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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.

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 ...

In soybean, there is a lack of research aiming to compare the performance of machine learning (ML) and deep learning (DL) methods to predict more than one agronomic variable, such as days to maturity (DM), plant height (PH), and grain yield (GY). As these variables are important to developing an overall precision farming model, we propose a machine learning approach to predict DM, PH, and GY for soybean cultivars based on multispectral bands. The field experiment considered 524 genotypes of soybeans in the 2017/2018 and 2018/2019 growing seasons and a multitemporal–multispectral dataset collected by embedded sensor in an unmanned aerial vehicle (UAV). We proposed a multilayer deep learning regression network, trained during 2000 epochs using an adaptive subgradient method, a random Gaussian initialization, and a 50% dropout in the first hidden layer for regularization. Three different scenarios, including only spectral bands, only vegetation indices, and spectral bands plus vegetation indices, were adopted to infer each variable (PH, DM, and GY). The DL model performance was compared against shallow learning methods such as random forest (RF), support vector machine (SVM), and linear regression (LR). The results indicate that our approach has the potential to predict soybean-related variables using multispectral bands only. Both DL and RF models presented a strong (r surpassing 0.77) prediction capacity for the PH variable, regardless of the adopted input variables group. Our results demonstrated that the DL model (r = 0.66) was superior to predict DM when the input variable was the spectral bands. For GY, all machine learning models evaluated presented similar performance (r ranging from 0.42 to 0.44) for each tested scenario. In conclusion, this study demonstrated an efficient approach to a computational solution capable of predicting multiple important soybean crop variables based on remote sensing data. Future research could benefit from the information presented here and be implemented in subsequent processes related to soybean cultivars or other types of agronomic crops.

Variable nitrogen(N) rate fertilization based on remote sensing is challenging for cotton production fields, but active crop canopy sensors (ACS) appear as an alternative to make this practical on farm since they can be used at night as well. The crop spatial variability in inherent in crop production in general, and not on-the-go solutions can be used with this type of active sensing technologies. Thus, the purpose of this study was to investigate the potential of two vegetation indices to identify the N requirement variability for cotton plants and to develop prototype algorithms for topdressing nitrogen variable rate on commercial and experimental areas, using the N-sufficiency methodology based on virtual reference. The concept of virtual reference is to use a histogram to characterize the vegetation index of properly fertilized plants without establishing an N-rich plot as a reference strip. The experiment was conducted in strips with four different N rates (0, 45, 90 and 180 kgN ha-1) during the 2015, 2016, 2017 and 2018 crop seasons in partnership with large cotton producers in Mato Grosso and also in experimental area of Embrapa Agrosilvopastoral. Two algorithms for variable rate nitrogen fertilization for cotton were developed, namely: 1) N recommendation algorithm for cotton in commercial production system: N rate (kg.N ha-1) = -234.79 ISN2 + 49,879 ISN + 195.15; R² = 0.97; and 2) for cotton grown in experimental area: N dose (kgN ha-1) = -174.73 ISN2 - 107.21 ISN + 306.78; R² = 0.94.

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

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.

The objective of this work was to investigate the use of remotely sensed vegetation indices to improve the quality of yield maps. The method was applied to the yield data of twelve cornfields from the Data Intensive Farm Management project. The results revealed the need to time shift the yield values up to three seconds to better match the sensor readings with the geographic coordinates. The residuals of the yield prediction model were used to identify points with unlikely yield values for that location, as an alternative to traditional approaches using local spatial statistics, without any assumption of spatial dependence or stationarity. The temporal and spatial distribution of the standardized coefficients for each experimental unit highlighted the presence of trends in the data. At least five out of the twelve fields presented trends that could have been induced by data collection.

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.

