Predicting seed admixture in maize combining flowering characteristics and a Lagrangian stochastic dispersion model

The seed purity problem has received very low attention in maize coexistence studies, and has not been considered in prediction tools that simulate pollen flow and out-crossing between maize fields. To fill this gap we developed the Seed Admixture Model (SAMETH) able to predict seed admixture dispersion combining flowering characteristics (pollen shed, silks exertion) with a Lagrangian stochastic dispersion model. The model was tested with a dataset obtained from 20 fields in 2007 and in 2008, whose seeds were mixed with 1% of a homozygous blue-kernelled hybrid. The model was first calibrated with data from 6 fields and then validated with the data from the remaining 14 fields. Moreover, a sensitivity analysis was performed to test the consequences of different pollen quantities released by the admixture and the commercial hybrids. The measured seed admixture ranged from 0.7% to 6% and the model was able to simulate the seed admixture with r2 = 0.83. The sensitivity analysis showed that the model was sensitive to the absolute released pollen quantities but it was still able to predict seed admixture rather accurately. Because of its reliability, the model could become a useful tool for case study scenarios that involve seed admixture and for which field implementations would be too complex and time-consuming.

Research highlights▶ A model able to predict the dispersion of seed impurity in maize was developed. ▶ Simple algorithms for the description of silks exertion and pollen shed were used. ▶ The model accuracy was elevated (r2 between 0.77 and 0.83). ▶ The model showed an elevated sensitivity to the absolute released pollen quantities.