Predicting future climatic events is one of the key issues in many fields, whether in scientific or industrial areas. An artificial neural network (ANN) model, based on a backpropagation type, was developed in this study to predict the minimum air temperature of the following day from meteorological data using air temperature, relative humidity, radiation, precipitation, and wind direction and speed to detect the occurrence of radiative frost events. The configuration of the next day ANN prediction system allows operating with low-power computing machines; it is able to generate early warnings that can lead to the development of effective strategies to reduce crop damage, lower quality, and losses in agricultural production. This paper presents a procedural approach to an ANN, which was trained, validated, and tested in 10 meteorological stations in central Chile for approximately 8 yr (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). The overall mean results were classified by a confusion matrix and showed good performance in predicting minimum temperature with a mean square error (MSE) of 2.99 ºC for the network, 1.71 ºC for training, 1.77 ºC for validation, and 1.74 ºC for the testing processes. Frost detection results had an appropriate 98% overall mean accuracy (ACC), 86% sensitivity (TPR), and 2% error rate (ER). Differences and errors in frost detection can be attributed to several factors that are mainly associated with the accuracy of the sensors meteorological stations, local climatic and geographic conditions, and the number of parameters that enter in the ANN training processes.
The red mite, Oligonychus yothersi (McGregor) (Acari: Tetranychidae), is a serious pest of avocado (Persea americana Mill.) 'Hass' in Chile. Micro-coleopterans predators are observed in avocado orchards during outbreaks of O. yothersi, which are attracted by herbivore-induced plant volatiles (HIPVs). However, the damage to plants persists and new predators are needed. Due to its effectiveness and adaptability to dry climates, the western predatory mite Galendromus occidentalis (Nesbitt) (Acari: Phytoseiidae) has been adopted as a biological control agent on many crops. Ours objectives were to study the biological parameters of G. occidentalis preying on O. yothersi in controlled conditions and its attraction to volatile compounds from avocado shoots, live preys and four synthetic doses of methyl salicylate (MeSA) and ocimene using a Y-tube olfactometer. We found a net reproductive rate (R0) 34.41 offspring female -1 , intrinsic rate of increase (rm) 0.19 females female -1 d -1 , finite rate of increase (λ) 1.21 females female -1 , mean generation time (T) 18.45 d, and doubling time (DT) of 3.61 d. Immature phytoseiids took 7.32 d to reach adulthood with 90% survival. Female longevity and fecundity were 36.27 d, and 59 eggs female -1 , respectively. Phytoseiids show no attraction to O. yothersi volatiles or volatiles of O. yothersi-infested avocado shoots. Nevertheless, females showed a preference for synthetic MeSA and ocimene at 100 μg mL -1 . Our findings indicate that O. yothersi is a potential diet to rear G. occidentalis, and MeSA and ocimene could be used in lures to manipulate its behavior in avocado orchards.
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