| 000 | 02720nab|a22003857a|4500 | ||
|---|---|---|---|
| 001 | 65874 | ||
| 003 | MX-TxCIM | ||
| 005 | 20250806125140.0 | ||
| 008 | 20221s2022||||mx |||p|op||||00||0|eng|d | ||
| 022 | _a2052-4463 (Online) | ||
| 024 | 8 | _ahttps://doi.org/10.1038/s41597-022-01828-y | |
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 100 | 1 |
_aGangopadhyay, P.K. _8001710309 _gBorlaug Institute for South Asia _919900 |
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| 245 | 1 | 0 | _aA new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India |
| 260 |
_bNature Publishing Group, _c2022. _aUnited Kingdom : |
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| 500 | _aPeer review | ||
| 500 | _aOpen Access | ||
| 520 | _aThe 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. | ||
| 546 | _aText in English | ||
| 591 | _aGangopadhyay, P.K. : Not in IRS staff list but CIMMYT Affiliation | ||
| 597 |
_aNutrition, health & food security _bAccelerated Breeding _cGenetic Innovation _uhttps://hdl.handle.net/10568/129206 |
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| 650 | 7 |
_aAgriculture _2AGROVOC _91007 |
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| 650 | 7 |
_aGovernance _2AGROVOC _911146 |
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| 650 | 7 |
_aRemote sensing _2AGROVOC _91986 |
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| 650 | 7 |
_aData _2AGROVOC _99002 |
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| 650 | 7 |
_aCrop production _2AGROVOC _91063 |
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| 651 | 7 |
_2AGROVOC _93726 _aIndia |
|
| 700 | 1 |
_aShirsath, P.B. _8I1706976 _92421 _gBorlaug Institute of South Asia |
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| 700 | 1 |
_aDadhwal, V.K. _918399 |
|
| 700 | 1 |
_aAggarwal, P.K. _gBorlaug Institute for South Asia _8I1706967 _92418 |
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| 773 | 0 |
_tScientific Data _dUnited Kingdom : Nature Publishing Group, 2022. _x2052-4463 _gv. 9, art. 730 _w u444686 |
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| 856 | 4 |
_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/22381 |
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| 942 |
_cJA _n0 _2ddc |
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| 999 |
_c65874 _d65866 |
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