Dynamics of CO2 fluxes and controlling environmental factors in sugarcane (C4)–wheat (C3) ecosystem of dry sub-humid region in India

Patel, N. R.; Pokhariyal, Shweta; Chauhan, Prakash; Dadhwal, V. K.

doi:10.1007/s00484-021-02088-y

Cited by 21 publications

(20 citation statements)

References 72 publications

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“…The average growing season NEP was in the 5-6 gC/m 2 /d 1 . Patel et al (2021) also reported a negative correlation of NEP with temperature due to higher respiratory losses at higher temperatures. The site-scale studies can only talk about the intra-annual variation of carbon fluxes.…”

Section: Introductionmentioning

confidence: 92%

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Carbon fluxes in spring wheat agroecosystems in India

Reddy

Gahlot

Roy

et al. 2022

Preprint

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Carbon fluxes from agroecosystems contribute to the variability in the carbon cycle and atmospheric [CO 2 ]. In this study, we used the Integrated Science Assessment Model (ISAM) equipped with a spring wheat module to study carbon fluxes and their variability in spring wheat agroecosystems of India. First, ISAM was run in the site-scale mode to simulate the Gross Primary Production (GPP), Total Ecosystem Respiration (TER), and Net Ecosystem Production (NEP) over an experimental spring wheat site in the north India. Comparison with flux-tower observations showed that the spring wheat module in ISAM can match the observed flux patterns better than generic crop models. Next, regional-scale runs were conducted to simulate carbon fluxes across the country for the 1980-2016 period. Results showed that the fluxes vary widely, primarily due to variations in planting dates across regions. Fluxes peak earlier in the eastern and central parts of the country, where the crops are planted earlier. All fluxes show statistically significant increasing trends (p<.01) during the study period. The GPP, Net Primary Production (NPP), Autotrophic respiration (Ra), and Heterotrophic Respiration (Rh) increased at 1.272, 0.945, 0.579, 0.328, and 0.366 TgC/yr 2 , respectively. Numerical experiments were conducted to study how natural forcings like changing temperature and [CO 2 ] and agricultural management practices like nitrogen fertilization and water availability could contribute to the increasing trends. The experiments revealed that increasing [CO2], nitrogen fertilization, and water added through irrigation contributed to the increase of carbon fluxes, with nitrogen fertilization having the strongest effect.

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Section: Introductionmentioning

confidence: 92%

“…A few studies have looked at carbon fluxes at the site scale over spring wheat agroecosystems in northern India. These include Patel et al (2011) for the 2008-2009 growing season, Patel et al (2021) for the 2014-15 growing season, and Kumar et al (2021) for the 2013-14 growing season.…”

Section: Introductionmentioning

confidence: 99%

Carbon fluxes in spring wheat agroecosystems in India

Reddy

Gahlot

Roy

et al. 2022

Preprint

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“…The country-scale SCON run described in Section 2.5.2 was designed to provide a quantitative understanding of the spatiotemporal variability of carbon fluxes across the wheat-growing regions of India. Before evaluating the regional scale ISAM runs, we decided to compare the simulated NEP from SCON run with the carbon flux data from Patel et al (2011Patel et al ( , 2021.…”

Section: Spatio-temporal Variability Of Carbon Fluxes From Spring Whe...mentioning

confidence: 99%

“…A few studies have examined carbon fluxes at the site scale in northern Indian spring wheat agroecosystems. Patel et al (2011) for the 2008-2009 growing season, Patel et al (2021) for 2014-15 growing season, and Kumar et al (2021) for the 2013-14 growing season are examples of these studies. All studies found the typical U-shaped curve in the NEP at diurnal and seasonal scales.…”

Section: Introductionmentioning

confidence: 99%

Carbon fluxes in spring wheat agroecosystems in India

Reddy

Gahlot

Roy

et al. 2023

Preprint

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Abstract. Carbon fluxes from agroecosystems contribute to the variability of the carbon cycle and atmospheric [CO2]. This study used the Integrated Science Assessment Model (ISAM) to investigate carbon fluxes and their variability in Indian spring wheat agroecosystems. First, ISAM was run in site-scale mode to validate GPP, TER, and NEP over an experimental spring wheat site in north India. When compared to flux-tower observations, the spring wheat module in ISAM outperformed the generic crop model. Following that, regional-scale runs were performed to simulate carbon fluxes across the country from 1980 to 2016. The results revealed that fluxes vary significantly across regions, owing primarily to differences in planting dates. Fluxes peak earlier in the country's eastern and central regions, where crops are planted earlier. During the study period, all fluxes show statistically significant increasing trends (p.01). GPP, NPP, Autotrophic Respiration (Ra), and Heterotrophic Respiration (Rh) increased at 1.272, 0.945, 0.579, 0.328, and 0.366 TgC/yr2, respectively. Numerical experiments were conducted to investigate how natural forcings such as changing temperature and [CO2] levels and agricultural management practices such as nitrogen fertilization and water availability could contribute to the rising trends. The experiments revealed that increasing [CO2], nitrogen fertilization, and irrigation water contributed to increased carbon fluxes, with nitrogen fertilization having the most significant effect.

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“…The derived datasets show that the calculated GPP is capable to capture the seasonal variations in reference to GPP EC (Flux tower-based measurements). The open-source EC data (such as FLUXNET) primarily focused on forest/ natural vegetation, for the present study, two studies were taken as a reference that is used for dry season rice 48 and winter wheat 49 . The observed GPP EC in rice and wheat were aggregated and synced with MODIS interval as in this study and found to be correlated with calculated GPPCY-IN (Fig.…”

Section: Usage Notesmentioning

confidence: 99%

A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India

et al. 2022

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The present study describes a new dataset that estimates seasonally integrated agricultural gross primary productivity (GPP). Several models are being used to estimate GPP using remote sensing (RS) for regional and global studies. Using biophysical and climatic variables (MODIS, SBSS, ECWMF reanalysis etc.) and validated by crop statistics, the present study provides a new dataset of agricultural GPP for monsoon and winter seasons in India for two decades (2001–2019). This dataset (GPPCY-IN) is based on the light use efficiency (LUE) principle and applied a dynamic LUE for each year and season to capture the seasonal variations more efficiently. An additional dataset (NGPPCY-IN) is also derived from crop production statistics and RS GPP to translate district-level statistics at the pixel level. Along with validation with crop statistics, the derived dataset was also compared with in situ GPP estimations. This dataset will be useful for many applications and has been created for estimating integrated yield loss by taking GPP as a proxy compared to resource and time-consuming field-based methods for crop insurance.

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Dynamics of CO2 fluxes and controlling environmental factors in sugarcane (C4)–wheat (C3) ecosystem of dry sub-humid region in India

Cited by 21 publications

References 72 publications

Carbon fluxes in spring wheat agroecosystems in India

Carbon fluxes in spring wheat agroecosystems in India

Carbon fluxes in spring wheat agroecosystems in India

A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India

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