Caracterização morfométrica das bacias hidrográficas inseridas no município de Rio Verde, Goiás, como ferramenta ao planejamento urbano e agrícola

The morphological characterization of the watersheds provides a rapid evaluation of the sources and their respective potentials of degradation. Thus, this work aimed to make a morphological characterization of the basins inserted in the municipality of Rio Verde, Goiás, as a planning tool and urban and agricultural. Drainage area, perimeter, main bed length, compactness coefficient, deformation index, deformation index, drainage density, drainage network density, watercourse slope, order Stream, circularity index and concentration time. As the basins located in the urban area of Rio Verde and in the district of Ouroana have a medium tendency to hypotheses under normal conditions of precipitation, in a way that developed in an analysis of the growth and development of the city. However, as basins with greater potential to large floods were like circular form being a greater part of these occupied by agricultural areas, which can compromise a local economy. As other basins showed a lower propensity to floods. As morphometric variables will serve for future planning and regional environmental management, as well as for predicting floods in urban and agricultural areas.A caracterização morfométrica das bacias hidrográficas propicia a uma rápida avaliação dos mananciais e seus respectivos potenciais de degradação. Assim, com este trabalho objetivou-se fazer a caracterização morfométrica das bacias inseridas no município de Rio Verde, Goiás, como ferramenta ao planejamento e urbano e agrícola. Foram analisadas nove bacias hidrográficas dentro do município de Rio Verde. As bacias com maior potencial a grandes enchentes foram as que possuem forma circular sendo a maior parte destas ocupadas por áreas agricultáveis, o que pode comprometer a economia local. As variáveis morfométricas servirão para planejamentos futuros e gestão ambiental regional, assim como para a previsão de enchentes em áreas urbanas e agrícolas

Aplicação mecanizada de N-P-K individualizada na cultura da cana-de-açúcar

The Brazil is the largest producer of sugarcane, due to expansion of cultivated areas, mechanized fertilizer becomes very important for the increase in operating performance in this culture, but this fertilization may show some problems in the distribution of fertilizers, so that when not performing the characterization of fertilizers through the angle of repose, particle size and density of nutrients, among other. Thus, it becomes necessary to develop new technologies that enable an improvement in the application of fertilizers in the cultivation of sugarcane. In view of this, a new concept of fertilizer in development considered as a prototype because this performs individualized application of nitrogen, phosphorus and potassium, providing greater efficiency in the distribution of these over other fertilizer machine. With this work the objective was to evaluate the mechanical application of NPK individualized culture of sugarcane. The experiment was conducted in the Matão municipality in the area of sugarcane belonging to the Fazenda Cascavel, possessing approximately 1.66 ha experimental area. The experimental design was completely randomized (DIC), with three treatments and thirty replications for treatment. This design was established according to the criteria of quality control, and monitoring of variables held during fertilization operation. At the end of the evaluation period were collected 90 sample points in total, with 30 points per treatment. The treatments were: 1- mechanized fertilized, without herbicide; 2- Operation combined (simultaneous application of herbicide and fertilizer); and 3- Two operations (separate application of herbicide and fertilizer). It was concluded that the best operational quality through control charts was the third treatment, two operations (separate application of herbicide and fertilizer) for presenting less variability. The right side of fertilizer machine was the best for having applied ...O Brasil é o maior produtor mundial de cana-de-açúcar, devido a expansão de áreas cultivadas, a adubação mecanizada torna-se muito importante para o aumento do desempenho operacional nesta cultura, porém esta adubação pode demonstrar alguns problemas na distribuição de fertilizantes, como a não realização da caracterização dos fertilizantes por meio do ângulo de repouso, granulometria e densidade dos nutrientes, entre outros. Dessa forma, torna-se necessário o desenvolvimento de novas tecnologias que possibilitem uma melhora na aplicação de fertilizantes na cultura da cana-de-açúcar. À vista disso, um novo conceito de adubadora está em desenvolvimento considerada como protótipo, pois esta realiza aplicação individualizada de nitrogênio, fósforo e potássio proporcionando maior eficiência na distribuição destes em relação às outras adubadoras. Com este trabalho o objetivo foi avaliar aplicação mecanizada de N-P-K individualizada na cultura da cana-de-açúcar. O experimento foi desenvolvido no município de Matão em área de cana-de-açúcar pertencente à Fazenda Cascavel, possuindo 1,66 ha aproximadamente de área experimental. O delineamento experimental foi Inteiramente Casualizado (DIC), com três tratamentos e trinta repetições por tratamento. Este delineamento foi estabelecido conforme os critérios do controle de qualidade, sendo o monitoramento das variáveis realizado durante a operação de adubação. Ao final do período de avaliação foram coletados 90 pontos amostrais no total, sendo 30 pontos por tratamento. Os tratamentos foram: 1- Adubação mecanizada, sem aplicação de herbicida; 2- Operação conjugada (aplicação simultânea de herbicida e adubação); e 3- Duas operações (aplicação separada de herbicida e adubo). Concluiu-se que a melhor qualidade operacional por meio das cartas de controle foi o tratamento 3, sendo duas operações (aplicação separada de herbicida e adubo)..

