Amaranthus palmeri S. Watson has become a weed of economic importance throughout the South-eastern United States in the last 20 years (Webster and Nichols, 2012) being especially problematic in soyabean (Glycine max L. Merr.) and cotton (Gossypium hirsutum L.) fields (Bensch et al., 2003;Berger et al., 2015). Recently, multiple reports have indicated that this weed species is invading agricultural

Ethiopian mustard (Brassica carinata A. Braun) is a biofuel crop that was recently introduced in the southeastern United States. In order for this crop to be successful, there is a need to develop integrated weed management strategies that complement its rotation with summer cash crops. The objectives of this research were to evaluate the effect of previous season summer crops on winter weed emergence patterns during Ethiopian mustard growing season and to assess the impact of planting Ethiopian mustard on the emergence patterns of summer weed species. Gompertz models were fit to winter and summer weed emergence patterns. All models represented more than 80% of the variation with RMSE values less than 0.20. The emergence pattern for winter weed species was best described using Growing Degree Days (GDD) accumulation, and this model can be utilized for implementing weed control strategies at the critical Ethiopian mustard growth stages. The results also showed that summer weeds can emerge during the winter in northern Florida but do not survive frost damage, which might create off-season seed bank reductions before the summer crop growing season.

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Wild radish (Raphanus raphanistrum L.) is a weed found globally in agricultural systems. The facultative winter annual nature of this plant and high genetic variability makes modeling its growth and phenology difficult. In the present study, R. raphanistrum natural seed banks exhibited a biphasic pattern of emergence, with emergence peaks occurring in both fall and spring. Traditional sigmoidal models were inadequate to fit this pattern, regardless of the predictive environmental variable, and a corresponding biphasic model (sigmoidal + Weibull) was used to describe emergence based on the best parameters. Each best-fit chronological, thermal, and hydrothermal model accounted for at least 85% of the variation of the validation data. Observations on phenology progression from four cohorts were used to create a common model that described all cohorts adequately. Different phenological stages were described using chronological, thermal, hydrothermal, day length dependent thermal time, and day length dependent hydrothermal time. Integrating day length and temperature into the models was important for predicting reproductive stages of R. raphanistrum.

Adoption of the new biofuel crop carinata (Brassica carinata A. Braun) in the southeastern United States will largely hinge on sound agronomic recommendations that can be economically incorporated into and are compatible with existing rotations. Timing of weed control is crucial for yield protection and long term weed seed bank management, but predictive weed emergence models have not been as widely studied in winter crops for this purpose. In this work, we use observed and predicted emergence of a winter annual weed community to create recommendations for timing weed control according to weed and crop phenology progression. Observed emergence timings for four winter annual weed species in North Carolina were used to validate previously published models developed for winter annual weeds in Florida by accounting for temperature and daylength differences, and this approach explained over 70% of the variability in observed emergence. Emergence of stinking chamomile (Anthemis cotula L.) and cutleaf evening primrose (Oenothera laciniata Hill.) followed biphasic patterns comparable to wild radish (Raphanus raphanistrum L.), which were predicted with previously published models accounting for 82 and 84% of the variation, respectively. Using the predictive models for weed emergence and carinata growth, critical control windows (CCW) were estimated for Clayton, NC and Jay, FL, according to different planting dates. The results demonstrated how early planting coincided with the emergence of three competitive winter weeds, but early control could also remove a large proportion of the predicted emergence of these species. The framework for how planting timing will affect winter weed emergence and crop growth will be an instructive decision-making tool to help prepare farmers to manage weeds in carinata, but it could also be useful for weed management planning for other winter crops.

The cover image is based on the Original Article Windows of action for controlling palmer amaranth (Amaranthus palmeri) using emergence and phenology models by Theresa A. Reinhardt Piskackova et al., https://doi.org/10.1111/wre.12470.

Weed emergence models have the potential to be important tools for automating weed control actions; however, producing the necessary data (e.g., seedling counts) is time consuming and tedious. If similar weed emergence models could be created by deriving emergence data from images rather than physical counts, the amount of generated data could be increased to create more robust models. In this research, repeat RGB images taken throughout the emergence period of Raphanus raphanistrum L. and Senna obtusifolia (L.) Irwin and Barneby underwent pixel-based spectral classification. Relative cumulative pixels generated by the weed of interest over time were used to model emergence patterns. The models that were derived from cumulative pixel data were validated with the relative emergence of true seedling counts. The cumulative pixel model for R. raphanistrum and S. obtusifolia accounted for 92% of the variation in relative emergence of true counts. The results demonstrate that a simple image analysis approach based on time-dependent changes in weed cover can be used to generate weed emergence predictive models equivalent to those produced based on seedling counts. This process will help researchers working on weed emergence models, providing a new low-cost and technologically simple tool for data collection.