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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.

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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.

Precision agriculture involves studying and managing crop variations within fields that can affect crop yield. In precision agriculture farmers are adapting technology and advanced remote sensing techniques with different software to relieve decision making. Hyperspectral ground measurements can be used for giving timely information about crops in specific areas and thereby providing valuable data for decision makers. In this paper field spectroscopy measurements measured by ASD field Spec4 spectroradiometer were used to monitor the spectral response and differences of peanut crop vegetation cover reflectance due to bio-physical plant variables. The results of Tukey's HSD showed that blue, Red and NIR spectral zones are more sufficient in the monitoring differences between peanut growth stages than green, SWIR-1 and SWIR-2 spectral zones. The results of physiological spectral indices of growth stages showed significant correlations between varied classes productivity and spectral similarity measures, indicating that similarity between the samples' spectra decreases as the pigments concentration in the plant leaves increases. Furthermore, electromagnetic peanut crop mapping was successfully employed to simulate vegetation healthy effect on canopy structure and final yield.