The optimal land-use in the gerf land of central Sudan was determined from the social welfare point of view. The alternative land-use options considered were Acacia nilotica plantations, eucalyptus plantations and bananas. The evaluation technique adopted was cost-benefit analysis using the Little-Mirrlees-Squire-van der Tak (LMST) approach. The results indicate that eucalyptus plantations are the optimum land-use option on the basis of economic growth maximization (economic analysis) as well as maximization of economic growth with equity as a constraint (social analysis). The A. nilotica option came second to eucalyptus, while banana performed poorly with negative contribution in most cases. The financial analysis which used distorted market prices revealed that all three land-use alternatives were profitable, with eucalyptus being the most profitable and A. nilotica the least. Compared to the financial profitability, the economic profitability of the two forest options was considerably higher. In contrast, the profitability of the banana option was lowest among the three options. It is concluded that distorting agricultural policy had the impact of misallocating land to the optimal use. (c) 2004 Elsevier B.V. All rights reserved.
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Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R ChinaChinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China
Yang, Lina
Sun, Xu
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Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R ChinaChinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China
Sun, Xu
Peng, Ling
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Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R ChinaChinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China
Peng, Ling
Shao, Jing
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Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R ChinaChinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China
Shao, Jing
Chi, Tianhe
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Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R ChinaChinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China