Population Growth Research Articles

Significant Demographic Factors Fuelling Housing Deficit in Ado-Ekiti, Nigeria

This article assesses the significant demographic factors fuelling housing deficit in Ado-Ekiti, Nigeria. Using a mixed method approach as survey design coupled with a combination of purposive and stratified sampling technique, 1254 households were selected for administration of questionnaires during data collection process. Questionnaires, interviews were the major instruments of data collection from seven residential development corridors (RDCs) in Ado-Ekiti. Ministries, Agencies and Departments (MDAs) supported secondary data. Findings revealed that high population growth, rapid urbanisation and migration increased housing deficit in the study area. Hence, this study observed an increase in live births (162, 901) than deaths (45, 046) in Ado-Ekiti. Also, findings revealed an increase in building concentration (57.7%) from 2000-date. Similarly, this study observed an increase in challenges such as urban sprawl and infrastructure deficiency (L-INFRA) encountered as a result of rapid urbanisation. The study noted an increase in quantitative housing deficit (223,086) in 2023. Thus, a quantitative housing deficit of 334,554 housing units would arise in 2034.Furthermore, sharing of dwellings with other households (SHAD), a resultant effect of high migration trends constituted 43.4 percent across the RDCs in Ado-Ekiti. Also, improved infrastructural facilities (33.8%) was the modal reason for staying (RESTAY) in the study area. Using Paired T-test, this study further validated that a perfect positive relationship (1.000) exists between population growth and housing deficit in Ado-Ekiti. Therefore, this study recommends adoption of effective Family Planning Methods (FPMs), regional distribution of infrastructural facilities and conversion of under-utilised lands for an efficient housing delivery in Ado-Ekiti.

Energy security and its determinants in New Zealand.

New Zealand relies on imported fossil fuels for about 38% of its primary energy. The country's energy demand is expected to grow due to population and economic growth, which will put more pressure on the energy system. Besides, resource scarcity, energy price volatility, and environmental challenges have made energy security a major concern for New Zealand and other countries. Given the lack of significant research on the effects of energy security factors in New Zealand, this study aims to shed light on the primary determinants of energy security using the dynamic autoregressive distributed lag method based on time series data from 1978 to 2021. The study found that a long-run link exists between energy security and energy intensity (energy efficiency), renewable energy use, fossil fuel consumption, and global oil prices. Real GDP, renewable energy consumption, and energy security were found to improve energy security, while fossil fuel consumption and world oil prices had a negative impact. The study also revealed a one-way causality from real GDP, fossil fuel consumption, and renewable energy use to energy security. In contrast, the relationship between energy intensity and energy security is bidirectional. Simulation results showed that global crude oil prices have a lower impact on energy security compared to other variables and are most responsive to a 5% shock in fossil fuel consumption, followed by economic growth.

Place-based drivers and effective management of population growth and change in regional Australia

Place-based drivers and effective management of population growth and change in regional Australia

Application of a microbial and pathogen source tracking toolbox to identify infrastructure problems in stormwater drainage networks: a case study.

Water scarcity and increasing urbanization are forcing municipalities to consider alternative water sources, such as stormwater, to fill in water supply gaps or address hydromodification of receiving urban streams. Mounting evidence suggests that stormwater is often contaminated with human feces, even in stormwater drainage systems separate from sanitary sewers. Pinpointing sources of human contamination in drainage networks is challenging given the diverse sources of fecal pollution that can impact these systems and the non-specificity of traditional fecal indicator bacteria (FIB) for identifying these host sources. As such, we used a toolbox approach that encompassed microbial source tracking (MST), FIB monitoring, and bacterial pathogen monitoring to investigate microbial contamination of stormwater in an urban municipality. We demonstrate that human sewage frequently contaminated stormwater (in >50% of routine samples), based on the presence of the human fecal marker HF183, and often exceeded microbial water quality criteria. Arcobacter butzleri, a pathogen of emerging concern, was also detected in >50% of routine samples, with 75% of these pathogen-positive samples also being positive for the human fecal marker HF183, suggesting human municipal sewage as the likely source for this pathogen. MST and FIB were used to track human fecal pollution in the drainage network to the most likely point source of contamination, for which a sewage cross-connection was identified and confirmed using tracer dyes. These results point to the ubiquitous presence of human sewage in stormwater and also provide municipalities with the tools to identify sources of anthropogenic contamination in storm drainage networks.IMPORTANCEWater scarcity, increased urbanization, and population growth are driving municipalities worldwide to consider stormwater as an alternative water source in urban environments. However, many studies suggest that stormwater is relatively poor in terms of microbial water quality, is frequently contaminated with human sewage, and therefore could represent a potential health risk depending on the type of exposure (e.g., irrigation of community gardens). Traditional monitoring of water quality based on fecal bacteria does not provide any information about the sources of fecal pollution contaminating stormwater (i.e., animals/human feces). Herein, we present a case study that uses fecal bacterial monitoring, microbial source tracking, and bacterial pathogen analysis to identify a cross-connection that contributed to human fecal intrusion into an urban stormwater network. This microbial toolbox approach can be useful for municipalities in identifying infrastructure problems in stormwater drainage networks to reduce risks associated with water reuse.

