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Row Crop Consolidation: Big Data, A Source of Knowledge

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If you’d like to catch up in the series discussing the major factors in row crop consolidation, check our previous articles discussing farmer age and resisting technology as the first two of the five major contributing factors. Technology as a major factor in consolidation has two facets: autonomous equipment and Big Data.  The last article in the series covered autonomous equipment as a factor in row crop consolidation. This article focuses on Big Data.

What is big data in agriculture?

For the last three or four years, a key buzz word in agriculture technology is Big Data. Personally, I’ve never seen a definition of Big Data. My suspicion is that the definition is as varied as the individuals using the term. So, I’m going to give you my definition of “Big Data” for this article:

The aggregation of all relevant and important crop-related information that comes from equipment, computers, the cloud, weather applications, and other electronic technology.

Now the amazing part of Big Data is that simply aggregating all of this data is useless without the following:

  1. It has to be cleaned up or scrubbed. For example, most machine data, when it comes from the equipment, is not very useful until it is organized, has any skips removed, mis-calibrations among multiple machines must be adjusted, start pass and/or end pass delays may need to be adjusted, and noise must be removed.
  2. It has to be organized with similar type data.
  3. It has to be standardized because you can’t compare apples to oranges. Whether or not the data has been calibrated to cart weights needs to be noted---without calibration, inferences may be drawn within a field, but we would be misled if we made comparisons across fields.
  4. You have to have tools that can take pieces of the Big Data from disparate and similar sources and integrate them, putting the pieces together in a meaningful fashion that converts the data to useful knowledge that can be utilized effectively. We can use Big Data methods only when we are certain which variables individual fields have been subject to, and cleaned data means putting the pieces together in a meaningful fashion that converts the data to useful knowledge.

Thus far, hardly anyone has been or is successful in putting those four steps together. There are systems developers who attempt to do specialized pieces but, in most instances, they do not have access to data or have not worked with farmers to ensure value, and they have not worked through standardized data processes. Some are attempting to complete those necessary steps to provide value to their data, but we have not seen much in the way of results.

Defining the components of big data in agriculture

One of the really interesting areas of Big Data would be to define the components. Some of those are:

  • Machine data
  • Agronomic
  • Weather
  • Financial and accounting
  • Processes
  • FSA reporting data
  • Crop insurance
  • Market data

This is not an all-inclusive list, but it gives the reader an understanding of the magnitude. It also illustrates the difficulty, because Big Data encompasses data from all those areas. What if one or two of these pieces of data are missing? Can you achieve the same results? The answer is obviously “Probably not.”

Big Data can only be analyzed and provide knowledge if the data being used is consistent, standardized and complete.

All data areas must be included in similar analysis.  If you believe the definition and the complexity of the components, it becomes pretty clear that we have not yet achieved the goal of using Big Data in its totality. Initially, we can and will use it for smaller analysis projects, but we are not going to be able to analyze all the data pieces until we can clean it up, standardize the individual pieces of data, and build the analytic tools.

Enterprise Resource Planning for crop farming

In my opinion, the most important tool in achieving successful utilization of Big Data is an on-farm ERP system. One of the critical components of that ERP system is the need to be set up to generate accurate, standardized, consistent, timely data. At FFG, we have built “AgVero,” the first ERP system for crop ag in North America. There are crop ERP systems in South America, but not in North America. We have some well-known, highly-publicized systems claiming ERP capabilities that are, in fact, MRP. Unfortunately, MRP systems lack the critical financial data.

The reason we can say the ERP system is the vital tool for utilizing Big Data in the agriculture industry is because it is, has been, and continues to be the vital tool in nearly every manufacturing industry in the world. ERP systems bring much-needed process management to allow integrated applications for managing the many technology-related business functions. Until crop ag producers adopt a standardized ERP system across producers, Big Data will continue to be an untapped and under-utilized resource that could be vitally important to producers.

In conclusion

The people who will be the most successful in consolidation will be the most effective users of standardized Big Data sets. Those who use Big Data successfully will not only collect and integrate data but will have people with time and expertise to analyze and apply the results in strategic managerial decision-making.  Knowledge, a huge competitive advantage, is the key to success, and an effective ERP system is the key to knowledge.

Stay tuned! The next article in this series on factors driving row crop consolidation will deal with capital access. If you would like to know more about FamilyFarms Group and how our system can help you and your operation deal with consolidation, please give us a call at 618-372-7400.

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