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International Journal of Innovation and Applied Studies
ISSN: 2028-9324     CODEN: IJIABO     OCLC Number: 828807274     ZDB-ID: 2703985-7
 
 
Thursday 12 December 2019

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Cereal Yields Forecasting using Remote Sensing and GIS Techniques : A Case Study of Ouled Saleh Commune, Region of Casablanca-Settat, Morocco


Volume 24, Issue 3, October 2018, Pages 1010–1019

 Cereal Yields Forecasting using Remote Sensing and GIS Techniques : A Case Study of Ouled Saleh Commune, Region of Casablanca-Settat, Morocco

Abdelhadi Mouchrif1 and Fouad Amraoui2

1 Faculty of Sciences Ain Chock, University of Hassan II, BP 5366 Maarif, Casablanca, Morocco
2 Faculty of Sciences Ain Chock, University of Hassan II, BP 5366 Maarif, Casablanca, Morocco

Original language: English

Received 29 April 2018

Copyright © 2018 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


In Morocco, Agriculture is a key sector of the national economy, playing crucial social and economic roles. The sector accounts for around 14 to 20% of the Gross Domestic Product (GDP) and represents 43% of all employment. Winter cereals (soft wheat, durum wheat and barley) are produced all over the country, occupying nearly 65% of agricultural lands and therefore cereal yields forecasting is a major tool for decision making, allowing for planning in advance actions like annual cereal imports or aids to farmers. The present study highlights the substantial contribution of remote sensing (RS) and Geographic Information Systems (GIS) techniques in predicting soft wheat yields at the rural commune of Ouled Saleh, Region Casablanca-Settat in Morocco. The forecasting methodology was based on two steps: First, a land cover map of the study area was produced using Sentinel imagery to identify agricultural zones; second, simple linear regression models were established between the Normalized Difference Vegetation Index (NDVI), derived from the Moderate Resolution Imaging Spectrometer (MODIS) and soft wheat yields over the period 2002-2012. Our results showed high correlations between the NDVI of agricultural lands, averaged over the period from February till March or April and soft wheat yields. Therefore, NDVI can be used as a predictor index to early forecast soft wheat yields one to two months before harvest.

Author Keywords: Remote sensing, GIS, NDVI, Ouled Saleh, soft wheat, yield forecasting.


How to Cite this Article


Abdelhadi Mouchrif and Fouad Amraoui, “Cereal Yields Forecasting using Remote Sensing and GIS Techniques : A Case Study of Ouled Saleh Commune, Region of Casablanca-Settat, Morocco,” International Journal of Innovation and Applied Studies, vol. 24, no. 3, pp. 1010–1019, October 2018.