Variabilidade espacial de Colletotrichum truncatum em campo de soja sob três níveis de sanidade de sementes

Abstract

RESUMOPalavras-chave adicionais: Máxima verossimilhança restrita, semivariograma esférico, transmissão.Objetivou-se caracterizar a variabilidade espacial da incidência de Colletotrichum truncatum (Schwein) em sementes colhidas de soja e determinar o melhor modelo e método de semivariograma que represente a incidência de C. truncatum dessas sementes. O experimento foi realizado em condições de campo na safra 2009/10, em 3 parcelas de 9,9 x 10 m, com sementes inoculadas com C. os métodos mínimos quadrados ordinários (OLS), mínimos quadrados ponderados (WLS), máxima verossimilhança (ML) e máxima verossimilhança restrita (REML). A validação cruzada foi empregada para escolha final do modelo e método do semivariograma. Em seguida, efetuaram-se a krigagem e o desvio padrão da krigagem. Os mapas de krigagem ilustraram a transmissão da semente para a semente e a sua variância. Verificou-se estrutura de dependência espacial da transmissão de C. truncatum via semente. O melhor modelo de semivariograma foi o esférico e o melhor ajuste foi o REML. Houve alcance de 0,95, 4,03 e 7,05 m para as parcelas com 0,8, 1,6 e 2,4% de sementes inoculadas respectivamente. Quanto maior o inóculo primário da parcela, maior foi a transmissão para as sementes próximas à fonte de inóculo.This study aimed to characterize the spatial variability of Colletotrichum truncatum (Schwein) incidence among harvested soybean seeds and to determine the best semivariogram model and method to represent C. truncatum incidence among these harvested seeds. The experiment was conducted under field conditions in 2009/10 crop cycle, in 3 plots of 9.9 x 10 m, using seeds inoculated with C. truncatum. The inoculum corresponded to 0.8, 1.6 and 2.4% of the total sown seeds. Three meshes were marked with GNSS receivers, totaling 112 points in each plot spaced at 1.3 m on the row. At the end of soybean cycle, seeds were harvested, dried and analyzed for their health according to the blotter test, considering the 336 demarked points. ABSTRACTto the collected data by using the methods ordinary least squares (OLS), weighted least squares (WLS), maximum likelihood (ML) and restricted maximum likelihood (REML). Cross-validation was used for the final selection of the semivariogram model and method. Then, kriging and kriging standard deviation were performed. The kriging maps illustrated the seed-to-seed transmission and its variance. Spatial dependence was found for C. truncatum transmission through the seeds. The best semivariogram model was spherical and the best fit was REML. There was a range of 0.95, 4.03 and 7.05 m for plots with 0.8, 1.6 and 2.4% inoculated seeds, respectively. The higher the primary inoculum of the plot, the higher the transmission to seeds near the inoculum source.A geoestatística tem por objetivos caracterizar a variabilidade espacial dos atributos do solo e das plantas e fazer estimativa, utilizando o princípio da variabilidade espacial, a fim de se identificar inter-relações desses atributos no espaço e no tempo, além de permitir estudar padr...

REACTIONS OF LIMA BEAN (Phaseolus lunatus L.) ACCESSIONS TO Colletotrichum truncatum

“…Sanitary levels in soybean (Glycine max L.) seeds and reactions to C. truncatum were evaluated by Noetzold et al (2014), who concluded that the fungus can stay in latency for almost the entire crop cycle and that this depends on the soil and climatic conditions, so identification and quantification of the symptoms can be difficult. Thus, the adoption of preventive methods of control is essential.…”

Transmission potential of Colletotrichum lindemuthianum (race 65) in association with bean seeds under controlled conditions

“…A noteworthy study conducted by Noetzold et al (2014) on spatial variability of Colletotrichum truncatum in soybean showed that this pathogen is able to infect the seeds coming from infected seeds, without symptoms appearing throughout the crop cycle.…”

Semivariogram models for estimating fig fly population density throughout the year

“…Mora & Beer (2013) used the spherical model to study the spatial variability of coffee (Coffea arabica L.) fine roots, under Erythrina shade. Noetzold et al (2014) evaluated the spatial variability of Colletotrichum truncatum on soybean (Glycine max L.) field with the spherical, exponential, Gaussian, and K-Bessel models, and found the best adjustment for the spherical model. However, most studies with geostatistical models use few semivariograms and do not contemplate the possibility that data sets may show a different spatial structure with time.…”