Regulating the content of white clover in mixed swards using grass-suppressing herbicides

A preliminary investigation evaluated six grass-suppressing herbicides applied on two occasions in late winter to a predominantly ryegrass ley containing only 15% ground cover of white clover. Substantial increases in clover growth, estimated visually, and flower head numbers per unit area were recorded in the first summer after treatment with 2·8 kg ha^-1 carbetamide, 0·8 kg ha^-1 propyzamide and 0·6 kg ha^-1 paraquat. To achieve these increases, visual estimates suggested that spring growth of grass was reduced by 40–80%. However, grass growth recovered fully by mid-summer on the majority of the treatments.
The following year five of the herbicides were compared in a field experiment. Dry matter (DM) and nitrogen (N) assessments of the grass and legume components were made at three harvests in the first growing season and a single harvest in the second year. Carbetamide, paraquat and, especially, propyzamide increased the proportion of clover in the DM (to 89% in the case of 1·2 kg ha^-1 propyzamide); in general, using herbicides to raise clover contents above 20% lead to reductions in spring grass growth of about 70%. However, such reduction was offset by subsequent increased growth so that total annual yields were largely unaffected. The increased legume content resulted in an increased N concentration in both grass and legume components, measured in the second summer. At this time, the greatest increase in total N yield (up to 35%) was recorded from 0·6 kg ha^-1 propyzamide. Potential uses to achieve legume dominance by grass-suppression are suggested and the needs for further research are outlined.     

Can an integrated management approach provide a basis for long-term prevention of weed dominance in Australian pasture systems?

Broad-leaved weeds in pasture, such as Carduus nutans, Onopordum spp. and Echium plantagineum are a major problem for graziers in southern Australia. Previous attempts to combat these weeds with a single technique have only resulted in short-term success. An approach to long-term control, combining biological control with different grazing and herbicide strategies, was evaluated in an integrated weed management (IWM) programme, in south-eastern Australia. This IWM study was one of the few that has focused on biological control agents. During the field trials, the impacts of grazing and herbicide treatments on the weed and biological control agents, as well as on pasture composition, were monitored. This paper concentrates on the part of the study that focuses on the role and importance of pasture composition as part of weed management. The main pasture components were monitored using botanal , a sampling technique for estimating species composition and pasture yield in the field. IWM is a long-term ecological approach and after 3 years, major trends were just becoming apparent. This study shows that pasture composition can be manipulated to increase productivity and sustainability. It demonstrates that broad-leaved weeds can be reduced when high level pasture background management and chemical control are combined.     

Variability of larval Baltic sprat (Sprattus sprattus L.) otolith growth: a modeling approach combining spatially and temporally resolved biotic and abiotic environmental key variables

Plankton sampling was conducted in the Baltic to obtain sprat larvae. Their individual drift patterns were back‐calculated using a hydrodynamic model. The modelled positions along the individual drift trajectories were subsequently used to provide insight into the environmental conditions experienced by the larvae. Autocorrelation analysis revealed that successive otolith increment widths of individual larvae were not independent. Otolith increment width was then modelled using two different generalized additive model (GAM) analyses (with and without autocorrelation), using environmental variables determined for each modelled individual larval position as explanatory variables. The results indicate that otolith growth was not only influenced by the density of potential prey but was controlled by a number of simultaneously acting environmental factors. The final model, not considering autocorrelation, explained more than 80% of the variance of otolith growth, with larval age as a factor variable showing the strongest significant impact on otolith growth. Otolith growth was further explained by statistically significant ambient environmental factors such as temperature, bottom depth, prey density and turbulence. The GAM analysis, taking autocorrelation into account, explained almost 98% of the variability, with the previous otolith increment showing the strongest significant effect. Larval age as well as ambient temperature and prey abundance also had a significant effect. An alternative approach applied individual‐based model (IBM) simulations on larval drift, feeding, growth and survival starting as exogenously feeding larvae at the back‐calculated positions. The IBM results revealed optimal growth conditions for more than 97% of the larvae, with a tendency for our IBM to slightly overestimate larval growth.