Market Beef Animal Market Beef Animal Anatomical Parts
Introduction
Beef cattle (including buffaloes) are the third nearly numerous farmed animals worldwide (after poultry and pigs), with 71.61 one thousand thousand tonnes produced globally in 2018. In the same year, the Eu produced 10.64 million tonnes of beef meat (1). Globally, cattle meat production has more than doubled since 1961, increasing from 28 meg tonnes per year to 68 million tonnes in 2014 (1). In numerical terms, between 2000 and 2050 the global cattle population might abound from ane.5 billion in 2000 to 2.half dozen billion (2). Beef cattle farming practices differ substantially beyond the globe, ranging from extensive to intensive and using different breeds (three). Each rearing arrangement presents specific challenges for brute welfare (4–6) and, while guidance on best practice is available for some of the identified problems, cognition gaps persist, for instance in the areas of disease prevention and monitoring, optimisation of live send, use of environmental features, and enrichments (4). Specific issues are associated to the management, transport and rearing of male calves coming from the dairy industry, which are of limited commercial value (seven).
The science of animal welfare has considerably evolved from the 1990s to the present mean solar day, and with it the recognition that animals are sentient beings, deserving of "a expert life," which includes opportunities to feel positive affective states (viii). It is therefore of interest to investigate if and under what respects the sustained global increase in cattle production has propelled an interest in researching beefiness cattle welfare. This paper uses text mining (TM) and topic analysis (TA) to analyse the scientific literature on beef cattle welfare published in English from 1990 until 2019 to better understand the most important topics discussed in bookish publications and their evolution over fourth dimension. TM is defined as "The knowledge discovery process which looks for identifying and analysing useful information on information which is interesting to users from big amounts of textual data" and equally such it is unique in its ability to analyse concept relationships "in lodge to observe new structures, patterns or associations" and to "notice new facts and trends about the globe itself" (9). More in detail, TM tin be used to summarise or cluster information into charts or maps; identify hidden structures (and associations) between concepts or groups or concepts; extract subconscious associations between textual elements; provide an overview of the contents of a big collection of documents; categorise texts by discovering relevant groupings (ix). In other words, TM and TA represent tools that can produce a preliminary thematic screening of large numbers of documents to reveal a structured "map" of textual knowledge (10, 11) by uncovering recurrent topics and latent themes when the set of documents to analyse is big. For these reasons, TM is increasingly beingness used in the scientific literature as a tool to place themes and time to come research avenues across a broad range of topics, including fauna welfare studies (12, 13).
Materials and Methods
Data Fix
A literature search protocol was prepare to identify peer-reviewed papers with at least an English abstract that covered the topic of beef cattle welfare using Scopus®, the abstracts and citation database of Elsevier©. The keywords used were "bovine meat welfare," "meat cattle welfare," "veal welfare," "beef welfare," "beef cattle welfare," and "heifer welfare." The search was performed in January 2020. The initial timespan considered was between 1960 and 2019. Every bit less than 1 relevant paper per year was published in the period 1960–1989, only papers published from 1990 onwards were retained for the full statistical analysis. An electronic Excel workbook was used to collect the data extracted from the identified papers. The spreadsheet was built in a 2-style table format considering every paper (record) as a row and its descriptive information in columns. A list of the descriptors used and additional information on information format are provided as Supplementary Materials. All datasets were merged and overlapping records were erased. Reasons for automatic exclusion were: no author bachelor, no source available, certificate type erratum, no certificate type bachelor. Additionally, two reviewers (EN and BC) independently screened the titles and abstracts for relevance to the research topic (i.e., papers dealing with one or more aspects of beef cattle welfare). The criteria for transmission exclusion of the papers were (1) wrong topic or focus (for instance, social and economic welfare) or (2) wrong species or sector (e.one thousand., welfare of dairy cattle). Citations were excluded from the database if ane or both criteria for exclusion were called by both reviewers for the same paper. Disagreements were resolved by consensus with the mediation of FG. The geographical localisation of each record was derived based on the affiliation of the corresponding writer/commencement author. Some descriptive statistics of the selected records were prepared to profile the scientific corpus based on data recorded from the Scopus database. A regression analysis of the number of published papers on years was performed to calculate the trend by year of the scientific involvement for this topic.
