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\"Taking care of cows isn't always easy, but the reward is great: milk! Dairy farms are an important food source. This book takes a look at how cows live on dairy farms. From grazing in fields to life in the barn and how they are milked, young readers will love learning about these amazing animals and how they help give us food\"--Provided by publisher.

Aim: The present study was carried out to determine load of total bacteria, Escherichia coli and Salmonella spp. in dairy farm and its environmental components. In addition, the antibiogram profile of the isolated bacteria having public health impact was also determined along with identification of virulence and resistance genes by polymerase chain reaction (PCR) under a one-health approach. Materials and Methods: A total of 240 samples of six types (cow dung - 15, milk - 10, milkers' hand wash - 10, soil - 10 water - 5, and vegetables - 10) were collected from four dairy farms. For enumeration, the samples were cultured onto plate count agar, eosin methylene blue, and xylose-lysine deoxycholate agar and the isolation and identification of the E. coli and Salmonella spp. were performed based on morphology, cultural, staining, and biochemical properties followed by PCR. The pathogenic strains of E. coli stx1, stx2, and rfbO157 were also identified through PCR. The isolates were subjected to antimicrobial susceptibility test against 12 commonly used antibiotics by disk diffusion method. Detection of antibiotic resistance genes ereA, tetA, tetB, and SHV were performed by PCR. Results: The mean total bacterial count, E. coli and Salmonella spp. count in the samples ranged from 4.54±0.05 to 8.65±0.06, 3.62±0.07 to 7.04±0.48, and 2.52±0.08 to 5.87±0.05 log colony-forming unit/g or ml, respectively. Out of 240 samples, 180 (75%) isolates of E. coli and 136 (56.67%) isolates of Salmonella spp. were recovered through cultural and molecular tests. Among the 180 E. coli isolates, 47 (26.11%) were found positive for the presence of all the three virulent genes, of which stx1 was the most prevalent (13.33%). Only three isolates were identified as enterohemorrhagic E. coli. Antibiotic sensitivity test revealed that both E. coli and Salmonella spp. were found highly resistant to azithromycin, tetracycline, erythromycin, oxytetracycline, and ertapenem and susceptible to gentamycin, ciprofloxacin, and imipenem. Among the four antibiotic resistance genes, the most observable was tetA (80.51-84.74%) in E. coli and Salmonella spp. and SHV genes were the lowest one (22.06-25%). Conclusion: Dairy farm and their environmental components carry antibiotic-resistant pathogenic E. coli and Salmonella spp. that are potential threat for human health which requires a one-health approach to combat the threat.

Describes what life is like for dairy cows on a Wisconsin farm, telling how they are milked, what they eat, and what they produce besides milk.

Effective dairy farm management requires the regular estimation and prediction of pasture biomass. This study explored the suitability of high spatio-temporal resolution Sentinel-2 imagery and the applicability of advanced machine learning techniques for estimating aboveground biomass at the paddock level in five dairy farms across northern Tasmania, Australia. A sequential neural network model was developed by integrating Sentinel-2 time-series data, weekly field biomass observations and daily climate variables from 2017 to 2018. Linear least-squares regression was employed for evaluating the results for model calibration and validation. Optimal model performance was realised with an R2 of ≈0.6, a root-mean-square error (RMSE) of ≈356 kg dry matter (DM)/ha and a mean absolute error (MAE) of 262 kg DM/ha. These performance markers indicated the results were within the variability of the pasture biomass measured in the field, and therefore represent a relatively high prediction accuracy. Sensitivity analysis further revealed what impact each farm’s in situ measurement, pasture management and grazing practices have on the model’s predictions. The study demonstrated the potential benefits and feasibility of improving biomass estimation in a cheap and rapid manner over traditional field measurement and commonly used remote-sensing methods. The proposed approach will help farmers and policymakers to estimate the amount of pasture present for optimising grazing management and improving decision-making regarding dairy farming.