Brazilian soils have natural high chemical variability; thus, apparent electrical conductivity (ECa) can assist interpretation of crop yield variations. We aimed to select soil chemical properties with the best linear and spatial correlations to explain ECa variation in the soil using a Profiler sensor (EMP-400). The study was carried out in Sidrolândia, MS, Brazil. We analyzed the following variables: electrical conductivity - EC (2, 7, and 15 kHz), organic matter, available K, base saturation, and cation exchange capacity (CEC). Soil ECa was measured with the aid of an all-terrain vehicle, which crossed the entire area in strips spaced at 0.45 m. Soil samples were collected at the 0-20 cm depth with a total of 36 samples within about 70 ha. Classical descriptive analysis was applied to each property via SAS software, and GS+ for spatial dependence analysis. The equipment was able to simultaneously detect ECa at the different frequencies. It was also possible to establish site-specific management zones through analysis of correlation with chemical properties. We observed that CEC was the property that had the best correlation with ECa at 15 kHz. Os solos brasileiros apresentam grande variabilidade química natural e com isso a condutividade elétrica aparente (CEa) pode auxiliar na interpretação das variações de produtividade das culturas. O objetivo deste trabalho foi selecionar atributos químicos do solo com a melhor correlação linear e espacial, para explicar a variabilidade da CEa do solo utilizando o aparelho Profiler modelo EMP-400. O estudo foi realizado no município de Sidrolândia, MS, e as variáveis avaliadas no solo foram: CE 2 kHz, CE 7 kHz, CE 15 kHz, matéria orgânica, K disponível, saturação de bases e capacidade de troca catiônica (CTC). A medição da CEa do solo foi feita com auxílio de um quadricículo, percorrendo a área total com espaçamento de 0,45 m entre passadas. As amostras de solo foram coletadas na profundidade de 0-20 cm, totalizando 36 amostras de solo em aproximadamente 70 ha. Para cada atributo analisado, realizou-se a análise descritiva clássica, utilizando o programa SAS, e para a dependência espacial, usou-se o GS+. O equipamento foi capaz de detectar simultaneamente as CEas nas diferentes frequências; por meio da análise de correlação com os atributos químicos do solo, foi possível estabelecer as zonas específicas de manejo. O atributo do solo que melhor se correlacionou com a CEa foi a CTC, para a frequência de 15 kHz.

Trees in the production systems can effectively reduce hot weather-induced stress in the Brazilian Midwest. High temperatures cause changes in animals daily routine, and trees into pastures can promote benefits. The aim of this research was to evaluate the behavior of dairy heifers in silvopastoral systems in the state of Mato Grosso, Brazil. A herd of 24 crossbreed heifers (3/4 and 7/8 Holstein/Zebu), 350kg average weight, was evaluated over three seasons. Piatã grass was managed under three shade levels: full-sun, moderate-shade, and intensive-shade provided by 10 to 12m high Eucalyptus trees. Behavior data were collected every 15 minutes from 8:30h to 16h. Shade availability significantly impacted heifer behavior, mainly affecting grazing frequency and time during the hottest hours. Grazing behavior was affected by shade levels during the different seasons. Heifers showed preferred grazing times. Heifers in the intensive-shade system visited shady areas during the hottest hours throughout the seasons. Heifers in the full sun-system avoided grazing during the warmer times, ceasing feeding activities. Our results from the Brazilian Midwest showed that shade availability causes breed heifers to change their daily routine. RESUMO: A presença de árvores em sistemas de produção animal pode reduzir o estresse provocado pelo calor no centro-oeste brasileiro. As altas temperaturas induzem a uma mudança na rotina dos animais e, as árvores dentro da pastagem podem promover benefícios. O objetivo desta pesquisa foi avaliar o comportamento de novilhas leiteiras em sistemas integrados no Mato Grosso, Brasil. Um grupo de 24 novilhas cruzadas (3/4 e 5/8 Holandês: Gir) de 350kg foi avaliado em três estações do ano. O capim piatã foi manejado sob três níveis de sombreamento: pleno sol e sombreamentos moderado e intenso promovidos por eucaliptos com 12m de altura. O comportamento das novilhas foi medido a cada 15 minutos entre 8h30min às 16h. A disponibilidade de sombra modificou o padrão de comportamento das novilhas, afetando a frequência e o tempo de pastejo nas horas mais quentes do dia. O comportamento de pastejo foi afetado pelos níveis de sombra e, variou nas diferentes estações do ano. As novilhas em sistemas com sombreamento intenso procuraram pela sombra nas horas mais quentes do dia durante as três estações do ano avaliadas. Já, as novilhas nas condições de pastagem a pleno sol, não pastejaram durante as horas mais quentes do dia, pois interrompiam suas atividades e permaneciam inativas durante grande período do dia. A disponibilidade de sombra induz novilhas cruzadas a mudarem sua rotina diária nas condições do Centro-Oeste brasileiro.