Text Mining
A TM analysis was performed on the abstracts of the selected papers to detect important patterns in text data as described past Wang et al. (10) and Contiero et al. (12). This technique converts text into numeric information and highlights the word frequency distributions. The text pre-processing consisted in three steps: tokenisation, filtering and stemming (14). Tokenisation is the process of finding words, separating them and reducing them to lowercase. Filtering is also chosen cease-word removal (exclusion of characters such every bit punctuation and blanks, exclusion of stop words such as articles, prepositions, and conjunctions, etc.). Stemming reduces word variants to their root form and we used Porter stemming algorithm to perform this feature (15). In add-on, keywords used in the bibliographic search were removed to avoid poor discriminative information due to their presence in almost all abstracts retrieved (x). The words were organised into a matrix that contains the documents along the rows and the terms forth the columns (so-called document-term matrix). A term frequency-inverse document frequency technique (TFIDF) was used to attribute a relative weight to words (xvi). This represents the frequency of a term adjusted for how widely it is used, thus reflecting how important a give-and-take is in the whole collection of documents. The words with the greatest relevance (TFIDF ≥ 8) were represented equally histogram. A deject of the most relevant words (TFIDF≥5) was also created (https://www.wordclouds.com/) in which a bigger graphic symbol size indicates a higher TF-IDF value. The statistical assay was conducted with R packet (2017) using the libraries tm, stringr, and SnowballC.
Topic Modelling
Topic modelling is a tool to uncover the structure of meaningful themes among collections of documents also as to discover subconscious textual patterns [something similar to a principal component analysis of a given dataset of words; (17)]. Latent Dirichlet resource allotment (LDA), one of the almost popular approaches to perform topic modelling analysis, was practical for the text mining of our abstract corpus (10). A single topic tin exist described as a multinomial distribution of words, and a single document can be described equally a multinomial distribution of latent topics. This model provides both a topic representation of all the documents and the word distributions of all the topics, in an iterative process implemented using a Gibbs sampling. At the finish of the iterative process, a posterior distribution was calculated to estimate the topic mixture of each document (by counting the proportion of words assigned to each topic within that document) and the words associated to each topic (by counting the proportion of words assigned to each topic overall). We used LDA function with Gibbs sampling option of the topicmodels package in R (18). The individual topics were presented equally an unstructured set of words using the bar histogram representation, where every bar relative to every discussion is proportional to the probability of the discussion within a topic (beta value). The cumulative probability of the 10 well-nigh probable words for unlike numbers of topics was calculated.
The number of topics needs to be stock-still a-priori. Equally the "ideal" number is in general not known, several models with unlike number of topics were fitted and measures of evaluation were calculated. In a first approach, the perplexity of holdout and training datasets was calculated. Perplexity measures how well a probability model predicts a sample. A lower perplexity score indicates better generalization performance (19). The certificate-term matrix was split in two parts: the showtime one, which included eighty% of the documents, was used equally training dataset and the last i every bit test dataset (hold-out set). For different numbers of topics (from 2 to 20) LDA models were fitted on the training dataset. Using the results obtained in the preparation dataset, the perplexity index was calculated both for the training and the holdout datasets. This machine learning arroyo permits to test the adequacy of a model developed in a preparation dataset measuring its operation on an concur-out dataset. A second approach to set the number of topics is based on the harmonic mean of the likelihood of a fix of samples generated by the Gibbs sampler (xx). In this case a higher value of the harmonic mean is amend. A hierarchical cluster analysis arroyo was adopted for the topics analyses with different number of topics. The topic-give-and-take matrix (first 100 almost likely words) was transformed to binary information with a 1/0 to indicate presence of a word in a given topic. Finally, a tendency analysis of the proportion of each topic by year was performed to examination the dynamics of all topics over fourth dimension.
To explore the relationship between topics, we performed hierarchical clustering analysis. The results are presented as Supplementary Materials.
Results
Descriptive Statistics
The distribution of the results of the initial bibliographic search past string on titles, abstracts, and keywords is shown in Table 1. The most numerous manufactures concerned the string "beef welfare" (81%), followed by "beef cattle welfare" (60%), "meat cattle welfare" (41%), "heifer welfare" (30%), "bovine meat welfare" (15%) and, lastly, "veal welfare" (11%). After removal of overlapping records and manual removal of irrelevant ones, 983 records were retained for farther assay.
Table 1. Bibliographic search strings for the text mining assay on the welfare of beef cattle carried out on titles, abstracts and keywords of peer-reviewed literature in English published between 1990 and 2019.
