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O preparo convencional do solo ainda é um procedimento comum na agricultura brasileira e, este contexto, o uso de grades pesadas é utilizado por diversos produtores. Assim, o conhecimento da variabilidade dos parâmetros de desempenho desta operação pode ser útil para que se possa obter melhor qualidade do processo. Desta forma, objetivou-se neste trabalho avaliar a qualidade operacional do preparo do solo realizado por meio de uma grade pesadas utilizando-se ferramentas do controle estatístico de processo. O trabalho foi desenvolvido no município de Sinop – MT, em solo de textura argilosa e apresentando teor de água no solo igual a 25,74%. A avaliação da qualidade operacional foi obtida tomando-se 33 pontos amostrais em intervalos e 1,5 minutos, utilizando-se como indicadores de qualidade da operação a rotação do motor, o consumo horário, específico e operacional, força e potência na barra de tração e consumo energético por unidade de área trabalhada. Os indicadores de qualidade relacionados ao consumo de combustível, exceto o consumo específico, apresentaram padrão de agrupamento dos dados, enquanto que os indicadores relacionados à força de tração apresentaram agrupamento e tendência. Com base nos indicadores avaliados, o processo foi considerado instável, resultando em uma operação de baixa qualidade.

Predicting sugarcane yield by quality allows stakeholders from research centers to industries to decide on the precise time and place to harvest a product on the field; hence, it can streamline workflow while leveling up the cost-effectiveness of full-scale production. °Brix and Purity can offer significant and reliable indicators of high-quality raw material for industrial processing for food and fuel. However, their analysis in a relevant laboratory can be costly, time-consuming, and not scalable. We, therefore, analyzed whether merging multispectral images and machine learning (ML) algorithms can develop a non-invasive, predictive framework to map canopy reflectance to °Brix and Purity. We acquired multispectral images data of a sugarcane-producing area via unmanned aerial vehicle (UAV) while determining °Brix and analytical Purity from juice in a routine laboratory. We then tested a suite of ML algorithms, namely multiple linear regression (MLR), random forest (RF), decision tree (DT), and support vector machine (SVM) for adequacy and complexity in predicting °Brix and Purity upon single spectral bands, vegetation indices (VIs), and growing degree days (GDD). We obtained evidence for biophysical functions accurately predicting °Brix and Purity. Those can bring at least 80% of adequacy to the modeling. Therefore, our study represents progress in assessing and monitoring sugarcane on an industrial scale. Our insights can offer stakeholders possibilities to develop prescriptive harvesting and resource-effective, high-performance manufacturing lines for by-products.

The aims of this study were: i) to compare no-till areas in two municipalities located in different regions of Brazil, along with the influence on CO2Flux and GPP, and ii) to verify the difference ...

Using spectral data to quantify nitrogen (N), phosphorus (P), and potassium (K) contents in soybean plants can help breeding programs develop fertilizer-efficient genotypes. Employing machine learning (ML) techniques to classify these genotypes according to their nutritional content makes the analyses performed in the programs even faster and more reliable. Thus, the objective of this study was to find the best ML algorithm(s) and input configurations in the classification of soybean genotypes for higher N, P, and K leaf contents. A total of 103 F2 soybean populations were evaluated in a randomized block design with two repetitions. At 60 days after emergence (DAE), spectral images were collected using a Sensefly eBee RTK fixed-wing remotely piloted aircraft (RPA) with autonomous take-off, flight plan, and landing control. The eBee was equipped with the Parrot Sequoia multispectral sensor. Reflectance values were obtained in the following spectral bands (SBs): red (660 nm), green (550 nm), NIR (735 nm), and red-edge (790 nm), which were used to calculate the vegetation index (VIs): normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), green normalized difference vegetation index (GNDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), modified chlorophyll absorption in reflectance index (MCARI), enhanced vegetation index (EVI), and simplified canopy chlorophyll content index (SCCCI). At the same time of the flight, leaves were collected in each experimental unit to obtain the leaf contents of N, P, and K. The data were submitted to a Pearson correlation analysis. Subsequently, a principal component analysis was performed together with the k-means algorithm to define two clusters: one whose genotypes have high leaf contents and another whose genotypes have low leaf contents. Boxplots were generated for each cluster according to the content of each nutrient within the groups formed, seeking to identify which set of genotypes has higher nutrient contents. Afterward, the data were submitted to machine learning analysis using the following algorithms: decision tree algorithms J48 and REPTree, random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR, used as control). The clusters were used as output variables of the classification models used. The spectral data were used as input variables for the models, and three different configurations were tested: using SB only, using VIs only, and using SBs+VIs. The J48 and SVM algorithms had the best performance in classifying soybean genotypes. The best input configuration for the algorithms was using the spectral bands as input.

