How Data Analytics is Transforming Agriculture

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How data analytics is transforming agriculture. Pham, X., & Stack, M.

Journal

Business Horizons, January–February 2018, Volume 61, Issue 1, 125-133

Reviewer

Dr. Luciano Castro, Clinical Associate Professor

Summary

“How Data Analytics is Transforming Agriculture” by Xuan Pham and Martin Stack presents the rise of precision agriculture and data analytics and elaborates on the consequences to agriculture at two different but interconnected levels.

First, on the industry macro level, it tries to answer the following: how will the industry look in the future? As a result of mergers, acquisitions and new entrants, what type of companies will survive new dynamics and be participating in the agricultural industry in the future? On the second level, it elaborates on the following: what should companies excel at to be able to succeed in the marketplace? In other words, what sort of skills, competences or resources might companies possess in order to compete in a new marketplace?

Considering the entire value chain—the sequence of companies and transactions from ag input manufacturers, ag retailers, farmers, processors, the food industry, food retailers and food consumers—the authors chose to focus their discussion on the upstream connections that link input suppliers, ag input retailers and farmers, as this is where most impactful innovations are happening. Reflecting on the questions previously mentioned will help guide us in understanding what is coming next and how to best prepare ourselves in a dynamic agriculture value chain environment.

What this means for Food and Agricultural Business

On the macro level and in a nutshell, precision agriculture and data analytics are pushing for a more integrated view on farmers and farming. This has reshaped (or forced a reshape) in the industry structure because it is challenging industry segment boundaries on the traditional siloed view of inputs: fertilizers, seed, chemical and machinery.

For conventional farming, the guiding goal was to steadily increase production. Farmers used to treat each input as a separate entity without paying attention to the integrated nature of all input decisions and different ways to read performance. They combined seeds, fertilizers and chemicals to maximize yields. Productivity output was preferred to input cost minimization. The structure of the market and the nature of competition did not promote a holistic, integrated set of relationships between the input suppliers and the farmers. 

In precision agriculture and data analytics reality, things have changed considerably. There is an emphasis on the collection and utilization of vast amounts of data to make better agricultural decisions. Examples of machine data include fuel rate, speed, direction, hydraulics and diagnostics. Examples of agronomic data are planting and fertilizing target and actual population, spacing, total acres, moisture levels at harvest time and grain temperature. The combination of data with variables such as those mentioned above should produce different scenarios and results.

This process requires an integrated view on the farmer journey from planning through harvesting and trading. As a consequence, it demands a need for a holistic view on the agricultural production process. It is not necessarily attached to a pre-defined industry structure. This is redefining industry boundaries since firms are being forced to review their basic operations and answer the fundamental question, “what business am I in?” Producers will come with more information on the whole and integrated production process, which will challenge traditional suppliers.

For example, companies such as Bayer-Monsanto (chemical and seed industries) and John Deere (machinery industry) have seen each other before in different markets and could easily cooperate in different initiatives. However, after they both launched FieldView and FarmSight respectively, they crossed each other’s path and found themselves competing for the data management systems and the pursuit of their own line of business to influence customers on their input decisions. Similarly, Syngenta started its AgriEdge platform, AGCO launched Fuse and Agrium (now Nutrien) launched Echelon, among many others. All of these organizations found themselves competing and cooperating at some point. They each had to make decisions related to what extent their platforms would be “open” or “closed”, allowing for more or less potential partnerships. However, integration is needed for real value creation.

Therefore, we may expect greater changes in the industry structure toward integrated businesses that will be able to manage a diverse set of information and deliver best results for farmers. New entrants or recently born companies may succeed in this market independently or as acquired units of existing large companies, precisely because they have this holistic view of farming and will better integrate information related to farmers’ decision making. Although, the ability to collect, interpret and capitalize on the data being generated requires a very different set of capabilities by farmers and input suppliers. That’s the second level of impact on the micro level, and it should define market success further on.

If one considers a general data value chain, there are three types of data players that can be identified. The first are those that are able to generate or collect data, named data holders. This is a basic and starting competence. The second are those that are known as data specialists–the ones mining the data for information generation that can strengthen a firm’s position in the market. They may supply information for customers to deal with by themselves.

The third and most important type is the segment with the big data mindset–companies that can take advantage of the option value of data. These are the strategists–or companies–that will play with the generated data and create value for customers or for the entire agricultural value chain. Data are managed by data strategists through being reused for different purposes, recombined with other data for the creation of powerful insights, or extended for the use of new applications that are not yet defined. From Pham and Stack’s perspective, John Deere and Monsanto were the only ones with the big data mindset in the industry, and they are the ones leading the process so far. 

These key capabilities differentiated into three types are those that precision agricultural reality is pushing players to have. However, companies that wish to win in a data value chain should be flexible and open. They must be fully informed on data analytics and open for innovation and strategic change. This brings a different competition pattern. Therefore, we may expect to see entrants in the industry that have these competences. More and more companies from the IT space like Google, IBM or Amazon will appear proposing to make a difference, and ag-techs will populate the marketplace due to their integrated perspective and data management natures. This is all because a new set of capabilities is being required.

Clearly, differentiation strategies will be based less on products and services and more on an organization’s ability to deliver and integrate an agriculture solution platform and take advantage of all of the information generated from fields and turn it into deliverable value from data analytics.

Precision agriculture and data analytics are therefore pushing for a new agriculture input industry structure, causing a new set of capabilities to be required. As an agribusiness company at any side of the agricultural vale chain, it is very important to consider this new reality and reflect on how to better incorporate these changes. I suggest asking yourself, what does this have to do with my business, and how should I review the strategic direction of my business considering precision agriculture and data analytics?

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