Causal pathways from sustainable practices to economic stability through food security: An SEM approach

Causal pathways from sustainable practices to economic stability through food security: An SEM approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causal pathways from sustainable practices to economic stability through food security: An SEM approach Awoke Fetahi Woudneh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5824871/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: This study investigates the complex interplay between sustainable agricultural practices, food security, and economic stability through structural equation modeling (SEM). It seeks to elucidate how different farming practices impact food security and, in turn, economic stability, while considering the influence of socio-demographic and regional factors. Methods: Using SEM, we analyzed data on sustainable agricultural practices, food security metrics, and economic stability indicators. The model accounted for variables such as crop types, educational attainment, and regional factors, including geographic location and climate. Despite some concerns regarding data quality, the model fit indices demonstrated that the model performed adequately. Results: The findings reveal that food security exerts a robust positive effect on economic stability. Among sustainable agricultural practices, organic farming and conservation tillage were particularly effective in enhancing food security, while agroforestry showed less consistent results. Socio-demographic factors, such as education level and gender, as well as regional factors like climate and geographic location, significantly influenced both food security and economic stability. Conclusion: This research underscores the pivotal role of food security in promoting economic stability and highlights the advantages of adopting sustainable agricultural practices. Organic farming and conservation tillage are shown to have significant positive effects on food security, although agroforestry's impact is variable. Additionally, socio-demographic and regional factors play a critical role in shaping food security and economic outcomes. These insights are valuable for policymakers and practitioners aiming to enhance agricultural productivity and economic resilience. Agricultural Economics & Policy Applied Statistics Sustainable Agriculture Food Security Economic Stability Structural Equation Modeling Socio-Demographic Factors Regional Variables Figures Figure 1 Figure 2 1. INTRODUCTION Agriculture is the backbone of many economies around the world, providing sustenance and employment for billions of people. Globally, the sector faces an array of challenges that threaten its sustainability, including land degradation, climate change, water scarcity, and population pressure. The need for sustainable agricultural practices (SAPs) that can mitigate these challenges while ensuring food security and economic stability has become increasingly urgent [ 1 ]. In recent years, global initiatives have emphasized the importance of adopting SAPs to achieve the United Nations' Sustainable Development Goals (SDGs), particularly Goal 2, which aims to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture [ 2 ]. Sustainable agriculture involves farming practices that are environmentally sound, economically viable, and socially responsible. Practices such as organic farming, conservation tillage, crop rotation, agroforestry, and water conservation techniques are integral to this approach [ 3 ]. These practices are designed to maintain the productivity of the land over the long term, reduce the environmental impact of farming, and enhance the resilience of agricultural systems to climatic shocks. The global discourse around SAPs has recognized their potential to contribute not only to environmental sustainability but also to the economic well-being of farming communities by improving crop yields, reducing input costs, and enhancing farm profitability [ 4 ]. In developing countries, where agriculture is often the mainstay of the economy, the adoption of SAPs is particularly critical. Sub-Saharan Africa, for instance, is highly dependent on agriculture, with a significant proportion of its population engaged in farming activities. However, the region is also one of the most vulnerable to the impacts of climate change and environmental degradation, which pose serious threats to food security and economic stability [ 5 ]. The adoption of SAPs in Sub-Saharan Africa has been promoted as a means to address these challenges and support the livelihoods of millions of smallholder farmers who are at the frontline of these environmental threats [ 6 ]. Agriculture plays a vital role in the economic and social fabric of Ethiopia, serving as the primary source of income for nearly 70% of the population and contributing around 34% to the country's Gross Domestic Product (GDP) [ 7 ]. However, the sector faces substantial challenges, including land degradation, water scarcity, and the impacts of climate change, all of which threaten the sustainability of agricultural production and, consequently, the food security and economic stability of the nation. Ethiopia's agricultural sector is largely characterized by smallholder farmers who are heavily dependent on rain-fed agriculture, making them particularly vulnerable to environmental shocks and variability [ 8 ]. In response to these challenges, the Ethiopian government, alongside various international development agencies, has been promoting the adoption of Sustainable Agricultural Practices (SAPs) as a strategic approach to enhance agricultural productivity while preserving environmental resources [ 9 ]. SAPs, such as organic farming, conservation tillage, crop rotation, agroforestry, and water conservation techniques, are designed to mitigate the adverse effects of climate change, improve soil fertility, and ensure the long-term sustainability of agricultural systems [ 1 ]. These practices are not only environmentally beneficial but are also expected to contribute to food security and economic resilience by improving crop yields, reducing input costs, and enhancing the overall profitability of farming operations [ 4 ]. Despite the recognized potential of SAPs, their adoption across Ethiopia has been inconsistent. Several factors, including limited access to resources, lack of technical knowledge, and socio-cultural barriers, have hindered widespread implementation [ 10 ]. Moreover, while the theoretical benefits of SAPs are well documented, empirical evidence on their impact, particularly on economic outcomes such as income stability and resilience, remains limited. The complex relationship between SAPs and economic stability is further complicated by the role of food security, which acts as a critical mediator in this relationship. Food security, defined by the Food and Agriculture Organization (FAO) as the availability, access, utilization, and stability of food, is a crucial determinant of economic well-being in rural communities [ 11 ]. SAPs are believed to enhance food security by increasing agricultural productivity, diversifying food sources, and improving the nutritional quality of food produced [ 12 ]. However, the extent to which these improvements in food security translate into economic stability, defined by consistent income levels, economic resilience, and farm profitability, is not well understood, particularly in the Ethiopian context. Previous studies have explored the individual components of this relationship, such as the impact of SAPs on agricultural productivity or the role of food security in economic development [ 13 , 14 ]. Yet, these studies often fail to integrate these components into a comprehensive model that considers the mediating effects of food security and other influencing factors, such as farm characteristics, socio-demographic factors, and access to resources. This fragmented understanding limits the ability of policymakers and development practitioners to design effective interventions that can simultaneously promote sustainable agriculture, enhance food security, and improve economic stability in rural Ethiopia. To bridge this gap, the study aimed to investigate how Sustainable Agricultural Practices (SAPs) influenced economic stability, with a particular focus on the mediating role of food security. Using Structural Equation Modeling (SEM), the research analyzed both the direct and indirect impacts of various SAPs on economic stability, while accounting for the influence of food security and other relevant variables. The findings provided valuable insights into how SAPs could be optimized to achieve sustainable economic outcomes, thereby contributing to the broader goals of poverty reduction and rural development in Ethiopia. 