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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
28/07/2022 |
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Data da última atualização: |
03/02/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
ARENAS-CALLE, L. N.; HEINEMANN, A. B.; SILVA, M. A. S. da; SANTOS, A. B. dos; RAMIREZ-VILLEGAS, J.; WHITFIELD, S.; CHALLINOR, A. J. |
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Afiliação: |
LAURA N. ARENAS-CALLE, University of Leeds; ALEXANDRE BRYAN HEINEMANN, CNPAF; MELLISSA ANANIAS SOLER DA SILVA, CNPAF; ALBERTO BAETA DOS SANTOS, CNPAF; JULIAN RAMIREZ-VILLEGAS, Alliance of Biodiversity International and CIAT; STEPHEN WHITFIELD, University of Leeds, Leeds; ANDREW J. CHALLINOR, University of Leeds, Leeds. |
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Título: |
Rice management decisions using process-based models with climate-smart indicators. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Frontiers in Sustainable Food Systems, v. 6, article 873957, Jul. 2022. |
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ISSN: |
2571-581X |
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Idioma: |
Inglês |
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Conteúdo: |
Irrigation strategies are keys to fostering sustainable and climate-resilient rice production by increasing efficiency, building resilience and reducing Greenhouse Gas (GHG) emissions. These strategies are aligned with the Climate-Smart Agriculture (CSA) principles, which aim to maximize productivity whilst adapting to and mitigating climate change. Achieve such mitigation, adaptation, and productivity goals- to the extent possible- is described as climate smartness. Measuring climate smartness is challenging, with recent progress focusing on the use of agronomic indicators in a limited range of contexts. One way to broaden the ability to measure climate-smartness is to use modeling tools, expanding the scope of climate smartness assessments. Accordingly, and as a proof-of-concept, this study uses modeling tools with CSA indicators (i.e., Greenhouse Intensity and Water Productivity) to quantify the climate-smartness of irrigation management in rice and to assess sensitivity to climate. We focus on a field experiment that assessed four irrigation strategies in tropical conditions, Continuous Flooding (CF), Intermittent Irrigation (II), Intermittent Irrigation until Flowering (IIF), and Continuous soil saturation (CSS). The DNDC model was used to simulate rice yields, GHG emissions and water inputs. We used model outputs to calculate a previously developed Climate-Smartness Index (CSI) based on water productivity and greenhouse gas intensity, which score on a scale between - 1 (lack of climate-smartness) to 1 (high climate smartness) the climate-smartness of irrigation strategies. The CSS exhibited the highest simulation-based CSI, and CF showed the lowest. A sensitivity analysis served to explore the impacts of climate on CSI. While higher temperatures reduced CSI, rainfall mostly showed no signal. The climate smartness decreasing in warmer temperatures was associated with increased GHG emissions and, to some extent, a reduction in Water Productivity (WP). Overall, CSI varied with the climate-management interaction, demonstrating that climate variability can influence the performance of CSA practices. We conclude that combining models with climate-smart indicators can broaden he CSA-based evidence and provide reproducible research findings. The methodological approach used in this study can be useful to fill gaps in observational evidence of climate-smartness and project the impact of future climates in regions where calibrated crop models perform well. MenosIrrigation strategies are keys to fostering sustainable and climate-resilient rice production by increasing efficiency, building resilience and reducing Greenhouse Gas (GHG) emissions. These strategies are aligned with the Climate-Smart Agriculture (CSA) principles, which aim to maximize productivity whilst adapting to and mitigating climate change. Achieve such mitigation, adaptation, and productivity goals- to the extent possible- is described as climate smartness. Measuring climate smartness is challenging, with recent progress focusing on the use of agronomic indicators in a limited range of contexts. One way to broaden the ability to measure climate-smartness is to use modeling tools, expanding the scope of climate smartness assessments. Accordingly, and as a proof-of-concept, this study uses modeling tools with CSA indicators (i.e., Greenhouse Intensity and Water Productivity) to quantify the climate-smartness of irrigation management in rice and to assess sensitivity to climate. We focus on a field experiment that assessed four irrigation strategies in tropical conditions, Continuous Flooding (CF), Intermittent Irrigation (II), Intermittent Irrigation until Flowering (IIF), and Continuous soil saturation (CSS). The DNDC model was used to simulate rice yields, GHG emissions and water inputs. We used model outputs to calculate a previously developed Climate-Smartness Index (CSI) based on water productivity and greenhouse gas intensity, which score on a scale between - 1... Mostrar Tudo |
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Palavras-Chave: |
Climate-smart agriculture; Climate-smart indicators; Climate-smartness; DNDC; Water productivity. |
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Thesagro: |
Arroz; Clima. |
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Thesaurus NAL: |
Climate models; Crop models; Greenhouse gas emissions. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/246232/1/fsfs-2022.pdf
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Marc: |
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022 $a2571-581X
100 1 $aARENAS-CALLE, L. N.
