Major Districts Research Articles

PSInSAR-Based Time-Series Coastal Deformation Estimation Using Sentinel-1 Data

Coastal areas are highly dynamic regions where surface deformation due to natural and anthropogenic activities poses significant challenges. Synthetic Aperture Radar (SAR) interferometry techniques, such as Persistent Scatterer Interferometry (PSInSAR), provide advanced capabilities to monitor surface deformation with high precision. This study applies PSInSAR techniques to estimate surface deformation along coastal zones from 2017 to 2020 using Sentinel-1 data. In the densely populated areas of Pasni, an annual subsidence rate of 130 mm is observed, while the northern, less populated region experiences an uplift of 70 mm per year. Seawater intrusion is an emerging issue causing surface deformation in Pasni’s coastal areas. It infiltrates freshwater aquifers, primarily due to excessive groundwater extraction and rising sea levels. Over time, seawater intrusion destabilizes the underlying soil and rock structures, leading to subsidence or gradual sinking of the ground surface. This form of surface deformation poses significant risks to infrastructure, agriculture, and the local ecosystem. Land deformation varies along the study area’s coastline. The eastern region, which is highly reclaimed, is particularly affected by erosion. The results derived from Sentinel-1 SAR data indicate significant subsidence in major urban districts. This information is crucial for coastal management, hazard assessment, and planning sustainable development in the region.

Comparative analysis of machine learning and statistical models for cotton yield prediction in major growing districts of Karnataka, India

BackgroundCotton is one of the most important commercial crops after food crops, especially in countries like India, where it’s grown extensively under rainfed conditions. Because of its usage in multiple industries, such as textile, medicine, and automobile industries, it has greater commercial importance. The crop’s performance is greatly influenced by prevailing weather dynamics. As climate changes, assessing how weather changes affect crop performance is essential. Among various techniques that are available, crop models are the most effective and widely used tools for predicting yields.ResultsThis study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka, India, utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors. The artificial neural networks (ANNs) performed superiorly with acceptable yield deviations ranging within ± 10% during both vegetative stage (F1) and mid stage (F2) for cotton. The model evaluation metrics such as root mean square error (RMSE), normalized root mean square error (nRMSE), and modelling efficiency (EF) were also within the acceptance limits in most districts. Furthermore, the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district. Specifically, the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum temperature had a major influence on cotton yield in most of the yield predicted districts. These differences highlighted the differential interactions of weather factors in each district for cotton yield formation, highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.ConclusionsCompared with statistical models, machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield formation at different growth stages. This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield. Thus, the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.

Disease management and biosecurity adoption status in finfish aquaculture: insights from Bangladesh

The finfish aquaculture sector of Bangladesh plays a vital role in food security and the national economy. However, its sustainability is often jeopardized by disease outbreaks. This study evaluated disease management practices, biosecurity adoption status, and the factors influencing disease occurrence and biosecurity adoption in Bangladesh’s finfish aquaculture sector. Data were collected from 400 farmers across 6 major fish-producing districts through an online survey tool covering farmer demographics, fish culture practices, disease management practices, and adoption of biosecurity measures. Results revealed that most farmers were male (87%), experienced (> 10 years, 53%), literate, full-time (78%) operators, managing small-scale farms (< 0.45 ha), and favored polyculture technique. Disease occurrence ranked among the top two challenges, affecting approximately 70% of the studied farmers. Common disease management practices included water quality management and chemical therapeutics use (91% each), antibiotics use (53%), and other operational modifications. Overall, biosecurity adoption among farmers was approximately 68%, with 56% identified as high adopters. Widely adopted biosecurity practices included regular farm monitoring and having proper feed storage (96% each) while avoiding restocking (14%) was the least adopted. Significant (P < 0.05) regional variations were observed in farmer demographics, fish culture practices, disease occurrences, and biosecurity adoption levels. Significant associations between disease occurrence and biosecurity adoption with several socio-demographic and operational factors emphasized the need for tailored interventions. The findings thus underscore the importance of area-specific biosecurity guidelines and strategic approaches to enhance biosecurity compliance, reduce disease risks, and ensure the sustainability of the aquaculture sector.

