Multivariate analysis and GIS approaches for modeling and mapping soil quality and land suitability in arid zones.

Assessing soil quality marks the initial step in precision farming and agricultural management. Developing countries like Egypt face numerous hurdles in ensuring food security due to increasing populations and limited agricultural resources. A geographic information system (GIS) and multivariate analysis were utilized in the current work to evaluate and map a soil quality index (SQI). Moreover, the land suitability of the land for two plantations of the tree's oak (Quercus robur), and pine (Pinus silvestris), respectively was assessed using a parametric approach. A total of 82 soil profiles were selected to fulfill the objectives of the study. Based on the samples' PC scores, and agglomerative hierarchical clustering (AHC, the data was divided into two clusters: Cluster I and Cluster II, which collectively account for approximately 57% and 43% of the total data, respectively.. . The findings indicated that land suitability for planting Q. robur planted identified 2.14% of the research area as highly suitable (S1), 37.98% as moderately suitable (S2), and 59.89% as not suitable (N). Furthermore, the assessment of suitability for P. silvestris indicated that 50.88% of the investigated area was classified into: S1, 48.73% as S2, and 0.39% as N, which means it is not suitable for conservation activities. The research identified that soil depth beside excessive salinity and calcium carbonate as the primary soil constraints in the area in both clusters. The average soil depth, ECd and CaCO3 were 113.62 ± 12.41, 17.27 ± 10.23, 16.83 ± 6.57 in Cluster 1 and 45.43 ± 15.21, 22.42 ± 12.43, 21.55 ± 5.63 in Cluster II. The study demonstrates that integrating multivariate analysis with GIS enables a precise and streamlined assessment of the Soil Quality Index (SQI). Soil suitability modelling underscores the importance of implementing efficient management practices to attain agricultural sustainability in arid regions, particularly amidst intensive land utilization pressures.

Enhancing Maize Yield and Soil Health through the Residual Impact of Nanomaterials in Contaminated Soils to Sustain Food.

Studying the impact of residual soil nanomaterials is a promising challenge for sustainable agricultural development to improve soil health and crop productivity. The objective of this study is to assess the long-term impacts of 50, 100, and 250 mg kg-1 soil of nanobiochar (nB) and nano-water treatment residues (nWTR) on the fertility, biological activity, and yield of maize (Zea mays L.) growing in heavy metal-contaminated soils. The results showed that when nB and nWTR were added in larger quantities, the concentrations of lead (Pb), nickel (Ni), cadmium (Cd), and cobalt (Co) extracted with DTPA decreased. With the addition of nB or nWTR, it also showed a significant increase in exchangeable cations, cation exchange capacity (CEC), soil fertility, soil organic matter (OM), microbial biomass carbon (MBC), and a decrease in soil salinity and sodicity. Catalase and dehydrogenase activities rose as nB addition increased, while they decreased when nWTR addition increased. In comparison to the control, the addition of nB and nWTR greatly boosted maize yield by 54.5-61.4% and 61.9-71.4%, respectively. These findings suggest that the researched nanomaterials' residual effect provides an eco-friendly farming method to enhance the qualities of damaged soils and boost maize production. Our research suggested that adding recycling waste in the form of nanoparticles could immobilize heavy metals, improve soil characteristics, and increase the soil's capacity for productivity.

A case study of a real-time internet of things system for site-specific potato crop management in El-Salhia Area-Egypt.

The site-specific management is the technology that considers the natural variability within the same field of factors related to crop growth to improve its management practices such that the agricultural treatments are varied for field's small production zones saving resources and environment, and improving crop quality and size. Since site-specific decisions are not far from the Fourth Industrial Revolution and the concept of processes automation, this work addresses improving the process of spatial variability analysis and thus supporting management decisions by developing a system-entitled EGYPADS-based on the Internet of Things and its enabling technologies. EGYPADS automates data collection, zones delineation according to their land suitability evaluation, and maps generation. The paper addresses a case study of potato crop in a specific area in Egypt, El-Salhia, in which eighty-five sites were chosen as main dataset for the modeling process during different stages of crop growth. Three management zones were recognized of the selected field based on the differentiation in their land suitability characteristics, representing about 5%, 65%, and 30% of the total area, respectively. The structure, screens, and services of EGYPADS are described in this paper. EGYPADS offered services include: management zones delineation using absolute and virtual coordinates, Land Suitability Assessment (LSA), data entry from field in real-time as well as from excel sheets, saving maps in suitable format for variable rate application, real-time and historical data processing, centralized management, and flexible formulation of events and related actions. The implementation of EGYPADS was verified. The system dynamically produces non-contiguous isobands, each representing a specific range of parameter values, and can be properly exported for use by other programs or smart machinery. It was proven that EGYPADS supports more than one land with different geometry, area, location, and number of nodes. EGYPADS was compared with the traditional LSA method, and was found to produce similar management zones.