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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
25/03/2024 |
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Data da última atualização: |
25/03/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SOARES, F. M.; PIRES, L. F.; GARCIA, M. C.; CORADIN, L.; GHILARDI-LOPES, N. P.; SILVA, R. R.; CARVALHO, A. M. de; GAVAI, A.; BOUZEMBRAK, Y.; MACULAN, B. C. M. dos S.; KOFFLER, S.; MONTEDO, U. B.; DRUCKER, D. P.; SANTIAGO, R.; CARVALHO, M. C. P. de; LIMA, A. C. da S.; GABRIEL, H. D. E.; FRANÇA, S. G. M. de; ALMEIDA, K. R. de; SANTOS, B. J. dos; SARAIVA, A. M. |
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Afiliação: |
FILIPI MIRANDA SOARES, UNIVERSIDADE DE SÃO PAULO, UNIVERSITY OF TWENTE; LUÍS FERREIRA PIRES, UNIVERSITY OF TWENTE; MARIA CAROLINA GARCIA, CENTRO UNIVERSITÁRIO BELAS ARTES DE SÃO PAULO; LIDIO CORADIN, PLANTS FOR THE FUTURE PROJECT; NATALIA PIRANI GHILARDI-LOPES, UNIVERSIDADE FEDERAL DO ABC; RUBENS RANGEL SILVA, CENTRO UNIVERSITÁRIO UNA; ALINE MARTINS DE CARVALHO, UNIVERSIDADE DE SÃO PAULO; ANAND GAVAI, UNIVERSITY OF TWENTE; YAMINE BOUZEMBRAK, WAGENINGEN UNIVERSITY AND RESEARCH; BENILDES COURA MOREIRA DOS SANTOS MACULAN, UNIVERSIDADE FEDERAL DE MINAS GERAIS; SHEINA KOFFLER, UNIVERSIDADE DE SÃO PAULO; UIARA BANDINELI MONTEDO, UNIVERSIDADE DE SÃO PAULO; DEBORA PIGNATARI DRUCKER, CNPTIA; RAQUEL SANTIAGO, UNIVERSIDADE FEDERAL DE GOIÁS; MARIA CLARA PERES DE CARVALHO, UNIVERSIDADE DE SÃO PAULO; ANA CAROLINA DA SILVA LIMA, UNIVERSIDADE FEDERAL DE MINAS GERAIS; HILLARY DANDARA ELIAS GABRIEL, UNIVERSIDADE DE SÃO PAULO; STEPHANIE GABRIELE MENDONÇA DE FRANÇA, UNIVERSIDADE DE SÃO PAULO; KAROLINE REIS DE ALMEIDA, UNIVERSIDADE DE SÃO PAULO; BÁRBARA JUNQUEIRA DOS SANTOS, UNIVERSIDADE DE SÃO PAULO; ANTONIO MAURO SARAIVA, UNIVERSIDADE DE SÃO PAULO. |
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Título: |
Citizen science data on urban forageable plants: a case study in Brazil. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Gigabyte, v. 2024, p. 1-16, Feb. 2024. |
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ISSN: |
2709-4715 |
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DOI: |
10.46471/gigabyte.107 |
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Idioma: |
Inglês |
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Conteúdo: |
This paper presents two key data sets derived from the Pomar Urbano project. The first data set is a comprehensive catalog of edible fruit-bearing plant species, native or introduced to Brazil. The second data set, sourced from the iNaturalist platform, tracks the distribution and monitoring of these plants within urban landscapes across Brazil. The study includes data from the capitals of all 27 federative units of Brazil, focusing on the ten cities that contributed the most observations as of August 2023. The research emphasizes the significance of citizen science in urban biodiversity monitoring and its potential to contribute to various fields, including food and nutrition, creative industry, study of plant phenology, and machine learning applications. |
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Palavras-Chave: |
Áreas urbanas; Biodiversidade urbana; Ciência cidadã; Ciência vegetal; Citizen science; Plantas forrageiras. |
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Thesagro: |
Botânica; Ciência Animal; Ecologia. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1163118/1/AP-Citizen-science-2024.pdf
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Marc: |
LEADER 02127naa a2200493 a 4500
001 2163118
005 2024-03-25
008 2024 bl uuuu u00u1 u #d
022 $a2709-4715
024 7 $a10.46471/gigabyte.107$2DOI
100 1 $aSOARES, F. M.
