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We are developing a real-time, data-integrated, data-driven, continuous decision-making engine, The Dairy Brain, by applying precision farming, big data analytics, and the Internet of Things. This is a transdisciplinary research and extension project that engages multidisciplinary scientists, dairy farmers, and industry professionals. Dairy farms have embraced large and diverse technological innovations such as sensors and robotic systems, and procured vast amounts of constant data streams, but they have not been able to integrate all this information effectively to improve whole-farm decision making. Consequently, the effects of all this new smart dairy farming are not being fully realized. It is imperative to develop a system that can collect, integrate, manage, and analyze on- and off-farm data in real time for practical and relevant actions. We are using the state-of-the-art database management system from the University of Wisconsin-Madison Center for High Throughput Computing to develop our Agricultural Data Hub that connects and analyzes cow and herd data on a permanent basis. This involves cleaning and normalizing the data as well as allowing data retrieval on demand. We illustrate our Dairy Brain concept with 3 practical applications: (1) nutritional grouping that provides a more accurate diet to lactating cows by automatically allocating cows to pens according to their nutritional requirements aggregating and analyzing data streams from management, feed, Dairy Herd Improvement (DHI), and milking parlor records; (2) early risk detection of clinical mastitis (CM) that identifies first-lactation cows under risk of developing CM by analyzing integrated data from genetic, management, and DHI records; and (3) predicting CM onset that recognizes cows at higher risk of contracting CM, by continuously integrating and analyzing data from management and the milking parlor. We demonstrate with these applications that it is possible to develop integrated continuous decision-support tools that could potentially reduce diet costs by $99/cow per yr and that it is possible to provide a new dimension for monitoring health events by identifying cows at higher risk of CM and by detecting 90% of CM cases a few milkings before disease onset. We are securely advancing toward our overarching goal of developing our Dairy Brain. This is an ongoing innovative project that is anticipated to transform how dairy farms operate.
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Big Data , Sistemas Computacionais , Indústria de Laticínios/métodos , Tomada de Decisões , Mastite Bovina/diagnóstico , Animais , Bovinos , Doenças dos Bovinos/diagnóstico , Doenças dos Bovinos/genética , Doenças dos Bovinos/fisiopatologia , Sistemas Computacionais/normas , Indústria de Laticínios/economia , Indústria de Laticínios/estatística & dados numéricos , Dieta/veterinária , Feminino , Humanos , Lactação , Estudos Longitudinais , Mastite Bovina/genética , Mastite Bovina/fisiopatologia , Leite/economia , Necessidades NutricionaisRESUMO
A survey to explore the challenges and opportunities for dairy farm data management and governance was completed by 73 farmers and 96 non-farmers. Although 91% of them find data sharing beneficial, 69% are unfamiliar with data collection protocols and standards, and 66% of farmers feel powerless over their data chain of custody. Although 58% of farmers share data, only 19% of them recall having signed a data share agreement. Fifty-two percent of respondents agree that data collected on farm belongs only to the farmer, with 25% of farmers believing intellectual property products are being developed with their data, and 90% of all said companies should pay farmers when making money from their data. Farmers and non-farmers are somewhat concerned about data ownership, security, and confidentiality, but non-farmers were more concerned about data collection standards and lack of integration. Sixty-two percent of farmers integrate data from different sources. Farmers' most used technologies are milk composition (67%) and early disease detection (56%); most desired technologies are body condition score (56%) and automatic milking systems (46%); most abandoned technologies are temperature and activity sensors (14%) and automatic sorting gates (13%). A better understanding of these issues is paramount for the industry's long-term sustainability.