Looking at the trend for the number of papers per publication year, from 1990 to 1996 fewer than x papers on beef cattle welfare were published annually, whereas from 1996 to 2019 there was a significant increase of three papers per twelvemonth (interpolation of information using a regression analysis from 1990 to 2019: y = 3.28x-6529 R two=0.85; Figure 1).
Figure 1. Peer-reviewed articles on the welfare of beef cattle published within the menstruation 1990–2019. From 1996 onwards at that place was a meaning increase in the number of papers published annually (by interpolation: 3/year).
Nearly one-half of the identified papers had first or corresponding authors based in Europe (members of the European Economical Area and Switzerland; 46%). The second nigh of import area of provenance of authors was North America (25%), followed past South America (11%), Oceania (9%), Asia (6%) and, lastly, Africa (3%).
A breakdown of articles per country of the European block is shown in Figure 2.
Effigy 2. Distribution by European land of peer-reviewed papers on the welfare of beef cattle based on the nationality of the first/corresponding authors and published between 1990 and 2019.
The distribution of published papers by journal championship (with at to the lowest degree x papers published on the topic in the flow considered) is shown in Figure 3. The most frequent publishing sources of the retained papers were scientific journals dealing with animal (product) science, animal welfare and behaviour, and meat quality, whereas the journals focused on veterinarian science were a less important publishing channel (15%).
Figure 3. Distribution of published papers by periodical title (with at least 10 papers published on the topic in the period considered). The most frequent publishing sources of the retained papers were scientific journals dealing with fauna (production) science, beast welfare and behaviour, and meat quality, whereas the journals focused on veterinary scientific discipline were a less important publishing channel (15%).
Research articles represented the most common type of retained paper (76%) followed past reviews (xiii%), briefing papers (6%), and others (five%). The papers were published in 310 scientific journals. As a whole, the 983 articles had collected—as of January 2020—a total of 15,208 citations. The virtually cited commodity was published in 2004, collecting 392 citations, followed past an article published in 2010 that nerveless 235 citations.
Text Mining Results
Text mining analysis was performed to identify the about of import words of the data corpus. The pre-processing of the information produced 106,108 words and later reduction of sparseness (exclusion of the "rare words") i,490 terms were retained from the selected 983 documents. The nearly relevant words according to the TFIDF ponderation system (TFIDF≥5) are represented in Figure 4 equally a deject in which the size of the font is proportional to the TFIDF of every word. Looking at the first 10 word stems, the most important was calv- (TFIDF=18.4), followed by transport (TFIDF=thirteen.8), product (TFIDF=12.3), slaughter (TFIDF=eleven.6), system and subcontract (TFIDF=x.7), consum- (TFIDF=10.5), stun- (TFIDF=10.one), castrat- (ix.9), and behaviour (nine.half dozen).
Figure 4. Cloud representation of the most relevant words (stems) in the database (TFIDF≥5). The relative importance of the terms is reflected by their size.
Topic Visualisation
Nosotros created a visualisation (histograms) of the x nearly probable words of the topics in LDA taking into account 5, ten, xv and 20 topics, respectively. The cumulative probability of the ten most frequent words for LDA with five topics was smaller than that for LDA with ten, fifteen and 20 topics. The average cumulative probability of the ten most frequent words was 0.19, 0.26, 0.31, and 0.35 for 5, 10, 15, and 20 topics, respectively. This is because if a small-scale number of topics is assumed, a few words may not convey a topic meaning sufficiently and different issues will be lumped together (10). By expanding the number of topics causeless it is possible to discover additional themes. We tried several approaches to select an optimal number of topics. The first was based on the calculation of the perplexity index for a training and a test dataset, respectively (xix). A second approach was based on the harmonic means of the likelihood of different models obtained with unlike number of topics (xx). The results of these two approaches are shown in Figure v.
Figure five. (A) Perplexity index of grooming (green) and test (red) datasets for different number of topics. (B) Harmonic mean of the likelihood of a gear up of samples generated past the Gibbs sampler.
As expected (Effigy 5A), the perplexity of the preparation dataset was lower than the 1 calculated for the test set. Both curves decreased as the number of topics increased. In Figure 5B the harmonic means was instead increasing. No local minimum or maximum were found for perplexity or harmonic means. The ii functions were monotonically decreasing or increasing, respectively, according to the increasing number of topics. Therefore, no clear proffer on the ideal number of topics could exist derived from these analyses.
