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  • Data  (4)
  • 2020-2024  (2)
  • 2015-2019  (2)
  • 1935-1939
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  • 1
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Biswas, Haimanti; Shaik, Aziz Ur Rahman; Bandyopadhyay, Debasmita; Chowdhury, Neha (2017): CO 2 induced growth response in a diatom dominated phytoplankton community from SW Bay of Bengal coastal water. Estuarine, Coastal and Shelf Science, 198, 29-42, https://doi.org/10.1016/j.ecss.2017.07.022
    Publication Date: 2024-03-15
    Description: The ongoing increase in surface seawater CO2 level could potentially impact phytoplankton primary production in coastal waters; however, CO2 sensitivity studies on tropical coastal phytoplankton assemblages are rare. The present study investigated the interactive impacts of variable CO2 level, light and zinc addition on the diatom dominated phytoplankton assemblages from the western coastal Bay of Bengal. Increased CO2supply enhanced particulate organic matter (POC) production; a concomitant depletion in δ13CPOM values at elevated CO2 suggested increased CO2 diffusive influx inside the cell. Trace amount of Zn added under low CO2 level accelerated growth probably by accelerating Zn-Carbonic Anhydrase activity which helps in converting bicarbonate ion to CO2. Almost identical values of delta 13CPOM in the low CO2 treated cells grown with and without Zn indicated a low discrimination between 13C and 12C probably due to bicarbonate uptake. These evidences collectively indicated the existence of the carbon concentration mechanisms (CCMs) at low CO2. A minimum growth rate was observed at low CO2 and light limited condition indicating light dependence of CCMs activity. Upon the increase of light and CO2 level, growth response was maximum. The cells grown in the low CO2 levels showed higher light stress (higher values of both diatoxanthin index and the ratio of photo-protective to light-harvesting pigments) that was alleviated by both increasing CO2 supply and Zn addition (probably by efficient light energy utilization in presence of adequate CO2). This is likely that the diatom dominated phytoplankton communities benefited from the increasing CO2 supply and thus may enhance primary production in response to any further increase in coastal water CO2 levels and can have large biogeochemical consequences in the study area.
    Keywords: Abundance; Alkalinity, total; Alkalinity, total, standard deviation; Aragonite saturation state; Bicarbonate ion; Biomass/Abundance/Elemental composition; Bottles or small containers/Aquaria (〈20 L); Calcite saturation state; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard deviation; Carbon, organic, particulate; Carbon/Nitrogen ratio; Carbonate ion; Carbonate system computation flag; Carbon dioxide; Change; Change, standard deviation; Chlorophyll a; Chlorophyll a, standard deviation; Chlorophyll a/particulate organic carbon ratio; Coast and continental shelf; Community composition and diversity; Diatoxanthin index; Diatoxanthin index, standard deviation; Entire community; EXP; Experiment; Experiment duration; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Growth/Morphology; Growth rate; Indian Ocean; Irradiance; Irradiance, standard deviation; Laboratory experiment; Light; Local Time; Micro-nutrients; Nitrogen, inorganic, dissolved; Nitrogen, inorganic, dissolved, standard deviation; Nitrogen, organic, particulate; OA-ICC; Ocean Acidification International Coordination Centre; Partial pressure of carbon dioxide, standard deviation; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Pelagos; pH; pH, standard deviation; Phosphate; Phosphate, standard deviation; Pigments, light harvesting; Pigments, light harvesting, standard deviation; Pigments, photo-protective; Pigments, photo-protective, standard deviation; Pigments, photo-protective/light harvesting ratio; Pigments, photo-protective/light harvesting ratio, standard deviation; Pigments, total; Pigments, total, standard deviation; Primary production/Photosynthesis; Salinity; Silicate; Silicate, standard deviation; Species; Temperature, water; Time point, descriptive; Treatment; Tropical; Type; Visakhapatnam_coast; δ13C, particulate organic carbon
    Type: Dataset
    Format: text/tab-separated-values, 3913 data points
    Location Call Number Expected Availability
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  • 2
    Publication Date: 2024-04-20
    Description: The site scale observations of the Indian crops are very rarely available for public access. Students at all agricultural institutes across India carry out experiments on Indian crops as part of their curriculum and report the results in a thesis. The thesis from such institutes is uploaded to the Krishikosh repository (https://krishikosh.egranth.ac.in). To fill the gap of absence of crop data on Indian crops, we started to look at this repository and collect data. We have collected and harmonized crop phenology and agricultural management data of major Indian crops such as spring wheat and rice from these theses. This dataset is the first of its kind, providing and combining weather data from several crop-growing regions. We are combining data from 83 theses related to rice and data from 64 theses related to spring wheat. These data are from 48 individual sites, a few of which have multiple growing seasons.
