ABSTRACT Precision agriculture can improve the decision-making process in agricultural production, as it gathers, processes and analyzes spatial data, allowing, for example, specific fertilizer application in each location. One of the proposals to deal with spatial heterogeneity of the soil or the distribution of chemical properties is to define application zones (homogeneous subareas). These zones allow reducing both spatial variability of the yield of the crop under study and of the environmental impacts. Considering the soil data, application zones can also represent strata or indicators to direct future soil sampling, thus seeking sample size reduction, for example. This study aimed to obtain an optimized sampling redesign using application zones generated from the assessment of five clustering methods (Fuzzy C-means, Fanny, K-means, McQuitty and Ward). Soil samples were collected in an agricultural area located in the city of Cascavel-Paraná-Brazil, and analyzed in the laboratory to determine the soil chemical properties, referring to four soybean harvest years (2013-2014, 2014-2015, 2015-2016 and 2016-2017). The application zones were obtained through a dissimilarity matrix that aggregates information about the Euclidean distance between the sample elements and the spatial dependence structure of the properties. Subsequently, an optimized sampling redesign, with reduction of the initial sample points, was obtained in these application zones. For the harvest years under study, the K-means and Ward clustering methods efficiently defined the application zones, dividing the study area into two or three application zones. Among the reduced sample configurations obtained by the optimization process, when comparing the initial sample configuration, the one optimized by 25 % (selecting 75 % of the initial configuration points, which corresponds to 76 sample points) was the most effective in terms of the accuracy indices (overall accuracy, Kappa, Tau). This fact indicates greater similarity between the thematic maps of these sample configurations. In this way, the reduced sample configurations could be used to generate the application zones and reduce the costs regarding the laboratory analyses involved in the study.

Resumo: A informação na agricultura é relevante, pois mantém o produtor conectado a acontecimentos de natureza produtiva e comercial. O objetivo do artigo é identificar e analisar os fatores determinantes para o acesso à informação por produtores de hortaliças na região sul de Moçambique. A pesquisa foi explicativa, descritiva, com abordagem qualitativa, quantitativa, de natureza aplicada e de cunho primário e secundário. Para a coleta de dados em campo, aplicaram-se questionários a 384 produtores rurais que produzem e comercializam hortaliças, escolhidos aleatoriamente em uma amostragem probabilística estratificada. A pesquisa ocorreu entre fevereiro e junho de 2019, e a análise de dados foi descritiva e econométrica por meio do modelo logit binário. Como resultado, o perfil dos produtores foi de pessoas com idade entre 19 e 85 anos, dos quais 50% não tinham formação escolar, formado por 46% de homens e 54% de mulheres que cultivavam em pequenas áreas de forma intensiva. As variáveis explicativas estatisticamente significativas e determinantes para o acesso à informação foram: idade; pertence a uma cooperativa/associação de produtores; experiência na produção; satisfação com o retorno financeiro; assistência técnica; acesso ao crédito; rendimento produzido por safra; uso de telefone na negociação; forma usada para comercializar a produção; e gestão da produção.

Abstract: Information in agriculture is relevant, as it keeps the producer connected with events of a productive and commercial nature. The article aims to identify and analyze the determinants of access to information for vegetable producers in the southern region of Mozambique. The research was explanatory, descriptive, with a qualitative and quantitative approach and an applied nature. The research was primary and secondary. For data collection in the field, questionnaires were distributed to 384 rural producers who produce and commercialize vegetables. The participants were chosen randomly in stratified probabilistic sampling. The research took place between February and June 2019. Data analysis was descriptive and econometric, using the binary logit model. As a result, the profile of the producers was made up of people aged between 19 and 85 years, of which 50% have no school education, composed of men (46%) and women (54%) who farm intensively in small areas. The statistically significant explanatory variables and determinants to access to information were: age, belonging to a cooperative/producer association, experience in production, satisfaction with financial return, technical assistance, access to credit, yield produced per harvest, use of the telephone in negotiation, the form used to commercialize the produce and production management.

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 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 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 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.