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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. [ABSTRACT FROM AUTHOR]

Through innovations and technologies, industrial revolutions have brought great developments and, mutually, challenges. We are currently experiencing the fourth industrial revolution, or Industry 4.0. In this context, humanity experiences new possibilities of thinking and acting through the combined advancement of technology and the internet. This article aimed to verify if there is a relationship between learning industry 4.0 practices and the level of education in a higher education institution, based on the perception of students, professors and employees. Through a case study with a quantitative approach, a questionnaire adapted from the study by Mian et al. (2020), with a sample of 143 respondents. To assess the relationship between qualitative variables, the Chi-Square independence test was applied. The results infer that in the sense of knowledge of industry 4.0, the term and its technologies, there is a lack of greater knowledge of the researched public at the University. Regarding practices, there is neutrality for collaborative projects and implementation of tools. It is concluded that the link with the institution is a significant factor to justify knowledge of industry 4.0 technologies. Thus, students and professors, from different areas, are more up-to-date on the subject when compared to the category of employees. [ABSTRACT FROM AUTHOR]

Vegetation Indices are important for agricultural monitoring and the assessment of biophysical attributes of different crops. In this context, the objective of this study was to evaluate the relationships between normalized difference vegetation indices (NDVIs) obtained by different terrestrial remote sensors (active and passive), with respect to biophysical aspects of soybeans and beans. With the NDVI GS (GreenSeeker 505 handheld active sensor), it was possible to obtain a better leaf area index and height estimate for both crops, as well chlorophyll index for soybeans. The NDVI FS (FieldSpec4 model Standard-Res passive sensor) is highlight in being able to estimate values for intercepted photosynthetically active radiation, mainly for soybeans. By using NDVIs with active and passive terrestrial remote sensors, it was possible to create models to estimate the biophysical parameters for soybeans and beans. [ABSTRACT FROM AUTHOR]

The objective of this research was to distinguish and estimate cultivated areas of soybean and corn in Paraná State, Brazil, in the 2014/2015 crop season. The main obstacle in mapping summer crops using vegetation index images is to separate the cultivated areas with soybean and corn. These crops planted in a similar period present similar spectral signatures. Thus, with the use of Data Mining techniques (DM) and Artificial Neural Network (ANN) it was possible to carry out the crop mapping, even for those that present similarities in spectral-temporal profile of vegetation indexes. The accuracy obtained in the mappings resulted in a KI (Kappa Index) of 0.78 and 89% of OA (overall accuracy) indicating a high accuracy in the separation of soybean and corn summer crops. [ABSTRACT FROM AUTHOR]

The main land use and land cover (LULC) changes that a given area passes over the time can be evaluated by using spatial-temporal analysis of satellites images. Then, it is possible to identify the LULC changes, as well as the main causes of environmental impacts. The objective of this paper was to analyze the LULC changes of the main agricultural lands cultivated in the Alto Paraguai Basin (BAP). This paper focused on the summer crops (soybean and corn) and the analysis of agricultural expansion. The results, considering a16-year comparison, showed an increase of 40.60% in the expansion of agricultural areas. The evaluation of the accuracy showed the efficiency of the methodology of agricultural mapping, presenting a Kappa Index of 0.85 for the 2000/2001 and 0.86 for the 2015/2016 crop seasons. [ABSTRACT FROM AUTHOR]

This study aims to assess the economic feasibility and the perception of the farmers in community Salette in Palotina-PR with relation to the installation of a rural tank. With a quantitative approach, this paper is constituted by three stages of research developed from May to July 2016: sizing of the tank to the searched property, project investment analysis and evaluation of the perception of farmers in relation to the installation of rural tank. To determine the size of the uptake capacity and rain water storage it was used Rippl method presented by Tomaz (2003). For analysis of the economic viability was used Net Present Value, Internal Rate of Return and payback and to evaluate the perception of farmers it was used the application of a questionnaire to the 15 farmers with property in Salette community in Palotina-PR. As a result it was observed that the installation of tank in the studied property did not provide economic opportunity, however the implementation of the project can be considered with the greatest impact on environmental variable, as an alternative source of water in the countryside which can contribute to the preservation of drinking water available, being only intended for noble purposes such as human consumption. [ABSTRACT FROM AUTHOR]

