ABSTRACT Crop growth simulation models such as WOFOST and DSSAT are useful, but require several inputs that sometimes are not available, especially in developing areas. In addition, measured data is usually time and labor-intensive. In search of faster and easier methods for soybean estimates, this study presents a lower input requiring methodology for yield estimation. This study combines the FAO-33 yield model with the agro-ecological zone approach for soybean yield estimations using mostly indirect data. Sowing and harvest dates and yield were collected from 74 soybean commercial farms. Agrometeorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used. Fifty farms (66%) were used to calibrate the model and 24 farm areas (33%) were used for evaluation purposes. Two methodologies (FAO-56 and Thornthwaite and Mather) for water balance and actual evapotranspiration (ETa) estimations were used. The comparison of yield estimations and observations showed that the use of low data input to obtain reasonable accuracy, with a mean error of −310 kg ha−1 and a mean absolute percentage error of 23.3%.
ABSTRACT This study aimed to identify whether there is an association between types of storage and categories of commercialization and use of grains, group the types of storage with the categories of commercialization, and group static and dynamic capacities of the units in the state of Paraná, Brazil. The data were obtained from the Brazilian Registry System of Storage Units for the 2014/2015 season. The association between variables under study was carried out with the chi-square test of independence and correspondence analysis. The cluster analysis consisted of the unweighted pair group method with arithmetic mean and considered a measure of mixed dissimilarity obtained for sets composed of qualitative and quantitative variables. A significant association was observed between the type of silo battery with the grain usage characteristic and cooperatives that commercialize the grain for use in the domestic market (CICOOPT); between the bulk warehouse and cooperatives that commercialize the grain in the foreign market (CIECOOPT); and between the silo and grain sellers who commercialize the grain in the domestic market (CI). Most types of storage units were grouped in Group 1, with a predominance of the CI characteristic and small to medium size static and dynamic capacity.
ABSTRACT Spatial variability description of soil chemical properties by thematic maps depends substantially on suitable geostatistical models. One of the parameters composing a geostatistical model is nugget effect. This study aimed to evaluate the simultaneous influence of nugget effect and sampling design on geostatistical model estimation and estimation of soil chemical properties at unsampled sites, considering simulated data. Our results will be used as scientific basis for spatial variability analyses of soil chemical properties in agricultural areas. Given the simulation results and agricultural data, we concluded that the high nugget effect values obtained here reduced spatial estimation efficiency. Moreover, a systematic sampling design promoted the least accurate estimates of geostatistical model and at non-sampled sites. Despite that, these nugget effect estimates should be kept in the analysis. However, further studies will be needed to investigate which factors are responsible for such high nugget effect values.
ABSTRACT. This study had two objectives - firstly, to analyze the total static and dynamic capacity of agricultural storage in Paraná State, Brazil and secondly, to verify if the storage followed the growth of grain production. The study was performed by mesoregion for the 2013/2014 and 2014/2015 crop years. The methodology used was descriptive from an agricultural database of the Secretariat of Agriculture and Supply (SEAB), of the National Register System of Storage Units (SICARM), interviews were also made with agroindustrial cooperatives and official agencies. It was identified that in Paraná State there is an insufficiency of 17.75% of total static capacity of warehouses to comply with the total grain production (soybean, 1st and 2nd corn crops, and wheat). The results showed that the total dynamic capacity of warehouses is sufficient in the mesoregions of Eastern Center, Southern Center, Northern Center, and Metropolitan. Therefore, storage units vary uniformly in most municipalities, not following the growth of total grain production in the state of Paraná.
ABSTRACT In the study of spatial variability of soil attributes, it is essential to define a sampling plan with adequate sample size. This study aimed to evaluate, through simulated data, the influence of parameters of the geostatistical model and sampling configuration on the optimization process, and resize and reduce the sample size of a sampling configuration of a commercial area composed of 102 points. For this, an optimization process called genetic algorithm (GA) was used to optimize the efficiency of the geostatistical model estimation based on the Fisher information matrix. The simulated data evidenced that the variation of the nugget effect or practical range did not significantly alter the sample size. GA was efficient in reducing the sample size, determining for soil chemical attributes a sample size between 30 and 40 points (29.41 to 39.22% of the initial sampling grid). The presence of spatial dependence was observed for all soil chemical attributes in the two sampling configurations (initial and optimized). The optimized sampling configuration evidenced an increase in trend intensity in the north direction and a more efficient estimation of parameters of the linear spatial regression model.
