Egypt is currently witnessing an extensive desert greening plan with a target of adding one and a half million feddans to the agricultural area. The present study evaluates the soil quality in the western desert fringes of the Nile Delta using three indicator datasets, which involve the total dataset (TDS), the minimum dataset (MDS), and the expert dataset (EDS). Three quality index models are included: the Additive Soil Quality Index (SQI-A), the Weighted Additive Soil Quality Index (SQI-W), and the Nemoro Soil Quality Index (SQI-N). Linear and nonlinear scoring functions are evaluated for scoring soil and terrain indicators. Thirteen soil quality indicators and three terrain indicators were measured in 397 sampling sites for soil quality evaluation. Factor analyses determined five soil and terrain indicators for the minimum dataset and their associated weights. The linear scoring functions reflected the soil system functions more than nonlinear scoring functions. Soil quality estimation by the minimum dataset (MDS) and Weighted Additive Soil Quality Index (SQI-W) is more sensitive than that by SQI-A and SQI-N quality models to explain soil quality indicators. The moderate soil quality grade is the largest quality grade in the studied area. The minimum dataset of soil quality indicators could assist in reducing time and cost of evaluating soil quality and monitoring the temporal changes in soil quality of the region due to the increased agricultural development.

Agro-management zones recently became the backbone of modern agriculture. Delineating management zones for Variable-Rate Fertilization (VRF) can provide important ecological benefits and better sustainability of the new Egyptian farming projects. This article aims to represent an approach for delineating management zones using Spatial Multicriteria Evaluation (SMCE) within irrigated peanut pivot situated at the eastern Nile Delta, Egypt. The results indicated that soil data, such as soil texture, soil type, the elevation of the landscape, and slope, allow for sampling the study area into similar classes and in smaller units, along with a crop productivity map. The effects of the variability in soil characteristics within the field on Peanut yields are predicted by the soil suitability model. In addition, final management zones map a varied amount of nutrients that could be added to different pivot zones. In conclusion, mapping soil units with a sufficient number of field observations within each class provided an acceptable accuracy, and a good spatial distribution of the suitability classification was achieved. Hence, agro-management zones are essentially needed for policymakers in a specific field in order to furnish an evaluation about the transformations at a territorial scale and for studying the strategies to realize environmental sustainability and to reduce the territorial impacts.

Land cover map of North Sinai was produced based on the FAO-Land Cover Classification System (LCCS) of 2004. The standard FAO classification scheme provides a standardized system of classification that can be used to analyze spatial and temporal land cover variability in the study area. This approach also has the advantage of facilitating the integration of Sinai land cover mapping products to be included with the regional and global land cover datasets. The total study area is covering a total area of 20,310.4km2 (203,104hectare). The landscape classification was based on SPOT4 data acquired in 2011 using combined multispectral bands of 20m spatial resolution. Geographic Information System (GIS) was used to manipulate the attributed layers of classification in order to reach the maximum possible accuracy. GIS was also used to include all necessary information. The identified vegetative land cover classes of the study area are irrigated herbaceous crops, irrigated tree crops and rain fed tree crops. The non-vegetated land covers in the study area include bare rock, bare soils (stony, very stony and salt crusts), loose and shifting sands and sand dunes. The water bodies were classified as artificial perennial water bodies (fish ponds and irrigated canals) and natural perennial water bodies as lakes (standing). The artificial surfaces include linear and non-linear features