What’s in store for Asian smallholder farmers in the Big Data hype?

By Camille Anne Mendizabal (World Agroforestry Centre) | December 29, 2017

Exploring how Big Data’s potentials can be used to enhance Asian farmers’ climate resiliency.


Smart tractors, agribots, survey drones, texting cows—these may seem like agriculture buzzwords, but with Big Data accelerating agricultural digitalization, these may soon come into fruition and be seen in farms in Asia.

What caused the hype?

The information age we are in now provided four technological milestones which paved the way for the digitalization of agriculture through Big Data.

First among these milestones is the improvement of peoples’ access to smartphones and data services. This provided opportunities for them to access agriculture information that could guide them in making farm-related decisions.

Secondly, the increased availability of cheaper smart agriculture sensors also helped farmers in monitoring their farms and adapting their practices to changing climatic conditions and environmental factors.

Another milestone that hastened the digitalization of agriculture is the improvement of the quality of satellite information and satellite images which led to better and more updated climate forecasts. Lastly, in our enhanced ability to analyse and interpret data provides better for support climate-smart agriculture (CSA) research and development efforts.

What is Big Data’s niche in CSA?

If the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) succeeds in utilizing Big Data, its biggest impacts can be seen in improving the following stages in the market-value chain: planning, selecting quality inputs, on-farm production, storage, and access to markets.

In the planning stage, Big Data is deemed most useful in helping farmers decide what to plant and when to plant it. It can also be used to guide farmers in selecting high-quality inputs. During production stage, applied data can potentially improve precision and adaptability of farming interventions.

Digitally warehouse receipts and digitally-enabled harvest loans may help reduce postharvest losses while they are stored. Moreover, the improved climate forecasts can also provide farmers with timely reminders and alerts on climate-related risks which are useful in monitoring farm operations and preventing yield loss.

Harnessing Big Data’s potential also enables the combination of climate forecasts with micro-insurance systems to further enhance farmers’ resilience to climate risks.

Big Data can also be sourced from social media. Through this, we can make the most out of the farmers’ groups established through social media platforms, Facebook posts and tweets by using them to build information database.

During the Joint CCAFS SEA-SA CSA Workshop in Hanoi, a special panel discussion tackled the potential of using Big Data to increase agricultural productivity, and at the same time manage climate-related risks. Photo: Duong Minh Tuan/ICRAF

Can smallholder farmers benefit from Big Data?

Despite the rosy picture that Big Data presents, it cannot be denied that we still have a long way to go before we can reap the benefits from it and before these benefits trickle down to smallholder farmers. As Andrew Jarvis, one of the Flagship Leaders of CCAFS said:

Big Data provides huge promise, but a handful of success stories for smallholder farmers.”

Dr. Leocadio Sebastian, CCAFS Southeast Asia programme leader, raised concerns about how using big data can be used further widen the digital divide. As of now, only commercial farms have access to technologies which can make sense of big data.

Unfortunately, 76% of the farmers in Asia are smallholder farmers, the majority of which do not have access to these technologies. Hence, the challenge now is for CCAFS to help make it work for this 76%.

Social differentiation in access and illiteracy in using these technologies also pose a challenge in this digitalization. Thus, CCAFS should work on downscaling information from forecasts to something more comprehensible and more relevant to farmers’ context.

How can CCAFS make Big Data work for smallholder farmers?

As of now, there is an insufficient publicly available data on agriculture which can be used to build a sustainable data ecosystem that scientists, extension workers and farmers can access. Building an information ecosystem on CSA that is more accessible to people and resolving data privacy issues could help address such problems.

Moreover, building the capacity of a new generation of agricultural scientists and field agronomists to enhance their skills not only in analyzing, and interpreting data, but more importantly in providing farmers with comprehensible, personalized, and actionable information should now be prioritized.

Creating an enabling environment for establishing public-private partnerships can also help resolve privacy issues in utilizing big data and can help maximize available technologies owned by public and private sectors to further develop information services for farmers.

If these abovementioned challenges are resolved, the rosy picture of modernized, climate-smart agriculture that now seems as a hype can finally be turned into reality.

Camille Anne Mendizabal is the junior communications specialist for the World Agroforestry Centre Philippines. She is also a communication consultant with the CCAFS SEA program.

