MODELING INLAND VALLEY SUITABILITY FOR RICE CULTIVATION
The demand for rice (Oryza sativa) in Ghana is increasing at a rate of 11.8% from 939, 920 t in 2010. Though there has been some increase in production it does not match the increase in consumption. This study seeks to determine the most suitable areas for inland valley rice cultivation using comput...
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Veröffentlicht in: | ARPN journal of engineering and applied sciences 2013-01, Vol.8 (1), p.9-19 |
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description | The demand for rice (Oryza sativa) in Ghana is increasing at a rate of 11.8% from 939, 920 t in 2010. Though there has been some increase in production it does not match the increase in consumption. This study seeks to determine the most suitable areas for inland valley rice cultivation using computer based models for selected sites (15km by 15km) in the Brong Ahafo Region (BAR) and Western Region (WR) of Ghana. A sensitivity analysis was carried out by excluding the least contributing parameters and varying their weights to detennine highly suitable areas. Finally, 12 most sensitive input parameters were identified from the original 22. These were used to model for five suitability classes (highly suitable, suitable, moderately suitable, marginally suitable and not suitable). The model results based on parameters having equal weights showed that 0.5% and 11.8% (BAR); and 1.4% and 21.4% (WR) of the area were highly suitable and suitable respectively. Using unequal weights, 0.8% and 7.6% (BAR); and 0.9% and 13.6% (WR) of the area were highly suitable and suitable, respectively. The study successfully mapped out suitable areas for rice cultivation using spatial models based on limited data set, which can be adopted for use elsewhere. |
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Though there has been some increase in production it does not match the increase in consumption. This study seeks to determine the most suitable areas for inland valley rice cultivation using computer based models for selected sites (15km by 15km) in the Brong Ahafo Region (BAR) and Western Region (WR) of Ghana. A sensitivity analysis was carried out by excluding the least contributing parameters and varying their weights to detennine highly suitable areas. Finally, 12 most sensitive input parameters were identified from the original 22. These were used to model for five suitability classes (highly suitable, suitable, moderately suitable, marginally suitable and not suitable). The model results based on parameters having equal weights showed that 0.5% and 11.8% (BAR); and 1.4% and 21.4% (WR) of the area were highly suitable and suitable respectively. Using unequal weights, 0.8% and 7.6% (BAR); and 0.9% and 13.6% (WR) of the area were highly suitable and suitable, respectively. 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Though there has been some increase in production it does not match the increase in consumption. This study seeks to determine the most suitable areas for inland valley rice cultivation using computer based models for selected sites (15km by 15km) in the Brong Ahafo Region (BAR) and Western Region (WR) of Ghana. A sensitivity analysis was carried out by excluding the least contributing parameters and varying their weights to detennine highly suitable areas. Finally, 12 most sensitive input parameters were identified from the original 22. These were used to model for five suitability classes (highly suitable, suitable, moderately suitable, marginally suitable and not suitable). The model results based on parameters having equal weights showed that 0.5% and 11.8% (BAR); and 1.4% and 21.4% (WR) of the area were highly suitable and suitable respectively. Using unequal weights, 0.8% and 7.6% (BAR); and 0.9% and 13.6% (WR) of the area were highly suitable and suitable, respectively. The study successfully mapped out suitable areas for rice cultivation using spatial models based on limited data set, which can be adopted for use elsewhere.</abstract><tpages>11</tpages></addata></record> |
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subjects | Computer simulation Consumption Cultivation Demand Mathematical models Sensitivity analysis Valleys |
title | MODELING INLAND VALLEY SUITABILITY FOR RICE CULTIVATION |
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