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.

A avaliação do conteúdo relativo de clorofila nas folhas fornece uma medida indireta do estado nutricional das plantas e pode ajudar os agricultores no ajuste da quantidade de nitrogênio (N) a ser aplicada em cobertura no milho. Objetivou-se neste trabalho validar o uso do clorofilômetro na detecção e correção da deficiência de N ao longo do ciclo vegetativo da cultura. O experimento foi conduzido em parcelas estabelecidas numa lavoura comercial, sendo o estado nutricional das plantas monitorado semanalmente com o uso do clorofilômetro. Os tratamentos incluíram uma testemunha (0kg ha-1) e quatro doses de N em cobertura (45, 90, 135 e 180kg ha-1), considerando-se o índice de suficiência de nitrogênio (ISN) de 0,95, o qual foi monitorado com o clorofilômetro, a partir de um tratamento referência (500kg ha-1 de N). O uso do clorofilômetro indicou a aplicação de N até 180kg ha-1, extrapolando as quantidades requeridas para máxima eficiência técnica (144kg ha-1) ou econômica (105kg ha-1). O estabelecimento de parcelas referência possibilita usar o clorofilômetro como indicador da necessidade de aplicação de N em cobertura durante o ciclo do milho, não obstante, o uso do valor de ISN 0,95 durante todo o ciclo da cultura pode não ser a estratégia mais apropriada. The evaluation of the chlorophyll relative content in the leaves provides an indirect measure of the plant's nutritional status and can help farmers to adjust the quantity of nitrogen (N) to be applied as top dressing fertilization for corn. The aim of this work was to validate the use of chlorophyll meter for detection and correction of N deficiency during the crop vegetative growth. The experiment was carried out in plots under commercial crop conditions and the corn nutritional status was monitored weekly with the chlorophyll meter. Six treatments of top dressing N included a check (0kg ha-1) and four doses (45, 90, 135 and 180kg ha-1), considering the nitrogen sufficiency index (NSI) of 0.95 monitored with the chlorophyll meter based on a reference treatment (500kg ha-1). The chlorophyll meter indicated top dress N application for corn up to 180kg ha-1, exceeding the quantities required to maximize the technical (144kg ha-1) or economic (105kg ha-1) efficiency. The establishment of reference plots makes the chlorophyll meter an useful indicator of the need of N top dressing during the corn growth, however, the use of the 0,95 NSI value throughout the crop cycle may not be the most appropriate strategy.

A obtenção de altas produtividades de milho (Zea mays L.) torna-se difícil, dentre outros fatores, pela complexa quantificação dos requerimentos de nitrogênio (N) da cultura do milho ao longo do seu ciclo, o que dificulta o seu dimensionamento, ainda mais, considerando a variada capacidade de suprimento de N em solos brasileiros. Nesse sentido, os teores relativos de clorofila surgem como uma opção para o manejo do N. Objetivou-se, neste estudo avaliar a sensibilidade do clorofilômetro em indicar o estado nutricional do milho em resposta ao N, em solos com teores de argila contrastantes e sob diferentes condições de suprimento de N. Para isso, foram realizados experimentos em condições controladas e de lavoura. Em condições controladas, foram testados três solos com teores de argila contrastantes e cinco doses de N, sendo avaliados, aos 55 dias após a emergência (estádio prévio ao VT), os teores relativos de clorofila, de N foliar e a produção de matéria seca da parte aérea. Em condições de lavoura foram avaliados seis tratamentos, resultantes da combinação de três doses de nitrogênio em cobertura, na presença e ausência de calcário, sendo a maior dose de N correspondente à condição na qual não haveria limitação de disponibilidade quanto ao suprimento do nutriente. A nutrição nitrogenada foi monitorada semanalmente com uso do clorofilômetro. Foram avaliados também os teores de N na folha e no grão, o N mineral no solo, a matéria seca da parte aérea e a produtividade de grãos. Os resultados permitiram concluir que há sensibilidade do clorofilômetro em detectar diferenças no estado nutricional do milho em resposta a diferentes características de solo e de suprimento de N, servindo como indicador da necessidade de adubação nitrogenada de cobertura.