Predicting maize yield using spectral information, temperature, and different irrigation management through machine learning algorithms provide information in a fast, accurate, and non-destructive way. The use of multispectral sensor data coupled with irrigation management in maize allows further exploration of water behavior and its relationship with changes in spectral bands presented by the crop. Thus, the objective of this study was to evaluate, by means of multivariate statistics and machine learning techniques, the relationship between irrigation management and spectral bands in predicting maize yields. Field experiments were carried out over two seasons (first and second seasons) in a randomized block design with four treatments (control and three additional irrigation levels) and eighteen sample repetitions. The response variables analyzed were vegetation indices (IVs) and crop yield (GY). Measurement of spectral wavelengths was performed with the Sensefly eBee RTK, with autonomous flight control. The eBee was equipped with the Parrot Sequoia multispectral sensor acquiring reflectance at the wavelengths of green (550 nm ± 40 nm), red (660 nm ± 40 nm), red-edge (735 nm ± 10 nm), and NIR (790 nm ± 40 nm). The blue length (496 nm) was obtained by additional RGB imaging. Data were subjected to Pearson correlations (r) between the evaluated variables represented by a correlation and scatter plot. Subsequently, the canonical analysis was performed to verify the interrelationship between the variables evaluated. Data were also subjected to machine learning (ML) analysis, in which three different input dataset configurations were tested: using only irrigation management (IR), using irrigation management and spectral bands (SB+IR), and using irrigation management, spectral bands, and temperature (IR+SB+Temp). ML models used were: Artificial Neural Network (ANN), M5P Decision Tree (J48), REPTree Decision Tree (REPT), Random Forest (RF), and Support Vector Machine (SVM). A multiple linear regression (LR) was tested as a control model. Our results revealed that Random Forest has higher accuracy in predicting grain yield in maize, especially when associated with the inputs SB+IR and SB+IR+Temp.

In environmental research, remote sensing techniques are mostly based on orbital data, which are characterized by limited acquisition and often poor spectral and spatial resolutions in relation to suborbital sensors. This reflects on carbon patterns, where orbital remote sensing bears devoted sensor systems for CO2 monitoring, even though carbon observations are performed with natural resources systems, such as Landsat, supported by spectral models such as CO2Flux adapted to multispectral imagery. Based on the considerations above, we have compared the CO2Flux model by using four different imagery systems (Landsat 8, PlanetScope, Sentinel-2, and AisaFenix) in the northern part of the state of Mato Grosso, southern Brazilian Amazonia. The study area covers three different land uses, which are primary tropical forest, bare soil, and pasture. After the atmospheric correction and radiometric calibration, the scenes were resampled to 30 m of spatial resolution, seeking for a parametrized comparison of CO2Flux, as well as NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical Reflectance Index). The results obtained here suggest that PlanetScope, MSI/Sentinel-2, OLI/Landsat-8, and AisaFENIX can be similarly scaled, that is, the data variability along a heterogeneous scene in evergreen tropical forest is similar. We highlight that the spatial-temporal dynamics of rainfall seasonality relation to CO2 emission and uptake should be assessed in future research. Our results provide a better understanding on how the merge and/or combination of different airborne and orbital datasets that can provide reliable estimates of carbon emission and absorption within different terrestrial ecosystems in southern Amazonia.