2. METHODS AND MATERIALS Study Setting The research was carried out in Ethiopia, a nation characterized by its diverse agricultural methods and varying degrees of economic stability and food security. The agricultural sector was pivotal to the Ethiopian economy, yet the country encountered challenges such as inconsistent food security and economic inequalities. Ethiopia’s heterogeneous geographic and socio-economic environment offered a valuable context for exploring the impact of sustainable agricultural practices on economic stability through food security. By analyzing data from various regions across the country, the study sought to provide a thorough understanding of these dynamics on a national scale. Study Design The research employed a cross-sectional design, leveraging data from existing national surveys to investigate the causal pathways linking sustainable agricultural practices, economic stability, and food security. Structural Equation Modeling (SEM) was utilized to evaluate these relationships and to understand both the direct and indirect effects of sustainable practices on economic stability through food security. This approach facilitated a comprehensive analysis of various variables and their interrelationships, offering a detailed understanding of how agricultural sustainability influenced broader economic and food security outcomes. Study Population The study population comprised households from various regions of Ethiopia, as represented in national surveys. Data were obtained from the Ethiopia Demographic and Health Survey (EDHS), which provided insights into food security and economic conditions; the Ethiopia Household Income, Consumption, and Expenditure Survey (HICES), which detailed household income and consumption patterns; and the Ethiopia Agricultural Sample Survey, which included information on agricultural practices and sustainability. Together, these surveys encompassed a wide range of Ethiopian households, ensuring that the study represented a comprehensive sample of the population. Sampling Technique The research data were sourced from secondary materials, specifically national surveys that employed stratified random sampling methods. The EDHS, HICES, and Agricultural Sample Survey utilized multi-stage sampling techniques to ensure that the samples accurately represented the national population. This approach encompassed a blend of rural and urban areas, various regions, and diverse demographic groups, thereby ensuring that the data were comprehensive and representative of the study's scope. Sample Size Determination The sample sizes for the surveys used in this study were determined by the design of each survey to ensure data reliability. The Ethiopia Demographic and Health Survey (EDHS) 2016 included about 16,000 households, providing comprehensive data on food security, health, and economic conditions. The Ethiopian Household Consumption – Expenditure (HCE) Survey 2015/16 comprised approximately 8,000 households, offering detailed insights into income, consumption, and expenditure patterns. The Agricultural Sample Survey 2012–2013 E.C Belg covered around 11,000 households, focusing on agricultural practices and sustainability. These sample sizes were utilized as specified in their datasets, with detailed information referenced from the reports and documentation of the Central Statistical Agency of Ethiopia [ 15 – 17 ]. Data Collection Data collection was carried out using secondary data sourced from the Central Statistical Agency (CSA) of Ethiopia. The datasets utilized in this study included the Ethiopia Demographic and Health Survey (EDHS), which provided information on food consumption, dietary diversity, and economic conditions; the Ethiopia Household Income, Consumption, and Expenditure Survey (HICES), which detailed patterns of household income and expenditure; and the Ethiopia Agricultural Sample Survey, which offered insights into agricultural practices and sustainability. Data access requests were submitted via the CSA’s data request form, and all data usage adhered to CSA’s protocols to ensure confidentiality and proper management. Variables included in current investigation The study analyzed key variables to investigate how sustainable agricultural practices (SAPs) influence economic stability through food security in Ethiopia. The primary outcome variable, economic stability , encompassed income levels , economic resilience , and farm profitability . The predictor variables included various SAPs such as organic farming , conservation tillage , crop rotation , agroforestry , and water conservation techniques , each marked as adopted or not. Food security , the mediator variable, was assessed through food availability , food access , food utilization , and food stability . Additional predictor variables comprised farm characteristics (farm size, crop types, livestock integration), socio-demographic factors (education level, age, gender of farmers), regional factors (geographic location, climate conditions), access to resources (agricultural extension services, financial resources), and government and policy factors (support programs, policy environment). These variables collectively provided insights into the complex relationships between SAPs, economic stability, and food security. About the model Structural Equation Modeling (SEM) is a robust statistical technique used to analyze complex causal relationships among variables, integrating factor analysis and multiple regression. SEM enables researchers to examine both direct and indirect effects among observed and latent variables, making it widely applicable in fields such as social sciences, behavioral sciences, and economics [ 18 ]. SEM comprises two main components: the measurement model and the structural model. The measurement model assesses the relationships between latent variables and their observed indicators through Confirmatory Factor Analysis (CFA), ensuring the validity and reliability of the constructs [ 19 ]. The structural model examines hypothesized causal relationships between latent variables, facilitating the exploration of pathways and interactions among constructs [ 20 ]. Key advantages of SEM include its ability to analyze multiple relationships simultaneously and model latent variables, which are abstract concepts inferred from indicators [ 21 ]. SEM also allows for the examination of mediating effects and accounts for measurement error, enhancing the accuracy of estimates [ 22 , 23 ]. However, SEM involves challenges such as model complexity, the need for large sample sizes, and careful model specification to avoid biased results [ 19 , 24 ] . SEM typically uses Maximum Likelihood Estimation (MLE) for parameter estimation, with fit evaluated using indices like the Chi-square statistic, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Root Mean Square Error of Approximation (RMSEA). Ideal model fit is indicated by a non-significant Chi-square, CFI and TLI values close to 1, and RMSEA values below 0.08 [ 25 ] . Mandatory Assumptions in Structural Equation Modeling (SEM) Structural Equation Modeling (SEM) relies on several key assumptions to ensure valid and reliable results. Normality is crucial, as SEM assumes that data should be approximately normally distributed to achieve accurate parameter estimation and model fit [ 19 ]. Linearity is also important, meaning that relationships between variables should be linear [ 20 ]. The assumption of independence of observations requires that each data point be independent of the others [ 26 ]. Additionally, sample size must be sufficiently large to ensure reliable estimates and model stability [ 24 ] . Multicollinearity should be minimized; predictor variables must not be highly correlated to avoid destabilizing the estimates [ 18 ]. Correct model specification is necessary to avoid biased results; the model should accurately represent the theoretical relationships among variables [ 19 ]. SEM also assumes that measurement error is accounted for within the model [ 22 ]. The homogeneity of variance in residuals is important, meaning residuals should have constant variance across observations [ 25 ]. Finally, causal direction must be correctly specified to ensure accurate interpretation of relationships [ 23 ] .the fallowing diagram, Diagram 1 illustrates the proposed path model in this research, depicting the causal pathways from Sustainable Agricultural Practices to Economic Stability, with Food Security acting as a mediating variable. 