Polinização por Apis mellifera em soja em soja transgênica [Glycine max (L.) Merrill] Merrill] Roundup ReadyTM cv. BRS 245 RR e convencional cv. BRS 133.

RESUMO: O objetivo deste estudo foi verificar a influência de Apis mellifera na produção de grãos e qualidade de sementes da soja transgênica Glycine max (L.) Merrill Roundup ReadyTM e convencional. A soja transgênica foi plantada intercalada com a convencional, em 18 parcelas, em três tratamentos: gaiolas com abelhas A. mellifera, gaiolas sem abelhas e áreas descobertas, com livre visitação de insetos. Na soja transgênica, em três parcelas de cada tratamento, foi aplicado glifosato, 30 dias após a emergência. Os parâmetros analisados foram: produção de grãos; número de vagens por planta; peso de 100 sementes e porcentagem de germinação das sementes. Não houve diferença entre as cultivares, entretanto a produção de 2.757,40 kg ha-1 obtida na área coberta por gaiola com abelhas, e 2.828,47 kg ha-1 na área livre para visitação de insetos foram superiores a 2.000,53 kg ha-1 da área coberta por gaiola sem abelhas. O número de vagens por planta foi maior na área coberta por gaiola com abelhas (38,28) e área livre (32,65), quando comparado com o da área coberta por gaiola sem abelhas (21,19). O peso médio de 100 sementes e a germinação das sementes não foram diferentes entre as cultivares e nem entre os tratamentos. Conclui-se que, para as cultivares estudadas, houve benefício na produção de grãos de 37,84%, quando foi permitida a visita de abelhas. ABSTRACT. Pollination by Apis mellifera in transgenic soy (Glycine max (L.) Merrill) Roundup Ready? cv. BRS 245 RR and conventional cv. BRS 133. This research was carried out to evaluate the influence of Africanized honeybees in grain production and seed quality of Glycine max (L.) Merrill Roundup Ready? transgenic soy, as well as of conventional soy. Transgenic soy was interposed with conventional soybean, in 18 plots and three treatments: covered area with Africanized honeybees, covered area without honeybees, and uncovered area with free insect visitation. The herbicide Glyphosate was applied on three plots of each treatment of transgenic soy, 30 days after emergence. Grain production, number of pods/plant, weight per 100 seeds, and seed germination percentage were evaluated. There was no difference among cultivars; however, the production in the covered area with honeybees (2757.4 kg ha-1) and in the uncovered area (2828.47 kg ha-1) were higher than in the covered area without honeybees (2000.53 kg ha-1). The number of pods/plant was greater than in the covered area with honeybees (38.28) and in the uncovered area (32.65) as compared to the covered area without honeybees (21.19). The weight per 100 seeds seed germination did not differ among cultivars or treatments. It can be concluded that, for these cultivars, there was a rise in grain production of 37.84% when honeybee visits were allowed.

Obtenção e caracterização de farinhas de soja das cultivares BRS 267, BRS 257 e VMAX.

2015

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.