    Keywords: Agriculture; Akola; Anand; Author(s); Bapatla; Bengaluru; Bhubaneswar; Binary Object; Brahmavara; Chatha; Coimbatore; Compilation; Cooch Behar; Country; crop yield; Dhadesugur; Dharwad; Event label; Faizabad; Field measurement; Gwalior; Identification; IND_RI_AKO_1977; IND_RI_AKO_1978; IND_RI_AKO_1979; IND_RI_BAP_2002; IND_RI_BEN_2016; IND_RI_BHU_2014; IND_RI_BRA_2017; IND_RI_COM_1985; IND_RI_COM_1986; IND_RI_DEL_1966; IND_RI_DEL_1967; IND_RI_DHA_2017; IND_RI_FAZ_2000; IND_RI_FAZ_2001; IND_RI_HYD_2010; IND_RI_JAB_1987; IND_RI_JAB_2009; IND_RI_JAB_2010; IND_RI_JAB_2011; IND_RI_JAB_2019; IND_RI_JOR_1997; IND_RI_JOR_1998; IND_RI_JOR_1999; IND_RI_KAS_2002; IND_RI_KAU_2008; IND_RI_KAU_2015; IND_RI_KUT_2013; IND_RI_KUT_2015; IND_RI_KUT_2016; IND_RI_KUT_2018; IND_RI_KUT_2019; IND_RI_MAD_1984; IND_RI_MAD_1985; IND_RI_MAN_2010; IND_RI_MEE_2019; IND_RI_PAL_1989; IND_RI_PAL_1990; IND_RI_PAL_1991; IND_RI_PAL_1992; IND_RI_PAL_1993; IND_RI_PAL_1997; IND_RI_PAL_1998; IND_RI_PAL_1999; IND_RI_PAL_2000; IND_RI_PAL_2001; IND_RI_PAL_2020; IND_RI_PAN_2005; IND_RI_PAN_2006; IND_RI_PAN_2010; IND_RI_PAN_2011; IND_RI_PAN_2012; IND_RI_PAN_2016; IND_RI_PAN_2017; IND_RI_PAN_2019; IND_RI_PON_2021; IND_RI_RAI_1973; IND_RI_RAI_1977; IND_RI_RAI_1984; IND_RI_RAI_1985; IND_RI_RAI_2009; IND_RI_RAI_2012; IND_RI_RAI_2015; IND_RI_RAI_2016; IND_RI_RAI_2018; IND_RI_RAI_2019; IND_RI_RAJ_2001; IND_RI_RAJ_2002; IND_RI_RAN_2015; IND_RI_RAN_2019; IND_RI_RED_2000; IND_RI_RED_2001; IND_RI_REW_2007; IND_RI_REW_2009; IND_RI_SRI_2012; IND_RI_TIR_1989; IND_RI_TIR_1994; IND_RI_TIR_1995; IND_RI_TIR_1996; IND_RI_VAD_2016; IND_RI_VAD_2017; IND_RI_VAD_2018; IND_RI_VAR_2016; IND_RI_VEL_1985; IND_SW_ANA_1964; IND_SW_ANA_1965; IND_SW_ANA_1968; IND_SW_ANA_1969; IND_SW_CHA_1997; IND_SW_CHA_1998; IND_SW_COB_1997; IND_SW_COB_1998; IND_SW_COB_2000; IND_SW_COB_2001; IND_SW_DHA_1998; IND_SW_DHA_1999; IND_SW_DHA_2000; IND_SW_DHA_2002; IND_SW_DHA_2016; IND_SW_FAZ_2003; IND_SW_FAZ_2004; IND_SW_FAZ_2020; IND_SW_GWA_2011; IND_SW_INR_1986; IND_SW_JAB_2013; IND_SW_JAB_2014; IND_SW_JAI_2013; IND_SW_JAI_2014; IND_SW_JOB_1970; IND_SW_JOB_1977; IND_SW_JOB_1983; IND_SW_JOB_1984; IND_SW_JOB_1998; IND_SW_JOB_1999; IND_SW_JOB_2000; IND_SW_JOB_2002; IND_SW_JOB_2015; IND_SW_JOB_2016; IND_SW_LUD_2011; IND_SW_LUD_2012; IND_SW_MEE_2011; IND_SW_MEE_2012; IND_SW_MEE_2013; IND_SW_NAD_1972; IND_SW_NAD_1973; IND_SW_NAD_1990; IND_SW_NAD_1991; IND_SW_NAD_1999; IND_SW_NAD_2000; IND_SW_NAD_2001; IND_SW_NAD_2002; IND_SW_NAD_2005; IND_SW_NAD_2006; IND_SW_NAD_2007; IND_SW_NAD_2013; IND_SW_NAD_2014; IND_SW_PAN_2007; IND_SW_PAN_2008; IND_SW_PAR_2001; IND_SW_PAR_2005; IND_SW_PAR_2009; IND_SW_PAR_2019; IND_SW_RAN_1995; IND_SW_RAN_1996; IND_SW_RAN_1997; IND_SW_RAN_2013; IND_SW_RAN_2014; IND_SW_RAN_2017; Indore; Jabalpur; Jaipur; Jobner; Jorhat; Journal/report