Use of growing degree indicator for developing adaptive responses: A case study of cotton in Florida

Significant variabilities in planting and harvesting dates of crops have been observed throughout Florida in recent decades, indicating a change in their phenology. This study innovatively uses an agroecosystem indicator, growing degree days (GDD), to understand the change in cotton crop phenology t...

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Autores principales:	Anjali Sharma, R. Deepa, Sriramana Sankar, Mikela Pryor, Briyana Stewart, Elijah Johnson, Aavudai Anandhi
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Climate variability and change Temperature change Phenological stages Adaptation and mitigation strategies Driver-Pressure-State-Impact-Responses (DPSIR) framework Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/0c35ac23602f46688edb3adbdb8137f4
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spelling	oai:doaj.org-article:0c35ac23602f46688edb3adbdb8137f42021-12-01T04:45:00ZUse of growing degree indicator for developing adaptive responses: A case study of cotton in Florida1470-160X10.1016/j.ecolind.2021.107383https://doaj.org/article/0c35ac23602f46688edb3adbdb8137f42021-05-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21000480https://doaj.org/toc/1470-160XSignificant variabilities in planting and harvesting dates of crops have been observed throughout Florida in recent decades, indicating a change in their phenology. This study innovatively uses an agroecosystem indicator, growing degree days (GDD), to understand the change in cotton crop phenology throughout the region and develop adaptation strategies using the Driver‐Pressure‐State‐Impact‐Response (DPSIR) framework. GDD is the amount of heat absorbed by the growing stages of cotton. It is computed from temperature simulations obtained from the 21 models participating in the Coupled Model Inter-comparison Project Phase 5 (CMIP5) for the historical (1950–2005) and future scenarios (Representative concentration pathway (RCP) 8.5, 2006–2100) at a spatial resolution of 0.125°x0.125°. The future projections from the 21 models show an increase in surface temperature ranging from 3.5 °C to 5.5 °C. Additionally, the variability in dates for the different phenological stages shows an early occurrence of the simulation’s growth stages. Historically, the minimum and maximum ranges of trend shift towards the funnel’s negative side in the RCP 8.5 scenarios. The trends are estimated for two time-periods during historical (1950–1975 and 1976–2005) and future (2006–2050 and 2015–2100) periods of time. They ranged from −3.5 to 3.4 days per decade and −3.6 to 0 (no change) days per decade, respectively, among the six stages namely: emergence stage, the appearance of the first square, the appearance of the first flower, peak blooming, first open boll, and defoliation. Warming accelerated plant growth and shortened the growing period, which is translated to develop adaptation strategies for a climate-resilient crop production system, using casual chain/loops and the DPSIR framework. Identifying the multiple adaptation strategies for levels of adaptation and degree of climate change and variability can be used by different stakeholders and policymakers as a guide for making decisions to adapt cotton to climate change better. Although this methodology is applied to the cotton crop in Florida, it can be used for other crops and regions of the world.Anjali SharmaR. DeepaSriramana SankarMikela PryorBriyana StewartElijah JohnsonAavudai AnandhiElsevierarticleClimate variability and changeTemperature changePhenological stagesAdaptation and mitigation strategiesDriver-Pressure-State-Impact-Responses (DPSIR) frameworkEcologyQH540-549.5ENEcological Indicators, Vol 124, Iss , Pp 107383- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Climate variability and change Temperature change Phenological stages Adaptation and mitigation strategies Driver-Pressure-State-Impact-Responses (DPSIR) framework Ecology QH540-549.5
spellingShingle	Climate variability and change Temperature change Phenological stages Adaptation and mitigation strategies Driver-Pressure-State-Impact-Responses (DPSIR) framework Ecology QH540-549.5 Anjali Sharma R. Deepa Sriramana Sankar Mikela Pryor Briyana Stewart Elijah Johnson Aavudai Anandhi Use of growing degree indicator for developing adaptive responses: A case study of cotton in Florida
description	Significant variabilities in planting and harvesting dates of crops have been observed throughout Florida in recent decades, indicating a change in their phenology. This study innovatively uses an agroecosystem indicator, growing degree days (GDD), to understand the change in cotton crop phenology throughout the region and develop adaptation strategies using the Driver‐Pressure‐State‐Impact‐Response (DPSIR) framework. GDD is the amount of heat absorbed by the growing stages of cotton. It is computed from temperature simulations obtained from the 21 models participating in the Coupled Model Inter-comparison Project Phase 5 (CMIP5) for the historical (1950–2005) and future scenarios (Representative concentration pathway (RCP) 8.5, 2006–2100) at a spatial resolution of 0.125°x0.125°. The future projections from the 21 models show an increase in surface temperature ranging from 3.5 °C to 5.5 °C. Additionally, the variability in dates for the different phenological stages shows an early occurrence of the simulation’s growth stages. Historically, the minimum and maximum ranges of trend shift towards the funnel’s negative side in the RCP 8.5 scenarios. The trends are estimated for two time-periods during historical (1950–1975 and 1976–2005) and future (2006–2050 and 2015–2100) periods of time. They ranged from −3.5 to 3.4 days per decade and −3.6 to 0 (no change) days per decade, respectively, among the six stages namely: emergence stage, the appearance of the first square, the appearance of the first flower, peak blooming, first open boll, and defoliation. Warming accelerated plant growth and shortened the growing period, which is translated to develop adaptation strategies for a climate-resilient crop production system, using casual chain/loops and the DPSIR framework. Identifying the multiple adaptation strategies for levels of adaptation and degree of climate change and variability can be used by different stakeholders and policymakers as a guide for making decisions to adapt cotton to climate change better. Although this methodology is applied to the cotton crop in Florida, it can be used for other crops and regions of the world.
format	article
author	Anjali Sharma R. Deepa Sriramana Sankar Mikela Pryor Briyana Stewart Elijah Johnson Aavudai Anandhi
author_facet	Anjali Sharma R. Deepa Sriramana Sankar Mikela Pryor Briyana Stewart Elijah Johnson Aavudai Anandhi
author_sort	Anjali Sharma
title	Use of growing degree indicator for developing adaptive responses: A case study of cotton in Florida
title_short	Use of growing degree indicator for developing adaptive responses: A case study of cotton in Florida
title_full	Use of growing degree indicator for developing adaptive responses: A case study of cotton in Florida
title_fullStr	Use of growing degree indicator for developing adaptive responses: A case study of cotton in Florida
title_full_unstemmed	Use of growing degree indicator for developing adaptive responses: A case study of cotton in Florida
title_sort	use of growing degree indicator for developing adaptive responses: a case study of cotton in florida
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/0c35ac23602f46688edb3adbdb8137f4
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Use of growing degree indicator for developing adaptive responses: A case study of cotton in Florida

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