Article Disclaimer: This article was published by the CCAFS-CGIAR and retrieved on 01/06/2018 and posted here for information and educational purposes only. The views and contents of the article remain those of the authors. We will not be held accountable for the reliability and accuracy of the materials. If you need additional information on the published contents and materials, please contact the original authors and publisher. Please cite the authors, original source, and INDESEEM Inc. accordingly.

Climate services for smarter farming – what’s it all about?


Dr. Julian Ramirez-Villegas, a Climate Impacts Scientist, at CIAT and the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Photo by: Neil Palmer / CIAT

 | Oct 5, 2017


Over the last few years, CIAT, CCAFS and their partners* have been doing groundbreaking work to provide climate information to help farmers make smarter decisions. Having achieved success in Colombia and Honduras, now the team wants to take climate services to the rest of the world. Dr. Julian Ramirez-Villegas, a Climate Impacts Scientist, talked to us in-depth about what makes this a revolutionary approach that can enable farmers to thrive in a changing climate.

What is a climate service?

A climate service is basically the act of providing specific pieces of information about the climate in a systematic and sustained way to allow a user to make a decision. Climate affects crop productivity quite significantly. Globally, it’s been estimated that maybe a third of global crop production depends on climate. So you need to be able to understand what the climate effects are and be able to manage your crops.

So if you’re a farmer, accurate and reliable climate information is really important — it can help you make decisions about what crop to grow, which variety of that crop to plant, and when best to plant it.

Sustainably providing this kind of information in the right formats and means to farmers, extension agents, or other people that are helping farmers make decisions — that’s what constitutes a climate service.

Can you give a real-world example?

In parts of Casanare and Meta — departments in Colombia — where farmers grow rice or maize, there’s a single growing season, from around May to September. But during that season, you can have pretty unstable rainfall: It can rain a lot for a few days, but then it can stay dry for a few days. This uncertainty hinders crop productivity because it affects the growth of the crop significantly.

Also, the areas can experience relatively long dry spells, so if you’re a farmer, the ideal situation is to be really sure that there’s going to be rain for the few days after planting. If there isn’t, then the seed that you put in the soil won’t germinate, and all the seed that you purchased will go to waste.

So a valuable climate service in that particular case would involve providing reliable information about the most reliable time to plant.

How do you generate that information?

We’re used to forecasts on television giving us weather information for the next few days, and these help us take decisions in our daily lives. But farmers need reliable information over the course of months — seasonal climate forecasts as well as weather forecasts. Seasonal conditions are harder to predict. The new information technology tools and approaches enable us to generate reliable climatic predictions that farmers can trust.

Typically, we try to answer questions like: Are the next few months going to be wetter or dryer than normal? Or are they going to be around normal? And for that information, part of the process involves looking at weather records to try to construct possible seasonal predictions. We feed this information into crop models and use big data analytics, which allow us to calculate how a particular crop behaves under certain climatic conditions. The model will show us the likelihood of a crop performing well or poorly if planted at a particular time, or will tell us which varieties may perform best under the expected conditions. The models can be quite precise: They can tell you that if you plant your crop between the 15th and the 20th of May, for example, then you’re very likely to achieve the highest productivity.

How do the models account for the fact that climate change is also happening? Doesn’t it mean that the future won’t be like the past?

In our mathematical models, we include the long-term trend. However, in a general sense, we should always be reminded that “all models are wrong, but some are useful.” In some instances, when climate change leads to more extreme climatic conditions that have not been experienced in the past, the type of statistical models that we are using may not work well. But this is why it is very important to continuously and closely monitor local climatic conditions. This will allow us to identify where and when extreme conditions may be increasing, and make our models “learn” from these events, too.

Where do you get the historical climate information? Can you download it online or do you have to request it from the weather agency?

Many of the weather agencies we interact with are working toward having online systems where you can download directly, or make a request online. At the moment, we request it, and they’re happy to share it. Colombia has a policy for open meteorological data, which makes our work very effective and efficient there. We also recognize the hard work that IDEAM, the Colombian meteorological agency, puts into data collection, curation, and sharing.

We can typically get 15-30 years of climate information for a given location. In some cases, we can get up to 40 years. It depends on how long the meteorological agency has been recording the climate.

What kinds of recommendations do you provide to farmers?

For rice and maize, which are the two crops that we work on most in Colombia, the analysis tells you basically three things: firstly, whether a farmer in a particular locality should plant or not plant — because there might be a risk of crop failure; secondly, if the farmer should plant, then when they should do it; and thirdly, which crop variety they should plant, based on the likely seasonal climate.

