Residual Square Research Articles

The link between gender inequality, financial development, and economic growth in Nigeria: A spectral Granger causality approach

AbstractThe male and female genders have crucial roles to play in economic advancement. Thus, the main reason for this research is to pinpoint the causal link between gender inequality (GPIN), financial development (FIDX), inflation (INFL), and economic growth (GDPC) in Nigeria from the year 1980–2020 utilizing the Spectral Granger and Pairwise Granger causality methods and “Residual Augmented Least Square (RALS) cointegration. The RALS result show confirms the stationarity properties of the variables. The Spectral Granger causality outcomes show that GPIN granger causes GDPC in the long, medium, and short–term. In addition, the FIDX granger causes GDPC in the long and short–term, while the medium–term impact is insignificant. For INFL, causality only occurs in the long–term. On the other hand, the Pairwise Granger causality results show that GPIN and FIDX granger cause GDPC, while INFL is insignificant. Therefore, if GPIN and FIDX can predict economic expansion, policymakers need to create policies that can reduce diverse forms of inequalities, promote financial development, and reduce inflation to the barest minimum.

The impact of renewable and non-renewable energy consumption on aggregate output in Pakistan: robust evidence from the RALS cointegration test.

Over the past three decades, Pakistan's energy consumption has surged due to industrialization, population growth, and development activities. To meet the escalating energy demands, the country has primarily relied on thermal power projects, which are financially burdensome and environmentally detrimental, compared to hydropower projects. This reliance exposes Pakistan to global oil price shocks and environmental degradation. To address this dilemma, this empirical research investigates the impact of both non-energy factors (labour and capital) and energy-specific factors (renewable and non-renewable) on Pakistan's aggregate output, using annual time-series data from 1980 to 2021. The analysis employs the newly established Residual Augmented Least Square (RALS) cointegration test and the Autoregressive Distributed Lag (ARDL) methodology to estimate the long-term cointegrating relationship among the examined variables. The empirical findings demonstrate that both non-energy and energy-specific factors positively and significantly influence Pakistan's long-term aggregate output. However, petroleum consumption exerts a positive but insignificant influence on Pakistan's long-term aggregate output. The study recommends diversifying the energy supply mix to include more hydroelectricity, non-hydroelectric renewables (mainly solar and wind), and natural gas. Specifically, transitioning from imported, expensive, and more greenhouse gas (GHG)-generating petroleum products to domestically produced natural gas could potentially reduce Pakistan's trade deficit and its vulnerability to global oil price shocks. Besides the economic benefits, shifting from non-renewable energy sources (specifically oil) to renewable energy would enhance Pakistan's image and increase its geopolitical influence over neighboring countries. Additionally, the study emphasizes the need to encourage private sector participation in renewable energy projects and suggests implementing effective carbon tax policies to mitigate CO2 emissions and foster economic growth.

Identifying the influence of climate policy uncertainty and oil prices on modern renewable energies: novel evidence from the United States

Abstract The policy uncertainty surrounding climate change can intensify the urgency of implementing climate policies and influence investment decisions, thereby serving as a catalyst for policy transformation. In this context, given its withdrawal from and subsequent re-commitment to the Paris Agreement, the United States (US) may experience policy uncertainty over climate change. Given this backdrop, it is of paramount importance to explore how the uncertainties caused by shifts in climate policy affect clean energy. This study delves into the impact of climate policy uncertainty and oil prices on modern renewable energy sources, such as solar, wind, geothermal, and biofuels. The most recent monthly data from 1989 to 2023 are used by conducting the Residual Augmented Least Squares (RALS) methodology. The empirical findings show that the uncertainty surrounding climate policy is leading to a shift in the US energy composition, with a focus on solar energy, wind energy, and biofuels. Moreover, the fluctuation in oil prices has a significant effect on the deployment of wind and biofuels, but no discernible effect on solar power. These findings provide significant insights for aligning climate policy in the US with Sustainable Development Goal (SDG) 7, highlighting the crucial importance of encouraging investments in solar energy, wind energy, and biofuels. Graphical abstract

