The agricultural industry is currently confronted with a formidable challenge. Agribusinesses will have to feed more people with fewer resources as the population grows and farmable land shrinks.
The United States Department of Agriculture (USDA) predicts that food demand will increase by 70-100 percent by 2050. Simultaneously, environmental concerns and limited resources limit how farms can expand.
Agricultural yields must improve, but farms cannot expand as they have in the past. Data analytics is the solution to this problem.
Data analytics – using new technologies to gain insight from digital data – is more common in other industries than farming. Still, agriculture has much to gain from it. Before farmers can start capitalizing on these technologies, though, they must acquire the necessary tools.
The first step in data analytics is collecting data, which farms can do through the internet of things (IoT). Wireless, interconnected devices like soil sensors, drones, and weather-tracking solutions gather crucial information and transmit it to a single, accessible location. Despite being fairly new, these technologies are increasingly accessible, with farms set to install 12 million of these sensors by 2023.
Farms will also need cloud storage solutions to store and organize this data. They’ll then use machine learning algorithms to analyze it and produce actionable insights. While agribusinesses can design their own models, off-the-shelf solutions are also available.
Having the necessary technologies is just part of the equation. Once farms have the devices and software they need, they can use them to improve crop yields through these five strategies.
The first way data analytics can improve yields is by discovering which crops to grow during a given season. Rotating crops can increase active crop time, in turn maximizing yields, but determining which plants are best suited to each season can be challenging. Data analytics makes the process far faster and more accurate.
Machine learning can analyze large data sets faster than humans and find connections they may miss. Consequently, they’re ideal for weighing factors like weather patterns and soil quality to determine which crops will be the most productive.
As a new season approaches, farmers can feed soil data, temperature readings, drone footage, and satellite imagery to predictive algorithms. These models can then produce a complete picture of the upcoming season’s conditions, suggesting crops that are best suited to them. Farms can then plant items that produce the best yields in these conditions.
Another critical role of data analytics in agricultural yields is managing resources. Farms need to provide crops with enough water, fertilizer, and pesticides, but overuse of these resources can be harmful and limit global food production. Machine learning can analyze real-time soil conditions to enable precision agriculture, helping farmers produce as much as possible while using minimal resources.
IoT soil sensors can monitor water and nutrient levels in the ground and adjust irrigation and fertilizer systems accordingly. That way, they ensure plants have what they need without wasting resources.
These precision systems are also a crucial part of becoming more sustainable. Sustainability can be challenging in agriculture since even cows can produce greenhouse gas emissions, and large farms often require extensive resources. Minimizing consumption by adapting to real-time needs helps offset these practices to reduce farms’ ecological footprints.
Similarly, farms can use data analytics to monitor crop health. Just like with human diseases, catching potentially harmful plant conditions early can minimize their damage and ensure long-term health. To do this, agribusinesses need real-time insight into their crops.
Analytics models can look at data from soil sensors to see how plants are consuming nutrients and growing, indicating their health. Alternatively, machine vision systems can analyze footage of crops from drones or other cameras to find signs of disease or nutrient deficiency. These systems can then alert farmers, informing quick, effective action.
Even models trained on relatively small datasets have proven 95% accurate in identifying crop diseases. With tools like this, farms could recognize potential issues far faster, letting them respond as quickly as possible to minimize their impact and preserve high yields.
Determining when to plant and harvest crops can also impact yields. While years of agricultural experience can inform fairly accurate schedules, changing climates and unforeseen weather patterns can change farm conditions from year to year. Data analytics can help agribusinesses understand and respond to these shifts.
Growing seasons aren’t as regular as they may seem at first. Coffee, for example, can grow year-round, but heavy rains interrupt the season in some climates. Farms must have an up-to-date and comprehensive view of the factors affecting ideal planting and harvesting times to produce the most successful harvests.
Analytics models can look at historical weather trends and compare them to more recent data to predict incoming conditions. Combining that data with changing soil factors can reveal the best time to plant different crops and, similarly, the best time to harvest them.
A more easily overlookable part of maximizing yields is ensuring safe, efficient transport after harvesting. The world loses roughly 14% of all food produced between harvest and retail, effectively shrinking otherwise significant crop yields. If farms want to make the most of their harvests, they need to optimize the supply chain through data analytics.
Telematics systems provide real-time information on vehicle locations that analytics models can then use to find optimal routes. These insights can make fleets more efficient by helping drivers find and avoid congested areas or other inefficient routes. Consequently, crops will spend less time in transit, preventing spoilage before reaching shelves.
Other IoT systems can monitor shipment quality in real-time. If they detect something wrong, like a broken refrigerated trailer, they can alert drivers to alter their course. Agribusinesses can then safely deliver their crops before they go bad.
As these technologies advance and become more common, their potential use cases will expand. One of the most promising future applications of data analytics for agricultural yields is genomics.
Early studies show that machine learning can identify genes that enable plants to grow with less fertilizer. Similar data analytics models could reveal other helpful genes to help scientists modify crops to produce more resilient, resource-efficient crops. As these practices grow and successful cases emerge, it’ll become easier for farms to grow more with less.
As data technology advances, it will also become more affordable. Devices like IoT sensors and cloud platforms will then become accessible to smaller farms, bringing these benefits to the entire industry, not just large agribusinesses.
Data analytics may be new to agriculture, but the practice is becoming increasingly crucial. As pressure grows to feed more people with fewer resources, farms must capitalize on analytics technologies to meet modern demands.
These five steps can help agribusinesses make the most of data technology to improve crop yields. They can then reduce their expenses and ecological footprints while growing profits.