In its various shapes and forms, aquaculture is one of the most complex animal production industries today. Be it farming in lakes, ponds, or artificial setups, aquaculture is a biotechnology that encompasses many complex steps, from larval rearing to controlling water chemistry to managing complex feeding processes. Even at the simplest fish or shrimp farms, success depends on the correct balance of applied biology, husbandry and technology.
In the last few decades, research projects around the world have added much knowledge and know-how on the culture of various species using different farming methods. Nutritional requirements for many species are known, and feed production technologies have reached quite advanced levels.
Today aquaculture is called upon to solve some of the critical problems caused by an expanding human population coupled with the unprecedented destruction of fisheries resources. Aquaculture’s growth, however, is leading to pressures on both the environment and raw materials.
In answer, farmers are finding lower-impact feed sources and becoming more efficient in the use of existing resources. Since the public image of aquaculture directly determines the value of what it produces, the industry is also reducing impacts on water resources and seeking greater efficiencies in attaining a sustainable mode of production.
Going forward, research performed in laboratory settings must be more directly related to practices at commercial operations, where variability in the farming environment, diseases and other causes of stress can negatively affect production outcomes. For example, studies in the salmon industry have shown that despite the use of advanced technologies, 10 to 40 percent of the feed used is left uneaten.
At pangasius farms, where the species can theoretically be grow with a feed-conversion ratio of 1, conversion values are often above 2.5.
The existing expertise regarding health and sanitation practices, feeds and feeding management, husbandry processes, and operations and human resources management could all, if well implemented, significantly improve the industry’s performance. However, the implementation of this knowledge is a limiting factor for aquaculture today.
The “inventor” of modern business management, Peter Drucker, may not have said it, but the quotation “If you can’t measure it, you can’t improve it” attributed to him is considered a maxim in today’s management science. It basically stresses the key role that information should play in management decisions. This is an area where aquaculture, as many other primary production industries, is very deficient.
There is very little automation in this industry, and due to its complexity, improvements will take a long time. Therefore the men and women working in aquaculture represent the key resource upon which business success or failure will depend. If we are to gather information to better manage our fish and shrimp farms, we need to get the men and women working in the field to gather it in a reliable and timely manner so that it can be used in good management decisions.
Aquaculture is an industry in which each daily feeding counts toward the success of the business and understanding of the health and environmental conditions matters for the good biological performance of the livestock. Yet in aquaculture companies around the world, the workforce tends to be somewhat poorly paid, poorly trained and usually expected to do repetitive tasks under difficult weather conditions..
No matter how good the processes at a farm are, the people who work in the field must be motivated to do a good job to achieve satisfactory results. Motivation is the result of three factors that impact performance in a positive way: having a purposeful mission, autonomy to progress according to the mission and mastery of the skills necessary to perform well.
These factors should be addressed in any management program, especially in industries like aquaculture. They require a management attitude that values the workforce, but also mechanisms where information is gathered as operations happen, in real time, and feedback given frequently to workers as to the quality of their performance. Staff members who are engaged in their work are motivated. Training should, ideally, be supplied almost continuously to support staff in their everyday decisions.
Aquaculture is clearly a complex industry, which today struggles with issues of efficiency and sustainability that can be improved greatly by better management. People play a key role, and improving motivation will deliver results. Data and information link all the above and act as the glue that binds performance, people and management in a way that delivers the further efficiencies needed to make aquaculture even more viable and sustainable.
The use of technologies that allow real-time data collection by workers and applies the information from every person at a fish or shrimp farm. Data collection focuses on feeding, environmental parameters, stock behavior and health status, infrastructure conditions and other parameters that affect farm performance.
The vast majority of this data has to be collected and recorded by workers at the very moment they perform their assigned tasks. Today we have technologies that make this possible and accessible to even small family businesses through the use of cloud technologies and mobile equipment such as smartphones and electronic tablets.
Data-recording applications can deliver knowledge such as the best growth and feeding models scientists have developed. They can also provide a form of crowd-learning advice to farmers that derives from the experiences of hundreds or thousands of other operations similar to theirs.
