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. </jats:p

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. </jats:p

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. </jats:p

Determining appropriate interventions to mainstream nutritious orphan crops into African food systems

Nutritious ‘orphan’ crops could (re)diversify African food systems, but appropriate means to bring this about are required. A review of the literature on crop intervention options suggested success and failure factors in promotion, but indicated little about the relative importance of production-versus consumption-based measures and how these interact. An analysis of secondary crop production data indicated that addressing food policies could be valuable for orphan crop mainstreaming, but, as with literature review, did not provide clear guidance on the importance of different interventions. A survey of experts suggested that cross-disciplinary teams are important for developing mainstreaming strategies, but revealed no clear consensus on the importance of particular measures for specific orphan crops. We discuss the implications of these findings