A model of long-term population growth with an application to Central West Argentina.

We propose an Ideal Specialization Model to help explain the diversity of population growth trajectories exhibited across archaeological regions over thousands of years. The model provides a general set of expectations useful for guiding empirical research, and we provide a concrete example by conducting a preliminary evaluation of three expectations in Central West Argentina. We use kernel density estimates of archaeological radiocarbon, estimates of paleoclimate, and human bone stable isotopes from archaeological remains to evaluate three expectations drawn from the model's dynamics. Based on our results, we suggest that innovations in the production of food and social organization drove demographic transitions and population expansion in the region. The consistency of population expansion in the region positively associates with changes in diet and, potentially, innovations in settlement and social integration.

Media coverage and local cooperation in immigration enforcement

ABSTRACT Public concern about immigrant criminality in the U.S. is reflected in news coverage of immigration, which tends to associate the topic with ‘illegality’ and crime. Although these media characterizations have increased in recent years as local policy action on immigration has intensified, little research has examined whether or how these two phenomena are connected. This study assesses whether local television news coverage of immigration and crime increases the likelihood of county sheriffs adopting 287(g) agreements deputizing officers to enforce immigration law. Employing fixed effect logistic regressions, this study finds that sheriffs’ departments in counties exposed to more news coverage of immigration and crime are more likely to adopt 287(g) agreements. These increases are particularly pronounced in counties with Republican sheriffs as well as areas experiencing above-average immigrant population growth, suggesting that news media may act as an external, politicizing agent that turns seemingly innocuous demographic changes into events that warrant punitive policy action. These findings position news media as an important context of immigrant reception that can trigger threatened responses in local immigration policy.

A Comprehensive Analysis of Remittances and Basic Level Government School Enrollment in Nepal

This study aims to examine the factors that impact student enrollment in government schools at the basic level in Nepal. The data were collected from secondary sources publication over the periods of 2000-2022. The Autoregressive Distributed Lag (ARDL) model was applied to analyze the correlations between enrollment and economic factors, such as per capita GDP, population growth, and remittances, in both the short-term and long-term. The results suggest that remittances have a significant negative impact on enrollment, raising concerns about the absence of parents due to working abroad. On the other hand, the level of per capita GDP had a positive effect on the accessibility of education, highlighting the importance of economic development. The population growth did not have a significant impact in this model. The co-integration test suggests a strong and lasting connection between the variables, while the Granger causality tests reveal that remittances and per capita GDP had a significant impact on enrollment. These findings offer valuable insights for policymakers aiming to improve access to high-quality education for every child in Nepal. The paper also discussed potential areas for future research, such as investigating the underlying causes of the negative impacts of remittances and addressing the issue of serial correlation in the model.

Natural coral recovery despite negative population growth.