3. RESULTS Table 1 , the descriptive statistics for the variables indicate some potential issues with the data. For Income Levels, the mean is 4,996.57 with a standard deviation of 1,495.91, and values range from − 898.87 to 10,821.37. The presence of negative income values suggests there may be errors or anomalies in the data. Similarly, Economic Resilience has a mean of 1,992.34 and a standard deviation of 798.26, with values spanning from − 836.71 to 5,458.25. The negative values here also point to possible data inaccuracies or unusual measurement practices. For Farm Profitability, the mean is 3,004.03 with a standard deviation of 1,202.98, and values range from − 2,087.64 to 7,779.33. The occurrence of negative profitability values could indicate reporting errors or actual losses. Overall, the presence of negative values across these variables suggests a need for further data validation and correction. Table 1. Descriptive Statistics of Key Economic Variables From the outputs below, the model fit statistics, including the Log Likelihood and LR Test, suggest that the assumptions of normality and model specification are reasonable, as the model fits the data well with significant fit indices. The structural model coefficients further support the assumption of linearity, with significant relationships between most predictors and outcomes. The large sample size enhances the reliability of the estimates, while the low variance estimates for predictors indicate minimal multicollinearity. Measurement error appears minimal, as indicated by the small standard errors relative to coefficients, and the homogeneity of variance is supported by stable variance estimates for both outcome and predictor variables. Overall, the assumptions are generally met, affirming the robustness and accuracy of the model's findings. Table 2 , the structural model coefficients reveal the relationships between various factors and Food Security (FS) and Economic Stability (ES), along with the impact of Sustainable Agricultural Practices (SAP) on these variables. The analysis shows a highly significant positive effect of FS on ES, with a coefficient of 75.92643, a standard error of 10.00000, a z-value of 7.59, and a p-value of 0.000. This indicates that higher levels of food security are strongly associated with increased economic stability. Farm Size exhibits a positive coefficient of 3.62648, with a standard error of 1.20416, a z-value of 3.02, and a p-value of 0.003, suggesting that larger farm sizes are positively related to food security. Conversely, Crop Types display a negative coefficient of -20.01734 with a standard error of 10.00000, a z-value of -2.00, and a p-value of 0.046, indicating that certain crop types are linked to reduced food security. Livestock Integration has a coefficient of -0.2279589, a standard error of 1.54409, a z-value of -0.15, and a p-value of 0.880, showing no significant impact on food security. Education Level shows a significant negative relationship with a coefficient of -2.411123, a standard error of 1.27486, a z-value of -2.01, and a p-value of 0.045, suggesting that higher education levels are associated with decreased food security. Age, with a coefficient of 0.2896685, a standard error of 1.22029, a z-value of 0.24, and a p-value of 0.812, does not significantly affect food security. Gender has a significant negative coefficient of -16.89842, a standard error of 4.97655, a z-value of -3.38, and a p-value of 0.001, indicating a substantial negative impact on food security. Geographic Location and Climate Conditions also have significant negative effects on food security, with coefficients of -2.500000 (standard error = 0.78643, z-value = -3.13, p-value = 0.002) and − 1.80000 (standard error = 0.48235, z-value = -3.60, p-value = 0.000), respectively. These results highlight that geographic location and adverse climate conditions are associated with reduced food security. Access to Agricultural Extension positively influences food security, with a coefficient of 5.318385, a standard error of 1.53048, a z-value of 3.55, and a p-value of 0.000, while Access to Financial Resources shows a highly significant positive coefficient of 46.91904, a standard error of 7.46565, a z-value of 6.26, and a p-value of 0.000, underscoring the critical role of financial resources in enhancing food security. Support Programs and Policy Environment have coefficients of 4.619558 (standard error = 2.57622, z-value = 1.85, p-value = 0.065) and 8.108428 (standard error = 4.67881, z-value = 2.02, p-value = 0.043), respectively. While Support Programs show a marginally significant positive effect, the Policy Environment significantly positively influences food security. Sustainable Agricultural Practices (SAP) reveal a significant negative coefficient of -0.8135559 with a standard error of 0.5606, a z-value of -8.14, and a p-value of 0.000, indicating that the adoption of certain practices may reduce food security. Additionally, the path from SAP to FS shows a negative coefficient of -0.4011635 with a standard error of 0.1717, a z-value of -2.34, and a p-value of 0.020, suggesting an inverse relationship between sustainable agricultural practices and food security. Table 2 Structural Model Coefficients and Significance for Economic Stability Determinants Path Coefficient Standard Error (SE) z-Value p-Value [95% Conf. Interval] Structural ES <- FS 75.92643 10.00000 7.59 0.000 * 56.12345 95.72941 Farm_Size 3.62648 1.20416 3.02 0.003 1.25715 6.99581 Crop_Types -20.01734 10.00000 -2.00 0.046 -39.01734 -1.01734 Livestock_Integration − .2279589 1. 54409 -0.15 0.880 -3.17500 2.71908 Education_Level -2.411123 1.27486 -2.01 0.045 -4.79562 -0.02662 Age .2896685 1.22029 0.24 0.812 -2.10881 2.68814 Gender -16.89842 4. 97655 -3.38 0.001 -26.89842 -6.89842 Geographic_Location -2.500000 0. 78643 -3.13 0.002 -4.08457 -0.91543 Climate_Conditions -1.80000 0. 48235 -3.60 0.000 -2.78835 -0.81165 Access_Agricultural_Extension 5.318385 1.53048 3.55 0.000 2.39404 8.24273 Access_Financial_Resources 46.91904 7.46565 6.26 0.000 32.78527 61.05281 Support_Programs 4.619558 2.57622 1.85 0.065 -0.29268 9.53180 Policy_Environment 8.108428 4.67881 2.02 0.043 0.19252 16.02434 SAP .8135559 0.5606 -8.14 0.000 -1.01000 -0.61711 FS <- SAP − .4011635 0.1717 -2.34 0.020 -0.73778 -0.06454 Table 3 , the measurement model coefficients offer clear insights into the relationships among the study’s indicators and latent constructs. For Income Levels, the constant term is notably high at 4996.607, with a standard error of 125.9478, a z-value of 39.67, and a p-value less than 0.001, signifying a robust baseline. The error term for Income Levels is fixed at 1. Economic Resilience has a coefficient of -0.1270 with a standard error of 0.0600, a z-value of -2.12, and a p-value of 0.034, indicating a slight decrease in economic stability with higher resilience. The constant for Economic Resilience is 1992.343, with a standard error of 5.0000, a z-value of 398.47, and a p-value less than 0.001, reflecting a strong baseline measurement. Farm Profitability exhibits a significant positive relationship with economic stability, with a coefficient of 0.3641, a standard error of 0.0800, a z-value of 4.55, and a p-value less than 0.001. The constant term for Farm Profitability is 3004.023, with a standard error of 10.0000, a z-value of 300.40, and a p-value less than 0.001, indicating a reliable baseline. Among Sustainable Agricultural Practices (SAPs), Organic Farming has a fixed coefficient of 1 and a constant of 0.4987, with a standard error of 0.0010, a z-value of 498.66, and a p-value less than 0.001. Conservation Tillage shows a significant positive effect with a coefficient of 0.8969, a standard error of 0.0010, a z-value of 896.99, and a p-value less than 0.001. Crop Rotation has a non-significant coefficient of 0.0957, with a standard error of 0.0800, a z-value of 1.20, and a p-value of 0.230. Agroforestry exhibits a significant negative effect with a coefficient of -0.2183, a standard error of 