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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Corte. Para informações adicionais entre em contato com cnpgc.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Corte. |
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Data corrente: |
12/12/2023 |
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Data da última atualização: |
12/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
BRETAS, I. L.; VALENTE, D. S. M.; OLIVEIRA, T. F. DE; MONTAGNER, D. B.; EUCLIDES, V. P. B.; CHIZZOTTI, F. H. M. |
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Afiliação: |
IGOR LIMA BRETAS, UNIVERSIDADE FEDERAL DE VIÇOSA; DOMINGOS SARVIO MAGALHÃES VALENTE, UNIVERSIDADE FEDERAL DE VIÇOSA; THIAGO FURTADO DE OLIVEIRA, UNIVERSIDADE FEDERAL DE VIÇOSA; DENISE BAPTAGLIN MONTAGNER, CNPGC; VALERIA PACHECO BATISTA EUCLIDES, CNPGC; FERNANDA HELENA MARTINS CHIZZOTTI, UNIVERSIDADE FEDERAL DE VIÇOSA. |
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Título: |
Canopy height and biomass prediction in Mombaça guinea grass pastures using satellite imagery and machine learning. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Precision Agriculture, v. 24, n. 4, p. 1638–1662, 2023. |
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DOI: |
https://doi.org/10.1007/s11119-023-10013-z |
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Idioma: |
Inglês |
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Notas: |
Published online: 17 April 2023. |
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Conteúdo: |
ABSTRACT - Remote sensing can serve as a promising solution for monitoring spatio-temporal variability in grasslands, providing timely information about diferent biophysical parameters. We aimed to develop models for canopy height classifcation and aboveground biomass estimation in pastures of Megathyrsus maximus cv. Mombaça using machine learning techniques and images obtained from the Sentinel-2 satellite. We used diferent spectral bands from the Sentinel-2, which were obtained and processed entirely in the cloud computing space. Three canopy height classes were defned according to grazing management recommendations: Class 0 (<0.45 m), Class 1 (0.45–0.80 m) and Class 2 (>0.80 m). For modeling, the original database was divided into training data (85%) and test data (15%). To avoid dependency between our training and test datasets and ensure greater generalization capacity, we used a spatial grouping evaluation structure. The random forest algorithm was used to predict canopy height and aboveground biomass by using height and biomass feld reference data obtained from 54 paddocks in Brazil between 2016 and 2018. Our results demonstrated precision, recall, and accuracy values of up to 73%, 73%, and 72%, respectively, for canopy height classifcation. In addition, the models showed reasonable predictive performance for aboveground fresh biomass (AFB) and dry matter concentration (DMC; R2=0.61 and 0.69, respectively). We conclude that the combined use of satellite imagery and machine learning techniques has potential to predict canopy height and aboveground biomass of Megathyrsus maximus cv. Mombaça. However, further studies should be conducted to improve the proposed models and develop software to implement the tool under feld conditions. MenosABSTRACT - Remote sensing can serve as a promising solution for monitoring spatio-temporal variability in grasslands, providing timely information about diferent biophysical parameters. We aimed to develop models for canopy height classifcation and aboveground biomass estimation in pastures of Megathyrsus maximus cv. Mombaça using machine learning techniques and images obtained from the Sentinel-2 satellite. We used diferent spectral bands from the Sentinel-2, which were obtained and processed entirely in the cloud computing space. Three canopy height classes were defned according to grazing management recommendations: Class 0 (<0.45 m), Class 1 (0.45–0.80 m) and Class 2 (>0.80 m). For modeling, the original database was divided into training data (85%) and test data (15%). To avoid dependency between our training and test datasets and ensure greater generalization capacity, we used a spatial grouping evaluation structure. The random forest algorithm was used to predict canopy height and aboveground biomass by using height and biomass feld reference data obtained from 54 paddocks in Brazil between 2016 and 2018. Our results demonstrated precision, recall, and accuracy values of up to 73%, 73%, and 72%, respectively, for canopy height classifcation. In addition, the models showed reasonable predictive performance for aboveground fresh biomass (AFB) and dry matter concentration (DMC; R2=0.61 and 0.69, respectively). We conclude that the combined use of satellite imagery and ma... Mostrar Tudo |
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Palavras-Chave: |
Pecuária de precisão. |
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Thesagro: |
Biomassa; Pastagem; Sensoriamento Remoto; Tecnologia. |
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Thesaurus Nal: |
Biomass; Pasture management; Remote sensing; Tropical grasslands. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02758naa a2200313 a 4500
001 2159571
005 2023-12-12
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s11119-023-10013-z$2DOI
100 1 $aBRETAS, I. L.
245 $aCanopy height and biomass prediction in Mombaça guinea grass pastures using satellite imagery and machine learning.$h[electronic resource]
260 $c2023
500 $aPublished online: 17 April 2023.