Agricultural productivity growth can simultaneously increase profit and reduce pollution. Yet, the impact of productivity growth on both has not been quantified. The objective of our study was to develop an approach to quantify the extent to which agricultural productivity growth can increase profit and reduce pollution. Focusing on nitrogen pollution, we applied the approach to a sample of 341 intensive Dutch dairy farms for the years 2006–2017. Using a Bennet–Lowe formulation, we measured economic and nitrogen productivities over time and across farms. We applied Data Envelopment Analysis to determine the potential for productivity growth from reducing economic and nitrogen inefficiencies and assessed the impact on profit and nitrogen pollution levels. Using a two-stage by-production model, we set profit maximisation as the overarching objective to account for the economic production behaviour of farmers. We found that if laggard farmers adopted the best practices of their best peers, they could on average increase annual gross profit by 34% and simultaneously reduce the N surplus by 50% during the time period, which is a win–win situation for farmers and the environment. The magnitude of these gains corroborates the suggestion that productivity growth could be a game-changer for agricultural sustainability.

The addition of dairy cow manure—stored in manure pits—to field soil has the potential to introduce not only organic nutrients but also mammalian microbial communities and antimicrobial resistance genes (ARGs) to soil communities. Using shotgun sequencing paired with functional metagenomics, we showed that microbial community composition changed between fresh manure and manure pit samples with a decrease in gut-associated pathobionts, while ARG abundance and diversity remained high. In agricultural settings, microbes and antimicrobial resistance genes (ARGs) have the potential to be transferred across diverse environments and ecosystems. The consequences of these microbial transfers are unclear and understudied. On dairy farms, the storage of cow manure in manure pits and subsequent application to field soil as a fertilizer may facilitate the spread of the mammalian gut microbiome and its associated ARGs to the environment. To determine the extent of both taxonomic and resistance similarity during these transitions, we collected fresh manure, manure from pits, and field soil across 15 different dairy farms for three consecutive seasons. We used a combination of shotgun metagenomic sequencing and functional metagenomics to quantitatively interrogate taxonomic and ARG compositional variation on farms. We found that as the microbiome transitions from fresh dairy cow manure to manure pits, microbial taxonomic compositions and resistance profiles experience distinct restructuring, including decreases in alpha diversity and shifts in specific ARG abundances that potentially correspond to fresh manure going from a gut-structured community to an environment-structured community. Further, we did not find evidence of shared microbial community or a transfer of ARGs between manure and field soil microbiomes. Our results suggest that fresh manure experiences a compositional change in manure pits during storage and that the storage of manure in manure pits does not result in a depletion of ARGs. We did not find evidence of taxonomic or ARG restructuring of soil microbiota with the application of manure to field soils, as soil communities remained resilient to manure-induced perturbation. IMPORTANCE The addition of dairy cow manure—stored in manure pits—to field soil has the potential to introduce not only organic nutrients but also mammalian microbial communities and antimicrobial resistance genes (ARGs) to soil communities. Using shotgun sequencing paired with functional metagenomics, we showed that microbial community composition changed between fresh manure and manure pit samples with a decrease in gut-associated pathobionts, while ARG abundance and diversity remained high. However, field soil communities were distinct from those in manure in both microbial taxonomic and ARG composition. These results broaden our understanding of the transfer of microbial communities in agricultural settings and suggest that field soil microbial communities are resilient against the deposition of ARGs or microbial communities from manure.

