Fused SDT/IRT Models for Mixed-Format Exams.

A psychological framework for different types of items commonly used with mixed-format exams is proposed. A choice model based on signal detection theory (SDT) is used for multiple-choice (MC) items, whereas an item response theory (IRT) model is used for open-ended (OE) items. The SDT and IRT models are shown to share a common conceptualization in terms of latent states of "know/don't know" at the examinee level. This in turn suggests a way to join or "fuse" the models-through the probability of knowing. A general model that fuses the SDT choice model, for MC items, with a generalized sequential logit model, for OE items, is introduced. Fitting SDT and IRT models simultaneously allows one to examine possible differences in psychological processes across the different types of items, to examine the effects of covariates in both models simultaneously, to allow for relations among the model parameters, and likely offers potential estimation benefits. The utility of the approach is illustrated with MC and OE items from large-scale international exams.

Chinese women's years of education and subjective well-being: an empirical analysis based on ordered Logit model and coupling coordination model.

In modern society, the improvement of women's education level has become one of the important indicators of national development and social progress. Although there are many useful explorations on the relationship between education and subjective well-being, the research on women's years of education and subjective well-being is very limited. The article focuses on women's years of education to determine whether and how to affect subjective well-being. This study is based on the China general social survey in 2021. The ordered Logit model was used to analyze the impact of women's years of education on subjective well-being, and a binary coupling coordination model was constructed to test the above two variables. The results show that the longer the education years of women, the stronger the subjective well-being. The benchmark regression results show that women's years of education have positive and negative effects on subjective well-being through economic status, physical and mental health, ecological environment, social cognition and personal cognition. The analysis of coupling coordination degree shows that the coupling between the years of education and subjective well-being of women in coastal areas and economically developed areas is the strongest, and the subjective well-being is better realized by increasing the years of education. Based on the above research results, this paper provides some practical suggestions for improving women's subjective well-being, and provides some valuable references for women to effectively balance husband-wife relationship, family relationship and work relationship, improve women's years of education and better obtain happiness.

Public preferences and willingness to pay for environmental benefits of straw return: Empirical evidence from Northeast China.

Straw management is an important issue for many developing countries. In China, straw return is an effective way of straw management, involving substantial financial subsidies and impacts on public welfare through environmental improvements. The public plays a significant role as a financial source and beneficiary, and sustainable straw return policies need to respond to the public's interests and concerns. Assessments of the public's heterogeneous preferences and willingness to pay can provide useful social insights into straw return policies. However, public opinion has not received sufficient attention. To fill this gap, this study utilized random parameter logit and latent class models based on a choice experiment survey data to assess willingness to pay and preference heterogeneity for environmental benefits of straw return. The results show that urban residents were willing to pay 20.71 CNY, 17.37 CNY, and 11.79 CNY, respectively, for increasing 1% soil organic matter content, clean air days, and chemical fertilizer reduction through straw return. Respondents' preferences were significantly influenced by their socioeconomic and cognitive characteristics. Specifically, respondents with higher income and more understanding of straw return policy have a significant preference for more attributes; older, lower-income, and less understanding groups have a significant preference for fewer attributes. The findings will not only enhance the accuracy and effectiveness of straw return policies, helping to form sustainable straw return policies, but also highlighting the necessity of broadening the scope of analysis for sustainable agricultural practices to provide a more comprehensive assessment, especially for developing countries facing straw management problems.

Heterogeneity in crash patterns of autonomous vehicles: The latent class analysis coupled with multinomial logit model.

