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 | Acesso ao texto completo restrito à biblioteca da Embrapa Pecuária Sudeste. Para informações adicionais entre em contato com cppse.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Pecuária Sudeste. |
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Data corrente: |
13/01/2023 |
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Data da última atualização: |
13/01/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CARRER, M. J.; SOUZA FILHO, H. M. DE; VINHOLIS, M. de M. B.; MOZAMBANI, C. I. |
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Afiliação: |
MARCELO JOSÉ CARRER, Federal University of São Carlos - UFSCar; HILDO MEIRELLES DE SOUZA FILHO, Federal University of São Carlos - UFSCar; MARCELA DE MELLO BRANDAO VINHOLIS, CPPSE; CARLOS IVAN MOZAMBANI, Federal University of São Carlos - UFSCar. |
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Título: |
Precision agriculture adoption and technical efficiency: an analysis of sugarcane farms in Brazil. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Technological Forecasting and Social Change, v. 177, apr. 2022, 121510. |
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Páginas: |
10 p. |
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DOI: |
https://doi.org/10.1016/j.techfore.2022.121510 |
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Idioma: |
Inglês |
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Conteúdo: |
Precision Agriculture Technologies (PATs) are at the core of the fourth revolution in farming technology, also called Agriculture 4.0. This study evaluates the determinants of PATs adoption and its impacts on technical efficiency (TE) and technology gap ratio (TGR) of sugarcane farms in the state of Sao ? Paulo, Brazil. A selectivity correction model for stochastic frontiers is combined with a metafrontier production function approach to estimate the role of a set of determinants of PATs adoption and its impacts on TE and TGR. In person interviews with 131 sugarcane farmers provided cross-sectional farm level data from the 2018/19 crop year. The estimates of a sample selection equation showed that farming size, farmer?s schooling and technical assistance positively affect PATs adoption by sugarcane farmers. Estimates of stochastic production frontiers (SPFs) and metafrontier revealed that the average of the TE and TGR scores of adopters are higher than those of non-adopters. The managerial gaps (TE) between adopters and non-adopters are considerably wider than their technology gaps (TGR). The adoption of PATs subsidizes farmers decision-making process which increased the efficiency in inputs use, an important issue for economic and environmental sustainability in sugarcane farming |
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Palavras-Chave: |
Sample selection; Technical efficiency; Technology adoption. |
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Thesaurus Nal: |
Precision agriculture; Sugarcane. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02086naa a2200241 a 4500
001 2150973
005 2023-01-13
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.techfore.2022.121510$2DOI
100 1 $aCARRER, M. J.
245 $aPrecision agriculture adoption and technical efficiency$ban analysis of sugarcane farms in Brazil.$h[electronic resource]
260 $c2022
300 $a10 p.
520 $aPrecision Agriculture Technologies (PATs) are at the core of the fourth revolution in farming technology, also called Agriculture 4.0. This study evaluates the determinants of PATs adoption and its impacts on technical efficiency (TE) and technology gap ratio (TGR) of sugarcane farms in the state of Sao ? Paulo, Brazil. A selectivity correction model for stochastic frontiers is combined with a metafrontier production function approach to estimate the role of a set of determinants of PATs adoption and its impacts on TE and TGR. In person interviews with 131 sugarcane farmers provided cross-sectional farm level data from the 2018/19 crop year. The estimates of a sample selection equation showed that farming size, farmer?s schooling and technical assistance positively affect PATs adoption by sugarcane farmers. Estimates of stochastic production frontiers (SPFs) and metafrontier revealed that the average of the TE and TGR scores of adopters are higher than those of non-adopters. The managerial gaps (TE) between adopters and non-adopters are considerably wider than their technology gaps (TGR). The adoption of PATs subsidizes farmers decision-making process which increased the efficiency in inputs use, an important issue for economic and environmental sustainability in sugarcane farming
650 $aPrecision agriculture
650 $aSugarcane
653 $aSample selection
653 $aTechnical efficiency
653 $aTechnology adoption
700 1 $aSOUZA FILHO, H. M. DE
700 1 $aVINHOLIS, M. de M. B.
700 1 $aMOZAMBANI, C. I.
773 $tTechnological Forecasting and Social Change$gv. 177, apr. 2022, 121510.
