The role of interspecific variability and herbicide pre-adaptation in the cinmethylin response of Alopecurus myosuroides

BACKGROUND: Cinmethylin is an inhibitor of plant fatty acid biosynthesis, with in-plant activity caused by its binding to fatty acid thioesterases (FAT). The recent registration of cinmethylin for pre-emergence herbicidal use in the UK represents a new mode of action (MOA) for control of the grassweed blackgrass (Alopecurus myosuroides). To date there is little published information on the extent of blackgrass’ inter-population variability in sensitivity to cinmethylin, nor on any potential effect of existing non-target-site resistance (NTSR) mechanisms on cinmethylin efficacy. RESULTS: Here we present a study of variability in cinmethylin sensitivity amongst 97 UK blackgrass populations. We demonstrate that under controlled conditions, a UK field-rate dose of 500 g ha-1 provides effective control of the tested populations. Nevertheless, we reveal significant inter-population variability at doses below this rate, with populations previously characterised as strongly NTSR displaying the lowest sensitivity to cinmethylin. Assessment of paired resistant “R” and sensitive “S” lines from standardised genetic backgrounds confirms that selection for NTSR to the acetyl-CoA-carboxylase inhibitor fenoxaprop, and the microtubule assembly inhibitor pendimethalin, simultaneously results in reduced sensitivity to cinmethylin at doses below 500 g ha-1. Whilst we find no resistance to the field-rate dose, we reveal that cinmethylin sensitivity can be further reduced through experimental selection with cinmethylin. CONCLUSION: Cinmethylin therefore represents a much-needed further MOA for blackgrass control, but needs to be carefully managed within a resistance monitoring and integrated weed management (IWM) framework to maximise the effective longevity of this compound

Real‐options water supply planning: Multistage scenario trees for adaptive and flexible capacity expansion under probabilistic climate change uncertainty

Planning water supply infrastructure includes identifying interventions that cost‐effectively secure an acceptably reliable water supply. Climate change is a source of uncertainty for water supply developments as its impact on source yields is uncertain. Adaptability to changing future conditions is increasingly viewed as a valuable design principle of strategic water planning. Because present decisions impact a system's ability to adapt to future needs, flexibility in activating, delaying, and replacing engineering projects should be considered in least‐cost water supply intervention scheduling. This is a principle of Real Options Analysis, which this paper applies to least‐cost capacity expansion scheduling via multistage stochastic mathematical programming. We apply the proposed model to a real‐world utility with many investment decision stages using a generalized scenario tree construction algorithm to efficiently approximate the probabilistic uncertainty. To evaluate the implementation of Real Options Analysis, the use of two metrics is proposed: the value of the stochastic solution and the expected value of perfect information that quantify the value of adopting adaptive and flexible plans, respectively. An application to London's water system demonstrates the generalized approach. The investment decisions results are a mixture of long‐term and contingency schemes that are optimally chosen considering different futures. The value of the stochastic solution shows that by considering uncertainty, adaptive investment decisions avoid £100 million net present value (NPV) cost, 15% of the total NPV. The expected value of perfect information demonstrates that optimal delay and early decisions have £50 million NPV, 6% of total NPV. Sensitivity of results to the characteristics of the scenario tree and uncertainty set is assessed