditions. As the adoption of agroecological farming practices can have diverse effects (positive as well as negative) on the different dimensions of working conditions, we recommend that future research should take into account the multiple dimensions contributing to working conditions, using a multidimensional approach that also should allow interactions between dimensions to be understood. To our knowledge, such an approach has not yet been used to study livestock farmers’ working conditions in agroecological farming systems. Moreover, the dimensions studied could be broadened to assess whether agroecology fulfills the promise of providing farm workers with honorable and fulfilling employment, as well as other aspects of social sustainability.

Data availability

The data will be made available on from the corresponding author upon reasonable request.

Change history

08 july 2021.

A Correction to this paper has been published: https://doi.org/10.1007/s13593-021-00709-9

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Acknowledgments

The authors thank the three anonymous reviewers of the journal for their valuable comments on the previous version of the manuscript.

This paper is part of the LIFT (“Low-Input Farming and Territories – Integrating knowledge for improving ecosystem-based farming”) project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 770747.

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Duval, J., Cournut, S. & Hostiou, N. Livestock farmers’ working conditions in agroecological farming systems. A review. Agron. Sustain. Dev. 41 , 22 (2021). https://doi.org/10.1007/s13593-021-00679-y

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Article Contents

Literature cited, social aspects of livestock farming around the globe.

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Gesa Busch, Social aspects of livestock farming around the globe, Animal Frontiers , Volume 13, Issue 1, February 2023, Pages 3–4, https://doi.org/10.1093/af/vfac084

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Livestock currently plays an important and outstanding role in societies around the globe. Thereby, keeping livestock fulfills various features in societies. These range from very obvious aspects such as providing food security, proteins, and income for farmers, to more hidden aspects such as fulfilling cultural or religious roles, providing ecosystem services, and satisfying their owners’ passion for keeping and caring for animals—to name just a few. Besides such positive aspects, animal farming is also controversially discussed and negative impacts on the sustainability of food production need to be considered when thinking about the future roles of farm animals in food systems and societies. For example, discussions about how to solve the global climate crisis have intensified and animal production is an important piece, both as one of the causes of global warming and as part of the solution—depending on regions and systems.

The foregoing shows that human societies maintain complex relationships with farm animals. These relationships shape how animals are cared for and what husbandry systems prevail. Furthermore, the way farm animals are kept and treated also impacts citizens’ views of the livestock sector in an interactive manner. The latter becomes very clear when we think about animal welfare problems in many production systems that made their way to scientific and public discussions about the future of livestock farming systems in many countries—very much in the global North, but increasingly also in countries of the global South.

Depending on the economic situation and natural conditions such as climate and vegetation, different social aspects of livestock farming are prominent in different regions of the world. The aim of this issue is to bring together different aspects and views on social aspects of livestock production in order to open a discussion on the complex tasks that livestock perform in different parts of the world and on the resulting challenges. To this end, five articles are compiled in this issue.

Headey (2023) contributes with an economic analysis on how dairy can help to solve the malnutrition crisis in developing countries. He explores cross-country disparities in dairy consumption by young children, as dairy is a high-potential food for addressing child malnutrition in low and middle income countries. Wealth and relative price differences, as well as refrigeration access and water quality (for powdered milk consumption) are predictors for dairy consumption—but large variations between regions exist. Some ideas on how to support dairy consumption to improve child nutrition are presented.

Birhanu et al. (2023) analyze in their article how smallholder poultry production can help with poverty reduction and food security under the light of increasing global food prices in the developing world—a topic that has even gained more importance in the light of current inflation, rising food prices and hunger around the world. In times of increasing food prices, smallholder poultry production can contribute to household income and animal source food consumption. Given these findings, advances in poultry production and productivity could provide opportunities for food security and household income—but require research and development support for the smallholder poultry sector.

Parlasca et al., (2023) reflect on how and why animal welfare concerns evolve in developing countries. They start by the observation that animal welfare concerns do not receive the same recognition by policy, law and consumers in developing countries as they do in higher income countries and point to knowledge and action gaps that might limit more animal friendly production. In countries where livestock farming has largely commercialized, citizen views on the sector are becoming more critically and sensitive to welfare issues but they still do not yet largely affect buying decisions. In order to improve welfare through increased willingness-to-buy, better understanding of citizen and consumer views are needed but also other stakeholders need to be taken into account.

Chen et al., (2023) focus on a specific animal welfare issue in one country: the importance of cage-free eggs in China. Although China has a long history of cage-free practices, market shares are in the niche and 90% of production takes place in caged systems. Recently, consumer demand for ‘free range’ products with associated higher animal welfare is increasing. Eggs marketed as cage-free may provide at least some opportunity for consumers seeking higher animal welfare products in a market where welfare labeling is largely not accessible and identifiable by consumers.

Finally, Kühl et al., (2023) elaborate on the role of trust and deception when buying organic animal products in Germany. They show that consumer trust is highly important for the success of the organic label that is associated with higher animal welfare standards by many people. However, many consumers are only little informed about husbandry systems and feelings of deception can arise when consumers become aware of possible gaps between individual expectations and farm realities. The organic sector should therefore be interested in consumer expectations and possibly adapt handling conditions in organic farming to better align with consumer expectations in order to prevent further trust losses and deception – and finally to prevent loosing market shares.

With this issue of Animal Frontiers, I hope to stimulate discussion about these topics and hope that you appreciate reading the versatile contributions included. Many thanks to all authors and reviewers who contributed to the creation of this issue.

About the Author

Gesa Busch is professor for “Food consumption and Wellbeing” at the University of Applied Sciences Weihenstephan-Triesdorf in Germany. Trained as an Agricultural Economist, she is passionate about working at the interface of social and animal sciences. Her research focuses on consumer behavior in the food domain, sustainable transformation of food systems, sustainable livestock production, and public debates surrounding (livestock) farming. Gesa received her PhD in 2016 from Göttingen University on the topic of “Animal farming and society: communication management between agriculture and the public”. After completing her PhD, she worked at the Free University of Bolzano-Bozen in Italy (2016-2019) on topics of sustainable mountain farming and consumer behavior before she returned to Göttingen and joined the group of “Marketing for Food and Agricultural Products”. Since September 2022 she teaches and researches as a professor in Weihenstephan-Triesdorf.

Birhanu , M. , R. Osei-Amponsah , F. Obese , and T. Dessie . 2023 . Smallholder poultry production in the context of increasing global food prices: roles in poverty reduction and food security . Anim. Front . 13(1):17–25.
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Kühl , S. , E. Bayer , and M. Schulze . 2023 . The role of trust, expectation and deception when buying organic animal products . Anim. Front .13(1):40–47. doi: 10.1093/af/vfac080

Parlasca , M. , I. Knößlsdorfer , G. Alemayehu , and R. Doyle . 2023 . How and why animal welfare concerns evolve in developing countries . Anim. Front .13(1):26–33. doi: 10.1093/af/vfac082

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Sustainable Livestock Farming: Environmental & Ethical Implications

• June 5, 2023
• Agriculture , Sustainable Agriculture

Sustainable livestock farming is an approach that aims to minimize the environmental impact and address ethical concerns associated with conventional farming practices. By assessing the environmental implications and adopting responsible feed management strategies, we can work towards a more sustainable future for the livestock industry.

Assessing Environmental Implications:

Livestock farming, while essential for meeting the growing global demand for meat and dairy products, has significant environmental implications. However, sustainable practices can help mitigate these issues:

• Land Use: Sustainable livestock farming emphasizes efficient land use. By optimizing grazing systems and integrating livestock with crop production, farmers can reduce the need for additional land and preserve natural habitats.
• Water Management: Proper water management is crucial in sustainable farming. Practices such as rainwater harvesting, efficient irrigation techniques, and the prevention of water pollution from animal waste contribute to sustainable water use.
• Greenhouse Gas Emissions: Livestock production is a significant contributor to greenhouse gas emissions. Sustainable practices focus on reducing emissions by improving feed efficiency, managing manure, and investing in renewable energy sources on farms.
• Biodiversity Conservation: Responsible livestock farming takes into account the preservation of biodiversity. Farmers can adopt agroforestry systems, which combine trees with livestock, to provide shade, shelter, and additional income while promoting biodiversity.

Ethical Implications of Sustainable Livestock Farming:

Alongside environmental considerations, sustainable livestock farming also addresses ethical concerns related to animal welfare and food safety:

• Animal Welfare: Sustainable practices prioritize the well-being of animals. Providing adequate space, access to pasture, and proper healthcare ensures a higher quality of life for livestock.
• Antibiotic Use: Overuse of antibiotics in conventional farming can lead to the emergence of antibiotic-resistant bacteria. Sustainable livestock farming promotes responsible antibiotic use, limiting the need for routine antibiotic treatment and promoting animal health.

Feed Management Strategies:

Effective feed management strategies play a crucial role in sustainable livestock farming. By adopting these practices, farmers can reduce environmental impact and enhance animal health:

• Sustainable Feed Sources: Choosing sustainable feed sources such as locally grown crops, algae-based feeds, and by-products from the food industry reduces the environmental footprint of livestock farming.
• Precision Feeding: Precision feeding involves tailoring the diet of each animal to meet its specific nutritional requirements. This approach reduces feed waste, improves feed efficiency, and minimizes environmental pollution.
• Crop-Livestock Integration: Integrating crops and livestock allows for efficient use of resources. For example, livestock can graze on crop residues, reducing the need for additional feed while returning nutrients to the soil.

Q: Does sustainable livestock farming lead to higher costs for consumers?

A: While sustainable farming practices may initially involve higher production costs, they contribute to long-term environmental and social benefits. As the demand for sustainable products increases, economies of scale and technological advancements can help reduce costs.

Q: Can sustainable livestock farming meet the global demand for meat?

A: Yes, sustainable practices can meet the global demand for meat while reducing environmental impact. By optimizing production systems, improving feed efficiency, and reducing waste, sustainable farming can provide high-quality meat while preserving resources.

Q: What can consumers do to support sustainable livestock farming?

A: Consumers can make a difference by choosing sustainably produced meat and dairy products. Look for labels such as “organic,” “grass-fed,” or “pasture-raised.” Reducing meat consumption and opting for plant-based alternatives can also have a positive impact on sustainability.

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Editorial article, editorial: livestock production and the functioning of agricultural ecosystems.

• 1 Department of Biology, University at Albany, Albany, NY, United States
• 2 Department of Wildland Resources, Utah State University, Logan, UT, United States

Editorial on the Research Topic Livestock Production and the Functioning of Agricultural Ecosystems

Approximately 37% (5.1 ×10 7 km 2 ) of the Earth's land mass is used for agriculture, but only about 11% of that is capable of large-scale crop production ( World Bank, 2021a , b ). As such, about 26% of the Earth's agricultural land can only be used to produce animals. Despite trends toward veganism in some countries, the consumption of meat is increasing globally, and sustained consumption of the vegan diet is rare ( Lal ; van Vliet et al. ). Although domestication of animals began nearly 11,000 years ago, our species' relationship with livestock has been challenging from the beginning. From the first efforts at domestication to the present day, livestock have been zoonotic disease vectors ( Diamond, 1998 ). Livestock management has challenged the integrity of ecosystems globally ( Kleppel ; Teague and Kreuter ) and has created social, animal welfare and public health concerns in both developed and developing countries. Clearly, the relationship between humans and the animals we raise for food is paradoxical. On the one hand, they are the source of nourishment, particularly protein, for most of the people on this planet. On the other, they and the practices used to raise them are major contributors to the pollution of surface waters, the degradation of air quality, the emission of greenhouse gases, the destruction of soil, vegetation and biodiversity, and the incitement of social unrest. While it is unlikely that any of this would or could compel Homo sapiens to turn again to a hunter-gatherer lifestyle, it is worth considering animal agriculture from both positive and negative perspectives, endeavoring to resolve those aspects of practice that threaten our environment and social systems, and to replace them with beneficial practices and approaches to produce food for a rapidly growing, but endangered human population on this fragile planet.

To respond to this challenge, we have compiled a collection of research and review articles dedicated to the many facets of Livestock Production and the Functioning of Agricultural Ecosystems . In fact, livestock and humans can contribute to meeting such challenges by integrating key principles underlying the adaptive and dynamic interactions of plants, animals, and humans with their environment into existing and novel management practices that foster ecosystem health and biodiversity. The nine papers presented here address the topic from different perspectives, providing readers with a range of questions and challenges associated with animal agriculture. The subjects examined in this issue are wide ranging, from the benefits of non-fiber carbohydrates in forage for beef cattle, to the comparison of different philosophies of pasture and rangeland management, to the resolution of conflicts between herders and crop farmers, as well as herders and lions, to the benefits of an omnivorous over an herbivorous diet. Several themes emerge from this compendium. Pre-eminent among them are the: (i) importance of biodiversity in animal agriculture, (ii) impacts of different management approaches to livestock and the environment, and (iii) effects of animal agriculture on human well-being and wildlife conservation. There are numerous overlaps among themes. For instance, biodiverse livestock operations may not only help mitigate certain climate change impacts, but they may increase economic stability, particularly in the developing world.