520 $aABSTRACT - Remote sensing can serve as a promising solution for monitoring spatio-temporal variability in grasslands, providing timely information about diferent biophysical parameters. We aimed to develop models for canopy height classifcation and aboveground biomass estimation in pastures of Megathyrsus maximus cv. Mombaça using machine learning techniques and images obtained from the Sentinel-2 satellite. We used diferent spectral bands from the Sentinel-2, which were obtained and processed entirely in the cloud computing space. Three canopy height classes were defned according to grazing management recommendations: Class 0 (<0.45 m), Class 1 (0.45–0.80 m) and Class 2 (>0.80 m). For modeling, the original database was divided into training data (85%) and test data (15%). To avoid dependency between our training and test datasets and ensure greater generalization capacity, we used a spatial grouping evaluation structure. The random forest algorithm was used to predict canopy height and aboveground biomass by using height and biomass feld reference data obtained from 54 paddocks in Brazil between 2016 and 2018. Our results demonstrated precision, recall, and accuracy values of up to 73%, 73%, and 72%, respectively, for canopy height classifcation. In addition, the models showed reasonable predictive performance for aboveground fresh biomass (AFB) and dry matter concentration (DMC; R2=0.61 and 0.69, respectively). We conclude that the combined use of satellite imagery and machine learning techniques has potential to predict canopy height and aboveground biomass of Megathyrsus maximus cv. Mombaça. However, further studies should be conducted to improve the proposed models and develop software to implement the tool under feld conditions.
650 $aBiomass
650 $aPasture management
650 $aRemote sensing
650 $aTropical grasslands
650 $aBiomassa
650 $aPastagem
650 $aSensoriamento Remoto
650 $aTecnologia
653 $aPecuária de precisão
700 1 $aVALENTE, D. S. M.
700 1 $aOLIVEIRA, T. F. DE
700 1 $aMONTAGNER, D. B.
700 1 $aEUCLIDES, V. P. B.
700 1 $aCHIZZOTTI, F. H. M.
773 $tPrecision Agriculture$gv. 24, n. 4, p. 1638–1662, 2023.
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Registro original: |
Embrapa Gado de Corte (CNPGC) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Semiárido. Para informações adicionais entre em contato com cpatsa.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
19/02/2021 |
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Data da última atualização: |
10/10/2022 |
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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: |
ALBUQUERQUE, I. R. R. de; ARAUJO, G. G. L. de; VOLTOLINI, T. V.; MOURA, J. H. de A.; COSTA, R. G.; GOIS, G. C.; COSTA, S. A. P.; CAMPOS, F. S.; QUEIROZ, M. A. A.; SANTOS, N. M. de S. S. |
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Afiliação: |
ITALO RENEU ROSAS DE ALBUQUERQUE; GHERMAN GARCIA LEAL DE ARAUJO, CPATSA; TADEU VINHAS VOLTOLINI, CPATSA; JOSÉ HELDER DE ANDRADE MOURA; ROBERTO GERMANO COSTA; GLAYCIANE COSTA GOIS; SAMIR AUGUSTO PINHEIRO COSTA; FLEMING SENA CAMPOS; MÁRIO ADRIANO ÁVILA QUEIROZ; NILMARA MÉRCIA DE SOUZA SÁ SANTOS. |
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Título: |
Saline water intake effects performance, digestibility, nitrogen and water balance of feedlot lambs. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Animal Production Science, v. 60, n. 13, 1591-1597, 2020. |
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DOI: |
https://doi.org/10.1071/AN19224 |
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Idioma: |
Inglês |
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Conteúdo: |
Water availability is often a limiting factor for herds in the arid and semi-arid regions worldwide. Aim: This study aimed to evaluate the productive performance and nutritional status of Santa Inês sheep receiving water containing different salinity levels. Methods: Thirty-two intact Santa Inês lambs, with an average body weight of 21.76±1.25 kg, were distributed in a completely randomized design with four treatments (total dissolved solids (TSD) levels in the drinking water: 640, 3200, 5760 and 8320 mg/L TDS) and eight animals per treatment. Intake, apparent nutrient digestibility, water balance, nitrogen balance, and performance of animals were evaluated. Key Results: TDS levels in water promoted an increasing linear effect for water intake offered via drinking trough (P = 0.01), total water intake (P = 0.02), total urine excretion (P = 0.02), total water excretion via urine (P = 0.01), water excretion via feces (P = 0.04), total water excretion (P = 0.01), absorbed water (P = 0.02), total nitrogen intake (P = 0.04), and water intake to total weight gain ratio (P = 0.01). No effect of increasing TDS levels was observed for dry matter and nutritional fractions intake, nutrient digestibility, water intake via food, total feces excretion, water balance, nitrogen excretion via feces, nitrogen excretion via urine, total nitrogen excretion, absorbed nitrogen, and nitrogen