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

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cg.authorship.typesCGIAR and developing country instituteen
cg.contributor.affiliationBorlaug Institute for South Asiaen
cg.contributor.affiliationInternational Maize and Wheat Improvement Centeren
cg.contributor.affiliationNational Institute of Advanced Studiesen
cg.contributor.donorCGIAR Trust Funden
cg.contributor.initiativeAccelerated Breeding
cg.coverage.countryIndia
cg.coverage.iso3166-alpha2IN
cg.coverage.regionSouthern Asia
cg.creator.identifierPrasun Gangopadhyay: 0000-0002-2549-3097
cg.creator.identifierParesh Shirsath: 0000-0003-3266-922X
cg.creator.identifierPramod Aggarwal: 0000-0002-1060-7602
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.1038/s41597-022-01828-yen
cg.isijournalISI Journalen
cg.issn2052-4463en
cg.issue1en
cg.journalScientific Dataen
cg.placeUnited Kingdomen
cg.reviewStatusPeer Reviewen
cg.subject.actionAreaGenetic Innovation
cg.subject.impactAreaNutrition, health and food security
cg.volume9en
dc.contributor.authorGangopadhyay, Prasun K.en
dc.contributor.authorShirsath, Paresh B.en
dc.contributor.authorDadhwal, Vinay K.en
dc.contributor.authorAggarwal, Pramod K.en
dc.date.accessioned2023-03-06T14:53:13Zen
dc.date.available2023-03-06T14:53:13Zen
dc.identifier.urihttps://hdl.handle.net/10568/129206
dc.titleA new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for Indiaen
dcterms.abstractThe 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.en
dcterms.accessRightsOpen Access
dcterms.audienceScientistsen
dcterms.available2022-11-27
dcterms.bibliographicCitationGangopadhyay, P. K., Shirsath, P. B., Dadhwal, V. K., & Aggarwal, P. K. (2022). A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01828-yen
dcterms.issued2022-11-27
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherSpringeren
dcterms.subjectagricultureen
dcterms.subjectgovernanceen
dcterms.subjectremote sensingen
dcterms.subjectdataen
dcterms.subjectcrop productionen
dcterms.typeJournal Article

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