Understanding the heterogeneity in autonomous vehicle (AV) crash patterns is crucial for enhancing the safety and public acceptance of autonomous transportation systems. In this paper, 584 AV collision reports from the California Department of Motor Vehicles (CA DMV) were first extracted and augmented by a highly automatic and fast variable extraction framework. Crash damage severities, classified as none, minor, moderate, and major, were set as the dependent variables. Factors including crash, road, temporal, vehicle, and environment characteristics were identified as potential determinants. To account for the heterogeneity inherent in crash data and identify key factors influencing the damage severity in AV crashes, a methodology integrating the latent class analysis and multinomial logit model was employed. Two heterogeneous clusters were determined based on the skewed distributions of vehicle status and driving mode. The model estimation results indicate a positive association between severe crash damage and some risk factors, such as head-on, intersection, multiple vehicles, dark with street lights, dark without street lights, and early morning. This study also reveals significant differences among the variables influencing the damage severity across two distinct subclasses. Moreover, partitioning the AV crash dataset into heterogeneous subsets facilitates the identification of critical factors that remain obscured when the dataset is analyzed as a whole, such as the evening indicator. This paper not only enhances our understanding of AV crash patterns but also paves the way for safer AV technology.

Drivers of variation in fuel choice for cooking: From the selected towns of North Shoa, Oromia Regional State, Ethiopia.

This study aimed to examine household fuel choice behaviour and drivers of variation in fuel choice for cooking. It utilizes descriptive statistics, ordered logit model and generalized ordered logit model to analyze the influence of independent variables upon dependent variables. The result shows that, mixed fuels are the dominant sources of energy with 43.75 % followed by unclean fuel with 33.25 % and then clean fuel with 23 %. This confirms 'households' Fuel-Stacking behaviour. The result of the ordered logit model suggest that variables like family size, per monthly income, the gender of household head, household ownership of electric meter, ownership of housing unit, marital status, age, occupation, educational levels of household heads, and the number of the adult females are statistically significant at 1 %, 5 %, and 10 % while, place of residence and occupation (self-non agriculture) are statistically insignificant to determine fuel choice. Identifying the fuels which are chosen by households should serve as a guide for government and policymakers in the formulation and implementation of policies and strategies that will guarantee optimal access to clean energy sources. Therefore, the government should improve the supply and distribution of electric meters by subsidizing them.

Applying survey weights to ordinal regression models for improved inference in outcome-dependent samples with ordinal outcomes.

Researchers often use outcome-dependent sampling to study the exposure-outcome association. The case-control study is a widely used example of outcome-dependent sampling when the outcome is binary. When the outcome is ordinal, standard ordinal regression models generally produce biased coefficients when the sampling fractions depend on the values of the outcome variable. To address this problem, we studied the performance of survey-weighted ordinal regression models with weights inversely proportional to the sampling fractions. Through an extensive simulation study, we compared the performance of four ordinal regression models (SM: stereotype model; AC: adjacent-category logit model; CR: continuation-ratio logit model; and CM: cumulative logit model), with and without sampling weights under outcome-dependent sampling. We observed that when using weights, all four models produced estimates with negligible bias of all regression coefficients. Without weights, only stereotype model and adjacent-category logit model produced estimates with negligible to low bias for all coefficients except for the intercepts in all scenarios. In one scenario, the unweighted continuation-ratio logit model also produced estimates with low bias. The weighted stereotype model and adjacent-category logit model also produced estimates with lower relative root mean square errors compared to the unweighted models in most scenarios. In some of the scenarios with unevenly distributed categories, the weighted continuation-ratio logit model and cumulative logit model produced estimates with lower relative root mean square errors compared to the respective unweighted models. We used a study of knee osteoarthritis as an example.

Unveiling the risks of speeding behavior by investigating the dynamics of driver injury severity through advanced analytics.

Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables using SHAP (Shapley Additive Explanations), while a mixed logit model was utilized to model driver injury severity accounting for unobserved heterogeneity in the data collection process. The results revealed a complex interplay of various factors that contribute to driver injury severity in single-vehicle crashes. These factors included driver characteristics such as demographics (male and female drivers, age below 26 years and between 35 and 45 years), driver actions (reckless driving, driving under the influence), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (non-interstate highways, undivided and divided roadways with positive barriers, curved roadways), environmental conditions (clear and daylight conditions), vehicle characteristics (motorcycles, displacement volumes up to 2500 cc and 5,000-10,000 cc, newer vehicles, Chevy and Ford vehicles), crash characteristics (rollover, run-off-road incidents, collisions with trees), temporal characteristics (midnight to 6 AM, 10 AM to 4 PM, 4th quarter of the analysis period: October to December, and the analysis year of 2017). The findings emphasize the significance of driving behavior and roadway design to speeding behavior. These aspects should be given high priority for driver training as well as the design and maintenance of roadways by relevant agencies.

