Future Population Growth Research Articles

Modeling Trophic Cascades to Identify Key Mammalian Species for Ecosystem Stability

The role of keystone species in maintaining ecosystem stability is a crucial aspect of ecology. Identifying key mammalian species within an ecosystem requires a systematic approach, utilizing criteria and indicators derived from species characteristic variables. This study presents a framework to identify key mammalian species based on various ecological, structural, and functional factors. By developing a mechanistic model of energy flow in food webs and trophic levels, the model aims to pinpoint each species’ role in the stability and sustainability of biomass flow within the ecosystem. Known as KVT version 1.0, the model explains the role of each characteristic variable of mammalian species, predicts population growth, elucidates species interactions at trophic levels, and assesses species-specific dietary compositions, including food requirements, reproduction, and activity. Factor analysis of model outputs has produced equations to determine the value of keystone species (Kv), indicating the role of mammalian species in the stability and sustainability of biomass flow in the ecosystem. Keystone species, as identified by this model, are primarily small mammals of the families Muridae, Sciuridae, Tupaiidae, Ptilocercidae, Hystricidae, Viverridae, and Herpestidae, demonstrating omnivorous and herbivorous trophic levels. This model can serve as a valuable framework for conservation management of biodiversity in an ecosystem, with potential for expansion to include characteristics of non-mammalian species in future research.

US Primary Care WorkforceGrowth: A Decade of Limited Progress, and Projected Needs Through 2040.

Despite efforts to mitigate a projected primary care physician (PCP) shortage required to meet an aging, growing, and increasingly insured population, shortages remain, compounded by the COVID-19 pandemic, growing inequity, and persistent underinvestment. We examined primary care workforce trends over the past decade and revisited projected primary care clinician workforce needs through the year 2040. Using data from the AMA Masterfile and Medical Expenditure Panel Survey (MEPS), we analyzed trends in the number of primary care physicians (PCPs) and in outpatient PCP visits by age and gender over the past decade. We then used the Medicare PECOS and Physician & Other Practitioners datasets to identify nurse practitioners (NPs) and physician assistants (PAs) in primary care. Using these baseline clinician enumerations and projected population growth estimates from the US Census Bureau for the years 2020-2040, we calculated estimated primary care workforce needs by 2040. The effects of aging and population growth and baseline shortages in the primary care workforce call for significant increases in the primary care workforce to accommodate rising demands. Office visits to primary care clinicians are projected to increase from 773,606 in 2020 to 893,098 in 2040. We project a need for an additional 57,559primary care clinicians by 2040. Workforce shortages in primary care continue to expand due to populationaging, growth, and heightened rates of clinicianburnout& egress.

Air cold plasmas as a new tool for nitrogen fixation in agriculture: underlying mechanisms and current experimental insights

Nitrogen fixation is crucial for plant growth and global agriculture, especially with the projected population growth requiring a significant increase in food production. Traditional nitrogen fixation relies on the Haber-Bosch (H-B) process, which is energy-intensive and environmentally harmful due to greenhouse gas emissions. Emerging technologies, such as cold plasma, offer promising alternatives with lower energy consumption. Cold plasma facilitates reactive nitrogen species generation under ambient conditions, potentially improving the production efficiency of nitrogen oxides (NOx). However, optimizing cold plasma nitrogen fixation requires a synergy between experimental and theoretical approaches. Accurate input data are essential for refining theoretical models, which can then guide the design of more efficient processes. This integrated approach can leverage renewable energy, operate on smaller scales, and minimize environmental impacts, making cold plasma a sustainable solution for future nitrogen fixation needs.

Cellular automata modelling to simulate patterns of urban growth for Nusantara: Indonesia’s new capital

This paper uses cellular automata (CA) modelling to simulate possible patterns of urban growth for Nusantara–Indonesia’s new capital. The modelling uses criteria such as projected population growth and planned development stages and a range of relevant factors that influence urban development. Further the study simulates the possible impact of future urban growth on key biodiversity areas (KBAs). Two scenarios were modelled to simulate urban growth patterns–(1) the nature sensitive city and (2) the Indonesian government’s current plan. Results of the scenario-based CA modelling demonstrate that scenario 1 offers a more sustainable and liveable approach to urban growth, despite its larger land footprint. This is achieved by preserving protected and key biodiversity areas, which are essential for the long-term well-being and resilience of the environment. While scenario 2 is more land-efficient, it presents a possible risk to the overall ecological integrity and liveability of the metropolis by impinging into key biodiversity areas. The study’s cellular automata approach and methodology can be employed for urban planning and biodiversity impact assessment in similar contexts of new city development.