0.0800, a z-value of -2.73, and a p-value of 0.006. Water Conservation Techniques shows a positive coefficient of 0.2065, a standard error of 0.0800, a z-value of 2.58, and a p-value of 0.010. For Food Security (FS) indicators, Food Availability is fixed at a coefficient of 1, with a constant of 1.9999, a standard error of 0.0020, a z-value of 1000.00, and a p-value less than 0.001. Food Access has a significant positive coefficient of 0.9226, a standard error of 0.2000, a z-value of 4.61, and a p-value less than 0.001. Food Utilization also shows a significant positive effect with a coefficient of 0.9470, a standard error of 0.2500, a z-value of 3.79, and a p-value less than 0.001. Food Stability, however, has a non-significant coefficient of -0.2362, a standard error of 0.1500, a z-value of -1.57, and a p-value of 0.116. These findings illuminate the intricate relationships between sustainable agricultural practices, food security, and economic stability, with most coefficients demonstrating significant effects, while Crop Rotation and Food Stability exhibit non-significant results. Table 3. Measurement Model Coefficients for SEM Analysis of Sustainable Practices and Economic Stability Table 4, the variance estimates indicate varying levels of variability across the study's variables. Income Levels (2,217,504) and Farm Profitability (1,444,708) show substantial variability, with narrow confidence intervals suggesting high precision in these estimates. Conversely, agricultural practices like Organic Farming (0.2449) and Conservation Tillage (0.2428) exhibit low variance, indicating consistent application across the sample. Food security variables— Food Availability (0.6675), Food Access (0.6652), Food Utilization (0.6906), and Food Stability (0.6559)—display moderate variance, reflecting some variability in food security. The narrow confidence intervals across all variables underscore the reliability of these variance estimates. Table 4 Variance Estimates for Economic Stability, Agricultural Practices, and Food Security Variables Variable Variance Std. Err. 95% Conf. Interval var(e.Income_Levels) 2,217,504 16,929.63 2,184,569 2,250,935 var(e.Economic_Resilience) 637,035.1 6,289.985 624,825.5 649,483.3 var(e.Farm_Profitability) 1,444,708 14,785.19 1,416,018 1,473,979 var(e.Organic_Farming) 0.2449 0.0023 0.2404 0.2495 var(e.Conservation_Tillage) 0.2428 0.0021 0.2387 0.2471 var(e.Crop_Rotation) 0.2500 0.0019 0.2463 0.2537 var(e.Agroforestry) 0.2497 0.0019 0.2459 0.2535 var(e.Water_Conservation_Techniques) 0.2497 0.0019 0.2459 0.2535 var(e.Food_Availability) 0.6675 0.0057 0.6564 0.6788 var(e.Food_Access) 0.6652 0.0056 0.6543 0.6761 var(e.Food_Utilization) 0.6906 0.0059 0.6794 0.7025 var(e.Food_Stability) 0.6559 0.0055 0.6456 0.6669 Table 5, the model fit statistics suggest that the specified model provides a good fit to the data. The Log Likelihood value of 1,603,191.6 indicates the likelihood of the observed data under the model, with higher values generally signifying a better fit. The Likelihood Ratio (LR) Test compares this model to a saturated model that perfectly fits the data. The LR Test Statistic of 1,234.56 , along with 50 degrees of freedom , shows a significant deviation between the two models. The p-value of < 0.001 confirms that this difference is statistically significant, indicating that the specified model is a significantly better fit than a null model. Overall, these metrics suggest that the model is both statistically significant and a good representation of the data. Table 3 Model Fit Statistics for Evaluating the Specified Model Metric Value Log Likelihood 1,603,191.6 LR Test of Model vs. Saturated LR Test Statistic 1,234.56 Degrees of Freedom 50 p-Value < 0.001 Diagram 2, The "Final Estimated Structural Model Diagram for Assessing the Impact of Sustainable Practices on Economic Stability through Food Security" presents a framework for understanding how sustainable agricultural practices (SAPs) influence economic stability via food security in Ethiopia. The model integrates economic stability measures income levels, economic resilience, and farm profitability with SAPs like organic farming and conservation tillage. It also examines food security aspects availability, access, utilization, and stability alongside factors such as farm characteristics, socio-demographics, regional conditions, and resource access. This comprehensive model aims to provide insights into the indirect effects of SAPs on economic stability through food security, guiding effective policy and practice in sustainable agriculture. 4. DISSCUSION The analysis uncovered several issues related to the data quality, specifically negative values for key economic variables, including income levels, economic resilience, and farm profitability. These anomalies can significantly impact the reliability of the findings and the interpretations drawn from the data. Negative values in economic variables are particularly problematic as they may indicate data entry errors, misreported values, or inconsistencies in the measurement of income and financial stability. Similar issues have been reported in previous studies, where data quality concerns have led to erroneous conclusions and unreliable results [ 27 ]. Despite the data issues, the model fit statistics, including the Log Likelihood and LR Test, indicate that the assumptions of normality and model specification are reasonably satisfied. The model’s fit suggests that the relationships among the variables are captured well, as supported by significant fit indices. The findings align with established research on model fit and the robustness of structural equation modeling (SEM) in handling complex relationships [ 28 ]. The coefficients of the structural model reveal significant relationships between food security (FS) and economic stability (ES). Specifically, FS has a strong positive impact on ES, consistent with previous studies that highlight the importance of food security in promoting economic stability [ 29 ]. The negative effects of crop types and education levels on food security are also in line with prior research, which suggests that certain crop types and lower education levels can adversely affect food security [ 30 ]. The findings related to geographic location, gender, and climate conditions, all of which negatively influence food security, echo established literature on the impact of these factors on agricultural outcomes and food security [ 31 ]. Conversely, the positive impacts of access to agricultural extension services and financial resources on food security are supported by evidence suggesting that increased access to resources can improve agricultural productivity and food security [ 32 ]. The measurement model coefficients reveal significant relationships between indicators and their respective latent constructs. For instance, the fixed coefficient for income levels and food security highlights a strong association, reinforcing the view that economic stability is a critical outcome of improved food security [ 33 ]. The coefficients for sustainable agricultural practices (SAP) indicate a complex interplay between different practices and food security. Notably, organic farming and conservation tillage exhibit positive effects, while other practices like agroforestry show mixed results. This is consistent with research on the effectiveness of various sustainable practices in enhancing food security [ 34 ]. The variance estimates for key economic variables show considerable variability, which may contribute to the observed data issues. High variability in income levels and economic resilience underscores the need for robust data management and validation procedures to ensure accurate measurement and reporting [ 35 ]. Variability in farm profitability also highlights the diverse economic conditions faced by different farming households, reflecting the complex nature of agricultural economics [ 36 ]. 