Basic human values and the adoption of cryptocurrency.

Cryptocurrency is an attempt to create an alternative to centralized financial systems using blockchain technology. However, our understanding of the psychological mechanisms that drive cryptocurrency adoption is limited. This study examines the role of basic human values in three stages of cryptocurrency adoption-awareness, intention to buy, and ownership-using the Theory of Planned Behavior (TPB). Logistic regression analysis was conducted on a quota sample of 714 German adults, and the results showed that openness-to-change values increased the likelihood of cryptocurrency awareness, while self-enhancement values increased the likelihood of intention to buy and ownership. These findings were consistent even after controlling for demographic characteristics, attitudinal beliefs, and perceived behavioral control, which are important factors in the TPB. The results suggest that basic human values may influence an individual's decision to adopt cryptocurrency, but the transition from awareness to ownership may be influenced by socio-economic opportunities available to interested individuals.

Cannabis consumers' preferences for legal and illegal cannabis: evidence from a discrete choice experiment.

BACKGROUND: In U.S. states that legalized and commercialized recreational cannabis, cannabis sales in illegal markets are still sizable or even larger than those in legal markets. This study aimed to assess cannabis consumers' preferences for purchasing cannabis from legal and illegal markets and estimate the trade-offs under various policy scenarios. METHODS: 963 adults were recruited, who used cannabis in the past year and lived in a state with recreational cannabis legalization. In a discrete choice experiment, participants chose purchasing cannabis from a legal dispensary or an illegal dealer with varying levels in product attributes including quality, safety, accessibility, potency, and price. Mixed logit models were used to analyze preferences. RESULTS: The likelihood of choosing legal cannabis increased with a higher quality, the presence of lab test, a shorter distance to seller, a higher tetrahydrocannabinol level, and a lower price. The likelihood of choosing illegal cannabis increased with a higher quality, a shorter distance to seller, and a lower price. Among product attributes, quality and accessibility were perceived to be the most important for legal cannabis and price was perceived to be the most important for illegal cannabis. Policy simulations predicted that improving quality, ensuring safety, allowing delivery services, increasing dispensary density, and lowering prices/taxes of legal cannabis may reduce illegal cannabis market share. CONCLUSIONS: In the U.S., cannabis consumers' preferences for illegal cannabis were associated with both legal and illegal cannabis product attributes. Policies regulating legal cannabis markets should consider potential spillover effects to illegal markets.

Is there a LINKAGE between credit access, land use, and crop diversification in achieving food security? Evidence from cocoa-producing households in Nigeria.

Cocoa farmers in Nigeria adopt crop diversification to safeguard the food security of their households. Although credit and land are thought to play a vital role in crop diversification, they continue to have limited access to credit and land. This study investigated the linkages between access to credit, land use, crop diversification, and food security with a focus on cocoa farming households. A multistage sampling procedure was used to obtain data for the study. Data were analyzed with the aid of descriptive statistics, the Heifindahl index, the Tobit regression model, the food consumption score, and the ordered Logit regression model. The results for the entire respondents showed mean values of 55 years for age, 31 years for farming experience, 6 people for household size, and 5 ha for farm size. Heifindahl index shows 38.67 % of the respondents had low crop diversification in the study area. Tobit regression model reveals that access to credit, farming experience, cooperative organization, access to extension service, farm size, distance to farms, and labour are the main albeit significant factors that determine crop diversification among cocoa farming households. Food consumption score revealed that 46.67 % were poor, 30.67 % were at the borderline and about 27.67 % were within the acceptable threshold. The ordered logit model revealed that crop diversification index, formal education, access to credit, farm size, land use, and farming experience have a significant influence on the food security of households. The study concluded that there is a positive relationship between access to credit, land use, crop diversification, and food security. Therefore, the government and financial institutions should make credit facilities accessible to cocoa farmers to improve their livelihood.