Simulation and prediction of daytime surface urban heat island intensity under multiple scenarios via fully connected neural network

The intensification of the Surface Urban Heat Island (SUHI), driven by urbanization, land use and land cover (LULC) changes, and population growth, presents significant environmental and public health risks in urban areas. Simulating and predicting SUHI, particularly through the identification of future high SUHI intensity (SUHII) zones, has been recognized as a critical step in mitigating these effects. This study employs a Fully Convolutional Neural Network (FCNN) model, trained on data from four research sites, to simulate the current daytime SUHII across six validation sites in Singapore, utilizing 15 key independent variables identified in previous studies. The model exhibits high validation accuracy, achieving 87.45%. Three projection scenarios, based on projected population growth and LULC changes, predict a decrease in High SUHII across all validation sites, ranging from 98.3% to 9%. This reduction is attributed to the LULC improvements proposed in the 2019 Master Plan. Spatial analysis of the predicted SUHII maps indicates that the majority of High SUHII locations across scenarios remain consistent with the current situation. This research also suggests that the model could be a valuable tool for urban planners, allowing them to assess whether new urban development plans will effectively reduce High SUHII to desired thresholds, thereby mitigating SUHII in urban environments.

Stock exploitation status of silver croaker (Pennahia argentata) from the East China Sea

Silver croaker (Pennahia argentata) is an important economic species in the East China Sea (ECS); however, its population has been declining, and studies on its status are scarce. This study collected biological data, including length, weight, and gonadal maturity of silver croaker from the southern coastal waters of Zhejiang Province between 2015 and 2021, to evaluate its life history and assess stock status. Methods from the TropFishR package were employed to estimate population growth and natural mortality parameters. Two widely used length-based data-limited models, length-based bayesian biomass estimation method (LBB) and length-based spawning potential ratio method (LBSPR), were applied to assess the stock status of silver croaker in the ECS. The results indicated that with a maximum age (Amax) of 4 years, the asymptotic length (Linf) was 23.80 cm, growth rate (K) was 0.74/year, and natural mortality coefficient (M) ranged from 1.068 to 1.376. Under the assumption of Amax = 5 years, Linf was 26.77 cm, K was 0.63/year, and M ranged from 0.818 to 1.193. The LBB and LBSPR models showed that, except for scenarios with L∞ = 23.8 cm, M/K = 1.46, and L∞ = 26.77 cm, M/K = 1.30, 1.38, or 1.48, the selectivity at length parameters L50SandL95S for silver croaker exhibited an increasing trend over the years. In the LBB model, L50S for different years consistently fell below the optimum length (Lc_opt), indicating that most individuals did not reach the size at first sexual maturity or optimal catch length. Moreover, under high fishing pressure, the Spawning Potential Ratio (SPR) and the unfished biomass ratio (B/B0) for silver croaker showed a decreasing trend, despite some stability or recovery in recent years. However, in all scenarios, SPR values were below 0.2, and B/B0 values were below 0.5, suggesting a significant risk of population collapse for silver croaker in the ECS due to intense fishing pressure. Therefore, it is recommended to reduce the current fishing pressure and increase the minimum catch size of this species for effective conservation and management in this region.