Assessing Cybersecurity Awareness Among Bank Employees: A Multi-Stage Analytical Approach Using PLS-SEM, ANN, and fsQCA in a Developing Country Context

Assessing Cybersecurity Awareness Among Bank Employees: A Multi-Stage Analytical Approach Using PLS-SEM, ANN, and fsQCA in a Developing Country Context

Service Provider Empathy and Responsiveness for Customer Satisfaction in the Telecommunications Sector

Customer satisfaction remains one of the crucial determinants of customer patronage and eventual organizational performance. Exploring service provider empathy and responsiveness for customer satisfaction in the telecommunications sector was the main thrust of this paper. The effects of empathy and responsiveness as dimensions of service quality were examined to establish their effect (if any) on satisfaction of mobile network users in the three major senatorial districts in Edo state. Appropriate research questions in alignment with the objectives of the study were raised, coupled with corresponding hypotheses which were tested to conduct the study. The study employed cross sectional research design executed through a survey using a total of 400 respondents as sample derived from a population of 7.1 million subscribers using the Krecjie and Morgan table for sample size determination. Data was obtained through a well- structured instrument designed in a five form Likert scale. The instrument was established to be reliable by using Cronbach alpha test. The obtained alpha values were over 0.7 for all constructs of the variables studied, indicating a high degree of items consistency. We analyzed the data using regression analysis for hypotheses testing. The results show significant effect of service provider responsiveness and service provider empathy on customer satisfaction. From the findings of the study, it is recommended that the telecommunication companies should boost customer satisfaction.

Resource Utilization and Profitability of Rice Production in The Terai Region of Nepal

Rice is the most important staple food crops in Nepal; however, the national yield of rice is low which has led in insufficient supply and heavy annual import. So, this study assessed the profitability and resource utilization of main and spring season rice production. A Semi-structured interview schedule was administered in 2022 among 360 randomly selected main-season rice-producing farmers across three major rice-producing districts of the Terai region of Nepal. Similarly, it conducted four Focus Group Discussions (FGDs) to collect necessary data for spring season rice from two Terai districts within similar agro-ecological regions. Cobb-Douglas production function was used to assess the resource utilization in main season rice production. The result revealed that the total cost and revenue of spring season rice production per hectare was higher, with a benefit-cost ratio of 1.63 compared to the main season. It was found that major resources such as the cost of seed, farm yard manure (FYM), chemical fertilizer, micronutrients, and pesticides were underused. On the other hand, the cost of labor, tillage, and harvesting were found to be overused in the main season of rice production. The proportion of the increase in rice income to the increase in costs of inputs for main season rice production was less, as indicated by the return to scale being less than unity. Hence, the study recommends to focus on addressing on prioritization to increase the cropping area under spring season rice and optimization in the use of resources supported by improved technologies to improve profitability and increase farmers’ income from rice this can eases the constraints in the domestic supply of rice and thereby contribute to lowering the annual volume of rice import. SAARC J. Agric., 22(2): 153-167 (2024)

Synergy Between Smallholder Palm Oil Replanting Policies and Green Energy Initiatives: A Study of Impacts and Policies in Indonesia

People’s Palm Oil Replanting (PSR) and green energy projects are crucial Indonesian initiatives aimed at achieving economic and environmental sustainability. This study explores the synergies between these programs, analyzing their impacts and interactions within natural resource management and sustainable energy. Using a qualitative case study approach, data were collected through stakeholder interviews, policy document analyses, and field surveys across five major palm oil-producing districts in Indonesia. The findings show that integrating PSR and green energy initiatives can enhance land productivity, reduce carbon emissions, and improve peatland management. PSR projects, focused on rejuvenating aging oil palm plantations and adopting environmentally friendly technologies, support more efficient bioenergy production. Simultaneously, green energy initiatives promote renewable energy use and incentivize sustainable practices. However, challenges such as limited collaboration, budget constraints, and farmer resistance persist. To address these, this study proposes a policy framework including financial support, technical training for farmers, and improved stakeholder communication. By examining the intersection of agricultural and energy policies, this research highlights strategies to achieve sustainability goals and provides actionable insights for policymakers to design integrated, effective approaches that maximize the benefits of these initiatives

High mean corpuscular volume as a predictor of esophageal cancer: A cohort study based on the Japanese Shizuoka Kokuho Database.

