Home » Big Data in Agriculture: How Big Data Transforms the Sector

Big Data in Agriculture: How Big Data Transforms the Sector

Veronica May 16, 2023

BigDataScalability.com – When you think about big data, agriculture is almost certainly not the first sector that comes to mind. After all, what does data have to do with making decisions across the farm? The reality is, big data in agriculture is a real game-changer. Big data can change the sector for the better.

This is especially true nowadays. Why? Because farmers deal with plenty of data already these days. Leveraging big data, farmers can maximize crop yields, meet food demand, manage farm equipment and supply chain problems better, and many more.

Below, we explain the ways big data transform agriculture, the 4 V’s, and the benefits of big data in agriculture.

What Is Big Data?


Let’s start with getting to know what big data is first. So, what exactly is big data? Big data is a collection of vast amounts of data, can’t be processed using conventional applications and tools. The sheer size of big data alone already makes it difficult to store, let alone process.

Currently, big data is measured in petabytes and exabytes. It won’t be long before big data scale even larger as we generate more data. Unlike some other hot topics of today, big data is not a passing trend. If anything, it will stay and transform every sector including agriculture.

For many years, agriculture has been regarded as an intuitive space as farmers do their operations based on intuition. There is also a generational aspect as farmers pass their wisdom to the next generation.

However, with today’s problems like climate change and the depletion of viable farmland, things are more complex. Being aware of nature and its whim is still necessary, but it is no longer enough. Like it or not, agriculture must be done less intuitively and more data-driven. Big data can help in this.

Big Data in Agriculture Sector


The agriculture sector is currently facing an enormous challenge: meeting the food demand of a global population that is expected to grow to 10 billion by 2025. But that’s not all. The feat must be accomplished while maintaining sustainable agricultural systems.

There are also additional obstacles that make things even more difficult: depletion of water resources and increasingly unpredictable weather. This is where big data can help. Leveraging big data, farmers can make better, more informed decisions and improve their operations.

With real-time data at hand, farmers can gain insights into how they should adapt to unpredictable weather, maximize yield on existing farmlands, anticipate disease outbreaks, and many more.

How Big Data Transforms the Sector

So how does big data in agriculture transform the sector? There are at least 4 ways in which big data transform the agriculture sector. Namely, product optimization, supply chain management, pesticides and fertilizers, and equipment optimization.

Product Maximization


The number of farmers who are using big data in agriculture is growing, and so is the frequency of usage. Farmers are using big data to assess soil conditions, weather, rainfalls, water usage, optimal harvesting time, and other factors to maximize productivity from the farmland they cultivate.

Technologies based on the Internet of Things (IoT) are necessary to accomplish the task. Today, IoT devices have become increasingly common in agriculture. They can be used in

  • Soil
  • Seeding equipment
  • Plants
  • Applicators and sprayers
  • Tractors and loaders

Every year, the demand for food is increasing. Using these technologies, farmers are better equipped to make the most of existing farmlands and meet the increasing demand.

Supply Chain Management


Big data in agriculture transforms the supply chain management aspect. No farm can operate alone. No farm is a lonely island. Each farm has a significant role to play in the supply chain that consists of

  • Farms
  • Logistic companies
  • Wholesalers
  • Manufacturers
  • Retail stores and supermarkets

Using big data, companies can reduce transportation time, optimize food delivery, and maintain the optimal production volume.

Pesticides and Fertilizers


Farmers use pesticides and fertilizers to protect and support crops. When used properly, these chemicals can help maximize crop yields and ensure that there will be plenty to reap when the harvesting time comes.

As beneficial as they are, these chemicals pose dangers when not used properly. That is why effective, ethical, and safe usage of pesticides and fertilizers is necessary.

In addition, there are guidelines and regulations that must be followed regarding the use of chemicals on farms. Some of them come from manufacturers and some others from regulatory bodies.

How does big data in agriculture help? Using big data, there won’t be any guesswork anymore. With big data, farmers can make sure that they use precisely the right amount of pesticides and fertilizers.

The benefits are twofold. On one hand, big data ensures that farmers follow the guidelines and regulations. On the other, with the proper use of chemicals, crops are protected and supported. This, in turn, may increase the profitability of the farms.

Equipment Optimization


Farmers can use big data in agriculture to optimize equipment. Using big data, farmers can

  • keep their farming equipment in top condition
  • maintain every machine and vehicle according to guidelines provided by their manufacturer
  • predict potential problems and glitches in advance

As a result, farmers won’t need to worry about downtimes caused by equipment failures. Modern farms that leverage big data can gain constant, actionable insights into every device and machine. This helps to ensure that every device and machine is fully operational when needed.