Examining drivers injury severity for manual and automatic transmission vehicles-involved crashes: Random parameter mixed logit model with heterogeneity in means and variances.

The effect of vehicle transmission type on driver injury severities have not been thoroughly studied. The study used four-year historical crash data that occurred between the year 2019 and 2022 in Ghana. The data shows 1856 and 2272 crashes for automatic and manual transmission, respectively. The study examined the factors influencing driver injury severity in crashes involving vehicles with manual and automatic transmissions, using Random Parameter Mixed Logit Model to account for heterogeneity in the dataset. It was observed that use of manual transmission is related to a higher risk of incapacitating and fatal injuries compared to automatic transmission. Specifically, for automatic transmission vehicle-involved crashes, factors related to fatal injury were overaged vehicles, public transport, morning and evening peak hours, head-on and rollover crashes. Crashes involving saloon cars and low age cars were associated with incapacitating injury whiles rainy weather condition was related to both fatal and incapacitant injuries. Regarding manual transmission, fatal injury was associated with crashes involving male and novice drivers, cars, pickup trucks, HGV, public transports, morning and evening peak hours, rainy weather conditions and curved roads. Also, buses, private cars and trip distance were related to incapacitating injury. The rollover crashes and overaged vehicles were also associated with both fatal and incapacitating injuries. Four random parameters demonstrated heterogeneity in means, with two factors influencing the variances of two parameters for automatic transmission model. For the manual transmission model, five random parameters showed heterogeneity in means, with four variables influencing the variances of three parameters. These findings are valuable for policymakers, manufacturers, and drivers in implementing targeted interventions and safety measures to promote road safety.

Investigating the influencing factors of vaccination decisions for newly developed and established vaccines: a comparative study based on latent class logit models in China.

Background: The factors influencing vaccination decision-making for newly developed vaccines may be similar to and different from those for established vaccines. Understanding these underlying differences and similarities is crucial for designing targeted measures to promote new vaccines against potential novel viruses. Objective: This study aims to compare public vaccination decisions for newly developed and established vaccines and to identify the differences and similarities in the influencing factors. Method: A discrete choice experiment (DCE) was conducted on 1,509 representatives of the general population in China to collect data on preferences for the coronavirus disease 2019 (COVID-19) and influenza vaccines, representing the newly developed and established vaccines, respectively. The latent class logit model was used to identify latent classes within the sample, allowing for an analysis of the factors distinctly influencing choices for both types of vaccines. Result: Participants valued similar attributes for both vaccines. However, concerns about sequelae were more significant for the newly developed vaccine, while effectiveness was prioritized for the established vaccine. Class membership analysis revealed these differences and similarities were significantly correlated with age, health, yearly household income, acquaintances' vaccination status, and risk perception. Conclusion: The study highlights the need for tailored communication strategies and targeted vaccination interventions. For the newly developed vaccines, addressing concerns about side effects is more crucial. For long-standing vaccines, emphasizing their effectiveness can enhance uptake more significantly. Engaging healthcare providers and community influencers is essential for both vaccines to increase public confidence and vaccination rates. Clear communication and community engagement are critical strategies for addressing public concerns and misinformation, particularly during periods of heightened concern.

Investigating factors affecting injury severity of single-vehicle run-off-road crashes.

