Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models.

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept beyond time-dependent measures to other variables of interest. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Ni ? no/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictors.

Assessment of Injuries Caused by Anastrepha fraterculus (Wied.) (Diptera: Tephritidae) on the Incidence of Bunch Rot Diseases in Table Grape.

Resumo: Anastrepha fraterculus (. Wied) é a principal praga de uvas de mesa (Vitis vinifera) na Região Sul do Brasil. Neste estudo, o objetivo foi investigar o efeito da punção de frutas por fêmeas adultas e infestação larvária por A. fraterculus na ocorrência da doença podridões na uva (cultivar "Itália"). Abstract: Anastrepha fraterculus (Wied.) is the main insect pest of table grapes (Vitis vinifera) in the Southern Region of Brazil. In this study, we aimed to investigate the effect of fruit puncturing by adult females and larval infestation by A. fraterculus on the occurrence of bunch rot disease in the grape (cultivar ?Itália?) by evaluating grapes (a) punctured for oviposition by females of A. fraterculus, sterilized in laboratory with novaluron (40 mg L−1) and further spray-inoculated separately with Botrytis cinerea (1 × 106 conidia mL−1), Glomerella cingulata (1 × 106 conidia mL−1), and bacteria and yeast that cause sour rot (1 × 105 cells mL−1), (b) grapes punctured for oviposition by non-sterilized females with pathogen spraying, (c) grapes with mechanical wounds and pathogen spraying, (d) grapes with no wounds and with pathogen spraying, (e) grapes punctured for oviposition by A. fraterculus chemically sterilized in laboratory with novaluron, (f) grapes punctured for oviposition by A. fraterculus non-sterilized in laboratory with novaluron, (g) grapes with mechanical wounds, and (h) grapes with no sterilization or pathogen spraying. Our data indicated that the mechanical and oviposition wounds caused by A. fraterculus increased the percentage of grapes infected by B. cinerea, G. cingulata, and microorganisms of acid rot. The grape puncturing by A. fraterculus and the mechanical wound allows the penetration of B. cinerea and microorganisms leading to acid rot. We conclude that the fruit fly A. fraterculus may facilitate phytopathogens penetration leading to bunch rots in the table grape Itália.