Identifying the drivers and constraints to adoption of IPM among arable farmers in the UK and Ireland

BACKGROUND

Arable crops in temperate climatic regions such as the UK and Ireland are subject to a multitude of pests (weeds, diseases and vertebrate/invertebrate pests) that can negatively impact productivity if not properly managed. Integrated pest management (IPM) is widely promoted as a sustainable approach to pest management, yet there are few recent studies assessing adoption levels and factors influencing this in arable cropping systems in the UK and Ireland. This study used an extensive farmer survey to address both these issues.

RESULTS

Adoption levels of various IPM practices varied across the sample depending on a range of factors relating to both farm and farmer characteristics. Positive relationships were observed between IPM adoption and farmed area, and familiarity with IPM. Choice of pest control information sources was also found to be influential on farmer familiarity with IPM, with those who were proactive in seeking information from impartial sources being more engaged and reporting higher levels of adoption.

CONCLUSION

Policies that encourage farmers to greater levels of engagement with their pest management issues and more proactive information seeking, such as through advisory professionals, more experienced peers through crop walks, open days and discussion groups should be strongly encouraged.

Measuring the unmeasurable? A method to quantify adoption of integrated pest management practices in temperate arable farming systems

BACKGROUND

The impetus to adopt integrated pest management (IPM) practices has re-emerged in the last decade, mainly as a result of legislative and environmental drivers. However, a significant deficit exists in the ability to practically monitor and measure IPM adoption across arable farms; therefore, the aim of the project reported here was to establish a universal metric for quantifying adoption of IPM in temperate arable farming. This was achieved by: (i) identifying a set of key activities that contribute to IPM; (ii) weighting these in terms of their importance to the achievement of IPM using panels of expert stakeholders to create the metric (scoring system from 0 to 100 indicating level of IPM practised); (iii) surveying arable farmers in the UK and Ireland about their pest management practices; and (iv) measuring level of farmer adoption of IPM using the new metric.

RESULTS

This new metric was found to be based on a consistent conception of IPM between countries and professional groups. The survey results showed that, although level of adoption of IPM practices varied over the sample, all farmers had adopted IPM to some extent (minimum 32.6 [corrected] points, mean score of 67.1), [corrected] but only 15 [corrected] of 225 farmers (5.8%) had adopted more than 67.1% [corrected] of what is theoretically possible, as measured by the new metric.

CONCLUSION

We believe that this new metric would be a viable and cost-effective system to facilitate the benchmarking and monitoring of national IPM programmes in temperate zone countries with large-scale arable farming systems. © 2019 Society of Chemical Industry.

Changes in field dose-response curves for demethylation inhibitor (DMI) and quinone outside inhibitor (QoI) fungicides against Zymoseptoria tritici, related to laboratory sensitivity phenotyping and genotyping assays

BACKGROUND

Insensitivity of Zymoseptoria tritici to demethylation inhibitor (DMI) and quinone outside inhibitor (QoI) fungicides has been widely reported from laboratory studies, but the relationships between laboratory sensitivity phenotype or target site genotype and field efficacy remain uncertain. This article reports field experiments quantifying dose-response curves, and investigates the relationships between field performance and in vitro half maximal effective concentration (EC₅₀ ) values for DMIs, and the frequency of the G143A substitution conferring QoI resistance.

RESULTS

Data were analysed from 83 field experiments over 21 years. Response curves were fitted, expressed as percentage control, rising towards an asymptote with increasing dose. Decline in DMI efficacy over years was associated with a decrease in the asymptote, and reduced curvature. Field ED₅₀ values were positively related to in vitro EC₅₀ values for isolates of Z. tritici collected over a 14-year period. Loss of QoI efficacy was expressed through a change in asymptote. Increasing frequency of G143A was associated with changes in field dose-response asymptotes.

CONCLUSION

New resistant strains are often detected by resistance monitoring and laboratory phenotyped/genotyped before changes in field performance are detected. The relationships demonstrated here between laboratory tests and field performance could aid translation between laboratory and field for other fungicide groups. © 2017 Society of Chemical Industry.

Evaluation of Probabilistic Disease Forecasts

The statistical evaluation of probabilistic disease forecasts often involves calculation of metrics defined conditionally on disease status, such as sensitivity and specificity. However, for the purpose of disease management decision making, metrics defined conditionally on the result of the forecast-predictive values-are also important, although less frequently reported. In this context, the application of scoring rules in the evaluation of probabilistic disease forecasts is discussed. An index of separation with application in the evaluation of probabilistic disease forecasts, described in the clinical literature, is also considered and its relation to scoring rules illustrated. Scoring rules provide a principled basis for the evaluation of probabilistic forecasts used in plant disease management. In particular, the decomposition of scoring rules into interpretable components is an advantageous feature of their application in the evaluation of disease forecasts.

Characterisation of Ramularia collo-cygni laboratory mutants resistant to succinate dehydrogenase inhibitors

BACKGROUND

Ramularia collo-cygni (Rcc) is responsible for Ramularia leaf spot (RLS), a foliar disease of barley contributing to serious economic losses. Protection against the disease has been almost exclusively based on fungicide applications, including succinate dehydrogenase inhibitors (SDHIs). In 2015, the first field isolates of Rcc with reduced sensitivity to SDHIs were recorded in some European countries. In this study we established baseline sensitivity of Rcc to SDHIs in the United Kingdom and characterised mutations correlating with resistance to SDHIs in UV-generated mutants.