This study aimed to identify and investigate the contributing factors influencing injury severity in single-vehicle run-off-road (ROR) crashes, which are known for their high severity. The primary objective was to analyze and compare the impact of these factors across three distinct vehicle classes: passenger cars, sport utility vehicles (SUVs), and pickups. A mixed logit model with heterogeneity in mean and variance was developed to analyze the injury severity outcomes in ROR crashes for the three vehicle classes. The model accounted for the potential variations in the impact of contributing factors across different vehicle types. The study revealed several significant variables consistently influencing injury severity across all three vehicle classes. These included driver age, alcohol or drug usage, seatbelt utilization, airbag deployment, higher travel speeds, and the vehicle model year post-2010. Notably, as driver age increased, the impact on changes in injury severity outcomes was more pronounced for pickup drivers compared to those operating passenger cars and SUVs. Among the common findings was the highly effective role of seatbelt usage in mitigating injury severity in ROR crashes. Additionally, passenger cars were associated with increased injury severity, particularly at relatively higher travel speeds exceeding 75 mph when contrasted with SUVs and pickups traveling between 61 and 75 mph. The study highlights the importance of considering vehicle class-specific factors in analyzing injury severity in ROR crashes. Recommendations include further in-depth investigations into distinct factors contributing to injury severity within each vehicle class and utilizing more extensive crash datasets to gain additional insights for enhancing road safety.

How to maximize the travelers' shift to rail transit in a Chinese valley city after bus fare adjustment.

Existing studies have not analyzed travelers' travel modal shift behavior after bus fare adjustment for special urban topography. In order to fill this gap and explore the strategies that can effectively encourage the shift of valley city travelers to rail transit after the adjustment of bus fare, the price adjustment perception and topographic space perception are introduced to expand the theory of planned behavior (TPB). On this basis, an integrated model combining structural equation model (SEM) and Mixed Logit model (MLM) is established to analyze the factors of travelers' modal shift behavior of in the valley city after bus fare adjustment, and elastic analysis is carried out. Taking Lanzhou as an example, questionnaire data and traffic data were collected for example analysis. The results show that fare adjustment perception (FAP) and topographic space perception (TSP) have the same significant impact on travel modal shift intention (TMSI) and behavior (TMSB) as subjective norms (SN), shift behavior attitude (SBA) and perceived behavior control (PBC), and the travel characteristics and psychological perception sensitivity of travelers in the valley city are heterogeneous. After the adjustment of bus fares in valley city, the shift to rail transit is the most. When the subjective norms, fare adjustment perception and topographic space perception of travelers in the valley city increase by 50 %. The sharing rate of rail transit is increased by 10.284 %. The effective way to increase the sharing rate of rail transit is to increase the combined factors of subjective norms, topographic space perception and fare adjustment perception. Compared with the Multinomial Logit model and the Logit model combined with structural equation model, the goodness of fit and prediction accuracy of the proposed integrated model are improved.

Determinants of active trachoma among rural children aged 1-9 years old in Aw-Bare Wereda, Somali Region of Ethiopia: a cross-sectional study.

INTRODUCTION: Chronic and highly contagious, trachoma is a condition characterized by recurrent bacterial infection with ocular strains of Mycoplasma trachoma. It spreads through fingers, flies, and fomites, especially in situations where there is overcrowding. If untreated, the illness may result in blindness. Trachoma is an ancient disease and has previously been a significant public health problem in many areas of the world, including parts of Europe and North America. There are at least 400 million cases of active trachoma in the world, 8 million of which have resulted in blindness. Trachoma is a serious public health issue that is very common in Ethiopia. Therefore, the objective of this study is to identify the determinants of active trachoma among rural children aged 1-9 years old in Aw-bare woreda, Somali region of Ethiopia. METHOD: A cross-sectional community-based study involving children aged 1-9 who lived in six selected rural kebeles in the Awbare woreda Somali region and carried out using an ordinal logistic regression model. The study comprised 377 children in total. Our sample youngsters were chosen through a two-stage cluster sampling procedure. Then also chose our sample kebeles by simple random sampling. The main environmental, personal, and demographic factors that influenced the outcomes of active trachoma status were modeled using partial proportional odds modeling and descriptive statistics. RESULT: The study showed that the prevalence of active trachoma was found to be 47.7%. The covariate secondary level of education of mother OR = 1.357; 95% CI (1.051, 1.75), P-value = 0.0192, Inside house cooking place of children family OR = 0.789:95% CI (0.687, 0.927), P-value = 0.0031, children stay at home OR = 2.203:95%CI (1.526, 3.473), P-value = 0.0057,rich income family OR = 1.335:95%CI(1.166,1.528),P-value = 0.0001,Amount of water fetched per day OR = 2.129,95%CI(1.780,2.547),P-Vaue = 0.0001 were significant effect on active trachoma. PPOM represents the best fit as it has the smallest AIC and BIC. It is also more parsimonious. CONCLUSION: The mother's educational level, the location where the children spent the majority of their time indoors cooking, the fly density during the interview, the family's income, the child's age in years, the distance to the water source, the quantity of water fetched daily, and the number of people sharing a room have all been found to be significant predictors of the child's active trachoma status. Thus, increasing maternal education, access to clean water, and socioeconomic position are all crucial measures in preventing trachoma. Preventing trachoma also involves reducing the number of kids in a room and enhancing activities linked to personal cleanliness, such as giving kids a thorough facial wash to remove debris and discharge from their eyes.