Climate policy uncertainty and energy impacts on trade openness and foreign direct investment in the United States: Evidence from the RALS co‐integration test

AbstractClimate change is becoming an increasingly pressing global issue, and understanding how climate policy uncertainty affects trade openness and foreign direct investment is crucial. This study provides contemporary insights into this complex relationship, particularly in the context of the United States, which is a major player in global trade and investment. The study employed a monthly dataset spanning from January 1990M01 to December 2020M12. Co‐integration exists between all the variables specified by employing Residual Augmented Least Squares (RALS‐EG), climate policy uncertainty, and fossil fuel consumption adverse influence upon trade openness and foreign direct investment, economic growth, and renewable energy consumption positive impact upon trade openness and foreign direct investment via the use of fully modified ordinary least square (FMOLS), and Dynamic Ordinary Least Squares (DOLS). The Fourier Toda‐Yamamoto causality reveals that climate policy uncertainty, economic growth, renewable energy, and trade openness exhibit bidirectional causality, while fossil fuels exhibit unidirectional causality. Based on the findings, policymakers should prioritize reducing climate policy uncertainty to create a more stable environment for trade and foreign direct investment (FDI). Clear and consistent climate policies can mitigate the negative impact of uncertainty on trade openness and FDI.

Determination of valsartan and pitavastatin using synchronous spectrofluorimetry and augmented least squares chemometric models: A comparative study with greenness and blueness assessment.

Hypertension and hyperlipidemia are two common conditions that require effective management to reduce the risk of cardiovascular diseases. Among the medications commonly used for the treatment of these conditions, valsartan and pitavastatin have shown significant efficacy in lowering blood pressure and cholesterol levels, respectively. In this study, synchronous spectrofluorimetry coupled to chemometric analysis tools, specifically concentration residual augmented classical least squares (CRACLS) and spectral residual augmented classical least squares (SRACLS), was employed for the determination of valsartan and pitavastatin simultaneously. The developed models exhibited excellent predictive performance with relative root mean square error of prediction (RRMSEP) of 2.253 and 2.1381 for valsartan and pitavastatin, respectively. Hence, these models were successfully applied to the analysis of synthetic samples and commercial formulations as well as plasma samples with high accuracy and precision. Besides, the greenness and blueness profiles of the determined samples were also evaluated to assess their environmental impact and analytical practicability. The results demonstrated excellent greenness and blueness scores with AGREE score of 0.7 and BAGI score of 75 posing the proposed method as reliable and sensitive approach for the determination of valsartan and pitavastatin with potential applications in pharmaceutical quality control, bioanalytical studies, and therapeutic drug monitoring.

Geopolitical risk and tourism in Turkey

PurposeThe purpose of this paper is to show the effect of Turkey's geopolitical risk on the number of international tourist arrivals to the country. When Turkish economy in 2019 is analyzed, it is seen that the share of tourism in national income is 11%. For this reason, national economy is significantly affected by changing of the number of international tourist arrivals. Security problems are an important variable affecting tourist arrivals.Design/methodology/approachThe paper focused on secondary data for the period 2000–2019 for macroeconomic variables. Accordingly, the number of international tourist arrivals was added as a dependent variable, geopolitical risk as an independent variable, gross domestic product (GDP) and economic freedom index as control variables and inflations as an external variable to the model. The residual augmented least squares–the autoregressive distributive lag (RALS-ADL) cointegration test and the dynamic ordinary least squares (DOLS) coefficient estimator were used. It allows for more robust results to be obtained when the residues do not have a normal distribution.FindingsThe RALS-ADL cointegration test result shows that there is a cointegration relationship between variables at a 1% significance level. Moreover, the DOLS coefficient estimator results indicate that an increase in economic freedom and GDP increase the number of international tourists, whereas an increase in the Geopolitical Risk Index and inflation decreases the number of international tourism arrival. It can be said that tourists consider the security and economic stability of the host country when making tourism decisions.Originality/valueTurkey is one of the most risky developing countries, as well as one of the most popular travel destinations. When the literature is examined, it has been found that studies for Turkey usually determine the relationship between the variables for a short period of time. However, to ensure sustainable growth and environment of confidence, the long-run relationship between variables should be determined so that policymakers can make more impactful decisions. Therefore, the aim of this study is to make a literature contribution, taking into account the long-term effects. In addition, unlike other studies, this study fills the gap in literature using the RALS-ADL cointegration test, which produces robust estimators.

