Your search found 6 records
1 Holden, S.; Shiferaw, B.; Pender, J. 2001. Market imperfections and land productivity in Ethiopian highlands. IFPRI discussion paper - Environment and Production Technology Division. iii, 34p. (EPTD discussion paper no.76)
(Location: IWMI-HQ Call no: P 5934 Record No: H029408)
2 Holden, S.; Shiferaw, B.; Pender, J. 2005. Policy analysis for sustainable land management and food security in Ethiopia: a bioeconomic model with market imperfections. Washington, DC, USA: IFPRI. x, 76p. (IFPRI Research Report 140)
Sustainable agriculture ; Food security ; Land management ; Land degradation ; Crop production ; Livestock ; Constraints ; Models ; Households ; Income ; Drought ; Case studies / Ethiopia
(Location: IWMI-HQ Call no: 338.1 G136 HOL Record No: H038589)
3 Gebregziabher, G.; Namara, Regassa E.; Holden, S.. 2009. Poverty reduction with irrigation investment: an empirical case study from Tigray, Ethiopia. Agricultural Water Management, 96(12):1837-1843. [doi: https://doi.org/10.1016/j.agwat.2009.08.004]
Irrigation effects ; Groundwater irrigation ; Households ; Income ; Poverty ; Case studies ; Models / Ethiopia / Tigray
(Location: IWMI HQ Call no: e-copy only Record No: H042488)
(0.19 MB)
The regional government of Tigray has invested millions of dollars to develop irrigation schemes as a strategy of poverty reduction. However, there has been limited attempt to analyze whether these investments have attained their stated objectives of poverty reduction and overall socio-economic enhancement. Therefore, we endeavor to: (1) evaluate the impacts of access to small-scale irrigation on farm household’s income and poverty status, (2) contribute to the scant literature on irrigation and poverty reduction in Ethiopia, and (3) provide information for policy makers. We examine a representative sample of 613 farm households (331 irrigators and 282 non-irrigators) drawn using three-stage stratified sampling with Probability Proportional to Size. We find that the average income of non-irrigating households is less than that of the irrigating households by about 50%. The overall average income gain due to access to irrigation ranges from 4000 Birr to 4500 Birr per household per annum. We find also that farming income is more important to irrigating households than to non-irrigating households, and off-farm income is negatively related with access to irrigation.
4 Gebregziabher, Gebrehaweria; Holden, S.. 2011. Does irrigation enhance and food deficits discourage fertilizer adoption in a risky environment? Evidence from Tigray, Ethiopia. Journal of Development and Agricultural Economics, 3(10):514-528.
Food shortages ; Agricultural production ; Fertilizers ; Drought ; Risks ; Investment ; Income ; Poverty ; Rain ; Households ; Analysis ; Environmental effects / Ethiopia / Tigray
(Location: IWMI HQ Call no: e-copy only Record No: H044348)
http://www.academicjournals.org/jdae/PDF/Pdf2011/Sept/26%20Sept/Gebregziabher%20and%20Holden.pdf
https://vlibrary.iwmi.org/pdf/H044348.pdf
https://vlibrary.iwmi.org/pdf/H044348.pdf
(0.32 MB) (370.46KB)
The northern Ethiopian highland in general and the Tigray region in particular is a drought prone area where agricultural production risk is prevalent. Moisture stress is a limiting factor for improved agricultural input mainly fertilizer use. Lack of capital and consumption smoothing mechanisms limits households’ investment in production enhancing agricultural inputs, possibly leading into poverty trap. Using a Cragg (Double Hurdle) model, we analyzed how rainfall risks, access to irrigation and food deficits affect the probability that farm households’ use fertilizer and given that the probability is positive and significant, the amount (intensity) of fertilizer use. Accordingly, we found that households were more likely to use fertilizer and that they used significantly higher amounts of fertilizer on their irrigated plots than on rain-fed plots. Furthermore, households with access to irrigation were more likely to use fertilizer, but the intensity (amount) of fertilizer they used was not significantly different from those households without access to irrigation. In investigating the effect of rainfall risk on fertilizer use, we found that fertilizer use was significantly higher in areas with higher average rainfall and in areas with lower rainfall variability. In general, irrigation was found significantly important for fertilizer adoption mainly in areas with low rainfall and high rainfall variability. Finally, we investigate the effect of food deficit on fertilizer adoption and found that both food self-sufficient and food deficit households were less likely to use fertilizer as coping mechanism. However, among those who decided to adopt, the food deficit households used higher amount of fertilizer than the food self-sufficient.
