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 | Acesso ao texto completo restrito à biblioteca da Embrapa Meio Ambiente. Para informações adicionais entre em contato com cnpma.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
28/12/2021 |
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Data da última atualização: |
15/03/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
FLACK-PRAIN, S.; SHI, L.; ZHU, P.; ROCHA, H. R. da; CABRAL, O. M. R.; HU, S.; WILLIAMS, M. |
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Afiliação: |
SOPHIE FLACK-PRAIN, University of Edinburgh; LIANGSHENG SHI, Wuhan University; PENGHUI ZHU, Wuhan University; HUMBERTO RIBEIRO DA ROCHA, IAG-USP; OSVALDO MACHADO RODRIGUES CABRAL, CNPMA; SHUN HU, Wuhan University; MATHEW WILLIAMS, University of Edinburgh. |
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Título: |
The impact of climate change and climate extremes on sugarcane production. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Global Change Biology Bioenergy, v. 13. n. 3, p. 408-424, 2021. |
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DOI: |
https://doi.org/10.1111/gcbb.12797 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Sugarcane production supports the livelihoods of millions of small-scale farmers in developing countries, and the bioenergy needs of millions of consumers. Yet, future sugarcane yields remain uncertain due to differences in climate projections, and because the sensitivity of sugarcane ecophysiology to individual climate drivers (i.e. temperature, precipitation, shortwave radiation, VPD and CO2) and their interactions is largely unresolved. Here we ask: how sensitive is sugarcane yield to future climate change, including climate extremes, and what are its key climate drivers? We combine the Soil-Plant-Atmosphere model with detailed time-series measurements from experimental plots in Guangxi, China, and Sao Paulo State, Brazil. We first calibrated and validated modelled carbon and water cycling against field flux and biometric data. Second, we simulated sugarcane growth under the historical climate (1980-2018), and six future climate projections (2015-2100). We computed the 'yield-effect' of each climate driver by generating synthetic climate forcings in which the driver time series was alternated to that of the historical median. In Guangxi, median yield and yield lows (i.e. lower decile) were relatively insensitive to forecast climate change. In Sao Paulo, median yield and yield lows decreased under all future climates projections (x over bar = -4% and -12% respectively). At Guangxi, where moisture stress was low, radiation was the principal driver of yield variability (yield-effect x over bar = -1.2%). Conversely, high moisture stress at Sao Paulo raised yield sensitivity to temperature (yield-effect x over bar = -7.9%). In contrast, a number of other modelling studies report a positive effect of increased temperatures on sugarcane yield. We ascribe the disparity between model predictions to the representation of key phenological processes, including the link between leaf ageing and thermal time, and the role of ageing in driving leaf senescence. We highlight climate sensitivity of phenological processes as a key focus for future research efforts. MenosAbstract: Sugarcane production supports the livelihoods of millions of small-scale farmers in developing countries, and the bioenergy needs of millions of consumers. Yet, future sugarcane yields remain uncertain due to differences in climate projections, and because the sensitivity of sugarcane ecophysiology to individual climate drivers (i.e. temperature, precipitation, shortwave radiation, VPD and CO2) and their interactions is largely unresolved. Here we ask: how sensitive is sugarcane yield to future climate change, including climate extremes, and what are its key climate drivers? We combine the Soil-Plant-Atmosphere model with detailed time-series measurements from experimental plots in Guangxi, China, and Sao Paulo State, Brazil. We first calibrated and validated modelled carbon and water cycling against field flux and biometric data. Second, we simulated sugarcane growth under the historical climate (1980-2018), and six future climate projections (2015-2100). We computed the 'yield-effect' of each climate driver by generating synthetic climate forcings in which the driver time series was alternated to that of the historical median. In Guangxi, median yield and yield lows (i.e. lower decile) were relatively insensitive to forecast climate change. In Sao Paulo, median yield and yield lows decreased under all future climates projections (x over bar = -4% and -12% respectively). At Guangxi, where moisture stress was low, radiation was the principal driver of yield variabi... Mostrar Tudo |
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Palavras-Chave: |
Climate sensitivity. |
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Thesagro: |
Cana de Açúcar; Clima; Mudança Climática; Produtividade. |
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Thesaurus Nal: |
C4 plants; Climate change; Climate models; Crop yield; Sugarcane. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02999naa a2200325 a 4500
001 2138503
005 2022-03-15
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1111/gcbb.12797$2DOI
100 1 $aFLACK-PRAIN, S.
245 $aThe impact of climate change and climate extremes on sugarcane production.$h[electronic resource]
260 $c2021
520 $aAbstract: Sugarcane production supports the livelihoods of millions of small-scale farmers in developing countries, and the bioenergy needs of millions of consumers. Yet, future sugarcane yields remain uncertain due to differences in climate projections, and because the sensitivity of sugarcane ecophysiology to individual climate drivers (i.e. temperature, precipitation, shortwave radiation, VPD and CO2) and their interactions is largely unresolved. Here we ask: how sensitive is sugarcane yield to future climate change, including climate extremes, and what are its key climate drivers? We combine the Soil-Plant-Atmosphere model with detailed time-series measurements from experimental plots in Guangxi, China, and Sao Paulo State, Brazil. We first calibrated and validated modelled carbon and water cycling against field flux and biometric data. Second, we simulated sugarcane growth under the historical climate (1980-2018), and six future climate projections (2015-2100). We computed the 'yield-effect' of each climate driver by generating synthetic climate forcings in which the driver time series was alternated to that of the historical median. In Guangxi, median yield and yield lows (i.e. lower decile) were relatively insensitive to forecast climate change. In Sao Paulo, median yield and yield lows decreased under all future climates projections (x over bar = -4% and -12% respectively). At Guangxi, where moisture stress was low, radiation was the principal driver of yield variability (yield-effect x over bar = -1.2%). Conversely, high moisture stress at Sao Paulo raised yield sensitivity to temperature (yield-effect x over bar = -7.9%). In contrast, a number of other modelling studies report a positive effect of increased temperatures on sugarcane yield. We ascribe the disparity between model predictions to the representation of key phenological processes, including the link between leaf ageing and thermal time, and the role of ageing in driving leaf senescence. We highlight climate sensitivity of phenological processes as a key focus for future research efforts.
650 $aC4 plants
650 $aClimate change
650 $aClimate models
650 $aCrop yield
650 $aSugarcane
650 $aCana de Açúcar
650 $aClima
650 $aMudança Climática
650 $aProdutividade
653 $aClimate sensitivity
700 1 $aSHI, L.
700 1 $aZHU, P.
700 1 $aROCHA, H. R. da
700 1 $aCABRAL, O. M. R.
700 1 $aHU, S.
700 1 $aWILLIAMS, M.
773 $tGlobal Change Biology Bioenergy$gv. 13. n. 3, p. 408-424, 2021.
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