Analyzing the stress management intervention impacting the mindfulness and well-being of employees.

Developing employee well-being as well as lowering stress may have several benefits for organizations. Stress management intervention techniques utilized via organizations help to develop worker mindfulness and well-being, and reduce stress. To review stress management interventions to determine the effectiveness of diverse interventions in improving the mindfulness and well-being of employees working in an organization. This work is split into two main sections: collection of data and analysis of data. First, data collection is carried out. The structured questionnaire was organized and distributed to managers and working employees of an organization. Based on the developed hypothesis, the questionnaire was arranged and all queries were compulsory. Subsequently, the collected data were performed by Structural Equation Modeling (SEM) and One Way Analysis of Variance (ANOVA) analysis. The Standard Mean Residual Root Square for the baseline model of mindfulness and well-being of employees working in an organization is 0.2156, which is better than threshold value. From the analysis, the goodness of fit and significant relation among the stress intervention techniques as well as mindfulness and well-being of employees in an organization has been found.

Cross-cultural adaptation, reliability, and validity study of the Turkish version of the “Spinal Cord Injury-Falls Concern Scale”

Purpose This study aims to conduct a Turkish validity and reliability study of the Spinal Cord Injury-Falls Concern Scale (SCI-FCS). Methods The study involved 134 volunteer patients with spinal cord injury. Various validity analyses, including content validity analysis, Confirmatory Factor Analysis (CFA), Pearson product-moment correlation coefficient, and Cronbach’s alpha value based on a single application for reliability, were employed. The Falls Efficacy Scale-International (FES-I) was used for criterion validity. Results All the standardized regression coefficients for the single factor measurement model established with 16 items in the scale are above 0.84. The CFA model, also with a single factor and 16 items, yields the following fit indices: Root-Mean-Square Error of Approximation value of 0.15, Standardized Root Mean Residual Squares value of 0.06, Chi-square value of 2.20, and Comparative Fit Index and Non-Normed Fit Index values of 0.98. The scores they received from the SCI-FCS and the scores they received from the FES-I scale have a statistically significant positive correlation (p < 0.01). The test-retest reliability coefficient value is 0.81, and the Cronbach Alpha value is 0.97. Conclusion The Turkish version of the SCI-FCS is a valid and reliable tool for determining fall concerns for patients with spinal cord injury.

Mechanisms of data mining in analyzing the effects and trends of news dissemination

Abstract Data mining techniques can help news organizations and media practitioners extract valuable information from a large amount of unorganized information, such as by analyzing the effect of news dissemination. In data mining, multiple linear regression is a widely used technical model. In this paper, we utilize the classic “cognition-attitude-behavior” analysis framework to construct a regression analysis model of the news communication effect. We solve the positional parameters according to the principle of least squares, ensuring that the regression value and all observations have the minimum residual square. We then solve the system of linear equations using Clem’s law and the Gaussian elimination method. The constructed models are used to analyze the news dissemination effect of Guangzhou’s city image on traditional news platforms and microblog self-media platforms, respectively. It is found that the news videos of “cultural image” and “ecological image” have a significant positive effect on the news dissemination effect. The longer the duration of the news video, the better the dissemination effect. In terms of the influence of event-related online opinion leaders on news dissemination, the number of comments and likes have a significant positive influence, with coefficients of 0.778 and 0.059, respectively, and both are significant at the 1% level. In addition to the number of comments and likes, the influencing factors on the dissemination of related news are the number of fans, whether there is V authentication, and whether there is microblogging membership. After conducting two empirical data mining analyses, we verified the validity of the multiple linear regression model in this paper by analyzing the effect and trend of news dissemination.

