Mercury in soils and sediments from gold mining liabilities in Southern Amazonia

The 1980-1990 Amazonian gold rush left an enormous liability that increasingly has been substituted by developing fish aquaculture. This work aimed at the identification of the mercury levels in the environment, associated with fish farms located in the North of Mato Grosso State, Southern Amazon. Sediment and soil samples were analyzed for total organic carbon and total mercury. Results indicate that the chemical characteristics of the sediment largely depend on the management procedures of the fish pond (liming, fish food used and fish population). The soils presented relatively low concentrations when compared with other data from the literature.

Cover layers to the growth of trees and shrobs over a sulfide spoil from gold mining

This work was done at a gold mine company in Paracatu, MG, Brazil, and was conducted from March 2000 to November 2005. The substrate (spoil) studied was a phillite rock which contains sulfides such as pyrite and arsenopyrite. This study aimed to evaluate the survival and growth of plant species on different combinations of substrate layers over the spoil. These layers were a cover layer and a sealing layer, both deposited over the spoil. The treatment 1 had saprolite (B1) in the sealing layer (SL) and B1 with liming (B1L) in the cover layer (CL). The treatment 2 had B1 in SL and B1L + soil with liming (SoL) in the CL. The treatment 3 had B1 + SoL in the SL and B1L in the CL. The treatment 4 had B1 + SoL in the SL and B1L + SoL in the CL. The plant species used were Acacia farnesiana, A. holosericea, A. polyphylla, Albizia lebbeck, Clitoria fairchildiana, Flemingia sp., Mimosa artemisiana, M. bimucronata e Enterolobium contortisiliquum. Forty and 57 months after planting, collardiameter, height, and living plants were evaluated. The greatest survival rate was oobservedintreatmentwith B horizon of an Oxisoil in both layers, with 80 %. In general, M. bimucronata and A. farnesiana species showed the highest survival rate. The arsenic-content by Mehlich 3 in the cover layer ranged from 0.00 to 14.69 mg dm- 3 among treatments. The experimental results suggest that layers combinations above the sulfide substrate allow the rapid revegetation of the spoil.

POPULATION STRUCTURE AND PRODUCTION OF COPAIBA OLEORESIN BETWEEN VALLEYS AND HILLSIDES OF THE MINING AREA OFTROMBETAS RIVER - PARÁ1

ABSTRACT We aimed in this work to study natural populations of copaiba (Copaifera multijuga Hayne) on the Monte Branco mountain at Porto Trombetas-PA, in order to support sustainable management and the exploitation of oleoresin from copaiba. We studied the population structure of copaiba on hillsides and valleys of the south face of Monte Branco, within Saracá Taquera National Forest, where bauxite ore was extracted in the biennium 2013-2014 by Mineração Rio do Norte (MRN). We produced a 100% forest inventory of the specie and of oleoresin extraction in order to quantify the potential production of the remaining area. The density of copaiba individuals with DBH > 30 cm was 0.33 individuals per hectare in the hillside and 0.25 individuals per hectare in the valley. Both environments presented a density of 0.28 individuals per hectare. The average copaiba oleoresin yield was 0.661±0.334 liters in the hillside and 0.765±0.280 liters in the valley. The average value of both environments together (hillside and valley) was 0.714±0.218 liters. From all individuals with DBH over 30 cm, 38 (58%) produced some amount of oleoresin, averaging 1.113±0.562 liters in the hillside, 1.329±0.448 liters in the valley and 1.190±0.355 liters in both environments together. The results show the need for planning the use of the surroundings of the study area in order to reach the required volume of copaiba to make feasible the sustainable management of oleoresin extraction in the region.

NATURAL REGENERATION STRATUM AS AN INDICATOR OF RESTORATION IN AREA OF ENVIRONMENTAL COMPENSATION FOR MINING LIMESTONE, MUNICIPALITY OF BARROSO, MG, BRAZIL

ABSTRACT This study was conducted in a forest under restoration process, which belongs to the company Holcim Brasil S/A, in the municipality of Barroso, state of Minas Gerais (21º00'to 22º00'S and 43º00' to 44º00'W), where 40 plots (2 x 2 m) were set, spaced at 10 m, forming eight strata parallel to the watercourse present in the area. Floristic composition and natural regeneration stratum were characterized, and the formed strata allowed evaluating whether the riparian vegetation and watercourse influence on the local regeneration. It was found 162 individuals of 13 families, 18 genera and 22 species, and 10,125 individuals/ha were estimated. Successional classes from pioneer and early secondary and zoochory dispersion syndrome prevailed among species and individuals. The watercourse and riparian vegetation did not exercise significant influence (p> 0.05) on the number of species and regenerating individuals among the different strata of the forest. The diversity index of Shannon-Wiener (H') and equability of Pielou (J') were 2.691 and 0.870, respectively. The species Psidium guajava and Myrtaceae families presented the highest VI (value of importance). Natural regeneration analysis showed the low floristic diversity in the area, suggesting that corrective management actions should be adopted.

Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.

Efficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization

Among the challenges of pig farming in today's competitive market, there is factor of the product traceability that ensures, among many points, animal welfare. Vocalization is a valuable tool to identify situations of stress in pigs, and it can be used in welfare records for traceability. The objective of this work was to identify stress in piglets using vocalization, calling this stress on three levels: no stress, moderate stress, and acute stress. An experiment was conducted on a commercial farm in the municipality of Holambra, São Paulo State , where vocalizations of twenty piglets were recorded during the castration procedure, and separated into two groups: without anesthesia and local anesthesia with lidocaine base. For the recording of acoustic signals, a unidirectional microphone was connected to a digital recorder, in which signals were digitized at a frequency of 44,100 Hz. For evaluation of sound signals, Praat® software was used, and different data mining algorithms were applied using Weka® software. The selection of attributes improved model accuracy, and the best attribute selection was used by applying Wrapper method, while the best classification algorithms were the k-NN and Naive Bayes. According to the results, it was possible to classify the level of stress in pigs through their vocalization.

Using data mining to identify factors that influence the degree of leg injuries in broilers

Locomotor problems prevent the bird to move freely, jeopardizing the welfare and productivity, besides generating injuries on the legs of chickens. The objective of this study was to evaluate the influence of age, use of vitamin D, the asymmetry of limbs and gait score, the degree of leg injuries in broilers, using data mining. The analysis was performed on a data set obtained from a field experiment in which it was used two groups of birds with 30 birds each, a control group and one treated with vitamin D. It was evaluated the gait score, the asymmetry between the right and left toes, and the degree of leg injuries. The Weka ® software was used in data mining. In particular, C4.5 algorithm (also known as J48 in Weka environment) was used for the generation of a decision tree. The results showed that age is the factor that most influences the degree of leg injuries and that the data from assessments of gait score were not reliable to estimate leg weakness in broilers.

Data mining techniques for identification of spectrally homogeneous areas using NDVI temporal profiles of soybean crop

The aim of this study was to group temporal profiles of 10-day composites NDVI product by similarity, which was obtained by the SPOT Vegetation sensor, for municipalities with high soybean production in the state of Paraná, Brazil, in the 2005/2006 cropping season. Data mining is a valuable tool that allows extracting knowledge from a database, identifying valid, new, potentially useful and understandable patterns. Therefore, it was used the methods for clusters generation by means of the algorithms K-Means, MAXVER and DBSCAN, implemented in the WEKA software package. Clusters were created based on the average temporal profiles of NDVI of the 277 municipalities with high soybean production in the state and the best results were found with the K-Means algorithm, grouping the municipalities into six clusters, considering the period from the beginning of October until the end of March, which is equivalent to the crop vegetative cycle. Half of the generated clusters presented spectro-temporal pattern, a characteristic of soybeans and were mostly under the soybean belt in the state of Paraná, which shows good results that were obtained with the proposed methodology as for identification of homogeneous areas. These results will be useful for the creation of regional soybean "masks" to estimate the planted area for this crop.

Vocalization data mining for estimating swine stress conditions

This study aimed to identify differences in swine vocalization pattern according to animal gender and different stress conditions. A total of 150 barrow males and 150 females (Dalland® genetic strain), aged 100 days, were used in the experiment. Pigs were exposed to different stressful situations: thirst (no access to water), hunger (no access to food), and thermal stress (THI exceeding 74). For the control treatment, animals were kept under a comfort situation (animals with full access to food and water, with environmental THI lower than 70). Acoustic signals were recorded every 30 minutes, totaling six samples for each stress situation. Afterwards, the audios were analyzed by Praat® 5.1.19 software, generating a sound spectrum. For determination of stress conditions, data were processed by WEKA® 3.5 software, using the decision tree algorithm C4.5, known as J48 in the software environment, considering cross-validation with samples of 10% (10-fold cross-validation). According to the Decision Tree, the acoustic most important attribute for the classification of stress conditions was sound Intensity (root node). It was not possible to identify, using the tested attributes, the animal gender by vocal register. A decision tree was generated for recognition of situations of swine hunger, thirst, and heat stress from records of sound intensity, Pitch frequency, and Formant 1.