There’s no standard set of recommendations. They are tailored depending on the climate predictions for that season, and on the local conditions and knowledge of technicians and farmers. Agronomy in a way is a kind of recipe, but you need to ensure you get the ingredients right for each situation.

How do you make sure this information gets to farmers?

We have a series of different delivery mechanisms, and when I say “we,” it’s actually not only CIAT, but also our partners in these countries.

We have been creating new tools and knowledge, but at the same time, we have been building the capacity of farmers’ organizations, to empower them and help them embrace this knowledge. We’re also working with teams of people who not only run models, but who also look at the climate conditions, and to interpret the outputs of models and convert that into advice for farmers.

We have also set up and been working through platforms called technical agroclimatic committees. These are roundtables of people from different institutions, including those within the meteorological service and different farmers’ organizations for different crops, so you have climate experts and farmers sitting together. The committees are able to issue the forecast as a joint output, along with recommendations for farmers in a given region. That comes typically in the form of a bulletin. Because these agroclimatic committees are local, they provide very specific information, and as such, they have been quite effective: Whoever comes to the meeting leaves with a clear set of recommendations and a clear idea of what might be coming weather-wise in the next few months. They then are able to share these with the farmers they work with.

What are some crucial elements to providing successful climate services?

First, providing a climate service is a two-way communication process with users. You need to talk to the users of the service, and you need to make sure that the information is tailored to the needs of those users.

Building the capacity of farmers organizations was crucial to our work in Colombia. The project we have there provided them with funding, which allowed them to hire people to develop tools with us. However, now they are themselves funding their teams, thus preserving the analytical capacity in-house, and being less dependent on external funding. Of course, external funding always helps to explore new topics and expand work, but the core capacity is now there.

With experts from IDEAM, farmer organizations, and other institutions, we also developed an online platform that automatically provides forecasts. With the help of farmers and technicians, we were able to make this much more tailored to users. It was a lot of work, but it increased the sense that they belong to the process. It empowered them and helped make the tool much more locally relevant and useful.

So you can see that providing climate services is the work of many people. Even inside CIAT, there are more than 30 people working on it. And outside CIAT, there are farmers’ organizations, secretaries or ministries of agriculture, meteorological service providers, and climate experts from the International Research Institute for Climate and Society (IRI) at Columbia University. It’s in no small part thanks to IRI that we and our partners know what climate prediction tools exist and how to use them.

This work dates back a number of years, and it’s had many, many players without whom we could not have not pursued this.

So what makes climate services a unique proposition to farmers and for CIAT?

Before we started our first project in Colombia, under an agreement with the Ministry of Agriculture, a lot of people were aware of the importance of the climate but didn’t know what to do about it. After going to the field, we realized that Colombian farmers were planting based on what happened last year. So if I planted on the first of May last year and I got a good crop, then I’ll do the same this year. With the amount of climate variability that we have here in Colombia, particularly rainfall, that’s a recipe for disaster. You cannot expect that climate conditions are going to be exactly the same from one year to the other: At the extreme, you might have a La Niña cycle this year, so it’s very wet, and an El Niño cycle next year, meaning it’s hotter and drier. It just wouldn’t make sense to apply exactly the same strategy across time. There was clearly a gap for a service that would systematically provide information about what to do when certain climate conditions are coming.

CIAT, of course, has been working on climate change for a long time, and leads the CGIAR Research Program of Climate Change, Agriculture, and Food Security (CCAFS). We’re collaborating with, for example, IRI at Columbia University, to be able to build tools that can connect what climate scientists are producing to insights that are relevant to farmers. We’re also able to use our network of partners to reach farmers. And this is where we see our role and comparative advantage: in building that bridge to connect hardcore climate scientists with farmers in the field.

What are the challenges to providing climate services? How do you address them?

Right now, we’re working with farmers’ organizations, who are empowered with the tools; they provide information to thousands of farmers. But there are many farmers that don’t belong to any farmer organization. They are typically small-scale farmers who are difficult to reach by typical extension services or communication channels. Also, particularly in Latin America, there are many regions that don’t have government-sponsored extension services at all. That makes it more difficult to reach these farmers with climate information. Plus, they’re often the most vulnerable farmers.