The theme of biodiversity is central to articles by Lal , Dumont et al. , and Rowntree et al. , who consider the implications of animal-crop-tree farming, and multi-animal species integration from different perspectives. As Dumont et al. point out, “… the diversity of system components and interactions among these components can increase productivity, resource-use efficiency and farm resilience.” These ideas are captured in the life cycle assessment conducted by Rowntree et al. on a farm that practices multi-species rotational grazing in Clay County, Georgia, USA. They are extended further by Lal , who demonstrates the links between integrated agriculture, environmental quality and social well-being within the context of the UN's Sustainable Development Goals.

The second theme, the environmental and nutritional impacts of different management approaches , focuses on forage quality and emerging livestock and grazing-land management approaches. Villalba et al. remind readers of the importance of non-structural or non-fiber carbohydrates in beef cattle nutrition and they recommend the incorporation of such nutritional sources, particularly legumes, into forages for livestock on pasture. Teague and Kreuter , and Kleppel focus on regenerative methods of livestock production. Regenerative agriculture emphasizes soil health and the restoration of ecosystem services. Kleppel compares the environmental impacts of regenerative and conventional animal agriculture, the latter being associated with practices that have become mainstream since the end of World War II. He suggests that relative to conventional practice, regenerative techniques favor restoration and maintenance of environmental quality and ecosystem services. Teague and Kreuter emphasize that, “[s]cientists partnering with farmers and ranchers… who have improved their… resource base and excel financially have documented… sound environmental, social, and economic outcomes.”

The third emergent theme in this issue, the effects of animal agriculture on human well-being and wildlife conservation , focuses on human health, and on interactions among disparate human communities, and humans and non-humans. van Vliet et al. present a comprehensive analysis of the nutritional importance of the omnivorous human diet, making the case for the synergistic nutritional complementarity of plant- and animal-based foods. They extend the discussion to popular plant-based meat alternatives, showing that extensive processing and lack of animal-based nutrients prevents them from being nutritionally complete substitutes for animal foods. Jablonski et al. describe the resolution of stress between Maasai herders and African lions, caused by increased lion depredation on livestock, by identifying and correcting weaknesses in herding practices. Similarly, Alary et al. document the reduction of stress between Bedouin herders who have long used the western edge of the Nile Delta to graze their livestock and newly arrived farmers seeking to cultivate the land.

Ultimately, this volume speaks to the breadth of researchable questions associated with animal agriculture, the integrated context of thematic areas within the discipline, and the obvious role that livestock production can play, not only in the food supply, but to human health, social welfare, and the future of Earth's ecosystems. Animal agriculture is in a state of transition. Changes are occurring in the ways we manage livestock and produce food and fiber from them. Answers to many of the questions raised in these papers remain elusive. Many will be controversial. But if good research raises more questions than it answers, then this issue should prove a useful stimulus for new research into the 11,000-year old practice of cultivating animals for food and fiber.

Author Contributions

GK, FP, and JV contributed substantively to the production of this document. GK prepared the initial draft of this Editorial. FP and JV reviewed and revised the draft. All authors approved the submitted version of the article.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We are grateful to the authors who contributed manuscripts to this issue, and to the many reviewers and paper editors, who contributed their time and efforts to ensure the success and quality of the peer review process.

Diamond, J. (1998). Guns, Germs and Steel. New York, NY: Norton.

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World Bank (2021a). Agricultural Land (% of Land Area) . Available online at: https://data.worldbank.org/indicator/AG.LND.AGRIS.ZS (accessed March 31, 2021).

World Bank (2021b). Arable Land (% of Land Area) . Available online at: https://data.worldbank.org/indicator/AG.LND.ARBL.ZS (accessed March, 31 2021).

Keywords: livestock management, grazing impacts, health and meat, livestock and social welfare, integrated agriculture

Citation: Kleppel GS, Provenza FD and Villalba JJ (2021) Editorial: Livestock Production and the Functioning of Agricultural Ecosystems. Front. Sustain. Food Syst. 5:690016. doi: 10.3389/fsufs.2021.690016

Received: 01 April 2021; Accepted: 14 May 2021; Published: 21 June 2021.

Edited and reviewed by: Ivette Perfecto , University of Michigan, United States

Copyright © 2021 Kleppel, Provenza and Villalba. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Gary S. Kleppel, gkleppel@albany.edu

This article is part of the Research Topic

Livestock Production and the Functioning of Agricultural Ecosystems: Volume I

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How Does Livestock Farming Affect Climate Change?

Animal farming is a major source of greenhouse gas emissions, including methane and nitrous oxide.

Explainer • Climate • Industry

Words by Grace Hussain

The connection between livestock farming and climate change has never been more clear. Raising animals for food uses extraordinary amounts of water, causes deforestation and contributes heavily to greenhouse gas emissions, making the practice of farming animals severely damaging to the climate and overall planetary health. Research suggests that a number of shifts, including dietary change , can help bring down food-related climate emissions. 

How Does Farming Livestock Affect Climate Change?

Raising animals on farms for food production takes a tremendous toll on the health of the environment. Animal agriculture is a contributor to greenhouse gas emissions, including nitrous oxide and methane, water pollution and the destruction of forests and other wild areas that help to regulate the planet’s atmosphere. 

How Does Livestock Farming Contribute to Greenhouse Gas Emissions?

The two main greenhouse gasses produced by the practice of rearing farmed animals are methane and nitrous oxide. Globally, raising animals for food contributes at least 16.5 percent of greenhouse gas pollution.  

Nitrous Oxide

Nitrous oxide is almost 300 times more potent than carbon dioxide when measured on a scale of 100-year Global Warming Potential. A number of farming practices add to nitrous oxide pollution, including soil management practices such as the application of synthetic and organic fertilizers to grow food both for people and animals, handling manure from raising animals for food and burning crop residues. According to EPA figures, these practices account for 74 percent of all nitrous oxide emissions from the United States. 

Accounting for about 11 percent of all U.S. anthropogenic greenhouse gas emissions, methane has an impact 25 times greater than carbon dioxide. The agriculture sector is the largest source of methane emissions in the United States, by EPA estimates. 

Livestock Methane Emissions

Ruminant animals commonly raised for food, including cattle, goats and sheep, emit methane as they digest their food through a process known as enteric fermentation . During this process, microbes in the animals’ digestive tracts decompose and ferment plant parts such as cellulose, starches, sugars and fiber. This process is incredibly effective — ruminants like cows can eat plants and crop waste that humans can’t thanks to their largest stomach chamber called the “ rumen ” — but a byproduct of this process is the toxic pollutant methane, released into the atmosphere primarily through animal burps. 

Methane from livestock manure is another source of emissions, especially significant from concentrated animal feeding operations, or CAFOs, of hogs and dairy cattle that store manure as a liquid. 

How Does Deforestation Affect Climate Change?

Forests and other wild areas of land like savannas play an important role in storing carbon that would otherwise be released into the atmosphere. Unfortunately, forests and other natural ecosystems across the globe are being destroyed to make way for urban expansion, logging, mining and agriculture. 

The largest forest on earth is the Amazon rainforest, which covers 2.72 million square miles and stretches into nine different countries. Considered one of Earth’s most important terrestrial carbon reserves , the Amazon stores an estimated 123 billion tons of carbon. 

In addition to the role these ecosystems play storing carbon, forests also stabilize soil with their roots, preventing erosion. When forests are destroyed the soil itself is also able to hold less water, increasing the likelihood of flooding to nearby communities. Deforestation in some areas can also lead to an increased likelihood of drought as the water cycle is disrupted. 

The largest driver of deforestation in the Brazilian Amazon rainforest is animal agriculture, which has been tied to 75 percent of coverage loss. Loggers and farmers in the Amazon cut down trees to create ranches where cattle and other farmed animals can live and graze, and also to create fields for growing corn and soy to feed farmed animals.

When forests are destroyed, whether by fire or converted for growing animal feed, the carbon dioxide once stored is released into the atmosphere. Perhaps even worse, these actions also deprive the land of its ability to store carbon, described by researchers as a lost ‘ opportunity cost’ for climate action that can only be recovered if the land is reforested or rewilded.  

How Does Eating Meat Affect Climate Change?

Greenhouse gasses.

Food-related greenhouse gas emissions come from a variety of sources throughout the animal farming supply chain. Sources include burps and manure from the animals themselves, the storage of their manure, the use of fertilizer on the fields used to raise them, fuel for transport, the land used to feed and raise them and the heating and machinery required for animal agriculture production. 

Water Usage and Pollution

Feeding and raising animals as livestock uses far more water than growing crops like soy or lentils. Beef production requires 15,415 liters per kilogram of meat, 112 liters per gram of protein and 153 liters per gram of fat. One third of all the water used by the animal agriculture sector goes toward the production of beef. Another 19 percent goes to dairy cattle for the production of milk and other dairy products. 

Livestock farming also pollutes waterways, disproportionately impacting Black and Indigenous communities, as well as other communities of color. This pollution comes mostly from manure pits or lagoons created to hold the waste of the thousands of animals housed on factory farms. When pits leak or overflow, the nitrogen and other contaminants in the manure pollute local water sources, causing or exacerbating numerous health problems in the surrounding communities. To avoid overflow, farmers often apply too much manure to fields, which also leads to pollution runoff.

How Does Climate Change Affect Livestock?

The production of meat and other animal products is a large contributor to climate change, which in turn makes life worse for the millions of animals living on factory farms. 

Heat Stress

A central feature of industrialized agriculture is its efficiency, achieved by packing thousands of animals together into a relatively small area to feed them for slaughter. The tight quarters in which these animals are living, coupled together with rising temperatures , leads to metabolic disruptions, damage to the body’s cells and immune suppression, which in turn make disease, infection and death more likely. 

Why Are Some People Saying Beef Production Is Only a Small Contributor to Emissions?

Some proponents defend beef by pointing to the livestock industry’s increasing capacity for producing more meat from each cow slaughtered. Since the 1970s, the number of cattle needed to meet the demand for beef in the United States has fallen by about 50 million. 

The industry has made this shift thanks to intensive breeding resulting in cows that grow faster and larger than their parents and grandparents. The 90 million cattle that are being raised to meet the demand for beef today, for example, are supplying more meat per animal than 140 million cattle were in the 1970s. 

Fewer cattle does mean lower greenhouse gas emissions but industry efficiency alone is not enough to reach the climate targets set out in the Paris Climate Agreement to limit global warming. Climate research points to deploying a number of simultaneous strategies to bring down food-related emissions, including dietary change in countries that currently consume the most beef. In the U.S., for example, Americans eat four times more than the global average.

Another common argument given by those in the camp playing down beef emissions is that cattle being raised for beef directly contribute only 3 percent of U.S. greenhouse gas emissions. That percentage leaves out climate impacts from land use, like deforestation for grazing and growing feed. 

Will Eating Less Meat Help Reduce Climate Change?

Eating less meat is one of the most impactful ways to reduce our personal or household contribution to climate change. In fact, plant-based foods have a carbon footprint 10 to 50 times smaller than animal-derived products on average. Choosing to eat vegetarian also decreases water consumption by between one-third and one-half compared with a diet that contains meat. Wasting less food is another powerful form of household climate action.

Future Action : Food System Transition

Industrial animal farming is detrimental to ecosystems and communities, as well as to the health of the planet on which we all depend. 

One powerful yet challenging form of climate action is food system change. To begin the work of shifting food systems away from its current central focus on animal proteins, several advocacy groups are working with farmers to transition out of the livestock industry. One example is Transfarmation , an organization that works with poultry and hog producers to grow crops like mushrooms and hemp rather than raise animals for food. These efforts are just one small part of the much-needed collective transition to a more plant-rich food system.

Independent Journalism Needs You

Grace covers farming and agricultural policy. Her reporting has been published in Truthdig and the Good Men Project. She holds her MS in Animals and Public Policy from Tufts University.

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7 Lessons Learned from Showing Livestock

Getting cattle show ring-ready provides a wealth of opportunities for instilling good values in our youth.

One show halter and one show stick guide a 1,000-pound animal. Those seemingly simple pieces of equipment have harnessed a lifetime of lessons for Liz, Maddie, and Grace Tusha.

"I don't think kids really understand what they are learning as they show their animals," says Tim Tusha of Garner, Iowa. "Now that our two oldest daughters, Liz, 20, and Maddie, 18, are in college, they are realizing they gained a lot of skills while showing cattle. I knew it as a parent. It is why our family started this project."

Solid foundation is central Raising children to be decent people means devoting a great deal of time guiding them down the right path. Showing cattle has helped Tim and wife Ellen build a family-focused foundation centered around instilling good values.

"Kids need structure. We wanted our three girls to have a reason to get up in the morning and to have a purpose," says Tim, a fifth-generation farmer.

"For us, showing cattle wouldn't be successful if it wasn't a family project. I'm not saying it's always been great times in the barn because it's a lot of hard work," he says.

"It's a good family bonding time, because everyone has a role to play. It's a team effort," says youngest daughter Grace.