Made available in DSpace on 2022-04-28T19:52:41Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-04-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Sampling trees in natural environment can be used in studies ranging from floristic composition and phytogeography to management and growth modelling, and accurate inventories are based on highly labor-intensive methods. Relying on hyperspectral approach, this study aimed to differentiate spectral libraries of four Amazon tree species. We first prepared the spectroradiometer data on representative bands on foliar biochemistry, followed by reflectance inflection difference and finally, we applied vegetation indices. Next, the discriminant analysis was reasoned on multivariate approach, were successfully discriminated the spectral curves related to each of evaluated tree species. By visual analysis, some regions of the electromagnetic spectrum with higher differentiation in reflectance responses can be seen, in portions of the visible spectrum (0.5–0.65 μm), near-infrared (0.913–1.25 μm) and short-wave infrared 2 (2.1–2.5 μm). There was a higher contribution in distinguishing between species based on specific RID (Reflectance Inflection Difference) heights, such as seen on specific representative bands, where RID approach reached 99.87% of data variability related to principal component 1 (PC1) and 99.72% for leaf structure-based bands in PC1. Principal component analysis applied to the vegetation indices brought satisfactory results, with PC1 highly related to the variability of the vegetation indices results (99.37%). Adopting this approach in hyperspectral data at the leaf level and well-defined classes results in good responses. We emphasize the importance of using combined vegetation indices, with greater contributions by indices developed for quantization or absorption of electromagnetic radiation by chlorophyll, which are based in the visible region. These results can improve further research by using remote sensing techniques, as create brand-new data for Amazonian tree species policymaking, conservation and research. Federal University of Mato Grosso (UFMT) Post-Graduate Program in Environmental Sciences (PPGCAM), Mato Grosso State University of Mato Grosso (UNEMAT), Mato Grosso State University of Mato Grosso (UNEMAT), Alta Floresta, Mato Grosso University of São Paulo (USP) Institute of Biosciences Department of Botany Louisiana State University (LSU) AgCenter School of Plant Environmental and Soil Sciences State University of São Paulo (UNESP) Jaboticabal Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul, Mato Grosso do Sul State University of São Paulo (UNESP) Jaboticabal CAPES: 001 CNPq: 303767/2020-0 CNPq: 309250/2021-8

Pilotless aircraft systems will reshape our critical thinking about agriculture. Furthermore, because they can drive a transformative precision and digital farming, we authoritatively review the contemporary academic literature on UAVs from every angle imaginable for remote sensing and on-field management, particularly for sugarcane. We focus our search on the period of 2016–2021 to refer to the broadest bibliometric collection, from the emergence of the term “UAV” in the typical literature on sugarcane to the latest year of complete publication. UAVs are capable of navigating throughout the field both autonomously and semi-autonomously at the control of an assistant operator. They prove useful to remotely capture the spatial-temporal variability with pinpoint accuracy. Thereby, they can enable the stakeholder to make early-stage decisions at the right time and place, whether for mapping, re-planting, or fertilizing areas producing feedstock for food and bioenergy. Most excitingly, they are flexible. Hence, we can strategically explore them to spray active ingredients and spread entomopathogenic bioagents (e.g., Cotesia flavipes and Thricrogramma spp.) onto the field wherever they need to be in order to suppress economically relevant pests (e.g., Diatraea saccharalis, Mahanarva fimbriolata, sugarcane mosaic virus, and weeds) more precisely and environmentally responsibly than what is possible with traditional approaches (without the need to heavily traffic and touch the object). Plainly, this means that insights into ramifications of our integrative review are timely. They will provide knowledge to progress the field’s prominence in operating flying machines to level up the cost-effectiveness of producing sugarcane towards solving the sector’s greatest challenges ahead, such as achieving food and energy security in order to thrive in an ever-challenging world.

