Spatial patterns of the leaf crown borer Eupalamides cyparissias (Cramer, 1775) (Lepidoptera: Castiniidae) in coconut (Cocos nucifera L.) tropical region.

The leaf crown borer Eupalamides cyparissias (Cramer, 1775) is an important pest of coconut (Cocos nucifera L.) and other palms (Arecaceae) of economic importance, attacking the base of leaves, inflorescences, and infructescences, increasing fruit abortion. The objective of this study was to evaluate the spatial correlation of the infestation rate of E. cyparissias in coconut plantation blocks in the Brazilian Amazon, from January to December 2019, in the city of Santa Izabel, Pará, Brazil. The study area is a farm subdivided into 157 blocks of a commercial plantation of the green dwarf coconut. The Local Moran's Index was used to evaluate the existence of spatial autocorrelation of the E. cyparissias infestation rate in the 157 blocks with neighboring blocks. The infestation rate was calculated by the ratio between the number of plants attacked by the borer and the total number of plants in a block. There is a significant correlation of the symptomatology of the attack by E. cyparissias in the blocks of the experimental area, which indicates an aggregated pattern of distribution. There is no significant correlation between the attack by the borer and the age of the coconut tree; however there is a significant correlation between the attack by the borer and proximity to forest areas. These results indicate that forest regions are foci of infestation by the borer in coconut plantations.

Using Geostatistical Analysis to Evaluate the Presence of Rotylenchulus reniformis in Cotton Crops in Brazil: Economic Implications.

In recent years, the productivity of cotton in Brazil has been progressively decreasing, often the result of the reniform nematode Rotylenchulus reniformis. This species can reduce crop productivity by up to 40%. Nematodes can be controlled by nematicides but, because of expense and toxicity, application of nematicides to large crop areas may be undesirable. In this work, a methodology using geostatistics for quantifying the risk of nematicide application to small crop areas is proposed. This risk, in economic terms, can be compared to nematicide cost to develop an optimal strategy for Precision Farming. Soil (300 cm(3)) was sampled in a regular network from a R. reniformis-infested area that was a cotton monoculture for 20 years. The number of nematodes in each sample was counted. The nematode number per volume of soil was characterized using geostatistics, and 100 conditional simulations were conducted. Based on the simulations, risk maps were plotted showing the areas where nematicide should be applied in a Precision Farming context. The methodology developed can be applied to farming in countries that are highly dependent on agriculture, with useful economic implications.