Exploring the Relationship Between Oil Prices and Economic Growth in Türkiye

This study investigates the existence of a long-run relationship between economic growth and oil prices in Türkiye, using annual data for the period 1987-2019. First, the stationarity of the variables is evaluated using the Residual Augmented Least Squares (RALS) based ADF unit root test. Second, the RALS-based cointegration test is used to investigate the long-run relationship between the variables. The findings indicate that there is no long-run relationship between economic growth and oil prices in Türkiye. As a result, it is concluded that the series does not return to equilibrium in the long run and oil price shocks during the financial turmoil may affect economic growth.

Augmented least squares, a powerful chemometric approach for the spectroscopic analysis of the antiretroviral therapy abacavir, lamivudine and dolutegravir in their ternary mixture

Augmented least squares models such as concentration residual augmented classical least squares (CRACLS) and spectral residual augmented classical least squares (SRACLS) are powerful chemometric approaches that can be applied for spectroscopic analysis of many pharmaceutical compounds. Herein, both CRACLS and SRACL have been employed for UV spectral analysis of three antiretroviral therapy namely abacavir (ACV), lamivudine (LMV) and dolutegravir (DTG) in their ternary mixture. A partial factorial design has been utilized for calibration set construction then both CRACLS and SRACLS models have been optimized regarding the number of iterations and principal components, respectively, using a leave-one-out cross-validation procedure. It was found that a higher number of iterations and principal components were required for modelling the minor component DTG indicating more augmentation procedures to improve the models’ accuracy. Validation of the proposed models was performed using external validation set of 13 mixtures and different validation parameters have been evaluated regarding models’ predictive abilities. Both models showed excellent performance for analyzing ACV and LMV with relative root mean square error of prediction (RRMSEP) below 2 %. However, higher RRMSEP values around 5 % were observed for the minor component DTG suggesting that these models should be utilized with caution when analyzing minor components in mixtures. Furthermore, the suggested models have been applied for analyzing ACV, LMV and DTG in their pharmaceutical formulation and excellent agreement was observed between the suggested models and the reported chromatographic method posing these models as powerful chemometric approaches for quality control analysis of many pharmaceutical compounds.

A New Regression Model for the Prediction of the Stress-Strain Relations of Different Materials.

Experimental flow stress-strain data under different stress states are often used to calibrate the plastic constitutive model of anisotropic metal materials or identify the appropriate model that is able to reproduce their plastic deformation behavior. Since the experimental stress-strain data are discrete, they need to be mathematically returned to a continuous function to be used to describe an equivalent hardening increment. However, the regression results obtained using existing regression models are not always accurate, especially for stress-strain curves under biaxial stress loading conditions. Therefore, a new regression model is proposed in this paper. The highest-order term in the recommended form of the new model is quadratic, so the functional relationships between stress-strain components can be organized into explicit expressions. All the experimental data of the uniform deformation stage can be substituted into the new model to reasonably reproduce the biaxial experimental stress-strain data. The regression results of experimental data show that the regression accuracy of the new model is greatly improved, and the residual square sum SSE of the regression curves of the new model reduced to less than 50% of the existing three models. The regression results of stress-strain curves show significant differences in describing the yield and plastic flow characteristics of anisotropic metal materials, indicating that accurate regression results are crucial for accurately describing the anisotropic yielding and plastic flow behaviors of anisotropic metal materials.