Infrared region of electromagnetic spectrum has remarkable applications in crop studies. Infrared along with Red band has been used to develop certain vegetation indices. These indices like NDVI, EVI provide important information on any crop physiological stages. The main objective of this research was to discriminate 4 different soybean varieties (BMX Potencia, NA5909, FT Campo Mourao and Don Mario) using non-imaging hyperspectral sensor. The study was conducted in four agricultural areas in the municipality of Deodapolis (MS), Brazil. For spectral analysis, 2400 field samples were taken from soybean leaves by means of FieldSpec 3 JR spectroradiometer in the range from 350 to 2500 nm. The data were evaluated through multivariate analysis with the whole set of spectral curves isolated by blue, green, red and near infrared wavelengths along with the addition of vegetation indices like (Enhanced Vegetation Index - EVI, Normalized Difference Vegetation Index - NDVI, Green Normalized Difference Vegetation Index - GNDVI, Soil-adjusted Vegetation Index - SAVI, Transformed Vegetation Index - TVI and Optimized Soil-Adjusted Vegetation Index - OSAVI). A number of the analysis performed where, discriminant (60 and 80% of the data), simulated discriminant (40 and 20% of data), principal component (PC) and cluster analysis (CA). Discriminant and simulated discriminant analyze presented satisfactory results, with average global hit rates of 99.28 and 98.77%, respectively. The results obtained by PC and CA revealed considerable associations between the evaluated variables and the varieties, which indicated that each variety has a variable that discriminates it more effectively in relation to the others. There was great variation in the sample size (number of leaves) for estimating the mean of variables. However, it was possible to observe that 200 leaves allow to obtain a maximum error of 2% in relation to the mean.

This research aimed to analyze the possibility to estimate and automatically map large areas of soybean cultivation through the use of MODIS (Moderate-Resolution Imaging Spectroradiometer) images. ...

Boron (B) is an essential element whose deficiency results in rapid inhibition in the growth of plants, acting on their meristematic growth. Real-time monitoring of B fertilization in eucalyptus is helpful for guiding precision diagnosis and efficient management of plant boron nutrition. This research hypothesizes that different boron levels alter the reflectance of different wavelengths in eucalyptus. In this context, the objective of this study was to identify spectral ranges that can be used to monitor the boron status in eucalyptus plants. The experiment was carried out in a greenhouse, in which the treatments consisted of increasing boron levels in the form of boric acid (17% of B), whose levels varied from deficit to toxicity. Thus, five treatments were established: no boron, 1, 10, 20, and 40 mg/dm3 of boron. The remote sensing data used were bands, heights, and vegetation indices calculated after obtaining the spectral curves in each treatment. Our findings show that it is possible to accurately distinguish the boron levels in eucalyptus using hyper and multispectral bands. The 350–371 nm spectral range can be used for detecting boron-deficient plants. Plants with adequate boron levels can be identified by using the 426–444 nm, 1811–1910 nm, 1948–2115 nm, and 2124–2208 nm spectral ranges. Finally, the 425–475 nm spectral range can be used to find boron-toxicity plants.

Monitoring soybean areas by remote sensing is extremely useful, especially in Brazil, which has a large territorial extension and where soybean cultivation has spread to all regions of the country. In this sense, the development of remote sensing techniques that enable the quantification and discrimination of soybean areas and now in cultivated cultivar level is of crucial importance for the soybean production chain in Brazil. This study aimed to discriminate soybean cultivars as a function of different hyperspectral bands using the sensor-system MSI-Sentinel-2 (Vis-NIR-SWIR) as a simulation and sample sizes using multivariate statistics to determine if the specific bands of this sensor are capable of performing such discrimination. Four soybean cultivation areas in the Midwest region cultivated with four cultivars (BMX Potencia, NA5909, Don Mario, and FT Campo Mourao) were assessed. Spectral readings from each sample soybean leaf were performed, and a total of 2400 vegetation spectral readings were obtained. Data were composed of 28 bands and 22 reflectance factor height (RID) values for each soybean cultivar. Multivariate statistical analysis was performed to verify the association between soybean cultivars and their relationship with hyperspectral bands, as well as to verify the possibility of cultivar differentiation based on hyperspectral bands. The results obtained demonstrated to be possible discriminate soybean cultivars by using multivariate techniques applied to multi and hyperspectral data. The bands that contributed significantly (>5%) to cultivar differentiation in order of importance were: B26, B27, A17, A21, A20 and A14. Discriminant analysis was efficient in the cultivar classification, and canonical variable analysis revealed bands associated with specific discrimination of each cultivar. Bands that most contributed to cultivar discrimination were also identified for MSI orbital sensor.