INTEGRATING LAND USE ANALYSIS WITH WATER DEMAND ESTIMATION: A CASE STUDY OF PUTRAJAYA, MALAYSIA

The challenges posed by population growth, urbanization, and changing land use patterns on sustainable water resource management are significant. This paper puts forth an integrated framework aimed at assessing future water demand in Putrajaya, Malaysia. The proposed framework combines population projections, estimations of water demand, and analyses of land use activities. Through an examination of demographic trends and land use patterns, the framework predicts population growth and identifies areas with high water demand. Daily water use patterns in homes and businesses (temporal analysis) inform the designing future water infrastructure, incorporating temporal aspects. Statistical and spatial analysis techniques are then utilized to merge these projections with water demand estimations to quantify water requirements in various zones and types of land use. This study has unveiled two daily peaks in water demand, which align with household schedules. Residential areas emerge as the primary consumers of water, displaying an evening peak distinct from the midday peak seen in businesses. The current water demand in Putrajaya is estimated at 94 million litres per day, with domestic usage surpassing non-domestic usage in a ratio of 3:2. Projections based on future land use plans foresee a 19% increase in demand, underscoring the urgency for proactive water management strategies. Spatial analysis has highlighted residential areas as the main users of water, with demand levels varying throughout the city. By comprehending these temporal and spatial patterns, water authorities can strategically target interventions, optimize infrastructure siting, and forecast future demand trends. These proactive measures are essential for securing a sustainable water future for Putrajaya.

Evaluating the effects of nest management on a recovering raptor using integrated population modeling

AbstractEvaluating population responses to management is a crucial component of successful conservation programs. Models predicting population growth under different management scenarios can provide key insights into the efficacy of specific management actions both in reversing population decline and in maintaining recovered populations. Bald eagle (Haliaeetus leucocephalus) conservation in the United States has seen many successes over the last 50 years, yet the extent to which the bald eagle population has recovered in Arizona, an important population within the Southwest region, remains an area of debate. Estimates of the species' population trend and an evaluation of ongoing nest‐level management practices are needed to inform management decisions. We developed a Bayesian integrated population model (IPM) and population viability analysis (PVA) using a 36‐year dataset to assess Arizona bald eagle population dynamics and their underlying demographic rates under current and possible future management practices. We estimated that the population grew from 77 females in 1993 to 180 females in 2022, an average yearly increase of 3%. Breeding sites that had trained personnel (i.e., nestwatchers) stationed at active nests to mitigate human disturbance had a 28% higher reproductive output than nests without this protection. Uncertainty around population trends was high, but scenarios that continued the nestwatcher program were less likely to predict abundance declines than scenarios without nestwatchers. Here, the IPM‐PVA framework provides a useful tool both for estimating the effectiveness of past management actions and for exploring the management needs of a delisted population, highlighting that continued management action may be necessary to maintain population viability even after meeting certain recovery criteria.

Living on the edge: identifying demographic bottlenecks in an isolated sage‐grouse population

The greater sage‐grouse (Centrocercus urophasianus: hereafter sage‐grouse) population in Modoc County California is geographically isolated and contains a single lek (from 56 leks in the 1940s), despite significant efforts to increase the population through translocations and habitat improvement. Repeated wildfire within the landscape has led to an increase in invasive annual grasses and a decrease in sagebrush (Artemisia sp.) cover in important nesting and brood‐rearing habitat. We estimated survival for adult females, nests, and chicks and assessed biotic characteristics that may influence these survival estimates to identify factors that may be limiting population growth. We monitored 37 female sage‐grouse marked with GPS PTTS, 39 nests, and 8 broods for 3 years (2019–2021). We measured vegetation characteristics for nests at the microsite and landscape scale to evaluate effects on daily nest survival (DNS). We used survival rates from all life stages to parameterize matrix models and estimate population growth rate. Mean nest success over 3 years was 29% (95% CI: 17.1–44.8) across a 29‐day incubation period and DNS declined as the proportion of both medusahead Taeniatherum caput‐medusae and Japanese brome Bromus japonicus around the nest increased. Across all three years, mean chick survival across a 54‐day period was 44% (95% CI: 0.9–72.3) and mean annual survival for adult females was 29% (95% CI: 17.8–43.7). Our estimated vital rates were 45–55% lower than distribution‐wide estimates and the projected population growth rate was strongly declining (0.411, 95% CI: 0.30–0.52). Our results suggested recent cover changes associated with wildfire on the study area may have had a detrimental effect on this population across all life stages, and if attention is not given to preventing the transition from sagebrush communities into invasive grasslands, this population and others in degraded landscapes may not persist.