Lastly, nosotros considered the hierarchical clustering of the topics obtained with 5, ten, fifteen and 20 topics. The inside-class variance component deemed for 89, 96, 96, and 97% of variance, respectively. The maximum increase was achieved between five and ten topics; a plateau was reached with higher values. This means, in exercise, that beyond ten topics there was no improvement in the capability of the model.
Taking into account the outcomes of these 3 analyses, we selected the LDA with 15 topics. The 10 about probable words in the 15 topics are reported in Figure 6.
Effigy 6. Histograms representing the most relevant words per topic in the LDA with 15 topics (beta=probability that a word belongs to a given topic).
Based on the most relevant words and the papers belonging to each topic, we tentatively attributed a theme to each topic (Table ii). The showtime 3 topics per number of papers published nether each topic were topic 12 (effects of send and slaughter on carcass quality), topic two (stakeholders' perceptions), and topic 8 (efficiency and environmental sustainability). Topic iv (food prophylactic and public health) closely followed topic eight. The cumulative probabilities (cp) calculated for the ten nearly relevant words in the topics showed that topics v (calf behaviour and direction), 8 (efficiency and environmental sustainability) and 12 (furnishings of ship and slaughter on carcass quality) were the most important statistically (cp = 0.50, 0.41 and 0.38, respectively).
Table 2. Topics that emerge from the LDA analysis.
Tabular array 3 shows the results of the trend analysis for the flow 1990 to 2019. For each topic, the percentage of papers published in the considered fourth dimension interval was analysed every bit a function of the year. The estimated regression coefficient represents the variation (increase/subtract depending on the sign of the coefficient) in the pct of published papers for each incremental unit of the time. In that location was a significant increase in the number of papers dealing with topics two (stakeholders' perceptions), 7 (risk factors for health and mortality), 9 (health and welfare of heifers) and 14 (prevention and handling of common bovine diseases), whereas the numbers of papers published on topic 15 (new economic models) decreased in the considered time frame. Topic 12 (effects of send and slaughter on carcass quality), although the kickoff for number of papers published over time, had a stable tendency, whereas topic 2 (stakeholders' perceptions) was the i showing the almost pronounced trend toward increase.
Tabular array three. Trend analysis of the xv LDA topics by year (1990–2019).
Discussion
LDA practical to academic papers on beef cattle welfare captured several fields of investigation that are of known relevance, for instance pain relief during invasive procedures, the furnishings of transport and slaughter, the management of heifers and calves, and the treatment and prevention of disease. This feature—being able to place known facts—has been proposed as testify of the trustworthiness of the text mining algorithms (ten). Additionally, the terms "heifer" and "calf" were included in the search keywords, and the topic analysis recaptured them, which is an indicator of the soundness of the methodology used. LDA modelling as well revealed other topics that are of increasing societal interest, such as the link between animal welfare, sustainability and wider market issues, and the attitudes and perceptions of stakeholders such as consumers and farmers.
The geographical distribution of the get-go authors shows that half of the papers had first authors in Europe, followed by the Usa. As well having a potent focus on scholarly publishing in English language, these two geographical areas accept similar beefiness production information [10.64 million tonnes for the EU vs. 12.22 million tonnes for the Us; (two)]. Within the European union, the United kingdom had the highest proportion of start authors, which does not surprise, as the Great britain is the cradle of the farmed animal welfare motility in Europe (21, 22) as well as being an important beef producing land. The start ten countries with high proportions of first authors are as well among the principal beefiness producing countries in Europe, with the exception of Switzerland, which all the same, just like the UK, has a long-standing tradition of protecting animal welfare [since 1992, Switzerland has recognised in its constitution the concept of animal dignity as well every bit fauna well-being; (23)]. Compared to other fields of research, in which Asia (Red china in particular), Due south America and Oceania are very well-represented in terms of scientific publications, beef cattle welfare literature in English was less popular.
From 1960 to 1989, our search criteria identified on average less than one relevant newspaper per twelvemonth published in English on beef cattle welfare. This is not surprising if we consider the history of animal welfare science. Equally highlighted by Broom (24), in the 1970s and early 1980s the term fauna welfare was used just still not defined, and many considered information technology to be unscientific. Only in the early 1990s did scientists agree that creature welfare is measurable, and hence that it is a scientific concept (24). Our results show a steady increase in the number of papers on beef cattle welfare published yearly in English starting from 1996. Considering that cattle numbers worldwide increased in a gradual and consistent manner from the 1960s onwards (1), the number of published research papers on beef cattle welfare appears to have been markedly influenced by the evolution of animal welfare scientific discipline.
