Is big data analytics only for big corporations and for data scientists? Well, that’s only partly true because, in this article, you are going to explore how big data is shaping farming and agriculture. Artificial Intelligence (AI) and other analytics processes big data or rather large volumes of data to better farming prospects.
According to Bhaskar Chakravorti at Tuft’s University, AI can harness big data to help farming generate $2.3 trillion every year. With detailed insights into operational and financial activities, farmers can plan and predict their harvest yields, fertilizer requirements, ROI, and optimization strategies for the next crop.
Well selling snow to an Eskimo isn’t any natural salesmanship. So, here you are going to see four ways in which big data analytics is poised to improve the global farming scenario.
Improved Prediction through Big Data
Experienced farmers may argue that big data is a late 21st-century technology. Farming thrived even before big data came into existence. Farm animals could predict a storm or an earthquake, and migratory birds would bring the news of famine. But we realize that time has changed. Natural disasters are on the rise and not enough farm animals to own the responsibility of the ailing agrarian condition of the world.
With big data and other monitoring technologies, crisis prediction has become simpler. By feeding historical data into a system, improved data science can effectively boost farming. Not only farmers but their distributors too can benefit through insights into supply chain distribution. With drones, land assessment has become simpler. It efficiently predicts if a given year is suitable for a particular or how some areas of the farming land is unfit for a specific crop.
Empower through Predictive Analytics
Time we advance the discussion around the agricultural supply chain. Farming is a serious business, and all our accomplishments will fall flat without food in our stomachs. If you are a big data enthusiast, you will concur how predictive modeling techniques can help farmers to plan or act on erratic weather conditions, consumer demands, trends, and more significant industrial concerns striking the supply chain unawares.
With big data, farmers can scale themselves to the market demands and participate in the decision making of the price their produce deserves. With massive access to insights, farmers can appoint their locally selected distributors, and retailers with more significant market share, and more. Data, when harvested right can help small farmers unionize against more giant corporations to fight for their visibility within the larger scheme of things.
Calculate Risks Real-Time
We often talk about calculated risks. But how do we calculate risk without supporting data? So far, we have been calculating consequences based on our own experience often biased. But with big data, farming communities benefit from various risk-assessment reports free from human prejudices. Imagine yourself as a small-time farmer in a village of Punjab. How are you going to know your risks of signing up for exporting your produce for earning a deserving livelihood?
With big data, data is always real-time. It helps in creating better risk alerts as and when policies evolve; regulations become convoluted or more stringent. A proper risk assessment factors in every potential threat, whether it is human-made or natural. Not only assessment; with data, decision-makers can also find their solution, implementing techniques, and eventual impact highlighting strategies that worked failed or could do better.
Big Data Analytics: Enabling Exposure to World Affairs
According to a report published by the Food and Agriculture Organization (FAO), Rome, Italy, “Projections show that feeding a world population of 9.1 billion people in 2050 would require raising overall food production by some 70 percent between 2005/07 and 2050.” From this projection, we can easily conclude that farmers need exposure to the various demands in the market so they can plan their crops accordingly.
FAO estimates that while global farming is expanding, there is a steep decline in the diversity of the crops. Sometimes it is monoculture, and sometimes, the killing of pollinators killed by pathogens and pesticides (World Biodiversity Council IPBES). So how can we expect a farmer in the remote of Norway to know about these to readjust their existing operations if it is not through harnessing big data?
Big Data Analytics: Conclusion
AGCO Corporation is using “AWS services Redshift, S3, and SageMaker” to harness big data in the manufacturing of its agricultural machinery, helping farmers and distributors sell globally. Many corporations and startups are working on big data. AI, Machine Learning (ML), and other technologies help farmers to satisfy an for the ever-swelling population.
Honestly, at this time, it is not only about how to satisfy food requirements; it is also about a considerable shift in dietary intake. For example, in most countries, IBS patients are given the low FODMAP diet. This impacts crops rich in carbohydrate and sugar. Through big data, farming research centers can educate local farmers on how to improve their crop distribution.