ABSTRACT The goal of this study was to use the spatial bootstrap method to model the spatial dependence structure of soybean yield and soil chemical attributes in an agricultural area. The study involved developing confidence intervals in probability plots to determine the probability distributions assumed by the data; determine the empirical distributions of the semivariances and model parameters, allowing to obtain statistics and confidence intervals; and to construct maps for the variables. The quantile-quantile plots indicated that the data follows a normal distribution. The confidence intervals for the semivariances helped to model the spatial dependence structure, and the descriptive statistics of the bootstrap replicates of the model parameters allowed to test the consistency of the estimates. The soil chemical attributes (calcium, potassium, and organic matter) were at levels suitable for soybean cultivation. However, the pH was below the ideal range in most of the study area, and water stress during cultivation decreased the mean yield. Therefore, according to the results, a recommendation to the farmer is to correct the soil pH to increase the yield.
ABSTRACT This work aimed to study the spatial autocorrelation of the total static capacity storage, the total number of warehouses in 2013/2014 (CONAB) and the average of the total grain production (soybean, corn 1st and 2nd crops and wheat) in the harvest years 2008/2009 to 2013/2014 (SEAB) in Paraná State, Brazil. The study was based on Moran's global autocorrelation index, Moran's local and Moran's bivariate correlation. It was possible to identify regions with low and high total grain production. There was positive spatial autocorrelation for the Total Static Storage Capacity (TSSC) and Total Quantity of Warehouses (TQW). For the total grain production, significant spatial autocorrelation were found. The total static storage capacity showed similarity between the studied regions. When evaluating the bivariate spatial correlation between Total Production of Harvested Grains (TPHG) in relation to the total static storage capacity and total quantity of warehouses, the presence of positive spatial correlation was observed. The results indicated that Moran's global autocorrelation and local indexes showed significant patterns of spatial autocorrelation, as well as the bivariate spatial correlation indexes in the studied variables.
Abstract The uniformity of water application is an important factor in the evaluation of sprinkler irrigation systems. This uniformity depends on the type of sprinkler and its operating conditions, such as the arrangement and spacing between the sprinklers in the area; velocity and wind direction during the period of water application and the pressure variation of the irrigation system. The objective of this study was to model, analyze and compare the structure of spatial dependence, as well as the spatial variability of the water depths applied by a sprinkler irrigation system with compensating and non-compensating sprinklers, using geostatistical methods and measurements of accuracy or similarity between the applied water depth maps. The experiment was carried out in an agricultural area, in the city of Cascavel-Paraná-Brazil. A total area of 10 × 10 m was used, with 04 compensating and 04 non-compensating sprinklers installed at a height of 1.5 m. For each type of sprinkler, water levels were measured in 100 collectors spaced 1 × 1 m in the study area in 32 trials. On each test sprinkling was carried out for one hour. The conditions of wind, temperature and air humidity were evaluated at the beginning of each test and at 10-minute intervals with a climatological station. As the geostatistical analysis showed the existence of directional trends, the coordinates were incorporated as covariates to the linear spatial model in the study of the spatial dependence of the average depth of the irrigation water for the two types of sprinklers. The spatial dependence structure that best fits the data when using the compensating sprinklers was the Gaussian model and when the non-compensating sprinklers were used, it was the exponential model. The spatial variability maps of average irrigation water depth (mm) of the trials, obtained by universal kriging, revealed that for both sprinklers there was an increase in the mean level average values in the northwest-southeast direction (135° in the azimuth system) in the area under study, influenced by wind direction and velocity during the execution of the experiment.
ABSTRACT Spatial variability depends on the sampling configuration and characteristics associated with the georeferenced phenomenon, such as geometric anisotropy. This study aimed to determine the influence of the sampling design on parameter estimation in an anisotropic geostatistical model and the spatial estimation of a georeferenced variable at unsampled locations. Datasets were simulated with geometric anisotropy, considering five values for the anisotropic ratio (1, 2, 3, 4, 5), and three sampling designs: lattice, random and lattice plus close pairs. The simulation results were used as a reference to select anisotropic models to describe the spatial dependence structure in chemical soil properties. For each dataset (with either simulated or chemical soil properties), the values of the georeferenced variables at unsampled locations were estimated by kriging, considering estimated isotropic and anisotropic geostatistical models. The choice of the sampling design influenced the spatial estimation of the georeferenced variable and the quality of the estimation of the geostatistical anisotropic model. The incorporation of geometric anisotropy in the spatial estimation of simulated data sets and soil chemical properties produced differences in the spatial estimation and improved the level of detail of subregions in thematic maps.
