The agricultural industry faces many challenges, including climate change, fluctuations in demand and supply, workforce lockdowns, and supply chain disruptions. There is a consensus among the industry stakeholders that the present situation needs a much more robust supply chain system. Farmers must use cutting-edge technologies to enhance their resilience and reduce potential risks. Big Data analytics in agriculture has emerged as a transformative tool. Leveraging a pool of data can help in solving problems by forecasting accuracy and boosting operational efficiency.
Source: Freepik
Primary Benefits of Big Data Analytics in Agriculture:
1. Better yield and production forecasting –
Through predictive analytics, Big Data helps in more accurate yield and production of forecasts that enable farmers to plan and manage the resources in a much better way, thus improving productivity.
2. Faster delivery –
With better analysis of logistics and supply chain data, the stakeholders can facilitate quicker delivery of goods to the distributors with less delays. Thus, improving the efficiency of the supply chain.
3. Real-time decision making –
Utilizing Big Data analytics in agriculture facilitates real-time decisions and alerts as data from fields and equipment can be analyzed constantly. It allows immediate intervention when needed, reducing the chance of losses.
4. Predictive maintenance –
This is one of the most important benefits of applications of big data analytics in agriculture. Big Data analytics help in predicting the chances of equipment failure, allowing preventive maintenance. This is a proactive approach that increases the lifespan of the farming equipment and reduces downtime.
5. Better energy efficiency –
Big Data helps in optimizing energy use. With smart metrics and data analytical capabilities, water and electricity usage can be reduced, leading to much more sustainable farming practices.
6. Data-driven decision making –
Decision-making based on business performance and production data allows strategic planning and resource allocation, contributing to higher profitability.
7. Optimized farming practices –
Big Data in agriculture helps to identify the correlations between the field conditions, weather, and commodity data, enabling much more efficient irrigation, fertilization, and harvesting of crops.
8. Demand prediction –
Being able to predict the demand for seeds, fertilizers, and animal feed, big data empowers agribusiness suppliers to prepare for demand spikes, ensuring a consistent supply.
9. Innovative pricing programs –
Big data analytics in agriculture can help devise new pricing programs that align with the demand and supply. This becomes specifically relevant when the product demand is correlated with the commodity pricing.
10. Supply chain management –
Big data analytics in agriculture enhances supply chain management by helping farmers trace their products through the supply chain. This empowers the distributors, retailers, and other key stakeholders to offer products and services that meet the needs of the agricultural market in the best possible way.
Application of Big Data Analytics in Agriculture in the Best Possible Way:
There are different ways Big Data analytics in agriculture can be used in the best possible way.
1. Continuous Monitoring –
Farmers need technologies that help with constant monitoring of the different parameters that directly impact yield and profitability. Wireless sensors and variable rate technologies empower farmers to track different agricultural factors like weather conditions, temperature, nitrate content, plant health, soil quality, and weed and pest detection. The primary data sources include:
- Traditional enterprise data – This is the data obtained from operational systems that offer valuable information about day-to-day activities and overall productivity.
- Sensor data – This includes data from the farm’s field sensors (like temperature, rainfall, humidity, and sunlight), farm equipment sensors (tractors, plows, and harvesters), and sensor data from harvested goods and livestock delivery vehicles.
- Trade and financial data – Commodities, trading, and financial forecast data offer a much better and broader picture of the market trends.
- Weather data – Detailed information on the weather conditions will help in better farming decisions.
- Genomic research data – Animal and plant genomics data can offer valuable breeding and disease management insights.
- Geospatial and satellite data – The data sources can help predict crop yields and identify potential problem areas on the farm.
2. Data Accuracy –
Technologies that accurately collect and store data are crucial to making reliable agritech solutions. These technologies are crucial to offer farmers accurate data for better decision-making.
3. Capitalize Automation –
This is one of the crucial applications of big data analytics in agriculture. Increasing automation in crop cultivation and livestock production systems is vital. Automated tools and equipment facilitate precision agriculture and enable continuous, accurate data collection. This leads to better and more efficient farming practices, better productivity, and reduced waste.
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