The Benefits of Big Data in Agriculture


Previously, we discussed the ways in which big data in agriculture transforms the sector. But what exactly are the benefits of big data in the sector?

The benefits are plenty and will increase as the use of big data in agriculture grows. Here are a few examples.

1. Increasing productivity and innovation

Farmers can leverage big data in agriculture to increase productivity and innovation. With technologies such as soil sensors, weather tracking, GPS-equipped tractors, and others, farmers now have unprecedented visibility not just into operations but also opportunities to maximize existing resources.

Utilizing real-time data, farmers can gain insights into when, where, and how to plant, down to the smallest detail. This, in turn, increases productivity and innovation.

2. Greater understanding of environmental challenges

Besides the growing demand for food, farmers must also face environmental challenges. Increasingly unpredictable weather, changing insect behaviors due to weather, severe drought, and storms impact the agriculture supply chain in one way or another.

Using big data in agriculture, farmers can gain a greater understanding of these challenges. And with greater understanding, farmers can adapt to the challenges and make better, more informed decisions.

Leveraging big data enables farmers to maximize opportunities and prepare for challenges, all without wasting their resources.

3. Improving profits and reducing waste

To remain profitable, agribusinesses have to find ways to demonstrate value and keep innovating. This can be accomplished by leveraging big data. With big data, agribusinesses can gain insights into sales-related issues and how they can solve them.

Using real-time data, agribusinesses can make more informed, timely, and data-driven decisions. Agribusinesses also gain visibility into pricing, which helps them make decisions based on profitability. Real-time data also help agribusinesses to reduce waste, enabling them to operate more efficiently.

4. Improving supply chain management

The current value chain in agribusiness is quite siloed. Not to mention communication and collaboration efforts in the supply chain need improvements, too. This is another aspect where big data can help.

Farmers can take advantage of big data analytics to trace their products through the supply chain. This enables farmers to communicate valuable information regarding product offerings and services to distributors, retailers, as well as other key stakeholders.

Big Data in Agriculture: The 4 V’s


Big data is characterized by the 4 V’s. These V’s are volume, velocity, variety, and veracity. Each V describes a certain aspect of big data. Below we explain each V in an agricultural sense.

1. Volume

Volume refers to the size of collected data. When it comes to big data, data is measured in petabytes (1 petabyte = 1,024 terabytes) or exabytes (1 exabyte = 1,024 petabytes). That’s how big it is. It will only increase from this time forward as more and more data is collected.

In agriculture, the larger the volume of the data is the more opportunities there are to understand more. This also comes with a downside, however. As the volume of the data grows larger, so does its complexity.

Those who want to leverage big data in agriculture must find ways to filter, sort, analyze and access the data. Otherwise, the volume of information wouldn’t be as useful as it should be.

2. Velocity

While volume refers to the size of the data, velocity refers to how fast the data is generated. Data is useful and important for decision-making. But the right timing is necessary, too. What good is data if it arrives too late?

Back then, data took a long time to generate. Today, with the advance in technology, it is possible to get data in real-time. This enables us to make not only better but also faster decisions.

Using more advanced reliable processing sources and transmission devices, we can increase the velocity of data further. As a result, we can make decisions on time and even ahead of time using predictive analytics.

3. Variety

After size and speed, there is variety. In big data, data can come in structured, unstructured, as well as semi-structured forms. Things are even more complex in agriculture. We have to deal with not only various forms of data but also the type of data that we create.

Rather than getting only a yield map by year-end, we have ways to capture, collect, and inform various conditions that have happened throughout the year. From root growth, soil moisture, and temperature to the low-yielding spot. Such is the variety of big data in agriculture.

Think of it like a puzzle. The more pieces you have, the clearer the puzzle picture will be. Of course, with more puzzle pieces at hand, the more complex they are to put together.

4. Veracity

There is also veracity. The term refers to the quality of the data. In other words, it is about trustworthiness and accuracy. For data to be useful, it needs to have value and provide answers.

If the quality of the data is low, analyzing it may not give us good answers. Faulty data won’t help us make good decisions. The good news is having more transparency and automation can help to manage the veracity of big data in agriculture.

Big data is changing the agriculture sector. What everyone needs to understand is that big data isn’t a passing trend. With the advance in technology, the applications of big data will only grow. Of course, this applies to the agriculture sector, too.

There is no doubt that big data in agriculture causes disruption. But, like any other sector, there are many benefits to derive from big data. Leveraging big data, farmers can make better, more informed decisions and improve their operations.

Related Posts