balance (P>0.05). No difference was observed in the performance of Santa Inês sheep consuming water with TDS levels up to 8320 mg/L, which presented a daily mean weight gain of 0.105 kg/day. Conclusions: Water with total dissolved solids levels up to 8320 mg/L can be used as drinking water of Santa Inês sheep. Implications: Therefore, the use of water from wells, which is usually saline, may be an alternative to supplying small ruminants. MenosWater availability is often a limiting factor for herds in the arid and semi-arid regions worldwide. Aim: This study aimed to evaluate the productive performance and nutritional status of Santa Inês sheep receiving water containing different salinity levels. Methods: Thirty-two intact Santa Inês lambs, with an average body weight of 21.76±1.25 kg, were distributed in a completely randomized design with four treatments (total dissolved solids (TSD) levels in the drinking water: 640, 3200, 5760 and 8320 mg/L TDS) and eight animals per treatment. Intake, apparent nutrient digestibility, water balance, nitrogen balance, and performance of animals were evaluated. Key Results: TDS levels in water promoted an increasing linear effect for water intake offered via drinking trough (P = 0.01), total water intake (P = 0.02), total urine excretion (P = 0.02), total water excretion via urine (P = 0.01), water excretion via feces (P = 0.04), total water excretion (P = 0.01), absorbed water (P = 0.02), total nitrogen intake (P = 0.04), and water intake to total weight gain ratio (P = 0.01). No effect of increasing TDS levels was observed for dry matter and nutritional fractions intake, nutrient digestibility, water intake via food, total feces excretion, water balance, nitrogen excretion via feces, nitrogen excretion via urine, total nitrogen excretion, absorbed nitrogen, and nitrogen balance (P>0.05). No difference was observed in the performance of Santa Inês sheep consuming water with TD... Mostrar Tudo |
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Palavras-Chave: |
Consumo de água salina; Desempenho produtivo. |
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Thesagro: |
Água Salina; Estado Nutricional; Nutrição Animal; Ovino. |
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Thesaurus NAL: |
Ruminant nutrition; Saline water; Sheep. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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Marc: |
LEADER 02888naa a2200349 a 4500
001 2130136
005 2022-10-10
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1071/AN19224$2DOI
100 1 $aALBUQUERQUE, I. R. R. de
245 $aSaline water intake effects performance, digestibility, nitrogen and water balance of feedlot lambs.$h[electronic resource]
260 $c2020
520 $aWater availability is often a limiting factor for herds in the arid and semi-arid regions worldwide. Aim: This study aimed to evaluate the productive performance and nutritional status of Santa Inês sheep receiving water containing different salinity levels. Methods: Thirty-two intact Santa Inês lambs, with an average body weight of 21.76±1.25 kg, were distributed in a completely randomized design with four treatments (total dissolved solids (TSD) levels in the drinking water: 640, 3200, 5760 and 8320 mg/L TDS) and eight animals per treatment. Intake, apparent nutrient digestibility, water balance, nitrogen balance, and performance of animals were evaluated. Key Results: TDS levels in water promoted an increasing linear effect for water intake offered via drinking trough (P = 0.01), total water intake (P = 0.02), total urine excretion (P = 0.02), total water excretion via urine (P = 0.01), water excretion via feces (P = 0.04), total water excretion (P = 0.01), absorbed water (P = 0.02), total nitrogen intake (P = 0.04), and water intake to total weight gain ratio (P = 0.01). No effect of increasing TDS levels was observed for dry matter and nutritional fractions intake, nutrient digestibility, water intake via food, total feces excretion, water balance, nitrogen excretion via feces, nitrogen excretion via urine, total nitrogen excretion, absorbed nitrogen, and nitrogen balance (P>0.05). No difference was observed in the performance of Santa Inês sheep consuming water with TDS levels up to 8320 mg/L, which presented a daily mean weight gain of 0.105 kg/day. Conclusions: Water with total dissolved solids levels up to 8320 mg/L can be used as drinking water of Santa Inês sheep. Implications: Therefore, the use of water from wells, which is usually saline, may be an alternative to supplying small ruminants.
650 $aRuminant nutrition
650 $aSaline water
650 $aSheep
650 $aÁgua Salina
650 $aEstado Nutricional
650 $aNutrição Animal
650 $aOvino
653 $aConsumo de água salina
653 $aDesempenho produtivo
700 1 $aARAUJO, G. G. L. de
700 1 $aVOLTOLINI, T. V.
700 1 $aMOURA, J. H. de A.
700 1 $aCOSTA, R. G.
700 1 $aGOIS, G. C.
700 1 $aCOSTA, S. A. P.
700 1 $aCAMPOS, F. S.
700 1 $aQUEIROZ, M. A. A.
700 1 $aSANTOS, N. M. de S. S.
773 $tAnimal Production Science$gv. 60, n. 13, 1591-1597, 2020.
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