Improving Data Analytics to Forecast Crop Yields and Make Better Decisions
Agricultural retailers depend on data to make informed decisions, as well as to predict future growth. Crop data enables farmers and agricultural retailers to forecast future crop growth and generate strategies regarding marketing, sales, and logistics.
However, the collection and collaboration of farm data is still a significant pain point for agricultural retailers and farmers alike. According to the Wall Street Journal, less than half of farmers surveyed by consulting firm McKinsey are using farm management software, and only 25% are using remote-sensing and precision agriculture hardware.
That said, incorporating these technologies can have significant benefits for farmers and retailers. The U.S. Department of Agriculture found that farmers using digital soil maps produced about 49% higher winter wheat yields in 2017 compared to those who didn't.
Here, we'll explore what challenges ag retailers and farmers face in their data and how they can improve their data analytics for better forecasting and decision-making.
The Challenges of Agricultural Data Analytics
Ag retailers play a pivotal role in the management and interpretation of farm data, bridging the gap between technology and the everyday practicalities of farming. They are at the forefront of driving and shaping the value proposition of ag data management.
However, the sheer volume of data they must manage and analyze can cause roadblocks and hinder decision-making. Other challenges can also make forecasting and planning difficult.
Disparate Data
Farm data, gathered from diverse sources such as soil samples, weather patterns, and regional sales numbers, provides valuable insights into farming practices, crop yields, and market trends. The challenge lies in managing and analyzing this vast, diverse data to extract meaningful and actionable insights.
Ag retailers and farmers struggle with data from separate sources because it is often formatted incongruently or is not otherwise manageable from an analytical perspective. For example, it can be difficult to combine and analyze weather and climate data alongside crop yield data both types of data come from different technology sources.
Unequal Access and the Need for Regulatory Frameworks
A crucial challenge in the ag data landscape is the potential inequities between farmers and large corporations. Farmers often lack access to the valuable information collected from their fields. This discrepancy is further exacerbated by the lack of a comprehensive legal and regulatory framework that ensures equitable access to this information.
To mitigate these issues, a robust regulatory framework is required. This would not only ensure farmers' access to the data collected from their fields but also protect them from any potential exploitation by large corporations. In this way, a more equitable ag data landscape can be fostered.
The Dilemma of Advanced Technologies
The advent of advanced technologies like Artificial Intelligence (AI) and the Internet of Things (IoT) has opened up new possibilities in ag data management. However, these technologies come with their own set of challenges. They require substantial amounts of data for effective operation, which can be hard to manage and analyze.
As the ag retailers continue to navigate these challenges, it becomes essential to strike a balance. A balance between harnessing the potential of advanced technologies and effectively managing the data they require. This balance will be key to unlocking the true potential of ag data management.
Harnessing the Power of Agricultural Data Analytics
The adage that knowledge is power holds particularly true in the world of farming. The more information ag retailers and farmers have about their land, crops, and farming methods, the more effectively they can manage their resources, maximize their yield, and minimize their environmental impact.
This is where advanced big data analytics come into play. The latest analytics tools can help ag retailers analyze large volumes of farm data from disparate sources to predict outcomes, anticipate problems, and make more informed decisions.
Here are some of the ways the agricultural industry can make data analytics more effective.
Adopt Standardized Data Sharing Practices
Data is not just for the retailers, though. Sharing data with farmers is one of the most potent steps an ag retailer can take. When farmers have access to data insights, they can make decisions that increase crop yields, reduce water usage, and improve the quality of their products.
It's a win-win situation that fosters collaboration and innovation.
Implement Emerging Data Technologies
Emerging technologies like Artificial Intelligence (AI) are also transforming the agriculture industry. AI applications can analyze data at lightning speed, providing forecasts, recommendations, and assistance in decision-making to improve farm operations.
These smart tools are like having a personal advisor for each farmer, guiding them towards the best decisions based on data.
Use Precision Farming Techniques
Furthermore, precision farming techniques, which use technology to make more effective interventions, can help ag retailers with farm data management. These techniques can pinpoint where resources are needed most, reducing waste and boosting efficiency.
The future of farming is precise, efficient, and data-driven.
Advocate for Strong Legal and Regulatory Guidance
As farmers and ag retailers embrace the power of data, they must not forget the importance of legal and regulatory frameworks. Farmers must have access to the information collected from their fields, and the technologies developed using their data. This is a matter of fairness and transparency, and it's essential for building trust in this digital farming revolution.
Where legal and regulatory guidance is lacking, ag retailers must band together to lobby government officials. Leaders of the agricultural industry can provide important input in the passing of new laws and regulations.
Transforming Agricultural Operations
By harnessing the power of agricultural data analytics, ag retailers can transform their operations, leading to increased crop yields, reduced water usage, and improved product quality. In essence, they can make smarter, data-driven decisions that benefit everyone - from the farmer to the end consumer.
If you'd like to learn more about improving your data analytics operations, don't miss the Smart Harvesting Summit on cultivating precision, yield, and sustainability with farmer-driven innovation. It’s happening at the Rancho Bernardo Inn in San Diego, California from February 21 to 22, 2024.