The Hidden Effects of Dairy Farming on Public and Environmental Health in the Netherlands, India, Ethiopia, and Uganda, Considering the Use of Antibiotics and Other Agro-chemicals

People in developing countries currently consume on average one-third the meat and one-quarter of the milk products per capita compared to the richer North, but this is changing rapidly. The amount of meat consumed in developing countries over the past has grown three times as much as it did in the developed countries. The Livestock Revolution is primarily driven by demand. Poor people everywhere are eating more animal products as their incomes rise above poverty level and as they become urbanized. By 2020, the share of developing countries in total world meat consumption will expand from 52% currently to 63%. By 2020, developing countries will consume 107 million metric tons (mmt) more meat and 177 mmt more milk than they did in 1996/1998, dwarfing developed-country increases of 19 mmt for meat and 32 mmt for milk. The projected increase in livestock production will require annual feed consumption of cereals to rise by nearly 300 mmt by 2020. Nonetheless, the inflation-adjusted prices of livestock and feed commodities are expected to fall marginally by 2020, compared to precipitous declines in the past 20 y. Structural change in the diets of billions of people is a primal force not easily reversed by governments. The incomes and nutrition of millions of rural poor in developing countries are improving. Yet in many cases these dietary changes also create serious environmental and health problems that require active policy involvement to prevent irreversible consequences.

Bacterial resistance is a rapidly escalating threat to public health as our arsenal of effective antibiotics dwindles. Therefore, there is an urgent need for new antibiotics. Drug discovery has historically focused on bacteria growing in planktonic cultures. Many antibiotics were originally developed to target individual bacterial cells, being assessed in vitro against microorganisms in a planktonic mode of life. However, towards the end of the 20th century it became clear that many bacteria live as complex communities called biofilms in their natural habitat, and this includes habitats within a human host. The biofilm mode of life provides advantages to microorganisms, such as enhanced resistance towards environmental stresses, including antibiotic challenge. The community level resistance provided by biofilms is distinct from resistance mechanisms that operate at a cellular level, and cannot be overlooked in the development of novel strategies to combat infectious diseases. The review compares mechanisms of antibiotic resistance at cellular and community levels in the light of past and present antibiotic discovery efforts. Future perspectives on novel strategies for treatment of biofilm-related infectious diseases are explored.

The objective of this study was to use pyrosequencing of the 16S rRNA genes to describe the microbial diversity of bovine milk samples derived from clinically unaffected quarters across a range of somatic cell counts (SCC) values or from clinical mastitis, culture negative quarters. The obtained microbiota profiles were used to distinguish healthy, subclinically and clinically affected quarters. Two dairy farms were used for the collection of milk samples. A total of 177 samples were used. Fifty samples derived from healthy, culture negative quarters with a SCC of less than 20,000 cells/ml (group 1); 34 samples derived from healthy, culture negative quarters, with a SCC ranging from 21,000 to 50,000 cells/ml (group 2); 26 samples derived from healthy, culture negative quarters with a SCC greater than 50,000 cells/ml (group 3); 34 samples derived from healthy, culture positive quarters, with a SCC greater than 400,000 (group 4, subclinical); and 33 samples derived from clinical mastitis, culture negative quarters (group 5, clinical). Bacterial DNA was isolated from these samples and the 16S rRNA genes were individually amplified and pyrosequenced. All samples analyzed revealed great microbial diversity. Four bacterial genera were present in every sample obtained from healthy quarters (Faecalibacterium spp., unclassified Lachnospiraceae, Propionibacterium spp. and Aeribacillus spp.). Discriminant analysis models showed that samples derived from healthy quarters were easily discriminated based on their microbiota profiles from samples derived from clinical mastitis, culture negative quarters; that was also the case for samples obtained from different farms. Staphylococcus spp. and Streptococcus spp. were among the most prevalent genera in all groups while a general multivariable linear model revealed that Sphingobacterium and Streptococcus prevalences were associated with increased 10 log SCC. Conversely, Nocardiodes and Paenibacillus were negatively correlated, and a higher percentage of the genera was associated with a lower 10 log SCC.