Data Science In Agriculture
Farm management no longer relies on paper and people organization like the Farmer’s Almanac or an older farmer’s experience. Gathering and processing information has been revolutionized. A new world of decision-making based on data science in agriculture is here. In fact, it has been here for longer than we may realize.
DTN has been developing unrivaled agribusiness tools since the 1980s. And as time has progressed, so has the level of information and insights we provide. Your business will benefit from up-to-date proprietary agribusiness information and analytical tools to help your customers make better decisions, minimize risk, improve efficiency, and drive profit.
This article will outline the factors driving innovation in the food and agriculture sectors, the role of data science in the field and how you can find the best solutions for your farmers.
Agtech on the rise
The agtech industry is experiencing exponential growth, as proved by the increased demand for data analytics in agriculture. Startups, new products, consolidation and significant investments from venture capitalists are drivers of this change.
Notably, venture capital investments in the agtech sector almost doubled between 2019 and 2020, totaling $5 billion. Since 2010, nearly $16 billion has been raised across the agtech industry. What is driving this appetite for growth in data science for modern agriculture?
Factors That Drive Innovation in Agriculture
Agriculture has moved from small-scale and labor-intensive farming (until the 1920s) to the industrial stage (from 1920 to 2010). We are now in a third stage, grounded in making data-driven decisions based on insights across a range of variables.
Many factors have influenced the growth of data science within agriculture, but here we will consider three.
UN predictions that the global population will be 9.8 billion by 2050 reveal opportunities and challenges for the agricultural industry. Demand for food production will increase, but this food must also be produced without further damaging the environment.
Meeting these needs in a way that does not deplete or endanger the earth’s resources creates a massive opportunity for precision agriculture. Data science comes into play as agronomists aim to increase yields using fewer inputs (pesticides, water, and land).
Agricultural data collected from small and large operations can be used to map out the efficient use of arable land to produce without creating future problems.
Availability of Data
Global interconnectedness through the internet has led to the migration of ideas and sharing of best practices. Data is more accessible than ever before, and machine learning has made it easily and quickly updatable.
The result? Agricultural businesses now have access to practices that help them improve their operations globally based on historical evidence, detailed weather and agronomic information and tools that better help them manage complex markets.
Cutting-edge mobile devices and apps put all the information your customers need literally at their fingertips. With only an internet connection necessary, the availability of big data has been a significant driver in the adoption of data analysis within agriculture.
Changing Consumer Demands and Buying Habits
Today’s consumer is educated, empowered and cautious when it comes to their purchases.
Where food comes from is a priority to these people, and many are willing to pay more for quality products or those grown in a particular fashion, such as organic and local. This increase in demand for transparency has put pressure on the agricultural industry to be more open about its supply chain and practices.
Farmers need access to the latest information and technology to meet these demands.
Data science allows farmers to track their crops, animals, and water usage and know what pests or diseases are present in their fields. It also provides local insights into historical data, including past land usage and hyper-local weather insights. Additionally, it enables them to do this quickly and easily.
While the producer’s historical knowledge and gut feeling will always have a place in agriculture, it’s more informed than it ever has been.
Applications of Data Science in Agriculture
Investing in data science can generally be divided into three categories: capital investments, service investments and knowledge.
Capital investments may include computer hardware or software, sensors, and other technology. Service investments are those services that transform the data collected into actionable insights. Finally, knowledge can include content services that allow farmers to remain up-to-date on what is happening in the market and expert advice on these ever-changing conditions.
Let’s review just some of the data science applications.
A significant part of crop quality depends on the weather, whether we are talking about growing conditions or transportation and storage.
Weather intelligence tools from DTN (like ClearAg) will make them your trusted partner. These tools provide actionable weather intelligence, predictive models and applied meteorological expertise.
Accurate, real-time information from DTN will help farmers decide when to plant, irrigate, and harvest. In other words, data science is helping farmers to get the most out of every acre.
Maintaining a field is both a science and an art, and fertilizer is an essential piece of that process. Fertilizer rates, timing, and placement can differentiate between a bumper crop and a disappointing one.
Deciding which fertilizer to use, where to apply it and how much to use depends on many factors. These include soil properties, water composition, land type, irrigation techniques, and forecasted weather.
Artificial intelligence can help farmers make these decisions based on data collected from their unique properties.
Data collected can develop models that predict where and when problems will appear. These could include pest issues, draught expectations and effects, and even consumer demand and the ever-changing markets.
Additionally, data can help producers improve the efficiency of any treatments based on meteorological data and field conditions. Tools like DTN Agronomy will help you better collaborate and communicate with your clients.
Data science helps producers be more efficient with their current acreage and helps facilitate decision-making about expansion.
Not all data tools are equal
Data science is the collection of data and the interpretation, analysis, and modeling of data. Poor or insufficient data can be more dangerous than no data, so you need to trust the information you use to make decisions.
Providing insufficient data to your customers can irreparably damage your relationship with them and your brand’s reputation within the market. Therefore, it makes sense to have an experienced industry partner with extensive current and historical data that influences their solutions.
Your client’s data should be the most robust, comprehensive, and complete information possible. Offering actionable insights based on a full picture of what is happening on your producers’ farms will drive your success.
DTN’s Agribusiness Solutions
The agribusiness market is in a state of constant change. Weather disruptions, market shifts, legislative requirements, and competitive changes present variables that affect your planning and your bottom line every day.
DTN has our fingers on the pulse, providing daily reports of business-critical, real-time information to capitalize on market trends. Our award-winning agriculture newsrooms will keep you up-to-date on the industry.
When it comes to providing operational intelligence that you can trust, DTN is the clear leader. We have more than 40 years of experience in the agribusiness industry and continue to grow our suite of products design to support our customers. And providing information and insights is all that we do, so you can trust that what we provide is free from bias or favoritism.
Learn more today about our robust suite of tools and what they can do for your business.