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022			_a1932-6203 (Online)
024	8		_ahttps://doi.org/10.1371/journal.pone.0309982
040			_aMX-TxCIM
041			_aeng
100	1		_aTiwari, V. _928432
245	1	0	_aAdvancing food security : _bRice yield estimation framework using time-series satellite data & machine learning
260			_aSan Francisco (United States) : _bPublic Library of Science, _c2024.
500			_aPeer review
500			_aOpen access
520			_aTimely and accurately estimating rice yields is crucial for supporting food security management, agricultural policy development, and climate change adaptation in rice-producing countries such as Bangladesh. To address this need, this study introduced a workflow to enable timely and precise rice yield estimation at a sub-district scale (1,000-meter spatial resolution). However, a significant gap exists in the application of remote sensing methods for government-reported rice yield estimation for food security management at high spatial resolution. Current methods are limited to specific regions and primarily used for research, lacking integration into national reporting systems. Additionally, there is no consistent yearly boro rice yield map at a sub-district scale, hindering localized agricultural decision-making. This workflow leveraged MODIS and annual district-level yield data to train a random forest model for estimating boro rice yields at a 1,000-meter resolution from 2002 to 2021. The results revealed a mean percentage root mean square error (RMSE) of 8.07% and 12.96% when validation was conducted using reported district yields and crop-cut yield data, respectively. Additionally, the estimated yield of boro rice varies with an uncertainty range between 0.40 and 0.45 tons per hectare across Bangladesh. Furthermore, a trend analysis was performed on the estimated boro rice yield data from 2002 to 2021 using the modified Mann-Kendall trend test with a 95% confidence interval (p < 0.05). In Bangladesh, 23% of the rice area exhibits an increasing trend in boro rice yield, 0.11% shows a decreasing trend, and 76.51% of the area demonstrates no trend in rice yield. Given that this is the first attempt to estimate boro rice yield at 1,000-meter spatial resolution over two decades in Bangladesh, the estimated mid-season boro rice yield estimates are scalable across space and time, offering significant potential for strengthening food security management in Bangladesh. Furthermore, the proposed workflow can be easily applied to estimate rice yields in other regions worldwide.
546			_aText in English
597			_bTransforming Agrifood Systems in South Asia _dCGIAR Trust Fund _dUnited States Agency for International Development (USAID) _dCereal Systems Initiative for South Asia (CSISA) _dBill & Melinda Gates Foundation (BMGF) _uhttps://hdl.handle.net/10568/169956
650		7	_aFood security _2AGROVOC _91118
650		7	_aRice _2AGROVOC _91243
650		7	_aCrop yield _2AGROVOC _91066
650		7	_aMachine learning _2AGROVOC _911127
650		7	_aClimate change adaptation _2AGROVOC _95511
650		7	_aSatellites _2AGROVOC _93815
650		7	_aData _2AGROVOC _99002
700	1		_aThorp, K. _937903
700	1		_aTulbure, M.G. _932416
700	1		_aGray, J.M. _937169
700	1		_aKamruzzaman, M. _937904
700	1		_aKrupnik, T.J. _gSustainable Agrifood Systems _8INT3222 _9906
700	1		_aSankarasubramanian, A. _937905
700	1		_aArdon, M. _937906
773	0		_dSan Francisco (United States) : Public Library of Science, 2024. _gv. 19, no. 12, art. e0309982 _tPLoS ONE _x1932-6203
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/35399
942			_cJA _n0 _2ddc
999			_c68384 _d68376