Data has become not just a necessity but a lifeline in today’s agriculture and agribusiness operations. You can understand the history and current realities, and perhaps project into the future of an agribusiness by simply analysing the available data in that enterprise. According to Dataways, Agricultural data refers to data gathered and assessed from various sources within the agricultural industry. It includes information relevant to farming operations like crop yields, field locations, pesticide and fertiliser requirements, weather and rainfall patterns, soil conditions, etc. Furthermore, it entails information across the agricultural value chains including data on finances, markets, processing, distribution, consumers’ needs, behaviour, and many other relevant data within the agricultural landscape.
In the last five decades, agriculture has undergone tremendous changes in addressing global food security challenges. The integration of technology and digitisation of operations in agriculture enables these changes. We are at a vista of connectivity in agriculture while utilising technologies in different forms, scales, and intensities across various processes and activities throughout the spectrum of agriculture and its associated industries.
Raw data in its unprocessed state is likened to raw food that can’t be eaten. Data needs to be processed to make meaning out of it. The end product of data processing is information.
There was a day I visited a friend’s poultry farm. He is such a meticulous person and kept all the required records. Data about the birds, feeding rates, vaccination programmes, disease infestation, weight, period of production, etc. Although his farm was filled with data he couldn’t make any informed decision out of it.
When he subjected his data to processing he marvelled at the information he got. The processed information indicated some patterns in egg production, weight gain, disease attacks, and the effects of the change in its feeds. This information assisted him in getting to the root cause of many issues on his farm and also workable action plans that have helped his farm. The decisions he made afterward optimised his farm and increased his profitability.
IMPORTANCE OF AGRICULTURAL DATA COLLECTION
On-field data tracking and collection simply enhances the efficiency and effectiveness of farming operations which also help farmers better manage their operations.
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Data availability and its processing into information assist farmers and agribusiness owners to make unique decisions that addresses specific needs of their enterprise.
The information obtained from various sensors and systems for example and their in-depth analysis can help agribusiness owners to avoid unnecessary activities that contribute little or nothing to productivity. Examples of on-field operations that can utilise the possibilities in data collection and analysis are: optimisation of irrigation schedules, precise application of fertilisers and pesticides, prediction and prevention of disease outbreaks and general improvement in management.
DATA ANALYTICS
Data analytics enables farmers to make informed decisions that replace traditional guesswork and avoidable uncertainties. By leveraging data from various sources in the enterprise, one can operate a precise and relatively predictable agribusiness. This is in contrast to the high risk and unpredictability that percolated farming and agribusinesses decades ago.
According to Cropin, Data analytics is the process of examining raw data to uncover patterns, trends, and intelligence that can enable informed decision-making. It involves collecting, cleaning, contextualising, overlaying, and analysing data to extract meaningful intelligence.
Technology has made data capture more inclusive, detailed, and comprehensive. Some of the technologies used today include:
1. Sensors and IoT devices: They are utilised to monitor environmental conditions, plant and animal behaviour, crop health, machinery operating parameters, etc.
2. Satellite imaging: These utilise signals from the satellite in analysing crop growth, yield predictions, and weather patterns. In other cases, the signals are connected to devices like drones to analyse a field for weed control, precise spraying of chemicals, field mapping, etc.
3. Drones: Capturing high-resolution images for precision farming and activities.
4. Weather stations: Providing real-time weather data.
Furthermore, software applications and frameworks designed can take, store and analyse data in various operations in agribusinesses. Examples include RegenIQ– a data-driven framework to promote the adoption of regenerative agriculture and Greeneye technology– an organisation utilising data for sustainable farming through artificial intelligence.
BENEFITS OF DATA ANALYTICS IN AGRICULTURE
The benefits of data analytics in agriculture cannot be over-emphasised. Some of the benefits of data analytics in agriculture are hereunder listed.
- Increased yields: Data-driven decisions improve crop and livestock productivity.
- Reduced waste: Optimised resource usage minimises waste.
- Improved resource allocation: Data informs decisions on labour, equipment, and inputs.
- Enhanced sustainability: Data-driven farming and agribusiness operations promote environmentally friendly practices.
- Better supply chain management: Data improves logistics, storage, and distribution.
- Increased value chain integration and efficiency: Data-driven insights help the value chains be more robust, integrated, and sustainable.
- Increased revenue and profit: Data capture and analysis can make agribusinesses reduce their input costs considerably and increase their revenue and profit.
CHALLENGES OF DATA IN AGRICULTURE
While data-driven agriculture holds immense potential, possibilities and opportunities for a digitalized agriculture and sustainable future, there are challenges that hamper the advancements or full adoption in other cases. Some of the challenges include:
- Data quality and standardisation: Sources of data usually have various degrees of accuracy, reliability formats, and are quite difficult to integrate and analyse effectively. The use of data integration tools will have to be utilised to assemble those data and integrate in a centralised location for processing.
- Interoperability between systems: According to Codeless Platforms, Data interoperability relies on the implementation of common standards and protocols that dictate how data is formatted, transmitted, and interpreted. These standards ensure that data from one system can be comprehended and utilised by another. Managing inconsistent information across multiple sources is a major challenge for data analytics. In addition, using specialists in this regard comes at a high cost.
- Cybersecurity concerns: Security of the data and data breach concerns are major challenges.
- Limited digital literacy among farmers: Some farmers and agribusiness entrepreneurs are not literate in the use of data and how it operates. The full adoption and implementation process across various operations in the value chain of data analytics will be achieved faster than expected when active stakeholders know and understand how data works.
To overcome these challenges, the agriculture industry is gradually moving towards the functionalities of the under-listed as a form of remedy:
- Cloud-based platforms: Integrating data from various sources.
- Artificial intelligence (AI) and machine learning (ML): Analysing complex (big) data sets using various AI and ML platforms.
- Blockchain technology: Ensuring data security and transparency using block chain technology is becoming relevant.
Organisations are collaborating and integrating operations to provide an efficient, robust, error-free data management infrastructure for agriculture. Recently CropX and CNH partnered to Streamline data management. Many organisations like Deepak Fertilizers, Vineview, and others are innovating in the data management space in agriculture.
CONCLUSION
Data is revolutionising and reengineering the face of modern agriculture, enabling farmers and other value chain actors to optimise resources, make informed decisions, and improve yields and productivity. As the agricultural landscape continues to evolve, the importance of data will only grow and its utilisation will expand vertically and horizontally along the value chains. By addressing challenges and embracing emerging technologies, we can unlock the full potential of data-driven agriculture for a sustainable future.