Demographic processes that ensure the recovery and resilience of marine populations are critical as climate change sends an increasing proportion on a trajectory of decline. Yet for some populations, recovery potential remains high. We conducted annual monitoring over 9 years (2012-2020) to assess the recovery of coral populations belonging to the genus Pocillopora. These populations experienced a catastrophic collapse following a severe typhoon in 2009. From the start of the monitoring period, high initial recruitment led to the establishment of a juvenile population that rapidly transitioned to sexually mature adults, which dominated the population within 6 years after the disturbance. As a result, coral cover increased from 1.1% to 20.2% during this time. To identify key demographic drivers of recovery and population growth rates (λ), we applied kernel-resampled integral projection models (IPMs), constructing eight successive models to examine annual change. IPMs were able to capture reproductive traits as key demographic drivers over the initial 3 years, while individual growth was a continuous key demographic driver throughout the entire monitoring period. IPMs further detected a pulse of reproductive output subsequent to two further Category 5 typhoon events during the monitoring period, exemplifying key mechanisms of resilience for coral populations impacted by disturbance. Despite rapid recovery, (i.e., increased coral cover, individual colony growth, low mortality), IPMs estimated predominantly negative values of λ, indicating a declining population. Indeed, while λ translates to a change in the number of individuals, the recovery of coral populations can also be driven by an increase in the size of surviving colonies. Our results illustrate that accumulating long-term data on historical dynamics and applying IPMs to extract demographic drivers are crucial for future predictions that are based on comprehensive and robust understandings of ecological change.

Rethinking Urban Water Management Through Drivers-Pressures-States-Impacts-Responses Framework Application in Chennai, India.

Cities suffering water scarcity are projected to increase in the following decades. However, the application of standardized indicator frameworks for assessing urban water resource management problems is on an early stage. India is expected to have the highest urban population facing water scarcity in the world by 2050. In this study, the authors assess how the Drivers-Pressures-States-Impacts-Responses framework, a causal framework adopted by the European Environment Agency, can contribute to evaluate water management challenges in cities and apply it to Chennai, India´s fourth-largest urban agglomeration. The framework proved to be a helpful tool for the evaluation of water management challenges in cities by disentangling relationships between environmental indicators and structuring dispersed data that allows a better understanding for policymakers. The main drivers identified in Chennai were population growth and economic development which generated impacts such as loss of aquatic ecosystems, low water table, low water quality, and reduction of biodiversity and human health. As a response, better urban planning, projects for new water infrastructure, and water bodies restoration have been implemented. Nevertheless, Chennai keeps facing difficulties to achieve proper water management. The severe hit of the COVID-19 pandemic on the Indian economy and its future management will be key for achievements related to water management.

Assessing pollution vulnerability in groundwater: A case study of Kala Shah Kaku industrial complex

ABSTRACT Various factors, such as industrialization, population growth, and urbanization, have caused water quality and availability to decline worldwide, resulting in water crises in many regions and millions of annual deaths from water-related diseases. The study aimed to assess the groundwater quality in Kala Shah Kaku, an industrial area in central Punjab, Pakistan. Groundwater samples from six locations were analyzed for physical and chemical parameters and heavy metals. The results revealed that Kala Shah Kaku's groundwater is unsuitable for drinking due to exceeding the physical and chemical parameters range. The study identified industrial effluents from nearby industries as the primary source of pollution in the area's groundwater. These findings emphasize the urgent need for remedial measures to address industrial pollution and protect groundwater resources in Kala Shah Kaku.

Identification of landscape features structuring movement connectivity for Namibian elephants

ContextHuman modification of landscapes poses a significant threat to wildlife, particularly in Africa where population growth and land conversion are expected to increase. Habitat loss and fragmentation have led to declines in wildlife populations, highlighting the need to identify and preserve critical habitats, including core use areas and connectivity between them. Most recently, the identification of habitat corridors has become a key objective.ObjectivesOur study objectives are to (1) empirically quantify connectivity across the Kunene—Etosha landscape in Northwestern Namibia using GPS tracking data on wild African elephants, and (2) assess the landscape features (i.e., geologic, biotic, and human-made) influencing connectivity and corridor types (e.g., fast movement corridors versus slow multi-use movement corridors).MethodsWe used GPS telemetry data from 66 elephants collared in Northwestern Namibia to empirically quantify connectivity using a graph theoretic approach and assess landscape features influencing connectivity. Based on the ‘movescape’ approach, we identify different types of corridors and examined how landscape features differed across these corridors using multiple regression models on locations classified into different types of use categories by machine learning algorithms.ResultsOur results revealed strong variation in connectivity across the landscape, with paths of high connectivity near water sources between the study areas. We found that factors related to water sources and human presence primarily influenced connectivity. Water holes serve as hubs across the ecosystem for both male and female elephants with lower use areas peripheral to areas with water. Connectivity between Kunene and Etosha National Park was relatively rare among the collared elephants, but we highlight the key areas used to move between the two regions.ConclusionWater was the key feature structuring space use, and human presence influenced connectivity between water points, highlighting the importance of landscape planning in relation to limited water sources and human activities. Our results suggest that focusing management efforts on areas where water is limited for both elephants and humans will be important to reduce conflict and maintain ecosystem connectivity.