publisher; Journal/report title; Kanke, Ranchi; Kaul; Kuthulia; Literature survey; Ludhiana; Madurai; Mandya; Meerut; Nadia; New delhi; Palampur; Pantnagar; Parbhani; phenology; Poonch; Publication type; Raipur; Rajavanthi; Rajendranagar; Ranchi; Reddipalli; Rewa; rice; Spring wheat; Srikakulam; Srinagar; Tirupati; Uniform resource locator/link to source data file; Vadgaon Maval; Varanasi; Vellayani; Year of publication
    Type: Dataset
    Format: text/tab-separated-values, 1470 data points
    Location Call Number Expected Availability
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  • 3
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Biswas, Haimanti; Jie, Jin; Li, Ying; Zhang, Guosen; Zhu, Zhuoyi; Wu, Ying; Zhang, Guoling; Li, Yanwei; Liu, Sumei; Zhang, Jing (2015): Response of a natural phytoplankton community from the Qingdao coast (Yellow Sea, China) to variable CO2 levels over a short-term incubation experiment. Current Science, 108(10), 1901-1909, https://www.currentscience.ac.in/Volumes/108/10/1901.pdf
    Publication Date: 2024-03-22
    Description: Since marine phytoplankton play a vital role in stabilizing earth's climate by removing significant amount of atmospheric CO2, their responses to increasing CO2 levels are indeed vital to address. The responses of a natural phytoplankton community from the Qingdao coast (NW Yellow Sea, China) was studied under different CO2 levels in microcosms. HPLC pigment analysis revealed the presence of diatoms as a dominant microalgal group; however, members of chlorophytes, prasinophytes, cryptophytes and cyanophytes were also present. delta 13CPOM values indicated that the phytoplankton community probably utilized bicarbonate ions as dissolved inorganic carbon source through a carbon concentration mechanism (CCM) under low CO2 levels, and diffusive CO2 uptake increased upon the increase of external CO2 levels. Although, considerable increase in phytoplankton biomass was noticed in all CO2 treatments, CO2-induced effects were absent. Higher net nitrogen uptake under low CO2 levels could be related to the synthesis of CCM components. Flow cytometry analysis showed slight reduction in the abundance of Synechococcus and pico-eukaryotes under the high CO2 treatments. Diatoms did not show any negative impact in response to increasing CO2 levels; however, chlorophytes revealed a reverse tend. Heterotrophic bacterial count enhanced with increasing CO2 levels and indicated higher abundance of labile organic carbon. Thus, the present study indicates that any change in dissolved CO2 concentrations in this area may affect phytoplankton physiology and community structure and needs further long-term study.