At 17, the high school senior has spent more than half of her life showing cattle. "I started when I was 7," she recalls. "I really fell in love with it in middle school."

As Grace enters her final year of showing cattle, she, along with her parents, reflect on their journey and the valuable skills acquired along the way. Here are seven lessons they learned.

Lesson #1 Be responsible . "I've definitely learned responsibility by waking up every morning at 6 a.m. I knew if I didn't do what I needed to do in the barn, it was going to take me two steps backward," says Grace.

Lesson #2 Manage time. Grace juggles her time in the barn not only with schoolwork but also with sports. "Before I even get to the first show, I have spent more than 100 hours caring and working with my show cattle," she notes. "I've learned to set time to do homework in order to get everything done and to meet deadlines."

At the shows, the Tushas wash animals early to allow enough time for the cattle to eat and lie down before they are fit and shown.

Lesson #3 Be confident. "When I was in the show ring in the past, I wasn't as confident as I am today," says Grace. "Over the years, my confidence has definitely grown."

For example, she's no longer afraid to use the show stick to set an animal up in its stance while the judge is looking. "It's important for the judge to see the animal's best side," she notes. "I've grown more comfortable using the show stick to help me better place feet and to calm an animal down."

Lesson #4 Deal with the different personalities. "We have been fortunate to have very tame steers the past few years," she says. "However, Big Boy does get riled up easily."

With 11 animals under her belt, getting to know each one and building trust are key.

"Brushing Big Boy as soon as we get him home and talking to him has helped," explains Grace.

Lesson #5 Be patient. "The first four or five months you have an animal is tough, because you have to break him in," she notes. "Stay with it, because he does eventually learn."

Lesson #6 Develop a strong work ethic. As the winner of the 2014 Iowa State Fair Reserve Grand Champion in her division, Grace knows it takes a strong work ethic to produce a quality animal.

"It's hard to describe the feeling of being in the show ring," she says. "It all comes down to that moment – the early mornings, the twice-a-day washings, the countless hours of caring for my animal. To me, it's all definitely worth it."

That pride in a job well done will resonate with her throughout her entire life. "Winning made me realize that all of my hard work paid off," notes Grace.

Lesson #7 Challenge yourself and others. "I can be the hardest guy in the world, but this process has helped our girls make great decisions throughout their young lives, and I trust they will continue to make good choices," says Tim. "I'm so proud of the women they've become. I've watched them develop into three young ladies who are genuinely good people."

• Watch video of the Tusha family's journey to the show ring.

Paragraph on Farming 100, 150, 200, 300 and 400 + Words

Paragraph on Farming 100 Words

Farming is an important industry that provides us with food and other products. It is a complex process that involves many different people and skills. We hope this article has given you a better understanding of what farming is and how it works. Agriculture is the process of producing food, feed, fiber, and other desired products by the cultivation of certain plants and the raising of livestock. Agriculture has been a vital part of human civilization for over 10,000 years. It is responsible for the production of most of the world’s food supply. Today, agriculture employs about 1.3 billion people around the world, making it one of the largest economic sectors.

Paragraph on Farming 150 Words

Farming is an essential part of our society, providing us with the food we need to survive. It is also a way of life that many people enjoy and find fulfilling. If you are thinking about becoming a farmer, we hope this article has given you some useful information to help you get started. There are many different types of farming, so be sure to do your research and choose the one that best suits your needs and interests. With hard work and dedication, you can be a successful farmer! Agriculture is the process of producing food, feed, fiber and other desired products by the cultivation of certain plants and the raising of domesticated animals. Agriculture was the key development in the rise of sedentary human civilization, whereby farming of domesticated species created food surpluses that supported the development of more centralized societies. The study of agriculture is known as agricultural science.

Paragraph on Farming 200 Words

Agriculture is the science, art, and practice of cultivating plants and animals for food, fiber, and other products used to sustain life. Agriculture was the key development in the rise of sedentary human civilization, whereby farming of domesticated species created food surpluses that enabled people to live in cities. The history of agriculture began thousands of years ago. After gathering wild grains beginning at least 105,000 years ago, nascent farmers began to plant them around 11,500 years ago. Pigs, sheep, and cattle were domesticated over 10,000 years ago. Plants were independently cultivated in at least 11 regions of the world. Industrial agriculture based on large-scale monoculture in the twentieth century came to dominate agricultural output, though about 2 billion people still depended on subsistence agriculture into the twenty-first century. Farming is an essential part of our society. It provides us with the food we need to survive and the materials we need to build our homes and clothes. Farmers work hard every day to ensure that we have the resources we need to live our lives. Without them, we would be lost. We are grateful for all they do and hope that you will join us in supporting them by buying local, organic products whenever possible.

Paragraph on Farming 300 Words

Agriculture is the science, art, and practice of cultivating plants and livestock. Agriculture was the key development in the rise of sedentary human civilization, whereby farming of domesticated species created food surpluses that enabled people to live in cities. The history of agriculture began thousands of years ago. After the industrial revolution, agriculture was modernized with advances in technology, including the tractor and combine harvester. Agriculture is now a global industry, with over one billion people employed in farms and related industries. With the right knowledge and tools, agriculture can be a very rewarding experience. Not only will you get to enjoy the fruits of your labor, but you’ll also be contributing to the food supply for your community. We hope that this article has helped you learn more about agriculture and what it takes to be successful in this field. With these tips in mind, we’re sure that you’ll be able to produce bountiful crops in no time. The agriculture industry is vital to the economy and provides a wide range of career opportunities. If you’re interested in pursuing a career in agriculture, there are a few things you should keep in mind. First, make sure you have a strong interest in the subject matter. Second, research the different types of careers available in agriculture so that you can find one that matches your skills and interests. And finally, don’t be afraid to get your hands dirty – literally! Agriculture is a hands-on industry, so getting experience working with crops or animals will give you an edge when applying for jobs. The agricultural industry is vital to the global economy, providing food and other products for people all over the world. Though it faces challenges, such as climate change and pests, agriculture has a long history of adaptability and resilience. With the right policies in place, the agricultural industry can continue to thrive and provide for people around the world for many years to come.

Paragraph on Farming 400 + Words

Farming is an agricultural process that involves activities like planting, harvesting, and raising livestock. It is one of the oldest human occupations and has been practiced for thousands of years. Today, farming is still an important part of many cultures around the world and plays a significant role in the economy.

What is Farming?

Farming can be defined as the process of growing crops or raising animals for food or other products. Farming is an ancient practice that has been essential to human survival for millennia. Today, farming continues to play a vital role in providing the world with food and other resources. There are many different types of farming, each with its own set of challenges and rewards. Some farmers grow crops such as wheat, corn, and rice; others raise livestock such as cattle, pigs, and chickens. Still others focus on specialty crops or animals, such as fruits and vegetables, fish, or bees. No matter what type of farming you pursue, there are a few core principles that will help you succeed. These include choosing the right location, using the best seeds and technology, and having a strong marketing plan. With these basics in place, you can start reaping the rewards of a thriving farm business.

The Different Types of Farming

Farming is a vital part of the agricultural industry, and there are many different types of farming that take place around the world. Some of the most common types of farming include:

• Arable Farming: This type of farming takes place on land that is suitable for growing crops. Arable farmers typically grow crops such as wheat, barley, and oats.
• Livestock Farming: This type of farming involves raising animals for meat, milk, or other products. Common livestock animals include cows, pigs, sheep, and chickens.
• Fruit and Vegetable Farming: This type of farming involves growing fruits and vegetables for consumption. Farmers typically grow a variety of fruits and vegetables, depending on the region in which they live.
• Dairy Farming: This type of farming involves raising cows for milk production. Dairy farmers typically have large herds of cows and produce milk on a large scale.
• Poultry Farming: This type of farming involves raising chickens for meat or egg production. Poultry farmers typically have large numbers of chickens and use special equipment to keep them healthy and well-fed.

The History of Farming

Farming is an ancient practice that dates back thousands of years. It is thought to have originated in the Fertile Crescent, which is a region in the Middle East that includes modern-day Iraq, Syria, and Lebanon. From there, it spread to other parts of the world, including Europe, Asia, and Africa. Farming allowed early humans to settle down and build civilizations. It also allowed them to domesticated plants and animals, which led to the development of agriculture. Agriculture allowed humans to produce food more efficiently, which led to population growth and the rise of cities and civilizations. Today, farming is still an important part of the world economy. It employs millions of people and produces food for billions of people. In developed countries, farming is often mechanized and uses modern technology to increase productivity. In developing countries, farming is often more traditional and relies on manual labor.

The Pros and Cons of Farming

Farming is a tough job, there’s no doubt about it. But it can also be a very rewarding profession, both financially and personally. If you’re thinking about becoming a farmer, it’s important to weigh the pros and cons before making your decision. On the plus side, farming offers a great way to live a healthy, active lifestyle. If you love being outdoors and working with your hands, then farming may be the perfect career for you. Farming can also be a very peaceful and relaxing way to spend your days. And of course, there’s the satisfaction that comes from producing your own food and knowing where it came from. On the downside, farming is hard work. Long hours are often required, and the work can be physically demanding. There’s also a lot of responsibility involved in running a farm, as you’ll be responsible for the health and well-being of your animals. And finally, there’s always the risk of crop failure or other problems beyond your control that can lead to financial losses. So, those are some of the pros and cons of farming. Ultimately, only you can decide if this is the right profession for you. But if you’re up for the challenge and are prepared

How to Start a Farm

Do you have a passion for farming? If you’re interested in starting your own farm, there are a few things you need to know. First, you need to decide what type of farm you want to start. Are you interested in growing vegetables, fruits, grains, or raising livestock? Once you’ve decided on the type of farm you want to start, research the best location for your farm. You’ll also need to obtain the necessary permits and licenses from your state or local government. Once you’ve taken care of the business side of things, it’s time to start planning your farm. You’ll need to develop a crop rotation plan, build infrastructure like fences and greenhouses, and purchase the necessary equipment. Depending on the type of farm you’re starting, you might need to purchase seeds or animals. And don’t forget about marketing! You’ll need to let people know about your farm so they can come and buy your products. Starting a farm can be a lot of work, but it can also be very rewarding. By following these steps, you can turn your dream of owning a farm into a reality.

In conclusion, farming is an essential part of life and the economy. Farmers provide us with food and other products we need to survive. They also help to maintain the environment by planting trees and taking care of the land. Farming is a demanding job, but it can be very rewarding.

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Division of Extension

Composting Mortalities

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• Introduction

On-farm composting is an approved method to dispose of livestock mortalities. Advantages include increased biosecurity, timely disposal of mortalities, low risk of environmental contamination, low cost, and relatively simple to do. Composting can be used for occasional mortality, emergency livestock mass casualties, and disease outbreaks.

Table of Contents

What is composting, carbon-to-nitrogen ratio, temperature, when is the composting process complete, pile construction, site selection, additional resources, peer reviewers.

Composting is an aerobic (with oxygen) recycling process where microorganisms break down organic material in a controlled environment to produce a stable product called humus. There are many ways to compost livestock mortalities. However, there are a few basics that are universal to all systems. Well-managed composting provides aerobic (oxygen-loving) microorganisms with the proper environment to grow and rapidly break down the mortality.

One of the key factors in proper composting is the carbon-to-nitrogen ratio. The optimum carbon-to-nitrogen ratio should be 25:1, with composting occurring as low as 10:1 and as high as 50:1. If carbon levels are too low, high ammonia odors will escape. The primary nitrogen source is the mortality (typically about 5:1 C:N). The carbon is supplied by the bulking material/co-compost/carbon source material used to make the pile. Common materials used as a carbon source are sawdust, woodchips, finely chopped corn stalks, chopped straw, bedded pack manure, and separated manure solids. Corn silage and chopped hay could be used if other materials cannot be found. There are many considerations when choosing a carbon source, the first being availability. Beyond availability, another thing to consider is the amount of carbon that can be provided for absorbency to handle any liquids released from the carcass during the process, a structure that can allow for proper oxygen exchange, insulating factor, and particle size. Particle size should be in the range of .25-1 inch. Previously used composted material should be used as 50% or less of the carbon source.

Table 1. Carbon to Nitrogen (C:N) and percent moisture values of common carbon sources

The ideal moisture range for composting is 40-60%. A rule of thumb for proper moisture content would be to squeeze a handful of material; the material should hold together but not be so wet that water can be squeezed from the solids, like a damp sponge. If the compost is too dry, bacteria growth will be slowed and decomposition will take longer. If there is too much moisture, there will not be enough air exchange, and the bacterial type will change from aerobic to anaerobic. Anaerobic bacteria produce sulfur and ammonia-containing gasses that produce strong offensive odors.