In these cases, other communication channels should be used. For example, radio would be much more effective because it is particularly good at reaching those in remote areas. But then it’s not only about the mechanism; it’s also about what you are communicating. In many localities that we’ve worked in, people would say, “Yeah on the radio I get the forecast, I get the climate predictions, but if I live in the Cauca Valley and the forecast is for the Andean region, how is that going to be useful for me?” We need to make the forecasts locally relevant.

Radio is just one example. Text messages, or even TV, could also work. There’s one very interesting example in Rwanda. There, CIAT is in the process of establishing a system whereby you have a TV screen located in district agricultural offices, which constantly provides climate predictions that are tailored to agriculture in that locality. I thought that was quite a neat idea.

Another key challenge is improving the accuracy of the prediction models. You say, “OK, you’re making climate predictions, so you’re telling me what might happen in the next six months. Is that really accurate?” The analysis that we’ve done suggests that the predictions are accurate about 80 percent of the time. This is actually a really high success rate, but we need to find ways of making the predictions yet more accurate, if we want to reduce the climate risks associated with farming to a minimum.

Do you think 100 percent accuracy is possible?

No natural phenomenon is 100 percent predictable. But we can reach greater levels of accuracy with better models. This would require significant investment in research on climate prediction.

Not only that; in some cases, we’ve realized there’s also an issue of data quality. So in regions where data quality is poor or where you have very few weather stations, the climate is more challenging to predict.

What’s next for CIAT’s climate service work?

Some of the prediction methods and mathematical models that we’re using are of a lot of interest to CIAT and partners in Africa and in Asia. So we really want to improve knowledge exchange across regions. To do this, we need to ensure that everyone knows there is a climate service framework and that we’re able to fit projects into that framework. Next, we want to take full advantage of the tools that we have or that we’re developing, to reduce duplication and enhance integration.

We see of course the area of climate services growing as we go into the future. Right now, we’re working with the U.S. Agency for International Development (USAID) to create a climate service suitability map. This map would take into account factors such as level of climate predictability, the difference between potential and actual yields in different regions, and the levels of food insecurity and malnutrition. It would show hot spots, where if you invest in climate services, you might be very effective at getting development outcomes. Once we get this work done, it should help USAID reorient its investment in its different priority countries.

So we expect a lot of growth, and I think so far we’re getting a lot of traction.

Would you say climate services is like the missing recipe to farming success?

Yes, though that’s not to say that it all works perfectly, but we’ve made enormous progress, and right now, we estimate that 300,000 farmers are receiving climate information as a result of our work. It’s a great start, but there’s a lot more to do.

CIAT wants to make this work truly global. We’ve proven that it works, that farmer organizations and farmers like and embrace it, and that it can save them money and boost productivity. Imagine if we could implement similar systems in sub-Saharan Africa or South East Asia — there are potentially millions and millions that could benefit.

* CIAT’s partners in providing climate services include the Colombian National Federation of Rice Growers (FEDEARROZ), Colombian Association for Fruits and Vegetables (ASOHOFRUCOL), the National Federation of Cereal and Grain Legume Growers (FENALCE), the National Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM), National Directorate of Science and Technology, Honduras (DICTA),  Agronet, Local Technical Agro-climatic Committees (LTACs), the Permanent Committee for Contingencies (COPECO), and the Secretariat of Agriculture and Livestock (SAG). Funders include the Colombian Ministry of Agriculture and Rural Development (MADR), the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the Climate Services for Resilience Development (CSRD) Program of the U.S. Agency for International Development (USAID), and The Nature Conservancy (TNC).

Article Disclaimer: This article was published by CIAT and retrieved on 10/5/2017 and reposted here for information and educational purposes only. The views and contents of the article remain those of the original authors and publisher. We will not be held accountable for the reliability, accuracy, and validity of the published materials. If you need additional information about the contents and materials of the article, please contact the original authors and publisher. INDESEEM is an emerging nonprofit, research and development organization which seeks to enhance development partnerships in developing countries to achieve the sustainable development goals by 2030 and beyond. Please cite article accordingly. Thank You.



Using Big Data to Help Combat Malnutrition in Africa


Source: Farming First, 2017


Dr. Debisi Araba, CIAT


In this guest blog for the #SDG2countdown campaign‘s week on SDG2.2: ending malnutrition, Dr. Debisi Araba outlines a new big data initiative designed to detect malnutrition before disaster strikes. Dr. Araba is a member of the Malabo Montpellier Panel, set up to provide evidence and promote dialogue for better outcomes in Agriculture and Food Security in Africa, and serves as Africa Director for the International Center for Tropical Agriculture (CIAT.)

