Data-driven Farming: How BIG DATA will change the future of Agriculture

Big data refers to a combination of structured, semi-structured and unstructured data that can be mined in order to guide both immediate and future decision-making.

With the advancement in technology, big data is being used across many industries. Within the agriculture sector, big data refers to the use of data science techniques to capture and analyse complex datasets in order to positively impact agricultural productivity and optimise farming operations.

Key Benefits of Data Analytics in Agriculture

Better Supply Chain Management

According to some estimates, nearly 25-30% of the global food produce is lost along the food supply chain. With burgeoning population, the agri-food supply chains are also becoming increasingly complex and facing challenges in mitigating food wastage and delivering safe, nutritious and high-grade quality food. However, this can be achieved by leveraging big data that will streamline supply chains and link farmers directly to the market, among other things.

Cost Savings

There is no denial of the fact that big data has tremendous potential in revolutionizing agricultural operations. Many agribusinesses are searching ways to improve production techniques in order to achieve economies of scale i.e. reduction in costs.

In this scenario, big data could lay the foundation for identifying correlations between farm field, weather and commodity data for optimal irrigation, crop harvesting and optimal feeding, thereby leading to overall cost reduction.

Demand Anticipation

BIg data analytics is useful to anticipate demand for seeds, fertilizers as well as animal feed, enabling the supplier to take pragmatic steps to match production to demand.

Better Decision-making

Big Data solutions can help improve not only forecasting but also operational efficiency while analyzing a variety of data sources for better insights.

Agtech start-ups, for example, can establish new pricing programs to help manage demand consistent with available supply. It is generally seen that demand for some products is often strongly linked with commodity pricing. The ability to better predict pricing changes could be useful for pro-active allocation of supplies.

Conclusion

Although big data cannot be considered as a silver bullet to solve the current agricultural crisis, application of big data in tandem with the existing agricultural interventions could prove fruitful in the long run.