The state of Paraná is one of the largest corn producers in Brazil, cultivating in two growing seasons. This study evaluated bivariate correlations between precipitation and the second-crop corn yield between the first 10 days of April and the first 10 days of October using spatial statistics. The spatial analysis of yield was evaluated using geostatistical models and validated by cross-validation. Spatial analysis was performed using the local indicator of spatial association and a cross-semivariogram, and the co-dispersion coefficient was used to validate the correlation between variables. Cross-validation identified the exponential model as the best fit, indicating a spatial dependence of 317.26 km. Spatial associations between municipalities were significant throughout the state of Paraná, Brazil. The cross-semivariogram allowed fitting the wave, spherical, Gaussian, and exponential models, with a spatial correlation of 37.04 to 570.38 km between the precipitations and yield. Moreover, the co-dispersion coefficient validated the correlation between the two variables for all 10-day periods. [ABSTRACT FROM AUTHOR]

cInterdisciplinary Centre for Energy Planning, State University of Campinas, Campinas, SP CEP 13083-896, Brazil (Received 14 June 2014; accepted 21 January 2015) 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. [ABSTRACT FROM AUTHOR]

The knowledge of Sowing Dates (SD), Maximum Vegetative Development Dates (MVDD), and Harvest Dates (HD) of crops is important for estimating and forecasting large-scale productivity. Prior information on harvest date of crops is also useful in optimizing grain reception logistics and improving business decision-making in agro-industrial companies. This study aimed to develop a method to predict the HD of soybeans from MVDD, which was obtained from crop maps in the state of Paraná between 2011/2012 and 2013/2014 crop years. TIMESAT software was used to perform a seasonal trend analysis of enhanced vegetation index (EVI) time series from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. After estimating time intervals between MVDD and HD (MH), applied to each mapped pixel, we calculated the 3 year MH average (average of the interval between MVDD and HD for the 3 crop years) for mesoregion and state levels, respectively. The MH by mesoregion was added to MVDD at a regional level, generating HDFORECAST, which is a prediction of the HD. Our results were compared with information from 44 monitored farms in the state in the 2013/2014 crop year. The findings showed that, for soybeans, MH by mesoregion can be used to predict HD with a Mean Error (ME) of 4.84 days, Mean Absolute Error (MAE) of 6.84 days, Root Mean Square Error (RMSE) of 8.12 days, Willmott modified coefficient (d) of 0.9, and Pearson correlation coefficient (r) of 0.88. Our study has produced valuable information for crop productivity models, as well as for logistics of grain transportation and storage in agro-companies, which can anticipate operations knowing when grains will be harvested and delivered to the centres. [ABSTRACT FROM AUTHOR]

Brazilian farming influences directly the worldwide economy. Thus, fast and reliable information on areas sown with the main crops is essential for planning logistics and public or private commodity market policies. Recent farming practices have embraced remote sensing to provide fast and reliable information on commodity dynamics. Medium-to-low resolution free orbital images, such as those from Landsat 8 and Sentinel 2, have been used for crop mapping; however, satellite image processing requires high computing power, especially when monitoring vast areas. Therefore, cloud data processing has been the only feasible option to deal with a large amount of orbital data and its processing and analysis. Thus, our goal was to develop a method to map the two main crops (soybeans and corn) in Paraná, one of the major Brazilian state producers. Landsat-8, Sentinel-2, SRTM+, and field data from 2016 to 2018 were used with the Simple Non-Iterative Clustering segmentation method and the Continuous Naive Bayes classifier, to identify cropped areas. A minimum global accuracy of 90% was found for both crops. Comparison with field data showed correlations of 0.96 and agreement coefficients no lower than 0.86. This ensures mapping quality when using Sentinel and/or Landsat imagery on the GEE platform. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Swine and poultry agroindustry consume a lot of thermal energy in their process. • Eucalyptus woodchip is widely used in Brazil to supply this energy demand. • In the IPF it is possible to have livestock and chip production in the same land. • With a GIS the potential of chip production in IPF in western Paraná was estimated. • 4.24% of the pasture area in IPF supplies the woodchip demand of agroindustry's. Abstract The increase in the world population has caused a rise in the demand for animal protein, particularly that of swine and poultry. Brazil is among the world's largest producers of these two commodities, with the western region of Paraná accounting for a significant fraction of this production. To industrialize this raw material requires a large amount of energy, and much of this demand is supplied by eucalyptus biomass in the form of woodchips originating from distant regions, which increases the cost of production. The Livestock-Forestry Integration (LFI) system, which is encouraged by the national government program of Low Carbon Agriculture (LCA), is a eucalyptus production system that is growing in Brazil. In the Western region of Paraná, there is widespread availability of pasture areas which could be used to produce eucalyptus in this type of system, and meet the biomass demand of agroindustries. The objective of this work was to explore the expansion of eucalyptus plantations under an LFI scenario for agroindustries in Western Paraná, Brazil. In addition to estimating the area with the potential for expansion of agriculture by means of Landsat-8 satellite images, digital elevation models integrated with the Geographic Information System geoprocessing and the software ArcGIS were used. It was found that conversion of 4.24% of the pasture areas into LFI would meet the current demand for woodchips of the agroindustries that slaughter poultry and swine in Western Paraná. Using 8.04% would satisfy the demand until 2025. It was verified that there are 3972.10 km2 available for expansion of agriculture in the region. The methodology used here serves as a reference for future work. [ABSTRACT FROM AUTHOR]

