Geostatistics applied to the study of the spatial distribution of Tibraca limbativentris in flooded rice fields

Abstract

Tibraca limbativentris (rice stem bug) is an insect highly injurious to the rice crop in Brazil. The aim of this research was to define the spatial distribution of the T. limbativentris and improve the sampling process by means of geostatistical application techniques and construction of prediction maps in a flooded rice field located in the "Planalto da Campanha" Region, Rio Grande do Sul (RS), Brazil. The experiments were conducted in rice crop in the municipality of Itaqui - RS, in the crop years of 2009/10, 2010/11 and 2011/12, counting fortnightly the number of nymphs and adults in a georeferenced grid with points spaced at 50m in the first year and in 10m in the another years. It was performed a geostatistical analysis by means adjusting semivariogram and interpolation of numeric data by kriging to verify the spatial dependence and the subsequent mapping population. The results obtained indicated that the rice stem bug, T. limbativentris, has a strong spatial dependence. The prediction maps allow estimating population density of the pest and visualization of the spatial distribution in flooded rice fields, enabling the improvement of the traditional method of sampling for rice stem bug

Sampling grids for monitoring Euschistus heros infestation in soybean

“…In general, CV values are inversely proportional to average values, and higher infestation of insects tend to provide lower CV, as also reported by Riffel et al (2012) and Pazini et al (2015), studying the spatial variability of soybean defoliating caterpillars (Anticarsia gemmatalis and Pseudoplusia includes) and rice stalk stink bug (Tibraca limbativentris), respectively. High CV values are commonly associated with counting data, and reflects the variability of the number found point-to-point (Chiba et al 2010).…”

“…Pazini et al (2015) also found high CVs (i.e., > 100%) in infestation data of stalk stink bug (Tibraca limbativentris) in rice fields. It shows that regardless of species, bedbugs have similar spatial pattern of distribution in the field.…”

“…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.…”

“…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). Therefore, a suitable dimensioning of the sampling grid is crucial to achieve both technical and economic feasibility of the specific management of pests (Pazini et al 2015).…”

“…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). Therefore, a suitable dimensioning of the sampling grid is crucial to achieve both technical and economic feasibility of the specific management of pests (Pazini et al 2015). For instance, using larger pest sampling grids (i.e., few evaluation points per area) makes the field work easier, less time-consuming, and cheaper; however, it can compromise the quality of information obtained in the field (Carleton et al 2013), leading to improper management decisions.…”