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An overwhelming absence of information on areas risk toward invasive species introduction results out of the significant time and labour requirements of species-specific analyses which complicates management. We aimed to develop a generic modelling approach to identify hotspots for plant pest introductions. We assessed the risk of presence in Europe for the whole group of 248 invasive species on the priority lists (A1 and A2) of the European and Mediterranean Plant Protection Organization. Global georeferenced data on a wide range of potential predictors related to climate, soils, water, and anthropogenic factors were collected, and an elastic-net machine learning algorithm was trained on around 341 000 observations across the globe to predict new introduction of invasive species as a function of the predictors. The algorithm was tuned and trained for nine setups resulting from the combinations of three approaches to generating background data and three cross-validation techniques.

Price responses following reductions in supply due to the invader are likely to redistribute economic impacts to consumers. This market response is expected to be particularly strong due to the inelastic nature of the production system. Vigilance among growers and citizens is critical to prevent an introduction and further spread of the disease. Communication of pest risk assessments to the public is key to fostering a better understand of mitigation strategies. As risks are shared between consumers and producers, invasive species should be contextualized as a societal challenge.

The study estimated the input-specific technical inefficiency among Dutch indoor vegetable farms for the period 2006–2016 and identified the sources of inefficiencies for the specified inputs.

Our objective was to empirically quantify the effects of spatial spillovers on outputand input-specific technical inefficiency in Dutch arable crop farms. First, a non-para-metric directional distance function was computed using DEA to estimate technicaland scale inefficiency scores for output, productive inputs and damage abatement inputs (pest control spending). Second, a spatial econometric model was estimated which incorporates regressors for spatial lags of farm characteristics alongside other non-lagged explanatory variables and time-period fixed effects. We use both the inverse distance weight matrix and the binary k-nearest-neighbours weight matrix and also estimate the distance cut-off and the optimal number of neighbours rather than imposing rules of thumb.

Xylella fastidiosa is one of the most dangerous plant-pathogenic bacteria worldwide. Regulatory measures were enacted in response to the detection of the subsp. pauca (Xfp) in Italian olives in 2013, but the current impact is nevertheless major. We developed a spatially explicit bio-economic model to compute potential future economic impact of the Xfp strain. Uncertainty on spread is accounted for by simulating different scenarios. The majority of orchards were found to be within climatically suitable territory. Even under slow disease spread and the ability to replant with resistant cultivars, projections of future economic impact in affected countries run in the billions of Euros. Our findings highlight the importance of minimizing disease spread and implementing adaptation measures in affected areas.