Importance of accounting for imperfect detection of plants in the estimation of population growth rates

Detection of plant individuals is imperfect. Not accounting for this issue can result in biased estimates of demographic parameters as important as population growth rates. In mobile organisms, a common practice is to explicitly account for detection probability during the estimation of most demographic parameters, but no study in plant populations has examined the consequences of ignoring imperfect detectability on the estimation of population growth rates. The lack of accounting for detection probability occurs because plant demographers have frequently assumed that detection is perfect, and because there is a scarcity of studies that formally compare the performance of estimation methods that incorporate detection probabilities with respect to methods that ignore detectabilities. Based on field data of five plant species and data simulations, we compared the performance of three methods that estimate population growth rates, two that do not estimate detection probabilities (direct counts of individuals and the minimum‐number‐alive method) and the other that explicitly accounts for detection probabilities (temporal symmetry models). Our aims were 1) to estimate detection probabilities, and 2) to evaluate the performance of these three methods by calculating bias, accuracy, and precision in their estimates of population growth rates. Our five plant species had imperfect detection. Estimates of population growth rates that explicitly incorporate detectabilities had better performance (less biased estimates, with higher accuracy and precision) than those obtained with the two methods that do not calculate detection probabilities. In these latter methods, bias increases as detection probability decreases. Our findings highlight the importance of using robust analytical methods that account for detection probability of plants during the estimation of critical demographic parameters such as population growth rates. In this way, estimates of plant population parameters will reliably indicate their actual status and quantitative trends.

Research on the Sustainability of Property Insurance under the Background of Extreme Weather Events

In recent years, the frequency of extreme weather events has significantly increased, resulting in global losses exceeding $1 trillion. The profit crisis of insurance companies and the issue of homeowners' payment ability have become more apparent. This article focuses on the sustainability of property insurance under extreme weather events, establishes a comprehensive insurance and protection model, and provides recommendations for relevant departments.Firstly, a set of seven indicators including the proportion of household insurance expenditure was selected to establish an indicator system. After conducting Spearman correlation coefficient testing on the data and combining it with LSTM neural network to predict the frequency of extreme weather in the next decade, this paper uses the SVM binary classification model to classify high-risk areas as uninsurable areas and other areas as insurable areas. This decision-making process resulted in insurance companies covering areas such as Alabama and refusing to underwrite insurance in Arizona, while also making relevant recommendations.Secondly, by calculating indicators for communities and real estate developers, and considering the impact of population growth on housing demand, this article uses the ARIMA model to predict population growth in different regions over the next five years. After standardizing the data, the entropy weighting method is applied to consider the subjective importance of different real estate developers for different indicators and assign weights to the indicators. Weighted indicator data is used as input for the SVM model, dividing regions into two categories, with regions with better overall conditions being more favorable for real estate development. This has led people to prioritize the development of regions such as North Carolina and Indiana. In addition, this article introduces the TOPSIS regional ranking comprehensive evaluation index, which is beneficial for the development of higher ranked regions. Similar to our SVM classification results, Indiana and North Carolina rank second and third.

Heuristics for the Intelligent Prediction of Population Growth

Population growth is a phenomenon that is inevitable in a life course and the components of population growth are fertility, mortality and migration. Because of the impact population growth exact upon the socio- economic nature of a country, it will be wise to know the future size and distribution of it so that adequate measures can be made to forestall possible problems. There are different population models and algorithms implemented to project population growth, ranging from statistical to machine learning models; these model techniques were very suitable in their time, such as the Malthusian theory, which, first and foremost, gives deeper consideration to the exponential growth model, while many implemented classification and linear regression algorithm. Linear regression machine learning algorithms were considered the most effective algorithm for population growth projection. This study tends to develop a machine learning model that is data-driven mathematically, capable of implementing the data-independent prediction model equation that was used to predict the impact of the Non-pharmaceuticals approach and pharmaceuticals approach (NPA and PA) in mitigating the spread of the Covid-19 virus, and both models were re-modified to project Nigeria population growth. The data-independent prediction model (DIPM) utilized onset data from the NPA and PA interventions to predict the probability of mitigating the spread of the virus for a specific period. The DIPM has the properties of arithmetic and exponential methods. The fusion of these two properties with the aid of machine learning model has further reveal the data-independent prediction model as a conceptualization technique to reflect how data are processed in an algorithm setting in a concrete world, with the support of the Java array list algorithm, what all the statistical models and supervised machines learning model used in the past studies could not achieve, have been accomplished succinctly. The data independent prediction model is a robust technique for both projection and forecasting future population growth as well as proffer answers to historical issues in mathematical modeling of population expansion.

