Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling
Inland valleys (IVs) in Africa are important landscapes for rice cultivation and are targeted by national governments to attain self-sufficiency. Yet, there is limited information on the spatial distribution of IVs suitability at the national scale. In the present study, we developed an ensemble mod...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/107122 |
| _version_ | 1855515271859535872 |
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| author | Akpoti, K. Kabo-bah, A.T. Dossou-Yovo, Elliott Ronald Groen, T.A. Zwart, Sander J. |
| author_browse | Akpoti, K. Dossou-Yovo, Elliott Ronald Groen, T.A. Kabo-bah, A.T. Zwart, Sander J. |
| author_facet | Akpoti, K. Kabo-bah, A.T. Dossou-Yovo, Elliott Ronald Groen, T.A. Zwart, Sander J. |
| author_sort | Akpoti, K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Inland valleys (IVs) in Africa are important landscapes for rice cultivation and are targeted by national governments to attain self-sufficiency. Yet, there is limited information on the spatial distribution of IVs suitability at the national scale. In the present study, we developed an ensemble model approach to characterize the IVs suitability for rainfed lowland rice using 4 machine learning algorithms based on environmental niche modeling (ENM) with presence-only data and background sample, namely Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Maximum Entropy (MAXNT) and Random Forest (RF). We used a set of predictors that were grouped under climatic variables, agricultural water productivity and soil water content, soil chemical properties, soil physical properties, vegetation cover, and socio-economic variables. The Area Under the Curves (AUC) evaluation metrics for both training and testing were respectively 0.999 and 0.873 for BRT, 0.866 and 0.816 for GLM, 0.948 and 0.861 for MAXENT and 0.911 and 0.878 for RF. Results showed that proximity of inland valleys to roads and urban centers, elevation, soil water holding capacity, bulk density, vegetation index, gross biomass water productivity, precipitation of the wettest quarter, isothermality, annual precipitation, and total phosphorus among others were major predictors of IVs suitability for rainfed lowland rice. Suitable IVs areas were estimated at 155,000–225,000 Ha in Togo and 351,000–406,000 Ha in Benin. We estimated that 53.8% of the suitable IVs area is needed in Togo to attain self-sufficiency in rice while 60.1% of the suitable IVs area is needed in Benin to attain self-sufficiency in rice. These results demonstrated the effectiveness of an ensemble environmental niche modeling approach that combines the strengths of several models. |
| format | Journal Article |
| id | CGSpace107122 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1071222023-12-08T19:36:04Z Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling Akpoti, K. Kabo-bah, A.T. Dossou-Yovo, Elliott Ronald Groen, T.A. Zwart, Sander J. land suitability rice agricultural production environmental modelling linear models forecasting uncertainty water productivity soil water content rainfed farming climatic data soil chemicophysical properties socioeconomic environment valleys pollution environmental engineering Inland valleys (IVs) in Africa are important landscapes for rice cultivation and are targeted by national governments to attain self-sufficiency. Yet, there is limited information on the spatial distribution of IVs suitability at the national scale. In the present study, we developed an ensemble model approach to characterize the IVs suitability for rainfed lowland rice using 4 machine learning algorithms based on environmental niche modeling (ENM) with presence-only data and background sample, namely Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Maximum Entropy (MAXNT) and Random Forest (RF). We used a set of predictors that were grouped under climatic variables, agricultural water productivity and soil water content, soil chemical properties, soil physical properties, vegetation cover, and socio-economic variables. The Area Under the Curves (AUC) evaluation metrics for both training and testing were respectively 0.999 and 0.873 for BRT, 0.866 and 0.816 for GLM, 0.948 and 0.861 for MAXENT and 0.911 and 0.878 for RF. Results showed that proximity of inland valleys to roads and urban centers, elevation, soil water holding capacity, bulk density, vegetation index, gross biomass water productivity, precipitation of the wettest quarter, isothermality, annual precipitation, and total phosphorus among others were major predictors of IVs suitability for rainfed lowland rice. Suitable IVs areas were estimated at 155,000–225,000 Ha in Togo and 351,000–406,000 Ha in Benin. We estimated that 53.8% of the suitable IVs area is needed in Togo to attain self-sufficiency in rice while 60.1% of the suitable IVs area is needed in Benin to attain self-sufficiency in rice. These results demonstrated the effectiveness of an ensemble environmental niche modeling approach that combines the strengths of several models. 2020-03 2020-02-18T06:47:39Z 2020-02-18T06:47:39Z Journal Article https://hdl.handle.net/10568/107122 en Limited Access Elsevier Akpoti, K.; Kabo-bah, A. T.; Dossou-Yovo, E. R.; Groen, T. A.; Zwart, Sander J. 2020. Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling. Science of the Total Environment, 19p. (Online first) doi: 10.1016/j.scitotenv.2019.136165 |
| spellingShingle | land suitability rice agricultural production environmental modelling linear models forecasting uncertainty water productivity soil water content rainfed farming climatic data soil chemicophysical properties socioeconomic environment valleys pollution environmental engineering Akpoti, K. Kabo-bah, A.T. Dossou-Yovo, Elliott Ronald Groen, T.A. Zwart, Sander J. Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling |
| title | Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling |
| title_full | Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling |
| title_fullStr | Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling |
| title_full_unstemmed | Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling |
| title_short | Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling |
| title_sort | mapping suitability for rice production in inland valley landscapes in benin and togo using environmental niche modeling |
| topic | land suitability rice agricultural production environmental modelling linear models forecasting uncertainty water productivity soil water content rainfed farming climatic data soil chemicophysical properties socioeconomic environment valleys pollution environmental engineering |
| url | https://hdl.handle.net/10568/107122 |
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