Tradicionalmente o manejo das plantas daninhas é realizado levando-se em consideração a infestação média destas plantas nas áreas agrícolas; sendo então adotadas estratégias de manejo de forma homogênea em toda a área. Porém, as plantas daninhas não se distribuem uniformemente, sendo comum o comportamento contagioso, formando manchas ou "reboleiras". No entanto, a localização e mapeamento de diferentes infestações são tarefas difíceis de serem executadas em larga escala e requerem metodologias adequadas. Desta forma, métodos de determinação dessa variabilidade espacial necessitam de melhores esclarecimentos e estudos científicos. Com o objetivo de estudar o comportamento da variabilidade espacial das plantas daninhas e seu respectivo banco de sementes com a utilização de ferramentas da agricultura de precisão, foram realizados quatro experimentos para avaliar esta variabilidade espacial e temporal e analisar a potencialidade que a mesma pode proporcionar para o manejo racional das plantas daninhas, principalmente a possibilidade da aplicação localizada de herbicidas. O primeiro experimento consistiu do mapeamento do banco de sementes ao longo de dois anos consecutivos no intuito de comparar sua variabilidade espacial com a flora emergente. Concluiu-se que existe uma correlação entre o banco de sementes e a flora emergente e entre bancos de sementes de anos consecutivos, principalmente para as plantas daninhas Ageratum conyzoides L. e Commelina benghalensis L.. Analisando a dependência espacial, foi detectada uma dependência entre 7 e 15 m para o banco de sementes e a flora emergente. Os mapas gerados mostraram uma grande variabilidade espacial das plantas daninhas, sendo observadas algumas tendências de estabilidade ao longo do tempo. No segundo experimento foram feitos dois mapeamentos da infestação da planta Panicum maximum Jacq., um durante a colheita da cultura de milho e outro após a colheita, para fins de aferição do método de mapeamento realizado durante a colheita. Foi constatada uma subestimação de 6% da área infestada pelo método de mapeamento durante a colheita, quando este foi comparado com o caminhamento numa grade regular após a colheita. Os dois métodos foram coincidentes em 45% das áreas marcadas. O terceiro experimento consistiu de uma avaliação da eficácia da aplicação localizada de herbicidas pós-emergentes na cultura da soja, baseada em mapas de infestação das plantas daninhas. Para isto foi feito uma amostragem numa grade de 6 x 6 m e, baseado na infestação em cada célula, foi pulverizado duas doses de herbicidas pós-emergentes dependendo da infestação de cada local. Foi obtida uma economia de produto da ordem de 18 e 44%, sendo a eficácia similar à aplicação convencional utilizando dose única e pulverização uniforme em área total. No quarto experimento foi realizada uma pulverização localizada de herbicida pré-emergente baseada no mapeamento prévio do banco de sementes das plantas daninhas; sendo verificado uma eficácia igual à convencional e uma economia de 22% de produto herbicida. Portanto, técnicas de mapeamento da variabilidade espacial das plantas daninhas são importantes ferramentas para que melhores decisões sejam tomadas dentro dos sistemas de produção agrícola, sendo de extrema urgência um maior conhecimento e embasamento técnico-científico de técnicas agronômicas que considerem esta variabilidade espacial. Traditionally the weed management has been done considering the overall weed infestation in the whole field and then herbicides are sprayed according to a single weed control recommendation for the field. But weeds are not uniformly distributed and normally have clumped populations showing weed patches presenting high spatial variability. This nature of the weed populations can be mapped, and so it can help the decisions associated with weed management on agricultural systems. However, localization and mapping of the weed clusters are very difficult tasks in large areas, and require adequate methodology. Therefore, determination method of this weed infestation variability in space must be scientifically studied. With the objective of studying the weed spatial variability and temporal behavior of weed seedlings infestations and their respective seed banks, four experiments were conducted to evaluate the spatial temporal behavior of the weeds and the potential of weed mapping technology could bring to the rational weed management. The aim of the first experiment was to evaluate the spatial temporal behavior of the weed seedlings and their respective seed banks using the mapping technology. A grid sampling was used to understand the seedling and seed bank behavior in the space and time. Correlations using "Spearman rank" test were observed, mainly in Ageratum conyzoides L. and Commelina benghalensis L. The spatial dependence was noted between 7 and 15 m for the weed seedlings and seed banks. The weed maps showed that the weeds have a high spatial variability making patches with some temporal stability. In the second experiment two weed surveys were done with the objective of mapping Panicum maximum Jacq. during and after corn harvest. The grid sampling method after harvest corn was used to evaluate the harvest mapping method. It was underestimated about 6% of the weed infestation using the harvest mapping method when it was compared with the grid sampling method after corn harvest. In this experiment the harvest mapping method had just 45% of efficiency in recording weed infestation areas. The third experiment was done in order to analyzed the post emergence herbicide efficacy in soybeans based on weed maps constructed with grid sampling mapping. The weed seedling was counted in a 6 by 6 m grid and, based on weed infestation of each grid, it was sprayed two post emergence herbicide rates according to the weed infestation. It was saved 18 to 44% of herbicides compared to broadcast application with the same herbicide efficacy. In the fourth experiment a pre emergence herbicide application was carried out in soybeans crop based on weed seed bank map and, it was observed that the site-specific application had similar efficacy saving 22% of herbicides. So, the weed mapping technology is an important tool to bear on decisions associated with crop production, and it is a priority for the implementation of this technology multiple sources of scientific knowledge in weed biology and management regarding to the weed spatial variability.

