How Big Data is Transforming Agriculture

Sometimes, we only recognize a transformation by looking back. However, today we can clearly see the innovations and changes happening in agriculture.

To produce more food, agriculture needs new products, methods, and technologies. To protect the planet, we must reduce chemical use and conserve water. Farmers want higher yields, lower costs, and better profits. Consumers demand safer and healthier food.

Big data is addressing these demands.

Big data is revolutionizing agriculture by processing the vast amounts of data generated from genetic technologies, water management, fertilizers, climate conditions, soil, machinery, and pest control. It offers new ways to acquire and utilize data for agricultural production and crop genetics. The value chain is shifting from large companies to smaller firms and individual farms, though large companies continue to innovate.

Transformation requires great ideas, new business models, and bold innovators. The big data industry is fostering the emergence of independent companies that bring fresh perspectives, methods, and practices. Traditional agricultural supply chain players must adapt to keep pace.

I believe big data has four major applications in agriculture:

• Seed Research: Faster discovery and creation of new plant genes using tools like genome sequencing and chromosome mapping.
• Precision Agriculture: While sometimes conflated with big data, precision agriculture uses big data analyses to inform farming decisions, which are then executed through precision farming technologies.
• Food Traceability: Sensors and analytics prevent spoilage and foodborne illnesses.
• Supply Chain Transformation: Information technology is revolutionizing both agricultural inputs and product supply chains.

Big Data Accelerates Crop Breeding

Traditional breeding methods take significant labor, funding, and at least a decade to develop superior crop varieties. Big data accelerates this process. Gene sequencing has seen explosive growth thanks to advancements in model organism studies and high-throughput, automated technologies.

Now, many experimental steps can be conducted in the cloud. Trials that previously required greenhouses or field experiments can now be simulated using chips for planning and hypothesis testing. Only seeds with high potential from preliminary screenings proceed to field trials. This shift has transformed breeding, reducing the need for physical presence in breeding stations as much of the work is now lab-based.

Traditional genetic engineering produced drought-tolerant, pest-resistant, and herbicide-resistant crops. New technologies are poised to develop varieties with better quality and environmental benefits. Future research priorities include high-calcium carrots, antioxidant-rich tomatoes, allergy-free nuts, antibacterial oranges, drought-resistant wheat, and nutrient-enriched cassava.

Big data enables faster, more efficient field production. Small labs with limited resources are becoming increasingly influential. Shared databases are driving innovation in plant genomics and cloud biology, spawning startups such as:

• Caribou Biosciences: CRISPR-Cas gene editing (secured $44.6M in funding).
• Benson Hill Biosystems: Big data analytics and cloud platforms for plant biotechnology ($8.05M in funding).
• Intrexon: Developing superior plant traits using innovative technologies.
• Cibus: Gene editing for herbicide-resistant crops.
• Arcadia Biosciences: Advanced breeding methods and biotechnology for new crop varieties.
• Precision Biosciences: Gene editing with an agricultural R&D focus ($25.65M in funding).
• Major players like DuPont/Dow, Syngenta, Monsanto, Bayer, and BASF are also actively investing in this field.

Data-Driven Agriculture: Better Results

Agricultural production involves complex interactions among biological, meteorological, and human factors. Producers have increasingly adopted precision agriculture technologies. Tools like GPS help farmers track yields, control equipment, monitor field conditions, and manage inputs with precision, significantly improving efficiency.

Big data companies can test crop traits, inputs, and environmental conditions across vast areas. These insights enable farmers to create tailored planting plans for specific fields and climates. For crop protection companies, big data supports personalized agrochemical solutions and crop management strategies.

Information is Power

Big data brings transparency to agriculture, potentially disrupting traditional supply chains. This transparency may unsettle retailers, distributors, seed companies, and agrochemical producers. By reducing input usage, big data increases growers’ profitability. Farmers using precise methods can achieve better results with fewer inputs, reducing costs by 30-40% while improving yields.

Despite low commodity prices and suboptimal farm profitability, new agricultural technologies are increasingly adopted. According to a report by Robert Hill of Caledonia, the adoption rate of new technologies doubled from 2013 to 2019, with 51% of farmers expressing interest in technology that directly impacts their livelihoods.

Big Data Startups Transforming Agriculture

Numerous companies are collecting, aggregating, and analyzing farm data. Their goal is to provide personalized services based on farm-specific conditions. Notable players include:

• Farmers Business Network: Helps farmers optimize planting strategies for maximum profitability ($24M in funding).
• Granular: Provides data visualization and decision-support tools ($50M in funding).
• Conservis: Cloud-based platform offering information tracking and analytics ($126.5M in funding).
• Trimble: Delivers location-based solutions for agriculture, construction, and more.
• Farmers Edge: Offers hardware and software platforms for data collection and analysis ($4.42M in funding).
• Iteris: Provides weather, water, soil, and crop growth insights via APIs and apps.

Major corporations like John Deere, Bayer, BASF, and DuPont are also building or acquiring data service platforms.

Food Traceability

Tracking food from farm to consumer helps prevent disease, reduce waste, and increase profits. As supply chains grow, traceability becomes crucial. Sensors, scanners, and analytics monitor the food supply chain, with GPS-enabled temperature and humidity sensors alerting stakeholders to anomalies.

Supply Chain Impact

Big data is challenging traditional agrochemical giants, empowering farmers to achieve maximum profitability with minimal inputs. Historically, innovation was confined to large corporations. Now, smaller companies are driving change, particularly in genetic and breeding technologies.

Farmers prefer objective, transparent information and are cautious about sharing data with agrochemical suppliers. Similarly, independent seed companies are reluctant to contribute data to public databases that might benefit major players. Big data startups, often unencumbered by legacy systems, are better positioned to deliver unbiased insights.

Conclusion

According to AgFunder, 2015 saw over 500 agriculture and food-related investment deals worth $4.6B. Startups are spearheading innovation across food, energy, and environmental sectors. While agricultural big data is still in its early stages, it’s rapidly gaining momentum. Entrepreneurs committed to transforming traditional models through innovation stand to reap significant rewards. This revolution promises benefits for innovators, the environment, farmers, and consumers alike—making it the best time for agricultural entrepreneurship.