Data Analytics in Agriculture

Big data analytics is revolutionising every industry that you can think of. It enhances decision making, analyses customer trends, tracks customer satisfaction, and identifies scopes for new products and services to meet the ever expanding market demands. Organisations get real-time marketing, product demand, sales, and financial data just by integrating information and systems to collect data.

Agricultural practices have evolved a great deal from conventional methods to more modernised techniques. Farmers no longer rely solely on recommendations from fellow farmers but on modern techniques and farming data. Harnessing insights coupled with historical data provides farmers with a detailed insight into which crops to be cultivated and the cultivation method that will prove beneficial for them.

Big data analytics is now modernising the age-old agricultural processes and streamlining irregularities, thereby enhancing efficiency in cultivation, irrigation, harvesting, supply chain management, and logistics, ensuring that there is minimal scope for risk while dealing with perishable goods.

How is Data Analytics Beneficial for Agribusinesses?

Ever since farmers started adopting precision agriculture and data-driven solutions, farmers have witnessed strong benefits in agricultural production.

  1. Enhanced Innovation and Productivity: Leveraging data and innovation to enhance productivity, boost yields and profits on a farm. Modern technology including soil sensors, GPS-equipped tractors, and weather tracking provides farmers with an unprecedented overview of their farm operations and opportunities to increase resources. Having access to real-time data allows farmers the convenience to decide when, where, and how to plant their crops, at a granular level. 
  1. Reduce Waste and Enhance Profits: For a profitable agribusiness, farmers need to innovate and come up with means to demonstrate real value. By incorporating a farming data analytics strategy, agricultural businesses can answer sales-related questions supported by data from a single platform. This creates the opportunity to make timely and evidence-based decisions. At the same time, farmers can gain pricing visibility, allowing them to remain profitable in their endeavours. At the same time, the accurate analysis will provide better opportunities at the customer level. 
  1. Better Supply Chain Management: The current agribusiness value chain requires improvements in both communication and collaboration efforts. ag data analytics help farmers trace their products through the supply chain, which in turn allows them to communicate valuable information to retailers, distributors, and other stakeholders regarding products and services.

 Data Analytics: Implementation

The three crucial factors that influence data analytics in agricultural farming include the combination of shared information, smart technology, and innovation. To reap maximum benefits from data analytics, agribusinesses must have the right farming equipment and a perfectly formulated strategy.

  1. Data Collection: Collecting data allows farmers to get aggregate data from a trusted, selected source that can simplify operations by storing the data in a single and secure location. 
  1. Data Standardisation: Accumulating multiple data sets in a single data structure creates an opportunity to compare, track trends in real-time, and uncover patterns in data to identify new opportunities. 
  1. Clean Data: To make data-driven decisions, it is necessary to ensure that the data you collect is clean, accurate, and complete. 
  1. Data Enrichment: To enhance forecasting and identify potential challenges, farmers need to have the opportunity to connect outside information including weather data, local soil analytics, and insect tracking. 
  1. Data Analysis:One of the crucial aspects of gaining value from the information you collect is the ability to analyse the data. Farmers need to learn the functioning of the tools to ensure that they fetch the result they desire. 

After the right technology and communication is decided, the next step would be to consider business strategy. The key steps to do so include:

  1. Understanding how the data analytics platform will support the overall business strategy of the organisation.
  2. Creating an analytics vision and setting target maturity levels for basic processes.
  3. Prioritising and developing a strategic roadmap, including both short-term and long-term goals.
  4. Developing a blueprint of the target architecture.

Conclusion

Data analytics empower farmers by providing them useful insights about their agriculture businesses that help them predict market conditions, determine consumer behaviour towards finished goods, consider inflation, and other variables that will help plan the entire process in advance, even before sowing the seeds. This becomes beneficial for farmers as they can maximise the return on investment and eliminate unnecessary loss.

For a more detailed understanding of how data analytics helps modern-day agribusiness, contact us at KG2, Australia’s largest independent farmer database.

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