The final components of proper composting are temperature, oxygen, and pH. As stated before, proper oxygen levels are needed to keep aerobic bacteria growing. The minimum oxygen level needed is 5%. Bacteria and fungi needed for this process need a neutral environment at around a pH of 7, ranging from 5-10. Temperature is key in bacterial growth, and temperatures rise as decomposition occurs. Temperatures between 110 -150 °F indicate that microbes are present and optimum decomposition occurs. The pile must be heated to 131°F for three consecutive days to destroy pathogens. When the pile temperatures drop below 110°F, this can indicate it is time to turn the pile to add oxygen and stimulate bacterial growth. Temperatures at or above 150 °F will decrease microbial activity. The length of time needed to compost mortalities depends on the mortality size and time of year (ambient air temperatures). In most cases, the first heating cycle will be done in three to six months, and the pile is turned for a second heating and possibly a third. With ideal conditions, it typically takes about three months for most of the soft tissue to be degraded and may take up to a year to completely decompose. Temperature monitoring will indicate when to turn.

Winter months can present challenges with freezing temperatures. New compost piles, constructed with unfrozen materials and fresh mortalities, will start to compost provided enough insulation material is used to retain the heat produced within the pile. Unfrozen recent mortalities can also be added to an existing actively composting pile provided enough insulating material is used. Mortalities, which freeze up, when used with cold materials to construct a compost pile will struggle to start the composting process.

Caution should be used to immediately place the fresh mortalities into a compost pile to avoid the toll of freezing temperatures. The cap material shown in Figure 1 can serve the purpose of insulation and maintaining heat within the pile. The thickness of the cap materials may need to be increased during winter months to increase its insulation capacity.

Figure 1. An example of a completed pile for composting multiple mortalities

When temperatures remain within a few degrees of ambient air temperature after turning, the process is complete. The mortality should be reduced to a few large brittle bones and in the case of sheep, some wool fibers, which can be buried, and the compost utilized as a soil amendment.

The number and frequency of mortalities and available facilities and materials will determine the type of composting setup. The number and size of the animals to be composted will determine the size of the pile for occasional composting needs. In general piles should be at least 3 feet deep if composting small animals (poultry, baby lambs) and not more than 8 feet deep for large animals to insure good composting. It is more practical to make longer piles than deeper piles for multiple larger animals. Piles can be placed in permanent locations, such as a 3-sided building with concrete walls with multiple bins/stages, outside with bales as walls, a single static pile, or a windrow. However, loading all of these systems is very similar. Structures with walls will reduce the amount of carbon source material needed.

• The basics to loading are to put down a layer of carbon source material about 24 inches deep and in an area large enough to have 24 inches of space between the mortality and the edge of the pile.
• Next is to lay the mortality on its side. At this point, the abdomen may be lanced to reduce bloating in the composting process and to allow microbes access to nitrogen.
• Next, cover the mortality with another 24-inchlayer of carbon source material. (Figure 1)
• If multiple mortalities are added at once, laythem in the pile so they are lying back-to-back. (Figure 2)
• If they are layered, place a 12-inch layer of carbon source material in between the mortality layers.
• If the pile is outdoors, crown the pile to facilitate the runoff of rain.
• A fence can prevent scavengers from disrupting the pile when working with outside compost systems.

Windrow systems are a good option if there are frequent mortalities. In a windrow system, mortalities are added end to end as they occur to create a long “windrow” of compost with turning occurring in sections.

Figure 2. Step by Step process for constructing a composting pile.

A multiple bin set-up, as shown in Figure 3., can help organize the composting process and provide protection from the wind. The example in Figure 3 shows low-cost dividers made of large round or square bales of corn stalks or similar material. Large livestock operations may consider constructing an appropriately sized building similar in floor plan to a commodity shed with a roof, permanent walls, and bin dividers to improve ease of management of composting mortalities. The plan’s design is intended for each phase to have its own bin. The initial burial and heating cycle would occur in the first bin. As the pile is turned, it is moved to the next bin. The first bin would then be available for starting new mortalities. Additional bins can be added to store bulking material before its use for composting and for completed compost if it cannot promptly be land applied. The advantages of a multiple-bin layout are additional protection from the wind and a systematic plan for large operations with greater composting needs. Farms with occasional mortalities may also construct a bin or shelter around a single pile if additional protection from the wind is needed. Costs need to be compared to benefits to determine the most efficient setup and design for a farm.

Figure 3. Example of a low-cost multiple bin setup using large bales

The compost facility/pile site should be high and dry. Environmental considerations must be made. The site should be located away from frequently used farm traffic routes yet be easily accessed by needed equipment, be close to carbon source storage, and be easy to get water to if the carbon source material becomes too dry. Public perception and neighbor relations should also be considered.

• Avoid wet areas; the location must be high and dry.
• Avoid slopes and areas with surface water flowduring rain events and snowmelt.
• Locate at least 3 feet above a high water table.
• Locate at least 300 feet from streams, ponds, or lakes in the same drainage area.
• Provide runoff collection/storage for the site incase of extreme water events.
• Ensure available in access in all types of weather conditions.
• Maintain suitable access to carbon source material storage.
• Locate a safe distance from buried and overhead utilities.
• Consider other farm traffic flows.
• Consider prevailing winds.
• Maintain biosecurity precautions.
• Consider aesthetics and landscaping – Screen view from neighbors or passing motorists.

Composting Dead Livestock: New solution to an old Problem. Iowa State University, 1999. https://datcp.wi.gov/Documents/iowacompostguide.pdf

Composting Animal Mortalities, D.E. Morse, et.al. July 2006, https://datcp.wi.gov/Documents/minnesotacompostguide.pdf

Natural Rendering: Composting Livestock Mortality and Butcher Waste, Cornell Waste Management Institute, Cornell Cooperative Extension, 2002 https://datcp.wi.gov/Documents/cornellcompostguide.pdf

Corus, E. (2020). Composting livestock and poultry carcasses. University of Minnesota Extension. Composting livestock and poultry carcasses | UMN Extension

Rozeboom, D. (2015). Carcass composting – A guide to mortality management on Michigan cattle farms. https://www.canr.msu.edu/resources/carcass_composting_a_guide_to_mortality_management_on_michigan_cattle_farms

Kapil Arora, Ph.D. Field Agricultural Engineer Iowa State University

Stanley (Jay) Solomon Extension Educator Natural Resources, Environment & Energy University of Illinois

William Halfman

Carolyn Ihde

Carolyn Ihde is a Small Ruminant Outreach Specialist for the University of Wisconsin-Madison, Division of Extension, Agriculture Institute.

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Precision Livestock Farming Research: A Global Scientometric Review

1 College of Economics and Management, Northeast Agricultural University, Harbin 150030, China; nc.ude.uaen@920108022s (W.T.); nc.ude.uaen@200108012b (L.C.); nc.ude.uaen@37800280a (X.D.)

2 Development Research Center of Modern Agriculture, Northeast Agricultural University, Harbin 150030, China

Wenjie Tang

Xiaoshang deng, associated data.

All data generated or analyzed during this study are included in this published article.

Simple Summary

In recent years, there has been a significant increase in research on precision livestock farming. The aim of this paper is to provide a comprehensive review of the current state of research on precision livestock farming. Using the visualization tool CiteSpace, this study creates knowledge maps to display data on research countries, institutions, author collaborations, and keyword networks. Through these analyses, this study objectively reveals the dynamics, development process, and evolutionary trends of precision livestock farming research while identifying the frontiers and hotspots in the field.

Precision livestock farming (PLF) utilises information technology to continuously monitor and manage livestock in real-time, which can improve individual animal health, welfare, productivity and the environmental impact of animal husbandry, contributing to the economic, social and environmental sustainability of livestock farming. PLF has emerged as a pivotal area of multidisciplinary interest. In order to clarify the knowledge evolution and hotspot replacement of PLF research, based on the relevant data from the Web of Science database from 1973 to 2023, this study analyzed the main characteristics, research cores and hot topics of PLF research via CiteSpace. The results point to a significant increase in studies on PLF, with countries having advanced livestock farming systems in Europe and America publishing frequently and collaborating closely across borders. Universities in various countries have been leading the research, with Daniel Berckmans serving as the academic leader. Research primarily focuses on animal science, veterinary science, computer science, agricultural engineering, and environmental science. Current research hotspots center around precision dairy and cattle technology, intelligent systems, and animal behavior, with deep learning, accelerometer, automatic milking systems, lameness, estrus detection, and electronic identification being the main research directions, and deep learning and machine learning represent the forefront of current research. Research hot topics mainly include social science in PLF, the environmental impact of PLF, information technology in PLF, and animal welfare in PLF. Future research in PLF should prioritize inter-institutional and inter-scholar communication and cooperation, integration of multidisciplinary and multimethod research approaches, and utilization of deep learning and machine learning. Furthermore, social science issues should be given due attention in PLF, and the integration of intelligent technologies in animal management should be strengthened, with a focus on animal welfare and the environmental impact of animal husbandry, to promote its sustainable development.

1. Introduction

The livestock industry serves as the foundation of the agricultural economy and constitutes the primary source of animal-derived product consumption. The growing demand for these products has led to a significant expansion in the scale of livestock breeding. However, traditional management practices that rely on farmers’ observations, judgment, and experience alone may not fulfill the requirements of modern large-scale livestock farming. Therefore, precision farming, with the support of information technology, has become an unavoidable trend in advancing modern livestock farming.

Since the 1970s, PLF technology has evolved considerably, beginning with individual electronic milk meters for cows and expanding to include behavior-based estrus detection, rumination activity monitoring and other related studies. Scholars have conducted extensive research on intelligent perception and analysis of individual animal information and behavior [ 1 ]. In 2003, the European Conference on Precision Livestock Farming (EC-PLF) was launched and held every two years to highlight the latest high-tech research advances in livestock farming. In 2012, The European Union launched the European Precision Livestock Farming Project (EU-PLF), with a mission to translate PLF technology into industrial practice. Participants in the project included influential universities in the field of PLF, such as Wageningen University and Research, Katholieke Universiteit Leuven and the University of Milan, as well as companies dedicated to developing PLF technology, including Facom, Soundtalks NV and GEA Farm Technologies. The project quantified key PLF indicators and guided the use of PLF systems such as video monitoring, sound monitoring, and environmental monitoring on farms, playing a vital role in promoting the research and application of PLF technology [ 2 ].

Due to the urgent requirement for integrating information technology, data science, artificial intelligence, and innovative animal husbandry development, China’s animal husbandry industry is rapidly advancing into a new era of integrated fusion innovation [ 3 ]. In 2012, the State Council issued the Opinions on Accelerating Agricultural Science and Technology Innovation to Continuously Enhance the Ability to Guarantee the Supply of Agricultural Products, in which it was proposed that a major breakthrough should be made in precision agriculture technology; in 2020, the Opinions on Promoting the High-Quality Development of the Livestock Farming Industry issued by the General Office of the State Council clearly proposed that the application of technologies such as big data, artificial intelligence, cloud computing, Internet of Things and mobile Internet in the livestock industry should be strengthened, and the intelligence level of environmental control of enclosures, precise feeding and animal disease monitoring should be improved. Driven by the policy, the development and application of precision technology in the livestock industry has received widespread attention. In recent years, the research on PLF in China has made great progress in animal respiration frequency detection, individual identification and behavioral analysis [ 4 , 5 , 6 ]. A lot of improvements and innovations have been made in the automated detection of animal body size and weight [ 7 , 8 ], and equipment and algorithms for environmental monitoring and prediction in livestock housing have been refined [ 9 , 10 ].

PLF is a multidisciplinary concept integrating information technology, data science, and innovative animal husbandry. Five primary research perspectives drive existing PLF research: animal science, veterinary medicine, computer science, agricultural engineering, and environmental science. The animal science perspective aims to optimize animal feeding and management by leveraging sensors and intelligent monitoring systems to real-time monitor and analyze animal behavior, physiological states, and environmental factors [ 11 , 12 ]. The veterinary medicine perspective focuses on intelligent prevention and diagnosis of animal diseases [ 13 , 14 ]. The computer science perspective focuses on the application of sensor networks, artificial intelligence, computer vision and other technologies in animal data monitoring, mining and collection [ 15 , 16 ]. The agricultural engineering perspective focuses on the mechanization of livestock farming and its automation technology to help farmers better manage their farms [ 17 , 18 ]. The environmental science perspective evaluates the environmental impact of using PLF technology as a mitigation strategy for livestock production [ 19 , 20 ]. The existing research on PLF has primarily focused on its technical aspects from a natural science perspective. However, there has been limited discussion on the evolution of basic knowledge and the emergence of new research focal points. In order to address this gap, this study examines PLF-related literature in the Web of Science database from 1973 to 2023. By utilizing the visualization tool CiteSpace, this study creates knowledge graphs to display data such as the research countries, institutions, author collaborations, and keyword networks. Through this analysis, this study objectively reveals the dynamics, developmental processes, and evolution trends of PLF research, while identifying frontiers and hotspots within the field. Ultimately, the aim of this study is to provide a comprehensive overview of PLF research status and scientific references for future research.