The present study aimed to analyze the influence of companies and personal characteristics on the financial remuneration of managers, in the western region of the State of Paraná. The theoretical basis, of this study, was from agribusiness activities to aspects associated with the financial remuneration of its managers. The methodological procedures characterize this study as an experimental research, developed through a quantitative approach. As for the data collection, they are from the Annual Social Information Ratio (RAIS), and the time cut was delimited for the year 2015, since the geographical cut refers to the municipalities of the Western region of the State of Paraná. Thus, the sample of this study has a total of 1,261 observations. For analysis of the data, the multiple log-linear regression technique was applied. It is concluded that managers are benefit from factors related to the characteristics of companies such as county and size, and also by personal characteristics such as educational level and company time. However, it may be possible to have wage discrimination, since the male sex earns more on average of 35.11% compared to the female sex. Even though it is an agricultural region, agribusiness-related activities tend to penalize the managers' remuneration. [ABSTRACT FROM AUTHOR]

This article aims to analyze comparatively the socioeconomic profile of consumers who value and those who do not value organic products in restaurants. To achieve this purpose, 392 customers were interviewed in two restaurants from a large supermarket network by means of a structured questionnaire. Regarding the cross-analysis between the profile of consumers and the enhancement of organic products, the research has found out that: (a) women value most organic products than men; (b) the higher the educational level of analyzed individuals, the greater the appreciation shown by them on the subject; (c) consumers with an income above three minimum wages value more organic products than persons with a lower income; however, from three minimum wages, the percentage of the valuation remains; and (d) consumers over 40 years of age value more organic products than younger consumers. It is emphasized that the findings presented in this article contribute to the formation of the body of scientific knowledge about organic consumers, that does not exclude, however, their need to be considered with caution, considering that they do not allow inferences that go beyond the investigated sample. As an empirical contribution, so, the broader understanding of these consumers, so as to better serve them, becomes an important aspect so shops can succeed. [ABSTRACT FROM AUTHOR]

Several colonisation projects were implemented in the Brazilian Legal Amazon in the 1970s and 1980s. Among these colonisation projects, the most prominent were those with the "fishbone" and "topographic" models. Within this scope, the settlements known as Anari and Machadinho stand out because they are contiguous areas with different models and structures of occupation and colonisation. The main objective of this work was to evaluate the dynamics of Land-Use and Land-Cover (LULC) in two different colonisation models, implanted in the State of Rondônia in the 1980s. The fishbone and topographic or Disorganised Multidirectional models were implemented in the Anari and Machadinho settlements, respectively. A 36-year time series of Landsat images (1984–2020) was used to evaluate the rates and trends in the LULC process in the different colonisation models. In the analysed models, a rapid loss of primary and secondary forests (anthropized areas) was observed, mainly due to the dynamics of its use, established by the Agriculture/Pasture relation with a heavy dependence on road construction. Understanding these two forms of occupation can help the future programs and guidelines of the Brazilian Legal Amazon and any tropical rainforest across the globe. [ABSTRACT FROM AUTHOR]

Spatial variability of hydraulic conductivity and water infiltration in the soil. When cultivated areas are used with intense agricultural production, the soil presents spatial and temporal alterations in its hydrophysical attributes so that the economical viability of the agricultural production depends on those attributes, requiring detailed studies consequently about the spatial variability of the soil. Thus, the objective of this work was to evaluate the spatial variability of the saturated hydraulic conductivity and water infiltration in the soil, in an area of 20 ha, characterized by intensive grain production, was used a grid of 50 x 50 m grating, using the Guelph Permeameter. In the spatial variability analyses, geostatistics methods were used. It was concluded that the hydrophysical attributes studied presented structures of spatial dependence and behaved proportionally, but did not show spatial correlation in the 50 m density of studied. [ABSTRACT FROM AUTHOR]