Trees in the production systems can effectively reduce hot weather-induced stress in the Brazilian Midwest. High temperatures cause changes in animals daily routine, and trees into pastures can promote benefits. The aim of this research was to evaluate the behavior of dairy heifers in silvopastoral systems in the state of Mato Grosso, Brazil. A herd of 24 crossbreed heifers (3/4 and 7/8 Holstein/Zebu), 350kg average weight, was evaluated over three seasons. Piatã grass was managed under three shade levels: full-sun, moderate-shade, and intensive-shade provided by 10 to 12m high Eucalyptus trees. Behavior data were collected every 15 minutes from 8:30h to 16h. Shade availability significantly impacted heifer behavior, mainly affecting grazing frequency and time during the hottest hours. Grazing behavior was affected by shade levels during the different seasons. Heifers showed preferred grazing times. Heifers in the intensive-shade system visited shady areas during the hottest hours throughout the seasons. Heifers in the full sun-system avoided grazing during the warmer times, ceasing feeding activities. Our results from the Brazilian Midwest showed that shade availability causes breed heifers to change their daily routine. Made available in DSpace on 2017-04-11T23:44:57Z (GMT). No. of bitstreams: 1 2016cpamtcarnevalligrazingheifersshadedtropicalgrasslands.pdf: 740464 bytes, checksum: fa1600388ad093daf370ffc7cdfee3d8 (MD5) Previous issue date: 2016-12-30