Mechanized fertilization: individual application of nitrogen, phosphorus and potassium in sugarcane

ABSTRACT With the expansion of areas of sugarcane cultivation, mechanised fertilisation is essential for providing improved operational performance, optimising the supply of nutrients and increasing productivity. However, this type of fertilisation may present deficiencies in the area of distribution, such as fluidity, separation, and particle size, among others. A new concept in fertilisation is therefore being developed, capable of carrying out the individual application of nitrogen, phosphorus and potassium, distributing these in various doses, with greater efficiency of application, employing precision farming and variable rates. The aim of this study was to evaluate the quality of the individual mechanised fertilisation of NPK in ratoon cane by means of Statistical Quality Control - SQC. The experiment was carried out in the city of Matão, in the State of São Paulo, with the experimental area making up part of the Cascavel Farm. The experimental design was based on the basic premise of spatial SQC, monitoring the individual mechanised fertilisation of NPK at 30 sampling points. For this treatment, the lots were composed of six rows of sugarcane, spaced 1.5 m apart, with an effective area of 1,571 m2. There was a sampling point every 30 m, where fertiliser was collected for 5 seconds. The helical metering system especially affected operational quality, as this was inefficient in its distribution. It was concluded that, due to adjustment of the helical metering system, individual application resulted in doses above those recommended for all fertilisers. Nitrogen (protected urea) showed the greatest variability in distribution, whereas phosphorus (MAP) displayed the highest operating quality due to the lower recommended volume

Does the Soil Tillage Affect the Quality of the Peanut Picker?

Machine harvesting is an essential step of crop production, considering a dynamic operation, and is subject to losses due to several factors that affect its quality. The objective of this study was to evaluate the quality of mechanized peanut pickers in the three soil tillage operations using Statistical Quality Control (SQC) tools. We conducted the experiments in a peanut field located at 21°20′23″ S and 47°54′06″ W of Brazilian peanut farmers. We used Statistic Control Quality (SQC) experimental design to monitor peanut losses during machine harvesting. The treatments evaluated were three soil tillage operations: conventional (CT), rotary tillers (RT), and hoe (RH). The quality indicators were collected inside the picker’s bulk tank. Statistical analyses used were descriptive statistics and SQC tools (run charts, control charts, and the Ishikawa diagram). The process was considered stable for indicators: whole pods (CT, RT, and RH), broken pods (CT, RT, and RH), and hatched pods (CT, RT, and RH), while the other indicators showed points that were out of control. With the application of SQC tools, it was possible to identify the factors that caused the increase of variability in peanut harvesting, listing the points to be improved to support decision-making, always aiming to increase this operation’s quality

Soil and satellite remote sensing variables importance using machine learning to predict cotton yield

Remote sensing (RS) in agriculture has been widely used for mapping soil, plant, and atmosphere attributes, as well as helping in the sustainable production of the crop by providing the possibility of application at variable rates and estimating the productivity of agricultural crops. In this way, proximal sensors used by RS help producers in decision-making to increase productivity. This research aims to identify the best feature importance ranking to the Random Forest Classifier to predict cotton yield and select which one best correlates with cotton yield. This work was developed in four commercial fields on a Newellton, LA, USA farm. We evaluated the cotton in different years as 2019, 2020, and 2021. The variables evaluated were: soil parameters, topographic indices, elevation derivatives, and orbital remote sensing. The soil sensor used was: GSSI Profiler EMP400 (soil electromagnetic induction sensor) at a frequency of 15 kHz, and the RS data were collected from satellite images from Sentinel 2 (passive sensor) and active sensor from LiDAR (Light Detection and Ranging). For training (70%) and validation (30%) of dataset results, Spearman correlation was used between sensors and cotton yield data, machine learning (Random Forest Classifier and Regressor - RFC and RFR). The metric parameters were the coefficient of determination (R²), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE). This study found that profiler, Sentinel-2 (blue, red, and green), TPI, LiDAR, and RTK elevation show the best correlations to predicting cotton yield

Control charts for initial seedling stand.

<p>IS: initial seedling stand. UCL: Upper control limit. LCL: Lower control limit. USCL upper specific.</p

Control charts for normal spacing.

<p>NS: Normal spacings. UCL: Upper control limit. LCL: Lower control limit. USCL: upper specific.</p

Soil classification and characteristics.

<p>Soil classification and characteristics.</p

Statistical process control applied to mechanized peanut sowing as a function of soil texture

<div><p>The successful establishment of agricultural crops depends on sowing quality, machinery performance, soil type and conditions, among other factors. This study evaluates the operational quality of mechanized peanut sowing in three soil types (sand, silt, and clay) with variable moisture contents. The experiment was conducted in three locations in the state of São Paulo, Brazil. The track-sampling scheme was used for 80 sampling locations of each soil type. Descriptive statistics and statistical process control (SPC) were used to evaluate the quality indicators of mechanized peanut sowing. The variables had normal distributions and were stable from the viewpoint of SPC. The best performance for peanut sowing density, normal spacing, and the initial seedling growing stand was found for clayey soil followed by sandy soil and then silty soil. Sandy or clayey soils displayed similar results regarding sowing depth, which was deeper than in the silty soil. Overall, the texture and the moisture of clayey soil provided the best operational performance for mechanized peanut sowing.</p></div

Percentage of samples within the specified limits for the initial seedling stand.

<p>Percentage of samples within the specified limits for the initial seedling stand.</p