Cattle farming is responsible for about 15% of Colombia's greenhouse gas emissions (GHGE). In the department of Cundinamarca, specialized dairy farms located in the high tropics contribute 14% of the national milk production, and 94% of them are small-scale producers. Therefore, mitigation strategies for dairy farms are needed to achieve national GHGE reduction targets. This study aims to quantify the carbon footprint (CF), through a Life cycle Assessment Methodology, of 82 specialized dairy farms at the farm gate in 3 regions of Cundinamarca: Central Savannah, West Savannah and Ubate Valley; and to identify the contribution of Acacia decurrens, Baccharis latifolia, and Sambucus peruviana to milk production increases and GHGE mitigation potential. The comparison of the effect of the tree species on the measured variables was carried out by analysis of variance under a completely random design. GHGE were calculated using the 2019 Refinement to 2006 IPCC guidelines and impact factors from databases. The emission factor for enteric methane from cows was estimated by considering the equation proposed by Niu et al. (Glob Chang Biol 24:3368–3389, 2018). The functional units corresponded to one kg fat and protein-corrected milk (FPCM) and one kg live weight gain in a cradle-to-farm-gate approach. For the 3 regions, enteric fermentation and manure left on pasture were the main on-farm sources of GHGE, and feed manufacturing was the main off-farm source. Milk CFs ranged from 1.5 to 2.2 kg CO2-eq kg FPCM−1. The inclusion Acacia decurrens, Baccharis latifolia, and Sambucus peruviana in cattle diets reduced the milk CF by 13–26% and increased milk yield by 19–37% in the three regions. Therefore, the inclusion of locally available forages in dairy cattle diets is a potential sustainable GHGE mitigation option that dairy farmers, from the Colombian high tropics, can adopt.

Animal welfare plays a key role in achieving sustainable development goals and addresses the growing concerns of citizens and consumers about animals and their products. This study, using the extended Theory of Planned Behavior (TPB), aims to examine the social and psychological factors influencing pro-animal behavior among dairy farmers. Despite its importance, this topic has been less explored in the research literature of sustainability. Data for the research were collected through a cross-sectional survey and a closed-ended questionnaire from two groups of traditional (n = 122) and factory (n = 208) dairy farmers in Iran. A random sampling approach was used to select the samples. SPSS 26 and SEM-PLS 3 software were used for data analysis, assessment of measurement and structural models, hypothesis testing, and model validation. The results showed that attitude (Beta = 0.43; Sig = 0.0001), perceived behavioral control (Beta = 0.31; Sig = 0.0001), and subjective norms (Beta = 0.11; Sig = 0.01) had statistically significant and positive impacts on pro-animal behavior. According to the results, awareness of the consequences of pro-animal behavior had a statistically significant and positive impact on attitude towards pro-animal behavior (Beta = 0.54; Sig = 0.0001) and perceived behavioral control (Beta = 0.30; Sig = 0.0001). Additionally, perceived behavioral control (Beta = 0.12; Sig = 0.006) and subjective norms (Beta = 0.17; Sig = 0.0001) had a positive and significant impact on attitude towards pro-animal behavior. In general, the independent variables in the validated model of present study could account for 48 percent of pro-animal behavior variance as the dependent variable. This study, by extending and applying the TPB to analyze pro-animal behavior, not only contributes theoretically to the sustainability literature but also provides practical and valuable insights to facilitate the development of pro-animal behaviors and improve animal welfare for stockmen, decision-makers, and responsible organizations.

Staphylococcus aureus is one of the most prevalent pathogens causing bovine mastitis in the world, in part because of its ease of adaptation to various hosts and the environment. This study aimed to determine the prevalence of S. aureus in Colombian dairy farms and its relationship with the causal network of subclinical mastitis. From thirteen dairy farms enrolled, 1288 quarter milk samples (QMS) and 330 teat samples were taken from cows with positive (70.1%) and negative California Mastitis Test (CMT). In addition, 126 samples from the milking parlor environment and 40 from workers (nasal) were collected. On each dairy farm, a survey was conducted, and the milking process was monitored on the day of sampling. S. aureus was identified in 176 samples, i.e., 138 QMS, 20 from teats, 8 from the milking parlor environment, and 10 from workers’ nasal swabs. Isolates identified as S. aureus underwent proteomics (clustering of mass spectrum) and molecular (tuf, coa, spa Ig, clfA, and eno genes) analysis. Regarding proteomics results, isolates were distributed into three clusters, each with members from all sources and all farms. Concerning molecular analysis, the virulence-related genes clfA and eno were identified in 41.3% and 37.8% of S. aureus isolates, respectively. We provide evidence on the circulation of S. aureus strains with limited variability among animals, humans, and the environment. The parameters with the lowest compliance in the farms which may be implicated in the transmission of S. aureus are the lack of handwashing and abnormal milk handling.