April 2018

April 2018

How Important are Red Lentils?

No one has to be reminded about the upheaval in the industry over the past 14 months.   As sellers of cash crops, it can be more difficult to step outside the day to day buying and selling and get a handle on how prices will interact going forward.   Maybe, it really is not that difficult.   Consider the AGT stock price at the end of March as it returns to below $20.00 / share.   I was thinking ‘that chart is familiar.’    Here are two screenshots showing the AGT stock and the #1 Red lentil chart from Statpub.

In 2014, AGT stock price and Red Lentils tracked around $18, peaking around $45, and returning to below $20 today.   It is hardly a coincidence as they chart in unison complete with a double top.   When red lentils ‘work’ the industry ‘works.’   When India needs red lentils, it is enough to carry an entire industry and apparently a stock price. 

Have a good month.

DGN  03/22/2018

March 2018


March 2018

Big Data and Smart Farms (excerpt – Science Direct)

In view of the technical changes brought forth by Big Data and Smart Farming, we seek to understand the stakeholder network around the farm. The literature suggests major shifts in roles and power relations among different players in existing agri-food chains. We observed the changing roles of old and new software suppliers in relation to Big Data and farming and emerging landscape of data-driven initiatives with the prominent role of big tech and data companies like Google and IBM. In Fig. 5, the current landscape of data-driven initiatives is visualized.

Fig. 5

Fig. 5. The landscape of the Big Data network with business players.

The stakeholder networks exhibits a high degree of dynamics with new players taking over the roles played by other players and the incumbents assuming new roles in relation to agricultural Big Data. As opportunities for Big Data have surfaced in the agribusiness sector, big agriculture companies such as Monsanto and John Deere have spent hundreds of millions of dollars on technologies that use detailed data on soil type, seed variety, and weather to help farmers cut costs and increase yields (Faulkner and Cebul, 2014). Other players include various accelerators, incubators, venture capital firms, and corporate venture funds (Monsanto, DuPont, Syngenta, Bayer, DOW etc.) (Lane, 2015).

Monsanto has been pushing big-data analytics across all its business lines, from climate prediction to genetic engineering. It is trying to persuade more farmers to adopt its cloud services. Monsanto says farmers benefit most when they allow the company to analyse their data – along with that of other farmers – to help them find the best solutions for each patch of land (Guild, 2014).

While corporates are very much engaged with Big Data and agriculture, start-ups are at the heart of action, providing solutions across the value chain, from infrastructure and sensors all the way down to software that manages the many streams of data from across the farm. As the ag-tech space heats up, an increasing number of small tech start-ups are launching products giving their bigger counterparts a run for their money. In the USA, start-ups like FarmLogs (Guild, 2014), FarmLink (Hardy, 2014) and 640 Labs challenge agribusiness giants like Monsanto, Deere, DuPont Pioneer (Plume, 2014). One observes a swarm of data-service start-ups such as FarmBot (an integrated open-source precision agriculture system) and Climate Corporation. Their products are powered by many of the same data sources, particularly those that are freely available such as from weather services and Google Maps. They can also access data gathered by farm machines and transferred wirelessly to the cloud. Traditional agri-IT firms such as NEC and Dacom are active with a precision farming trial in Romania using environmental sensors and Big Data analytics software to maximize yields (NEC, 2014).

Venture capital firms are now keen on investing in agriculture technology companies such as Blue River Technology, a business focusing on the use of computer vision and robotics in agriculture (Royse, 2014). The new players to Smart Farming are tech companies that were traditionally not active in agriculture. For example, Japanese technology firms such as Fujitsu are helping farmers with their cloud based farming systems (Anonymous, 2014c). Fujitsu collects data (rainfall, humidity, soil temperatures) from a network of cameras and sensors across the country to help farmers in Japan better manage its crops and expenses (Carlson, 2012). Data processing specialists are likely to become partners of producers as Big Data delivers on its promise to fundamentally change the competitiveness of producers.

Beside business players such as corporates and start-ups, there are many public institutions (e.g., universities, USDA, the American Farm Bureau Federation, GODAN) that are actively influencing Big Data applications in farming through their advocacy on open data and data-driven innovation or their emphasis on governance issues concerning data ownership and privacy issues. Well-known examples are the Big Data Coalition, Open Agriculture Data Alliance (OADA) and AgGateway. Public institutions like the USDA, for example, want to harness the power of agricultural data points created by connected farming equipment, drones, and even satellites to enable precision agriculture for policy objectives like food security and sustainability. Precision farming is considered to be the “holy grail” because it is the means by which the food supply and demand imbalance will be solved. To achieve that precision, farmers need a lot of data to inform their planting strategies. That is why USDA is investing in big, open data projects. It is expected that open data and Big Data will be combined together to provide farmers and consumers just the right kind of information to make the best decisions (Semantic Community, 2015).

4.4. Network management

4.4.1. Organization

Data ownership is an important issue in discussions on the governance of agricultural Big Data generated by smart machinery such as tractors from John Deere (Burrus, 2014). In particular, value and ownership of precision agricultural data have received much attention in business media (Haire, 2014). It has become a common practice to sign Big Data agreements on ownership and control data between farmers and agriculture technology providers (Anonymous, 2014a). Such agreements address questions such as: How can farmers make use of Big Data? Where does the data come from? How much data can we collect? Where is it stored? How do we make use of it? Who owns this data? Which companies are involved in data processing?

There is also a growing number of initiatives to address or ease privacy and security concerns. For example, the Big Data Coalition, a coalition of major farm organizations and agricultural technology providers in the USA, has set principles on data ownership, data collection, notice, third-party access and use, transparency and consistency, choice, portability, data availability, market speculation, liability and security safeguards (Haire, 2014). And AgGateway, a non-profit organization with more than 200 member companies in the USA, have drawn a white paper that presents ways to incorporate data privacy and standards (AgGateway, 2014). It provides users of farm data and their customers with issues to consider when establishing policies, procedures, and agreements on using that data instead of setting principles and privacy norms.

The ‘Ownership Principle’ of the Big Data Coalition states that “We believe farmers own information generated on their farming operations. However, it is the responsibility of the farmer to agree upon data use and sharing with the other stakeholders (…).” While having concerns about data ownership, farmers also see how much companies are investing in Big Data. In 2013, Monsanto paid nearly 1 billion US dollars to acquire The Climate Corporation, and more industry consolidation is expected. Farmers want to make sure they reap the profits from Big Data, too. Such change of thinking may lead to new business models that allow shared harvesting of value from data.

Big data applications in Smart Farming will potentially raise many power-related issues (Orts and Spigonardo, 2014). There might be companies emerging that gain much power because they get all the data. In the agri-food chain these could be input suppliers or commodity traders, leading to a further power shift in market positions (Lesser, 2014). This power shift can also lead to potential abuses of data e.g. by the GMO lobby or agricultural commodity markets or manipulation of companies (Noyes, 2014). Initially, these threats might not be obvious because for many applications small start-up companies with hardly any power are involved. However, it is a common business practice that these are acquired by bigger companies if they are successful and in this way the data still gets concentrated in the hands of one big player (Lesser, 2014). Gilpin (2015b), for example, concluded that Big Data is both a huge opportunity as a potential threat for farmers.