The main word stems that emerged from the TDIF analysis were, in descending order of importance, "calv-," "transport-," "product-," "slaughter," "arrangement-," "farm-," "consum-," "stun-," "castrat-," and "behaviour." Almost of these terms are too included in the showtime iii topics identified later LDA analysis, and therefore they will exist discussed below.
The identified topics are comprehensive as, likewise animate being welfare, they include animal health and behaviour, meat quality, sustainability, and the social dimension. The analysis of cumulative probability identified the three almost of import topics statistically. The nearly significant was "dogie behaviour and management" (topic five). The tokenised words belonging to this topic were "calv-, grouping-, behaviour-, observ-, differ-, dogie-, wean-, indic-, studi-, frequenc-." The articles retained in the database reveal a scientific focus on aspects of animal welfare at calving and the optimisation of calf welfare (based on behavioural and physiological indicators) to reduce stress and improve growth. Some aspects were common to the literature on beef and dairy cattle, whereas others were specific to each sector. A common aspect is the correct management of difficult calving (dystocia) from the perspective of the health and welfare of both the cow and calf (25, 26). Several studies also assessed weaning stress. The weaning process includes handling, a more than or less abrupt cow-calf separation and, sometimes simultaneously, transportation, sudden changes in the nutrition, and social reorganisation due to regrouping. All of the events taking identify around this phase are sources of stress and potential health issues for both beefiness and dairy calves (27, 28). Cows besides feel stress due to separation from their calves at weaning (28–thirty). Studies assessing methods to reduce stress effectually weaning typically rely on a mixture of behavioural and physiological indicators to compare the relative benefits of dissimilar separation strategies (eastward.1000., ii-pace weaning; (27, 31, 32). Treatment and transport also have an upshot on stress indicators and growth in beef calves (33). The veal sector presents specific challenges: male person dairy calves are typically separated from the cows immediately or 24–48 h subsequently birth. They are normally housed individually for the get-go weeks of life and weaning—the gradual alter in nutrition from milk or milk replacers to solid feed—can occur as early as 6 weeks of historic period. This is in itself a very stressful event with potentially long-lasting consequences (34). Research has shown that male dairy calves are given less care and attending than heifer calves due to their lower commercial value (7). One determination is that farmers need better data on colostrum feeding regime and pain management for these animals. Studies in the database also dealt with the management of calves kept in group housing to ameliorate animal welfare and maximise growth (35–37).
The second most important topic identified past the statistical assay was "efficiency and environmental sustainability" (topic 8). The give-and-take stems making upward this theme are "product-, system-, increase-, organ-, environs-, improv-, effici-, chang-, sustain-, nutrit-." The recurring theme in this grouping of papers is a recognition that the beef industry is nether public scrutiny for several respects (e.g., food prophylactic, environmental footprint, animate being welfare) and that several adaptations will exist necessary to address all of these concerns (38, 39). According to the selected literature, such adaptations can be possibly achieved by improving product efficiency and meat quality, addressing creature welfare issues, and diversifying rearing systems wherever possible, for instance past adopting organic or agro-ecological farming techniques (40, 41). Achieving and expanding the profitability of "alternative" beef rearing systems depends on a complex interplay of factors. These include market readiness and resource availability (42, 43), applied technical knowledge (41, 44), land suitability and availability (45), and whether information technology is possible to guarantee beast wellness and welfare under a range of climatic and geographical atmospheric condition (46–48). Remote sensor technology can assist in monitoring animal welfare in grazing systems (49, 50).