Ultimately, gathering all this data in one place allows us to understand the factors that really affect production – whether nutritional, environmental or husbandry-related – in the real world of farms, as opposed to the laboratory.
Becoming a data-driven farm is no small task, and it means much more than simply using a dashboard or having a data warehouse. To reach analytics maturity – i.e. the ability to manage and use data to make business decisions – data needs to become a core part of a farm’s culture.
Farms typically report numbers to meet regulations or measure progress. A data-driven farm goes beyond this, moving from reporting to analysis, action, and value:
- Using data to make proactive, not reactive decisions.
- Using data to answer, “why did this happen?” instead of “what happened?”
- Using data to influence business decisions and prescribe actions.
This starts with using the right system to automatically collect, organize, analyze, and translate data to real-time insight.
Too often, farms collect incomplete, inconsistent, or poorly formatted data. An investment in quality data collection, whether it’s through better sensors, cameras or other technology, both saves employee time and minimises the risk of human error. Higher-quality data also means higher-quality insights down the line. This answers questions like:
- How many times did we go over the government lice limit?
- How many mortalities did each site have?
- What were the average lice levels last year?
Farmers are inundated with tools to manage farm information. The data needed to make daily production decisions may be scattered between Excel sheets, emails, papers and databases. When information exists in silos, value is lost. Pooling all farm data into one place gives employees the most comprehensive, up-to-date picture of farm health at any given time answering questions such as:
- What were the environmental conditions when these lice numbers dipped?
- Did this mortality event occur after we treated for lice?
- How did mechanical treatments impact feeding this year?
Even the highest-quality dataset will mean nothing unless it’s accessible and searchable. Tools that slice, dice, analyse, and share data give farmers control over their information.
With filtering, grouping and aggregation, important trends and patterns will emerge to help inform decisions, answering questions such as:
- How is each generation comparing to the previous?
- Do certain cages consistently perform better?
- What are the factors leading to the highest fillet quality?
Fish health changes every day, and farmers could spend a lifetime digging deeper into analysis. But time is of the essence. Machine learning models will automatically ingest a constant stream of data and find an essentially infinite number of correlations and nuances between factors – insights impossible to find with the human eye. The models learn from information and adjust in real-time, not in retrospect. This gives farmers the tools to think ahead about questions such as these:
- What is my risk of PD this summer?
- How will my neighbours’ sea lice situations impact me?
- What is the optimal treatment option for this cage, in these conditions, and at this time?
The right foundation connects important information to those who need it sooner. In the end, though, humans are the ones that ask questions and make decisions – a data-driven farm combines the accuracy of computers with human intuition and experience.
A culture shift
Often, data-driven strategies stop at the executive level. A truly data-driven farm culture only gains traction when everyone, from the fish health biologists to financial planners, is empowered to use data to do their job better.
Solutions must be flexible. Data will have many different uses in and out of farm operations. Solutions cannot become a barrier to access: if it makes an employee’s job more difficult, he or she is not going to use it. Strategies need to meet the needs of all employees with an easy-to-use, flexible application.
Solutions must work with industry expertise. The aquaculture industry is full of unknowns, and many datasets are incomplete or poorly formatted. This doesn’t mean they’re not useful. The key is to augment this information with traditional industry knowledge. Data-driven solutions need to alert decision-makers, so they can combine insights with their expertise and experience.
Organizations must constantly test and improve. Each team needs to have confidence in and understand the value of data, so they’re empowered to collect better-quality information and use it to improve their work. And leaders should use this information to iteratively improve—strong data-driven strategies invest in a foundation but constantly revisit based on results.
In aquaculture, just a few hours can make or break fish health. Centralized data storage and alerts are key for farms located in remote areas, where information can take valuable time to get to those who need it. This can also mean the difference of millions in lost revenue: a 2019 report by Nofima and Kontali estimated that slaughtering 1 million fish at 3.5 kg rather than 5 kg costs a farm NOK 30 to 45 million (US$3.5 to 5.3 million).
A data-driven farm can not only reduce the risk of larger disasters but also continually optimise: more effectively using feed, expanding the feeding windows, minimising time in the water and more. With an intelligent system that is constantly and automatically learning, the potential impact is constantly growing.