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We have applied social network analysis (SNA) to data on voluntary cow movement through a sort gate in an automatic milking system to identify pairs of cows that repeatedly passed through a sort gate in close succession (affinity pairs). The SNA was applied to social groups defined by four pens on a dairy farm, each served by an automatic milking system (AMS). Each pen was equipped with an automatic sorting gate that identified when cows voluntarily moved from the resting area to either milking or feeding areas. The aim of this study was two-fold: to determine if SNA could identify affinity pairs and to determine if milk production was affected when affinity pairs where broken. Cow traffic and milking performance data from a commercial guided-flow AMS dairy farm were used. Average number of milked cows was 214 ± 34, distributed in four AMS over 1 year. The SNA was able to identify clear affinity pairs and showed when these pairings were formed and broken as cows entered and left the social group (pen). The trend in all four pens was toward higher-than-expected milk production during periods of affinity. Moreover, we found that when affinities were broken (separation of cow pairs) the day-to-day variability in milk production was three times higher than for cows in an affinity pair. The results of this exploratory study suggest that SNA could be potentially used as a tool to reduce milk yield variation and better understand the social dynamics of dairy cows supporting management and welfare decisions.
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This analysis is performed to obtain information on the current situation regarding phosphorus (P), cobalt (Co), copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn) concentrations in cow diets of commercial dairy herds in Québec, Canada, and to compare them with National Research Council recommendations. Data are collected on 100 Holstein dairy herds in Québec, Canada, and 4430 cows were involved. Rations are analyzed for selected minerals and cow requirements relative to the recommendations were calculated. Median percentages of mineral recommendations fulfilled by forage were 55%, 196%, 54%, 776%, 181%, and 44% for P, Co, Cu, Fe, Mn, and Zn, respectively. Daily dietary concentrations of P, Cu, Mn, and Zn decreased as lactation progressed, whereas Co and Fe were stable throughout lactation. Phosphorus was the mineral fed the closest to the requirements, cows below 21 days in milk were even underfed by 11%. All studied trace minerals were fed in excess for the majority of cows. Cobalt was fed on average 480% above requirements regardless of the stage of lactation. For Cu, Fe, Mn, and Zn, rations for cows below 21 days in milk were fed 23% (95% confidence interval: 15-32), 930% (849-1019), 281% (251-314), and 35% (22-47) above the recommendations, respectively, and were closer to the requirements than after 21 days in milk. These results show that most nutritionists are aware that precision feeding regarding P is important to minimize detrimental environmental impacts of dairy production. However, some efforts should be made to limit trace mineral overfeeding to ensure environmental resiliency.
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Data governance is a growing concern in the dairy farm industry because of the lack of legal regulation. In this commentary paper, we discuss the status quo of the available legislation and codes, as well as some possible solutions. To our knowledge, there are currently four codes of practice that address agriculture data worldwide, and their objectives are similar: (1) raise awareness of diverse data challenges such as data sharing and data privacy, (2) provide data security, and (3) illustrate the importance of the transparency of terms and conditions of data sharing contracts. However, all these codes are voluntary, which limits their adoption. We propose a Farmers Bill of Rights for the dairy data ecosystem to address some key components around data ownership and transparency in data sharing. Our hope is to start the discussion to create a balanced environment to promote equity within the data economy, encourage proper data stewardship, and to foster trust and harmony between the industry companies and the farmers when it comes to sharing data.
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Dairy farm decision support systems (DSS) are tools which help dairy farmers to solve complex problems by improving the decision-making processes. In this paper, we are interested in newer generation, integrated DSS (IDSS), which additionally and concurrently: (1) receive continuous data feed from on-farm and off-farm data collection systems and (2) integrate more than one data stream to produce insightful outcomes. The scientific community and the allied dairy community have not been successful in developing, disseminating, and promoting a sustained adoption of IDSS. Thus, this paper identifies barriers to adoption as well as factors that would promote the sustained adoption of IDSS. The main barriers to adoption discussed include perceived lack of a good value proposition, complexities of practical application, and ease of use; and IDSS challenges related to data collection, data standards, data integration, and data shareability. Success in the sustainable adoption of IDSS depends on solving these problems and also addressing intrinsic issues related to the development, maintenance, and functioning of IDSS. There is a need for coordinated action by all the main stakeholders in the dairy sector to realize the potential benefits of IDSS, including all important players in the dairy industry production and distribution chain.