The third virtually important topic dealt with "ship, slaughter and carcass quality" (topic 12). The discussion stems included in this topic are "slaughter, transport, carcass-, stun-, qualiti-, bruis-, load-, handl-, time, loss-." The identified literature reflects a tension between the economically driven pressure to increase slaughter speed on the i hand, and the need to minimise financial losses due to bruising, guarantee meat quality, and protect creature welfare and public wellness on the other (51). Minimising stress and suffering during the pre-slaughter and slaughter phases is an important component of overall cattle welfare, in role also due to societal expectations well-nigh how food animals should be killed (52, 53). From an economic perspective, stress and rough handling during send, lairage and slaughter tin can compromise carcass quality (54–56) with consequences that tin even impact the global market cost of beefiness originating from a specific land (57). One of the near studied aspects in this group of papers concerned the effects of send on cattle welfare and meat quality, as well as potential strategies to mitigate the associated risks (58–lx). In upshot, the financial losses due to rough treatment during the pre-slaughter phase can be significant (61). In the slaughter phase, stun quality is important to ensure that animals do non regain consciousness until death (62); to reduce health and safety risks for slaughterhouse operators (63); to preserve meat quality, every bit wrong stunning causes a surge in blood cortisol and the secretion of heat shock proteins (64). Non-stun slaughter presents specific challenges for animal welfare, such as time to loss of consciousness after the cervix cut (65). Some papers investigated dues- and post-mortem animal-based indicators to appraise and meliorate animal welfare on farm and during send (66, 67).
Although non included in the showtime three most statistically relevant topics, "castrat-" was among the starting time 10 well-nigh important word stems according to the TFIDF analysis and is included in Topic 3 ("pain management"). The castration of male calves or mature bulls is a common do in many parts of the globe and is carried out to facilitate management and prevent unwanted breeding (68). Castration can exist carried out upon arrival at the feedlot (69), in some cases together with other painful procedures [e.g., dehorning, branding (70)]. Physiological and behavioural indicators of inflammation and pain can last for days or weeks depending on the method and age of the animal at the time of castration (71, 72). The legal requirements on the provision of pain relief during castration and other painful procedures differ by geographical region and fifty-fifty by state. Nevertheless, there is increasing sensation on this topic and veterinary codes of practice also as some industry guidelines increasingly recommend the apply of anaesthesia and/or analgesia, especially when castrating older animals [see (73–75) for some examples]. Research papers on castration included in Topic 3 focused on the availability and potential effectiveness of methods to reduce or eliminate the acute and chronic pain associated with this procedure (69, 76–78). A painless alternative such as immunisation confronting GnRF (gonadotropin-releasing cistron) could be a viable pick according to some studies (79, lxxx). With a view to acknowledging scientific evidence on the acute and chronic pain caused by routine invasive husbandry practices, and to meet societal expectations on the ethical treatment of farmed animals, some authors have proposed a "3S" approach ("suppress, substitute, soothe"), which is the equivalent of the "3R" ("reduce, replace, refine") principle for animals used in research (81).
Some other interesting aspect that emerged from this study is the development of different topics over time. Topic north. ii ("stakeholders' perceptions") showed the most pronounced upward trend throughout the years. This topic deals with the attitudes, beliefs, expectations and preferences of different stakeholders (citizens/consumers, veterinarians, farmers, etc.) toward creature welfare and other attributes of beef meat. Such aspects have important implications for the treatment of animals on the one hand, and for the marketplace on the other, since they influence purchase decisions. Consumers' perceptions of the beef cattle industry—and the livestock industry at large—are constantly changing and tin can influence willingness to pay for meat produced and marketed in certain ways (82–84). At the same time, farmers' perceptions and beliefs can have a profound bear upon on their behaviour, and thus as well on animal welfare (85, 86). The aforementioned applies to cattle veterinarians (87, 88) and hauliers (89).
The second topic showing the most significant upward trend in terms of papers published was n. 7 ("risk factors for morbidity and mortality"). Papers in this thematic group deal with risk factors for health and mortality in all categories of beef cattle. They are all quite recent, dating from 2002 onwards. The topics that feature almost prominently in this thematic group are (a) antimicrobial use and resistance and (b) calf health, with item reference to factors affecting morbidity and early mortality. The ii topics are interdependent: veal calves are typically transported to specialised fattening facilities when still unweaned, sometimes passing via auction markets, and often with insufficient passive immunity (90). Transport over long distances, lairage and treatment at auctions, and mixing upon arrival at the fattening facility are all wellness and welfare challenges (90, 91). As a result, morbidity, mortality and antimicrobial use are still high in the veal sector, and solutions are needed to improve the health and welfare of veal calves, as well in light of the global fight against antimicrobial resistance (92–94). Other papers deal with mortality rates in cow-calf and beefiness fattening operations (95–97). The trend for an increase in the number of publications on these topics is very pronounced, showing a growing interest in improving animal wellness and welfare also equally a ways to protect public health.