Soil management practices for soy cultivation are considered necessary to sustain crop yields, in addition to implementing a synergy between sustainable agricultural practices and reducing environmental impacts. Thus, it is necessary to develop a model that can accurately and quickly distinguish between soil management treatments for soy based on spatial analysis. O objetivo desta pesquisa foi desenvolver uma metodologia aplicada na deteccao dos tipos de Plantios Convencional e Direto, atraves da integracao entre GEOBIA (Geographic Object-Based Image Analysis) e MD (Mineracao de Dados) nas areas identificadas com o cultivo da soja (Glycine max L. Merr.) no estado de Mato Grosso (MT). Para a identificacao das areas cultivadas com a cultura da soja foi utilizado o PCEI (Perpendicular Crop Enhancement), por meio de imagens multiespectrais do sensor MODIS (Moderate-Resolution Imaging Spectroradiometer). O processamento das bandas espectrais e os indices de vegetacao em uma serie temporal do satelite Landsat foram associadas as tecnicas de mineracao de dados e classificacao orientada a objeto devidamente tratados e aplicados uma segmentacao em multiresolucao para definicao dos objetos, com a selecao pre-definida dos alvos, sendo entao gerada uma Arvore de Decisao (AD) pelo algoritmo C4.5/J48 implementado no software Weka®. O algoritmo foi composto por 28 atributos e por 468 amostras de treinamento, representando todas as classes de plantio para obtencao de uma analise matricial de erro e estatistica Kappa. As estatisticas indicaram que a classificacao alcancou uma precisao geral de 95.29% e um coeficiente Kappa de 0.92. Os resultados revelaram que a combinacao das tecnicas GEOBIA e MD sao eficientes e promissoras para o processo de classificacao do tipo de tratamento de solo.

The least limiting water range (LLWR) is widely used as an agronomic indicator of soil physical quality, however few studies have reported its spatial variability, mainly in integrated crop-livestock systems (ICL). This study aimed to evaluate the spatial dependence of LLWR and its limits that establish the minimum physical and water restrictions for plant growth in an Oxisol under ICL systems. In addition, to verify correlations between LLWR limits and soil attributes that are commonly used to evaluate the soil compaction state. A georeferenced sampling mesh of 12 ha and 170 randomly distributed points was installed at an experimental station, which specifically studies five ICL systems and a no-tillage system with soybean and maize successions. The soil was sampled in 0.00–0.10 and 0.10–0.20 m layers for determinations of porosity, penetration resistance (PR), bulk density and LLWR. The results showed that LLWR presented spatial dependence and adjusted to the exponential model of semivariance, was positively correlated to macroporosity and total porosity and negatively correlated to bulk density, PR and microporosity. Among the parameters that compose it, the PR curve presented spatial dependence and explained the main changes in soil caused by management systems.