Real-time conflict risk at signalized intersection using drone video: A random parameters logit model with heterogeneity in means and variances.

Signalized intersections are crash prone. This can be attributed to driver errors, red light running behaviour, and poor coordination of conflicting traffic. It is anticipated that overall crash risk at signalized intersection would increase when mixed traffic like motorcycles is involved. In this study, a real-time prediction model for motorcycle and non-motorcycle involved conflict risk at the signalized intersection is proposed. For example, high-resolution vehicle and motorcycle trajectory data are extracted from drone videos using advanced computer vision techniques. Additionally, conflict types including rear-end, angle, and head-on conflicts are also considered. Then, the multinomial logit approach is adopted to model the propensity of severe and slight vehicle-vehicle and vehicle-motorcycle conflicts. Furthermore, the problem of unobserved heterogeneity is addressed using the random parameters model with heterogeneity in means and variances. Results indicate that risk of vehicle-vehicle conflict is significantly associated with vehicle speed and acceleration, and conflict type, and that of vehicle-motorcycle conflict is associated with vehicle speed and acceleration, motorcycle lateral speed, conflict type, and time to green signal. Findings should shed light to the development and implementation of optimal traffic signal time plan and traffic management strategy that can mitigate the potential crash risk, especially involving motorcycles, at the signalized intersection.

Preferences and Willingness to Pay for Herpes Zoster Vaccination Among Chinese Adults: Discrete Choice Experiment.

BACKGROUND: The incidence of herpes zoster (HZ) is rapidly increasing, causing both clinical and economic burdens in China. Very little is known about Chinese residents' HZ vaccine preferences and willingness to pay (WTP) for each vaccination attribute. OBJECTIVE: This study aims to elicit the preferences of Chinese urban adults (aged 25 years or older) regarding HZ vaccination programs and to calculate WTP for each vaccination attribute. METHODS: In this study, we interviewed 2864 residents in 9 cities in China. A discrete choice experiment was conducted to investigate the residents' preferences for HZ vaccination and to predict the uptake rate for different vaccine scenarios. A mixed logit model was used to estimate the preferences and WTP for each attribute. Seven attributes with different levels were included in the experiment, and we divided the coefficients of other attributes by the coefficient of price to measure WTP. RESULTS: Vaccine effectiveness, protection duration, risk of side effects, place of origin, and cost were proven to influence Chinese adults' preferences for HZ vaccination. The effectiveness of the HZ vaccine was the attribute that had the most predominant impact on residents' preferences, followed by protection duration. The residents were willing to pay CN ¥974 (US $145) to increase the vaccine effectiveness from 45% to 90%, and they would barely pay to exchange the vaccination schedule from 2 doses to 1 dose. It is suggested that the expected uptake could be promoted the most (by 20.84%) with an increase in the protection rate from 45% to 90%. CONCLUSIONS: Chinese urban adults made trade-offs between vaccine effectiveness, protection duration, place of origin, side effects, and cost of HZ vaccination. Vaccine effectiveness was the most important characteristic. The residents have the highest WTP (CN ¥974; US $145) for enhancing the effectiveness of vaccines. To maximize HZ vaccine uptake, health authorities should promote vaccine effectiveness.