Text mining with LDA is a methodology that enables researchers to take a practiced overview of the current state of a given domain or topic and provide indications for further enquiry if relevant, peculiarly when the number of documents to consider is big (ten). However, 2 of import limitations of our study are that the document selection was restricted to peer-reviewed research (Scopus) and to manufactures written in English or having at to the lowest degree an abstract in English. The selection of Scopus was based on the fact that information technology is a citational bibliometric database comprising a greater number of scientific journals compared to other databases. However, one limitation of Scopus is that it does not include grayness literature, which could have been an interesting source of boosted information. Concerning the literature in English, by including in our search criteria all relevant papers with at least an abstract in English language, we managed to comprehend a broad geographical range for our research. Notwithstanding, as already mentioned in the results, geographical areas that normally have a proficient scientific output in English language for other disciplines were less represented in our database. For case, papers with a first author located in South America, where beef production is economically and numerically important, represent 11% of our database. However, due to the specific challenges associated to the rearing, handling, send, and slaughter of beef cattle in that geographical expanse, it is plausible that text mining on papers written in Spanish and Portuguese, too as in other languages (e.chiliad., German, Chinese, etc.), volition reveal different trends and topics. For this reason, an analysis of non-English literature on beef cattle welfare certainly claim to exist carried out.
Based on the assay of the top ten word stems, the most important topics statistically, and the emerging trends, two considerations tin can be made. The starting time one is that animate being welfare is now perceived equally an of import component of beefiness cattle management, and one that can have a positive impact on animal and human health. This is perfectly in line with the OneHealth framework, specially concerning the global fight against antimicrobial resistance (98). The second consideration is that, based on our TM assay of the literature in English language, it would appear that inquiry on beef cattle welfare is increasingly addressing wider societal concerns that, albeit to a variable extent, are function of gimmicky global policy discussions on livestock farming. Such concerns include most notably environmental sustainability, but too product efficiency, painful husbandry procedures, also equally the attitudes of various stakeholders toward beef cattle farming.
Conclusions
Our LDA topic analysis of scholarly manufactures on beef cattle welfare published in English language betwixt 1990 and 2019 shows an increasing scrutiny into the health and welfare of calves, including behavioural aspects. At that place is besides a growing interest in sustainability issues and organic farming practices. Animal welfare during pre-slaughter and stunning, particularly during send, has an impact on meat quality and is therefore also an important inquiry topic. In this specific case, there is a clear convergence of involvement between financial gains and improved cattle welfare in the pre-slaughter and slaughter phases. The results indicate a detail focus on the welfare of calves, especially in the veal manufacture. Research is also increasingly assessing aspects of beefiness cattle welfare that are interlinked to meat quality, the social and environmental sustainability of the sector in relation to market opportunities, and public health. The issue of hurting relief during castration featured prominently and is likely to get increasingly important every bit societal scrutiny into the ethical treatment of farmed animals converges with the scientific evidence on the acute and chronic painfulness of routine husbandry practices. The topic showing the almost significant increase in popularity in the scientific literature on beef cattle welfare had to do with attitudes of consumers, farmers, and other stakeholders in the beefiness supply concatenation and their role in driving college welfare practices and market opportunities. Some other cluster of topics that has shown a marked increase in the literature since 2002 has to do with run a risk factors for morbidity and mortality, in detail in relation to the high use of antimicrobials in veal calf fattening facilities, which should exist reduced by meliorate addressing certain dogie health and welfare issues. Although in some cases the focus is all the same on animal health and production parameters such as meat quality, our analysis shows that research on beef cattle welfare is increasingly incorporating and analysing ecology and societal topics that are relevant for the development of future local and global policies on livestock productions. The identified topics represent a basic source of information that can be used for further and more detailed analyses (e.g., systematic reviews) focussed on specific research themes or geographical areas.
Data Availability Statement
The original contributions presented in the written report are included in the article/Supplementary Material, further inquiries can exist directed to the corresponding author/s.
Author Contributions
EN, FG, and GC contributed to the conceptualisation and methodology of the original draft. BC performed the formal assay. EN, BC, FG, and GC contributed to writing, reviewing, and editing the manuscript.
Conflict of Interest
The authors declare that the inquiry was conducted in the absenteeism of any commercial or financial relationships that could exist construed every bit a potential conflict of interest.
Acknowledgments
The authors wish to give thanks Reineke Hameleers for her insightful comments and suggestions on the offset draft of the commodity.
Supplementary Material
The Supplementary Material for this article can be found online at: https://world wide web.frontiersin.org/articles/10.3389/fvets.2020.588749/full#supplementary-fabric
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