A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India
Material type:
ArticleLanguage: English Publication details: Nature Publishing Group, 2022. United Kingdom :ISSN: - 2052-4463 (Online)
| Item type | Current library | Collection | Status | |
|---|---|---|---|---|
| Article | CIMMYT Knowledge Center: John Woolston Library | CIMMYT Staff Publications Collection | Available |
Peer review
Open Access
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.
Text in English
Gangopadhyay, P.K. : Not in IRS staff list but CIMMYT Affiliation
Nutrition, health & food security Accelerated Breeding Genetic Innovation
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