Chronic malnutrition affects one in four people in sub-Saharan Africa. This increases the region’s vulnerability during food crises and compromises the continent’s overall development. We know we need to improve the way we respond to food crises to protect African food production systems, farmers, and the people who depend on the food they produce.

Food crisis response

Currently, responses to food crises are reactive, not proactive. Signs of malnutrition may not be apparent until a food crisis erupts, and decision-makers lack the data to combat crises, making response coordination difficult.

There are various forces that influence nutrition and it can be difficult to identify how they converge to cause widespread problems. In addition, most governments and aid organizations use multiple metrics and separate tracking systems to measure malnutrition. With so much data to absorb, it can be easy to miss early indicators of trouble brewing before a crisis kicks in.

This makes it impossible to form proactive food policies and escape the trap of constantly reacting to disruptions rather than getting ahead of hunger. Interventions are also limited to the household or community level and rarely focus on national and regional systems.

How data will help

An innovative new approach to collating and analyzing large sets of data could enable this shift to early action. Through machine learning, computer programs track complex and constantly changing data from multiple sources in order to “learn” from them and make predictions.

The International Center for Tropical Agriculture (CIAT) is applying machine learning technology to search for early signs of potential crop failures, drought, rising food prices, and other factors that can trigger food shortages. Over time, this bespoke system – known as the Nutrition Early Warning System (NEWS) – will become “smarter” and more accurate so that data can be used to predict the likelihood of malnutrition threats before they occur, while also suggesting mitigating measures.

NEWS would enable governments, donors, farmers, health care providers, NGOs and food companies to contribute towards and implement more rapid, tailored interventions.

CIAT will coordinate the development of NEWS, to be deployed in collaboration with partners to alert decision-makers to nutrition threats well ahead of a crisis. Initially, CIAT will use NEWS to focus on boosting nutrition in sub-Saharan Africa. By picking up food shortage triggers, the system will give relief agencies, donors and governments information they need to make informed decisions about agricultural policies and programmes.

Ongoing surveillance is expected to provide multiple recommendations for future nutrition interventions. The recommendations can be tailored to the needs of individual countries through national “nutrition dashboards”.  These will further refine insights available through NEWS. The dashboards will be accessible via a secure website that will regularly monitor and post updates on key nutrition and food security indicators.



Refining NEWS for Africa

CIAT, which leads the CGIAR Platform for Big Data in Agriculture, has already seen success using big data approaches to tackle agricultural challenges. In 2014, some 170 farmers in Colombia avoided potentially catastrophic losses after CIAT experts used a machine learning algorithm to analyse weather and crop data. It revealed drought on the horizon, and farmers were advised to skip a planting season, saving them more than US$3 million.

We now need to work with partners to track indicators of malnutrition in West, East and Central, and Southern Africa and to create and fully develop NEWS.

The NEWS white paper calls for collaboration between governments, development and relief agencies to find robust methods to track malnutrition indicators. In particular, it urges potential partners who want to harness big data to address fundamental challenges linked to agriculture and nutrition in the developing world, to join CIAT’s effort to create and fully develop the potential of NEWS across Africa.


Article Disclaimer: This article was published by the Farming First and retrieved on 08/01/2017 and posted here for information and educational purposes only. The views and contents of the article remain those of the authors. We will not be held accountable for the reliability and accuracy of the materials. If you need additional information on the published contents and materials, please contact the original authors and publisher. Please cite the authors, original source, and INDESEEM accordingly.









Your Pot Habit Is Making Climate Change Worse


Photo Credit: stdesign/Shutterstock

Community Broadcaster: Using Big Data for Community Radio


Written by: Ernesto Aguilar. October 14, 2016

Amid 170,000-plus attendees and 2,700 sessions, it is hard to not be awed by the sheer spectacle that wasDreamforce, the annual conference hosted by customer relationship management tools developer and service provider Salesforce.com But beyond rumors it will buy Twitter, why should Salesforce.com and CRM matter to small and local community radio, and what can radio learn?

Whether it is Tony Robbins or U2, Dreamforce attracts big names and marquee corporations. For good reason — Salesforce.com has made a tremendous name for itself across many industries, from finance to retail to every sector of technology imaginable. Some of the world’s biggest nonprofits use it to manage donor relations.

“All good,” you say, “but who cares?”