ABSTRACT This study aims to quantify the uncertainties associated to the parameters of a Gaussian spatial linear model (GSLM) and the assumption of normality residuals in the modeling of the spatial dependence of the soybean yield as a function of soil chemical attributes. The spatial bootstrap methods were used to determine the point and interval estimators associated with the model parameters. Hypothesis tests were carried out on the significance of the model parameters and the quantile-quantile probability plot was elaborated to verify the data normality. The uncertainties associated to the parameters of the spatial dependence structure were quantified and the potassium content, phosphorus content and soil pH covariates were significant to explain the soybean yield mean. These covariates were used in the elaboration of a new model, which provided the elaboration of a contour map of soybean yield. Analysis of the quantile-quantile plot indicated that soybean yield data follow a normal probability distribution.
ABSTRACT: In geostatistical modeling of soil chemical properties, one or more influential observations in a dataset may impair the construction of interpolation maps and their accuracy. An alternative to avoid the problem would be to use most robust models, based on distributions that have heavier tails. Therefore, this study proposes a spatial linear model based on the slash distribution (SSLM) in order to characterize the spatial variability of soybean yields as a function of soil chemical properties. The likelihood ratio statistic (LR) was applied to verify the significance of parameters associated with the model. We evaluated the sensitivity of the maximum likelihood estimators by means of local influence analysis for both the soybean response and the linear predictor. In the proposed model, we analyzed data gathered from a commercial grain production area (127.18 ha) located in the western part of the state of Paraná (Brazil). The results showed that the slash distribution allowed us to adjust the high kurtosis of the data set distribution and the LR test confirmed that the soil chemical properties of phosphorus, potassium, pH, and organic matter were significant for the SSLM. Diagnostic analysis indicated that the atypical value of the sample set was not influential in the parameter estimation process. Construction of the interpolation map based on the proposed model is not affected when considering the atypical and/or influential observations. Thus, SSLM becomes a robust alternative in the study of soybean yield variability as a function of soil chemical properties, making it possible to investigate the productive potential of the areas.
ABSTRACT: The t-Student distribution has been used to the spatial dependence modelling of soybean yield as an alternative to the normal distribution, being used for data with heavier tails or discrepant values. However, a usual Student t-distribution does not allow direct comparisons of geostatistical methods with a normal distribution. The aim of this study was to assess the soybean yield spatial variability through a reparameterized t-Student linear model, comparing the results with those of a Gaussian linear model. For parameter estimation, a complete maximum likelihood (CML) method was used through an expectation-maximization (EM) algorithm. The maps constructed with both reparameterized t-Student and normal distributions are dissimilar and present a kappa index (K) equivalent to 0.64. The reparameterized t-Student distribution is an alternative in studying data with discrepant values, showing the ability to decrease the influence of these points.
ABSTRACT The survey information from growing regions, the interaction with the vegetation index and climatic variables is of great importance in the search for soybean productivity increase. Paraná is the second largest soybean producer in Brazil and presents great spatial variability, both in periods of the crop cycle as in soil and climate. The objective of this study was to analyze the spatial correlation of soybean productivity, the enhanced vegetation index (EVI) and agrometeorological variables (water balance, global radiation and average temperature) in the state of Paraná, on a decendial scale, using the Moran global autocorrelation index between the 2010/2011 and 2012/2013 crop years. Similarity was found in the average productivities in 2010/2011 and 2012/2013. In 2011/2012 the state average was 2.38 t ha−1 lower in 10.19% compared to the national average, caused by the water deficit in flowering and grain filling phases. As a consequence, spatial autocorrelation indicated a higher similarity in productivity among municipalities with a Moran index of 0.735. The use of vegetation indices and agrometeorological variables allowed the identification of different sowing periods between regions and great climatic variability, influencing the soybean productivity.