2. Concept of PLF

PLF integrates precision agriculture concepts to help farmers manage large-scale livestock farming through the use of sensors and actuators, representing the application of PA in livestock systems [ 21 ]. Daniel Berckmans first coined the term PLF and argued that continuous, direct, real-time monitoring or observation of animal status through PLF would enable farmers to rapidly identify and control problems related to animal health and welfare [ 22 ]. As scholars have explored PLF, a more unified perception of its concept has gradually emerged. PLF is a method for fine-grained management of modern livestock farming using process engineering principles and techniques [ 2 , 23 , 24 ], animal science [ 25 ] and information technology [ 2 , 25 , 26 ], and is a set of technologies used to monitor and control animal health, welfare, production, reproduction and environmental impacts in real-time [ 27 , 28 , 29 ], aiming to provide stakeholders with information as a basis for management decisions [ 24 , 30 ] to improve the management of large-scale livestock and poultry [ 21 , 31 ] to achieve economically, socially and environmentally sustainable farming [ 19 , 21 , 23 ]. Combining the above concepts, this study concludes that PLF is a series of fine management methods supported by information technology, based on real-time data collection and analysis, with the intelligent sensing and analysis of individual animal information and behavior as the core, aiming to improve animal productivity and animal welfare.

The widespread use of digital technologies has given birth to smart livestock farming (SLF) and digital livestock farming (DLF), whose concepts have some overlap and crossover with PLF. In order to clarify the connotation of PLF, it is necessary to further explore the concepts of SLF and DLF. In much of the literature, SLF is often attributed to PLF [ 31 , 32 ], but recent research proposes that SLF should be considered more of a successor to PLF [ 33 ]. PLF focuses on the digital processing of specific information to support stakeholder decision-making, while SLF is a knowledge-based concept that leverages information and communication technology (ICT) to manage cyber-physical livestock farms [ 33 , 34 ]. DLF incorporates the concepts of precision and smart farming that use modern technological tools, advanced equipment and comprehensive data management; it provides important insights, modeling approaches and actionable analytics and automation techniques to provide efficient, accurate and intelligent solutions for livestock farming [ 33 ]. The focus of DLF development is no longer on mere accuracy, but on integrating precise data into digital systems, achieving a transcendence of PLF [ 33 ]. Both SLF and DLF can be seen as PLF as a natural development based on PLF, reflecting the increasing integration of digital technologies in animal husbandry. The promotion and application of these new models will further improve the productivity and quality of animal husbandry, safeguard animal welfare, and promote the sustainable development of animal husbandry.

3. Materials and Methods

3.1. data collection.

Literature data collection is essential for review papers because its quantity and quality directly determine the effectiveness of article visualization [ 35 ]. In order to acquire high-quality literature pertaining to PLF research, we initially narrowed our search to prestigious academic journals, such as SCI and SSCI. Furthermore, to ensure that the collected literature was academically novel, we exclusively considered “Article” and “Review” document types. Finally, while meeting the above criteria, we aimed to collect as much literature data as possible, and thus chose the Web of Science database as our source of literature data. The Web of Science database is a major citation index database that has 10 sub-data sets, covering different time spans, over 21,000 high-quality academic journals in various fields, and 1.7 billion citations [ 36 ]. To ensure the comprehensiveness of the literature search, multiple search terms were set. The search terms used were: TS = (“accelerometer” or “sensor” or “GPS” or “automate” or “machine learning” or “big data” or “robot” or “computer vision” or “deep learning”) AND TS = (“cattle” or “cow” or “sheep” or “poultry” or “broiler” or “hen” or “pig”); TS = (“ruminal boluses”) AND TS = (“cattle” or “cow” or “sheep”); TS = (“PLF”), the retrieval period was “all years (1950–2023)”, the citation index was limited to “SCI-EXPANDED” and “SSCI”, and the document type was “Article” and “Review”. The preliminary search yielded 14,762 papers. These obtained papers were then re-evaluated by reading the titles and abstracts, and after removing duplicates and irrelevant content, 3658 papers were finally obtained that were highly relevant to the PLF topic.

3.2. Research Methodology

Bibliometrics employs mathematical and statistical methods to analyze literature quantitatively. It is widely used in various fields, including literature statistics, assessing the impact of journals or research institutions, tracking academic hotspots, and predicting research trends [ 37 ]. As such, bibliometrics offers a more objective view of research progress in specific fields of knowledge.

CiteSpace, a bibliometric analysis tool developed using Java language by Professor Chaomei Chen from Drexel University, applies co-citation analysis theory, pathfinding network algorithm, and minimum spanning tree algorithm to quantitatively analyze specific literature data [ 38 ]. It generates a series of visual graphs to detect the frontier of discipline development and provide an analysis of the potential dynamic mechanism of discipline evolution [ 39 ]. In CiteSpace networks, nodes represent different elements such as countries, institutions, and authors, where the size of a node indicates its frequency of occurrence, and connecting lines between nodes demonstrate cooperative links [ 40 ]. To quantify the significance of each node in the network, a centrality indicator known as betweenness centrality is used. A node with centrality > 0.1 is highlighted with a purple ring to emphasize the key points in the network [ 41 ]. The formula for calculating the betweenness centrality of each node is provided as follows:

In Equation (1), ρ j k refers to the number of shortest paths between node j and node k , with ρ j k ( i ) representing the number of those paths passing through node i [ 42 ].

Price Law is commonly used to measure the distribution of core authors across disciplines and data analysis, which is generally applicable to most research fields with a long statistical time and a large author collection, and the method often appears in bibliometric-related articles [ 37 , 38 , 43 ]. The formula is as follows:

In Equation (2), T P n represents the threshold value of the number of papers published by core authors, and N m a x represents the number of papers published by the most productive authors. When the number of papers published by an author is T P n and above, he or she is identified as a core author in this research field. According to Price Law, a core group of authors is formed when the number of papers published by core authors reaches half of the total number of research papers in a field [ 43 ].

Bradford’s law is an empirical law describing the law of literary dispersion, which is applied to measure the degree of connection between disciplines and describe the distribution of the number of relevant papers in published journals [ 43 ]. Bradford’s law posits that when journals are arranged in decreasing order based on the number of papers published on a subject, they can be categorized into core, relevant, and irrelevant regions, with approximately equal numbers of papers published in each region. Moreover, the number of journals in the three regions has a 1: a: a 2 relationship (a ≈ 5) [ 37 ]. Journals in core areas are considered to represent the latest level of research in a specific field [ 43 ].

In this study, the scientific knowledge graphs and empirical laws in bibliometrics were selected, with the help of the visual tool CiteSpace (6.2. R2), the research on PLF was visually analyzed. The parameters were set as follows: (1) time slicing from 1973 to 2023 at 1 year per slice; (2) the selection uses a modified g-index in each slice: k = 25, which means that data were extracted on the top 25 results for each time slice; (3) the node type was set as country/institution/author/keyword. The remaining parameters were the default settings. By drawing relevant knowledge graphs and charts, the research status, hotspots and dynamic frontiers of PLF were combed and summarized.

4. Results and Discussion

4.1. analysis of main features, 4.1.1. annual scientific production.

The annual number of PLF publications is a critical indicator of the discipline’s developmental level and research achievements, and evaluating the academic influence of research papers can be aided by the calculation of the annual average citation rate per article [ 44 ]. Figure 1 displays number of publications and annual average citation rate per article associated with PLF research. The citation frequency of recent publications has been relatively low due to the time it takes for important articles to be recognized and become widely cited, resulting in a fluctuating downward trend in the annual average citation rate of PLF research papers. The paper “Segmentation and Tracking of Piglets in Images” published by N.J.B. McFarlane and C.P. Schofield in 1995 has been widely cited. The two scholars developed an algorithm for segmenting and tracking piglets, providing valuable experience for subsequent improvement of tracking feature extraction and related algorithms [ 45 ]. The upward trend in the number of PLF publications between 1973 and 2023 suggests a growing interest in this research field. This trend may be divided into three phases: the germination stage (1973–1996), the exploration stage (1997–2016), and the rapid development stage (2017–2023). In the germination stage, limited by the level of animal husbandry development and information technology understanding, research content was relatively scant. Automatic milking and estrus detection were common topics [ 46 , 47 ]. The exploration stage focused on animal health and welfare due to concerns over disease transmission from animals to humans [ 22 ]. Scholars also focused on animal behavior monitoring and analysis, in addition to research on machine learning systems for livestock classification, pattern recognition, optimization, and prospective prediction [ 48 ]. As big data and cloud computing technologies matured and production demands grew, managing animals manually became more challenging [ 29 ], leading to a rapid increase in PLF literature. From 2017 to 2023, there were 2379 articles, accounting for 65.04% of the total literature sample. Researchers showed increased interest in artificial intelligence applications in livestock farming, such as deep learning and machine learning, leading to a rapid stage of PLF development.

Number of publications and annual average citation rate per article as determined using Web of Science data.

4.1.2. Countries

The analysis of the countries involved in research can uncover their collaborative relationship and provide a new perspective for evaluating countries’ academic influence [ 49 ]. Literature data were imported into CiteSpace software, with the node type of the functional selection area set to COUNTRY for analyzing all countries engaged in PLF research. A threshold value of 70 was set to highlight the countries with 70 or more papers, resulting in the creation of the co-occurrence network of countries ( Figure 2 ).

Countries co-occurrence network.

The distribution of connections between countries shows a cluster-like pattern with relatively tight links. Figure 2 displays 92 nodes and 619 connections, and the density of the national cooperation network is 0.1479, which is an indication of strong international collaboration in this field. The top five countries in the number of publications are the USA, China, UK, Australia, and Germany, all of which are major livestock production nations. PLF research is primarily concentrated in developed countries. The USA, as the country with the highest degree of modernization in livestock development, leads PLF research, accounting for 19.08% of the total literature [ 50 ]. As the world’s largest livestock producer, China’s livestock industry is transitioning towards scale and modernization, demanding information technology and intelligent farming technology. China launched various research and development programs since 2016, such as key technology and equipment for intelligent sensing of livestock and poultry breeding, and relevant enterprises and research institutes have carried out multi-level research and development and practice to promote smart livestock farming and unmanned pastures. Scholars focus on PLF research, with publications accounting for 11.45% of the total literature. UK, Australia and Germany are leaders in well-developed facility animal husbandry. They emphasize promoting the industrialization of livestock farming, intelligent sensing technology, and education and training to cultivate highly qualified farmers and herdsmen [ 51 ]. These countries have extensive applications of PLF technology.

Quantifying the importance of nodes in a network can be accomplished by measuring node centrality [ 49 ]. Combining the centrality to analyze the influence of the research countries ( Table 1 ), five countries stand out as having key influence in the field of PLF: USA, Spain, UK, Netherlands, and France. The international influence of the USA, Spain, and UK is particularly evident with centrality measures above 0.2. Although China has a high number of publications, it does not have significant centrality advantages compared to other countries. In contrast, Netherlands, France, and Spain, while not as numerous as China, have relatively high centrality levels and concentrate more on the influence of international cooperation. Developed countries have an early start in PLF research, more experience in related technology development and application, and closer communication and collaboration among countries while developing countries need to further strengthen cross-country cooperation in PLF.

Top 20 countries according to the number of publications.

4.1.3. Institutions

Analyzing the structural characteristics of research institutions is essential to identify influential institutions in the field and understanding inter-institutional cooperation [ 35 , 52 ]. To analyze the institutions involved in PLF research, we designated the node type as INSTITUTION in the CiteSpace function selection area. We then set the threshold value to 30 to highlight institutions with 30 or more papers, resulting in an institution co-occurrence network ( Figure 3 ). The top 20 institutions for the period 2019–2023 can be found in Supplementary Material Table S1 .

Institution co-occurrence network.

The distribution of research institutions in PLF is shown in Figure 3 . Wageningen University and Research, Katholieke Universiteit Leuven, University of Guelph, China Agricultural University and University of Sydney have a high number of publications and are representative institutions for PLF research. Wageningen University and Research is recognized as a world leader in agricultural and environmental sciences, with top-ranked disciplines in agricultural science, plant and animal science, and environmental science. The Katholieke Universiteit Leuven is highly ranked for mechanical engineering and has made significant contributions to the development and utilization of livestock technologies. The University of Guelph ranks in the top 10 to 50 globally for agronomy, veterinary medicine and environmental science and spends over half its budget on academic research. The University of Sydney is one of Australia’s top universities and has a prominent position in the field of agricultural sciences, artificial intelligence, and veterinary medicine. As the leading agricultural institution in China, China Agricultural University has a primary objective of promoting national innovation in agricultural science and technology and advancing modern agriculture. Its agricultural engineering discipline is of national key first-class and world-class standard, with several key laboratories focusing on research into the integration of intelligent agriculture systems and acquisition of agricultural information technology, among others, which have made invaluable contributions to PLF research. National research institutions such as INRAE and government departments such as Agriculture and Agri-Food Canada are also important in promoting the modernization of livestock farming and play a significant role in PLF research. From the attributes of the research institutions ( Table 2 ), the top 20 research institutions are mainly concentrated in universities in various countries, indicating that academic institutions attach great importance to PLF research and have made great contributions, being the main force in this field. From the geographical viewpoint of the research institutions, the main research institutions are unevenly distributed globally, with a concentration in Europe and North America.

Top 20 institutions according to the number of publications.

