Climate smart Agriculture Interventions in Selected Agricultural Growth Program-II Districts of Harari Region and Dire Dawa Administration: Double Cropping Practices

Usmane Ibsa Aliyi; Umer Abdulaziz Teha; Siraj Nasir; Magersa Oromiya; Urgesa Badaso; Urge Tashoma

doi:10.17352/2455-815x.000082

Climate smart Agriculture Interventions in Selected Agricultural Growth Program-II Districts of Harari Region and Dire Dawa Administration: Double Cropping Practices

International Journal of Agricultural Science and Food Technology ◽

10.17352/2455-815x.000082 ◽

2021 ◽

Vol 7 (1) ◽

pp. 014-019

Author(s):

Usmane Ibsa Aliyi ◽

Umer Abdulaziz Teha ◽

Siraj Nasir ◽

Magersa Oromiya ◽

Urgesa Badaso ◽

...

Keyword(s):

Agricultural Growth ◽

Double Cropping ◽

Cropping Practices ◽

Dire Dawa ◽

Smart Agriculture

Get full-text (via PubEx)

Mapping Cropping Practices on a National Scale Using Intra-Annual Landsat Time Series Binning

Remote Sensing ◽

10.3390/rs11030232 ◽

2019 ◽

Vol 11 (3) ◽

pp. 232 ◽

Cited By ~ 15

Author(s):

Philippe Rufin ◽

David Frantz ◽

Stefan Ernst ◽

Andreas Rabe ◽

Patrick Griffiths ◽

...

Keyword(s):

Time Series ◽

Water Resource ◽

Agricultural Systems ◽

Double Cropping ◽

Tasseled Cap ◽

Temporal Features ◽

Cropping Practices ◽

Greenhouse Cultivation ◽

Province Level ◽

Cropland Mapping

Spatially explicit information on cropland use intensity is vital for monitoring land and water resource demands in agricultural systems. Cropping practices underlie substantial spatial and temporal variability, which can be captured through the analysis of image time series. Temporal binning helps to overcome limitations concerning operability and repeatability for mapping large areas and can improve the thematic detail and consistency of maps in agricultural systems. We here assessed the use of annual, quarterly, and eight-day temporal features for mapping five cropping practices on annual croplands across Turkey. We used 2403 atmospherically corrected and topographically normalized Landsat Collection 1 L1TP images of 2015 to compute quarterly best-pixel composites, quarterly and annual spectral-temporal metrics, as well as gap-filled eight-day time series of Tasseled Cap components. We tested 22 feature sets for binary cropland mapping, and subsequent discrimination of five cropping practices: Spring and winter cropping, summer cropping, semi-aquatic cropping, double cropping, and greenhouse cultivation. We evaluated area-adjusted accuracies and compared cropland area estimates at the province-level with official statistics. We achieved overall accuracies above 90%, when using either all quarterly features or the eight-day Tasseled Cap time series, indicating that temporal binning of intra-annual image time-series into multiple temporal features improves representations of cropping practices. Class accuracies of winter and spring, summer, and double cropping were robust, while omission errors for semi-aquatic cropping and greenhouse cultivation were high. Our mapped cropland extent was in good agreement with province-level statistics (r2 = 0.85, RMSE = 7.2%). Our results indicate that 71.3% (±2.3%) of Turkey’s annual croplands were cultivated during winter and spring, 15.8% (±2.2%) during summer, while 8.5% (±1.6%) were double-cropped, 4% (±1.9%) were cultivated under semi-aquatic conditions, and 0.32% (±0.2%) was greenhouse cultivation. Our study presents an open and readily available framework for detailed cropland mapping over large areas, which bears the potential to inform assessments of land use intensity, as well as land and water resource demands.

Get full-text (via PubEx)