What are some ways Agribusinesses can use data analysis for food technology operations?

Increase farming productivity. Big data analytics in agriculture has already shown great results in forecasting crop production and improving crop yields. Improve farming operations. ... Stop migration of the labor force. ... Reduce food waste. 20% to 30% of food is wasted today at various stages of the supply chain. ...

Remote sensing can be used as an example of this. Invisible wave lengths of the light spectrum can be captured using orbital, aerial, or terrestrial platforms to generate vegetation indexes that could help to solve in-field issues or even predict those issues.

Knowing the real-time data of what crops are grown across the region can help in matching the demand and supply thus helping in decreasing the volatility of the food grain prices

In addition, the adoption of data analysis can also facilitate regulatory compliance and adherence to food safety standards. By closely monitoring and analysing various parameters throughout the food value chain, agribusinesses can ensure that their products meet the stringent safety and quality requirements set by regulatory authorities and consumer expectations. This not only enhances consumer trust but also bolsters the overall reputation and credibility of the agribusiness in the market.

One of the ways to use data in the agribusinesses is to collect it for obtain some certifications. In my experience, the use of data has help to some organizations of coffee in Latinamerica to collect data about production of farmers that compose this organization (with more than 100 farmers) and help to obtain certifications as GlobalGap. Also, the data is essential for create Digital Business Intelligence, because when a organization of farmers know data about production, crops, harvets in specific seasons, behaviour of the production according to climate phenomenals and others aspects, can visualize some results and make decisions for increase the productivity, its sales, and can be a growth driver for small producers.

Data analysis are currently and will increasingly be used to monitor origin, track products throughout the whole process from live animals and to the consumer. This will increasingly give useful and valuable data on i.e. processing yield, cost of processing etc. and how that correlates with different variables such as type of feed, different feed withdrawal plans for chicken to name one example. Discarded products as well as end product quality issus will also be recorded an put into perspective with the whole value chain where big data can potentially spot potential improvement points early and across the value chain that result to poor efficiency in processing or even end product quality issues.

Increasingly farm-level data will be needed to evidence ingredient claims made on pack by brands and retailers. For example, in Europe the EU Green Claims Directive comes into force in 2026 and will introduce a higher barrier to any environmental marketing claims. If your business wishes to continue to make these types of claims it's important to ensure that you have visibility over the way that your raw materials are grown and can access data relating directly to your product claim. This visibility can be achieved through a combination of supplier engagement, subscription to industry schemes and standards and aggregating relevant supply chain data.

Data analysis assists agribusinesses seeking to expand internationally in gaining a deeper understanding of the intricacies and complexities within global markets. This enables them to make well-informed decisions, optimizing their international trade potential, and identifying promising global investment opportunities.

Data analysis can enhance the marketing and sales performance of agribusinesses by providing a detailed understanding and segmentation of their customers, markets, and competitors. Additionally, data analysis facilitates the design and implementation of effective marketing strategies, campaigns, and promotions that increase brand awareness, loyalty, and sales. For example, through customer relationship management systems, such as databases, analytics, and communication tools, agribusinesses can gather and analyze data on customer behavior, preferences, feedback, and satisfaction, and then tailor their products, prices, and services accordingly.

The use of IoT in agribusiness often involves collecting data from sensors deployed in the field. These sensors can monitor parameters such as soil moisture, temperature, humidity, and even crop health. The data collected is then analyzed to gain insights into the condition of the crops and the environment. Farmers can use this analysis to make informed decisions about irrigation, fertilization, and pest control, optimizing resource usage and crop yields