ABSTRACT Precision agriculture (PA) allows farmers to identify and address variations in an agriculture field. Management zones (MZs) make PA more feasible and economical. The most important method for defining MZs is a fuzzy C-means algorithm, but selecting the variable for use as the input layer in the fuzzy process is problematic. BAZZI et al. (2013) used Moran’s bivariate spatial autocorrelation statistic to identify variables that are spatially correlated with yield while employing spatial autocorrelation. BAZZI et al. (2013) proposed that all redundant variables be eliminated and that the remaining variables would be considered appropriate on the MZ generation process. Thus, the objective of this work, a study case, was to test the hypothesis that redundant variables can harm the MZ delineation process. BAZZI This work was conducted in a 19.6-ha commercial field, and 15 MZ designs were generated by a fuzzy C-means algorithm and divided into two to five classes. Each design used a different composition of variables, including copper, silt, clay, and altitude. Some combinations of these variables produced superior MZs. None of the variable combinations produced statistically better performance that the MZ generated with no redundant variables. Thus, the other redundant variables can be discredited. The design with all variables did not provide a greater separation and organization of data among MZ classes and was not recommended.
RESUMO A agricultura de precisão proporciona aos agricultores identificar e tratar de forma adequada as variações encontradas na área agrícola. As zonas de manejo (ZMs) permitem a implantação da agricultura de precisão de forma viável e relativamente mais econômica. A forma mais importante para definir ZMs é usando o algoritmo fuzzy C-means. Um problema consiste em como selecionar a variável a ser usada como layer de entrada no processo fuzzy. Assim, o objetivo deste trabalho, foi testar a hipótese de que variáveis redundates podem prejudicar o processo de delineamento de ZMs. Este trabalho foi desenvolvido em uma área de 19,6 ha e 15 agrupamentos de ZMs foram gerados por meio do o algoritmo fuzzy C-means, dividindo-se em duas a cinco classes. Cada agrupamento usou uma composição diferente de variáveis, que são os atributos cobre, silte, argila, e altitude. Foi encontrado que algumas combinações dessas variáveis produziu melhores ZMs. Nenhuma combinação de variáveis produziu desempenho estatisticamente melhor que a ZM gerada apenas com as variáveis não redundantes. Assim, as variáveis redundantes podem ser descartadas. O agrupamento com todas as variáveis não forneceu maior separação e organização dos dados entre as classes de ZM, não sendo recomendado.
ABSTRACT Influencing points in agricultural spatial analysis may change considerably results on spatial dependence and hence map building. With regards to physico-chemical soil properties and crop yield, such maps should efficiently estimate current field conditions, being important for an agricultural site-specific management, optimizing thus input applications in order to increase yields. This study aimed to analyze hair-plot graphic techniques, with local influence (Ci and |Lmax|) to identify influencing points within a set of georeferenced spatial continuous data. These information were gathered from an experimental area with 167.35 hectares, wherein an agricultural site-specific management has been adopted. As a result, we obtained potentially influencing points and then outlined maps with and without the use of them. By comparing both maps, we could note by metric comparison that it is of major importance to identify those points on a spatial database. Thus, such investigations must be carried out to understand cases of unusual performance, since they considerably modify the generated maps.
RESUMO Na análise de dados espaciais em agricultura, a presença de pontos influentes pode alterar consideravelmente os resultados das análises de dependência espacial e, consequentemente, a construção dos mapas. Quando se referem a atributos físico-químicos do solo e da produtividade, os mapas devem representar uma estimativa eficiente das condições reais do campo, já que são importantes informações utilizadas para a manutenção de um sistema agrícola de manejo localizado, com a otimização da aplicação de insumos agrícolas, visando à maior produtividade. Este trabalho teve por objetivo apresentar as técnicas gráficas hair-plot, de influência local (Ci e |Lmax|) de identificação de observações influentes em dados contínuos espaciais georreferenciados, coletados em uma área experimental de cultivo comercial, com 167,35 hectares, onde o sistema agrícola de manejo localizado é adotado. Como resultados apresentam-se os pontos potencialmente influentes e os mapas construídos com e sem eles. Na comparação entre os mapas com e sem estes pontos, as métricas de comparação dos mapas mostraram a importância da identificação dos pontos influentes em uma base de dados espaciais. Sendo assim, a existência de pontos influentes deve ser investigada para entender o motivo de seu comportamento atípico, já que eles modificam, consideravelmente, os mapas gerados.