Big data is a valuable asset in today’s digital world. Organisations across various sectors now use big data analytics to unthread hidden information from vast datasets. This approach improves their efficiency and addresses the needs of their stakeholders.
The agricultural sector is one of the many industries benefiting from it, leading to smarter farming practices that yield higher output.
Below is a more detailed look at how it is transforming agriculture and improving farm efficiency and productivity.
What is big data in agriculture?
In agriculture, data analysis involves collecting, analyzing, and applying large and complex datasets. This approach improves decision-making, efficiency, productivity, and sustainability in farming and agribusiness.
It encompasses a wide range of information, including data from weather patterns, soil conditions, crop growth, machinery performance, market trends, etc.
What data is important to farmers?
For farming to be effective, farmers need to collect and analyse the following types of data:
- Soil: Qualities like fertility, humus content, and acidity are important for determining the soil’s suitability for planting. These factors help identify what types of crops are best suited for the conditions.
- Yield: Analysing crop yields is vital for determining areas of low fertility, thus allowing farmers to better rectify the issue.
- Farm Equipment: Employing big data applications in farming equipment or machinery will help farmers better monitor and manage their fleets. Among the important info they can learn includes tractor availability, service due dates, and fuel refill alerts.
- Plants: By analysing crop data, farmers can study plant performance even in remote areas. This can help in the allocation of fertilisers and pesticides to where they’re needed.
- Weather: Data from weather stations can help farmers monitor temperature changes and forecast weather conditions.
- Relief: Relief data determines the distribution of water and nutrients in the soil, which influences land fertility.
Why is data analysis important in agriculture?
The agricultural industry faces challenges like climate change, supply chain disruptions, and workforce issues. As such, big data analytics has risen as a response and solution to these issues. It offers an effective means of leveraging the vast amounts of data that agricultural organisations receive on a near-daily basis.
Role of big data analytics in agriculture
Big data analytics and data science are becoming the foundation for modern agricultural advancements. With this analysis, farmers can predict scenarios that improve future farming practices, leading to increased yields to support a growing global population.
Applications of data analytics in agriculture
The use of big data in agriculture encompasses various aspects within the agricultural sector:
Precision farming
Farmers can achieve greater precision in applying water, fertilisers, and pesticides. This is done through data collected from sensors, drones, and satellites. These technologies monitor crop health, soil conditions, and weather patterns.
Yield prediction
Data analytics is a game changer in predicting crop yields. Accurate yield predictions empower farmers to optimise planting and harvesting strategies. This optimisation leads to increased production and a reduced environmental footprint.
Pest and disease management
Farmers can spot the first signs of pest infestations or plant diseases by analysing data sets such as weather conditions and historical data.
Early detection allows for targeted interventions and reduced crop losses. This might involve applying specific pesticides or implementing preventative measures like crop rotation.
Soil health monitoring
Soil data analysis provides farmers with crucial information about nutrient levels, pH, moisture, and organic matter content. With this knowledge, farmers will better understand soil management. They can use sustainable farming practices like crop rotation or cover cropping to improve soil health in the long run.
Water management
Water is a precious resource, especially for farmers, and predictive analytics helps them better manage it. Farmers can analyse data on weather forecasts, soil moisture, and crop water needs. This allows them to optimise irrigation schedules and improve crop yields while conserving water.
Supply chain optimisation
Supply chain management uses data analytics to analyze data on factors like demand, transportation, storage, and distribution. This helps farmers streamline logistics, reduce waste, and optimize the distribution of agricultural products.
By delivering products to markets efficiently and on time, farmers can maintain product quality and customer satisfaction.
Market trend analysis and price forecasting
Farming success hinges not only on crop yields and delivery but also on selling crops at the right time and place. Analysing historical market trends and other data sets allows farmers to forecast demand, price trends, and consumer preferences. This information enables them to make informed decisions on what to plant and when to sell.
Risk assessment and management
Whether it’s climate change, pest outbreaks, or changing market trends, farmers can use data analytics to anticipate potential risks to their crops and investments. This preparation helps them adapt to changing conditions.
Livestock management
Farming is not all about crops, of course. Livestock forms the other half of the agricultural sector. Monitoring and analysing data related to livestock health, feeding patterns, and growth rates will grant farmers vital knowledge of their animals’ well-being. With this info at hand, they’ll be able to improve animal welfare and productivity.
Farm equipment optimisation
Farm equipment optimisation involves using data analytics to improve the efficiency and reduce the costs of agricultural machinery. Farmers can optimise machinery scheduling and utilisation by analysing data on equipment usage, performance, and maintenance.
This ensures that the equipment is used when it is most needed and in the most efficient manner possible.
Advantages of big data in agriculture
Agricultural data offers many benefits, including:
- Increased productivity: By optimising farming practices and forecasting crop production, farmers can achieve higher yields. This allows them to make more informed decisions on resource management.
- Cost reduction: Big data allows precise application of resources such as water, fertilisers, and pesticides. This, in turn, allows farmers to minimise input costs and reduce resource wastage.
- Risk mitigation: Data analytics helps predict, manage, and mitigate risks related to weather, pests, and diseases, reducing potential losses and improving food safety.
- Sustainability: Big data plays a vital role in environmentally-friendly farming. By analysing agricultural data, farmers can promote the efficient use of resources. This analysis also supports sustainable farming practices and reduces environmental impact.
- Improved decision-making: The information leveraged from vast agricultural data sets provides farmers with actionable insights. These insights help them make better decisions in planning, planting, and harvesting.
- Enhanced quality control: Big data analytics enables farmers to track their crops and livestock in real-time, ensuring consistent produce yields and quality.
- Market competitiveness: Big data offers data-driven strategies to help farmers stay competitive by aligning production with market demands.
Case study example for big data in agriculture research
To see the practical application of big data in agriculture, consider a leading palm oil producer. This producer optimised its extraction yield by automating the grading of Fresh Fruit Bunches (FFB).
The producer developed an automated FFB grading system that utilises a light normalisation module and deep learning algorithms. This system accurately assesses fruit ripeness and quality.
This data-driven approach minimises human error and standardises the grading process. As a result, it leads to more precise and efficient FFB classification. The impact was significant, with an 8-12% improvement in the baseline oil extraction rate.
In addition to optimising oil extraction, the automated grading system streamlined FFB handling, reducing labour costs and processing time. The accuracy of the system also provided valuable data for decision-making, further enhancing process optimisation and quality control.
The system reduced discrepancies in grading, which in turn minimised disputes between estates and mills, leading to increased profitability.
For more insights, explore the full case study on big data in agriculture here.
Conclusion
Farmers will be facing many challenges in the foreseeable future, from climate change to changing market demand trends. In light of this, big data in agriculture plays a crucial role in facing and managing these challenges.
In this increasingly digital era, big data and data analytics have become the backbone of modern farming practices and innovations.