Hear me out. The noncommercial media space, including community radio and public media, has much to learn from successful nonprofits using data and technology to grow. The analytics revolution that Salesforce.com and competitors have ushered into modern life is also a chance for community radio and public media to assess what is most important. It matters because contributors have new expectations. It also matters because technology can help stations focus less on paperwork and more on the relationships with their supporters.

Three key things at Dreamforce struck me.

Community radio can use technology to grow what people expect of it. At Dreamforce there were so many instances of nonprofits using data, mobile and service to engage supporters in ways that press community radio to consider how it can inspire members and underwriters, and expand its own service. One UK nonprofit takes public concerns for the homeless to smartphones by allowing geolocation of people in need to service providers. Black Girls Code and Code 2040leaders shared stories about how they made alliances with businesses work best for their constituencies. Discussions like this are incredibly instructive for community radio, which often fancies itself as a voice for localism and subcommunities. Technology gives a chance to realize these ideals in a new, dynamic and creative way.

Community radio needs to embrace the new normal of data. Community radio collects all manner of information — recordings, volunteer information, etc. — but is missing a golden opportunity to do what it does better. More and more nonprofits are seeing how important it is to use data to show donors they care. Others still struggle. On the corporate side, Apple can tell you what a customer prefers and what they buy. Similarly, more and more nonprofits can track what a donor supports most, their average gift and when they’re most inclined to give. This level of tracking is eschewed in some circles as invasive. However, the reality is that more people, particularly those who give to charity, are those who organizations need to value more. In my public media work, I’ve talked to many members who feel the fact an organization doesn’t know their giving habits equates to not caring about them personally. The world today has conditioned most people to expect connectedness as never before. They expect to give out an email address and assume an organization has their billing information and giving history on file. Yet a 2014 study indicates catering to the new expectations of customers is among the lowest priorities. Community radio would benefit by switching it up.

The touch always matters most. Among the tiny and massive nonprofits at Dreamforce, the objective of all of these cool gizmos was clear: to make each organization’s people more effective at what they do, and to enable them to have the most information possible for quality contacts with donor-members. Staff change, addresses change, but all nonprofits know their communication needs to be consistent and smart. The longtime supporter should have assurances that even new people know their importance to an organization, their history and what matters to them. A new donor should have regular, but unobstrusive, contact and a smooth ride into an organization’s world. As community radio leaders are well aware, it is tough to raise money and convert the casual observer to active giver. Technology can only enhance the contact, but it’s that moment that matters most.

Community and public radio, and, really, all nonprofits, have some common cause in how your average business relates to a consumer. Where a business is trying to sell you an aesthetic, such as trust, a community radio station wants you to give money out of a higher ideal: mission, culture or a contribution to the commons. Community radio outlets are special snowflakes all, but we share the same challenges. Dreamforce demonstrates but one example of ways to tackle our biggest puzzles.

Ernesto Aguilar is membership program director of theNational Federation of Community Broadcasters. NFCB commentaries are featured regularly at radioworld.com.


Article Disclaimer: This article was published at the Radio World and retrieved on 10/15/2016 and posted at INDESEEM for information and educational purposes only. The views, ideas, materials, and content of the article remains those of the author. Please cite the original source accordingly.


Researchers on Big Data: More than a lot of numbers!

Photo by 123RF.com

By Steve Sonka and Yu-Tien Cheng, University of Illinois November 03, 2015 | 7:01 am EST

Big Data — the current buzzword of choice. Today it’s very easy to be overwhelmed by the hype promoting Big Data. Farm media, newspapers and general media, and conference speakers all extol the future transforming effects of Big Data, stressing that “Big Data will be essential to our future, whatever it is.” The goal of this article, and the series of five that follow, is to begin to unravel that “whatever it is” factor for agriculture.

We’ll definitely explore “whatever it is” from a managerial, not a computer science, perspective. Potential implications for agriculture will be the primary emphasis of the following set of articles:

1.Big Data: More Than a Lot of Numbers! This article emphasizes the role of analytics enabling the integration of various data types to generate insights. It stresses that the “Big” part of Big Data is necessary but it’s the “Data” part of Big Data that’s likely to affect management decisions.

2.Precision Ag: Not the Same as Big Data But… Today, it’s easy to be confused by the two concepts, Precision Ag and Big Data. In addition to briefly reviewing the impact of Precision Ag, this article stresses that Big Data is much more than Precision Ag. However, Precision Ag operations often will generate key elements of the data needed for Big Data applications.

