While we may still grumble about the ability to forecast the weather from time to time, the amount of data collected about the weather is truly amazing and has been integrated into many helpful tools for managing crops. Tools to track and predict growing degree days (GDD) are a great example.
Pest prediction models utilize GDD data to track the life cycle of pests, providing important information on when a particular pest will be entering a life stage where it is likely to damage the crop. Regardless of how good and readily available this data has become, the actions taken with the information remain the most important aspect of whether the tool brings value. In most cases, the knowledge of a pest’s life cycle is simply the starting point of an integrated pest management (IPM) approach of scouting, thresholds, and other factors to determine if action is needed against the pest. They are not an automatic trigger for control measures.
The availability of GDD tracking tools have become valuable in tracking corn progress as well. Comprehensive data exist from seed companies on the average number of GDD a certain relative maturity hybrid needs to reach maturity, and numerous studies have provided valuable data on the number of GDD needed for the plant to reach certain milestones in its development. Further work, specific to silage, has provided estimates of the number of GDD, from planting or tasseling, needed for corn to reach target maturity.
These are useful tools; however, their limitations also need to be recognized. While heat units are an important aspect of corn plant development, the plant is growing in a dynamic system with numerous biotic and abiotic factors (water, pest, nutrition, and more) influencing how effectively it can utilize the heat units available.
As with pest management, GDD calculators are useful as a planning tool but are not a standalone answer. Combined with planting date and hybrid relative maturity information, early season use can provide a ballpark for harvest timing and allow for preliminary planning. As harvest approaches, using them as an early warning can help you be more efficient with your time as an indicator of when to begin more intensive in-field observations of crop maturity.
This last part is the key; final decisions should be made based on more intensive in-field observations. GDD data alone can result in incorrect decisions on timing. In addition to observing this at many farms over the years, this has played out several times in our own studies and the error can be in either direction. At a number of project site-years, in-season stressors have resulted in delayed crop progress with optimum harvest timing delayed by as much as a few weeks compared to known GDD targets. In these cases, harvesting by GDD data alone would have resulted in a wet immature crop, challenging fermentation and sacrificing yield and starch content.
Moving to a related aspect of harvest timing decision making, handheld near-infrared (NIR) units have become increasingly available for sampling forage dry matter (DM) and are a welcome tool to speed up DM testing compared to more time intensive methods such as Koster testers or air friers. These NIR units are great technology; however, to date our experience in obtaining accurate DM data from them has been very mixed. They are a complex machine relying on proper calibration of specific crops and moisture ranges. When calibrations are kept up and applied to the intended crop parameters, they will provide accurate and valuable data for decision making. When these details are missed results can be off significantly, in some cases leading to worse outcomes than if decisions were made with no data.
All data is powerful, only accurate data with context is helpful.
