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Remote sensing based agricultural water productivity assessment

Faruque, MD Jamal (2020) Remote sensing based agricultural water productivity assessment.

Link to full-text:https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2020/msc/wrem/faruque.pdf
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Abstract:Water productivity is a useful indicator to measure how efficiently water is used in crop production. In this study, FAO-WaPOR developed methodology and L1 (250m), L2 (100m), and L3 (30m) remote sensing datasets used to calculate water productivity in the Gezira Irrigation Scheme in Sudan from 2009 to 2020. In the summer season, average evapotranspiration and interception (ETI) of Cotton, Groundnut, and Sorghum were 455mm, 486mm, and 423mm, respectively. In the winter season, average ETI of Onion, Pigeonpea, and Wheat was 436mm, 354mm, and 372mm, respectively. From 2009 to 2014, GBWP and NBWP increased gradually and 2015 to 2018; the productivity was substantially low, whereas, in 2019, the productivity increased significantly. The average GBWP and NBWP of Cotton, Groundnut, and Sorghum was 0.51, 0.56, 0.48 kgm-3 and 0.48, 0.54, 0.43 kgm-3, respectively. In the winter season, GBWP and NBWP increased gradually from 2010 and peaked in 2014 then decreased steadily till 2020. The average GBWP and NBWP of Onion, Pigeonpea, and Wheat was 0.60, 0.59, 0.52 kgm-3 and 0.51, 0.59, and 0.53 kgm-3, respectively. The overall average water productivity derived from WaPOR datasets results in a lower value than similar types of studies. There might be several reasons or combinations of reasons that are responsible for the low water productivity in the scheme. WaPOR uses the CHIRPS v2 dataset as precipitation, compared with another independent dataset shows that CHIRPS v2 overestimates precipitation. From 2014, there was a transformation of MERRA to the GEOS-5 model for all three levels for the downscaling and retrieving of meteorological inputs, and this might result in inconsistency in the temporal WP assessment (e.g. 2014-15). This comment also holds for other satellite sources input data, e.g. NDVI, and albedo, which underwent sensor changes (e.g. MODIS to PROBA-V) during the 2009-2020 period. In the Gezira scheme, scattered settlements, bare areas as well as fallow lands are observed, which may also influence the aggregated response recorded by the sensor that results in low water productivity. Moreover, long-term political instability was affecting irrigation scheme management, and lack of farmer's trust also might have implications on water productivity. The results show that there is a considerable scope to increase water productivity in the Gezira Irrigation Scheme. The results also point out the need to further analyse and investigate the multiple causes of variations observed in the WP time series in the Gezira scheme. This is required for being able to use WP as a water use efficiency and performance indicator in agricultural water management.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
Link to this item:https://purl.utwente.nl/essays/85170
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