    Keywords: Alkalinity, total; Aragonite saturation state; Bacteria, heterotrophic; Bacteria, heterotrophic, standard deviation; Bicarbonate ion; Biomass/Abundance/Elemental composition; Bottles or small containers/Aquaria (〈20 L); Calcite saturation state; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, organic, dissolved; Carbon, organic, dissolved, standard deviation; Carbon, organic, dissolved + particulate, net production; Carbon, organic, dissolved + particulate, net production, standard deviation; Carbon, organic, particulate; Carbon, organic, particulate, standard deviation; Carbon/Nitrogen ratio; Carbon/Nitrogen ratio, standard deviation; Carbon/Phosphorus ratio; Carbon/Phosphorus ratio, standard deviation; Carbonate ion; Carbonate system computation flag; Carbon dioxide; Chlorophyll a; Chlorophyll a, standard deviation; Chlorophyll a/Chlorophyll b ratio; Chlorophyll a/particulate organic carbon ratio; Chlorophyll a/particulate organic carbon ratio, standard deviation; Coast and continental shelf; Community composition and diversity; Consumption of carbon, inorganic, dissolved; Consumption of carbon, inorganic, dissolved, standard deviation; Diatoxanthin index; Diatoxanthin index, standard deviation; Dissolved inorganic nitrogen, uptake; Dissolved inorganic nitrogen, uptake, standard deviation; Entire community; Fucoxanthin/chlorophyll a ratio; Fucoxanthin/chlorophyll a ratio, standard devitation; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Laboratory experiment; Lutein/chlorophyll a ratio; Lutein/chlorophyll a ratio, standard deviation; Neoxanthin/chlorophyll a ratio; Neoxanthin/chlorophyll a ratio, standard deviation; Nitrogen, inorganic, dissolved; Nitrogen, inorganic, dissolved, standard deviation; Nitrogen, inorganic, dissolved/Phosphorus, inorganic, dissolved ratio; Nitrogen, inorganic, dissolved/Phosphorus, inorganic, dissolved ratio, standard deviation; Nitrogen, organic, particulate; Nitrogen, organic, particulate, standard deviation; Nitrogen/Phosphorus ratio; Nitrogen/Phosphorus ratio, standard deviation; Nitrogen/Phosphorus uptake ratio; Nitrogen/Phosphorus uptake ratio, standard deviation; Nitrogen/Silicon ratio; Nitrogen/Silicon ratio, standard deviation; North Pacific; OA-ICC; Ocean Acidification International Coordination Centre; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Particulate organic phosphorus, standard deviation; Pelagos; pH; Phosphorus, inorganic, dissolved; Phosphorus, inorganic, dissolved, standard deviation; Phosphorus, organic, particulate; Phosphorus uptake; Phosphorus uptake, standard deviation; Picoeukaryotes; Picoeukaryotes, standard deviation; Primary production/Photosynthesis; Salinity; Silicate; Silicate, standard deviation; Silicate uptake; Silicon/Nitrogen