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Donnish Journal of Agricultural Research

November 2016 Vol. 3(4), pp. 022-034

ISSN: ISSN: 2984-8938

Copyright © 2016 Donnish Journals




Original Research Article


Optimizing Soil Fertility Management Decision in Mali by Remote Sensing and GIS


Djeneba Dembele1*, Kalifa Traore1, Mamadou Ballo2, Bocar Dit Sire BA3, Edward Mathew Osei Jnr4, Charles Quansh4

1Soil-Water-Plant Laboratory, Institute of Rural Economy, Mali.
2Department of Crop and Soil Science, Faculty of Agriculture, Kwame NíKrumah University of Science and Technology, Ghana.
3Danish Cooperation of Mali.
4Department of History-Geography, University of Mali, Mali.

Corresponding Author's Email: djenebademb@yahoo.fr

Accepted 23rd October, 2016.



Abstract


Understanding soil variability is significant for agriculture soil planning and management. Soil test is also a widely accepted methodology in nutrient management. However, its applicability is curtailed in Mali due to the high cost of implementation. Thus, soil fertility maps could be used as a soil fertility management decision support tool. In the current study, Remote Sensing, Geographic Information System and laboratory analysis were used to identify soil fertility status. Stratified randomized sampling was performed using landsat image and visual interpretation. 52 points were sampled at 0-20 cm depth and analysed to determine soil clay, sandy and silt content as well as soil pH, C, N, P and K. The combined use of visual interpretation, kriging and thematic analysis function of ArcGIS allowed determining clay, sand and silt spatial distribution. Soil texture triangle was used to identify the textural classes. Ordinary Kriging method was used to analyse the spatial variability of soil pH, C, N, P and K. Soil clay content was low (1.22 - 12%), soil sandy was high (47 - 85%), soil pH was from extremely to moderate acidic (4.7 - 6.1). Carbon, Nitrogen, Phosphorus and Potassium were below the critical levels, ranging from negligible to 0.4%; negligible to 0.03%; 2.22 to 5.5 mg/kg and from 0.01 to 0.07 cmol respectively. The overall current soil status was poor.

Keywords: Remote Sensing, Geographic Information System, Soil, Management, Decision, Mali.

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Cite This Article:

Djeneba Dembele, Kalifa Traore, Mamadou Ballo, Bocar Dit Sire BA, Edward Mathew Osei Jnr and Charles Quansh. Optimizing Soil Fertility Management Decision in Mali by Remote Sensing and GIS. Donnish Journal of Agricultural Research 3(4) 2016 pp. 022-034.


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