Digital soil mapping: evaluation of sampling systems for soil surveys and refinement of soil maps at lower cost using legacy data

In soil surveys, several sampling systems can be used to define the most representative sites for sample collection and description of soil profiles. In recent years, the conditioned Latin hypercube sampling system has gained prominence for soil surveys. In Brazil, most of the soil maps are at small scales and in paper format, which hinders their refinement. The objectives of this work include: (i) to compare two sampling systems by conditioned Latin hypercube to map soil classes and soil properties; (II) to retrieve information from a detailed scale soil map of a pilot watershed for its refinement, comparing two data mining tools, and validation of the new soil map; and (III) to create and validate a soil map of a much larger and similar area from the extrapolation of information extracted from the existing soil map. Two sampling systems were created by conditioned Latin hypercube and by the cost-constrained conditioned Latin hypercube. At each prospection place, soil classification and measurement of the A horizon thickness were performed. Maps were generated and validated for each sampling system, comparing the efficiency of these methods. The conditioned Latin hypercube captured greater variability of soils and properties than the cost-constrained conditioned Latin hypercube, despite the former provided greater difficulty in field work. The conditioned Latin hypercube can capture greater soil variability and the cost-constrained conditioned Latin hypercube presents great potential for use in soil surveys, especially in areas of difficult access. From an existing detailed scale soil map of a pilot watershed, topographical information for each soil class was extracted from a Digital Elevation Model and its derivatives, by two data mining tools. Maps were generated using each tool. The more accurate of these tools was used for extrapolation of soil information for a much larger and similar area and the generated map was validated. It was possible to retrieve the existing soil map information and apply it on a larger area containing similar soil forming factors, at much low financial cost. The KnowledgeMiner tool for data mining, and ArcSIE, used to create the soil map, presented better results and enabled the use of existing soil map to extract soil information and its application in similar larger areas at reduced costs, which is especially important in development countries with limited financial resources for such activities, such as Brazil.

Componentes principais na elaboração de índices de provas de desempenho em bovinos

The principal component analysis assists the producers in making decision of which evaluated features must be maintained in performance tests indexes, according to the variation present in these animals evaluated. The objective in this study was to evaluate a set of characteristics measured in a performance test in semifeedlot cattle of the Simmental and Angus breeds, by means principal component analysis (PC), aim to identify the features that represent most of the phenotypic variation for preparation of indexes. It was used data from 39 Angus and 38 Simmental bulls from the Santa Éster farm, located in Silvianópolis - MG. The performance test period was from october 2014 to february 2015. The features evaluated in the test were: final weight (FW), average daily gain weight (GW), respiratory rate (RR), haircoat temperature (HT) and rectal (RT), hair number (HN), hair length (HL), hair thickness (HT), muscularity (MUSC), racial characteristics, angulation, reproductive and balance (BAL), height of the front and back, width and length of croup, body length, depth and heart girth, subcutaneous fat thickness and rump (FTR), loin eye area and marbling (MAR). It was used PRINCOMP from SAS program for procedure the PC analysis. It was found that of the 27 features evaluated, the first four PC for Simmental breed explained 74% total variation data. The four PC selected with the corresponding weighting coefficients formed the following index: (0.27 * FW) + (0.47 * MUSC) + (0.50 * HL) + (0.39 * HT). Since the characteristics related to the adaptability of great importance for the studied breed, it was decided to keep the index of evidence for the Angus breed, the feature hair number, because there is a feature that presented a great variability and occupied one of the first principal component. Thus, the Angus index was composed by five features, with 79% total variation data, resulting in the following formula: (0.26 * FW) + (0.33 * BAL) + (0.58 * MAR) - (0.43 * FTR) – (0.38 * HN). By the principal component analysis it was possible to minimize the features number to be evaluated on performance tests from that farm, making the animal selection rapidly and accurate.