245 $aCitizen science data on urban forageable plants$ba case study in Brazil.$h[electronic resource]
260 $c2024
520 $aThis paper presents two key data sets derived from the Pomar Urbano project. The first data set is a comprehensive catalog of edible fruit-bearing plant species, native or introduced to Brazil. The second data set, sourced from the iNaturalist platform, tracks the distribution and monitoring of these plants within urban landscapes across Brazil. The study includes data from the capitals of all 27 federative units of Brazil, focusing on the ten cities that contributed the most observations as of August 2023. The research emphasizes the significance of citizen science in urban biodiversity monitoring and its potential to contribute to various fields, including food and nutrition, creative industry, study of plant phenology, and machine learning applications.
650 $aBotânica
650 $aCiência Animal
650 $aEcologia
653 $aÁreas urbanas
653 $aBiodiversidade urbana
653 $aCiência cidadã
653 $aCiência vegetal
653 $aCitizen science
653 $aPlantas forrageiras
700 1 $aPIRES, L. F.
700 1 $aGARCIA, M. C.
700 1 $aCORADIN, L.
700 1 $aGHILARDI-LOPES, N. P.
700 1 $aSILVA, R. R.
700 1 $aCARVALHO, A. M. de
700 1 $aGAVAI, A.
700 1 $aBOUZEMBRAK, Y.
700 1 $aMACULAN, B. C. M. dos S.
700 1 $aKOFFLER, S.
700 1 $aMONTEDO, U. B.
700 1 $aDRUCKER, D. P.
700 1 $aSANTIAGO, R.
700 1 $aCARVALHO, M. C. P. de
700 1 $aLIMA, A. C. da S.
700 1 $aGABRIEL, H. D. E.
700 1 $aFRANÇA, S. G. M. de
700 1 $aALMEIDA, K. R. de
700 1 $aSANTOS, B. J. dos
700 1 $aSARAIVA, A. M.
773 $tGigabyte$gv. 2024, p. 1-16, Feb. 2024.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Biblioteca(s): |
Embrapa Meio Ambiente; Embrapa Pecuária Sudeste. |
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Data corrente: |
06/03/2023 |
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Data da última atualização: |
23/03/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
ALMEIDA, S. L. H. DE; SOUSA, J. B. C.; NOGUEIRA, S. F.; PEZZOPANE, J. R. M.; TEIXEIRA, A. H. DE C.; BOSI, C.; ADAMI, M.; ZERBATO, C.; BERNARDI, A. C. de C.; SILVA, G. B. S. da; SILVA, R. P. DA. |
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Afiliação: |
SAMIRA LUNS HATUM DE ALMEIDA, São Paulo State University; JARLYSON BRUNNO COSTA SOUZA, São Paulo State University; SANDRA FURLAN NOGUEIRA, CNPMA; JOSE RICARDO MACEDO PEZZOPANE, CPPSE; ANTÔNIO HERIBERTO DE CASTRO TEIXEIRA, Federal University of Sergipe; CRISTIAM BOSI; MARCOS ADAMI, National Institute for Space Research; CRISTIANO ZERBATO, São Paulo State University; ALBERTO CARLOS DE CAMPOS BERNARDI, CPPSE; GUSTAVO BAYMA SIQUEIRA DA SILVA, CNPMA; ROUVERSON PEREIRA DA SILVA, São Paulo State University. |
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Título: |
Forage mass estimation in silvopastoral and full sun systems: evaluation through proximal remote sensing applied to the SAFER model. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Remote Sensing, v. 15, n. 3, jan. 2023. |
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Páginas: |
18 p. |
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DOI: |
ttps://doi.org/10.3390/rs15030815 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: The operational slowness in the execution of direct methods for estimating forage mass, an important variable for defining the animal stocking rate, gave rise to the need for methods with faster responses and greater territorial coverage. In this context, the aim of this study was to evaluate a method to estimate the mass of Urochloa brizantha cv. BRS Piatã in shaded and full sun systems, through proximal sensing applied to the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model, applied with the Monteith Radiation Use Efficiency (RUE) model. The study was carried out in the experimental area of Fazenda Canchim, a research center of Embrapa Pecuária Sudeste, São Carlos, SP, Brazil (21°57′S, 47°50′W, 860 m), with collections of forage mass and reflectance in the silvopastoral systems animal production and full sun. Reflectance data, as well as meteorological data obtained by a weather station installed in the study area, were used as input for the SAFER model and, later, for the radiation use efficiency model to calculate the fresh mass of forage. The forage collected in the field was sent to the laboratory, separated, weighed and dried, generating the variables of pasture total dry mass), total leaf dry mass, leaf and stalk dry mass and leaf area index. With the variables of pasture, in situ, and fresh mass, obtained from SAFER, the training regression model, in which 80% were used for training and 20% for testing the models. The SAFER was able to promisingly express the behavior of forage variables, with a significant correlation with all of them. The variables that obtained the best estimation performance model were the dry mass of leaves and stems and the dry mass of leaves in silvopastoral and full sun systems, respectively. It was concluded that the association of the SAFER model with the proximal