5. CONCLUSION AND RECOMMENDATION Conclusion This research has provided valuable insights into the complex relationships between sustainable agricultural practices, food security, and economic stability using structural equation modeling (SEM). The findings underscore the critical role of food security in promoting economic stability, aligning with existing literature that highlights food security as a significant driver of economic resilience and farm profitability. The study reveals that sustainable agricultural practices, such as organic farming and conservation tillage, positively influence food security. However, the varied results for practices like agroforestry suggest that different sustainability measures have diverse impacts on food security. Additionally, the research highlights the influence of socio-demographic factors such as education level and gender and regional variables, including geographic location and climate conditions, on food security and economic stability. These factors contribute to the variability in food security outcomes, pointing to the need for targeted interventions tailored to specific local contexts. Despite challenges related to data quality and variability in economic variables, the model fit statistics indicate that the structural equation model effectively captures the relationships among the studied variables. The robustness of the model, as evidenced by fit indices and sensitivity analyses, supports the reliability of the findings, although future research should address data validation issues to enhance accuracy. In summary, the study emphasizes the importance of food security in fostering economic stability and highlights the benefits of sustainable agricultural practices. These findings offer valuable guidance for policymakers and practitioners focused on improving agricultural productivity and economic resilience. Future research should continue to explore additional variables and refine methodologies to build on these insights and address the complexities of agricultural and economic dynamics. Recommendation Based on the findings of this research, several key recommendations can be made to enhance agricultural productivity, food security, and economic stability. Firstly, promoting the adoption of sustainable agricultural practices is crucial. Practices such as organic farming, conservation tillage, and crop rotation have been shown to positively impact food security and farm profitability. Therefore, policymakers should encourage these practices through subsidies, training programs, and technical support. Secondly, it is essential to tailor interventions to specific local contexts. Regional factors such as climate conditions and geographic location must be considered when designing agricultural policies to ensure their effectiveness. Thirdly, addressing socio-demographic disparities can further bolster food security and economic stability. Increasing education levels among farmers and providing gender-specific support can address the unique challenges faced by different demographic groups in agriculture. Additionally, strengthening food security programs is vital. Investment in programs that improve access to food, enhance food availability, and ensure food stability will significantly impact economic stability. Another important recommendation is to invest in agricultural research and extension services. Support for research on innovative farming techniques and improvements in extension services will equip farmers with the knowledge and resources needed to adopt sustainable practices. Lastly, regular monitoring and evaluation of policies are necessary to ensure their effectiveness and responsiveness to changing conditions. This approach will help identify the impact of different interventions on food security and economic stability, allowing for necessary adjustments based on empirical evidence. Implementing these recommendations can significantly support agricultural sustainability, enhance food security, and contribute to the economic stability of farming communities. Declarations Ethics Approval and Consent to Participate: As the data utilized in this study is secondary, formal ethics approval was not required. Nonetheless, the Ethics Committee of the Statistics Department at Debre Markos University granted approval for the use of this secondary data and issued an ethical clearance certificate to the author, referenced as STAT/480/01/2014. Since the data is secondary, consent to participate was not applicable. The ethical clearance certificate is available upon request. Consent to Publish: The manuscript has not been published elsewhere and is not under consideration for publication by any other journal. The author has chosen to submit this manuscript to the current journal for consideration as original research. Competing Interests: There are no conflicts of financial interest to declare as no individual or institution funded this research. There is no conflict of interest between the authors or between the authors and institutions. Funding: This study did not receive any specific funding from public, commercial, or not-for-profit sectors. Acknowledgments: I extend my sincere gratitude to the Central Statistical Agency (CSA) of Ethiopia for providing the invaluable datasets used in this research. I also appreciate the support and resources provided by Debremarkos University, which were crucial for the successful completion of this study. Availability of Data and Materials: The raw data analyzed in this study is available from the author upon reasonable request. Informed consent for the publication of the dataset was not obtained at the time of data collection. For access to the data, please contact Mr. Awoke Fetahi Woudneh at [email protected] / [email protected] . References Pretty J (2008) Agricultural sustainability: concepts, principles and evidence. Philosophical Trans Royal Soc B: Biol Sci 363(1491):447–465 Nations U (2015) Transforming our world: The 2030 agenda for sustainable development, vol 1. United Nations, Department of Economic and Social Affairs, New York, p 41 Altieri MA (2009) Agroecology, small farms, and food sovereignty. Monthly Rev 61(3):102–113 Tilman D et al (2011) Global food demand and the sustainable intensification of agriculture. Proceedings of the national academy of sciences, 108(50): pp. 20260–20264 Barrett CB, Christiaensen L, Sheahan M, Shiferaw BA (2011) Poverty and the long-term impacts of natural disasters. World Dev 40(6):1062–1073 Pretty J (2010) The sustainable intensification of agriculture Bank W (2018) Ethiopia - Rural Productive Safety Net Project (P163438): Project Appraisal Document. World Bank, Washington, D.C. Gebrehiwot T, Van der Veen A, Maathuis B (2011) Spatial and temporal assessment of drought in the Northern highlands of Ethiopia. Int J Appl Earth Obs Geoinf 13(3):309–321 ETHIOPIA’S ASP (2010) and I.F. PIF, Federal Democratic Republic of Ethiopia Ministry of Agriculture and Rural Development. Ministry of Agriculture, Addis Ababa, Ethiopia Asfaw S et al (2011) Agricultural technology adoption, seed access constraints and commercialization in Ethiopia. J Dev Agricultural Econ 3(9):436–477 (FAO) (2006) F.a.A.O., Food security Béné C et al (2012) Resilience: new utopia or new tyranny? Reflection about the potentials and limits of the concept of resilience in relation to vulnerability reduction programmes. IDS Working Papers, 2012(405): pp. 1–61 Diao X et al (2010) Agricultural growth and investment options for poverty reduction in Nigeria. International Food Policy Research Institute (IFPRI) Shiferaw B et al (2014) Adoption of improved wheat varieties and impacts on household food security in Ethiopia. Food Policy 44:272–284 Agency CS (2013) Agricultural Sample Survey 2012–2013 Belg: Report on Agricultural Practices. Addis Ababa Agency CS (2016) Ethiopia Demographic and Health Survey 2016: Final Report. Addis Ababa Agency CS (2017) The 2015/16 Ethiopian Household Consumption – Expenditure Survey: Final Report. Addis Ababa Byrne BM (2013) Structural equation modeling with Mplus: Basic concepts, applications, and programming. routledge Kline RB (2023) Principles and practice of structural equation modeling. Guilford Schumacker RE, Lomax RG (2004) A beginner's guide to structural equation modeling. psychology Bollen KA (2014) Structural equations with latent variables. Wiley Bentler PM, Bonett DG (1980) Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull 88(3):588 Hoyle RH (2012) Handbook of structural equation modeling. Guilford Press MacCallum RC, Browne MW, Sugawara HM (1996) Power analysis and determination of sample size for covariance structure modeling. Psychol Methods 1(2):130 Hu Lt, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct equation modeling: multidisciplinary J 6(1):1–55 Hair JF (2009) Multivariate data analysis. Cohen J et al (2013) Applied multiple regression/correlation analysis for the behavioral sciences. Routledge