The world population is expected to grow to 10 billion by 2050 according to the estimate shared by the United Nations. As a result, food production will need to grow by 56% compared to what it was in 2010, in order to support the needs of the growing population. Experts believe that agricultural production needs to be increased significantly over the coming years and agri data could hold the key to making this happen.
In this article, we will share a new way of exploring agri data that will help the industry tremendously in the future. But first, let’s understand what agri data is and how it’s currently being used in the agricultural industry.
What is agri data and how it’s used today
Agri data refers to the collection of information from a wide range of sources that are used for tracking and optimising the supply chain in the agricultural industry.
Big data analytics is becoming a crucial part of improving operations in just about every industry and agriculture is no exception. With the help of big data analytics, agri data can be analysed and integrated into actionable insights that can be used for forecasting, improving crop production, developing a better understanding of environmental challenges, and reducing waste, apart from improving efficiency and productivity within agribusinesses.
Traditionally, agri data has been analysed in two ways:
- Dataset-centric, where the agri data is analysed by linking together compatible datasets for making better sense of the data that is available.
- Space-time-centric, where the datasets are analysed in relation to other important factors like location and time, in order to provide better insights than the dataset-centric approach.
A new way of exploring agri data
Experts are recommending a radical approach for exploring agri data, that’s a bit different from the traditional approaches that are being utilised predominantly in the agriculture industry today. This new and relatively underused approach is referred to as the domain-centric approach, where the agri data can be linked together with the real world tangible concepts of agriculture, such as a specific crop, livestock or food item. It’s being regarded as highly crucial for overcoming the kind of complex challenges that the future may hold for mankind, such as unpredictable weather changes, food shortages and supply chain issues.
This new approach is expected to help the industry get better at ensuring end-to-end traceability, carbon accounting, and yield predictions. Moreover, it will save a significant amount of time for the users, while also flagging other pieces of information for them, that they may or may not have known as useful or relevant in their case. For example, when the users will look up a growing crop, the data model could also provide them with details of soil chemistry, weather forecast, satellite imagery, and drought risk calculation, among other pieces of information, that could potentially help them make better decisions.
What are the applications of this new approach of analysing agri data
Making drought predictions
Given that the vast majority of crop failures across the world are water-related, access to the important insights related to the availability of water supply, weather forecast, and the rate of evapotranspiration can help reduce crop failures significantly. This is only possible by following a domain-centric approach when it comes to analysing agri data.
Improving supply chain management
By closely tracking the various factors associated with the production, delivery and consumption of agricultural products, farmers and distributors can not only identify inefficiencies in the supply chains but also deliver their products faster and in a more cost-effective manner.
Transforming livestock care and production
It’s not uncommon for illnesses to spread quickly in a herd of cattle before farmers can even realise there is something wrong with them. By using this new approach of data analytics together with advanced sensors, farmers can not only prevent such problems but also monitor the fertility and periods of higher milk production, without much effort on their part.
Making better risk assessment
One of the most important applications of this new approach of data analysis and modelling would be to make farmers and agribusinesses better at risk assessment. With the help of IoT, drones, and satellite imagery, it’s now possible to collect and analyse various data points that can help everyone in the agricultural supply chain to reduce risk and consistently optimise for better outcomes.
Improving crop management
Without having access to data, even the most experienced farmers can sometimes have failed growing seasons, which can prove very costly for their business. With the help of bioprospecting, it becomes easier to identify and grow a variety of crops in a way that improves efficiency and reduces the margin of error, when it comes to crop management.
Gone are the days when agriculture was mostly guesswork. By adopting newer ways of exploring agri data, it’s now possible to achieve what was unthinkable just a few decades before.