Devido à existência de variabilidade espacial nos fatores que afetam o desenvolvimento do algodoeiro, faz-se necessário investigar a hipótese de que existem diferenças na dose de máximo retorno econômico da aplicação de nitrogênio (N) em cobertura e de fitorreguladores em áreas de cultivo de algodão. Assim, esse trabalho teve como objetivo investigar o potencial da utilização de sensores ativos de dossel para detecção da variabilidade espacial do desenvolvimento das plantas e da nutrição nitrogenada existente em áreas de produção de algodão, bem como estabelecer estratégias de gestão da variabilidade com o uso de taxas variáveis na adubação nitrogenada de cobertura e na aplicação de fitorreguladores no algodoeiro. Foram conduzidos experimentos em áreas comerciais de cultivo do algodoeiro localizadas em Chapadão do Céu - GO nas safras 2012/2013 e 2013/2014 e em Campo Verde - MT na safra 2014/2015. Avaliou-se o desempenho de dois modelos de sensores ativos de dossel em predizer a altura de plantas e o acúmulo de biomassa e N no algodoeiro, considerando dois sistemas de produção (espaçamento convencional e adensado) e vários estádios de desenvolvimento da cultura. Foram instaladas parcelas para calibração das curvas de dose resposta do algodoeiro ao nitrogênio em cobertura em diferentes condições de desenvolvimento da cultura relacionadas a variações no solo ou no histórico de produtividades no interior do talhão. Com base nos resultados dos experimentos foram desenvolvidos e validados algoritmos para aplicação em taxas variáveis de N e fitorreguladores, adotando-se o conceito de experimentos em nível de fazenda, com o modelo estatístico considerando a condutividade elétrica do solo como uma covariável e uma matriz de variância-covariância espacial, que foi ajustada pelo método da máxima verossimilhança. O desempenho geral dos sensores foi satisfatório, sendo capazes de predizer a altura de plantas e o acúmulo de biomassa e N no algodoeiro com valores de R2 acima de 0,90. Observou-se interação significativa com o espaçamento das fileiras e a fase de desenvolvimento da cultura, com performance inferior em situações de pequena variabilidade na cultura ou de acúmulo de biomassa muito grande. A resposta do algodoeiro ao nitrogênio aplicado em cobertura variou de acordo com as condições de cultivo de cada experimento. Maiores respostas foram observadas em áreas de médio e baixo potencial produtivo, existindo talhões e partes de talhões em que a aplicação de N não proporcionou incremento de produtividade, enquanto que em outras áreas observou-se aumento de produtividade expresso por uma relação quadrática entre a dose aplicada e a produtividade. No talhão com as maiores diferenças, a dose de máximo retorno econômico foi de 0 kg ha-1 na região de alto potencial produtivo, 165 kg ha-1 na região de médio potencial produtivo e 280 kg ha-1 na região de baixo potencial produtivo. Na análise dos experimentos em nível de fazenda, o melhor ajuste foi obtido no modelo que considera a dependência espacial dos resíduos, evidenciando que em experimentos dessa dimensão não se deve assumir que os princípios da estatística clássica são sempre satisfeitos. Em três áreas não foram observadas diferenças significativas entre a aplicação do N em cobertura em taxa fixa ou usando os algoritmos para aplicação em taxa variável, devido à homogeneidade da área e da interferência de outros fatores. Em uma das áreas observou-se vantagem para a aplicação em taxas variáveis usando o algoritmo que recomenda aplicação de maiores doses de N nos locais em que as plantas apresentam menor desenvolvimento vegetativo, com produtividade cerca de 100 kg ha-1 que a aplicação em taxa fixa e cerca de 300 kg ha-1 superior à obtida com a aplicação em taxas variáveis usando o algoritmo que recomenda aplicação de maiores doses de N nos locais em que as plantas apresentam-se mais vigorosas. A aplicação de fitorreguladores em taxas variáveis não apresentou efeito significativo na produtividade de algodão em caroço, entretanto o volume consumido dos produtos foi em média 18% menor na aplicação em taxa variável, principalmente em função das áreas que não foram aplicadas por apresentarem desenvolvimento vegetativo reduzido. Due to the existence of spatial variability in factors affecting cotton development, it is necessary to investigate the hypothesis that there are differences in the economic optimum fertilizer rates of top dressing nitrogen (N) and plant growth regulators (PGR) applications in cotton fields. Thus, the objective of this study was to investigate the potential of using active crop canopy sensors to detect the spatial variability of plant development and nitrogen nutrition in cotton fields and to establish strategies for managing spatial variability using variable rate technology to apply N and PGR in cotton. We conducted experiments in commercial cotton production fields located in the state of Goiás, in 2012/2013 and 2013/2014 seasons, and in the state of Mato Grosso, in 2014/2015 season. We evaluated the performance of two models of active crop canopy sensors to predict the height of plants and the accumulation of biomass and N in cotton, considering two production systems (conventional and narrow row spacing) and various growth stages. Within-field variations in the response curve to the increasing doses of nitrogen was evaluated under different crop development conditions related to variations in soil and historical yields. Based on the results of the experiments we developed and validated algorithms for variable rate application of N and PGR, using on-farm experiments. The statistical model considered the soil electrical conductivity as a covariate and a spatial variance-covariance matrix, which was adjusted by maximum likelihood. The overall performance of the sensors was satisfactory, predicting the height of plants and the accumulation of biomass and N in cotton with R2 values above 0.90. There was a significant interaction with the row spacing and the development stage of the crop, with lower performance in situations of low variability or very large biomass accumulation. The cotton response to top dressing nitrogen varied according to growing conditions of each experiment. Major responses were observed in areas of medium and low yield potential. There were fields and plots within fields in which the application of N did not increased yields, while in other areas there was a quadratic relationship between the dose applied and yield. In the field with the biggest differences, the economic optimum fertilizer rates of top dressing N was 0, 165 and 280 kg ha-1 in the high, medium and low yield potential zones, respectively. In the analysis of on-farm experiments, the best fit was obtained in the model that considered the spatial dependence of the residues, showing that experiments of this size we should not assume that the principles of classical statistics are always satisfied. In three areas, there was no significant differences between the application of N in fixed rate or using the algorithms for variable rate application, due to the homogeneity of the areas and the interference of external factors. In one of the areas, there was an advantage for application at variable rates using the algorithm that recommends application of higher doses of N in places where the plants have low vegetative development. The yields were about 100 kg ha-1 higher than the application of a fixed rate and 300 kg ha-1 higher than that obtained with the application of variable rates using the algorithm that recommends applying higher doses of N in places where the plants were more vigorous. The variable rate application of PGR had no significant effect on cottonseed yield, however the amount of products used was on average 18% lower in the variable rate application, mainly due to the not applied areas.

