Next generation soybean breeding
The graph above is the result of one model that graduate student Brent Christensen has developed comparing actual yields to yields predicted by the model. On the X axis is estimated yield based only on spectral data using the model. On the Y axis is the actual seed yield at harvest. Each box on the graph represents a Group III variety. Each diamond represents a Group IV variety.
If the model was predicting yields perfectly using the spectral data, all the boxes and diamonds would fall on the line. They don’t, but in most cases the estimated yield is fairly close to the predicted yield.
With this model, and using only the spectral data taken at the seed fill stage to make selections, we would have retained all of the highest yielding varieties by selecting the best half.
If we can repeat the kind of results we have achieved in the training population with experimental varieties from other populations, the precision should be accurate enough to cull out lines having a low yield potential at the earliest stage of evaluation. If we can discard low-yielding lines without having to harvest them and weigh the seed for yields, this will have tremendous value to the breeding program in terms of saving time, space, and money.
We only have two years of data so far. We are expanding our research into this new technology, developing more robust models, using different types of sensors, adding genotypes, and evaluating the methods of measurement. Also, this summer, we will test the use of aerial sensors in addition to the ground-based sensors.
Our goal is to be able to use spectral analysis to achieve a dramatic reduction in the cost of producing a unit gain in yield potential, and the results so far are promising.
This technology is also being evaluated for its ability to detect yield differences in wheat genotypes, in the program of USDA-ARS wheat geneticist Dr. Jesse Poland.