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Registro original: |
Embrapa Pecuária Sudeste (CPPSE) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
25/10/2023 |
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Data da última atualização: |
25/10/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
COSTA-NETO, G.; MATTA, D. H. da; FERNANDES, I. K.; STONE, L. F.; HEINEMANN, A. B. |
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Afiliação: |
GERMANO COSTA-NETO, CORNELL UNIVERSITY, Ithaca-NY; DAVID HENRIQUES DA MATTA, UNIVERSIDADE FEDERAL DE GOIÁS; IGOR KUIVJOGI FERNANDES, UNIVERSIDADE FEDERAL DE GOIÁS; LUIS FERNANDO STONE, CNPAF; ALEXANDRE BRYAN HEINEMANN, CNPAF. |
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Título: |
Environmental clusters defining breeding zones for tropical irrigated rice in Brazil. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Agronomy Journal, 2023. |
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ISSN: |
1435-0645 |
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DOI: |
https://doi.org/10.1002/agj2.21481 |
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Idioma: |
Inglês |
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Notas: |
Early view. |
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Conteúdo: |
Geographic and seasonal effects are important in driving selection decisions in rice breeding research. Adopting new strategies for characterizing environmental?phenotype associations is critical to understanding these effects, and the outcomes of their study could reflect the benefits of developing locally adapted cultivars. This study aimed to characterize Brazil's tropical irrigated rice (IR) environment, Latin America's largest rice production system. We integrated unsupervised (K-means clustering) and supervised (decision tree classifier) algorithms to identify environmental clusters (EC) based on historical yield data. The data set included 31 locations and 471 genotypes from 1982 to 2017. We used environmental features (EF), such as weather and geography, as input variables for our analysis, assuming the model as EC ∼ f (EF). Results indicate that the tropical IR production region can be divided into four primary breeding zones, with temperature emerging as a significant factor in the study area. After employing a linear mixed model analysis, we observed that the current relationship between genetics (G), environmental variation (E), and their interaction (G×E) in Brazil's tropical IR has a 1:6:2 ratio. However, when introducing our data-driven model based on EC, we reduced this ratio to 1:5:1. Therefore, the selection for local adaptability across a large region became more reliable. Our approach successfully identified EC in Brazil's tropical production region of IR, providing valuable insights for defining breeding zones and identifying more productive and stable seed production fields. MenosGeographic and seasonal effects are important in driving selection decisions in rice breeding research. Adopting new strategies for characterizing environmental?phenotype associations is critical to understanding these effects, and the outcomes of their study could reflect the benefits of developing locally adapted cultivars. This study aimed to characterize Brazil's tropical irrigated rice (IR) environment, Latin America's largest rice production system. We integrated unsupervised (K-means clustering) and supervised (decision tree classifier) algorithms to identify environmental clusters (EC) based on historical yield data. The data set included 31 locations and 471 genotypes from 1982 to 2017. We used environmental features (EF), such as weather and geography, as input variables for our analysis, assuming the model as EC ∼ f (EF). Results indicate that the tropical IR production region can be divided into four primary breeding zones, with temperature emerging as a significant factor in the study area. After employing a linear mixed model analysis, we observed that the current relationship between genetics (G), environmental variation (E), and their interaction (G×E) in Brazil's tropical IR has a 1:6:2 ratio. However, when introducing our data-driven model based on EC, we reduced this ratio to 1:5:1. Therefore, the selection for local adaptability across a large region became more reliable. Our approach successfully identified EC in Brazil's tropical production region... Mostrar Tudo |
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Thesagro: |
Arroz Irrigado; Genótipo; Meio Ambiente; Melhoramento Genético Vegetal; Oryza Sativa; Sistema de Produção. |
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Thesaurus NAL: |
Breeding; Climate models; Environmental factors; Genotype; Genotype-environment interaction; Rice; Tropical agriculture. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02650naa a2200361 a 4500
001 2157493
005 2023-10-25
008 2023 bl uuuu u00u1 u #d
022 $a1435-0645
024 7 $ahttps://doi.org/10.1002/agj2.21481$2DOI
100 1 $aCOSTA-NETO, G.
245 $aEnvironmental clusters defining breeding zones for tropical irrigated rice in Brazil.$h[electronic resource]
260 $c2023
500 $aEarly view.
520 $aGeographic and seasonal effects are important in driving selection decisions in rice breeding research. Adopting new strategies for characterizing environmental?phenotype associations is critical to understanding these effects, and the outcomes of their study could reflect the benefits of developing locally adapted cultivars. This study aimed to characterize Brazil's tropical irrigated rice (IR) environment, Latin America's largest rice production system. We integrated unsupervised (K-means clustering) and supervised (decision tree classifier) algorithms to identify environmental clusters (EC) based on historical yield data. The data set included 31 locations and 471 genotypes from 1982 to 2017. We used environmental features (EF), such as weather and geography, as input variables for our analysis, assuming the model as EC ∼ f (EF). Results indicate that the tropical IR production region can be divided into four primary breeding zones, with temperature emerging as a significant factor in the study area. After employing a linear mixed model analysis, we observed that the current relationship between genetics (G), environmental variation (E), and their interaction (G×E) in Brazil's tropical IR has a 1:6:2 ratio. However, when introducing our data-driven model based on EC, we reduced this ratio to 1:5:1. Therefore, the selection for local adaptability across a large region became more reliable. Our approach successfully identified EC in Brazil's tropical production region of IR, providing valuable insights for defining breeding zones and identifying more productive and stable seed production fields.
650 $aBreeding
650 $aClimate models
650 $aEnvironmental factors
650 $aGenotype
650 $aGenotype-environment interaction
650 $aRice
650 $aTropical agriculture
650 $aArroz Irrigado
650 $aGenótipo
650 $aMeio Ambiente
650 $aMelhoramento Genético Vegetal
650 $aOryza Sativa
650 $aSistema de Produção
700 1 $aMATTA, D. H. da
700 1 $aFERNANDES, I. K.
700 1 $aSTONE, L. F.
700 1 $aHEINEMANN, A. B.
773 $tAgronomy Journal, 2023.
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