In terms of research institutions’ collaboration, the density of the PLF cooperative network is 0.0047, indicating that the cooperative network is relatively sparse. While the centrality of Wageningen University and Research and the University of Guelph is significantly higher than that of other institutions and has an important connecting role in the cooperative network, the level of connection and collaboration between other universities and research institutes remains low. The relative independence of research led by each institution has led to a clear pattern of network segmentation. The deepening of connections between each representative institution is necessary to address this issue, as an academic community for PLF is yet to be formed.

4.1.4. Authors

Analyzing the structural characteristics of published authors helps to identify core authors in the research field, which further reflects the collaborative relationships among them [ 40 ]. Employing Price’s Law, we calculated that the number of papers published by core authors in PLF research exceeded six, resulting in 52 authors classified as core authors. The number of publications by these core authors totaled 713, which accounts for 19.49% of the total literature. A stable cooperative network should ideally reach 50%, indicating that a stable core author group has not yet been formed in this research field [ 53 ]. By setting the node type to AUTHOR in CiteSpace’s function selection area, we analyzed all authors involved in PLF research. We set the threshold to 10 to highlight the most prolific authors and obtained an author co-occurrence network ( Figure 4 ). Table 3 presents the top 10 most prolific authors.

Author co-occurrence network.

Top 10 prolific authors according to the number of publications.

The authors’ cooperative network consists of 1090 nodes and 1549 connections, with a density of 0.0026, indicating that while there are many scholars in PLF research, their cooperative relationship is not strong, resulting in “small concentration and large dispersion” characteristics. The concentration of authors is high, and most scholars have formed a relatively fixed cooperative group, which has initially formed a research group led by the core scholars Daniel Berckmans. However, there is still a need for stronger academic connections between different academic teams and authors, and the related research is scattered.

The authors’ research directions span a wide range of topics. Daniel Berckmans is a renowned PLF developer who focuses on creating real-time algorithms to monitor and improve the lives of individual humans and animals. Tomas Norton collaborates with Berckmans frequently, and his current research primarily involves the development of PLF technologies for animal health and welfare monitoring and management. Ilan Halachmi specializes in animal science, artificial intelligence, lameness, and computer vision. Claudia Bahr concentrates on utilizing automated animal monitoring techniques, including disease detection and lameness detection. Marcella Guarino’s research encompasses animal behavior, disease, production and welfare monitoring, and SLF. Jeffrey Rushen mainly deals with animal science, especially dairy cattle, lameness, animal-assisted therapy, and welfare and conducts research on cow lameness automatic detection and cow gait assessment. Trevor J. Devries’ primary areas of study are animal science, dairy cattle, milking and feeding behavior, including research on automatic milking systems and monitoring systems. Henk Hogeveen emphasizes animal health management and has achieved many research advancements in mastitis detection on dairy farms. Jeffrey M. Bewley’s focus is on precision dairy farming, the modernization of dairy facilities, and cow comfort and health. Sergio C. Garcia’s main interest is cow management and performance improvement through automatic milking systems.

4.1.5. Journals

Examining the structural characteristics of relevant journals can provide direction for literature collection and prior knowledge accumulation in the field, while also reflecting the theoretical and practical value of research within the area [ 54 ]. The 3658 sample papers on PLF research were published across 598 journals, with 19 core journals identified in accordance with Bradford’s law, as depicted in Figure 5 .

Journals in the core zone.

Computers and Electronics in Agriculture, Journal of Dairy Science, Animals, Sensors, and Biosystems Engineering are the top five journals in terms of publication volume. These five journals account for approximately 41.25% of the analyzed publications. According to JCR 2021, their average impact factor is 4.612, which indicates the development of influential perspectives within the field of PLF. Therefore, researchers engaged in relevant studies should keep track of the progress of these journals for high-quality PLF-related research. Based on impact factor, Computers and Electronics in Agriculture were ranked fourth and 23rd in the agriculture, multidisciplinary category and the computer science, interdisciplinary applications category, respectively, with Q1 ratings in both categories. Journal of Dairy Science was ranked sixth and 48th in the agriculture, dairy and animal science category and the food science and technology category, respectively, with Q1 and Q2 ratings in each of these categories. Animals were ranked 13th and 16th in the agriculture, dairy and animal science category and the veterinary sciences category, respectively, with Q1 ratings in both categories. Sensors was ranked 29th, 95th, and 19th in the chemistry, analytical category, engineering, electrical and electronic category, and the instruments and instrumentation category, respectively, with Q2 ratings for all three categories. Finally, Biosystems Engineering was ranked fourth and eighth in the agricultural engineering category and the agriculture, multidisciplinary category, respectively, with Q2 and Q1 ratings in these categories. These 19 journal subject categories relate mainly to animal science, veterinary science, computer science, agricultural engineering, and environmental science, highlighting the multidisciplinary research nature of PLF. Among these categories, animal science had the largest number of journals, and relevant journal publications accounted for approximately 34.20% of the analyzed publication, indicating that PLF is a hot topic of interest in animal science research.

4.2. Analysis of Research Cores

4.2.1. research hotspots.

Keywords constitute the core and essence of a paper as they offer a high-level summary of its content [ 49 ]. Analyzing keywords visually can facilitate the identification of hot topics and emerging trends in the target field [ 35 ].

The higher the frequency of keywords, the stronger their research popularity in a certain field. To obtain the keyword co-occurrence network ( Figure 6 ), we set the node type to KEYWORD and the threshold value to 55. Combined with Table 4 , “dairy cow” is the most frequently co-occurring keyword, followed by “cattle”, “behavior”, “system”, and others. Due to their high economic value and close relationship with human nutrition and health [ 25 ], “dairy cow” and “cattle” have become the focus of PLF research. The keyword “behavior” ranks third in frequency, and its related research includes animal behavior monitoring [ 55 ], behavior recognition [ 56 ], and behavior analysis [ 57 ]. Systems belong to PLF technology, and relevant research includes intelligent decision support systems [ 58 ], machine learning systems [ 48 ], electronic nose systems [ 59 ], and precision pig breeding systems [ 57 ], among others.

Keyword co-occurrence network.

Top 20 keywords according to the number of publications.

The top 20 high-frequency keywords can be categorized into three dimensions: (1) PLF technology, mainly including “system”, “machine learning”, “deep learning”, “model”, and “computer vision”. The technical methods used in PLF rely on systematic data collection and analysis and combine with machine learning, deep learning, and other technologies to achieve dynamic monitoring and management of livestock and poultry. This improves the efficiency of livestock and poultry breeding and the quality of livestock products. (2) The application objects of PLF technology, mainly including “dairy cow”, “cattle”, “cow” and “dairy cattle”. Most applications of PLF technology are based on monitoring devices attached to the animals’ necks, legs, and ears, with large animals providing more space for these devices; moreover, large animals have higher economic value [ 1 ]. Therefore, PLF technology is currently mainly applied to large animals. (3) The uses of PLF technology, mainly including “health”, “management”, “classification”, “animal welfare”, and “milk yield”. PLF technology can realize intelligent sensing, warning, and analysis of the livestock production environment. It provides accurate breeding, visual management, and intelligent decision-making for livestock production and effectively improves animal living conditions and welfare.

4.2.2. Research Directions

Keyword clustering analysis highlights the relationship between research directions and keywords, and a timeline analysis of keywords is to reveal the relationship between clusters further and the historical span of keyword sets [ 40 , 60 ]. Using CiteSpace software, we obtained the timeline network ( Figure 7 ) and clustering information table ( Table 5 ) for the PLF research’s keyword network. The horizontal axis indicates the year, and the vertical axis indicates the cluster. The higher the rank of the cluster label and the larger its size, the more keywords it includes. We selected the top six clusters with the largest size for analysis.

Timeline view of keyword network.

Keywords clustering information table.

Cluster #0 is deep learning, which incorporates 168 articles from 1991 to the present, encompassing hot words such as deep learning, computer vision, image processing, precision livestock farming, and object detection. Deep learning is a subset of machine learning that utilizes neural networks with three or more layers. These networks are designed to mimic the human brain’s behavior and learn from vast amounts of data. In recent years, deep learning has fueled the rise of many artificial intelligence applications and services that increase automation [ 61 , 62 ]. Within the field of PLF, deep learning is primarily utilized for animal behavior analysis on farms, tracking poultry and livestock’s health status, behavior habits, and production performance in real-time, and generating personalized management recommendations through data analysis. A convolutional neural network architecture was proposed by Pu et al. for recognizing chicken behavior within poultry farms, and this method achieves an accuracy rate of 99.17%, successfully resolving herd behavior image classification problems [ 63 ]. Wu et al. proposed a classification method for identifying lame cattle based on the YOLOv3 deep learning algorithm and relative step feature vector, which can intelligently detect lameness and improve dairy cow welfare [ 64 ]. Zhang et al. proposed EFMYOLOv3, a deep learning network based on bilateral filtering enhancement of thermal images, which automatically detects the eyes and breasts of dairy cows, enabling automatic recognition of dairy mastitis [ 65 ].

Cluster #1 is an accelerometer, which incorporates 145 articles from 1992 to the present, encompassing hot words such as accelerometer, GPS, feeding behaviour, grazing, and sheep. Accelerometers, as sensors that utilize the interaction between mass and spring, are capable of sensing acceleration and translating it into a functional output signal. Accelerometers have been prevalent in multiple research and industrial testing domains, such as controlling aircraft attitudes and detecting vehicle collisions. Within the field of PLF, accelerometers are among the most applied technologies and are primarily utilized for observing animal behavior [ 66 ]. Accelerometers, as a method for monitoring individual animal welfare, overcome manual challenges related to time, resources, and discrete sampling [ 67 ]. They can collect a wealth of behavioral information from animals, which can be used in combination with machine learning algorithms to classify and identify their behavior [ 68 , 69 ]. Yang et al. utilized two machine learning models, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), to analyze the data collected from accelerometers on broilers, achieving the classification of specific broiler behaviors [ 68 ]. Mei et al. validated the usefulness of utilizing 3D accelerometers and machine learning models for the identification of aflatoxicosis in broiler chickens [ 70 ]. Williams and Zhan found that the data from tail-mounted accelerometers showed high classification performance for standing and lying postures of dairy cows but performed poorly in classifying excretory events [ 71 ].

Cluster #2 is an automatic milking system, which incorporates 122 articles from 1992 to the present, encompassing hot words such as automatic milking system, mastitis, robotic milking, somatic cell count, and automatic milking. The automatic milking system can more accurately monitor the physiological and behavioral changes of dairy cows, quickly identifying and addressing health issues to ensure milk quality [ 72 , 73 ]. One important responsibility of automatic milking systems is to detect mastitis. Bausewein et al. assessed the accuracy rate of automatic milking systems in identifying clinical mastitis in Bavarian dairy herds in southern Germany [ 74 ]. Aerts et al. studied the effect of specific factors on milking efficiency using the decision tree technique and suggested that the year of automatic milking system operation, number of lactations, calving season, age at first calving and days in milk were related to milking efficiency [ 75 ]. As cows may be stressed during a transition to automated milking systems, which could alter their health and production performance, Morales-Pineyrua et al. investigated the connection between cow temperament, behavior, and production parameters, concluding that cow temperament affects behavior and production parameters [ 76 ].

Cluster #3 is lameness, which incorporates 109 articles from 1992 to the present, encompassing hot words such as lameness, locomotion score, heat stress, animal welfare, and locomotion. Lameness is a highly expensive disease for farm animals, particularly dairy cows, resulting in significant welfare and economic stress on the livestock industry [ 77 , 78 ]. Kang et al. proposed a spatiotemporal network comprising video downscaling and deep learning algorithms to detect cow lameness effectively, enhancing the accuracy of lameness detection in dairy cow walking videos [ 79 ]. Jiang et al. proposed a method for early detection of cow lameness combining machine vision techniques and deep learning algorithms, which can correctly detect lameness in cows and provide an innovative means of detecting lameness in cows [ 80 ]. Zheng et al. proposed a fused attention mechanism with a conjoined attention model to automatically track and monitor cattle legs in large-scale farms, providing an effective method for accurate tracking and lameness detection of cattle legs [ 81 ].

Cluster #4 is estrus detection, which incorporates 105 articles from 1992 to the present, encompassing hot words such as estrus detection, estrus, machine learning, reproductive performance, and ovulation. By monitoring and analyzing behavior patterns, such as calls and movements, we can identify whether livestock are in estrus and determine the best breeding time to enhance reproductive efficiency [ 82 , 83 ]. Higaki et al. employed sensors attached to the tail to collect surface temperature data of the ventral tail base of cattle during the estrous cycle and constructed an estrus detection model using machine learning technology for training data, which was tested for estrus detection ability [ 84 ]. Wang et al. developed a lightweight sow estrus detection approach based on acoustic data and a deep convolution neural network algorithm, which analyzed short- and long-frequency sow estrus sounds, providing an effective and accurate estrus monitoring and early warning system for pig farms [ 85 ]. Yu et al. proposed an ewe estrus recognition method based on a multi-target detection layer neural network, which can accurately and promptly recognize ewe estrus behavior in large-scale mutton sheep breeding, avoiding the stress caused by contact sensor detection [ 86 ].

