Degradation and sorption of fipronil and atrazine in Latossols with organic residues from sugarcane crop.

ABSTRACT: Organic residues from sugarcane crop and processing (vinasse, boiler ash, cake filter, and straw) are commonly applied or left on the soil to enhance its fertility. However, they can influence pesticide degradation and sorption. The objective of this study was to assess the effect of adding these organic residues on the degradation and sorption of fipronil and atrazine in two soils of the State of Mato Grosso do Sul, MS, Brazil. The degradation experiment was carried out with laboratory-incubated (40 days; 28°C; 70% field capacity) soils (0-10cm). The batch equilibration method was used to determine sorption. Fipronil (half-life values of 15-105 days) showed to be more persistent than atrazine (7-17 days). Vinasse application to the soil favored fipronil and atrazine degradation, whereas cake filter application decreased the degradation rates for both pesticides. Values for sorption coefficients (Kd) were determined for fipronil (5.1-13.2mL g-1) and atrazine (0.5-1.5mL g-1). Only straw and cake filter residues enhanced fipronil sorption when added to the soil, whereas all sugarcane residues increased atrazine sorption. RESUMO: Resíduos orgânicos do cultivo e processamento da cana-de-açúcar (vinhaça, cinzas, torta de filtro e palha) são usualmente aplicados ou deixados no solo para aumentar sua fertilidade, mas eles podem influenciar na degradação e sorção de agrotóxicos. O objetivo deste estudo foi avaliar o efeito da adição desses resíduos orgânicos no solo sobre a degradação e sorção do fipronil e da atrazina em dois solos no Estado de Mato Grosso do Sul, MS, Brasil. O experimento de degradação foi realizado com solos (0-10cm) incubados em laboratório (40 dias; 28°C; 70% da capacidade de campo). Para determinar a sorção, foi usado o método da batelada. Fipronil mostrou ser mais persistente (valores de meia-vida entre 15-105 dias) que atrazina (7-17 dias). O solo com adição de vinhaça favoreceu a degradação de fipronil e atrazina, enquanto adição da torta de filtro desacelerou o processo. Os valores do coeficiente de sorção (Kd) foram determinados para fipronil (5,1-13,2mL g-1) e atrazina (0,5-1,5mL g-1). Apenas os resíduos palha e torta de filtro aumentaram a sorção de fipronil quando adicionados ao solo, enquanto todos os resíduos aumentaram a sorção de atrazina.

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.