Mean corpuscular volume (MCV) is known to increase with alcohol and tobacco consumption, and is therefore a potential predictive marker for esophageal cancer onset. However, this potential has not previously been examined using a large database. This study aims to clarify whether MCV is a predictor of esophageal cancer onset using health checkup data from a comprehensive health insurance claims database of a major administrative district in Japan. Health checkup data for 582,342 individuals recorded between April 2012 and September 2020 in the Shizuoka Kokuho Database were analyzed. Risk factors were assessed using both univariable and multivariable Cox proportional hazards models. Within the cohort, 1,562 health checkup participants (0.27%) had been diagnosed with esophageal cancer during the study period. Multivariable analysis revealed that risk of esophageal cancer onset was predicted by hypertension, smoking, systolic blood pressure, alcohol consumption, alcohol use disorder, body mass index, low-density lipoprotein cholesterol, and MCV. The cutoff value of MCV for predicting esophageal cancer onset was 104.086 fl. These results suggest that it may be appropriate to carry out endoscopy to detect esophageal cancer when MCV, a well-known indicator of alcohol and tobacco consumption, is greater than 104 fl.

Farmer’s Knowledge, Perception, &amp; Management of Major Diseases of Ginger in Palpa District, Nepal

Ginger, a high-value spice crop of mid-hill regions in Nepal, is ranked fourth in global ginger production. Palpa is one of the major ginger-producing districts of Nepal. The survey was conducted in the Ginger Block of Agriculture Knowledge Center (AKC), Palpa using a semi-structured questionnaire, key information interview, and focus group discussion on the knowledge and management of ginger disease. Out of 905 respondents, 90 ginger farmers from the Bagnaskali, Nisdhi, and Purbakhola rural municipalities were randomly selected for the study. Both primary and secondary data were collected and analyzed through computer software like the Statistical Package for Social Science (SPSS) and Microsoft- Excel including simple descriptive statistics, chi-square test, and independent t-test. Rhizome rot (Index -value 0.95), was reported as a major disease followed by Bacterial wilt, leaf spot, soft rot, and storage mold and disease severity occurs 3-5 months day after sowing (DAS). Most farmers adopted the cultural method instead of the chemical method despite a high literacy rate and had a significant association with the use of chemical pesticides at a 5% level of significance. Furthermore, training had found a significant association with farmers’ knowledge level of diseases at a 1% level of significance. Farmers had perceived a 31.58 % yield loss due to disease, 40% loss of income, and agriculture income had found a positive influence on the adoption of disease management. Thus, effective and efficient disease management methods should be adopted to hinder yield loss and control disease.

Host Range and Distribution of Invasive Pest, Rugose Spiralling Whitefly Aleurodicus rugioperculatus Martin in Telangana, India

Rugose Spiraling Whitefly (RSW), Aleurodicus rugioperculatus, is an invasive pest that invaded India in 2016, damaging coconut and other crops in Tamil Nadu, Kerala, Karnataka, and Andhra Pradesh. RSW stresses plants by depleting water and nutrients and promotes sooty mold growth through honeydew excretion, reducing photosynthesis. The present study conducted, field surveys were conducted across major oil palm-growing districts of Telangana, including Bhadradri Kothagudem and Khammam, to assess the pest's distribution, damage intensity, infestation levels, and host range. This research provides critical insights for developing effective pest management strategies to mitigate the impact of RSW on economically significant crops. Fifty host plants from 29 families were identified as hosts for RSW. Of these, 10 plants, including coconut, oil palm, banana, sapota, mango, and guava, supported all life stages of RSW, while the remaining host plants exhibited only the egg stage. The damage caused by RSW was particularly severe in coconut, oil palm, sapota, mango, and guava, highlighting their susceptibility. This comprehensive study underscores the varying levels of host suitability and damage intensity across different plant species, providing valuable insights into the pest's biology and its impact on key crops.

Co-cultivation of Bacillus amyloliquefaciens and Trichoderma harzianum: Synergistic effects on plant growth and biocontrol of jasmine collar rot

