The Role of Data Standardization in Driving Efficiency Across the Agriculture Supply Chain

The COVID-19 pandemic has drastically disrupted global supply chains – from consumer products to the automotive industry, food to agriculture products, and technology companies, it felt like no area went untouched. Thel pandemic and the need for globalization combined with higher-yielding crops have put even more pressure on the agriculture supply chain

It’s important to remember that while growers and crop researchers can work on improving crop growth and yield, there is a need for business people, economists, and marketing specialists to get the harvested crop to consumers through the supply chain. 

Food shortages, increased prices, harvest losses, and other events can damage the supply chain that connects growers with consumers. With more open data sharing and improved data standardization, the agricultural supply chain has the ability to become more efficient and resilient to these events. 

This is where big data in agriculture comes in. There are over 150 million on-farm experiments conducted each year, yet only 2 million field trials are analyzed each year. It’s essential that we harness the power of big data in precision agriculture to drive innovation and improve agronomic production.

Big Data in Agriculture

The term “big data” in agriculture has become the norm for researchers, growers, and policy analysts. These data are generated by farmers, agronomists, government, NGOs, research centers, academic researchers, food and beverage companies, and more, relating to every aspect of the agricultural process. 

In agriculture, there are millions of potential data points that can be collected from the time that a seed is purchased, sown into a field, grown, harvested, and then is marketed and sold to a customer. These data points can help inform future decisions for any part of this process to increase quality, yield, profit, and sustainability. 

Over the last decade, digital technologies have evolved immensely with more experience, statistical algorithms, and computational power. As the infrastructure and computing capabilities continue to improve, it’s essential that we harness the power of big data in agriculture to solve bigger issues such as sustainability, reducing carbon emissions, food security issues, and crop nutrition.

The Challenges with Big Data

There are many challenges with big data that growers and researchers encounter, two of the main ones being: underutilized resources and data, and the inability to connect data insights to data action.

Oftentimes, researchers do not use collected data to its fullest extent, missing the potential gains. Many research groups and farmers conduct the same trials and experiments repeatedly but are unaware. Whether it’s poor data management or forgotten data, many data points simply go unused. This means that all of the effort, time, and resources that went into collecting the data point were essentially wasted.

The ultimate goal of most agronomic research is to aid farmers in their productions – whether that’s through increased yield, new tools, or management practices. But a common disconnect in the pipeline is between researchers and farmers, specifically when it comes to farmer adoption or having farmers leverage data insights. 

A researcher’s inability to translate research findings into actionable protocols and a farmer’s limited application of digital technology can drastically limit agricultural progress. 

The barriers that are commonly encountered with the extensive amount of big data need to be focused on immediately. Solutions to these challenges have the potential to help address the need for increased food supply with the world population expected to rise close to 10 billion by 2050. More effective operations, reduced uncertainties, and real-time decision-making could revolutionize agriculture, all of which relate to big data.  

The Benefits of Data Standardization in Agriculture

Many agri big data challenges can initially be addressed, or at least minimized, through data standardization. Data standardization, whether that’s creating a single standard of data collection/measurement or transforming data so that they’re all in the same units or scale, allows you to actually compare data across experiments. 

Data standardization in agriculture not only helps with the quality of data but its usability. In a field that has the ability to generate data on every single practice and application within the production (think daily rainfall, fertilizer application, planting date, wind speed, etc.), these data help farmers monitor their health in real-time and can help with future management decisions to optimize yield. 

Some of the benefits of data standardization in agriculture include: relating data points and harmonizing them, improving data quality through identifying anomalies and erroneous entries, and converting measurement units into one. All of these benefits, and more, are available with Agmatix’s Axiom technology, specifically geared towards big data in agriculture.

How Agmatix Can Help

Agmatix’s platform is able to ingest and standardize your agronomic data from multiple sources, regardless of format. Our agricultural data management focuses on the interoperability and reusability of agri big data. The automation pipelines allow you to analyze your data for immediate solutions, empower your production to be more sustainable and efficient, be resilient to the supply chain, and ultimately drive innovative decisions.

  • More Sustainable: Agmatix can help you reduce farmer adoption gaps and negative environmental impacts. Farmers are able to use digital tools to collect and analyze data on their own or utilize our decision support systems to help them make the best crop nutrient and sustainability decisions. Our tools quantify your production’s carbon footprint and drive more effective reporting.
  • More Efficient: Our platform helps coordinate multiple agronomic field trials locally, regionally, or globally, giving you a more comprehensive view for budget planning and oversight purposes. With this structured approach, your speed from trial to insights from agri big data is increased. Instead of having data sitting in silos of Excel files for years, untouched, our digitized approach allows for immediate use and analysis.
  • Resilient Supply Chain: By transferring knowledge and training to farmers and connecting them through a unified platform, Agmatix helps bridge a common gap in the pipeline. There are also localized plant nutrition protocols and other insights with organizations or individuals that you collaborate with to help streamline the supply chain process across groups.
  • Drive Innovation: Utilizing a centralized data hub for the analysis of trials accelerates the pipeline development of new technologies. This data standardization in agriculture helps synthesize data from numerous sources and unlock robust insights, driving new innovation. 

You may be interested in:
The Importance of Data Standardization and Harmonization to Innovation
Benefits of Sharing Agronomic Data and Using Data Standardization

Big Picture Impact

In a recent study by Ernst & Young, LLP, over 200 supply chain executives cited increased efficiency as their company’s top priority over the next year. These top executives also mentioned the need for increased implementation of AI and machine learning technologies to tackle problems that can pop up with big data. Traditional data management techniques and platforms are ill-equipped and less efficient when it comes to real-time data sharing and analysis across several organizations. 

Agmatix’s innovative ag-solutions help you manage and extract real value from big data. The platform and tools through Agmatix specifically assist with the data standardization of big data in precision agriculture to drive efficiency across the agriculture supply chain. With the versatility and scalability to assist with a single farmer’s field or dozens of global field trials, Agmatix’s technology is one that will help you unlock the true potential of your data.