Jerry Adriani Johann

Estimativa de áreas com culturas de verão no Paraná, por meio de imagens multitemporais EVI/Modis

O objetivo deste trabalho foi estimar e mapear as areas com as culturas de soja e milho, no Parana, com uso de imagens multitemporais EVI/Modis. Foram avaliados os anos‑safra de 2004/2005 a 2007/2008. Em razao da alta dinâmica temporal e da heterogeneidade de datas de semeadura das culturas no estado, foram utilizadas cenas que contemplavam as fases de pre‑plantio e de desenvolvimento inicial das culturas, para gerar a imagem de minimo EVI (IMIE), e cenas que consideravam o pico vegetativo das culturas, para gerar a imagem de maximo EVI (IMAE). Estas imagens foram utilizadas para gerar a composicao colorida RGB (R, IMAE; GB, IMIE), o que permitiu a confeccao de mascara das areas com soja e milho. As estimativas das areas de mascara por municipio foram comparadas com dados oficiais de producao agricola municipal, tendo-se observado bons ajustes (R²>0,84, d>0,95, c>0,85) entre os dados. Para a avaliacao da exatidao espacial das mascaras, imagens Landsat‑5/TM e AWiFS/IRS foram usadas como referencia para construcao da matriz de erros. Os resultados obtidos sao indicativos de que a metodologia proposta e altamente eficiente e pode ser utilizada para mapeamento dessas culturas.

Mapping and discrimination of soya bean and corn crops using spectro-temporal profiles of vegetation indices

The use of remote-sensing technology has been studied as a way to make the monitoring of agricultural crops more efficient, dynamic, and reliable. The use of data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has proved to be an interesting tool regarding the mapping of large areas, however, some challenges still need to be addressed. One of these is the identification of specific types of crops, especially when they have similar phenologies. The purpose of this study was to perform discrimination and mapping of soya bean and corn crops in the state of Paraná, Brazil, for the 2010/2011 and 2011/2012 crop years. A methodology using spectro-temporal profile information of the crops derived from vegetation indices (VIs), the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and the wide dynamic range vegetation index (WDRVI) based on MODIS data was appraised. This method generated a series of maps of the respective crops that were later qualitatively or quantitatively appraised. Some of the maps drawn showed a global accuracy rate above 80% and a kappa coefficient (κ) of over 0.7. The data areas showed an average difference of 6% for the cultivation of soya beans, and 11% for corn when compared to official data. The WDRVI and EVI were similar and showed better performance when compared to the NDVI in the assessments made. The results demonstrate that the soya bean crop was better mapped compared to corn, particularly in terms of the size of the crop area. The use of spectro-temporal profiles of the VIs assisted in obtaining important information, enabling better identification of crops from regional scale mapping using the MODIS data.

Variabilidade espacial dos atributos físicos do solo e da produtividade em um Latossolo Bruno distrófico da região de Cascavel, PR

Through geostatistics techniques, contour maps, were produced by interpolation using ordinary kriging, representing the spatial variability of the physical attributes; soil density [kg dm-3], soil water [g g-1] and penetration resistance [MPa] in the 0-10, 10-20 and 20-30 cm of depth, besides the soy bean productivity [t ha-1]. Soil attributes and yield data, derived from an unaligned stratified systematic sampling scheme, subdivided in portions with localized management (CML) and without localized management (SML) for the agricultural year 1998/99. The productivity maps in general presented a similar variability standard for the distribution in the non responding area as well as in the area with different chemical treatments applied in the CML plots. Where as the physical attributes presented a similar behavior for the two methods of management. Amongst the physical attributes studied, the penetration resistance in the 0-10 cm of depth was the variable which was best correlated with the productivity.

Mapping soya bean and corn crops in the State of Paraná, Brazil, using EVI images from the MODIS sensor

ABSTRACT This study aimed to map, separate, and estimate soya bean and corn crop areas in Paraná State, Brazil, in the harvest years 2012/13 and 2013/14, using the enhanced vegetation index (EVI) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Thus, two methodologies were integrated, the first considering heterogeneity on the dates of crop cycles, the scenes required to generate images of minimum and maximum vegetation indexes, creating a colour composite red, green, and blue (RGB), and identifying two cultures simultaneously. In the second methodology, soya bean and corn were identified and mapped using the selection of pure pixels and the supervised classification algorithm Spectral Angle Mapper (SAM). In order to avoid overlapping areas, we multiplied the results from the first and second methodologies to obtain the final separation. The final validation of the mapping was compared to official data, identifying high correlation to crops. Based on Medium-Resolution Linear Imaging Self-Scanner (LISS-III) and Land Remote Sensing Satellite (Landsat-8) images, the similarity of global accuracy (GA) and kappa accuracy indices was determined, being classified as good and excellent, respectively. It showed that the use of the two consortium methodologies for separation and overlap elimination of these crops in the state of Paraná was efficient.

PRAPRAG: software para planejamento racional de máquinas agrícolas

The software PRAPRAG is a tool used for choosing agricultural machines and implements that present the lowest cost per area or produced amount, as well as, to it makes the machines acquisition planning for the agricultural property, from both technical and economical points of view. It was used the programming language Borland Delphi 3.0. From the machine and implement handouts, it was created a database where the user can register and modify their characteristics of use. The software showed to be a useful and friendly tool. The software provides high speed, safety and reliability for the productive and economical process of the properties, at the selection and acquisition of agricultural systems, as well as for the determination of costs with the used labor.