5 Gebregziabher, Gebrehaweria; Namara, Regassa E.; Holden, S.. 2012. Technical efficiency of irrigated and rain-fed smallholder agriculture in Tigray, Ethiopia: a comparative stochastic frontier production function analysis. Quarterly Journal of International Agriculture, 51(3):203-226.
Irrigated farming ; Rainfed farming ; Agricultural development ; Smallholders ; Soil moisture ; Stochastic models ; Technical progress ; Analytical methods / Ethiopia / Tigray
(Location: IWMI HQ Call no: e-copy only Record No: H044980)
(0.21 MB)
Stochastic production frontiers of irrigated and rain-fed smallholder agriculture in Tigray, Ethiopia, were fitted to a random sample of irrigated and rain-fed plots to compare their technical efficiencies. Propensity Score Matching Method was applied to select rain-fed plots with comparable bio-physical attributes to irrigated plots that might have blurred the true efficiency differences between the two systems. Irrigated farms are on a higher production frontier with significant inefficiencies, while rain-fed farms are on a lower production frontier with high efficiency levels. Thus, there is considerable potential for increasing outputs by improving the efficiency of irrigation farms. Rain-fed systems need interventions in soil moisture management to move to a higher production frontier. The study underlines the need for correcting the sequence and mix of yield boosting technologies such as irrigation, improved seeds, and fertilizer that are promoted in arid environments such as Tigray. We recommend that water control must proceed or implemented in tandem with improved seeds and fertilizer technologies. Unless soil moisture is improved by investing in moisture improving technologies, the use of seed and fertilizer in moisture stress areas such as Tigray may have adverse effects.
6 Zhang, Y.; Chen, G.; Vukomanovic, J.; Singh, K. K.; Liu, Y.; Holden, S.; Meentemeyer, R. K. 2020. Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping. Remote Sensing of Environment, 247:111945. (Online first) [doi: https://doi.org/10.1016/j.rse.2020.111945]
Land cover mapping ; Imagery ; Urban development ; Landscape ; Remote sensing ; Semantic standard ; Databases ; Models ; Suburban areas / USA / North Carolina / Raleigh / Durham / Chapel Hill
(Location: IWMI HQ Call no: e-copy only Record No: H049774)
(7.14 MB)
Shadows are prevalent in urban environments, introducing high uncertainties to fine-scale urban land-cover mapping. In this study, we developed a Recurrent Shadow Attention Model (RSAM), capitalizing on state-of-the-art deep learning architectures, to retrieve fine-scale land-cover classes within cast and self shadows along the urban-rural gradient. The RSAM differs from the other existing shadow removal models by progressively refining the shadow detection result with two attention-based interacting modules – Shadow Detection Module (SDM) and Shadow Classification Module (SCM). To facilitate model training and validation, we also created a Shadow Semantic Annotation Database (SSAD) using the 1 m resolution (National Agriculture Imagery Program) NAIP aerial imagery. The SSAD comprises 103 image patches (500 × 500 pixels each) containing various types of shadows and six major land-cover classes – building, tree, grass/shrub, road, water, and farmland. Our results show an overall accuracy of 90.6% and Kappa of 0.82 for RSAM to extract the six land-cover classes within shadows. The model performance was stable along the urban-rural gradient, although it was slightly better in rural areas than in urban centers or suburban neighborhoods. Findings suggest that RSAM is a robust solution to eliminate the effects in high-resolution mapping both from cast and self shadows that have not received equal attention in previous studies.
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