Use of active canopy sensors to discriminate wheat response to nitrogen fertilization under no-tillage

Kapp, Claudio; Guimarães, Alaine Margarete; Caires, Eduardo Fávero

doi:10.1590/1809-4430-eng.agric.v36n5p886-894/2016

Cited by 15 publications

(14 citation statements)

References 25 publications

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“…As the value of these indices varies with the crop growth stage, the crop coefficient (kc) for irrigation management can be defined as its function (Kamble et al 2013). Other applications of these indices include the estimation of productivity, biomass, protein content, nitrogen fertilizer content and weed control (Yao et al 2012, Kapp Júnior et al 2016, Pantazi et al 2016.…”

Section: Palavras-mentioning

confidence: 99%

Vegetation indices in the prediction of biomass and grain yield of white oat under irrigation levels

Coelho

Rosalen

Faria

2018

Pesqui. Agropecu. Trop.

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Vegetation indices are widely used to indicate the nutritional status of crops, as well as to estimate their harvest yield. However, their accuracy is influenced by the phenological stage of evaluation and the index used. The present study aimed to evaluate the accuracy of the Normalized Difference Vegetation Index (NDVI) and Inverse Ratio Vegetation Index (IRVI) in the prediction of grain yield and biomass of white oat cultivated under irrigation levels, besides indicating the best phenological stage for evaluation. The irrigation levels consisted of 11 %, 31 %, 60 %, 87 % and 100 % of the maximum evapotranspiration, with four replicates. The mean values for NDVI and IRVI were determined using an active terrestrial sensor, at four phenological stages (4, 8, 10 and 10.5.4). The white oat grain yield and biomass may be estimated with a high precision using the NDVI and IRVI. The NDVI was more accurate than the IRVI. The grain yield estimate was more accurate from the flag leaf sheath appearance stage (10), whereas, for the biomass, the best estimate was for the kernel watery ripe stage (10.5.4).

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Section: Palavras-mentioning

confidence: 99%

Vegetation indices in the prediction of biomass and grain yield of white oat under irrigation levels

Coelho

Rosalen

Faria

2018

Pesqui. Agropecu. Trop.

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“…A avaliação com o índice de clorofila permite identificar o estado nutricional das plantas, especialmente em relação ao nitrogênio (10,11,6) . Outras aplicações desse índice são na estimativa de produtividade, biomassa, teor de proteína e no manejo da adubação nitrogenada (12,13,14) .…”

Section: Introductionunclassified

Estimativa Da Produtividade De Grãos Da Aveia-Branca Cultivada Sob Níveis De Irrigação Utilizando Clorofilômetro Portátil

Coelho

Leal

Filla

et al. 2018

Rev Cient Fac Educ e Meio Ambient

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Palavras-chave:Clorofila ICF Modelos RESUMO: A utilização de medidas de vegetação indiretas na agricultura, associado à modelos, é uma forma eficiente de estimar a produtividade das culturas. Objetivou-se verificar a acuracidade de modelos para estimar a produtividade da aveia-branca utilizando como variável leituras indiretas de clorofila, além de indicar a melhor fase e folha para a avaliação. O experimento de campo foi realizado entre maio e agosto de 2017, na FCAV/Unesp, Jaboticabal, SP. As lâminas de irrigação foram 100%, 87%, 60%, 31% e 11% da ETc para os tratamentos L5, L4, L3, L2 e L1, respectivamente. Cada tratamento apresentava 4 repetições. A leitura do índice clorofila (ICF) foi realizada pelo clorofilômetro Falker ClorofiLOG ® 1030 nas folhas bandeira e bandeira -1, em 5 plantas por parcela. As regressões foram submetidas à análise de variância (Teste F) e sua acuracidade verificada pela avaliação do coeficiente de determinação (R 2 ), raiz quadrada do erro médio (RSME) e erro médio (ME). Leituras indiretas de clorofila apresentam alta correlação com a produtividade da aveia-branca. A maior acurácia para a estimativa da produtividade da aveia foi a avaliação na folha bandeira e na fase vegetativa da cultura (R 2 = 0,81; RSME = 509 kg ha -1 ; ME = 0.0072 kg ha -1 ). ABSTRACT: The use of indirect measures of vegetation in agriculture, associated withmodels, is an efficient way of estimating crop productivity. The objective of this study was to verify the accuracy of models to estimate the yield of white oats using as indirect variable chlorophyll measures, besides indicating the best phase and leaf for the evaluation. The field experiment was carried out between May and August 2017 at FCAV/Unesp, Jaboticabal, SP. Irrigation depths were 100%, 87%, 60%, 31% and 11% ETc for treatments L5, L4, L3, L2 and L1, respectively. Each treatment had 4 replicates. The 1 AGRADECIMENTOS: Agradecemos ao Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) pela concessão da bolsa de mestrado ao primeiro autor e à Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) pela concessão de bolsas de estudos ao segundo e terceiro autores. 2 Atribuição CC BY: Este é um artigo de acesso aberto e distribuído sob os Termos da Creative Commons Attribution License. A licença permite o uso, a distribuição e a reprodução irrestrita, em qualquer meio, desde que creditado as fontes originais.

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“…2015, Junior et al 2016), and yield potential (Cao et al 2015, Cao et al 2016 in various crops. However, there have been few reports of comparisons of the different types of meters for disease assessment (Calcante et al 2012), and no reports on how different meters compare for evaluation of leaf spot diseases of peanut.…”

mentioning

confidence: 99%

Comparison of Trimble GreenSeeker and Crop Circle (Model ACS-210) Reflectance Meters for Assessment of Severity of Late Leaf Spot

Jordan¹,

Branch²,

Coffin³

et al. 2019

Peanut Science

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Four field experiments conducted in 2015 were used to examine the relationships among normalized difference vegetation index (NDVI) measurements from two canopy crop sensors and visual estimates of defoliation by late leaf spot ( Nothopassalora personata) of peanut ( Arachis hypogaea) the predominant foliar disease in this study. For each evaluation, reflectance was measured with each the two meters, and leaf spot severity was measured visually within as short a time as possible. Linear and quadratic regression models were used to characterize the relationships between percent defoliation from late leaf spot and NDVI measured with the GreenSeeker (NDVIGS) and Crop Circle (NDVICC) instruments and the relationships between NDVIGS and NDVICC. NDVIGS decreased with increasing percent defoliation according to linear or quadratic functions in three of the four trials, NDVICC decreased with increasing percent defoliation according to linear functions in three of four trials. In two of the four trials, NDVICC increased with increasing NDVIGS according to quadratic functions, but there was no significant regression for those variables in two trials. In three of the four trials, NDVICC linear regression had a better fit for predicting percent defoliation according to the coefficient of determination (R2). There was no indication for either instrument that the same NDVI reading corresponded with the same level of defoliation across trials. Results indicated that NDVI measurements from the two instruments are not interchangeable.

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Use of active canopy sensors to discriminate wheat response to nitrogen fertilization under no-tillage

Cited by 15 publications

References 25 publications

Vegetation indices in the prediction of biomass and grain yield of white oat under irrigation levels

Vegetation indices in the prediction of biomass and grain yield of white oat under irrigation levels

Estimativa Da Produtividade De Grãos Da Aveia-Branca Cultivada Sob Níveis De Irrigação Utilizando Clorofilômetro Portátil

Comparison of Trimble GreenSeeker and Crop Circle (Model ACS-210) Reflectance Meters for Assessment of Severity of Late Leaf Spot

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