Jasmine (Jasminum officinale) is a valuable and culturally significant flowering crop in India. Still, its cultivation is greatly impacted by collar rot disease, caused by Sclerotium rolfsii Sacc., resulting in yield losses of up to 50%. This study explores co-culture technology, utilizing Trichoderma harzianum TR(D)-4 and Bacillus amyloliquefaciens BC(M)-2, as an integrated single-product solution for the biological control of this pathogen. Field surveys conducted across major jasmine-growing districts in Tamil Nadu revealed a range of disease severity (20% - 52%) and incidence (29% - 64%). The collar rot pathogen, and biocontrol agents were isolated from surveyed locations and identified through morphological characteristics and molecular phylogenetic analysis based on ITS and 16S rDNA sequences. In vitro assays using dual-plate and paired-plate methods demonstrated that Bacillus amyloliquefaciens BC(M)-2 achieved inhibition rates of 71.34% and 78.27%, respectively, while Trichoderma harzianum TR(D)-4 exhibited inhibition rates of 86.27% and 75.43% against Sclerotium rolfsii SR(D)-5. Co-culturing these antagonistic strains synergistically improved antifungal effectiveness, achieving an 87% inhibition rate against S. rolfsii compared to their separate cultures. The synergistic interaction in co-culture promoted the production of novel compounds, including alpha-bisabolol (AB), bis(2-ethylhexyl) phthalate, and harziandione, which enhanced plant growth and inhibited S. rolfsii. Further planta studies confirmed that the co-culture significantly reduced disease incidence and enhanced plant growth in both pre-and post-inoculation strategies. This research highlights the potential of co-culturing T. harzianum TR(D)-4 and B. amyloliquefaciens BC(M)-2 as an effective and sustainable approach for managing collar rot disease in jasmine.

High Efficiency Irrigation System and Socio-Economic Development for the Farmers of Pakistan

This study examine the factors in the adoption of high-efficiency irrigation system (HEIS) for socio-economic development in southern Punjab, Pakistan. Multi-stage sampling is used to select the sample of 400 farmers (200 HEIS adopters and 200 non-adopters) from major districts of south Punjab. The results demonstrated that determinants including average landholding, age, education and awareness of HEIS all had a significant impact on the adoption of HEIS. The adoption rate of HEIS was also found to be impacted by energy crises, operational and maintenance issues, high installation prices and a lack of experienced employees. A higher proportion of participants strongly believed that HEIS might increase agricultural productivity while simultaneously saving water for future use. Additionally, respondents believed that shifting HEIS to high-value crops improves social status, particularly in rural areas. BCR values for wheat, guava and citrus using SI and DI were found to be larger than 1. The SI system NPV values for the wheat crop ranged from Rs. 248034 to Rs. 463191. Similarly, the results are consistent for DI for guava and citrus orchards (BCR &gt; 1 and NPV positive values demonstrate that HEIS project is more economically feasible and viable than TI). Moreover, the benefit-cost ratio demonstrated that, when compared to conventional irrigated growers, the yield and net benefit due to HEIS of a variety of crops, including citrus, wheat, and guava, improved significantly to 48.48%, 70%, 44.83%, 102%, 178% and 107%, respectively. Water productivity of HEIS-irrigated wheat, guava and citrus were calculated as 1.2, 2.6 and 2.5 kg/m3, respectively and increased highly significantly than TI. The findings suggested that government initiatives should focus to enhance adoption rate of HEIS. This could be accomplished by providing modest subsidized HEIS and imparting awareness to farmers about the socioeconomic benefits of HEIS.. KEYWORDS :Agriculture, Cost-benefit, HEIS, Socio-economic development, Sustainable, Water productivity, NPV.

A Retrospective Study of Urinary Schistosomiasis in the Eastern Cape Province, South Africa.

Schistosomiasis is caused by infection with trematode flukes of the genus Schistosoma. More than 700 million people worldwide are estimated to be susceptible to infection. In sub-Saharan Africa, schistosomiasis is the second most widespread neglected tropical disease after malaria. This retrospective investigation evaluated the incidence and impacts of schistosomiasis on communities across three major districts of the Eastern Cape province in South Africa using a cross-sectional retrospective observational analysis of secondary data from patients with microscopically confirmed schistosomiasis between 2019 and 2020. This study focused upon both rural and semi-urban areas, including Bizana, Butterworth, Centane, Elliotdale, Flagstaff, Idutywa, Lusikisiki, Libode, Mqanduli, Port St. Johns, Willowvale, and Mthatha. Data were obtained from three districts-Alfred Nzo, Amatole, and OR Tambo-covering both rural and semi-urban regions. This study included patients of all ages who submitted urine samples for schistosomiasis testing in the specified districts. A simple random sampling method was used to select 337 clinical records from the National Health Laboratory Service (NHLS) of Mthatha. Hospital records from the NHLS Microbiology Department of Mthatha were analyzed. St Barnabas Laboratory had the highest frequency of cases (34.1%), followed by Greenville Depot (17.8%) and Willowvale Laboratory (11.3%). Most cases were in the 10-19 age group (63.4%), followed by those under 10 years of age (24.9%). Male patients constituted 76.4% of the cases, while female patients accounted for 23.6%. Viable ova were observed in 98.2% of the samples. This study highlights a significant prevalence of schistosomiasis in the Eastern Cape province, with a higher incidence in rural areas and among males aged 10-19. These findings underscore the need for targeted public health interventions and continuous monitoring to control and prevent schistosomiasis in this region.

