Manasi Nachankar (India), with a background in computer engineering and founder of the Almería’s committee of the International Association of Students in Agricultural and Related Sciences (IAAS), enrolled in MIAM to specialize in Agribusiness and combine her work of specialization with her family business. In this article, she explains the main challenges of big data that need to be faced in the agri sector. Keep reading to discover them:
Big Data is revolutionizing the sector as there are multiple benefits that it can bring to farmers. However, it can be sometimes difficult to understand and use. In this article, we explain the most common challenges of Big Data so that you can anticipate them.
Data Quality and Standardization
One of the biggest challenges in using big data in agriculture is ensuring the quality and standardization of the data. Different data sources may have different formats, levels of accuracy, and reliability, which can make it difficult to integrate and analyze the data effectively.
With the advent of new technologies such as IoT sensors, drones, and satellite imagery, farmers can now gather vast amounts of data about their crops and operations. However, this data is often scattered across multiple platforms and is not always compatible with other systems. As a result, farmers need to invest in data integration tools that can help them bring all of their data together into one centralized location.
For example, a farmer might use IoT sensors to gather data about soil moisture levels, temperature, and other environmental factors. They could then use this data to optimize irrigation and fertilization schedules. By integrating this data with satellite imagery, weather forecasts, and other sources of information, farmers can make more informed decisions about their operations.
Limited Awareness and Skills
Many farmers and other stakeholders in the agriculture industry in Europe may not be aware of the potential benefits of big data or have the skills and expertise needed to collect and analyze the data effectively. Addressing this will require education and training programs to help build capacity and awareness among farmers and other stakeholders.
Skills and Expertise
Analyzing and interpreting big data requires specialized skills and expertise, such as data science, machine learning, and artificial intelligence. Many farmers and other stakeholders in the agriculture industry may not have these skills, which can limit their ability to leverage the potential benefits of big data.
Cost and ROI
Collecting and analyzing big data can be expensive, particularly for small farmers or those with limited resources. Ensuring a positive return on investment (ROI) can be challenging, particularly in the short term.
As you can see, there are some challenges that you have to bear in mind when we’re talking about big data in the agri sector. Fortunately, our Master in International Agribusiness Management provides you with a module that will give you the main keys to solving them
