Variabilidade espacial e temporal de plantas daninhas em Latossolo Vermelho argiloso sob semeadura direta

Abstract

RESUMO. O conhecimento da variabilidade espacial das plantas daninhas permite açôes de manejo localizado, que podem trazer o duplo beneficio da economia de insumos e da preservacáo da qualidade do amhiente. Este trabatho objetivou avaliar a distrihuiçâo espacúil de plantas daninhas num Latossolo Vermelho distrófico (LVd) argiloso em Campinas, Estado de Sao Paulo, no periodo entre 2003 e 2008. A área amostrada era de 3,42 hectares e apresentava 302 pontos amostráis distribuidos em grade regular de 10 x 10 m. Os coeficientes de variaçâo calculados foram classificados como altos e muitos altos, características de dados provenientes de contagem, e assimétricos, o que demonstra que a utilizaçâo de valores medios nao representa adequadamente a populaçâo amostral. A análise semivariográfica mostrou que, para as diferentes épocas estudadas, houve estrutura de dependencia espacial definida, tanto para as plantas separadas em fungáo do tipo de folha como para o número total délas. Os mapas ohtidos por krigagem e o indice de distribuiçâo evidenciaram o padrâo agregado da distribuiçâo espacial dos dados. Foi possivel delimitar zonas de manejo com diferenças de infestaçao da ordem de cinco a dez vezes no número total de plantas daninhas, confirmando a hipótese de que a análise geoestatistica pode ser utilizada como ferramenta auxiliar no manejo de forma satisfatória.Palavras-chave: geoestatistica, krigagem, mapeamento. ABSTRACT. Spatial and temporal variability of weed population in anOxisol under no-till system. The knowledge of weed spatial distribution allows specific site management that leads to herbicide savings and environmental quality protection. This work aimed to evaluate the spatial distribution of weeds in a clay Oxisol in Campinas, Sao Paulo State, Brazil during the period between 2003 and 2008. The sampled area measured 3.42 ha and featured 302 sampling points, in a 10 X 10 meter regular grid. Based on high and very high values of coefficient of variation and on its asymmetry, it was shown that inferences of mean values did not represent the sampled population of weeds. Also, the semivariograms showed a spatial pattern of data for weeds, even if they were separated according to its leaf morphology. The clustered weed distribution were well designed at the kriged maps and also observed according to distrihtition index. With the geostatistical approach, it was possible to clear mapping site-specific managing zones with 5 to 10 fold differences in weed count, in order to confirm that this technique can be used as ancillary tool in agricultural practices.

Selection of Grids for Weed Mapping

“…These results were similar to those found by Chiba et al (2010) when mapping the spatial variability of weeds using precision farming tools using 10 x 10 m cells, divided into three classes of infestation, who concluded that there was a defined spatial dependency structure for plants separated according to the type of leaf and for their total number. The maps obtained in this study show the aggregated pattern of spatial distribution of weeds.…”

Sampling grids for monitoring Euschistus heros infestation in soybean

“…High CV values are commonly associated with counting data, and reflects the variability of the number found point-to-point (Chiba et al 2010). Therefore, high CV values observed in this study suggest high data variability of the E. heros infestation in the area (Oliveira et al 2015), requiring the use of sampling plans with higher number of samples to efficiently reproduce this spatial variability in unsampled locals.…”

“…Several studies of pest population dynamics have reported that, under low infestation, the data do not follow a normal distribution curve (Reay-Jones et al 2010;Riffel et al 2012;Carleton et al 2013). In this case, using conventional sampling protocols, which takes into account the arithmetic mean value, non-normal data do not represent satisfactorily the variability of the insect population in the field (Chiba et al 2010). On the other hand, data normality is not a requirement of geostatistical analysis; however, asymmetric distributions with high number of outliers deserve attention (Webster and Oliver 2007).…”

“…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).…”

Spatial and temporal distribution of weeds in no-tillage system

“…Jaremtchuk et al (2008) emphasize that the mapping of weed population allows the monitoring of the infestation over time, enabling the development of management strategies that minimize the effect of competition with the crop, besides the saving of resources due to the application of varying volumes according to the infestation. Chiba et al (2010), studying spatial and temporal variability of weeds in a Red Latosol with clayey texture under notillage system, observed that weeds, both magnoliopsida and liliopsida, show a defined spatial distribution structure (nonrandom occurrence), allowing the use of geostatistical analysis to fit theoretical models of semivariance and construction of thematic maps. These authors also pointed out that the mapping of weeds indicated an aggregated structure of occurrence (localized groups), which may favor the localized application of herbicides, corroborating with studies conducted by Wiles & Brodhal (2004), who demonstrated that the spatial distribution pattern of weeds rarely occurs at random, because it is conditioned to the specific differences of each environment.…”