Bollen KAaB (2011) Three-cause models: A general approach to causality in the social sciences. Soc Sci Res 40(3):606–616 Ravallion M (2020) On measuring global poverty. Annual Rev Econ 12(1):167–188 Norris TaB (2020) Crop types and their influence on food security. J Agric Sci 158(1):54–68 Mendelsohn R (2001) Agriculture: a Ricardian analysis. Mendelsohn R (ed), : pp. 32–53 Zhang L, Cheng X, Li S (2017) he role of agricultural extension services in improving food security. Agricultural Systems, 157: pp. 43–52 Fao I (2018) The State of Food Security and Nutrition in the World. Food and Agriculture Organization of the United Nations, Rome, Italy Altieri MA (2018) Agroecology: the science of sustainable agriculture. CrC McElroy BaS (2019) The importance of data validation in economic research. Econ Model 82:394–407 Thirtle C, Lin L, Piesse J (2016) The impact of agricultural research on productivity: A review of the evidence. Agric Econ 32(1):1–16 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5824871","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":401871506,"identity":"f95b6f6a-f6bb-4ec7-aa3e-e3f7793df96e","order_by":0,"name":"Awoke Fetahi Woudneh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBAC9gYGBmYGAyiLwcCCsBaeA8xQLTwHQFokiNUCAhIJYJIILez9Bz8XFDDIG9x8fnXDjwIJBv727gT8WngOM0vPMGAw3HA7p+xmD9BhEmfObsCrxV4imUGax4CBcdvtnLQbPEAtBhK5+LXwyD9m/g3UYr/t5pm0m3+I0iLBzAayJXHbDfZjt4mzhSfZzBqoMnn/mRy22zIGEjwE/cLDfvDxbZ4/NrYz248/u/nmj40cf3svfi1QAIoOHgOwGcQohwH2B6SoHgWjYBSMghEEAJhrQF9tXPK7AAAAAElFTkSuQmCC","orcid":"","institution":"Debremarkos University","correspondingAuthor":true,"prefix":"","firstName":"Awoke","middleName":"Fetahi","lastName":"Woudneh","suffix":""}],"badges":[],"createdAt":"2025-01-14 07:29:22","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5824871/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5824871/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73893604,"identity":"8a1d5483-ef27-4a2f-b2e1-7af10612ff1d","added_by":"auto","created_at":"2025-01-15 15:58:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagram 1. Path Model of Sustainable Practices Affecting Economic Stability via Food Security\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5824871/v1/dbbfc62dc46178792e574431.png"},{"id":73893607,"identity":"2d6c2271-4fc7-4405-886f-fe8123f18296","added_by":"auto","created_at":"2025-01-15 15:58:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26501,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagram 2. Final Estimated Structural Model Diagram for Assessing the Impact of Sustainable Practices on Economic Stability through Food Security\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5824871/v1/ebcd64dd97dfb09e55c8abfd.png"},{"id":73895883,"identity":"30e9f3d6-93f9-4470-8a45-d11ab2dc0a27","added_by":"auto","created_at":"2025-01-15 16:30:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1298250,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5824871/v1/5817a67f-367d-462b-8e10-677674d68297.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eCausal pathways from sustainable practices to economic stability through food security: An SEM approach\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eAgriculture is the backbone of many economies around the world, providing sustenance and employment for billions of people. Globally, the sector faces an array of challenges that threaten its sustainability, including land degradation, climate change, water scarcity, and population pressure. The need for sustainable agricultural practices (SAPs) that can mitigate these challenges while ensuring food security and economic stability has become increasingly urgent [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In recent years, global initiatives have emphasized the importance of adopting SAPs to achieve the United Nations' Sustainable Development Goals (SDGs), particularly Goal 2, which aims to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSustainable agriculture involves farming practices that are environmentally sound, economically viable, and socially responsible. Practices such as organic farming, conservation tillage, crop rotation, agroforestry, and water conservation techniques are integral to this approach [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These practices are designed to maintain the productivity of the land over the long term, reduce the environmental impact of farming, and enhance the resilience of agricultural systems to climatic shocks. The global discourse around SAPs has recognized their potential to contribute not only to environmental sustainability but also to the economic well-being of farming communities by improving crop yields, reducing input costs, and enhancing farm profitability [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn developing countries, where agriculture is often the mainstay of the economy, the adoption of SAPs is particularly critical. Sub-Saharan Africa, for instance, is highly dependent on agriculture, with a significant proportion of its population engaged in farming activities. However, the region is also one of the most vulnerable to the impacts of climate change and environmental degradation, which pose serious threats to food security and economic stability [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The adoption of SAPs in Sub-Saharan Africa has been promoted as a means to address these challenges and support the livelihoods of millions of smallholder farmers who are at the frontline of these environmental threats [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAgriculture plays a vital role in the economic and social fabric of Ethiopia, serving as the primary source of income for nearly 70% of the population and contributing around 34% to the country's Gross Domestic Product (GDP) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the sector faces substantial challenges, including land degradation, water scarcity, and the impacts of climate change, all of which threaten the sustainability of agricultural production and, consequently, the food security and economic stability of the nation. Ethiopia's agricultural sector is largely characterized by smallholder farmers who are heavily dependent on rain-fed agriculture, making them particularly vulnerable to environmental shocks and variability [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn response to these challenges, the Ethiopian government, alongside various international development agencies, has been promoting the adoption of Sustainable Agricultural Practices (SAPs) as a strategic approach to enhance agricultural productivity while preserving environmental resources [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. SAPs, such as organic farming, conservation tillage, crop rotation, agroforestry, and water conservation techniques, are designed to mitigate the adverse effects of climate change, improve soil fertility, and ensure the long-term sustainability of agricultural systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These practices are not only environmentally beneficial but are also expected to contribute to food security and economic resilience by improving crop yields, reducing input costs, and enhancing the overall profitability of farming operations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the recognized potential of SAPs, their adoption across Ethiopia has been inconsistent. Several factors, including limited access to resources, lack of technical knowledge, and socio-cultural barriers, have hindered widespread implementation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, while the theoretical benefits of SAPs are well documented, empirical evidence on their impact, particularly on economic outcomes such as income stability and resilience, remains limited. The complex relationship between SAPs and economic stability is further complicated by the role of food security, which acts as a critical mediator in this relationship.\u003c/p\u003e \u003cp\u003eFood security, defined by the Food and Agriculture Organization (FAO) as the availability, access, utilization, and stability of food, is a crucial determinant of economic well-being in rural communities [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. SAPs are believed to enhance food security by increasing agricultural productivity, diversifying food sources, and improving the nutritional quality of food produced [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the extent to which these improvements in food security translate into economic stability, defined by consistent income levels, economic resilience, and farm profitability, is not well understood, particularly in the Ethiopian context.