MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03427nab|a22003977a|4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
68343 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MX-TxCIM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20241220092917.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
202412s2024||||mx |||p|op||||00||0|eng|d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
|---|
| International Standard Serial Number |
1569-8432 |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
|---|
| International Standard Serial Number |
1872-826X (Online) |
| 024 8# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
https://doi.org/10.1016/j.jag.2024.104284 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
MX-TxCIM |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Negussie, K.G. |
| 9 (RLIN) |
37758 |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Assessing land suitability for leguminous crops in the Okavango river basin : |
| Remainder of title |
A multicriteria and machine learning approach |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc. |
Netherlands : |
| Name of publisher, distributor, etc. |
Elsevier B.V., |
| Date of publication, distribution, etc. |
2024. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Peer review |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Open Access |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
This study aimed to create a model to identify land suitable for growing sunn hemp (Crotalaria juncea) and pigeon pea (Cajanus cajan) in the Okavango River basin of the Kavango East region of Namibia. Advanced tree-based ensemble learning models, including Random Forest, Extra Trees, Gradient Boosting, XGBoost and multivariate regression analysis , were employed to enhance analytical accuracy. The Random Forest and XGboost models exhibited outstanding performance, as evidenced by their respective accuracy values of 0.97 and 0.96. In addition, this study proposed an innovative approach through the integration of subjective and objective analytical methods, which are independent of one another. The subjective component of the analysis employed a Multi-Criteria Decision Making-Analytic Hierarchy Process (MCDM-AHP). On the other hand, the objective component used a data-driven multivariate approach supported by tree-based learning algorithms. Twenty-two variables were considered, encompassing climatic conditions, hydro-geomorphologic features, soil characteristics, vegetation patterns, and socio-economic factors. These variables played a crucial role to identify the most suitable areas for growing the selected leguminous crops. The MCDM-AHP method utilised expert evaluations to rank the importance of variables, identifying water sources, slope, and soil properties as key factors. A suitability mapping analysis revealed that 17.63% of the area was highly suitable and 62.77% moderately suitable, while 10% was less suitable and 9.59% unsuitable for growing these two legumes. According to the data driven methodology, soil fertility and nitrogen content emerged as key determinants for land suitability. This is particularly relevant for nitrogen-fixing leguminous crops such as sunn hemp and pigeon pea, which play a central role in improving soil quality and ensuring food security. |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
Text in English |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Decision making |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
8770 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Learning |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
11157 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Legumes |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
1963 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Land suitability |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
13797 |
| 651 #7 - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME |
|---|
| Geographic name |
Namibia |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
31616 |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Okavango river basin |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Field link and sequence number |
001714264 |
| Personal name |
Bisrat Gebrekidan |
| Miscellaneous information |
Sustainable Agrifood Systems |
| 9 (RLIN) |
37336 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Wyss, D. |
| 9 (RLIN) |
37759 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Kappas, M. |
| 9 (RLIN) |
37760 |
| 773 0# - HOST ITEM ENTRY |
|---|
| Title |
International Journal of Applied Earth Observation and Geoinformation |
| Related parts |
v. 135, art. 104284 |
| Place, publisher, and date of publication |
Netherlands : Elsevier B.V., 2024. |
| International Standard Serial Number |
1569-8432 |
| 856 4# - ELECTRONIC LOCATION AND ACCESS |
|---|
| Link text |
Open Access through DSpace |
| Uniform Resource Identifier |
https://hdl.handle.net/10883/35163 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Article |
| Suppress in OPAC |
No |
| Source of classification or shelving scheme |
Dewey Decimal Classification |