RESUMO
Soybean (Glycine max) is the world's most cultivated legume; currently, most of its varieties are Bt. Spodoptera spp. (Lepidoptera: Noctuidae) are important pests of soybean. An artificial neural network (ANN) is an artificial intelligence tool that can be used in the study of spatiotemporal dynamics of pest populations. Thus, this work aims to determine ANN to identify population regulation factors of Spodoptera spp. and predict its density in Bt soybean. For two years, the density of Spodoptera spp. caterpillars, predators, and parasitoids, climate data, and plant age was evaluated in commercial soybean fields. The selected ANN was the one with the weather data from 25 days before the pest's density evaluation. ANN forecasting and pest densities in soybean fields presented a correlation of 0.863. It was found that higher densities of the pest occurred in dry seasons, with less wind, higher atmospheric pressure and with increasing plant age. Pest density increased with the increase in temperature until this curve reached its maximum value. ANN forecasting and pest densities in soybean fields in different years, seasons, and stages of plant development were similar. Therefore, this ANN is promising to be implemented into integrated pest management programs in soybean fields.
RESUMO
Sampling plans are an essential part of integrated pest management programs. Sequential sampling plans enable rapid and low-cost assessment of pest densities. Thrips are emerging pests in soybean crops, and the main method used in pest control is chemical. In soybean crops, insecticides are applied mainly using tractors or airplanes. Thus, this work aimed to determine sequential sampling plans for thrips in soybean crops with insecticide applications using a tractor or airplane. Data were collected in 56 soybean fields, and each field was 20 ha. Sampling plans were determined and validated. The lower (m0) and upper (m1) limits of the sequential sampling plans were: m0 = 1.72 and m1 = 3.43 (by tractor applications) and, m0 = 2.27 and m1 = 4.53 thrips. sample-1 (by airplane applications). The slope (S) and the lower (h0) and upper (h1) intercepts of the sequential sampling plans were: S = 2.42, h0 = -5.79, and h1 = 5.79 (by tractor applications) and, S = 3.19, h0 = -6.83, and h1 = 6.83 (by airplane applications). Sequential sampling plans allowed for correct decisions to be made in all situations using a maximum of 10 samples. The sequential plan reduced the sampling effort by over 87% compared to conventional sampling plans. Therefore, these control decision-making systems have proven feasible and advantageous for implementing integrated pest management programs for controlling thrips species in soybean crops.
Assuntos
Inseticidas , Tisanópteros , Animais , Glycine max , Controle de Pragas/métodos , Produtos AgrícolasRESUMO
Spatial distribution studies of insect pests make it possible to determine their colonization and dispersal patterns. Watermelon (Citrullus lanatus (Thunb.) Matsum. et Nakai) is among the most frequently consumed fruits in the world, and the common blossom thrips, Frankliniella schultzei (Trybom) (Thysanoptera: Thripidae), is one of the most important insect pests of this plant. The objective of this study was to determine the spatial distribution of F. schultzei in commercial watermelon crops using geostatistics. The studied F. schultzei populations presented an aggregated distribution. The colonization of thrips began at the borders of the crops, especially in the areas located in the opposite direction to that of the prevailing winds. The highest densities of thrips occurred in crops that had cucurbits in the surrounding areas. When monitoring for F. schultzei populations, greater attention should be given to sampling that part of the crop located in the opposite direction to that of the prevailing winds because this is where colonization begins. Even at low densities, the aggregation points of thrips in the crop should be located and controlled so that they do not cause damage. In sampling programs for F. schultzei, samples should be taken at distances greater than 9 m apart because this is the distance up to which densities of this species show spatial dependence. Planting watermelon crops close to other cucurbits should be avoided, as these alternate hosts may act as a source of infestation by this pest.
Assuntos
Citrullus , Tisanópteros , Animais , Produtos Agrícolas , Frutas , InsetosRESUMO
Studying the spatial dynamics of pests allows the determination of abiotic and biotic factors affecting time and locations of pest attack to the crops. Such abiotic and biotic factors mainly include 1) climatic elements, 2) natural enemies, 3) phenological stage of plants, and 4) surrounding vegetation. Melon (Cucumis melo L. [Cucurbitales: Cucurbitaceae]) is among the most consumed fruit in the world, and the whitefly Bemisia tabaci (Gennadius) is among the main pests of this crop. This work aimed to determine the effects of surrounding vegetation, natural enemies, climatic elements, and stages of plants on the spatial dynamics of B. tabaci in commercial melon fields. Adult whitefly densities were monitored on four melon fields in a tropical climate region. Sampling location in crops was georeferenced. Experimental data were submitted to geostatistical analysis. The highest densities of B. tabaci occurred during hot periods experiencing lower rainfall, and when the surrounding area presented crops hosting B. tabaci, especially other melon farms. The density ratio of the predators (i.e., spiders and Geocoris sp.) were dependent on pest density. The pattern of field colonization by B. tabaci varies according to its density. Therefore, the surrounding vegetation, air temperature, and predators influence the spatial distribution of B. tabaci in melon fields. These results provide important information to melon farmers, assisting them to improve the management of B. tabaci in the field.