Cluster #5 is electronic identification, which incorporates 53 articles from 1993 to the present, encompassing hot words such as electronic identification, feeding behavior, transponder, traceability, and performance. An electronic identification (EID) system is a crucial element in the environment of PLF farms and the only technology currently mandatory under EU laws [ 21 ]. EID primarily involves the use of radio frequency identification tags (RFID) and can be primarily classified into three types: ear tags, boluses and injectable glass tags [ 21 , 66 ]. Ear tags offer simplicity, and low cost, and have the widest range of applications. Ruminal boluses are slightly more expensive, but are highly durable with a low loss rate, making them a popular choice in commercial farming operations. Injectable glass tags boast high reliability and safety but are limited in their use on farms due to the difficulty of removing them at the slaughterhouse. It has been demonstrated by Garcia et al. that the use of transponders is feasible for the EID of water buffaloes, and they recommended that electronic transponders be implanted in calves that are up to two months of age, as this reduces the physical rate of transponder loss and the loss of functionality [ 87 ]. Kandemir et al. employed electronic leg tags (ELT) and electronic ear tags (EET) to identify goats and concluded that the traceability of ear-tagged animals was inferior [ 88 ]. Non-invasive biometric identification could greatly enhance animal welfare and management in livestock farming. Shojaeipour et al. proposed the two-stage YOLOv3-ResNet50 algorithm, which demonstrated outstanding performance in detecting the mouth and nose area of cattle, rendering it appropriate for automated cattle biometric identification systems [ 89 ].

4.2.3. Research Frontiers

A keyword burst denotes a sudden surge in the frequency of keywords in a short duration, and analyzing keyword bursts can reveal the shift in research hotspots over different periods, and identify potential development trends and cutting-edge research [ 90 ]. Figure 8 presents the top 15 keywords of citation bursts, where “Year” indicates the year of the initial occurrence of the keyword, “Strength” implies the intensity of the burst for the keyword, and “Begin” and “End” designate the start and end of the keyword bursts, respectively. The dark blue section represents the timespan of the keyword’s appearance, while the red part represents the span of the keyword’s burst.

Top 15 keywords with the strongest citation bursts.

The burst of the keyword in PLF research mainly occurred after 1995. As for the impact cycle, “dairy cattle” had the longest duration of burst. Given the high economic value of dairy cattle and their important role in human nutrition and health, coupled with their susceptibility to diseases such as mastitis resulting in significant economic losses and animal welfare issues, the research of PLF technology in the dairy cattle industry has always been a hot topic [ 25 , 91 ]. Regarding the strength of keyword bursts, “deep learning” ranks first. In PLF, a large amount of monitoring data requires processing, and deep learning models can handle massive data, quickly and accurately detecting animal behaviors and anomalies, automatically diagnosing animal diseases, predicting potential diseases, and more [ 92 , 93 ]. The next term is “automatic milking”, the automatic milking system provides a detailed description of the milking process for each cow, recording many parameters of the milking process, which can help improve milk yield and quality [ 94 ]. The most recent bursts of keywords suggest the future direction of research. “Precision agriculture”, “deep learning” and “machine learning” are currently burst keywords. Precision agriculture (PA) is a management strategy that collects, processes, and analyzes relevant data to support management decisions in order to improve the sustainability of agricultural production [ 95 ]. PA can be divided into precision crop farming and PLF [ 96 ]. Most PA research has focused on intensive planting systems, and a study has shown that the utilization of PA in livestock management is limited in certain regions, indicating the restricted application of PLF [ 97 ]. Future research should prioritize the need for farmers to acquire more knowledge regarding PLF. To utilize vast amounts of data, machine learning has become an indispensable part of modern animal husbandry [ 98 ]. Machine learning primarily serves to monitor animal behavior and estimate economic balances for producers accurately [ 99 , 100 ]. Commonly used machine learning methods, such as artificial neural networks (ANN), random forests (RF), and support vector machines (SVM), are mostly used to extract or label livestock features (body weight, estrus events, growth performance) and to determine or predict relevant features [ 62 , 101 , 102 ]. Deep learning is a subdivision of machine learning that employs elaborate algorithms to detect high-level features from data facilitating better performance in image processing and classification problems, surpassing traditional machine learning [ 62 , 103 ]. Recently, the PLF field has shown extensive interest in deep learning-based livestock identification and localization [ 62 , 104 , 105 ]. Future research should further improve the models and networks for both machine learning and deep learning, modify the set of technologies to adapt to these two techniques, and develop automated systems for livestock tracking and health monitoring [ 100 , 103 ].

4.3. Analysis of Hot Topics

Highly cited articles are defined as those with excellent contributions to a specific research area and widely accepted research conclusions [ 106 ]. Analyzing these articles can help explore the knowledge base of PLF research and identify the current hot topics based on existing foundations [ 107 ]. To better display and enlighten readers of further research endeavors, we have collected and analyzed the top 10 highly cited articles in the field of PLF from the current quinquennial period that spans from 2019 to 2023 ( Table 6 ). It can be seen that the main themes of PLF research are social science in PLF, the environmental impact of PLF, information technology in PLF, and animal welfare in PLF. A list of PLF article titles for the period 2019–2023 can be found in the Supplementary Material Table S2 .

Top 10 Highly cited articles from 2019 to 2023 as determined using Web of Science data.

4.3.1. Social Science in PLF

Despite the abundance of research exploring PLF from a natural science perspective, social science literature investigating the social, economic, and institutional approaches to PLF has steadily increased as well [ 108 ]. These studies can be divided into five topics: (1) Adoption of PLF on farms, which covers factors influencing technology adoption [ 109 , 110 ], farmers’ attitudes towards technology [ 111 , 112 , 113 ], public perceptions of technology [ 114 , 115 ], experiences with PLF on farms [ 116 , 117 ] and the roles of different players in supporting PLF [ 118 , 119 ]. Comprehensive consideration of various factors can promote the implementation of PLF technology on farms. (2) Effects of PLF on farmer identity, farmer skills and farm work. PLF replaces regular physical work but introduces new tasks such as equipment maintenance, monitoring, and data interpretation, resulting in an increased mental workload [ 31 ]. How PLF impacts animal management, demanding different knowledge and skills among farmers, and better farmer advisory services are needed to facilitate optimal farm system adaptation [ 108 , 120 ]. Farmers, livestock, and PLF technologies should work together to achieve the coevolution of all elements [ 121 ]. (3) Ethical concerns in PLF. The use of precision technologies may objectify animals into mere data points, ignoring their complex emotional and social needs, and lead to a loss of connection between farmers and animals, which is detrimental to animal well-being [ 33 , 122 ]. Animal welfare issues could harm production and result in lower profitability, which raises economic sustainability issues [ 123 ]. (4) Economics and management in PLF, with studies exploring the costs and benefits of PLF technology [ 124 , 125 ] and investment decisions for PLF technology [ 126 , 127 ]. (5) Transformation of PLF. Recently, scholars tend to emphasize the element of real-time monitoring and control in their definitions of PLF [ 20 , 66 ]. This reflects the digital transformation of PLF, as the digitization of animal husbandry can achieve real-time monitoring and control over the entire production process through intelligent equipment and sensors [ 33 ]. The transformation of PLF can be divided into two stages: the first stage focuses on digitalization, while the second stage further distinguishes between DLF and SLF.

4.3.2. The Environmental Impact of PLF

The expansion of livestock farming has had numerous adverse effects on the environment, despite its contribution to increased livestock product supply. These impacts primarily manifest in two aspects. Firstly, water quality is compromised due to the excessive nutrients present in animal diets that are not fully absorbed, resulting in high levels of compounds in animal manure. Consequently, these substances gradually infiltrate the soil, leading to water pollution. Secondly, air pollution occurs through the processes of animal gastrointestinal fermentation and manure disposal, resulting in significant greenhouse gas emissions. Relevant studies indicate that approximately 15% of global emissions are attributed to animal husbandry, exerting a severe impact on air quality [ 128 ]. Therefore, the livestock industry must explore effective strategies to mitigate environmental risks while maintaining high production levels [ 27 ].

PLF aims to continuously monitor animal health and welfare in real-time. Reducing environmental pollution from animal husbandry is not its primary objective; however, it provides a valuable tool for mitigating the impact of animal husbandry on the environment [ 20 ]. Rather than relying on specialized technologies to directly reduce environmental pollution, PLF employs information technology to optimize management and minimize adverse environmental effects. Precision feeding systems, for instance, can optimize feed ratios to accurately meet the nutritional requirements of each animal, thus reducing the excretion of nutrients resulting from poor absorption and improving environmental quality [ 129 ]. Animals experiencing health and stress issues may lead to unnecessary gas emissions, but the timely identification of conditions such as lameness and mastitis through PLF technologies can help mitigate environmental pollution from animal husbandry. Furthermore, achieving high levels of livestock fertility can reduce greenhouse gas emissions by over 20% [ 130 ]. Some scholars have developed sensors, algorithms, and smart devices capable of detecting the optimal time for fertilization in animals, thereby enhancing conception rates and mitigating the greenhouse effect [ 129 , 131 ].

4.3.3. Information Technology in FLF

Utilizing modern information technology to address deficiencies and issues in traditional animal husbandry, exploring advanced models of animal husbandry development, and stimulating internal growth potential are crucial for promoting stable and sustainable animal husbandry development [ 132 ]. Recently, supported by “Internet+”, the innovation of the new generation of information technology represented by the Internet of things, blockchain, and machine learning has been promoted in animal husbandry.

The Internet of things (IoT) is an information carrier based on the Internet, traditional telecommunications networks, etc. IoT allows for communication between farm sensors, devices, and equipment [ 17 ]. For example, with the assistance of IoT, sensors and wireless communication technology can be embedded in wearable devices for collecting massive animal data [ 132 ]. IoT enables real-time capture, collection, and transmission of livestock data, allowing for tracking, intelligent identification, and efficient monitoring and management [ 55 , 133 , 134 ]. Additionally, the application of IoT involves a substantial amount of animal and environmental data, thus raising concerns about data privacy and security [ 135 ]. Ensuring secure storage, transmission, and usage of data as well as protecting user privacy are critical challenges that IoT needs to address.

Blockchain is a distributed ledger technology that boasts decentralization, tamper-resistance, and transparency. It can provide a secure and reliable platform for data storage and exchange. Blockchain has become an important technology in many applications of the PA discipline [ 135 ]. In livestock systems, blockchain is being used to enhance the traceability of livestock products, supply chain monitoring and tracking, as well as data security and assurance [ 136 , 137 , 138 ]. For example, AppliFarm is a leading blockchain platform that can track livestock data in the animal production sector. This platform can be used to provide digital proof of animal welfare and livestock grazing [ 135 ].

Machine learning (ML) is a branch of artificial intelligence, which uses data and algorithms to imitate human learning methods, so as to gradually improve the accuracy in application [ 98 ]. ML has become an indispensable technology in modern livestock farming and has been widely used in improving animal welfare and raising animal productivity [ 98 , 99 , 139 ]. On the one hand, animal welfare involves the health and well-being of animals and is closely related to product quality, and its evaluation indicators include physiological stress indicators and behavioral indicators. ML is mainly used to monitor animal behavior in order to monitor their health status and facilitate the detection of diseases at an early stage [ 97 , 140 , 141 ]. On the other hand, there are many problems with livestock production systems and ML methods are mainly applied to accurately predict and estimate relevant parameters to improve the economic efficiency of production systems [ 99 , 142 , 143 ].

4.3.4. Animal Welfare in PLF

Animal welfare means the physical and mental state of an animal in relation to the conditions in which it lives and dies [ 144 ]. An animal in a good state of welfare should be consistent with being healthy, comfortable, safe, well-fed, able to express normal patterns of behaviour, and free of unpleasant states such as pain, fear, and stress [ 144 ]. Animal welfare deals with the physical and mental state of animals and is a broad term that covers all aspects of coping with the environment and takes into account a wider range of feelings than those affecting health, and therefore, animal health is included in animal welfare as a significant part of animal welfare [ 145 ].

In PLF, the use of sensors, coupled with algorithms that combine images, sounds, movements and vital signs of animals, enables non-invasive monitoring of animals, which can improve animal welfare by detecting diseases at an early stage [ 146 , 147 , 148 ]. Animal sounds contain vital information on animal health and behavior. To address the scarcity of multi-species vocal classification algorithms, Bishop et al. proposed a multifunctional animal vocal algorithm using specific audio feature extraction techniques and machine learning models, which laid the foundation for the development of subsequent automatic animal vocal detection systems [ 149 ]. Mao et al. developed a chicken call signal recognition device based on a convolutional neural network model, which effectively avoided the problem of inefficient reliance on manual recognition [ 150 ]. Changes in animal behavior are powerful indicators of health and welfare problems, and automatic identification of animal behavior can provide a powerful tool for improving farm management and ensuring animal welfare [ 151 , 152 ]. Lameness is a common problem in dairy farms, and since continuous monitoring of cow lameness is too time-consuming, Warner et al. used a machine learning approach based on decision tree induction to detect the level of lameness risk in dairy herds [ 140 ].

