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

dc.creatorGangopadhyay, P.K.
dc.creatorShirsath, P.B.
dc.creatorDadhwal, V.K.
dc.creatorAggarwal, P.K.
dc.date2023-01-12T01:00:15Z
dc.date2023-01-12T01:00:15Z
dc.date2022
dc.date.accessioned2023-07-17T20:10:01Z
dc.date.available2023-07-17T20:10:01Z
dc.identifierhttps://hdl.handle.net/10883/22381
dc.identifier10.1038/s41597-022-01828-y
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7514128
dc.descriptionThe 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.
dc.languageEnglish
dc.publisherNature Publishing Group
dc.relationNutrition, health & food security
dc.relationAccelerated Breeding
dc.relationGenetic Innovation
dc.relationhttps://hdl.handle.net/10568/129206
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
dc.rightsOpen Access
dc.source9
dc.source2052-4463
dc.sourceScientific Data
dc.source730
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectClimatic Variability
dc.subjectWeather Risks
dc.subjectGross Primary Productivity
dc.subjectLight Use Efficiency
dc.subjectProduction Datasets
dc.subjectAGRICULTURE
dc.subjectGOVERNANCE
dc.subjectREMOTE SENSING
dc.subjectDATA
dc.subjectCROP PRODUCTION
dc.subjectinstitutional
dc.titleA new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India
dc.typeArticle
dc.typePublished Version
dc.coverageIndia
dc.coverageUnited Kingdom


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