sensor allowed us to obtain a fast, precise and accurate forage estimation method. MenosAbstract: The operational slowness in the execution of direct methods for estimating forage mass, an important variable for defining the animal stocking rate, gave rise to the need for methods with faster responses and greater territorial coverage. In this context, the aim of this study was to evaluate a method to estimate the mass of Urochloa brizantha cv. BRS Piatã in shaded and full sun systems, through proximal sensing applied to the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model, applied with the Monteith Radiation Use Efficiency (RUE) model. The study was carried out in the experimental area of Fazenda Canchim, a research center of Embrapa Pecuária Sudeste, São Carlos, SP, Brazil (21°57′S, 47°50′W, 860 m), with collections of forage mass and reflectance in the silvopastoral systems animal production and full sun. Reflectance data, as well as meteorological data obtained by a weather station installed in the study area, were used as input for the SAFER model and, later, for the radiation use efficiency model to calculate the fresh mass of forage. The forage collected in the field was sent to the laboratory, separated, weighed and dried, generating the variables of pasture total dry mass), total leaf dry mass, leaf and stalk dry mass and leaf area index. With the variables of pasture, in situ, and fresh mass, obtained from SAFER, the training regression model, in which 80% were used for training and 20% for testing the models. The SAFER was ... Mostrar Tudo |
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Palavras-Chave: |
Digital agriculture; ILPF; Proximal sensor. |
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Thesagro: |
Biomassa; Capim Urochloa; Gramínea Forrageira; Sistema de Informação Geográfica; Sombreamento. |
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Thesaurus NAL: |
Meteorological data; Urochloa brizantha. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1152139/1/Nogueira-Forage-mass-2023.pdf
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Marc: |
LEADER 03114naa a2200385 a 4500
001 2152139
005 2023-03-23
008 2023 bl uuuu u00u1 u #d
024 7 $attps://doi.org/10.3390/rs15030815$2DOI
100 1 $aALMEIDA, S. L. H. DE
245 $aForage mass estimation in silvopastoral and full sun systems$bevaluation through proximal remote sensing applied to the SAFER model.$h[electronic resource]
260 $c2023
300 $a18 p.
520 $aAbstract: The operational slowness in the execution of direct methods for estimating forage mass, an important variable for defining the animal stocking rate, gave rise to the need for methods with faster responses and greater territorial coverage. In this context, the aim of this study was to evaluate a method to estimate the mass of Urochloa brizantha cv. BRS Piatã in shaded and full sun systems, through proximal sensing applied to the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model, applied with the Monteith Radiation Use Efficiency (RUE) model. The study was carried out in the experimental area of Fazenda Canchim, a research center of Embrapa Pecuária Sudeste, São Carlos, SP, Brazil (21°57′S, 47°50′W, 860 m), with collections of forage mass and reflectance in the silvopastoral systems animal production and full sun. Reflectance data, as well as meteorological data obtained by a weather station installed in the study area, were used as input for the SAFER model and, later, for the radiation use efficiency model to calculate the fresh mass of forage. The forage collected in the field was sent to the laboratory, separated, weighed and dried, generating the variables of pasture total dry mass), total leaf dry mass, leaf and stalk dry mass and leaf area index. With the variables of pasture, in situ, and fresh mass, obtained from SAFER, the training regression model, in which 80% were used for training and 20% for testing the models. The SAFER was able to promisingly express the behavior of forage variables, with a significant correlation with all of them. The variables that obtained the best estimation performance model were the dry mass of leaves and stems and the dry mass of leaves in silvopastoral and full sun systems, respectively. It was concluded that the association of the SAFER model with the proximal sensor allowed us to obtain a fast, precise and accurate forage estimation method.
650 $aMeteorological data
650 $aUrochloa brizantha
650 $aBiomassa
650 $aCapim Urochloa
650 $aGramínea Forrageira
650 $aSistema de Informação Geográfica
650 $aSombreamento
653 $aDigital agriculture
653 $aILPF
653 $aProximal sensor
700 1 $aSOUSA, J. B. C.
700 1 $aNOGUEIRA, S. F.
700 1 $aPEZZOPANE, J. R. M.
700 1 $aTEIXEIRA, A. H. DE C.
700 1 $aBOSI, C.
700 1 $aADAMI, M.
700 1 $aZERBATO, C.
700 1 $aBERNARDI, A. C. de C.
700 1 $aSILVA, G. B. S. da
700 1 $aSILVA, R. P. DA
773 $tRemote Sensing$gv. 15, n. 3, jan. 2023.
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Embrapa Meio Ambiente (CNPMA) |
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