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Understanding variation in host-associated microbial communities is important given the relevance of microbiomes to host physiology and health. Using 560 fecal samples collected from wild chimpanzees (Pan troglodytes) across their range, we assessed how geography, genetics, climate, vegetation, and diet relate to gut microbial community structure (prokaryotes, eukaryotic parasites) at multiple spatial scales. We observed a high degree of regional specificity in the microbiome composition, which was associated with host genetics, available plant foods, and potentially with cultural differences in tool use, which affect diet. Genetic differences drove community composition at large scales, while vegetation and potentially tool use drove within-region differences, likely due to their influence on diet. Unlike industrialized human populations in the United States, where regional differences in the gut microbiome are undetectable, chimpanzee gut microbiomes are far more variable across space, suggesting that technological developments have decoupled humans from their local environments, obscuring regional differences that could have been important during human evolution. IMPORTANCE Gut microbial communities are drivers of primate physiology and health, but the factors that influence the gut microbiome in wild primate populations remain largely undetermined. We report data from a continent-wide survey of wild chimpanzee gut microbiota and highlight the effects of genetics, vegetation, and potentially even tool use at different spatial scales on the chimpanzee gut microbiome, including bacteria, archaea, and eukaryotic parasites. Microbial community dissimilarity was strongly correlated with chimpanzee population genetic dissimilarity, and vegetation composition and consumption of algae, honey, nuts, and termites were potentially associated with additional divergence in microbial communities between sampling sites. Our results suggest that host genetics, geography, and climate play a far stronger role in structuring the gut microbiome in chimpanzees than in humans.
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Much like humans, chimpanzees occupy diverse habitats and exhibit extensive behavioural variability. However, chimpanzees are recognized as a discontinuous species, with four subspecies separated by historical geographic barriers. Nevertheless, their range-wide degree of genetic connectivity remains poorly resolved, mainly due to sampling limitations. By analyzing a geographically comprehensive sample set amplified at microsatellite markers that inform recent population history, we found that isolation by distance explains most of the range-wide genetic structure of chimpanzees. Furthermore, we did not identify spatial discontinuities corresponding with the recognized subspecies, suggesting that some of the subspecies-delineating geographic barriers were recently permeable to gene flow. Substantial range-wide genetic connectivity is consistent with the hypothesis that behavioural flexibility is a salient driver of chimpanzee responses to changing environmental conditions. Finally, our observation of strong local differentiation associated with recent anthropogenic pressures portends future loss of critical genetic diversity if habitat fragmentation and population isolation continue unabated.
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Comportamento Animal , Evolução Molecular , Variação Genética , Componentes Genômicos , Repetições de Microssatélites , Pan troglodytes/genética , Migração Animal , Animais , Ecossistema , Interação Gene-Ambiente , Genética Populacional , Pan troglodytes/psicologia , Filogenia , Especificidade da EspécieRESUMO
Different protozoa and metazoa have been detected in great apes, monkeys and humans with possible interspecies exchanges. Some are either nonpathogenic or their detrimental effects on the host are not yet known. Others lead to serious diseases that can even be fatal. Their survey remains of great importance for public health and animal conservation. Fecal samples from gorillas (Gorilla gorilla) and humans living in same area in the Republic of Congo, chimpanzees (Pan troglodytes) from Senegal and one other from the Republic of Congo, Guinea baboons (Papio papio) from Senegal, hamadryas baboons (Papio hamadryas) from Djibouti and Barbary macaques (Macaca sylvanus) from Algeria, were collected. DNA was extracted and screened using specific qPCR assays for the presence of a large number of helminths and protozoa. Positive samples were then amplified in standard PCRs and sequenced when possible. Overall, infection rate was 36.5% in all non-human primates (NHPs) and 31.6% in humans. Great apes were more often infected (63.6%) than monkeys (7.3%). At least twelve parasite species, including ten nematodes and two protozoa were discovered in NHPs and five species, including four nematodes and a protozoan in humans. The prevalences of Giarida lamblia, Necator americanus, Enterobius vermicularis, Strongyloides stercoralis were similar between gorillas and human community co-habiting the same forest ecosystem in the Republic of Congo. In addition, human specific Mansonella perstans (5.1%) and other Mansonella spp. (5.1%) detected in these gorillas suggest a possible cross-species exchange. Low prevalence (2%) of Ascaris lumbricoides, Enterobius vermicularis, Strongyloides stercoralis were observed in chimpanzees, as well as a high prevalence of Abbreviata caucasica (57.1%), which should be considered carefully as this parasite can affect other NHPs, animals and humans. The Barbary macaques were less infected (7.2%) and Oesophagostomum muntiacum was the main parasite detected (5.8%). Finally, we report the presence of Pelodera sp. and an environmental Nematoda DNAs in chimpanzee feces, Nematoda sp. and Bodo sp. in gorillas, as well as DNA of uncharacterized Nematoda in apes and humans, but with a relatively lower prevalence in humans. Prevalence of extraintestinal parasites remains underestimated since feces are not the suitable sampling methods. Using non-invasive sampling (feces) we provide important information on helminths and protozoa that can infect African NHPs and human communities living around them. Public health and animal conservation authorities need to be aware of these infections, as parasites detected in African NHPs could affect both human and other animals' health.
