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Registro Completo |
Biblioteca(s): |
Embrapa Amazônia Oriental; Embrapa Soja. |
Data corrente: |
29/03/1993 |
Data da última atualização: |
12/08/2013 |
Autoria: |
UNITED NATIONS INDUSTRIAL DEVELOPMENT ORGANIZATION (New York). |
Título: |
Fertilizer manual. |
Ano de publicação: |
1967 |
Fonte/Imprenta: |
New York, 1967. |
Páginas: |
218 p. |
Idioma: |
Inglês |
Conteúdo: |
World survey: people, food and fertilizer; Role of fertilizer in agricultural production; Criteria for production versus importation of fertilizers; Demand for fertilizers; Marketing, distribution and pricing of fertilizers; General concepts and definitions; Production of ammonia; Criteria for production versus importation of ammonia; Production of ammonia salts, nitric acid and nitrates; Production of urea; Production of nitrogen solutions; Production of phosphate fertilizers; Production of potash fertilizers; Production of nitrophosphates and ammonium phosphates; Production of mixed fertilizers; Secondary and micronutrients; Location of fertilizer plants; Planning for development of the fertilizer industry; General problems of fertilizer projects in developing countries; A case study of a nitrogenous fertilizer project in a developing country; Plant investment and production costs. |
Palavras-Chave: |
Fertilizacao; Fertilization; Fertilizer; Micronutrient; Micronutriente; Plant. |
Thesagro: |
Fertilizante; Nitrogênio; Planta. |
Thesaurus Nal: |
nitrogen; soil. |
Categoria do assunto: |
-- |
Marc: |
LEADER 01498nam a2200253 a 4500 001 1963704 005 2013-08-12 008 1967 bl uuuu 00u1 u #d 100 1 $aUNITED NATIONS INDUSTRIAL DEVELOPMENT ORGANIZATION (New York). 245 $aFertilizer manual. 260 $aNew York$c1967 300 $a218 p. 520 $aWorld survey: people, food and fertilizer; Role of fertilizer in agricultural production; Criteria for production versus importation of fertilizers; Demand for fertilizers; Marketing, distribution and pricing of fertilizers; General concepts and definitions; Production of ammonia; Criteria for production versus importation of ammonia; Production of ammonia salts, nitric acid and nitrates; Production of urea; Production of nitrogen solutions; Production of phosphate fertilizers; Production of potash fertilizers; Production of nitrophosphates and ammonium phosphates; Production of mixed fertilizers; Secondary and micronutrients; Location of fertilizer plants; Planning for development of the fertilizer industry; General problems of fertilizer projects in developing countries; A case study of a nitrogenous fertilizer project in a developing country; Plant investment and production costs. 650 $anitrogen 650 $asoil 650 $aFertilizante 650 $aNitrogênio 650 $aPlanta 653 $aFertilizacao 653 $aFertilization 653 $aFertilizer 653 $aMicronutrient 653 $aMicronutriente 653 $aPlant
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Embrapa Amazônia Oriental (CPATU) |
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Registro Completo
Biblioteca(s): |
Embrapa Unidades Centrais. |
Data corrente: |
18/09/2014 |
Data da última atualização: |
02/06/2017 |
Autoria: |
FERRAZ, P. F. P.; YANAGI JUNIOR, T.; HERNÁNDEZ JULIO, Y. F.; CASTRO, J. de O.; GATES, R. S.; REIS, G. M.; CAMPOS, A. T. |
Afiliação: |
PATRICIA FERREIRA PONCIANO FERRAZ, UFLA; TADAYUKI YANAGI JUNIOR, UFLA; FABIÁN HERNÁNDEZ JULIO, UFLA; JAQUELINE DE OLIVEIRA CASTRO, UFLA; RICHARD STEPHEN GATES, University of Illinois; GREGORY MURAD REIS, UFLA; ALESSANDRO TORRES CAMPOS, UFLA. |
Título: |
Predicting chick body mass by artificial intelligence-based models. |
Ano de publicação: |
2014 |
Fonte/Imprenta: |
Pesquisa Agropecuária Brasileira, Brasília, DF, v. 49, n. 7, p. 559-568, jul. 2014. |
Idioma: |
Inglês |
Notas: |
Título em português: Predição da massa corporal de pintinhos por meio de modelos baseados em inteligência artificial. |
Conteúdo: |
The objective of this work was to develop, validate, and compare 190 artificial intelligence‑based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside our climate‑controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21‑day‑old hicks) ? with the variables dry‑bulb air temperature, duration of thermal stress (days), chick ageb (days), and the daily body mass of chicks ? was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro‑fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The NNs enable the simulation of different scenarios, which can assist in managerial decision‑making, and they can be embedded in the heating control system. |
Palavras-Chave: |
Artificial neural networks; Modeling; Neuro-fuzzy network; Thermal comfort. |
Thesaurus NAL: |
Animal welfare. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/108680/1/Predicting-chick-body.pdf
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Marc: |
LEADER 01954naa a2200265 a 4500 001 1995322 005 2017-06-02 008 2014 bl uuuu u00u1 u #d 100 1 $aFERRAZ, P. F. P. 245 $aPredicting chick body mass by artificial intelligence-based models. 260 $c2014 500 $aTítulo em português: Predição da massa corporal de pintinhos por meio de modelos baseados em inteligência artificial. 520 $aThe objective of this work was to develop, validate, and compare 190 artificial intelligence‑based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside our climate‑controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21‑day‑old hicks) ? with the variables dry‑bulb air temperature, duration of thermal stress (days), chick ageb (days), and the daily body mass of chicks ? was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro‑fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The NNs enable the simulation of different scenarios, which can assist in managerial decision‑making, and they can be embedded in the heating control system. 650 $aAnimal welfare 653 $aArtificial neural networks 653 $aModeling 653 $aNeuro-fuzzy network 653 $aThermal comfort 700 1 $aYANAGI JUNIOR, T. 700 1 $aHERNÁNDEZ JULIO, Y. F. 700 1 $aCASTRO, J. de O. 700 1 $aGATES, R. S. 700 1 $aREIS, G. M. 700 1 $aCAMPOS, A. T. 773 $tPesquisa Agropecuária Brasileira, Brasília, DF$gv. 49, n. 7, p. 559-568, jul. 2014.
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