Using weed emergence and phenology models to determine critical control windows for winter-grown carinata (Brassica carinata)

Abstract

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.

Shortening critical period of weed control at soybean by residual herbicide mixtures

“…This could be explained by some weed species able to germinate near or after the end of a pre-emergent herbicides soil residual effect. Considering the variability of weed traits, there are species able to manifest several flows during the crop growing season, such as Brassica carinata, Raphanus raphanistrum, Oenothera laciniata, and Anthemis cotula (Piskackova, Leon, 2022). In this study, the success in the control of D. insularis and C. benghalensis by pre-emergent herbicides can also be attributed because the control happened for germinating seedlings and the annual growing cycle of the weeds.…”

Developing a Localized Emergence Model of Echinochloa crus-galli to Predict Early Post-Herbicide Effectiveness in Maize

“…Many weed descriptive models are fit with data from bare-ground plots and/or with artificial seedbanks [6,10,21,22], but not all of these models are validated with weed emergence in the crop. It is therefore valuable to have support that an emergence model fit to data from a natural seedbank in bare ground can successfully predict emergence within the crop canopy, as seen in Figure 1 and supported in Table 2 Further applications of these locally validated models could be used in integrated models for timing weed control based on weed emergence and the critical period of weed control [23]. As demonstrated in Figure 2, herbicide applications that are timed according to crop stage may coincide at different proportions of the yearly expected weed emergence.…”

“…Empirical models that describe the expected emergence pattern of a weed species are interesting for their potential to implement precision timing in weed control [13,23]. Applications of such models are already in effect [9].…”

The impact of burial depth on Centaurea diluta emergence and modelling of its growth using a nonlinear regression and artificial neural network

“…14 Recently, it has been proposed that these problems can be avoided by using models that integrate weed phenology progression as a function of seedling emergence patterns to time control actions that reduce the risk of weed escapes. [15][16][17][18] Weed phenological models are based on growth responses to environmental factors such as temperature [thermal time (TT)] and more complex parameters that integrate several environmental factors such as moisture [hydrothermal (HTT)] and daylength [photothermal (PhTT)]. Generally, those biological times are related to the accumulation of emergence or biomass using sigmoidal regression models (SRM) such as Logistic and Gompertz.…”

“…This ultimately might allow escapes and potentially lead to resistance evolution 14 . Recently, it has been proposed that these problems can be avoided by using models that integrate weed phenology progression as a function of seedling emergence patterns to time control actions that reduce the risk of weed escapes 15–18 …”