Exploring Bayesian models to evaluate control procedures for plant disease

• Danilo Alvares ^[1] ; Carmen Armero ^[1] ; Anabel Forte ^[1] ; Luis Rubio ^[2]
  1. [1] Universitat de València

     Universitat de València

     Valencia, España

     
  2. [2] Instituto Valenciano de Investigaciones Agrarias

     Instituto Valenciano de Investigaciones Agrarias

     Moncada y Reixach, España

     
• Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 40, Nº. 1, 2016, págs. 139-152
• Idioma: inglés
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• Dialnet Métricas: 1 Cita
• Resumen

  • Tigernut tubers are the main ingredient in the production of orxata in Valencia, a white soft sweet popular drink. In recent years, the appearance of black spot s in the skin of tigernuts has led to important economic losses in orxata production because severely diseased tubers must be discarded. In this paper, we discuss three complementary st atistical models to assess the dis- ease incidence of harvested tubers from selected or treated seeds, and propose a measure of effectiveness for different treatments against the diseas e based on the probability of germina- tion and the incidence of the disease. Statistical methods f or these studies are approached from Bayesian reasoning and include mixed-effects models, Diri chlet-multinomial inferential processes and mixed-effects logistic regression models. Statistica l analyses provide relevant information to carry out measures to palliate the black spot disease and ach ieve a high-quality production. For instance, the study shows that avoiding affected seeds incr eases the probability of harvesting asymptomatic tubers. It is also revealed that the best chemi cal treatment, when prioritizing ger- mination, is disinfection with hydrochloric acid while sod ium hypochlorite performs better if the priority is to have a reduced disease incidence. The reducti on of the incidence of the black spots syndrome by disinfection with chemical agents supports the hypothesis that the causal agent is a pathogenic organism.

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