Understanding Balanophyllia regia Distribution in the Canary Islands: Effects of Environmental Factors and Methodologies for Future Monitoring

The future of marine ecosystems is at risk due to climate change and other human impacts. Specifically, due to ocean warming, some tropical species are expanding their populations while populations of temperate species are in regression, making the establishment of conservation measures imperative to maintain local biodiversity. In this study we establish a baseline on the distribution and abundance of the temperate coral Balanophyllia regia from the Canary Islands. We found that the main environmental factors determining B. regia’s distribution and abundance were sea surface temperature and hydrodynamic conditions. Areas under large wave action and colder environments enhanced this warm-temperate species’ development. Since its metabolic performance depends exclusively on the surrounding environment, we also propose a methodology to potentially monitor climate change on coastal habitats through this azooxanthellate calcified coral. Results of a tagging experiment showed that a concentration of 20 mg/mL of calcein during 6 h might be enough to in situ label polyps of B. regia without compromising corallite survival. Long-term monitoring of population abundances and growth rates of B. regia through calcein tagging will allow us to identify alterations in local ecosystems early and focus future conservation investments on the most vulnerable areas with higher ecological and economic value.

Honey bee populations surviving Varroa destructor parasitism in Latin America and their mechanisms of resistance

The honey bee (Apis mellifera) parasitic mite, Varroa destructor, is considered one of the main causes of colony losses in European honey bee (EHB) populations around the world. However, some EHB and Africanized honey bee (AHB) populations (derived from the African subspecies A. m. scutellata) that inhabit tropical and subtropical regions of the Americas, have survived varroa mite infestations in the absence of acaricide treatments. It is conceivable to expect that these honey bee populations, which have been subjected to natural selection over decades, would have developed resistance against V. destructor or possess pre-existing adaptations that allow them to survive mite parasitism. Here, we present a comprehensive literature review describing the spread of V. destructor and the honey bee populations occurring in Latin America (LA), and summarize the evidence of resistance of those populations to V. destructor. We also analyze reports describing the potential mechanisms of mite resistance and how they operate in those honey bee populations. Studies of a few EHB, as well as of numerous AHB populations exhibiting resistance to V. destructor in LA, unveil the existence of evolutionary adaptations that restrain V. destructor population growth and provide insight into the current host-parasite relationship. This review supports the notion that selective breeding of local honey bee populations from LA could be a viable strategy to manage varroa mite infestations in colonies.

Analyzing the impact of artificial intelligence on operational efficiency in wastewater treatment: a comprehensive neutrosophic AHP-based SWOT analysis.

The escalating global challenges of population growth, climate crisis, and resource depletion have intensified water scarcity, emphasizing the critical role of wastewater treatment (WWT) in environmental preservation. While discharging untreated wastewater poses extinction risks to various species, effective WWT operations are indispensable for ecosystem continuity and sustainable water sources. Recognizing the complexity of WWT management, this study delves into the potential of artificial intelligence (AI) in strategic planning and decision-making within the WWT domain. Through a comprehensive SWOT analysis, this study evaluates the strengths, weaknesses, opportunities, and threats associated with AI integration in WWT processes. Utilizing the SWOT analysis framework, key criteria are identified, and their importance weights are assessed via the interval-valued neutrosophic analytical hierarchy process (IVN-AHP). According to analysis, the strengths in WWT are crucial, but potential opportunities and threats should not be ignored. The results of the study highlight several key findings regarding the integration of AI in WWT processes. While concerns about the reduction in human resources and potential unemployment, as well as the activation time and high energy consumption of AI systems, are identified as significant challenges, the study underscores the success of AI in data analytics as a strong aspect. Specifically, advanced data analysis techniques and the ability to proactively prevent problems emerge as important strengths of AI in WWT. WWT operators and practitioners are encouraged to prioritize the adoption of advanced data analysis techniques and proactive problem-solving strategies to maximize the effectiveness of AI integration in WWT processes.

