Changes in weed species since the introduction of glyphosate-resistant cotton

Weed management practices in cotton systems that were based on frequent cultivation, residual herbicides, and some post-emergent herbicides have changed. The ability to use glyphosate as a knockdown before planting, in shielded sprayers, and now over-the-top in glyphosate-tolerant cotton has seen a significant reduction in the use of residual herbicides and cultivation. Glyphosate is now the dominant herbicide in both crop and fallow. This reliance increases the risk of shifts to glyphosate-tolerant species and the evolution of glyphosate-resistant weeds. Four surveys were undertaken in the 2008-09 and 2010-11 seasons. Surveys were conducted at the start of the summer cropping season (November-December) and at the end of the same season (March-April). Fifty fields previously surveyed in irrigated and non-irrigated cotton systems were re-surveyed. A major species shift towards Conyza bonariensis was observed. There was also a minor increase in the prevalence of Sonchus oleraceus. Several species were still present at the end of the season, indicating either poor control and/or late-season germinations. These included C. bonariensis, S. oleraceus, Hibiscus verdcourtii and Hibiscus tridactylites, Echinochloa colona, Convolvulus sp., Ipomea lonchophylla, Chamaesyce drummondii, Cullen sp., Amaranthus macrocarpus, and Chloris virgata. These species, with the exception of E. colona, H. verdcourtii, and H. tridactylites, have tolerance to glyphosate and therefore are likely candidates to either remain or increase in dominance in a glyphosate-based system.

Managing patches of glyphosate resistant Echinochloa colona: can they be eradicated?

Glyphosate-resistant Echinochloa colona L. (Link) is becoming common in non-irrigated cotton systems. Echinochloa colona is a small seeded species that is not wind-blown and has a relatively short seed bank life. These characteristics make it a potential candidate to attempt to eradicate resistant populations when they are detected. A long term systems experiment was developed to determine the feasibility of attempting to eradicate glyphosate resistant populations in the field. To this point the established Best Management Practice (BMP) strategy of two non-glyphosate actions in crop and fallow have been sufficient to significantly reduce the numbers of plants emerging, and remaining at the end of the season. Additional eradication treatments showed slight improvement on the BMP strategy, however were not significant overall. The effects of additional eradication tactics are expected to be more noticeable as the seed bank gets driven down in subsequent seasons.

Can patches of glyphosate-resistant Echinochloa colona be eradicated?

Glyphosate-resistant Echinochloa colona L. (Link) is becoming common in non-irrigated cotton systems. Echinochloa colona is a small seeded species that is not wind-blown and has a relatively short seed bank life. These characteristics make it a potential candidate to attempt to eradicate populations resistant to glyphosate when they are detected. A long term systems experiment was developed to determine the feasibility of attempting to eradicate glyphosate resistant populations in the field. After three seasons, the established Best Management Practice (BMP) strategy of two non-glyphosate actions in crop and fallow have been sufficient to significantly reduce the numbers of plants emerging, and remaining at the end of the season compared to the glyphosate only treatment. Additional eradication treatments showed slight improvement on the BMP strategy, however to date these improvements are not significant. The importance of additional eradication tactics are expected to become more noticeable as the seed bank gets driven down in subsequent seasons.

Inferential, non-parametric statistics to assess quality of probabilistic forecast systems

Many statistical forecast systems are available to interested users. In order to be useful for decision-making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and their statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of `quality’. However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what ‘quality’ entails and how to measure it, leading to confusion and misinformation. Here we present a generic framework to quantify aspects of forecast quality using an inferential approach to calculate nominal significance levels (p-values) that can be obtained either by directly applying non-parametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte-Carlo methods (in the case of forecast skill scores). Once converted to p-values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. Our analysis demonstrates the importance of providing p-values rather than adopting some arbitrarily chosen significance levels such as p < 0.05 or p < 0.01, which is still common practice. This is illustrated by applying non-parametric tests (such as KW and KS) and skill scoring methods (LEPS and RPSS) to the 5-phase Southern Oscillation Index classification system using historical rainfall data from Australia, The Republic of South Africa and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. We found that non-parametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system or quality measure. Eventually such inferential evidence should be complimented by descriptive statistical methods in order to fully assist in operational risk management.