Elucidating the Pathogen Variability of Northern Leaf Blight of Maize (Zea mays L.) in Bihar, India

Exserohilum turcicum is an emerging pathogen of maize causing the northern leaf blight (NLB) disease with severe losses. The disease prevails all over the area under production but has varying severity. A temperature of 13°C to 33°C, along with 75-84% relative humidity, favours the pathogen development and growth. Thus, the present investigation was carried out to assess the prevalence of the disease in Bihar and the cultural, morphological, and pathogenic variability among the population through extensive field surveys conducted in maize-growing areas of seven major maize-producing districts of Bihar, India. A total of twenty-one isolates were identified as E. turcicum, which showed varying colony characteristics, colony margin, margin colour, pigmentation, growth pattern, sporulation, conidial shape, conidial size, and conidial septa. However, all the isolates produced characteristic symptoms of cigar-shaped necrotic lesions with variation in disease reaction on the leaves of susceptible maize inbred line CM-202. The isolates were categorized into three phylogenetic clusters with a coefficient of 0.42 based on their variations. A similar trend was observed, and three clusters with a coefficient of 0.52 were confirmed based on the disease reaction under field conditions. The isolates constituting Cluster I were deemed high virulent, followed by Cluster II as moderate virulent, and Cluster III as low virulent. All the isolates were further identified as Setosphaeria turcica, a teleomorph stage of E. turcicum, through molecular identification using ITS sequence analysis. Conclusively, a higher variability among the pathogen population was present, thus leading to the disease emergence, and their virulence assessment will aid in the identification of races and simultaneously accelerate the resistance breeding programmes in maize.

Unraveling microbial landscapes: high-throughput amplicon sequencing reveals distinct bacterial communities and potential health risks in potable springs of the Indian Himalayas.

Spring water is a vital drinking resource for residents in the Eastern Himalayas' Sikkim, India. While our initial investigations into spring water quality highlighted concerning levels of fecal coliform bacteria, the bacterial community composition (BCC) of these springs remains largely unexplored. This study sought to elucidate the BCC of Himalayan spring water, exploring its effects on water quality and delving into the unique bacterial ecology of these high-altitude springs. Bacterial diversity was assessed using 16S rRNA gene amplicon (V3-V4) library sequencing. The Greengenes reference database facilitated the classification of de-novo assembled operational taxonomic units (OTUs). The findings of this study revealed Proteobacteria (39.78%), Planctomycetes (35.76%), Verrucomicrobia (32.65%), and Bacteroidetes (37.04%) as predominant phyla across the four major districts: East, West, South, and North. Additionally, distinct genera emerged as dominant in each district: Emticicia in the East, Prosthecobacter in the South, and Planctomyces in the North and West. Of potential health concern, pathogenic bacteria like Corynebacterium, Acinetobacter, Legionella, Mycobacterium, and Clostridium were detected, albeit in low abundance. Their presence, even in minor quantities, might indicate potential future health risks for the communities relying on these springs. However, a substantial portion of the bacterial sequence remained unidentified (> = 40.0%), showcasing no sequence similarity with the reference database. This intriguing "dark matter" in bacterial DNA hints at a potential treasure trove of yet-to-be-identified species. Future taxonomic profiling of these novel sequences may offer a deeper understanding of Himalayan springs' microbial makeup. Furthermore, these novel bacterial sequences will be instrumental in enhancing our global understanding of bacterial community structures and their ecological adaptations in high-altitude, low-temperature environments.