Adaptation strategies of smallholder farmers to climate variability and change in Konso, Ethiopia.

This study investigates the adaptation strategies employed by smallholder farmers in Konso, Ethiopia, to cope with climate change and variability. A survey of 355 smallholder households revealed that farmers utilize various indigenous techniques, including terracing, agroforestry, intercropping, and traditional irrigation practices, to adapt to climate change. The adoption of these strategies is influenced by factors such as education level, landholding size, income level, access to climate information, credit, and extension services. Despite these efforts, smallholder farmers in Konso continue to face challenges in ensuring food security due to recurrent droughts and unpredictable rainfall patterns. To enhance the sustainability of indigenous farming systems in Konso, this study recommends providing regular weather information, accessible credit services, crop insurance options, and capacity building through extension services, with a focus on inclusive approaches that cater to uneducated farmers.

A user-friendly method for estimating discrete choice land-use model in a panel data setting.

Land-use modeling stands as a pivotal tool in shaping sustainable development policies. With the rapid advancement of remote-sensing technology and the widespread adoption of satellite imagery-based land cover products, these datasets have emerged as primary sources for understanding land-use dynamics due to their high spatial and temporal resolutions. Yet, it remains challenging to effectively integrate such rich panel data into nonlinear econometric land-use models. This paper introduces a method to seamlessly incorporate land cover panel data into econometric models, enabling comprehensive utilization of temporal information within a single framework.-By capturing dynamic land-use patterns, the method enhances prediction accuracy while mitigating issues such as autocorrelated error terms commonly encountered in panel data analysis.-The method is straightforward to implement and applicable to many nonlinear models, making it particularly suitable for datasets with large sample sizes.

Effects of participatory forest management programs on Land use/land cover change and its Determinants in Alle District, southwest Ethiopia.

In order to create sustainable conservation policies for biodiversity, it is imperative that participatory forest management (PFM) be assessed. Forests contribute to the sustainability of the planet by controlling soil erosion in agricultural areas and by moderating the effects of climate change. However, Ethiopia's forest resources have been under intense pressure because of the increased demand for wood products and agricultural conversion. As one of the potential solutions, the PFM programme was implemented in 1990. This study set out to investigate the effects of the PFM programme on land use and land cover (LULC) in the Alle district of southwest Ethiopia, as well as the variables influencing community involvement and the obstacles to PFM implementation and community involvement. Changes in forest cover were detected using Landsat images from 1992, 2012, and 2022 obtained from Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI). Images were obtained during the dry season and were cloud-free. A total of 240 respondents were chosen by means of a straightforward random sampling technique, and survey data were collected using questionnaires, interviews, and field observations. Data were analyzed using ArcGIS 10.5, ERDAS Imagine 2015, SPSS version 20, and Excel 2010. The change in forest cover shows an increasing trend from 2012 to 2022. Again, grassland and wetland coverage in this study decreased rapidly. In the years 2012-2022, forest land increased from 462.7ha (74.8 %), to 569.8ha (92.1 %), while, the agricultural land, grassland, and wetland were reduced from 109.5ha (17.7 %) to 37.8ha (6.1 %), 31.9ha (5.2 %) to 0.0ha (0.0 %); 14.1 ha (2.3 %), to 10.8 ha (1.7 %) respectively. There have been beneficial developments in the forests over the last 30 years. The binary logistic regression model disclose that, land ownership had a negative impact on forest management participation, while other factors such as gender, education level, family size, TLU, access to credit, training, and law enforcement had a positive and significant (p < 0.05) effect on PFM practices. LULC change in study area causes rapid wetland ecosystem deterioration, which may result in the extinction of the most significant and ecologically valuable species and a loss of biodiversity in the environment. In this context, developing an integrated participatory approach requires rapid attention, and all farmers and stakeholders must be actively involved in PFM programs.