Temperature-Dependent Oviposition Models for Monochamus saltuarius (Coleoptera: Cerambycidae)

Monochamus saltuarius Gebler is a serious insect pest in Europe and East Asia regions, including Portugal, Spain, China, Japan, and Korea. It transfers the pine wood nematode Bursaphelenchus xylophilus to conifer trees, resulting in pine wilt disease (PWD). As temperature is a key factor influencing insect population dynamics, temperature-dependent models describing M. saltuarius oviposition could estimate population growth potential and evaluate outbreak risks. In this study, the longevity and fecundity of M. saltuarius females were measured under constant temperature conditions ranging from 20 to 32 °C, and temperature-dependent models were constructed. The longevity of M. saltuarius females ranged from 83.36 days to 22.92 days, with a total fecundity of 141 eggs and 52.77 eggs at 20 °C and 32 °C, respectively. To describe oviposition, we used a single-phase simulation describing oviposition as a single model and a two-phase simulation describing sexual maturation and oviposition as two separate models. These models effectively described M. saltuarius oviposition (r2 > 0.96) under constant temperature conditions, with the two-phase simulation demonstrating greater accuracy overall. Such models could facilitate assessments of PWD risks. The modeling framework of this study shows potential for predicting threats from various forestry and agricultural pests.

Acculturating to multiculturalism: a new dimension of dietary acculturation among Asian American, Native Hawaiian, and Pacific Islander women in the San Francisco Bay Area, USA

BackgroundDietary acculturation is the process by which diet and dietary practises from the environment of origin are retained or changed and/or those prevalent in a new environment are adopted. Despite rapid population growth the U.S., knowledge gaps exist on characterising dietary acculturation among Asian American, Native Hawaiian, and Pacific Islander communities (AANHPI). This study characterise dietary patterns in a sample representative of AANHPI on key demographic characteristics.MethodsData were from a 2013–2014 population-based case-control study in the San Francisco Bay Area, U.S. Survey items were adapted from dietary acculturation scales developed for AANHPI populations. Validated measures assessed social capital, social standing, discrimination and immigration experiences. A principal components factor analysis was conducted to characterise dietary patterns of acculturation.ResultsThree dietary patterns were identified: “Asian,” “Western,” and a distinct “Multicultural” factor. Respondents reporting a high-Asian diet tended to also report smaller social networks, higher levels of stress, and, among those born outside of the U.S., an educational standing that was better before immigration. Respondents reporting a high-Western diet tended to also report the highest level of discrimination. Those reporting a high-Multicultural diet tended to report higher neighbourhood collective efficacy.ConclusionsThe finding of a distinct “Multicultural” factor beyond the typical “Asian” and “Western” factors may reflect the multidirectional relationships between culture, diet, and dietary behavior, in which origin and destination cultures interact in complex ways and where foods from multiple ethnicities intermix.

Reproductive health protection: medical and organizational measures and legal regulation

. The article presents an analysis of medical-organizational and state-legal factors affecting the reproductive health of the population. The state is interested in maintaining natural population growth. Changes in legislation in the field of reproductive rights are considered in order to provide effective legal guarantees for the implementation of the right to use reproductive technologies. The legal framework has been defined, as well as a set of therapeutic and preventive measures aimed at improving the reproductive health of the population. The state-legal protection of the reproductive health of citizens, the inclusion of reproductive rights in strategic planning documents indicate that reproductive health is considered an important factor in the development of the country.

A Multi-Area Task Path-Planning Algorithm for Agricultural Drones Based on Improved Double Deep Q-Learning Net

With the global population growth and increasing food demand, the development of precision agriculture has become particularly critical. In precision agriculture, accurately identifying areas of nitrogen stress in crops and planning precise fertilization paths are crucial. However, traditional coverage path-planning (CPP) typically considers only single-area tasks and overlooks the multi-area tasks CPP. To address this problem, this study proposed a Regional Framework for Coverage Path-Planning for Precision Fertilization (RFCPPF) for crop protection UAVs in multi-area tasks. This framework includes three modules: nitrogen stress spatial distribution extraction, multi-area tasks environmental map construction, and coverage path-planning. Firstly, Sentinel-2 remote-sensing images are processed using the Google Earth Engine (GEE) platform, and the Green Normalized Difference Vegetation Index (GNDVI) is calculated to extract the spatial distribution of nitrogen stress. A multi-area tasks environmental map is constructed to guide multiple UAV agents. Subsequently, improvements based on the Double Deep Q Network (DDQN) are introduced, incorporating Long Short-Term Memory (LSTM) and dueling network structures. Additionally, a multi-objective reward function and a state and action selection strategy suitable for stress area plant protection operations are designed. Simulation experiments verify the superiority of the proposed method in reducing redundant paths and improving coverage efficiency. The proposed improved DDQN achieved an overall step count that is 60.71% of MLP-DDQN and 90.55% of Breadth-First Search–Boustrophedon Algorithm (BFS-BA). Additionally, the total repeated coverage rate was reduced by 7.06% compared to MLP-DDQN and by 8.82% compared to BFS-BA.