3.Big Data in Farming: Why Matters! Big Data applications generally create predictions based on analysis of what has occurred. Uncertainty in farming, based in biology and weather, means that the science of agriculture (the Why) will need to be integrated within many of the sector’s Big Data applications.

4.Big Data: Alive and Growing in the Food Sector! Big Data already is being extensively employed at the genetics and consumer ends of the food and ag supply chain. This article will stress the potential for capabilities and knowledge generated at these levels to affect new opportunities within production agriculture.

5.A Big Data Revolution: What Would Drive It? Management within farming historically has been constrained by the fundamental reality that the cost of real-time measurement of farming operations exceeded the benefits from doing so. Sensing capabilities (from satellites, to drones, to small-scale weather monitors, to soil moisture and drainage metering) now being implemented will materially lessen that constraint. Doing so will create data streams (or is it floods?) by which Big Data applications can profoundly alter management on the farm.

6.A Big Data Revolution: Who Would Drive It? Over the last 30 years, novel applications of information technology have caused strategic change in many sectors of the economy. This article draws on those experiences to inform our thinking about the potential role of Big Data as a force for change in agriculture.

Big Data: More Than a Lot of Numbers!

Innovation has been critical to increased agricultural productivity and to support of an ever increasing global population. To be effective, however, each innovation had to be understood, adopted, and adapted by farmers and other managers.

Although Big Data is relatively new, it is the focus of intense media speculation today. However, it is important to remember that Big Data won’t have much impact unless it too is understood, adopted and adapted by farmers and other managers. This article provides several perspectives to support that process.

Big Data Defined

“90% of the data in the world today has been created in the last two years alone” (IBM, 2012).

In recent years, statements similar to IBM’s observation and associated predictions of a Big Data revolution have become increasingly more common. Some days it seems like we can’t escape them!

Actually, Big Data and its hype are relatively new. As shown in Figure 1, use of the term, Big Data, was barely noticeable prior to 2011. However, the term’s usage literally exploded in 2012 and 2013, expanding by a factor of 5 in just two years.

With all new concepts, it’s nice to have a definition. Big Data has had more than its fair share. Two that we find helpful are:

•The phrase “big data” refers to large, diverse, complex, longitudinal, and/or distributed data sets generated from instruments, sensors, Internet transactions, email, video, click streams, and/or all other digital sources available today and in the future (National Science Foundation, 2012).

•Big Data is high-volume, -velocity, and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making (Gartner IT Glossary, 2012).

These definitions are impressive. However, they really don’t tell us how Big Data will empower decision makers to create new economic and social value.

From Technology to Value

In the next few paragraphs, we’ll move beyond those definitions to explore how application of Big Data fosters economic growth. In this article, we’ll present non-ag examples because today there is more experience outside of agriculture. The following articles in this series will focus on agriculture.

Big Data generally is referred to as a singular thing. It’s not! In reality, Big Data is a capability. It is the capability to extract information and craft insights where previously it was not possible to do so.

Advances across several technologies are fueling the growing Big Data capability. These include, but are not limited to computation, data storage, communications, and sensing.

These individual technologies are “cool” and exciting. However, sometimes a focus on cool technologies can distract us from what is managerially important.

A commonly used lens when examining Big Data is to focus on its dimensions. Three dimensions (Figure 2) often are employed to describe Big Data: Volume, Velocity, and Variety. These three dimensions focus on the nature of data. However, just having data isn’t sufficient.

Analytics is the hidden, “secret sauce” of Big Data. Analytics refers to the increasingly sophisticated means by which analysts can create useful insights from available data.

Now let’s consider each dimension individually:

Interestingly, the Volume dimension of Big Data is not specifically defined. No single standard value specifies how big a dataset needs to be for it to be considered “Big”.

It’s not like Starbucks; where the Tall cup is 12 ounces and the Grande is 16 ounces. Rather, Big Data refers to datasets whose size exceeds the ability of the typical software used to capture, store, manage, and analyze.

This perspective is intentionally subjective and what is “Big” varies between industries and applications. An example of one firm’s use of Big Data is provided by GE — which now collects 50 million pieces of data from 10 million sensors everyday (Hardy, 2014).

GE installs sensors on turbines to collect information on the “health” of the blades. Typically, one gas turbine can generate 500 gigabytes of data daily. If use of that data can improve energy efficiency by 1%, GE can help customers save a total of $300 billion (Marr, 2014)! The numbers and their economic impact do get “Big” very quickly.

