Data Analytics Transforming Agriculture
Data is on track to completely restructure and reform the agricultural market.
Updated
On this page
Updated November 2026. This was first published on 10 May 2020. The idea is the same. Any number below is what the original piece said then. It is not a new count.
Key takeaways
- Data is on track to completely restructure and reform the agricultural market.
- Economic analysts predict that the global agriculture analytics market will jump from $585 million in 2018 to a whopping $1.236 billion in 2023.
- The fuel behind this drastic increase is the precision that the agriculture market has developed.
Data is on track to completely restructure and reform the agricultural market. Economic analysts predict that the global agriculture analytics market will jump from $585 million in 2018 to a whopping $1.236 billion in 2023. The fuel behind this drastic increase is the precision that the agriculture market has developed. As more farmers adopt technical solutions, like advanced analytics, artificial intelligence, and machine learning, the better they can track supplies, evaluate probability, generate predictive models, and increase yields. There are four ways data analytics can contribute to changing the way we approach agriculture.
Food demand around the world is set to double in the next thirty years, and it will be up to farmers and their suppliers to exploit data and innovation to increase productivity to feed an ever-growing population. Farmers equipped with soil sensors, GPS tractors, and local weather apps can implement agricultural practices with unforeseen insights. They can better manage their resources ranging from seed, fertilizer, and pesticides, and at the same time, increase their productivity. One case study cites farmers using soil probes and sensors attached to their equipment and machinery to scan the farm as they move. The portal, the sensors are connected to will compare their data to other farmers, and offer suggestions based on various conditions. The journal of Nature found that ending nutrient overuse, Also to sustainable intensification, could increase the production of corn and grains by upwards of 30%.
Managing environmental challenges, particularly climate change, is another threat that data analytics can confront. Data-driven solutions can help farmers traverse shifts in environmental conditions as well as track crop health and various natural events such as pest or disease-based issues. By arming themselves with data, farmers can adjust their techniques to get ahead of adverse events and to alleviate the damages caused by environmental threats. According to Tufts University, by using smarter farming techniques, the industry could rake in $2.3 trillion in savings and business opportunities yearly. A not-so-insignificant chuck of those savings, $250 billion will come from artificial intelligence and analytics. These savings can act as a buffer against competition and a source for reinvestment in their farms.
Farming, like most other businesses, is subjected to supply chains, and agriculture’s supply chain is predicted to have some of the most drastic changes. After the application of analytics, farmers will have a much simpler time tracing their products in the supply chain, and the retailers and distributors will be better prepared to customize their services to the needs of the agricultural market.
The addition of analytics to agriculture is one of the essential technological additions in the last decade. As climate change threatening the world’s food supply, farmers need, now more than ever, to adapt to become more fruitful, economical, and they can do so by revolutionizing the agricultural sector.
Words this piece uses #
The draft already used these words. The lines below only say what they mean. They are not new findings.
Machine learning is software that gets better at a task by finding patterns in examples, instead of only following rules a person wrote.
In this kind of writing, artificial intelligence means software that does a task people used to judge by hand. The useful question is which task, and what happens when it is wrong.
What the original did not show #
The draft did not include a later count, a new quote, or a promise about today. Read the claims above as a record of the day it was written.
Questions and answers #
What is the main point? #
Data is on track to completely restructure and reform the agricultural market.
What else did the original piece say? #
Economic analysts predict that the global agriculture analytics market will jump from $585 million in 2018 to a whopping $1.236 billion in 2023.