Recognition of Organic Waste Objects Based on Vision Systems Using Attention Convolutional Neural Networks Models

Purpose: High population growth and increasing consumption patterns have resulted in significant organic waste production. The public often does not understand the correct way to deal with the problem of organic waste, including public awareness regarding the need for its management. Therefore, a system is needed to recognize waste objects based on various types. Currently, much research in this field has been studying object recognition, for example, the implementation of the Convolutional Neural Networks (CNN) model. However, there are still various challenges that must be addressed, including objects with diverse visual characteristics such as form, size, color, and physical condition. This research focuses on developing a system that enhances object recognition of waste, specifically organic waste, using an Attention Convolutional Neural Network (ACNN). By integrating attention mechanisms into the CNN model, this study addresses the challenges of recognizing waste objects with diverse visual characteristics. The proposed system seeks to improve the accuracy and efficiency of organic waste identification, which is crucial for advancing waste management practices and reducing environmental impact. Methods: This research combines a CNN architecture with an attention mechanism to create a better object detection environment called Attention-CNN (ACNN). The ACNN architecture employed consists of one layer input, three convoluted layers, three max-pooling layers, one attention layer, one flattened layer, four dropout layers, and two dense layers arranged in a certain way. Result: The research result shows that the model CNN with attention mechanism (ACNN) was slightly better at 86.93% than the standard model of CNN, which accounted for 86.70% in accuracy. Novelty: In general, the current use of CNN architecture to address waste object recognition problems typically employs standard architectures, resulting in lower accuracy for complex waste objects. In contrast, our research integrates attention mechanisms into the CNN architecture (ACNN), enhancing the model's ability to focus on relevant features of waste objects. This leads to improved recognition accuracy and robustness against visual variability. This distinction is important as it overcomes the limitations of standard CNN models in handling visually diverse and complex waste objects, thereby highlighting the novelty and contribution of our research.

Enhancing Peanut (Arachis hypogaea) Productivity and Biochemical Traits: A Comparison of Straw Mulch and Polythene Mulch Under Prolonged Salinity Stress

ABSTRACTThe escalating population growth has spurred a demand for increased oilseed production, necessitating urgent attention. However, the expansion of saline‐affected regions posed a significant obstacle to maintain peanut productivity in these areas. Thus, to tackle the productivity decline in saline‐affected regions, we investigated whether substituting polythene mulch with straw mulch, as part of an agronomic management strategy, could mitigate the rapid decrease in peanut productivity. Three mulching methods (control, polythene mulch and straw mulch) were employed to cultivate the crop (cv. TG 37A) under salinity levels of 0.5, 2, 4 and 6 dS m−1. As salinity levels increased, there was a notable decrease in germination percentage, growth, yield and biochemical characteristics, including pod and haulm yields were reported. Despite salinity reduced free amino acids and oil content, it exhibited significant increase in protein and sugar content. Saline irrigation water led to a reduction in pod yield, haulm yield and oil content by 24.67%, 23.84% and 5.07%, respectively, at a salinity level of 6.0 dS m−1 compared to the control with 0.5 dS m−1 salinity. Moreover, straw mulching resulted in a boost in pod yield, haulm yield and oil content by 30.09%, 4.83% and 1.75%, respectively, compared to the control. The reduction in pod yield and oil content under the interaction of mulching and salinity was 46.44% and 6.87% at M0S6, 21.42% and 4.44% at M1S6, and 7.55% and 3.87% at M2S6 compared to M0S0, M1S0 and M2S0, respectively. A similar trend was also observed in 100‐pod weight, 100‐kernel weight and shelling percentage. Accordingly, it was concluded that the declining trend in all attributes under straw mulching at various salinity levels surpassed polythene mulching, ensuring superior peanut production under salinity stress conditions.