How Agriculture Data Analytics is Helping Farmers Make Better Decisions
For most of history, farmers relied on the tribal knowledge of traditional farming techniques. Farmers made decisions based on the acquired knowledge of generations before them. Progress came slowly. And then came the industrial revolution, priming the farming engine for future progress. Starting in the mid-1990, monitoring technology and data analytics have become commonplace on the farm – ushering in the agricultural data revolution.
Yet, the concept of agriculture data analytics is changing. Farmers have been comparing the performance of livestock genetics, crop varieties, and machinery efficiency for as long as these technologies have been around. What has changed is the sheer quantity of data available and the speed and cost of analyzing all that data.
Agriculture Decision Support Systems (DSS)
The amount of data at the disposal of farmers and their advisors is increasing at breakneck speed. The data generated per farm is projected to exceed two million data points per day by 2030. Satellites measure soil topography, electronic ear tags monitor livestock behavior, and harvesters produce detailed GPS yield mapping. This all feeds into big data in agriculture - agriculture data analytics providing insights across different sources and producing predictions and prescriptions for immediate use on the farm. Processed through software called “decision support systems” or DSS, this data helps farmers make better decisions. Decisions that save the farmer time and money, improve environmental outcomes and make the farm more productive.
Six ways data analytics supports decision-making on the farm:
Improved Crop Yields:
Data analytics allows farmers to collect and analyze vast amounts of on-farm data. By leveraging this data, farmers can discover patterns and insights that were once impossible to detect. They can understand how different factors, such as soil composition, moisture levels, and weather patterns, affect their crops' performance. With this information, farmers can make changes to optimize their crops' growth and maximize their yields.
Better Management of Resources:
Farmers need to manage various resources, including water, labor, and fertilizer, to maintain their farms' productivity. Agriculture data analytics can help farmers make more informed decisions about allocating these resources. For example, by evaluating data about soil moisture levels, farmers can determine the exact time to plant to maximize seed germination. Data analytics can also help farmers optimize their labor resources by predicting when tasks like irrigating, spraying, and harvesting are likely most efficient.
Livestock Wellness Monitoring
Monitoring animal health and wellness is vital to ensuring a livestock operation thrives. Data from monitoring collars, feed bunks, and milking equipment can notify farmers when a potential health issue is brewing, when an animal is impacting the efficiency of the operation, or when she is ready to breed or calve. Agricultural data analytics can also help monitor feed. Feed is generally the highest overhead cost for livestock farmers, so ensuring the right kind and the right amount of feed is selected to optimize each animal's growth is important. With data-driven decisions, a farmer predicts health and production outcomes so that success is repeatable.
Improved Evaluation of Weather Patterns:
Weather is the common denominator among farmers for being chaotic and unpredictable. Farmers must decide about planting and harvesting based on various weather factors, such as temperature, precipitation, and wind. Agriculture data analytics can help farmers evaluate weather patterns more accurately, enabling them to make more informed decisions about when to plant and harvest. Farmers can also use data analytics to track weather trends and plan accordingly.
Farm-to-Table Transparency:
As consumers become more conscious of food safety and sustainability, farmers need to be able to trace their products' supply chain. Agriculture data analytics can help farmers, consumers, and all the parties in between better track their product's journey from farm to table. By using sensors and tracking tools, farmers can monitor the conditions in which their products are transported, ensuring they remain fresh and safe. Farmers can also use data analytics to track important sustainability metrics, such as their carbon footprint and water usage.
Reduced Risk:
Farming is a risky business. There are many variables that farmers have no control over, such as droughts, storms, and pests. Agriculture data analytics can help farmers reduce risks by providing real-time information about their farms. By detecting early warning signs of problems, such as crop disease outbreaks, farmers can take corrective action before the problem spreads, thereby minimizing their losses.
Importance of Agriculture Data Analytics
Data analytics has become an essential tool for farmers looking to make informed decisions about their farms. By leveraging vast amounts of data through decision support systems, farmers can gain valuable insights and make changes to optimize crop yields, allocate resources more efficiently, and reduce risks. Data analytics also provides farmers with helpful traceability information, helping them meet increasing consumer demands for transparency and sustainability. As the world's population grows and the demand for food increases, data analytics will continue to play an increasingly important role in the farming industry.