RESULTS

Five SDHI-resistant isolates were generated by UV mutagenesis. In four of these mutants a single amino acid change in a target succinate dehydrogenase (Sdh) protein was associated with decrease in sensitivity to SDHIs. Three of these mutations were stably inherited in the absence of SDHI fungicide, and resistant isolates did not demonstrate a fitness penalty. There were no detectable declines in sensitivity in field populations in the years 2010-2012 in the United Kingdom.

CONCLUSIONS

SDHIs remained effective in controlling Rcc in the United Kingdom in the years 2010-2012. However, given that the first isolates of Rcc with reduced sensitivity appeared in other European countries in 2015, robust antiresistance strategies need to be continuously implemented to maintain effective disease control. © 2016 Society of Chemical Industry.

Resolution of Probabilistic Weather Forecasts with Application in Disease Management

Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.

Development and use of microsatellite markers to study diversity, reproduction and population genetic structure of the cereal pathogen Ramularia collo-cygni

Ramularia collo-cygni (Rcc) is a major pathogen of barley that causes economically serious yield losses. Disease epidemics during the growing season are mainly propagated by asexual air-borne spores of Rcc, but it is thought that Rcc undergoes sexual reproduction during its life cycle and may also disperse by means of sexual ascospores. To obtain population genetic information from which to infer the extent of sexual reproduction and local genotype dispersal in Rcc, and by implication the pathogen's ability to adapt to fungicides and resistant cultivars, we developed ten polymorphic microsatellite markers, for which primers are presented. We used these markers to analyse the population genetic structure of this cereal pathogen in two geographically distant populations from the Czech Republic (n=30) and the United Kingdom (n=60) that had been sampled in a spatially explicit manner. Genetic diversity at the microsatellite loci was substantial, Ht=0.392 and Ht=0.411 in the Czech and UK populations respectively, and the populations were moderately differentiated at these loci (Θ=0.111, P<0.01). In both populations the multilocus genotypic diversity was very high (one clonal pair per population, resulting in >96% unique genotypes in each of the populations) and there was a lack of linkage disequilibrium among loci, strongly suggesting that sexual reproduction is an important component of the life cycle of Rcc. In an analysis of spatial genetic structure, kinship coefficients in all distance classes were very low (-0.0533 to 0.0142 in the Czech and -0.0268 to 0.0042 in the Scottish population) and non-significant (P>0.05) indicating lack of subpopulation structuring at the field scale and implying extensive dissemination of spores. These results suggest that Rcc possesses a high evolutionary potential for developing resistance to fungicides and overcoming host resistance genes, and argue for the development of an integrated disease management system that does not rely solely on fungicide applications.

Integrating Experience, Evidence and Expertise in the Crop Protection Decision Process

Generically, farm-scale crop protection decision making may be characterized as a process beginning with an initial assessment of disease risk followed by the accumulation of evidence related to current risk factors, leading to a risk prediction. What action is then taken depends on the response of the decision owner, taking into account previous experience, advice from trusted sources, alongside policy or legislative constraints on crop protection practice that are intended to mitigate any impacts that may transcend the farm scale. This process has commonalities with decision-making in the strategy of preventive medicine. This article delves into the clinical literature in order to provide a perspective on some recent discussions of shared decision making presented there, discussions that relate to issues also faced in sustainable crop protection.

Information graphs for binary predictors

Binary predictors are used in a wide range of crop protection decision-making applications. Such predictors provide a simple analytical apparatus for the formulation of evidence related to risk factors, for use in the process of Bayesian updating of probabilities of crop disease. For diagrammatic interpretation of diagnostic probabilities, the receiver operating characteristic is available. Here, we view binary predictors from the perspective of diagnostic information. After a brief introduction to the basic information theoretic concepts of entropy and expected mutual information, we use an example data set to provide diagrammatic interpretations of expected mutual information, relative entropy, information inaccuracy, information updating, and specific information. Our information graphs also illustrate correspondences between diagnostic information and diagnostic probabilities.

Information graphs for binary predictors

Binary predictors are used in a wide range of crop protection decision making applications. Such predictors provide a simple analytical apparatus for the formulation of evidence related to risk factors, for use in the process of Bayesian updating of probabilities of crop disease. For diagrammatic interpretation of diagnostic probabilities, the receiver operating characteristic is available. Here, we view binary predictors from the perspective of diagnostic information. After a brief introduction to the basic information theoretic concepts of entropy and expected mutual information, we use an example data set to provide diagrammatic interpretations of expected mutual information, relative entropy, information inaccuracy, information updating and specific information. Our information graphs also illustrate correspondences between diagnostic information and diagnostic probabilities.

Disease risk curves

Disease risk curves are simple graphical relationships between the probability of need for treatment and evidence related to risk factors. In the context of the present article, our focus is on factors related to the occurrence of disease in crops. Risk is the probability of adverse consequences; specifically in the present context it denotes the chance that disease will reach a threshold level at which crop protection measures can be justified. This article describes disease risk curves that arise when risk is modeled as a function of more than one risk factor, and when risk is modeled as a function of a single factor (specifically the level of disease at an early disease assessment). In both cases, disease risk curves serve as calibration curves that allow the accumulated evidence related to risk to be expressed on a probability scale. When risk is modeled as a function of the level of disease at an early disease assessment, the resulting disease risk curve provides a crop loss assessment model in which the downside is denominated in terms of risk rather than in terms of yield loss.