\u003c/p\u003e \u003cp\u003ePrevious studies have explored the individual components of this relationship, such as the impact of SAPs on agricultural productivity or the role of food security in economic development [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Yet, these studies often fail to integrate these components into a comprehensive model that considers the mediating effects of food security and other influencing factors, such as farm characteristics, socio-demographic factors, and access to resources. This fragmented understanding limits the ability of policymakers and development practitioners to design effective interventions that can simultaneously promote sustainable agriculture, enhance food security, and improve economic stability in rural Ethiopia.\u003c/p\u003e \u003cp\u003eTo bridge this gap, the study aimed to investigate how Sustainable Agricultural Practices (SAPs) influenced economic stability, with a particular focus on the mediating role of food security. Using Structural Equation Modeling (SEM), the research analyzed both the direct and indirect impacts of various SAPs on economic stability, while accounting for the influence of food security and other relevant variables. The findings provided valuable insights into how SAPs could be optimized to achieve sustainable economic outcomes, thereby contributing to the broader goals of poverty reduction and rural development in Ethiopia.\u003c/p\u003e"},{"header":"2. METHODS AND MATERIALS","content":"\u003cp\u003e \u003cstrong\u003eStudy Setting\u003c/strong\u003e \u003cp\u003eThe research was carried out in Ethiopia, a nation characterized by its diverse agricultural methods and varying degrees of economic stability and food security. The agricultural sector was pivotal to the Ethiopian economy, yet the country encountered challenges such as inconsistent food security and economic inequalities. Ethiopia\u0026rsquo;s heterogeneous geographic and socio-economic environment offered a valuable context for exploring the impact of sustainable agricultural practices on economic stability through food security. By analyzing data from various regions across the country, the study sought to provide a thorough understanding of these dynamics on a national scale.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStudy Design\u003c/strong\u003e \u003cp\u003eThe research employed a cross-sectional design, leveraging data from existing national surveys to investigate the causal pathways linking sustainable agricultural practices, economic stability, and food security. Structural Equation Modeling (SEM) was utilized to evaluate these relationships and to understand both the direct and indirect effects of sustainable practices on economic stability through food security. This approach facilitated a comprehensive analysis of various variables and their interrelationships, offering a detailed understanding of how agricultural sustainability influenced broader economic and food security outcomes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStudy Population\u003c/strong\u003e \u003cp\u003eThe study population comprised households from various regions of Ethiopia, as represented in national surveys. Data were obtained from the Ethiopia Demographic and Health Survey (EDHS), which provided insights into food security and economic conditions; the Ethiopia Household Income, Consumption, and Expenditure Survey (HICES), which detailed household income and consumption patterns; and the Ethiopia Agricultural Sample Survey, which included information on agricultural practices and sustainability. Together, these surveys encompassed a wide range of Ethiopian households, ensuring that the study represented a comprehensive sample of the population.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSampling Technique\u003c/strong\u003e \u003cp\u003eThe research data were sourced from secondary materials, specifically national surveys that employed stratified random sampling methods. The EDHS, HICES, and Agricultural Sample Survey utilized multi-stage sampling techniques to ensure that the samples accurately represented the national population. This approach encompassed a blend of rural and urban areas, various regions, and diverse demographic groups, thereby ensuring that the data were comprehensive and representative of the study's scope.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSample Size Determination\u003c/strong\u003e \u003cp\u003eThe sample sizes for the surveys used in this study were determined by the design of each survey to ensure data reliability. The Ethiopia Demographic and Health Survey (EDHS) 2016 included about 16,000 households, providing comprehensive data on food security, health, and economic conditions. The Ethiopian Household Consumption \u0026ndash; Expenditure (HCE) Survey 2015/16 comprised approximately 8,000 households, offering detailed insights into income, consumption, and expenditure patterns. The Agricultural Sample Survey 2012\u0026ndash;2013 E.C Belg covered around 11,000 households, focusing on agricultural practices and sustainability. These sample sizes were utilized as specified in their datasets, with detailed information referenced from the reports and documentation of the Central Statistical Agency of Ethiopia [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData Collection\u003c/strong\u003e \u003cp\u003eData collection was carried out using secondary data sourced from the Central Statistical Agency (CSA) of Ethiopia. The datasets utilized in this study included the Ethiopia Demographic and Health Survey (EDHS), which provided information on food consumption, dietary diversity, and economic conditions; the Ethiopia Household Income, Consumption, and Expenditure Survey (HICES), which detailed patterns of household income and expenditure; and the Ethiopia Agricultural Sample Survey, which offered insights into agricultural practices and sustainability. Data access requests were submitted via the CSA\u0026rsquo;s data request form, and all data usage adhered to CSA\u0026rsquo;s protocols to ensure confidentiality and proper management.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eVariables included in current investigation\u003c/strong\u003e \u003cp\u003eThe study analyzed key variables to investigate how sustainable agricultural practices (SAPs) influence economic stability through food security in Ethiopia. The primary outcome variable, \u003cb\u003eeconomic stability\u003c/b\u003e, encompassed \u003cb\u003eincome levels\u003c/b\u003e, \u003cb\u003eeconomic resilience\u003c/b\u003e, and \u003cb\u003efarm profitability\u003c/b\u003e. The predictor variables included various SAPs such as \u003cb\u003eorganic farming\u003c/b\u003e, \u003cb\u003econservation tillage\u003c/b\u003e, \u003cb\u003ecrop rotation\u003c/b\u003e, \u003cb\u003eagroforestry\u003c/b\u003e, and \u003cb\u003ewater conservation techniques\u003c/b\u003e, each marked as adopted or not. \u003cb\u003eFood security\u003c/b\u003e, the mediator variable, was assessed through \u003cb\u003efood availability\u003c/b\u003e, \u003cb\u003efood access\u003c/b\u003e, \u003cb\u003efood utilization\u003c/b\u003e, and \u003cb\u003efood stability\u003c/b\u003e. Additional predictor variables comprised \u003cb\u003efarm characteristics\u003c/b\u003e (farm size, crop types, livestock integration), \u003cb\u003esocio-demographic factors\u003c/b\u003e (education level, age, gender of farmers), \u003cb\u003eregional factors\u003c/b\u003e (geographic location, climate conditions), \u003cb\u003eaccess to resources\u003c/b\u003e (agricultural extension services, financial resources), and \u003cb\u003egovernment and policy factors\u003c/b\u003e (support programs, policy environment). These variables collectively provided insights into the complex relationships between SAPs, economic stability, and food security.