In the arid regions of south eastern Tunisia, the land use is predominated by olive trees cropping, where two main cultivation strategies can be found: using of water harvesting techniques to overcome the scarcity and variability of rainfall (in the Matmata mountains) and dryland farming (in the Jeffara plain). In these arid areas, soil moisture is the main limiting factor for crop growth and it should be monitored to benchmark different management options. Different conventional methods are available for point soil moisture monitoring, but the increased availability of remotely sensed data offers major opportunities for spatial analyses. The aim of this paper is to perform a comparative study on the soil water status for rainfed olive tree growing in three major landscape areas: in the mountains with traditional water harvesting check dams (called jessour), in the piedmont on floodwater harvesting (called tabias), and in the plain with full dryland farming conditions. Time series of Normalized Difference Infrared Index (NDII), derived from Landsat 7 satellite, were retrieved from the novel Google Earth Engine platform. NDII values were related to measured soil water content, which was taken at non-regular time intervals between 2009 and 2017. The analysis of NDII data, indicating the water content of the vegetation, shows that jessour can adequately ensure water supply for olive trees. Increased soil moisture conditions in the jessour areas are visible both in the dry and the humid seasons, indicating the effectiveness of this traditional water harvesting system. Moreover, our results show that Landsat 7 NDII values are correlated with the root-zone soil moisture in the monitoring sites (r2 ranging from 0.62 to 0.67), allowing the use of NDII to estimate soil water contents in our study area.
Effect of traditional check dams (jessour) on soil and olive trees water status in Tunisia / Castelli, G.; Oliveira, L.A.A.; Abdelli, F.; Dhaou, H.; Bresci, E.; Ouessar, M.. - In: SCIENCE OF THE TOTAL ENVIRONMENT. - ISSN 0048-9697. - ELETTRONICO. - 690:(2019), pp. 226-236. [10.1016/j.scitotenv.2019.06.514]
Effect of traditional check dams (jessour) on soil and olive trees water status in Tunisia
Castelli, G.;Bresci, E.;
2019
Abstract
In the arid regions of south eastern Tunisia, the land use is predominated by olive trees cropping, where two main cultivation strategies can be found: using of water harvesting techniques to overcome the scarcity and variability of rainfall (in the Matmata mountains) and dryland farming (in the Jeffara plain). In these arid areas, soil moisture is the main limiting factor for crop growth and it should be monitored to benchmark different management options. Different conventional methods are available for point soil moisture monitoring, but the increased availability of remotely sensed data offers major opportunities for spatial analyses. The aim of this paper is to perform a comparative study on the soil water status for rainfed olive tree growing in three major landscape areas: in the mountains with traditional water harvesting check dams (called jessour), in the piedmont on floodwater harvesting (called tabias), and in the plain with full dryland farming conditions. Time series of Normalized Difference Infrared Index (NDII), derived from Landsat 7 satellite, were retrieved from the novel Google Earth Engine platform. NDII values were related to measured soil water content, which was taken at non-regular time intervals between 2009 and 2017. The analysis of NDII data, indicating the water content of the vegetation, shows that jessour can adequately ensure water supply for olive trees. Increased soil moisture conditions in the jessour areas are visible both in the dry and the humid seasons, indicating the effectiveness of this traditional water harvesting system. Moreover, our results show that Landsat 7 NDII values are correlated with the root-zone soil moisture in the monitoring sites (r2 ranging from 0.62 to 0.67), allowing the use of NDII to estimate soil water contents in our study area.File | Dimensione | Formato | |
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