Twenty-day cohorts of corn poppy were grown in the presence or absence of barley, and seedling survival, biomass accumulation and allocation, plant reproduction, and seed dormancy were measured. Seedling survivorship and biomass accumulation differed strongly among cohorts and were influenced by crop competition. In the absence of crop competition, plants from the first three cohorts (emerging October to January) had 900% higher biomass and 160% more seeds per plant than later cohorts (emerging January to April). Crop competition reduced cohort fitness; for example, in 2003 to 2004, corn poppy biomass was reduced 57 to 96%, and seed production 77 to 97%. Seeds collected from plants that had emerged in spring were less dormant, and thus, germination and emergence of these seeds were higher (25% higher and 200 to 600% higher, respectively) than those for seeds collected from other cohorts. Environmental factors at the time of seed formation may be responsible for the observed differences in dormancy. Cohort-dependent emergence, growth, reproduction, and dormancy have relevant implications for corn poppy management and demography in agricultural systems in northeastern Spain. The dormant seeds produced by autumn to winter cohorts will be the main contributors to the seed bank and weed population shift in subsequent generations. For efficient corn poppy management, the control of cohorts emerging before or with the crop in a cereal field is essential.

The knowledge of weed distribution in a field is a key factor to manage weeds effectively. The feasibility of using weed distribution maps for site-specific weed control will largely depend on the stability of the spatial distribution of the populations. Seed banks are the most reliable way of telling the area's weediness, but the effect of regular herbicide applications on its stability is largely unknown. A field experiment was conducted during 3 yr in a winter wheat field under herbicide treatments with the aim of studying the seed bank's spatial distribution of prostrate knotweed and corn poppy and the spatiotemporal stability of their populations. Soil samples were taken each year on the same locations, and seed abundance was measured by germination in greenhouse. Both species accounted for more than 10% of the broad-leaved weed seed bank and they were selected for further analysis. Prostrate knotweed seed-bank density decreased 76% and corn poppy 88% in 3 yr. Spatial distribution was described by spherical isotropic semivariograms. Distance of spatial dependence (range) of prostrate knotweed and corn poppy decreased 33 and 11% respectively, and the spatial variability (sill) decreased 96 and 99%. Yearly spatial seed distribution was compared for each species and no temporal stability was observed over a 3-yr period. The lack of stability was attributed to the important decrease of seed density over time and the increase in the short-range variability (nugget). However, for prostrate knotweed, the location of minima and maxima were roughly the same between years, allowing farmers to extend the period of use of the weed distribution maps. Although spatial distribution of seed banks can be affected by processes that promote fast changes in the densities of weed populations, this fact does not mean that weed distribution maps could not be used in consecutive seasons.

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 ( 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.