Porphyry copper formation driven by water-fluxed crustal melting during flat-slab subduction

The prevailing view of the formation of porphyry copper deposits along convergent plate boundaries involves deep crustal differentiation of metal-bearing juvenile magmas derived from the mantle wedge above a subduction zone. However, many major porphyry districts formed during periods of flat-slab subduction when the mantle wedge would have been reduced or absent, leaving the source of the ore-forming magmas unclear. Here we use geochronology and thermobarometry to investigate deep crustal processes during the genesis of the Late Cretaceous–Palaeocene Laramide Porphyry Province in Arizona, which formed during flat-slab subduction of the Farallon Plate beneath North America. We show that the isotopic signatures of Laramide granitic rocks are consistent with a Proterozoic crustal source that was potentially pre-enriched in copper. This source underwent water-fluxed melting between 73 and 60 Ma, coincident with the peak of granitic magmatism (78–50 Ma), porphyry genesis (73–56 Ma) and flat-slab subduction (70–40 Ma). To explain the formation of the Laramide Porphyry Province, we propose that volatiles derived from the leading edge of the Farallon flat slab promoted melting of both mafic and felsic pre-enriched lower crust, without requiring extensive magmatic or metallogenic input from the mantle wedge. Other convergent plate boundaries with flat-slab regimes may undergo a similar mechanism of volatile-mediated lower-crustal melting.

AN INTEGRATED APPROACH TO IDENTIFY SUITABLE AREAS FOR THE LOCATION OF PUBLIC FACILITIES USING MULTI-CRITERIA ANALYSIS BASED ON GIS AND AHP IN THE CITY OF EL KHROUB, ALGERIA

Random and spontaneous urban growth is the result of massive natural population growth, as it is in all developing countries. As for our study air, it also results from the unloading of the mother city of Constantine to the satellite city of El-Khroub, which gave urbanization the power to take over its vast environment, An uneven distribution of public facilities in the city’s major districts, as well as traffic jams. This work attempted to determine the suitability of urban land for the location of public facilities in the city. It should be noted that land suitability assessment is a major factor in urban planning and management. Our land adequacy assessment is subject to the AHP model, calculating the weighting of various criteria including physical, socio-economic, environmental and urban. The final adequacy analysis revealed the proportion of land most suitable, estimated at 3.52% of the total area, covering mainly fallow land. The results of this study provide a detailed mapping of urban development potential, integrating both existing data and future projections.

Discovering the common latent structure of commercial districts focusing on the spatial co-occurrence relationship between stores

Store agglomeration in a commercial district is considered to follow synergistic and complementary relationships between stores. The micro-interactions, that is, co-occurrence relationships, between stores are thought to create commercial districts’ universality and characteristics. This study aims to empirically identify latent attributes that can explain the mechanism of store agglomeration from the co-occurrence relationships between stores in commercial districts, and to identify universal characteristics in store agglomeration. We represented store agglomeration as a store co-occurrence network with stores as nodes and co-occurrence relationships between stores as links in 10 major commercial districts in Japan. We estimated latent attributes of stores that generated the store co-occurrence network and empirically clarified the quantitative and qualitative nature of store agglomeration. The co-occurrence networks of stores with estimated latent attributes were compared across those of districts, and the co-occurrence patterns common to latent attributes were clarified. The estimated latent attributes were empirically shown to have more explanatory power for the store agglomeration than the conventional business category classification, suggesting their usefulness as a new classification axis for stores. In addition, by comparing 10 districts, common co-occurrence relationships were extracted, and the universal spatial structure of store agglomeration was empirically clarified.

Integrating solar-induced chlorophyll fluorescence with traditional remote sensing and environmental variables for enhanced rice yield prediction in Nepal using machine learning

Integrating solar-induced chlorophyll fluorescence with traditional remote sensing and environmental variables for enhanced rice yield prediction in Nepal using machine learning

AN INTEGRATED APPROACH TO IDENTIFY SUITABLE AREAS FOR THE LOCATION OF PUBLIC FACILITIES USING MULTI-CRITERIA ANALYSIS BASED ON GIS AND AHP IN THE CITY OF EL KHROUB, ALGERIA

Random and spontaneous urban growth is the result of massive natural population growth, as it is in all developing countries. As for our study air, it also results from the unloading of the mother city of Constantine to the satellite city of El-Khroub, which gave urbanization the power to take over its vast environment, An uneven distribution of public facilities in the city’s major districts, as well as traffic jams. This work attempted to determine the suitability of urban land for the location of public facilities in the city. It should be noted that land suitability assessment is a major factor in urban planning and management. Our land adequacy assessment is subject to the AHP model, calculating the weighting of various criteria including physical, socio-economic, environmental and urban. The final adequacy analysis revealed the proportion of land most suitable, estimated at 3.52% of the total area, covering mainly fallow land. The results of this study provide a detailed mapping of urban development potential, integrating both existing data and future projections.