THE IMPLICATIONS OF BREXIT REFERENDUM ON THE LONG-RUN RELATIONSHIP BETWEEN THE EURO AND POUND STERLING: RESIDUAL AUGMENTED LEAST SQUARE BASED EVIDENCE

Purpose- The aim of this study is to investigate the economic effects of Brexit from the viewpoint of exchange rates. This research studies the impact of Brexit on long-run relationship between euro and pound sterling. It is crucial to assess how Britain’s vote to leave the European Union affects long-term relationship between euro and pound sterling because such action may have significant financial and economic implications on both economies (Diez et al., 2019; Korus &amp; Celebi, 2019; Nagarakatte &amp; Natchimuthu, 2022). Methodology- This study utilizes daily trade-weighted real effective exchange rate indices for euro (EUR) and pound sterling (GBP) over the period of January 1, 2013 to December 31, 2019 to investigate the impact of Brexit referendum took place on June 23, 2016 on the long-run relationship between euro and pound sterling. We exclude European sovereign debt crisis and pandemic periods. As stated previously, this study considers June 23, 2016 as the reference to assess the impact of Brexit on the relationship between currencies. Therefore, our analysis involves three periods including full sample period, pre- and post-Brexit. Empirical investigation relies on residual augmented least squares (RALS) based approach. Due to Im &amp; Schmidt (2008), Im, Lee, &amp; Tieslau (2014), and Lee, Lee, &amp; Im (2015), we use RALS-based unit root test and cointegration analysis. While traditional tests (augmented Dickey-Fuller unit root and Engle-Granger cointegration) assume that error terms are normally distributed, which is a very strict assumption, RALS-based tests relax that condition. We also conduct Granger (non-) causality test proposed by Toda &amp; Yamamoto (1995) to investigate the direction(s) of possible causalities between euro and pound sterling for each period. Findings- RALS based unit root test results suggest that the time series of exchange rates contain unit root for full sample, pre- and post-Brexit periods. The only exception is the time series of sterling pound is I (0) in post-Brexit period. According to the unit root test results, we can conclude that foreign exchange markets in both economies follow weak form efficiency for all sample periods. However, this is not valid for the sub sample where the time series of pound sterling is stationary at level, in post-Brexit. RALS based cointegration test results show that there is no cointegration between the time series of EUR and GBP. This general result is confirmed by both models, RALS ADL and RALS EG2. However, we observe a cointegrating relationship between two currencies in post-Brexit period when we treat GBP as the dependent variable. According to the Granger non-causality test results, we fail to reject null hypothesis and conclude that there is no causality running from EA to UK and vice versa for all sample periods. Test results reveal one exception. In the post-Brexit period, EUR Granger causes GBP implying that the past values of EUR are useful to forecast GBP. Conclusion- Interdependence of world economies is increasing as the financial integration is deepening. Key political, economic, and financial developments in the global world should be followed and assessed. Such developments at home or abroad may influence the prospects. Therefore, it is crucial to assess the outcomes of Brexit-like events that clearly have impact on economic and financial environment to take necessary actions. Keywords: Exchange rates, Brexit referendum, Euro, Pound Sterling, RALS Cointegration Test JEL Codes: C32, F31, G15

The Prediction of The Drought Index in The Indragiri Watershed Using SARIMA and SPI Methods

Farmers are concerned about the problem of drought in agriculture because it will disrupt the production system and cause losses. In 2015, the Indragiri Hulu area experienced a drought on 273 hectares of agricultural land. This area is part of the Indragiri River Watershed in Riau Province. To prevent the impact of future droughts, forecasting is required. The level of drought is influenced by rainfall, so the Seasonal Auto Regressive Integrated Moving Average (SARIMA) method is used to model rainfall data. In addition, the Standardized Precipitation Index (SPI) is also used to analyze the drought index. After the analysis, it was found that the best model in forecasting is SARIMA model (1,0,1)(2,1,1)12 with an average residual square of 18.0175, and the drought index in the Indragiri watershed is currently included in the "Normal" category. The results of this forecasting are expected to assist the Riau Province National Disaster Management Agency in anticipating and mitigating the impact of future drought disasters.