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Human ethnographic knowledge covers hundreds of societies, whereas chimpanzee ethnography encompasses at most 15 communities. Using termite fishing as a window into the richness of chimpanzee cultural diversity, we address a potential sampling bias with 39 additional communities across Africa. Previously, termite fishing was known from eight locations with two distinguishable techniques observed in only two communities. Here, we add nine termite-fishing communities not studied before, revealing 38 different technical elements, as well as community-specific combinations of three to seven elements. Thirty of those were not ecologically constrained, permitting the investigation of chimpanzee termite-fishing culture. The number and combination of elements shared among individuals were more similar within communities than between them, thus supporting community-majority conformity via social imitation. The variation in community-specific combinations of elements parallels cultural diversity in human greeting norms or chopstick etiquette. We suggest that termite fishing in wild chimpanzees shows some elements of cumulative cultural diversity.
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Diversidade Cultural , Comportamento Social , Animais , Pan troglodytesRESUMO
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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BACKGROUND: Gut parasites exert an important influence on the gut microbiome, with many studies focusing on the human gut microbiome. It has, however, undergone severe richness depletion. Hygienic lifestyle, antimicrobial treatments and altered gut homeostasis (e.g., chronic inflammation) reduce gut microbiome richness and also parasite prevalence; which may confound results. Studying species closely related to humans could help overcome this problem by providing insights into the ancestral relationship between humans, their gut microbiome and their gut parasites. Chimpanzees are a particularly promising model as they have similar gut microbiomes to humans and many parasites infect both species. AIMS: We study the interaction between gut microbiome and enteric parasites in chimpanzees. Investigating what novel insights a closely related species can reveal when compared to studies on humans. METHODS: Using eighty-seven faecal samples from wild western chimpanzees (Pan troglodytes verus) in Senegal, we combine 16S rRNA gene amplicon sequencing for gut microbiome characterization with PCR detection of parasite taxa (Blastocystis sp., Strongyloides spp., Giardia duodenalis, Cryptosporidium spp., Plasmodium spp., Filariae and Trypanosomatidae). We test for differences in gut microbiota ecosystem traits and taxonomical composition between Blastocystis and Strongyloides bearing and non-bearing samples. RESULTS: For Blastocystis, twelve differentially abundant taxa (e.g., Methanobrevibacter), including Prevotella and Ruminococcus-Methanobrevibacter enterotype markers, replicate findings in humans. However, several richness indices are lower in Blastocystis carriers, contradicting human studies. This indicates Blastocystis, unlike Strongyloides, is associated to a "poor health" gut microbiome, as does the fact that Faecalibacterium, a bacterium with gut protective traits, is absent in Blastocystis-positive samples. Strongyloides was associated to Alloprevotella and five other taxonomic groups. Each parasite had its unique impact on the gut microbiota indicating parasite-specific niches. Our results suggest that studying the gut microbiomes of wild chimpanzees could help disentangle biological from artefactual associations between gut microbiomes and parasites.