Detecting Weak Signals for the Future of Poultry Farms - Juniper Publishers

Archives of Animal & Poultry Sciences - Juniper Publishers

Short Communication

Poultry farms require energy in order to provide the highest thermal comfort, air quality and proper luminosity for birds. Energy is necessary to handle food, equipment, litter and waste, mortality disposal and sometimes even to provide water. In addition, energy costs tend to continue to increase everywhere and there is a widespread public pressure to reduce odors and gas emissions, including CO² [1].

The Spanish startup Avir Smart Comfort has developed an infrared heating system which provides energy comfort in poultry farms, with a significant decrease in energy consumption [2]. This system improves productivity on livestock farms, by using an intelligent far-infrared heating system combined with sensors, data collection and data analysis. With the development of this technology, as well as the automation of the installation, suitable for the demands of the livestock sector, Avir is a leading company in this type of installation in Spain.

There is a global trend and a growing concern for food quality and safety, and it is no longer limited to the final product, so all processes that occur from the birth of the animal until reaching the consumer need to be considered.

In conclusion, there are many trends that entrepreneurs need to know in order to make the right decisions. Some of these trends are well known at a global level, as for instance, energy efficiency improving or carbon footprint reduction.

However, the biggest threat for academia, governments, entrepreneurs and companies is the steady pace of changes. In fact, companies very often show their inability to manage and anticipate these developments on time [3]. Markets have proven to be complex environments in which it can be very difficult tomake the right decision at the right time, but undoubtedly doing so can mark the good future of an organization. Therefore, to foresee the advent of new technologies and their socio-economic impact is a necessity for many stakeholders.

The development of new processes to facilitate decision making in organizations considering data from different internal and external sources becomes more and more important

One of the methodologies to detect future trends is the implementation of systems to detect future signals of the future. A weak signal is a change factor which is hardly discernible in the present but will constitute a strong tendency in the future [4].

These small changes mask the potential for the development of more significant phenomena and transcendental changes in the environment, hence the importance of being able to identify and monitor them as soon as possible. These phenomena, if they evolve to the point that they become relevant (strong signals), have the potential to reinforce an action plan or to obstruct it.

A system for the detection of weak signals of the future has been implemented [5] considering the three variables of the Hiltunen semiotic model [6]. This system consists of 6 stages, which are:

1. Collection and integration of information

2. ETL (Extract, transform and load)

3. Category assignation

4. Text mining focused on detecting weak signals

5. Semantic Analysis: Multiword expressions

6. Interpretation and evaluation

In order to decrease the carbon footprint of poultry farm, a good idea is to increase the use of green energy that is generated by solar panels. Knowing the future trends in the solar panel sector before those trends are mainstream, can make the difference and drive a startup to success.

Analyzing a dataset of more than forty thousand documents from three different types of sources: newspapers, scientific papers and social networks, a list of 33 terms have been detected as candidates for weak signals, which are: amorphous, annealing, boost, cavity, conservation, coverage, education, emerging, engineering, evolutionary, fast, financial, free, health, inner, instantaneous, insulation, interface, matrix, mitigation, nanoparticles, nuclear, office, political, reserves, robust, scheduling, self, splitting, squares, vacuum, window, zero. The map of results is shown in Figure 1.

In order to obtain more accurate results, other natural language processing tools should be added such as multi-word expressions, parts-of-speech tagging, bag of words recognition, regular expressions or sentiment analysis. Finally, groups of experts will be consulted to check the matching of their predictions with the obtained results.

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