Optimization-oriented exponential-polynomial-closure approach for analyzing nonlinear stochastic oscillators

A novel method named optimization-oriented exponential-polynomial-closure approach is proposed in this article. The main idea of this attempt is to extend the original exponential-polynomial-closure solution procedure methodologically by minimizing the resulted residual error square of the governing equation, which is achieved after an exponential polynomial is adopted as the approximate solution. The objective function for computing the parameters in the approximate solutions of nonlinear random oscillators is then formulated. The probabilistic solutions of the oscillators obtained by the presented approach are verified by the exact solutions in some special cases or by Monte Carlo simulation. Numerical examples indicate that the solutions attained by the presented approach match with the exact or Monte Carlo simulation solutions. The advantage of the presented solution procedure is that it can provide a much more accurate solution than the Equivalent Linearization approach and it is much more efficient than Monte Carlo simulation as demonstrated by the numerical examples.

Environmental implication of energy policies and private and public subsidies on infant mortality rate: a sustainable development study of India.

India has remarkably achieved some level of decline in infant mortality rate and increase in aged person through increase in life expectancy due to improvement on its health care sector but still remain amongst the countries with the highest rate of infant mortality within the Asian countries. Literature on environmental implication remains scarce, and for this we utilised India's data from 1975 to 2020 to research on this topic. Relevant scientific methods (residual Augmented Least Squares - RALS, Engle and Granger - EG, and its newly augmented version - RALS-EG) are adopted in this study. Further, to estimate the long-run elasticities of the regressors, the symmetric analyses, i.e., dynamic ordinary least squares (DOLS) and Engle and Granger causality test techniques, are employed. Findings according to DOLS revealed that renewable energy sources and social (GDP per capita) and public subsidies (general government final consumption expenditure) have lessening effect on infant mortality in India, whilst the private subsidies (gross capital formation), fossil fuels, and carbon dioxide cause an increase in infant mortality in India. This exposes renewable energy source as a mitigating factor in Indian environmental degradation which as well lessen the infant mortality level in India; hence, policy is suggested to be framed on improving renewable energy and health sectors. The graphical presentation of the abstract is done with the diagram below. The graph shows the interactions amongst the selected variables in this study. The impacts of the explanatory variables on the dependent variable are shown with different colours showing positive (green) and negative (red) impacts.

Proposal of a non-linear model to adjust in vitro gas production at different incubation times

This work aims to propose a new model named Gompertz-Von Bertalanffy bicompartmental (GVB), a combination of the models Gompertz and Von Bertalanffy. The GVB models is applied to fit the kinetic curve of cumulative gas production (CGP) of four foods (SS – sunflower silage; CS – corn silage; and the mixtures 340SS – 660 gkg-1 of corn silage and 340 gkg-1 of sunflower silage; and 660SS – 340 gkg-1 of corn silage and 660 gkg-1 of sunflower silage). The GVB fit is compared to models Logistic-Von Bertalanffy bicompartmental (LVB) and bicompartmental logistic (BL). All the process studied employed the semi-automatic “in vitro” technique of producing gases used in ruminant nutrition. The gas production readout was performed at times 2, 4, 6, 8, 10, 12, 15, 19, 24, 30, 48, 72, and 96 h. The data generated were used to estimate the models’ parameters by the least squared method with the iterative Gauss-Newton process. The data fit quality of the models was verified using the adjusted coefficient of determination criterion (), mean residual square (MRS), Akaike information criterion (AIC), and mean absolute deviation (MAD). Among the analyzed models, the LVB model presented the best quality of fit evaluators for CS. In contrast, the GVB model showed better quality of fit to describe CGP over time for 340SS, 660SS, and SS, presenting the highest values of () and the lowest values of MSR, AIC, and MAD.