5. Conclusions and Insights

5.1. conclusions.

This study analyzed 3658 articles on PLF research in the Web of Science database from 1973 to 2023 quantitatively using CiteSpace software. By examining the main characteristics, research cores and hot topics of PLF-related research, the following conclusions were drawn:

• (1) From the perspective of the characteristics of publishing, the number of research papers on topics related to PLF generally shows an increasing trend. The international cooperation of this research is strong, and the developed countries of livestock farming in Europe and America have a large number of papers and close cooperation among countries; the research institutions of this study are mainly universities, involving agriculture-related institutions in individual countries, and the Inter-institutional cooperation network is relatively loose but the group characteristics are obvious; Daniel Berckmans and his team have published the most articles, and the overall cooperation among scholars is characterized by “small concentration and large dispersion”, and the cooperation among scholars is weak; the research belongs to a multidisciplinary cross-fertilization research field, mainly including animal science, veterinary science, computer science, agricultural engineering and environmental science.
• (2) Research hotspots in PLF include precision dairy technology, precision cattle technology, intelligent systems, and animal behavior research. The hot words can be categorized into PLF technology, technology application objects, and technology use, with research directions focused on deep learning, accelerometer, automatic milking systems, lameness, estrus detection, and electronic identification, and the specific research contents intersecting. Scholars have paid more attention to deep learning and machine learning since 2021.
• (3) From the perspective of hot topics, the research on PLF mainly includes four hot topics: social science, environmental impact, information technology, and animal welfare. The literature on PLF from a social science perspective can be divided into five categories: adoption of PLF on farms, effects of PLF on farmer identity, farmer work and farm work, ethical concerns in PLF, economics and management of PLF, and transformation of PLF. PLF provides a valuable tool for mitigating the impact of animal husbandry on the environment by optimizing livestock management. The new generation of information technology represented by the Internet of Things, blockchain, and machine learning plays an important role in promoting the stable and sustainable development of animal husbandry. The combination of sensors and algorithms can effectively extract and analyze the images, sounds, movements and vital signs of animals, facilitating early detection of diseases and improving animal welfare.

5.2. Insights

PLF establishes a scientific management system and decision support system through information acquisition, processing, understanding and application, which can promote the low-cost, high-efficiency and safe development of increasingly large-scale livestock farming and realize the modernization of livestock farming. Future research on PLF can be deepened from the following three aspects:

• (1) Strengthen the exchange and cooperation of PLF research. Universities with outstanding contributions in the field of PLF, such as Wageningen University and Research, University of Guelph and Katholieke Universiteit Leuven, should actively hold academic exchange conferences or exchange programs, and other institutions should actively establish friendly exchange relations with these universities and carry out related project cooperation. Scholars should actively participate in international exchange meetings and forums on PLF, discuss and learn the research experience and latest achievements of PLF with scholars from all over the world, and strengthen extensive exchanges and cooperation among scholars.
• (2) Pay attention to the combination of multi-disciplines and multi-methods. The research content needs the intervention of many disciplines and fields such as animal science, veterinary science, computer science, agricultural engineering and environmental science; in terms of research methods, it is necessary to organically integrate various methods such as big data analysis and model analysis, and strengthen the innovative integration of information technology in animal husbandry, in order to promote the research and development of PLF.
• (3) Strengthen the application of deep learning, machine learning, and other technologies. Develop integrated intelligent detection channels that integrate lameness recognition, body condition scoring, weight estimation, respiratory heart rate sign measurement and other multi-functional features; give full play to the role of data decision support, and realize refinement management to improve animal welfare. In addition, it is necessary to consider the relationship between PLF and humans and animals from the perspective of social science and to guide farmers to change the concept of responsibility to deeply explore and give full play to the value of PLF.
• (4) Strengthen the focus and exploration of DLF. PLF is gradually transforming into DLF, and farmers, scientists and consumers as well as other stakeholders should consciously participate in and pay attention to following this process in order to accelerate the innovative integration of digital technology and animal husbandry and promote the development of animal husbandry.

There are also some shortcomings in this study: Firstly, we only included the relevant papers contained in the Web of Science database as the research object and directly used parameters such as citation frequency provided by Web of Science as our analysis indicators, which cannot fully represent the research results in the field of PLF. Further research in the later period can increase the related data of other academic retrieval databases; secondly, this study primarily considered the commonly used information technology in PLF when setting the search type, and to ensure simplicity, certain search terms with minimal results were removed. Inevitably some literature was overlooked. However, the research results of this study still have important representative significance and can continue to improve the setting of search types in the future.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ani13132096/s1 , Table S1: Top 20 institutions according to the number of publications during 2019–2023; Table S2. Summary of PLF article titles for the period 2019–2023.

Funding Statement

This research was funded by the National Natural Science Foundation of China for Young Scholars (grant number 72203034); the Emergency Project Fund of National Natural Science Foundation of China (grant number 71640017); the General Program of National Natural Science Foundation of China (grant number 71773134); the General Program of National Natural Science Foundation of China (grant number 72072125); the Natural Science Foundation of Heilongjiang Province (grant number LH2021G002).

Author Contributions

Conceptualization, W.T. and B.J.; methodology, W.T.; software, W.T. and X.D.; validation, W.T.; formal analysis, W.T.; resources, B.J.; data curation, W.T. and X.D.; writing—original draft preparation, W.T.; writing—review and editing, B.J. and L.C.; visualization, W.T.; supervision, B.J. and L.C.; project administration, B.J.; funding acquisition, B.J. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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Students learn to handle livestock like pros

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Working with livestock can be exciting and challenging—even intimidating for those who didn’t grow up on a farm. But even if you grew up in the city and have never interacted with large animals you can still learn to work with livestock. How? By joining Block & Bridle, a student organization at Cornell that gives students from all majors the chance to gain educational, hands-on interaction with livestock of all kinds. 

Block & Bridle has existed since 1916, but in 2023 members revised the format of the organization from competition-based to educational. “We want it to be a really inclusive environment where there are people of all backgrounds being able to learn different things,” said Adyson Miller, Block & Bridle president.

In the past, Block & Bridle focused primarily on competition and livestock shows where members handled and groomed livestock, they also participated in livestock judging, public speaking, presentations, and evaluations. The latest transition to a more hands-on, educational approach encourages an environment where students from all departments can learn what it means to care for animals, said Miller. A typical Block & Bridle meeting now focuses on clinics, mini-lectures and hands-on demonstrations. 

 “We want it to be a really inclusive environment where there are people of all backgrounds being able to learn different things.”  – Adyson Miller

The organization also discusses industry technology that requires livestock knowledge and care. “We go over what happens on the solar panel farms, the rotations that we do, the research that's going on there, why sheep are better fitted for solar panel farms, and just how the whole process goes throughout the summer,” said Miller. Participating in these meetings enables members within the field interest to envision what a future career might look like. It fosters a great environment to ask questions, get involved and show up when you can.  

These days, members come from a variety of backgrounds. “I’m from the city,” said Antonia Li ’24, public relations chair. “I have no livestock experience. I wasn’t raised on a farm or anything like that.” 

However, Block & Bridle has provided her with the confidence and skill set to care for large animals. “My confidence evolved immensely,” she said. “Not only does the physical time we get with the animals help, but the lectures before the clinic also give those who need it a little more aid.” 

Adding clinics to the club’s format this past year was a priority, said Li. “Clinics are a very important aspect we wanted to include this year because of the consistency and accessible times we can offer to our members. That’s essential because Cornell students are so busy during the semester,” she said. 

With a mini lecture before each hands-on clinic activity, members gain familiarity with each topic and have the opportunity to practice with the animals. All they have to do before the meeting is sign a waiver.

Miller and Li stressed that everyone is welcome to join at any time. “I'm really excited to see the growth of this club,” said Miller. “We used to be at 200 members, tops. Then when COVID hit, the club had to close down. We brought it back in 2022, and right now we have 40 active members. 

“Even though I’m proud of what we’ve accomplished in two years, I believe that we can do so much more in the future,” she added.

Izzy Escalante ’26 is a communication major and student writer for the Cornell CALS Department of Animal Science.

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AI won’t fix animal agriculture

"precision livestock farming tools won’t move the world toward less cruel and more climate-friendly . . . diets", by shann chongwattananukul - benny smith - faraz harsini.

This article originally appeared on Undark.

T oday's factory farms are monuments to humanity's unprecedented technological sophistication and our seemingly limitless capacity for cruelty.

Over the past half century, industrial farms have selectively bred animals to grow much faster and larger than their natural patterns, leading to health issues such as chronic pain. Meanwhile, these creatures are kept in harsh, crowded conditions and slaughtered inhumanely . The close confinement and unsanitary conditions on factory farms can breed zoonotic diseases and antibiotic-resistant bacteria .

What's more, scientists tell us that eliminating animal products from our plates is one of the best things we can do as individuals to mitigate climate change and other environmental threats .

Amid this moral, environmental, and public health catastrophe, meat industry technologists are proposing precision livestock farming, or PLF, as a solution. PLF is the use of digital tools to continuously monitor livestock parameters, offering precise information about farmed animals in real-time.

PLF is a small but rapidly growing field. Although these tools have been around since the early 2000s, 65 percent of all literature on the matter was published in the past five years alone. Improvements in sensor technology and the computing power to interpret the sensors' output are responsible for this. Artificial intelligence, or AI, in particular has given rise to many new PLF applications . Still, the reality of AI on farms, for now at least, is more mundane than what some may imagine: fewer robots, more surveillance cameras and buzzing sensors.

We are a group of university students, advocates, and scientists affiliated with the nonprofit Allied Scholars for Animal Protection. With calls to drastically reduce or eliminate meat, dairy, and egg consumption from many scientists and animal advocacy groups such as ours, the livestock industry is scrambling for ways to improve its public image.

The meat industry often uses exaggeration and greenwashing to assuage public concern. For example, "regenerative" or lower-carbon agricultural practices have received much hype, despite the fact that the most sustainable way to produce meat is to not produce it at all . PLF, touted for its supposed benefits to animal welfare, human health, and the environment, may be the meat industry's next marketing ploy.

Conveniently for the industry, PLF tools achieve all of these changes by increasing production efficiency. For example, precision nutrition technologies purport to give animals optimized, individualized diets, thus reducing food waste.

Meanwhile, physical afflictions can be ameliorated via automated systems that adjust indoor conditions in response to signs of animal distress. Sickness can hurt farm output by slowing animals' growth, so disease detection systems could improve efficiency by obviating the need for costly clinical testing.

    PLF, touted for its supposed benefits to animal welfare, human health, and the environment, may be the meat industry's next marketing ploy.  

It's important to note, though, that illness and misery do not seem to have been much of an impediment to production in the past. Respiratory illness is commonplace among farmed pigs , and bodily mutilations without pain relief are standard practice. It would be hard for factory farms to do worse on animal welfare than currently, but it is unlikely that incremental technological improvements will yield anything that could reasonably be described as humane.

We would be remiss not to mention emotion-monitoring systems , intended to capture animals' affective states and enable adjustments to improve welfare conditions. Once commercial options become available, they would likely be marketed as husbandry practices that maximize animal "happiness."

Advertisements that such tools herald a revolution in farm animal welfare miss the obvious point that no farm animal will ever be happy living their life in a tiny crate.

It seems more likely that industry insiders , who for decades have used bogus certifications labeling their products as " humane " to distract the public from their abuses, will use PLF as yet another means to delude the public about where their food comes from. There is no universal standard for assessing animal welfare, so the meat industry enjoys ample room for exaggeration.

    Even the worst-performing plant-based diet has a lower impact on land and climate than the best diet that includes animal products.  

It is plainly inaccurate to frame PLF as a net positive for farmed animals. PLF methods will only be implemented when they benefit the animal slaughter industry by increasing efficiency and bolstering production. In perpetuating the exploitation of animals, PLF fundamentally opposes their best interests.

Putting ethics aside, there are many reasons why the end of large-scale animal farming is crucial for the future of our planet. For example, global adoption of a plant-based diet could reduce agricultural land use by about 75 percent .

Instead of reforming animal agriculture, the world should move away from a food system based on animal products. Even the worst-performing plant-based diet has a lower impact on land and climate than the best diet that includes animal products. This shouldn't be too surprising, given that animal agriculture currently accounts for 80 percent of global farmland but only produces 17 percent of calories .

As consumers, it's easy to feel overwhelmed by the scale of global problems and our limited ability to make a difference. When it comes to what's on our plates, though, the benefits of opting for plant-based diets are clear: better public health, a more livable climate, and better lives for our fellow creatures. Incremental tweaks to animal agriculture through PLF are misleading to consumers and do not get us to the world we need.

Shann Chongwattananukul, Benny Smith, and Dr. Faraz Harsini work with Allied Scholars for Animal Protection , a U.S.-based nonprofit founded by Dr. Harsini that focuses on animal advocacy and educates university students on sustainable food systems. Dr. Harsini is a biomedical and food system senior scientist who studies alternative protein technology.

This article was originally published on Undark . Read the original article .

By Shann Chongwattananukul

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