Published in Agron. J. 103:1815–1826 (2011) Posted online 28 Sept 2011 doi:10.2134/agronj2011.0199 Copyright © 2011 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. I N management for corn using active crop canopy sensors (ACS) relies on the use of algorithms that can trigger on-the-go N fertilization in the field based on crop canopy reflectance. Optical sensing equipment that employs this approach is commercially available and these sensors rely on some version of a vegetation index to express crop reflectance (Shanahan et al., 2008; Eitel et al., 2008) and prescribe N rate application. There are different approaches and vegetation indices used to determine N rate based on these sensors, but the majority of algorithms use the nitrogen sufficiency index (NSI) approach previously proposed for chlorophyll meter readings (Varvel et al., 1997). For example, when the ratio between a targeted region in the field and a well-fertilized reference in the same field reaches a certain level, N fertilizer is needed according to a function that describes the relationship between yield and NSI readings (Bausch and Duke, 1996). Some N rate recommendation algorithms use yield potential that is determined by growing degree days and an estimate of biomass at the day of sensing (Raun et al., 2002). Several additional vegetation indices have been used to calculate N rate for corn and wheat using active canopy sensors, such as the green normalized difference vegetation index (GNDVI) (Dellinger et al., 2008), and the CI (Solari et al., 2008). Regardless of the approach used, an understanding of how these indices may be influenced by water stress and previous crop is needed. Previous work by Eitel et al. (2008) investigated the impact of water availability and N stress on leaf area index (LAI) in wheat using a multispectral radiometer and a chlorophyll meter. They showed that the ratio of the modified chlorophyll absorption ratio index to the second modified triangular vegetation index (MCARI/MTVI2) is sensitive to N and less susceptible to variable LAI caused by water stress. Another example of interaction between water and N stress in corn using remote sensing was the work done by Clay et al. (2006), where broad band widths were used to calculate different indices (NDVI, GNDVI, normalized difference water index [NDWI], and nitrogen reflectance index [NRI]), with the major conclusion being that water and N had additive effects on yield and optimum N rates (100–120 kg N ha–1) were similar across different water levels. There are other examples of indices used specifically to detect water stress (Zygielbaum et al., 2009), to determine chlorophyll content, and to estimate gross primary productivity (Lemaire et al., ABSTRACT

Stylosanthes macrocephala M. B. Ferr. et S. Costa é uma leguminosa utilizada sob consorciação em pastagens, adubação e recuperação de áreas degradadas. A falta de características morfológicas e agronômicas estáveis e de informações ecogeográficas dos locais de coleta dos acessos tem dificultado o melhoramento genético da espécie. A fim de obter descritores ecológicos, moleculares e avaliar a variabilidade genética da coleção de S. macrocephala, 87 acessos foram analisados com o auxílio do Sistema de Informações Geográficas (SIG) e de marcadores moleculares RAPD. Os acessos provieram de sete Estados, cinco bacias hidrográficas, sete tipos de vegetação e sete tipos de solos. As altitudes dos locais de coleta variaram de 1 a 1.298 m e a pluviometria anual média de 550 a 2.870 mm. A variabilidade de descritores ecológicos sugeriu diversidade adaptativa na coleção. Com base em 161 marcadores RAPD, verificou-se que as distâncias genéticas entre os acessos de S. macrocephala variaram entre 0,02 e 0,42. Com base nessas distâncias, dez grupos de similaridade genética foram estabelecidos. Observou-se tendência de separação por bacias hidrográficas e elevada variabilidade genética entre os acessos coletados nos estados da Bahia e de Minas Gerais. A alta variabilidade genética da coleção de S. macrocephala evidencia a importância desses acessos para futuros trabalhos de melhoramento genético. Stylosanthes macrocephala M. B. Ferr et S. Costa is a leguminous species used as forage, cover crop and as a pioneer plant to recover degraded areas. Inexistence of stable morphological descriptors and lack of ecogeographic information about collecting sites bring difficulties to the studies of this species. The objective of this work was to use the geographic information system (GIS) and RAPD markers to obtain ecological and molecular descriptors and to study the genetic variability of 87 S. macrocephala accessions. The accessions were collected from different ecogeographical areas in seven Brazilian States, five hydrographic regions, seven vegetation types and seven soil types. Collecting sites ranged from 1 to 1,298 m above sea level with annual rainfall from 550 to 2,870 mm. Accession distribution along those diverse ecosystems indicate high adaptive diversity. Using 161 RAPD markers, genetic distances between accessions ranged from 0.02 to 0.42. These genetic distances allowed the establishment of ten genetic groups. Accessions collected in specific hydrographic region tended to be grouped in the same genetic group. Accessions collected in Bahia and Minas Gerais States showed high genetic variability. The high genetic variability observed in the accessions showed the importance of this S. macrocephala collection for breeding programs.