Towards sustainable development: Drivers of aggregate and sectoral convergence in per capita non-methane volatile organic compound emissions of OECD countries in two centuries

The purpose of this study is to extend the current literature on convergence of pollutants in five different ways including the examination of the convergence of per capita non-methane volatile organic compound emissions in 20 OECD countries for the period, 1820–2019. According to our knowledge, this is the one of the first attempts to investigate the convergence of per capita non-methane volatile organic compound emissions. The convergence of per capita non-methane volatile organic compound emissions across four sectors (agriculture, industry, residential commercial and others; and waste) in these countries has also been investigated. A residual augmented least squares regression method has been used to investigate the stochastic convergence of the series. Furthermore, conditional convergence and sigma convergence of per capita non-methane volatile organic compound emissions have also been examined. The results provide support for stochastic convergence of the pollutants both at aggregate and sectoral levels. The conditional convergence analysis supports the existence of convergence and shows that population growth rate and real gross domestic product per capita are structural factors that facilitate the existence of convergence in most of the sectors. A policy implication from the results is that a combination of international coordination efforts and country-specific factors including the real gross domestic product per capita and population growth rate must considered, when initiating policies aimed at further reducing emissions in these countries.

Segmentation in Structural Equation Modeling Using a Combination of Partial Least Squares and Modified Fuzzy Clustering

The application of a structural equation modeling (SEM) assumes that all data follow only one model. This assumption may be inaccurate in certain cases because individuals tend to differ in their responses, and failure to consider heterogeneity may threaten the validity of the SEM results. This study focuses on unobservable heterogeneity, where the difference between two or more data sets does not depend on observable characteristics. In this study, we propose a new method for estimating SEM parameters containing unobserved heterogeneity within the data and assume that the heterogeneity arises from the outer model and inner model. The method combines partial least squares (PLS) and modified fuzzy clustering. Initially, each observation was randomly assigned weights in each selected segment. These weights continued to be iteratively updated using a specific objective function. The sum of the weighted residual squares resulting from the outer and inner models of PLS-SEM is an objective function that must be minimized. We then conducted a simulation study to evaluate the performance of the method by considering various factors, including the number of segments, model specifications, residual variance of endogenous latent variables, residual variance of indicators, population size, and distribution of latent variables. From the simulation study and its application to the actual data, we conclude that the proposed method can classify observations into correct segments and precisely predict SEM parameters in each segment.

Nonlinear model fitting analysis of feather growth and development curves in the embryonic stages of Jilin white geese (Anser cygnoides).

Poultry is subject to varying degrees of feather loss and feather pecking during production, which seriously affects the live appearance and carcass appearance of their commercial traits and greatly reduces the production profitability of the farming enterprise. It also has an impact on down production and quality in the case of geese. In this study, mathematical models (Logistic, Gompertz, and Von Bertalanffy) were used to assess feather growth and development during the embryonic period in Jilin white geese (Anser cygnoides) predicting the weight and length of feathers from the back, chest, and belly tracts at different embryonic ages, to determine which growth model more accurately described feather growth patterns. The result first showed that the primary feather follicles of the Jilin white goose developed at E14 and secondary feather follicles at E18; primary feather follicle density increased and then decreased, whereas secondary feather follicle density increased continuously and the primary and secondary feather follicles developed independently. Secondly, the embryonic feather growth followed a slow-fast-slow pattern, with feathers growing slowly from E12 to E18, quickly from E18 to E24, and then decreasing after E24 until just before emergence (E30). In addition, before E14, feathers were concentrated in the back tracts, and no feathers were found on the head, neck, chest, abdomen, or wings. By E22, the whole body of the embryo was covered with feathers, and the back feathers were the earliest and fastest to develop. Compared to the Gompertz and von Bertalanffy models, the logistic model fit (R2 = 0.997) was the highest, while the sum of residual squares (RSS = 25661.67), Akaike's information criterion (AIC = 77.600), Bayesian information criterion (BIC = 78.191), and mean square error (MSE = 2851.296) were the lowest. Therefore, the logistic model was more suitable for describing the changes in whole-body feather growth during the embryonic period in Jilin white geese. In conclusion, using the growth curve model to explain the relationship between feather growth and embryonic age in geese will potentially speed up the process of genetic improvement in Jilin white geese (A. cygnoides) and thus provide scientific support for molecular genetic breeding.