O nitrogênio (N) está envolvido na formação de clorofila, pigmento verde que permite às plantas capturar a luz solar através das folhas para a realização do processo de fotossíntese. Logo, uma planta de milho deficiente em N tem menores níveis de clorofila nas folhas, expressando cores de tonalidades verde claro ou amareladas, sintomas que indicam que a capacidade fotossintética e, consequentemente, o potencial produtivo da cultura está sendo comprometido pela nutrição insuficiente. Por outro lado, elevados teores de N nas plantas estão relacionados com níveis mais altos de clorofila nas folhas e altas taxas fotossintéticas. Made available in DSpace on 2018-04-25T00:37:23Z (GMT). No. of bitstreams: 1 Clorofilometrosdetectam.pdf: 2763195 bytes, checksum: d178a952e5d8fcadcf5658b323f008d2 (MD5) Previous issue date: 2016-04-11

The soil's nitrogen (N) supply can vary drastically in the field, spatially as well as temporally making any soil prediction difficult even with very detailed mapping. Consequently, a plant-based approach wherein the measured canopy can indicate the N needs in a reactive and spatially-variable way can be a better approach than mapping, because integrate the soil N supply and translate the crop need on-the-go. The first experiment evaluated the performance of various spectral indices for sensing N status of corn, where spectral variability might be confounded by water-induced variations in crop reflectance. We found that water and previous crops effects on vegetation indices (VI) must be considered, and also that some VIs are less susceptible to water with good ability for N differentiation. In the second experiment, the objective was to develop an approach that relies an local soil conditions as well as on active canopy sensor measurements for real-time adjustment of N application rate. We found that local variations in plant N availability must be considered to determine the optimal N rate on-the-go, and that the localized reference incorporated the spatial variability of the N-rich plot. Next, we determined the correlation between active canopy sensors assessments of N availability and ultrasonic sensor measurements of canopy height at several growth stages for corn. We found strong correlations between both sensors and that they had similar abilities to distinguish N-mediated differences in canopy development. The integrated use of both sensors improved the N estimation compared to the isolated use of either sensor. Based on these strong correlations, we developed an N recommendation algorithm based on ultrasonic plant height measurements to be used for on-the-go variable rate N application. Lastly, we evaluated the crop water status using infrared thermometry integrated with optical and ultrasonic sensors, we concluded that the integration of sensors was beneficial to detect water-stressed zones in the field, affecting yield and possibly promising to delineate zones for N and Water management. Tese (Doctor of Philosophy) - Faculty of The Graduate College, University of Nebraska. Orientadores: Richard Ferguson; John Shanahan.