Luciana Pagliosa Carvalho Guedes

Optimization of management zone delineation by using spatial principal components

Variable selection techniques were evaluated jointly with the Fuzzy C-means algorithm.A new variable selection approach, for defining management zones, was proposed.The new approach, named MPCA-SC, provided the best performance for the Fuzzy C-means.MPCA-SC provided management zones more viable from the viewpoint of field operations. Definition of management zones is the delimitation of sub-areas with similar topographic, soil and crop characteristics within a field. Among the many variables that can be used for this definition, those that are stable and spatially correlated with yield are more often recommended for use. Clustering algorithms such as Fuzzy C-means are also frequently applied to define management zones. Three variable selection techniques that can be applied with Fuzzy C-means are spatial correlation analysis, principal component analysis (PCA), and multivariate spatial analysis based on Morans index PCA (MULTISPATI-PCA). In this study, the efficiency of each of these three techniques used in conjunction with the clustering method was assessed. Furthermore, a new variable selection approach, named MPCA-SC, based on the combined use of Morans bivariate spatial autocorrelation statistic and MULTISPATI-PCA, was proposed and tested. The evaluation was performed by using data collected from 2010 to 2014 from three agricultural areas in Parana State, Brazil, with corn and soybean crops, generating two, three, and four classes. The delineated management zones were different according to the method used, and MPCA-SC provided the best performance for the Fuzzy C-means algorithm and the best variance reduction values of the data after the delimitation of the sub-areas. Furthermore, MPCA-SC provided management zones with greater internal homogeneity, making them more viable for implementation from the viewpoint of field operations.

Influence of incorporating geometric anisotropy on the construction of thematic maps of simulated data and chemical attributes of soil

The study on spatial variability of soil properties performed through geostatistical techniques allow us to identify the spatial distribution of phenomena by means of a spatial model that considers degree of dependence among observed data, depending on distance and also the direction that separate them, if there is geometric anisotropy, in other words, a directional trend in spatial continuity. However, the main difficulty in decision making regarding the use of anisotropic spatial model focuses on its relevance to the parameters that express the geometric anisotropy in a spatial model exercise in relation to the estimation space. This study aims at identifying the degree of influence of geometric anisotropy on the accuracy of spatial estimation using simulated data sets with different sample sizes and soil chemical properties such as: Fe, potential acidity (H + Al), organic matter and Mn. Comparing the isotropic and anisotropic models, especially for smaller sample sizes (100 and 169) showed an increased sum of squares of differences between predictions anisotropy factor (Fa) equals 2. Furthermore, from Fa equals 2.5, over 50% of the simulations showed values of overall accuracy (OA) of less than 0.80 and values for the concordance index Kappa (K) and Tau (T) from 0.67 to 0.80, indicating differences between thematic maps. Similar conclusions were obtained for chemical properties of the soil, from Fa equals 2, showing that there are relevant differences regarding the inclusion or not of geometric anisotropy.

Cassava starch extraction effluent treatment in a one phase tubular horizontal pilot reactor with support medium

The cassava starch industries generate a large volume of wastewater effluent that, stabilized in ponds, wastes its biogas energy and pollutes the atmosphere. To contribute with the reversion of this reality, this manipueira treatment research was developed in one phase anaerobic horizontal pilot reactor with support medium in bamboo pieces. The reactor was excavated into the ground and sealed with geomembrane in HDPE, having a volume equal to 33.6 m3 and continuous feeding by gravity. The stability indicators were pH, volatile acidity/total alkalinity ratio and biogas production. The statistical analyses were performed by a completely randomized design, with answers submitted to multivariate analysis. The organical loads in COD were 0.556; 0.670; 0.678 and 0.770 g L-1 and in volatile solids (VS) of 0.659; 0.608; 0.570 and 0.761 g L-1 for the hydraulic retention times (HRT) of 13.0; 11.5; 10.0 and 7.0 days, respectively. The reductions in COD were 88; 80; 88 and 67% and for VS of 76; 77; 65 and 61%. The biogas productions relatively to the consumed COD were 0.368; 0.795; 0.891 and 0.907 Lg-1, for the consumed VS of 0.524; 0.930; 1.757 and 0.952 Lg-1 and volumetric of 0.131; 0.330; 0.430 and 0.374 L L-1 d-1. The reactor remained stable and the bamboo pieces, in visual examination at the end of the experiment, showed to be in good physical conditions.

Optimization of sample design sizes and shapes for regionalized variables using simulated annealing

AbstractL.P.C. Guedes, M.A. Uribe-Opazo, and P.J. Ribeiro Junior. 2014. Optimization of sample design sizes and shapes for regionalized variables using simulated annealing. Cien. Inv. Agr. 41(1): 33-48. The spatial variability of structures in regionalized variables are defined with the aid of geostatistical techniques, which facilitate the estimation of values for these variables in unsampled localizations and generate thematic maps to be used in decision making for localized treatments in the area under study. The quality of these maps depends on the trustworthiness of these estimates that can be modified with the choice for the sample design. The objective of this work was to establish an optimal size and shape of the sample designs in order to enhance the efficiency of sampling plans for the prediction of space dependent variables. These designs were obtained with the use of a stochastic search method called Simulated Annealing. This method is based on a sampling grid with a large number of points. Here, it is initially used to consider simulated data sets with distinct spatial dependence structures and is then used to consider real data on soy productivity. The simulated results are used as reference for the achievement of the best sample design with the lowest number of sample points that can efficiently represent the spatial dependence structure of soy productivity in a commercial area harvested by the harvester monitor. The results reported for the simulations and soy productivity data show that the optimization process was efficient in determining sample designs with reduced size, especially when using the Global Accuracy as the measurement to be maximized.