A Thermal Time Model to Predict Corn Poppy (Papaver rhoeas) Emergence in Cereal Fields

Corn poppy is the most abundant broad-leaved weed in winter cereals of Mediterranean climate areas and causes important yield losses in wheat. Knowledge of the temporal pattern of emergence will contribute to optimize the timing of control measures, thus maximizing efficacy. The objectives of this research were to develop an emergence model on the basis of soil thermal time and validate it in several localities across Spain. To develop the model, monitoring of seedling emergence was performed weekly during the growing season in a cereal Field located in northeastern Spain, during 3 yr. Cumulative thermal time from sowing date was used as the independent variable for predicting Cumulative emergence. The Gompertz model was Fitted to the data set of emergences. A base temperature of 1.0 C was estimated through iteration for maximum Fit. The model accounted for 91% of the variation observed. Model validation in several localities and years showed general good performance in predicting corn poppy seedling emergence (R(2)(adj) values ranging from 0.64 to 0.99 and root-mean-square error from 4.4 to 24.3). Ninety percent emergence was accurately predicted in most localities. Results showed that the model performs with greater reliability when significant rainfall (10 mm) occurs within 10 d after crop sowing. Complemented with in-Field scouting, it may be a useful tool to better timing control measures in areas that are homogeneous enough regarding climate and crop management.