AGRIBUSINESS: MEAT SHARING AND CROWD COW IN THE RED MEAT VALUE CHAIN.

 We have been discussing about franchising as an option in the red meat value chain.  Discussions have spanned various models of making profit in the red meat value chain from owning an abattoir to running a meat shop.

Livestock business 

  The force of collaborations in form of franchising to grow a business is a tested and proven strategy. Crowd sourcing method can be remolded and used in the red meat value chain. Meat sharing aptly termed crowd cow.  scenario 4 

  Meat sharing and crowd cow: interested in the red meat value chain but dont fit into any other highlighted options? Then this is for you.

   Meat sharing and crowd cow; this method allows various individuals to buy parts of a cow, the cow is only slaughtered when all parts are sold. These are then dispatched to individuals or they pick up at your shop. 

                             

Meat sharing and crowd cow is  risk free because interested buyers pay before cow is delivered.  The business strategy is crowdfunding, different individuals are paying for shares in a cow. It is the business owner that will determine price per share based on the price of cow he intends to purchase.

 The meat sharing and crowd cow needs an advertising platform to get people informed of operation. The platform will announce dates and time of crowd cow with timeline to opt -in. When the  target is reached then the business owner moves in the abattoir to slaughter and pack the beef as desired by investor.

 Advantages of this model is 1) No risk attached because you pay to opt-in. 2) No waste or loss because the client has paid and must pick up . 3) No extra cost of freezing left-overs. 4) Business is based on demand per time, no slaughtering on  assumptions. 5) Minimal cost of start -up, a social media page and means of dispatch is all you need.

Tweets show daily rhythm of our thinking.

Tweets show daily rhythm of our thinking. A study of 800-million tweets finds distinct daily cycles in our thinking patterns. Our mode of thinking changes at different times of the day and follows a 24-hour pattern, according to new findings published in PLOS ONE. University of Bristol researchers were able to study our thinking behaviour by analysing seven-billion words used in 800-million tweets. Researchers in artificial intelligence (AI) and in medicine used AI methods to analyse aggregated and anonymised UK twitter content sampled every hour over the course of four years across 54 of the UK's largest cities to determine if our thinking modes change collectively. The researchers revealed different emotional and cognitive modalities in our thoughts by identifying variations in language through tracking the use of specific words across the twitter sample which are associated with 73 psychometric indicators, and are used to help interpret information about our thinking style.

Machine learning detects marketing and sale of opioids on Twitter.

Machine learning detects marketing and sale of opioids on Twitter.Using advanced machine learning, a cross disciplinary team of University of California San Diego researchers developed technology that mined Twitter to identify entities illegally selling prescription opioids online. Between June and November 2015, some 619,937 tweets containing the keywords codeine, Percocet, fentanyl, Vicodin, Oxycontin, oxycodone and hydrocodone were collected. The findings, published online in the American Journal of Public Health in October, detected 1,778 posts that were marketing the sale of controlled substances, 90 percent included hyperlinks to online sites for purchase.

Social media trends can predict tipping points in vaccine scares..

Social media trends can predict tipping points in vaccine scares.Analyzing trends on Twitter and Google can help predict vaccine scares that can lead to disease outbreaks, according to a study from the University of Waterloo. In the study, researchers examined Google searches and geocoded tweets with the help of artificial intelligence and a mathematical model. The resulting data enabled them to analyze public perceptions on the value of getting vaccinated and determine when a population was getting close to a tipping point. In the study, a tipping point represented the point at which vaccine coverage declines dramatically due to spreading fear, which could cause large disease outbreaks due to a loss of population immunity. As part of their study, the researchers collected tweets that mentioned measles-mumps-rubella vaccine and classified their sentiment using artificial intelligence computer programs. They also collected data on measles-related Google searches. Their mathematical theory on vaccine scares predicted what kind of early warning signals they should observe in the data. They found they were able to detect those signals in data from California before the 2014-15 Disneyland, California measles outbreak, which showed early warning signs of a tipping point two years before the outbreak occurred. Their mathematical model also predicted how the Disneyland outbreak helped push California back from the tipping point by making parents more afraid of the disease than the vaccine.

Spread of diseases in farmed animals shown using social media analysis.

The Spread of diseases in farmed animals can be shown using social network analysis according to a research published in the PLOS. 

Researchers have shown that looking at movements of operators and vehicles between farms in the same way we look at contacts in social networks can help explain the spread of dangerous infectious diseases of livestock, such as foot-and-mouth disease and avian influenza. This research can contribute to the development of more accurate tools for predicting the spread of livestock diseases and may help implement more effective biosecurity measures in farms.

This research, published in PLOS Computational Biology, can contribute to the development of more accurate tools for predicting the spread of livestock diseases and may help implement more effective biosecurity measures in farms. The research showed that the network of contacts originated from on-farm visits by veterinarians in dairy farms of Northern Italy displays hidden features that cannot be detected by simply looking at the frequency of visits and unveils patterns of infection otherwise unexplained. 

The authors discovered that veterinarians' movements produce an unexpectedly large number of potentially infectious contacts between farms that can quickly spread dangerous livestock diseases. The research, made possible by the availability of high-resolution data in space and time on veterinarian movements in the study area, shed light on the actual significance of operator movements in disease spread, a still poorly understood topic due to the highly diverse and complex nature of such movements and to privacy issues in data collection. 

 The researchers compared the role of veterinarian movements on diseases spread with those of animal exchange between farms, which is recognized as the most effective transmission route for livestock infectious diseases. 

 They found that co-occurrence of operator movements and animal exchanges is synergistic, largely amplifying the potential for disease propagation. The study shows how multilayer network analysis substantially improves the way diseases spread can be described, thus contributing to their control.