Growing Degree Day Model for North Dakota Soybean (6/28/12)
Growing Degree Days (GDDs) are calculated from the average temperature of each day for a period of time using the North Dakota Agricultural Weather Network data (ndawn.ndsu.nodak.edu). There are workable GDD models for various crops in North Dakota, such as corn, barley, wheat, canola, potato, sugarbeet, and sunflower (see NDAWN website). However there is not yet a model created for soybean. GDD can be used to predict the crop growth stages and help in making crop management decisions which are based on the growth stage of the soybean. Accumulated GDD is the method to determine the heat units accumulated at a given time during the growing season towards the maturity of the crop. In this study, we initially selected a threshold temperature of 50°F as a base temperature above which soybean would accumulate growth. Since corn uses the same base temperature, we used corn growing degree days to calculate accumulated GDD for soybean. However, soybean phenology is influenced by temperature and photoperiod. Soil moisture and nutrient stress also affect the development of soybean. Seed companies use a maturity group indication for each variety. Maturity groups used in North Dakota are a double zero (00), zero (0), and one (1).
Soybean variety trials were planted from 2007 through 2011 at Langdon, Carrington, central and southern test locations in North Dakota. Each scientist recorded the planting date and physiological maturity date for each soybean variety. There were different maturity groups at each location.
The accumulated GDD results based on 50°F base temperature show variation in growing degree days needed for soybean plants in the same maturity group to reach maturity in different years at a specific location and there are differences between locations. Companies all have their own test locations on which they base the maturity rating of their varieties; therefore, there is a substantial variability between varieties within a maturity group.
However, when we start to average the data sets over more locations and years the numbers start to show a more consistent trend (Table 1). Our initial evaluation seems to indicate that we can develop a predictive model but the model needs to be verified and adjusted as more information will become available. If we continue to collect GDDs for different maturity groups the averages across years will tend to become better predictors. These improved numbers can be included to refine the predictive power of a soybean GDD model for North Dakota.
Extension Agronomist Broadleaf Crops
NDSU State Climatologist