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Weather data at experimental agricultural sites across Indian croplands from 1960's to 2020 (2024)

Varma, Gudimetla Venkateswara ; Reddy, Kangari Narender ; Baidya Roy, Somnath ; [et al.]

PANGAEA

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Publication Date: 2024-05-30

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

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Indian cereal crops (wheat and rice) phenology and agricultural management data across Indian croplands from 1960's to 2020 (2024)

Varma, Gudimetla Venkateswara ; Reddy, Kangari Narender ; Baidya Roy, Somnath ; [et al.]

PANGAEA

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Publication Date: 2024-05-30

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

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Simulating impacts of wind farms on local hydrometeorology (2011)

Baidya Roy, Somnath

Elsevier

In: Journal of Wind Engineering and Industrial Aerodynamics. 2011; 99(4): 491-498. Published 2011 Apr 01. doi: 10.1016/j.jweia.2010.12.013.

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Publication Date: 2011-04-01

Print ISSN: 0167-6105

Electronic ISSN: 1872-8189

Topics: Energy, Environment Protection, Nuclear Power Engineering

Published by Elsevier

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Impact of environmental changes and land management practices on wheat production in India (2020)

Gahlot, Shilpa ; Lin, Tzu-Shun ; Jain, Atul K. ; [et al.]

Copernicus

In: Earth System Dynamics. 2020; 11(3): 641-652. Published 2020 Jul 24. doi: 10.5194/esd-11-641-2020.

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Publication Date: 2020-07-24

Description: Spring wheat is a major food crop that is a staple for a large number of people in India and the world. To address the issue of food security, it is essential to understand how the productivity of spring wheat varies with changes in environmental conditions and agricultural management practices. The goal of this study is to quantify the role of different environmental factors and management practices on wheat production in India in recent years (1980 to 2016). Elevated atmospheric CO2 concentration ([CO2]) and climate change are identified as two major factors that represent changes in the environment. The addition of nitrogen fertilizers and irrigation practices are the two land management factors considered in this study. To study the effects of these factors on wheat growth and production, we developed crop growth processes for spring wheat in India and implemented them in the Integrated Science Assessment Model (ISAM), a state-of-the-art land model. The model is able to simulate the observed leaf area index (LAI) at the site scale and observed production at the country scale. Numerical experiments are conducted with the model to quantify the effect of each factor on wheat production on a country scale for India. Our results show that elevated [CO2] levels, water availability through irrigation, and nitrogen fertilizers have led to an increase in annual wheat production at 0.67, 0.25, and 0.26 Mt yr−1, respectively, averaged over the time period 1980–2016. However, elevated temperatures have reduced the total wheat production at a rate of 0.39 Mt yr−1 during the study period. Overall, the [CO2], irrigation, fertilizers, and temperature forcings have led to 22 Mt (30 %), 8.47 Mt (12 %), 10.63 Mt (15 %), and −13 Mt (−18 %) changes in countrywide production, respectively. The magnitudes of these factors spatially vary across the country thereby affecting production at regional scales. Results show that favourable growing season temperatures, moderate to high fertilizer application, high availability of irrigation facilities, and moderate water demand make the Indo-Gangetic Plain the most productive region, while the arid north-western region is the least productive due to high temperatures and lack of irrigation facilities to meet the high water demand.

Print ISSN: 2190-4979

Electronic ISSN: 2190-4987

Topics: Geosciences

Published by Copernicus on behalf of European Geosciences Union.

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Layout optimization for offshore wind farms in India using the genetic algorithm technique (2020)

Reddy, Narender Kangari ; Baidya Roy, Somnath

Copernicus

In: Advances in Geosciences. 2020; 54: 79-87. Published 2020 Oct 17. doi: 10.5194/adgeo-54-79-2020.

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Publication Date: 2020-10-17

Description: Wind Farm Layout Optimization Problem (WFLOP) is a critical issue when installing a large wind farm. Many studies have focused on the WFLOP but only for a limited number of turbines and idealized wind speed distributions. In this study, we apply the Genetic Algorithm (GA) to solve the WFLOP for large hypothetical offshore wind farms using real wind data. GA mimics the natural selection process observed in nature, which is the survival of the fittest. The study site is the Palk Strait, located between India and Sri Lanka. This site is a potential hotspot of offshore wind in India. A modified Jensen wake model is used to calculate the wake losses. GA is used to produce optimal layouts for four different wind farms at the specified site. We use two different optimization approaches: one where the number of turbines is kept the same as the thumb rule layout and another where the number of turbines is allowed to vary. The results show that layout optimization leads to large improvements in power generation (up to 28 %), efficiency (up to 34 %), and cost (up to 25 %) compared to the thumb rule due to the reduction in wake losses. Optimized layouts where both the number and locations of turbines are allowed to vary produce better results in terms of efficiency and cost but also leads to lower installed capacity and power generation. Wind energy is growing at an unprecedented rate in India. Easily accessible terrestrial wind resources are almost saturated, and offshore wind is the new frontier. This study can play an important role while taking the first steps towards the expansion of offshore wind in India.

Print ISSN: 1680-7340

Electronic ISSN: 1680-7359

Topics: Geosciences

Published by Copernicus on behalf of European Geosciences Union.

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Estimating Trends and Variation of Net Biome Productivity in India for 1980–2012 Using a Land Surface Model (2017)

Gahlot, Shilpa ; Shu, Shijie ; Jain, Atul K. ; [et al.]

American Geophysical Union

In: Geophysical Research Letters. 2017; 44(22): 11573-11579. Published 2017 Nov 18. doi: 10.1002/2017gl075777.

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Publication Date: 2017-11-18

Description: In this paper we explore the trend in net biome productivity (NBP) over India for the period 1980–2012 and quantify the impact of different environmental factors, including atmospheric CO 2 concentrations ([CO 2 ]), land use and land cover change, climate, and nitrogen deposition on carbon fluxes using a land surface model, Integrated Science Assessment Model. Results show that terrestrial ecosystems of India have been a carbon sink for this period. Driven by a strong CO 2 fertilization effect, magnitude of NBP increased from 27.17 TgC/yr in the 1980s to 34.39 TgC/yr in the 1990s but decreased to 23.70 TgC/yr in the 2000s due to change in climate. Adoption of forest conservation, management, and reforestation policies in the past decade has promoted carbon sequestration in the ecosystems, but this effect has been offset by loss of carbon from ecosystems due to rising temperatures and decrease in precipitation. ©2017. American Geophysical Union. All Rights Reserved.

Print ISSN: 0094-8276

Electronic ISSN: 1944-8007

Topics: Geosciences , Physics

Published by American Geophysical Union

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