245 $aRice management decisions using process-based models with climate-smart indicators.$h[electronic resource]
260 $c2022
520 $aIrrigation strategies are keys to fostering sustainable and climate-resilient rice production by increasing efficiency, building resilience and reducing Greenhouse Gas (GHG) emissions. These strategies are aligned with the Climate-Smart Agriculture (CSA) principles, which aim to maximize productivity whilst adapting to and mitigating climate change. Achieve such mitigation, adaptation, and productivity goals- to the extent possible- is described as climate smartness. Measuring climate smartness is challenging, with recent progress focusing on the use of agronomic indicators in a limited range of contexts. One way to broaden the ability to measure climate-smartness is to use modeling tools, expanding the scope of climate smartness assessments. Accordingly, and as a proof-of-concept, this study uses modeling tools with CSA indicators (i.e., Greenhouse Intensity and Water Productivity) to quantify the climate-smartness of irrigation management in rice and to assess sensitivity to climate. We focus on a field experiment that assessed four irrigation strategies in tropical conditions, Continuous Flooding (CF), Intermittent Irrigation (II), Intermittent Irrigation until Flowering (IIF), and Continuous soil saturation (CSS). The DNDC model was used to simulate rice yields, GHG emissions and water inputs. We used model outputs to calculate a previously developed Climate-Smartness Index (CSI) based on water productivity and greenhouse gas intensity, which score on a scale between - 1 (lack of climate-smartness) to 1 (high climate smartness) the climate-smartness of irrigation strategies. The CSS exhibited the highest simulation-based CSI, and CF showed the lowest. A sensitivity analysis served to explore the impacts of climate on CSI. While higher temperatures reduced CSI, rainfall mostly showed no signal. The climate smartness decreasing in warmer temperatures was associated with increased GHG emissions and, to some extent, a reduction in Water Productivity (WP). Overall, CSI varied with the climate-management interaction, demonstrating that climate variability can influence the performance of CSA practices. We conclude that combining models with climate-smart indicators can broaden he CSA-based evidence and provide reproducible research findings. The methodological approach used in this study can be useful to fill gaps in observational evidence of climate-smartness and project the impact of future climates in regions where calibrated crop models perform well.
650 $aClimate models
650 $aCrop models
650 $aGreenhouse gas emissions
650 $aArroz
650 $aClima
653 $aClimate-smart agriculture
653 $aClimate-smart indicators
653 $aClimate-smartness
653 $aDNDC
653 $aWater productivity
700 1 $aHEINEMANN, A. B.
700 1 $aSILVA, M. A. S. da
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700 1 $aRAMIREZ-VILLEGAS, J.
700 1 $aWHITFIELD, S.
700 1 $aCHALLINOR, A. J.
773 $tFrontiers in Sustainable Food Systems$gv. 6, article 873957, Jul. 2022.
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Embrapa Arroz e Feijão (CNPAF) |
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