O objetivo deste trabalho foi identificar correlações entre o banco de sementes das plantas daninhas e os atributos da fertilidade dos solos. As amostragens de solo para determinação do banco de sementes foram feitas simultaneamente à amostragem para a análise da fertilidade do solo, utilizando uma grade amostral regular de 20 x 20 m. Os mapas de infestação e de atributos de fertilidade dos solos foram gerados pela técnica de interpolação por "krigagem". Os valores de correlação entre o banco de sementes de BRAPL e a saturação de alumínio (m%), a saturação de bases (V%) e o pH foram de, respectivamente, 0,84, -0,83 e -0,74. Portanto, quanto maior o valor de m% e menores os valores de V% e pH, maior a infestação do banco de sementes. Resultado semelhante foi observado para COMBE, porém os valores de correlação foram menores (-0,35 com V% e 0,28 com m%). O banco de sementes de CYPRO apresentou a mesma tendência da BRAPL (0,57 com m%, -0,63 com V%, -0,7 com Ca e -0,64 com o pH). Conclui-se que na área experimental houve alta correlação entre pH, Ca, V% e m% e o banco de sementes de plantas daninhas.

The objective of this work was to develop a fast and practical method of weed seedbank evaluation to generate spatially distributed maps for use in site-specific weed management. Soil cores were collected at 0.20 m depth, air-dried, and then submitted to seedling growth in greenhouse. The sampling grid of 20 by 20 m was georeferenced by Global Positioning System, obtaining 73 soil cores with three replicates. During the greenhouse trial, there were two peaks of weed seedling growth: one in 119 days after water irrigation and another after KNO3 application. Weeds seedbank maps were obtained at different stages of seedling growth. The Pearson correlation was 0.99 for Brachiaria plantaginea seedbank map, 0.95 for Commelina benghalensis, and 0.85 for Cyperus rotudus generated at 119 days compared with 392 days after seedling growth in the greenhouse. The Brachiaria plantaginea seedbank map evaluated at 35 days presented correlation of 0.97 with 392 days. It was concluded that, for site-specific weed seedbank management, the evaluation of seedling growth in greenhouse until the first emergence peak is enough to generate weed seedbank maps.

Metodologias de mapeamento de plantas daninhas em áreas agrícolas estão sendo utilizados a fim de gerar mapas de aplicação localizada de herbicidas, sendo que, este mapeamento vem sendo feito através de ferramentas da agricultura de precisão. O mapeamento das plantas daninhas gera então mapas de tratamentos de herbicidas que comandam pulverizadores capazes de realizar a aplicação localizada de herbicidas, aproveitando o comportamento contagioso inerente da comunidade das plantas daninhas, porém poucos experimentos relatam a eficiência desses métodos. Este experimento teve como objetivo comparar metodologias de mapeamento do capim-colonião (Panicum maximum), com base na avaliação visual durante e após a colheita da cultura de milho. Durante a colheita foi conduzido o monitoramento com avaliação visual, feito por uma pessoa devidamente treinada e pelo operador antecipadamente orientado. Após a colheita, a avaliação visual foi feita por amostragens, numa grade regular de 20 x 20 m. Foi observada uma subestimação de 6% da área infestada, com uma infestação de mais de 80% de cobertura pelo método de mapeamento durante a colheita, quando comparado com o caminhamento na grade regular após a colheita. Os dois métodos foram coincidentes em 45% da área marcada.