The Velocity dimension refers to the capability to acquire, understand, and respond to events as they occur. Sometimes it’s not enough just to know what’s happened; rather we want to know what’s happening. We’ve all become familiar with real-time traffic information available at our fingertips.

Google Map provides live traffic information by analyzing the speed of phones using the Google Map app on the road (Barth, 2009). Based on the changing traffic status and extensive analysis of factors that affect congestion, Google Map can suggest alternative routes in real-time to ensure a faster and smoother drive.

Variety, as a Big Data dimension, may be the most novel and intriguing. For many of us, our image of data is a spreadsheet filled with numbers meaningfully arranged in rows and columns.

With Big Data, the reality of “what is data” has wildly expanded. The lower row of Figure 3 shows some newer kinds of sensors in the world, from cell phones, to smart watches, and to smart lights.

Cell phones and watches can now monitor users’ health. Even light bulbs can be used to observe movements, which help some retailers to detect consumer behaviors in stores to personalize promotions (Reed, 2015). We even include human eyes in Figure 3, as it would be possible to track your eyes as you read this article.

The power of integrating across diverse types and sources of data is commercially substantial. For example, UPS vehicles are installed with sensors to track the engine performance, car speed, braking, direction, and more (van Rijmenam, 2014).

By analyzing these and other data, UPS is able to not only monitor the car engine and driving behavior but also suggest better routes, leading to substantial savings of fuel (Schlangenstein, 2013).

So, Volume, Variety, and Velocity can give us access to lots of data, generated from diverse sources with minimal lag times. At first glance that sounds attractive. Fairly quickly, however, managers start to wonder, what do I do with all this stuff?

Just acquiring more data isn’t very exciting and won’t improve agriculture. Instead, we need tools that can enable managers to improve decision-making; this is the domain of Analytics.

One tool providing such capabilities was recently unveiled by the giant retailer, Amazon (Bensinger, 2014). This patented tool will enable Amazon managers to undertake what it calls “anticipatory shipping”, a method to start delivering packages even before customers click “buy”.

Amazon intends to box and ship products it expects customers in a specific area will want but haven’t yet ordered. In deciding what to ship, Amazon’s analytical process considers previous orders, product searches, wish lists, shopping-cart contents, returns, and even how long an Internet user’s cursor hovers over an item.

Analytics and its related, more recent term, data science, are key factors by which Big Data capabilities actually can contribute to improved performance, not just in retailing, but also in agriculture. Such tools are currently being developed for the sector, although these efforts typically are at early stages.

So What?

In this discussion, we explored the dimensions of Big Data — 3Vs and an A. The Volume dimension links directly to the “Big” component of Big Data. Variety, Velocity and Analytics relate to the “Data” aspect. While Volume is important, strategic change and managerial challenges will be driven by Variety, Velocity, and especially Analytics.

Unfortunately, media and advertising tend to emphasize Volume; it’s easy to impress with really, really large numbers. But farmers and agricultural managers shouldn’t be distracted by statistics on Volume.

Big Data’s potential doesn’t rest on having lots of numbers or even having the world’s largest spreadsheet. Instead, the ability to integrate across numerous and novel data sources is key.

The point of doing this is to create new managerial insights that enable better decisions. While Volume and Variety are necessary, Analytics is what allows for fusion across data sources and new knowledge to be created.

Emphasizing the critical role of Variety of data sources and Analytics capabilities is particularly important for production agriculture. Individual farms and other agricultural firms aren’t likely to possess the entire range of data sources needed to optimize value creation.

Further, sophisticated and specialized Analytics competencies will be required. To be effective, however, the computer science competencies also need to be combined with knowledge of the business and science aspects of agricultural production.

At times this sounds complicated and maybe threatening. Visiting with a farmer from Ohio about this topic recently, he made a comment that is helpful in unraveling this complexity. He noted that effective use of Big Data for him as a Midwestern farmer is mainly about relationships.

The relevant question is, “Which input and information suppliers and customers can provide the Big Data capabilities for him to optimize his decisions?” And he noted, “For farmers, managing those relationships isn’t new!”

Article Disclaimer: This article was published by Agprofessional.com and was retrieved and posted at INDESEEM for information and educational purposes only. The views, opinions, thoughts, and information expressed in this article are those of the authors. Please cite the original and INDESEEM accordingly.



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