Hani M. Saoub

Modelling desertification risk in the north-west of Jordan using geospatial and remote sensing techniques

Remote sensing, climate, and ground data were used within a geographic information system (GIS) to map desertification risk in the north-west of Jordan. The approach was based on modelling wind and water erosion and incorporating the results with a map representing the severity of drought. Water erosion was modelled by the universal soil loss equation, while wind erosion was modelled by a dust emission model. The extent of drought was mapped using the evapotranspiration water stress index (EWSI) which incorporated actual and potential evapotranspiration. Output maps were assessed within GIS in terms of spatial patterns and the degree of correlation with soil surficial properties. Results showed that both topography and soil explained 75% of the variation in water erosion, while soil explained 25% of the variation in wind erosion, which was mainly controlled by natural factors of topography and wind. Analysis of the EWSI map showed that drought risk was dominating most of the rainfed areas. The combined effects of soil erosion and drought were reflected on the desertification risk map. The adoption of these geospatial and remote sensing techniques is, therefore, recommended to map desertification risk in Jordan and in similar arid environments.

Molecular and Field Comparison of Selected Barley Cultivars for Drought Tolerance

A field experiment was conducted using five most-planted barley (Hordeum vulgare L.) cultivars in Jordan — a country with 90% of its total land area receiving an annual rainfall of less than 200 mm. The objective of this study was to determine the response of these five cultivars evaluated under drought-stressed (DS) and non-stressed (NS) environments and to investigate polymorphism of 10 candidate genes for drought response. Drought susceptibility index (DSI) was used to rank cultivars according to their performance. Biological and grain yield for all cultivars was lower in DS compared with NS. Reduction in biological yield caused by drought stress ranged from 19% for ‘Mutah’ to 45% for ‘Rum’ cultivar. The least affected by drought stress was ‘Mutah’ with 13% percent reduction (PR) in grain yield. DSI for grain yield ranged from 0.33 for ‘Mutah’ to 1.41 for ‘Rum’. Depending on PR and DSI for biological and grain yield, ‘Mutah’ was the most tolerant cultivar. However, no clear differences were detected among cultivars for the tested genes. The use of this tolerant and improved genotype should be maximized in breeding and genetic studies to identify, map, and pyramid new drought-tolerance genes and to enhance sustainable farming systems.

Genetic and Phenotypic Variation Among Faba Bean Landraces and Cultivars

Landraces of crops can contain phenotypic and genotypic variability that can be exploited so that new lines can be developed. The phenotypic and genotypic variability among 11 Jordanian faba bean (Vicia faba L.) landraces and five imported cultivars were studied. Yield and 12 yield components and yield-related traits were evaluated at two locations. Analysis indicated significant variability among germplasm for all characters and among locations for 10 characters, in which final plant height, pods/plant, and seeds/pod were not significant. Genetic variability and relationships among germplasm were established using 12 single sequence repeat (SSR) primer pairs. Eleven primers were particularly efficient in amplification of banding patterns among germplasm. The overall polymorphic percentage for the 31 loci generated by 11 SSR markers was 84%. This was sufficient to estimate genetic variation among faba bean germplasm. The unweighted pair group method with arithmetic average ordered germplasm into three groups based on a Dice similarity coefficient of 0.71, range 0.7 to 0.89. Clustering based on phenotypical traits indicated five smaller groups that were consistent with the three larger SSR-based groups. These landraces can be used for future faba bean breeding programs and derivation of new plant lines.

Management optimization of dual-purpose barley (Hordeum spontaneum C. Koch) for forage and seed yield

Abstract Dual-purpose barley (Hordeum spontaneum C. Koch.) is a winter annual native to Southern Mediterranean regions. It is used to establish permanent pasture because it has a brittle rachis. Crude protein, crude fiber contents, and responses of dual-purpose barley to time of defoliation were investigated in the northern mountains of Jordan. Field trials were conducted in the 1999–2000 and 2000–2001 growing seasons in Samta (32° 23′N, 35°50′E) at an elevation of 1043 m. The highest protein contents (P ≤ 0.05) of 25% were recorded in February 2001. Protein content declined gradually and reached the lowest values (2.5%) at maturity. Clipping produced shorter plants, but did not impact tillering. Clipping individual plants on 28 February (14.3 and 10.2 g plant−1, respectively in 2000 and 2001) and 15 March (10.3 and 9.2 g plant−1, respectively in 2000 and 2001) did not reduce the plant shoot weight. Forage production from plants clipped on 28 February (2902 and 1274 kg ha−1, respective years), 15 March (1793 and 1394 kg ha−1, respectively in 2000 and 2001) and 15 April (1554 and 994 kg h−1, respectively in 2000 and 2001) were similar to forage production from unclipped plants. Clipping on 15 April inhibited seed production. Defoliation during early growth stages optimized seed yield and forage quantity and quality.

Remote sensing indices for monitoring land degradation in a semiarid to arid basin in Jordan

Spectral reflectance for soils and vegetation of the Yarmouk basin were correlated with surficial soil properties and vegetation biomass and cover. The overall aim of the study was to identify bands suitable for assessing soil and vegetation as indices for land degradation and desertification. Results showed that vegetation was well separated from soils in the shortwave infrared wavelength at 1480 nm. For most sites, the differences in the bandwidths (in the range of 8.5 nm to 90 nm) did not improve the differentiation of vegetation types. For all wavelengths, stronger correlation values (maximum R2 = 0.85) were obtained for vegetation cover when compared with biomass (maximum R2 = 0.54). Soil spectral reflectance tended to increase with salinity, with maximum correlations obtained in the blue wavelengths (470±10 nm, 485±90 nm), followed by green and the NIR bands, where R2 values were around 0.60. Comparing results from radiometer measurements with results obtained from ASTER image bands showed that correlations tended to decrease with decreased spatial resolution for the investigated soil properties. For all wavelengths, spectral reflectance of degraded soils was higher than that for natural vegetation and irrigated crops with partial surface cover. Results of the study showed that the use of remote sensing indices related to vegetation cover and soil salinity would be recommended to map the extent of land degradation in the study area and similar environments. However, spectral unmixing should be applied to improve the correlations between satellite remote sensing data and surficial soil properties.