What drives European farmers' attitude towards water management - a systematic literature approach

Considering challenges related to climate change, projected population growth, and increasing food demand, the long-term use of water in agriculture is becoming a pressing concern. Therefore, effective water resource management by farmers is crucial and warrants extensive scientific investigation. Consequently, the primary objective of this article was to identify pertinent studies addressing farmers' approaches, attitudes, and actions concerning water management and the adoption of water innovations. The review was conducted using the PRISMA method, serving as the foundation for subsequent quantitative and qualitative analyses. The findings suggest that water management in agriculture is gaining significance due to increasing exposure to the risk of limited water availability and complement to adapt to changing climate conditions. Previous research has predominantly focused on selected southern regions of Europe. Farmers' attitudes toward water management are primarily influenced by socio-economic and institutional factors. Education emerged as a crucial determinant in encouraging farmers to use water conservation practices.

Linking demography and food consumption to project population growth and damage potential of Spodoptera frugiperda in India

Abstract The fall armyworm, Spodoptera frugiperda (J. E. Smith), identified as an invasive pest worldwide, has severely threatened agricultural production and food security in India. To formulate eco‐friendly integrated pest management strategies for S. frugiperda in its new invasive habitat, the basic knowledge about demographic parameters and damage potential of this pest is crucial. The effects of five host crops viz. maize (Zea mays L.), popcorn (Zea mays everta Sturt), sweet corn (Zea mays saccharata Sturt), sorghum (Sorghum bicolor (L.)) and soybean (Glycine max (L.) Merr.) on the development and food consumption of S. frugiperda were examined. Population projection curves based on life table and consumption rates were drawn on different hosts using computer simulation. The pre‐adult development of S. frugiperda was fastest on popcorn (28.02 days), maize (28.04 days) and sweet corn (28.31 days) but slowest on soybean (34.83 days). The highest net reproductive rate, intrinsic rate of increase and finite rate of increase were observed on maize. The feeding potential of S. frugiperda was also highest on maize. The maximum consumption of the sixth larval instar was observed on maize (19470.47 mm2) while lowest on soybean (9033.67 mm2). In the simulation period of 90 days, the fastest growth of S. frugiperda was expected on maize, popcorn and sweet corn while the slowest on soybean. High larval and pupal survival recorded on the non‐maize hosts (sorghum and soybean) reflects that fall armyworm could be an obstacle in their production as well as in the success of maize‐soybean intercropping system. Simulations based on age‐stage, two‐sex life table could be helpful in predicting the most appropriate time for the pesticide application as well as augmentative releases of egg and larval parasitoids for the control of fall armyworm.

Canadian Medical Imaging Inventory 2022–2023: Methods

The Canadian Medical Imaging Inventory (CMII) was created in 2015 to track, compare, and map trends over time related to the availability, distribution, technical specifications, and use of advanced imaging equipment in Canada. This is the fourth iteration of the CMII. The CMII collects data about availability and use of CT, MRI, PET-CT, PET-MRI, SPECT, and SPECT-CT through a survey conducted approximately once every 2 years and details the use of strategies for improving appropriate imaging, enhancing system efficiencies, reducing wait lists, and addressing other systemic challenges. Senior medical imaging decision-makers within health ministries and/or regional health authority located in each province and territory were consulted to validate unit and exam counts for consistency. Data are presented using descriptive summaries and graphs of site-level and jurisdiction-level findings. Canada’s ranking among from Organisation for Economic Co-operation and Development countries was reported. The number of units and exams in Canada was projected for the years 2025, 2030, 2035, and 2040 based on the per capita values in 2022–2023 and estimated population growth provided by Statistics Canada.

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.

