<b>A sampling plan and spatial distribution for site-specific control of <i>Sphenophorus levis</i> in sugarcane

Abstract

ABSTRACT. Sphenophorus levis has become a major pest of sugarcane plantations in Brazil, causing significant losses in productivity. Few studies have been conducted on the spatial distribution of this pest in the field, which is essential for the development of sampling plans. We aimed to characterize the spatial distribution of S. levis in sugarcane, defining a reliable, practical and feasible sampling plan, as well as generating application maps for production areas, allowing for comparison of the consumption of inputs in a conventional system with site-specific application. The study was conducted in areas around Piracicaba, Brazil. First, we conducted sampling to evaluate the best sampling density to characterize the spatial dependence. The results showed that the procedure in which the evaluators entered every 20 rows of sugarcane and sampled every 20 m was sufficient to characterize the spatial dependence. In separate areas, the sampling was performed similarly to the previous step, and after generating the application maps, insecticide was applied on a site-specific basis. Even using the more strict control level defined by the producer, there was considerable saving for the pesticide used. In the two fields, the percentage of the infestation-free area was 56.1 and 80.7%.Keywords: geostatistics, site-specific application, precision agriculture, entomology, soil pests, sugarcane weevil.Plano de amostragem e distribuição espacial para controle localizado de Sphenophorus Levis em cana-de-açúcar RESUMO. Sphenophorus levis tem se tornado uma praga importante na cultura da cana-de-açúcar, causando perdas expressivas na produtividade. São raros os estudos sobre a distribuição espacial desta praga, e tais estudos são imprescindíveis para o desenvolvimento de planos de amostragem. O objetivo deste trabalho foi caracterizar a distribuição espacial de S. levis definindo um plano de amostragem confiável, prático e viável, assim como gerar mapas de aplicação localizada para poder intervir em áreas de produção, possibilitando comparar o consumo de insumo de uma aplicação convencional com uma aplicação localizada. O estudo se desenrolou em áreas na região de Piracicaba, Brasil. Primeiramente foi realizada uma amostragem para avaliar a melhor densidade amostral para caracterizar a dependência espacial da praga. Os resultados indicaram que onde os avaliadores entravam a cada 20 fileiras de cana e faziam uma amostragem a cada 20 m foi suficiente para caracterizar a dependência espacial. Em outras áreas foi realizado a amostragem seguindo o padrão das avalições prévias e foram gerados os mapas para aplicação localizada de inseticida. Mesmo utilizando o nível de controle mais conservador, houve uma considerável redução na quantidade de defensivo aplicado, a porcentagem de área sem a infestação da praga foi 56,1 e 80,7%.Palavras-chave: geoestatistica, aplicação localizada, agricultura de precisão, entomologia, praga de solo.

Sampling grids for monitoring Euschistus heros infestation in soybean

“…According to reported by Pavlu and Molin (2016), PNE does not mean necessarily that dataset presents a randomized pattern, but it may be a result of the large sampling grid sizes that prevented the detection of variability existing in the area. In addition, Soybean phenological scale as described by Ritchie et al (1985) and adapted by Yorinori (1996).…”

“…The difficulty to determine a consistent pattern of pest spatial variability led Reay-Jones et al (2010) and Pavlu and Molin (2016) to conclude that a more efficient strategy to recognize the spatial distribution pattern of the bedbugs in cotton and Sphenophorus levis in sugarcane fields is indirectly through the mapping of pest damages caused to crops, since crop damages are irreversible and statics. However, this alternative is not feasible for soybean crop, because damages caused by bedbugs to grains cannot be identified visually in field, but they need to be analyzed in laboratory.…”

“…Therefore, the infestation had a randomized pattern of spatial variability in the field (Pavlu and Molin 2016). The absence of spatial dependence of data may be associated to lower number of insect (< 1 individual per meter) found in these initial reproductive stage of soybean crop (i.e., R 1 to R 5 ) (Reay-Jones et al 2010) as well as due to high data dispersion, commonly observed in studies with counting dataset (Chiba et al 2010).…”

“…Consequently, these protocols indicate to make 10 to 15 pest sampling (subsamples) within the entire area, and the average infestation from all sampling is used for guiding whether a pest controlling strategy need to be implemented (Bueno et al 2013(Bueno et al , 2015. However, as several studies have reported the population of insect pests can also presents a structured spatial dependence (Burgio et al 2012;Riffel et al 2012;Pazini et al 2015;Pavlu and Molin 2016). Consequently, average population of insects does not represent the actual condition of most part of the sampled fields.…”

“…Studies have shown that site-specific application of pesticides can reduce the amount of product applied, leading to lower insecticide-induced resistance of pest insects, as well as it may contribute to the conservation of natural enemies (Reay-Jones et al 2010;Aita et al 2015;Pavlu and Molin 2016). On the other hand, the cost of pest sampling for identifying the spatio-temporal variability patterns and elaborating the management maps stands out as one of the major constraints for the economical sustainability of site-specific control of agricultural pests (Burgio et al 2012;Carleton et al 2013;Pazini et al 2015).…”