Variabilidade espacial da rentabilidade, perdas na colheita e produtividade do feijoeiro

Common bean is grown in all regions of Brazil and has an economic and social importance. It is a subsistente crop on small farms, however, there are some problems affecting its marketing, such as fluctuation of prices paid to producers. The harvesting methods are diverse and currently the most widely used in Brazil is the semi-mechanized one. In order to evaluate productivity, profitability, manual and mechanical losses of Juriti beans cultivar, it was carried out a survey of georeferenced data in an area of 10.50 ha, with a 50 x 50 m grid of regular sampling. To study the spatial variability, geostatistics techniques were used to draw thematic maps by kriging techniques. It was found out that for the 2005/2006 crop, the average productivity in this area was 6.77% higher than the regional average, reported by the SEAB in Parana. At harvest, the manual losses were lower (1.49%) than the mechanical (8.73%), and these, when added together, represented a total loss of 10.22% of average productivity. According to the maps, it was possible to identify the spatial distribution of variables across the studied area and register that there was a loss in 35.91% of cultivated area, which means that the beans sale profitability was less than the production cost, caused mainly by the low yield of this crop. Such information is important so that farmers know their acreage, as well as evaluate, by the profitability map, if eventually, any other crop would have a better return per area.

Comparison measures of maps generated by geostatistical methods

This study uses several measures derived from the error matrix for comparing two thematic maps generated with the same sample set. The reference map was generated with all the sample elements and the map set as the model was generated without the two points detected as influential by the analysis of local influence diagnostics. The data analyzed refer to the wheat productivity in an agricultural area of 13.55 ha considering a sampling grid of 50 x 50 m comprising 50 georeferenced sample elements. The comparison measures derived from the error matrix indicated that despite some similarity on the maps, they are different. The difference between the estimated production by the reference map and the actual production was of 350 kilograms. The same difference calculated with the mode map was of 50 kilograms, indicating that the study of influential points is of fundamental importance to obtain a more reliable estimative and use of measures obtained from the error matrix is a good option to make comparisons between thematic maps.

Modelo de regressão espacial para estimativa da produtividade da soja associada a variáveis agrometeorológicas na região oeste do estado do Paraná

This study presents the Spatial Lag Model (SAR) and Conditional Autoregressive Model (CAR) in order to investigate the association between soybean yield and agrometeorological variables related to medium temperature and global solar radiation. The study was realized with data from the agricultural years from 2005/2006 to 2007/2008 crops in the West Region of the state of Parana. As the agrometeorological data are available only for eight cities of the region in study, the estimates were obtained through the use of Thiessen polygons. The estimation of the parameters of the adjusted models was obtained using the method of maximum likelihood. The evaluation of the performance of models was held based on the coefficient of determination (R²), maximum value of the logarithm of the likelihood function and Bayesian Information Criterion of Schwarz (BIC). This study also allowed to verify the correlation and the spatial autocorrelation between soybean yield and the agrometeorological factors by analyzing spatial area, by uses of Global and Local uni and bivariate and significance tests. The study demonstrated that by means of performance indicators used, the SAR and CAR models offered better results than the classical multiple regression model.

Análise de agrupamento da variabilidade espacial da produtividade da soja e variáveis agrometeorológicas na região oeste do Paraná

O presente trabalho realizou uma analise de agrupamentos espacial por meio da estatistica multivariada, no intuito de investigar a relacao entre a produtividade da soja e as seguintes variaveis agrometeorologicas: precipitacao pluvial, temperatura media do ar, radiacao solar global e indice local de Moran (LISA) da produtividade. O estudo foi realizado com os dados das safras dos anos agricolas de 2000/2001 a 2007/2008 da regiao oeste do Estado do Parana. A identificacao do numero adequado de clusters para cada ano-safra foi obtida utilizando a minimizacao de desvios. O estudo mostrou a formacao de grupos de municipios utilizando as similaridades das variaveis em analise. A analise de agrupamento foi um instrumento util para melhor gestao das atividades de producao da agricultura, em funcao de que, com o agrupamento, foi possivel estabelecer similaridades que proporcionem parâmetros para melhor gestao dos processos de producao que traga, quantitativa e qualitativamente, resultados almejados pelo agricultor.

Spatial statistics applied to soybean production data from Paraná State for 2003-04 to 2009-10 crop-years

In the current study, we performed a soybean production spatial distribution analysis in Parana State. Seven crop-year data, from 2003-04 to 2009-10, obtained from the Parana Department of Agriculture and Supply (SEAB) were used to develop a Boxmap for each crop-year, show soybean production throughout this time interval. Morans index was used to measure spatial autocorrelation among municipalities at an aggregate level, while LISA index local correlation. For each index, different contiguity matrix and order were used and there was a significance level study. As a result, we have showed spatial relationship among cities regarding the production, which allowed the indication of high and low production clusters. Finally, identifying main soybean-producing cities, what may provide supply chain members with information to strengthen the crop production in Parana.