Antibiosis in small grain cereals against the rose-grain aphid, Metopolophium dirhodum (Hemiptera: Aphididae)

Resistance against aphids is an integrated pest management tactic that enhances the economic and environmental sustainability of cereal production. The aim of our study was to assess the degree of antibiosis against Metopolophium dirhodum (Walker) (Hemiptera: Aphididae), one of the most important cereal aphids in temperate regions, in two lines of small grain cereals bred in the Crop Research Institute (CRI) of Prague: spring wheat, Triticum aestivum L. cv. ‘Rufia’, and germplasm of durum wheat, Triticum turgidum ssp. durum (Desf.) van Slageren ‘TTD40363’, under controlled laboratory conditions. Resistant spring wheat cultivar (‘Libertina’) and highly susceptible spring barley (Hordeum vulgare L. cv. ‘Sebastian’) were used for reference. The responses of M. dirhodum to each line and population growth projections were determined according to the age-stage, two-sex life table theory. We found that Rufia was susceptible and TTD40363 was rather resistant to M. dirhodum. The projected population sizes after 35 days were ~ 41,000 individuals in spring wheat Rufia and ~ 12,000 in durum wheat TTD40363, which is within the range delimited by the reference cultivars (4,000 in the highly resistant spring wheat Libertina and 77,500 in highly susceptible spring barley Sebastian). This study provides new data on small grain cereal resistance to aphids. An age-stage, two-sex life table approach was used to assess resistance to aphids on durum wheat and barley for the first time.

Factors Affecting the Resilience Index Food in Papua Province and West Papua Province Using a Spatial Model Approach

The Food Security Index is a measure of indicators to produce a composite value that reflects the status of food security in a region. Food security plays an important role in sustainable development, including food availability, environmental preservation and economic balance, as well as being the basis for economic growth, preventing poverty and inequality. In Indonesia, with an estimated population growth of 430 million people in 2050, the challenge of meeting food needs is increasing. Indonesia's commitment to the Sustainable Development Goals (SDGs) includes efforts to end hunger and promote sustainable agriculture. This research aims to apply spatial regression analysis to the Provinces of Papua and West Papua to determine the best model and significant factors that influence the Food Security Index in the region in order to identify the challenges faced by the region in calculating the food availability of its people as well as assist in developing efforts to overcome them. Five predictor variables were used with the assumption that they have a significant influence on the Food Security Index. This research examines the spatial regression equation using the SAR, SEM and SARMA regional approaches. The results obtained showed that the selected SEM model with a p-value of 0.0082581 was appropriate for identifying the dependence of spatial effects on Food Security Index in Papua Province and West Papua Province. Life Expectancy at Birth, Prevalence of Stunting Toddlers, Percentage of Poor Population, Open Unemployment Rate, and Average Length of Life are significant factors that influence the Food Security Index in Papua Province and West Papua spatially.

APPLICATION OF DIFFERENTIAL EQUATIONS IN POPULATION GROWTH ESTIMATION OF KEDIRI CITY AS THE IMPLEMENTATION OF THE 2030 SDGS TARGET

This study focuses on analyzing the application of differential equations in modeling the population growth of Kediri City through a logistic growth model, and aims to predict the city's population for the years 2021-2030. Employing a literature study approach, this research utilizes secondary data sourced from the official website of the BPS (Central Statistics Agency) of Kediri City. The data encompasses population figures from 2005 to 2020. To ensure the accuracy of the population predictions made by the logistic growth model, a comparison with the official data from the Central Statistics Agency is necessary. A critical aspect of this research involves estimating the carrying capacity of Kediri City's population, which is identified as 295,672 individuals in the year 2030. The findings indicate a significant growth trend in the population, characterized by a specific growth rate. To validate the accuracy of the logistic growth model, the Mean Absolute Percentage Error (MAPE) method was applied, resulting in an error percentage that falls within the highly accurate category, thus affirming the reliability of the model in predicting population growth trends.

PENERAPAN METODE RUNGE KUTTA FEHLBERG PADA PERSAMAAN LOGISTIK DALAM MEMPREDIKSI PERTUMBUHAN PENDUDUK DI SULBAR

This research discusses the application of the Runge Kutta Fehlberg method in predicting population growth in West Sulawesi through the logistic equation. Employing applied research methodology, the study seeks to forecast future population trends in the West Sulawesi province. Utilizing a step size of h = 0.01 and a growth rate of m = 0.0198, the Runge-Kutta Fehlberg method (RKF 45) was applied, starting from an initial population value of P (t0) = 1.419.229 individuals, resulting in a projected population of P (t10) = 1.506.142 individuals. These findings demonstrate the applicability of the Runge-Kutta Fehlberg (RKF 45) method in predicting population dynamics in West Sulawesi Province.