uptake ratio; Silicon/Nitrogen uptake ratio, standard deviation; Silicon/phosphorus uptake ratio; Silicon/phosphorus uptake ratio, standard deviation; Silicon uptake, standard deviation; Synechococcus; Synechococcus spp., standard deviation; Temperate; Temperature, water; Treatment; Type; Violaxanthin/antheraxanthin ratio; Violaxanthin/antheraxanthin ratio, standard deviation; Violaxanthin/chlorophyll a ratio; Violaxanthin/chlorophyll a ratio, standard deviation; Violaxanthin/Zeaxanthin ratio; Violaxanthin/Zeaxanthin ratio, standard deviation; δ13C; δ13C, standard deviation; δ15N; δ15N, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 679 data points
    Location Call Number Expected Availability
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  • 4
    Publication Date: 2024-04-20
    Description: The weather has a significant impact on crop growth. In the Indian agroecosystem information on the weather during a crop's growing season measured at the experimental site is scarce. We have compiled and harmonized meteorological data measured at the experimental sites for rice and spring wheat crop from various Indian agricultural institutes. These theses are available as pdf at the Krishikosh repository (https://krishikosh.egranth.ac.in). This dataset is the first of its kind, providing and combining weather data from several crop-growing regions. We are combining data from 83 theses related to rice and data from 64 theses related to spring wheat. These data are from 48 individual sites, a few of which have multiple growing seasons.
    Keywords: Agriculture; Akola; Anand; Author(s); Bapatla; Bengaluru; Bhubaneswar; Brahmavara; Chatha; Coimbatore; Compilation; Cooch Behar; Country; crop yield; DATE/TIME; Date/time end; Dhadesugur; Dharwad; ELEVATION; Evaporation, daily mean; Evaporation, daily total; Event label; Faizabad; Field measurement; Gwalior; Humidity, relative; Humidity, relative, daily mean; Humidity, relative, maximum; Humidity, relative, minimum; Humidity, relative, monthly mean; Identification; IND_RI_AKO_1977; IND_RI_AKO_1978; IND_RI_AKO_1979; IND_RI_BAP_2002; IND_RI_BEN_2016; IND_RI_BHU_2014; IND_RI_BRA_2017; IND_RI_COM_1985; IND_RI_COM_1986; IND_RI_DEL_1966; IND_RI_DEL_1967; IND_RI_DHA_2017; IND_RI_FAZ_2000; IND_RI_FAZ_2001; IND_RI_HYD_2010; IND_RI_JAB_1987; IND_RI_JAB_2009; IND_RI_JAB_2010; IND_RI_JAB_2011; IND_RI_JAB_2019; IND_RI_JOR_1997; IND_RI_JOR_1998; IND_RI_JOR_1999; IND_RI_KAS_2002; IND_RI_KAU_2008; IND_RI_KAU_2015; IND_RI_KUT_2013; IND_RI_KUT_2015; IND_RI_KUT_2016; IND_RI_KUT_2018; IND_RI_KUT_2019; IND_RI_MAD_1984; IND_RI_MAD_1985; IND_RI_MAN_2010; IND_RI_MEE_2019; IND_RI_PAL_1989; IND_RI_PAL_1990; IND_RI_PAL_1991; IND_RI_PAL_1992; IND_RI_PAL_1993; IND_RI_PAL_1997; IND_RI_PAL_1998; IND_RI_PAL_1999; IND_RI_PAL_2000; IND_RI_PAL_2001; IND_RI_PAL_2020; IND_RI_PAN_2005; IND_RI_PAN_2006; IND_RI_PAN_2010; IND_RI_PAN_2011; IND_RI_PAN_2012; IND_RI_PAN_2016; IND_RI_PAN_2017; IND_RI_PAN_2019; IND_RI_PON_2021; IND_RI_RAI_1973; IND_RI_RAI_1977; IND_RI_RAI_1984; IND_RI_RAI_1985; IND_RI_RAI_2009; IND_RI_RAI_2012; IND_RI_RAI_2015; IND_RI_RAI_2016; IND_RI_RAI_2018; IND_RI_RAI_2019; IND_RI_RAJ_2001; IND_RI_RAJ_2002; IND_RI_RAN_2015; IND_RI_RAN_2019; IND_RI_RED_2000; IND_RI_RED_2001; IND_RI_REW_2007; IND_RI_REW_2009; IND_RI_SRI_2012; IND_RI_TIR_1989; IND_RI_TIR_1994; IND_RI_TIR_1995; IND_RI_TIR_1996; IND_RI_VAD_2016; IND_RI_VAD_2017; IND_RI_VAD_2018; IND_RI_VAR_2016; IND_RI_VEL_1985; IND_SW_ANA_1964; IND_SW_ANA_1965; IND_SW_ANA_1968; IND_SW_ANA_1969; IND_SW_CHA_1997; IND_SW_CHA_1998; IND_SW_COB_1997; IND_SW_COB_1998; IND_SW_COB_2000; IND_SW_COB_2001; IND_SW_DHA_1998; IND_SW_DHA_1999; IND_SW_DHA_2000; IND_SW_DHA_2002; IND_SW_DHA_2016; IND_SW_FAZ_2003; IND_SW_FAZ_2004; IND_SW_FAZ_2020; IND_SW_GWA_2011; IND_SW_INR_1986; IND_SW_JAB_2013; IND_SW_JAB_2014; IND_SW_JAI_2013; IND_SW_JAI_2014; IND_SW_JOB_1970; IND_SW_JOB_1977; IND_SW_JOB_1983; IND_SW_JOB_1984; IND_SW_JOB_1998; IND_SW_JOB_1999; IND_SW_JOB_2000; IND_SW_JOB_2002; IND_SW_JOB_2015; IND_SW_JOB_2016; IND_SW_LUD_2011; IND_SW_LUD_2012; IND_SW_MEE_2011; IND_SW_MEE_2012; IND_SW_MEE_2013; IND_SW_NAD_1972; IND_SW_NAD_1973; IND_SW_NAD_1990; IND_SW_NAD_1991; IND_SW_NAD_1999; IND_SW_NAD_2000; IND_SW_NAD_2001; IND_SW_NAD_2013; IND_SW_NAD_2014; IND_SW_PAN_2007; IND_SW_PAN_2008; IND_SW_PAR_2001; IND_SW_PAR_2005; IND_SW_PAR_2009; IND_SW_PAR_2019; IND_SW_RAN_1995; IND_SW_RAN_1996; IND_SW_RAN_1997; IND_SW_RAN_2013; IND_SW_RAN_2014; IND_SW_RAN_2017; Indore; Jabalpur; Jaipur; Jobner; Jorhat; Journal/report publisher; Journal/report title; Kanke, Ranchi; Kaul; Kuthulia; LATITUDE; Literature survey; Location; LONGITUDE; Ludhiana; Madurai; Mandya; Meerut; Nadia; New delhi; Number of wet days; Palampur; Pantnagar; Parbhani; Period; Poonch; Precipitation; Precipitation, monthly mean; Precipitation, monthly total; Publication type; Raipur; Rajavanthi; Rajendranagar; Ranchi; Reddipalli; Rewa; rice; Solar radiation; Spring wheat; Srikakulam; Srinagar; Standard meteorological week; Sunshine duration, daily; Temperature, air, daily mean; Temperature, air, maximum; Temperature, air, minimum; Temperature, air, monthly mean; Tirupati; Uniform resource locator/link to source data file; Vadgaon Maval; Varanasi; Vellayani; Water vapour pressure; Water vapour pressure, maximum; Water vapour pressure, minimum; Wind speed; Year of publication
    Type: Dataset
    Format: text/tab-separated-values, 80931 data points
    Location Call Number Expected Availability
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