Is Australia ready for triple gene herbicide stack technology?

Integration of multiple herbicide-resistant genes (trait stacking) into crop plants would allow over the top application of herbicides that are otherwise fatal to crops. The US has just approved Bollgard II® XtendFlex™ cotton which has dicamba, glyphosate and glufosinate resistance traits stacked. The pace of glyphosate resistance evolution is expected to be slowed by this technology. In addition, over the top application of two more herbicides may help to manage hard to kill weeds in cotton such as flax leaf fleabane and milk thistle. However, there are some issues that need to be considered prior to the adoption of this technology. Wherever herbicide tolerant technology is adopted, volunteer crops can emerge as a weed problem, as can herbicide resistant weeds. For cotton, seed movement is the most likely way for resistant traits to move around. Management of multiple stack volunteers may add additional complexity to volunteer management in cotton fields and along roadsides. This paper attempts to evaluate the pros and cons of trait stacking technology by analysing the available literature in other crop growing regions across the world. The efficacy of dicamba and glufosinate on common weeds of the Australian cotton system, herbicide resistance evolution, synergy and antagonisms due to herbicide mixtures, drift hazards and the evolution of herbicide resistance to glyphosate, glufosinate and dicamba were analysed based on the available literature.

Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield

Yield loss in crops is often associated with plant disease or external factors such as environment, water supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation. Herbicide drift can be a combination of improper practices and environmental conditions which can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLS-R) models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality, photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA) were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables were highly affected by the chemical. Four PLS-R models for predicting yield were developed according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes (RMSEP = 2.6 and R2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R2 = 0.84). In summary, the results of this study show that it is possible to accurately predict yield after a simulated herbicide drift of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective and non-destructive alternative based on the internal response of the cotton leaves.