Contributed by Dr. Amber Hauvermale, Washington State University
After more than 70 years, the Falling Numbers (FN) method remains the industry standard for detecting alpha-amylase, an enzyme in flour that can cause significant end-use quality problems in baked goods if present in high enough amounts. High levels of alpha-amylase result in low FN. Environmental causes of high alpha-amylase in susceptible wheat varieties include rain before harvest leading to preharvest sprouting (PHS) and cold weather during the soft dough stage of grain development causing late-maturity alpha-amylase (LMA).
The FN test is slow, expensive to run, requires a skilled operator, and is logistically impossible to perform in real-time at receival stations, limiting grain segregation at harvest. Consequently, growers are unable to efficiently evaluate the effects of combining grain from different fields, and grain handlers have no way of knowing the impact of mixing until weeks after crop receival. In years when weather related FN events are localized, the impact to the industry may be minor. However, when events are widespread, as was the case in 2016, the economic impacts are devastating.
Preventing economic losses from low FN wheat due to weather events is an intractable challenge currently faced by the wheat industry and a problem becoming more common due to increased climate variability. This year, the Foundation for Food and Agricultural Research (FFAR) funded a new project aimed at combating low FN. The timely investment by FFAR and matched funding by stakeholders was essential for supporting a broad cross-disciplinary collaboration between researchers at Washington State University, EnviroLogix, the McGregor Company, HighLine Grain Growers, the USDA, the Washington Grain Commission, the Wheat Marketing Center, the Canadian Grain Commission, and CIMMYT, all working to develop and deliver innovative and practical solutions throughout the grain chain.
With the development of inexpensive, fast, sensitive, and simple diagnostic immunoassays, farmers, grain handlers, and breeders will have tools for 1) sorting sound grain from compromised grain in real-time and onsite, 2) differentiation between PHS and LMA in the field, and to select against susceptibility in early breeding lines, and 3) developing modified methods for segregating and blending grain. Further through the development and use of predictive weather models to identify the likelihood for low FN events in a given year and location, those in the grain industry will have access to powerful tools for early identification.
For questions about this research or to participate in the ongoing efforts, please contact ahauvermale@wsu.edu.