Agriculture has conventionally been treated as an intuitive space with wisdom passed down from one generation to another. But today’s problems — like the changing climate and depletion of viable farmland — are more complex and urgent in nature.
The United Nations estimates that the global population will reach 9.8 billion by 2050, a 2.2 billion increase from now. This means that we need to step up our crop production significantly to cater to the growing number of people. Unfortunately, rapid urbanization and climate changes have claimed a major share of farmlands. In the United States alone, there has been a dip in the total area of farmlands from 913 million acres in 2014 to 899 million acres in 2018.
Today there is an urgent need to produce more food for the growing population - with less land to grow it on. In this article, let’s take a closer look into how big data and agtech (or agricultural technology) can help tackle this challenge.
Big Data and Agriculture: A Complete Guide now.
How big data can help agriculture
To counter the pressures of increasing food demand and climate changes, policymakers and industry leaders are seeking assistance from technology forces such as IoT, big data, analytics, and cloud computing.
IoT devices help in the first phase of this process — data collection. Sensors plugged in tractors and trucks as well as in fields, soil, and plants aid in the collection of real-time data directly from the ground.
Second, analysts integrate the large amounts of data collected with other information available in the cloud, such as weather data and pricing models to determine patterns.
Finally, these patterns and insights assist in controlling the problem. They help to pinpoint existing issues, like operational inefficiencies and problems with soil quality, and formulate predictive algorithms that can alert even before a problem occurs.
The adoption of analytics in agriculture has been increasing consistently; its market size is expected to grow from USD 585 million in 2018 to USD 1236 million by 2023, at a Compound Annual Growth Rate (CAGR) of 16.2%.
Best Practices for Building Big Data Analytics now.
Top 4 use cases for big data on the farm
The scope for big data applications is large, and we’ve only just begun to explore the tip of the iceberg. The ability to track physical items, collect real-time data, and forecast scenarios can be a real game changer in farming practices. Let’s take a look at a few use cases where big data can make a difference.
1. Feeding a growing population
This is one of the key challenges that even governments are putting their heads together to solve. One way to achieve this is to increase the yield from existing farmlands.
Big data provides farmers granular data on rainfall patterns, water cycles, fertilizer requirements, and more. This enables them to make smart decisions, such as what crops to plant for better profitability and when to harvest. The right decisions ultimately improve farm yields.
2. Using pesticides ethically
Administration of pesticides has been a contentious issue due to its side effects on the ecosystem. Big data allows farmers to manage this better by recommending what pesticides to apply, when, and by how much.
By monitoring it closely, farmers can adhere to government regulations and avoid overuse of chemicals in food production. Moreover, this leads to increased profitability because crops don’t get destroyed by weeds and insects.
3. Optimizing farm equipment
Companies like John Deere have integrated sensors in their farming equipment and deployed big data applications that will help better manage their fleet. For large farms, this level of monitoring can be a lifesaver as it lets users know of tractor availability, service due dates, and fuel refill alerts. In essence, this optimizes usage and ensure the long-term health of farm equipment.
4. Managing supply chain issues
McKinsey reports that a third of food produced for human consumption is lost or wasted every year. A devastating fact since the industry struggles to bridge the gap between supply and demand. To address this, food delivery cycles from producer to the market need to be reduced. Big data can help achieve supply chain efficiencies by tracking and optimizing delivery truck routes.
3 big data in agriculture case studies
Let’s do a deep dive into three case studies of how companies have leveraged big data effectively to solve issues plaguing the farming industry. This will help appreciate how big data solutions can make a real, hard-hitting impact on the ground.
1. Bayer uses big data to ensure sustainable food production
Crop protection has always been a challenge for farmers, given the vulnerability to weeds and insects. However, to administer the correct herbicide and neutralize the weed, farmers need to be able to identify the exact species. Bayer Digital Farming, a unit of the Bayer Group, developed an application that uses machine learning and artificial intelligence (AI) in weed identification.
Farmers upload photos of weeds in the app, which then matches the photo against a comprehensive Bayer database (with almost 100,000 photos) to recognize the species. This application intervenes at the right time, protects crops, and increases yields.
2. DTN uses big data to improve yields and profitability
Digital Transmission Network (DTN), a division of Schneider Electric, provides agricultural information solutions and market intelligence to its customers. Using DTN, farmers and commodity traders can access up-to-date weather and pricing data to better manage their business.
Faced with the challenge of managing a complex network of data sources — an ERP, financial applications, GIS, agronomy packages, and sensing applications — to render information in real-time for customers, DTN current method of connecting these systems was proving too expensive to maintain.
DTN invested in a modern data integration tool that consolidated data from multiple sources without having to write a ton of custom code. With a clean and consistent set of interfaces, DTN can now combine critical weather and agronomic data from fields to give accurate forecasts. Using DTN, farmers are able to improve yields and cut costs on the basis of these forecasts.
DTN has rapidly become an industry standard for agribusiness information sharing and has evolved into an information hub for a networked farming and agribusiness community.
The Definitive Guide to Data Integration now.
3. SMAG InVivo uses big data to empower precision farming
InVivo is France’s leading agricultural cooperative group with 220 members and €6.4 billion in sales. SMAG, its subsidiary, is the French leader in agronomic information systems. Its software is used by 80% of cooperatives and 50% of merchants in France.
While SMAG had developed many mobile applications to support farmers in their daily operations, SMAG wanted to pool all its data — 30 years of weather data history, satellite and drone images, and soil types — to make informed decisions faster. Their objective: use digitization to solve the food challenges of the 21st century.
Using a tool to help process the vast amount of stored and accumulated data, SMAG developed a complex agronomic Data Crop algorithm, allowing for the use of different types of data to optimize decision-making. For example, Data Crop enables users to track the progress of crops over the year and predict yields — a data point that has led to incredible wheat production results. Currently, 80% of French agricultural land under wheat cultivation is managed through Data Crop. SMAG plans to expand this to other crops and countries as well.
The cloud and the future of big data in agriculture
Success in farming has been largely dependent on favorable natural forces, but not anymore. The coming together of cloud computing and big data has ensured that farmers have sufficient data points to make good decisions.
Cloud computing has democratized the availability of huge computing power as data centers and storage are now available on a ‘pay-as-you-go’ model. This has made it possible to bring together knowledge repositories that contain data such as weather, irrigation practices, plant nutrient requirements, and several other farming techniques.
Cloud-based apps can guide farmers on how to adjust their production based on market demand and how to improve their yield and profitability. Today, a farmer can micromanage farming and all its accompanying activities — even before planting crops, it’s feasible to estimate the results by tweaking the variables involved.
Getting started with big data in agriculture
Big data can truly revolutionize the agricultural sector only by having a cloud-based ecosystem with the right tools and software to integrate various data sources. These tools should be able to consolidate data on climate, agronomy, water, farm equipment, supply chain, weeds, nutrients, and so much more to aid the farmer make decisions.
Talend Data Fabric achieves that by offering a single suite of self-service apps for data integration and data integrity. It lets you stream data from multiple sources in real-time and helps derive crucial insights on the basis of trusted quality data. Try Talend Data Fabric today.Try Talend Data Fabric