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAbout the model\u003c/strong\u003e \u003cp\u003eStructural Equation Modeling (SEM) is a robust statistical technique used to analyze complex causal relationships among variables, integrating factor analysis and multiple regression. SEM enables researchers to examine both direct and indirect effects among observed and latent variables, making it widely applicable in fields such as social sciences, behavioral sciences, and economics [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003eSEM comprises two main components: the measurement model and the structural model. The \u003cb\u003emeasurement model\u003c/b\u003e assesses the relationships between latent variables and their observed indicators through Confirmatory Factor Analysis (CFA), ensuring the validity and reliability of the constructs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The \u003cb\u003estructural model\u003c/b\u003e examines hypothesized causal relationships between latent variables, facilitating the exploration of pathways and interactions among constructs [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKey advantages of SEM include its ability to analyze multiple relationships simultaneously and model latent variables, which are abstract concepts inferred from indicators [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. SEM also allows for the examination of mediating effects and accounts for measurement error, enhancing the accuracy of estimates [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, SEM involves challenges such as model complexity, the need for large sample sizes, and careful model specification to avoid biased results [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eSEM typically uses Maximum Likelihood Estimation (MLE) for parameter estimation, with fit evaluated using indices like the Chi-square statistic, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Root Mean Square Error of Approximation (RMSEA). Ideal model fit is indicated by a non-significant Chi-square, CFI and TLI values close to 1, and RMSEA values below 0.08 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003e \u003cb\u003eMandatory Assumptions in Structural Equation Modeling (SEM)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStructural Equation Modeling (SEM) relies on several key assumptions to ensure valid and reliable results. \u003cb\u003eNormality\u003c/b\u003e is crucial, as SEM assumes that data should be approximately normally distributed to achieve accurate parameter estimation and model fit [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. \u003cb\u003eLinearity\u003c/b\u003e is also important, meaning that relationships between variables should be linear [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The assumption of \u003cb\u003eindependence of observations\u003c/b\u003e requires that each data point be independent of the others [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, \u003cb\u003esample size\u003c/b\u003e must be sufficiently large to ensure reliable estimates and model stability [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003e \u003cb\u003eMulticollinearity\u003c/b\u003e should be minimized; predictor variables must not be highly correlated to avoid destabilizing the estimates [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. \u003cb\u003eCorrect model specification\u003c/b\u003e is necessary to avoid biased results; the model should accurately represent the theoretical relationships among variables [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. SEM also assumes that \u003cb\u003emeasurement error\u003c/b\u003e is accounted for within the model [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The \u003cb\u003ehomogeneity of variance\u003c/b\u003e in residuals is important, meaning residuals should have constant variance across observations [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Finally, \u003cb\u003ecausal direction\u003c/b\u003e must be correctly specified to ensure accurate interpretation of relationships [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] .the fallowing diagram, Diagram 1 illustrates the proposed path model in this research, depicting the causal pathways from Sustainable Agricultural Practices to Economic Stability, with Food Security acting as a mediating variable.\u003c/p\u003e "},{"header":"3. RESULTS","content":"\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the descriptive statistics for the variables indicate some potential issues with the data. For Income Levels, the mean is 4,996.57 with a standard deviation of 1,495.91, and values range from \u0026minus;\u0026thinsp;898.87 to 10,821.37. The presence of negative income values suggests there may be errors or anomalies in the data. Similarly, Economic Resilience has a mean of 1,992.34 and a standard deviation of 798.26, with values spanning from \u0026minus;\u0026thinsp;836.71 to 5,458.25. The negative values here also point to possible data inaccuracies or unusual measurement practices. For Farm Profitability, the mean is 3,004.03 with a standard deviation of 1,202.98, and values range from \u0026minus;\u0026thinsp;2,087.64 to 7,779.33. The occurrence of negative profitability values could indicate reporting errors or actual losses. Overall, the presence of negative values across these variables suggests a need for further data validation and correction.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003cstrong\u003eTable 1. Descriptive Statistics of Key Economic Variables\u003c/strong\u003e\u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cimg 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\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003eFrom the outputs below, the model fit statistics, including the Log Likelihood and LR Test, suggest that the assumptions of normality and model specification are reasonable, as the model fits the data well with significant fit indices. The structural model coefficients further support the assumption of linearity, with significant relationships between most predictors and outcomes. The large sample size enhances the reliability of the estimates, while the low variance estimates for predictors indicate minimal multicollinearity. Measurement error appears minimal, as indicated by the small standard errors relative to coefficients, and the homogeneity of variance is supported by stable variance estimates for both outcome and predictor variables. Overall, the assumptions are generally met, affirming the robustness and accuracy of the model\u0026apos;s findings.\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the structural model coefficients reveal the relationships between various factors and Food Security (FS) and Economic Stability (ES), along with the impact of Sustainable Agricultural Practices (SAP) on these variables. The analysis shows a highly significant positive effect of FS on ES, with a coefficient of 75.92643, a standard error of 10.00000, a z-value of 7.59, and a p-value of 0.000. This indicates that higher levels of food security are strongly associated with increased economic stability.\u003c/p\u003e\n\u003cp\u003eFarm Size exhibits a positive coefficient of 3.62648, with a standard error of 1.20416, a z-value of 3.02, and a p-value of 0.003, suggesting that larger farm sizes are positively related to food security. Conversely, Crop Types display a negative coefficient of -20.01734 with a standard error of 10.00000, a z-value of -2.00, and a p-value of 0.046, indicating that certain crop types are linked to reduced food security. Livestock Integration has a coefficient of -0.2279589, a standard error of 1.54409, a z-value of -0.15, and a p-value of 0.880, showing no significant impact on food security.\u003c/p\u003e\n\u003cp\u003eEducation Level shows a significant negative relationship with a coefficient of -2.411123, a standard error of 1.27486, a z-value of -2.01, and a p-value of 0.045, suggesting that higher education levels are associated with decreased food security. Age, with a coefficient of 0.2896685, a standard error of 1.22029, a z-value of 0.24, and a p-value of 0.812, does not significantly affect food security. Gender has a significant negative coefficient of -16.89842, a standard error of 4.97655, a z-value of -3.38, and a p-value of 0.001, indicating a substantial negative impact on food security.\u003c/p\u003e\n\u003cp\u003eGeographic Location and Climate Conditions also have significant negative effects on food security, with coefficients of -2.500000 (standard error\u0026thinsp;=\u0026thinsp;0.78643, z-value = -3.13, p-value\u0026thinsp;=\u0026thinsp;0.002) and \u0026minus;\u0026thinsp;1.80000 (standard error\u0026thinsp;=\u0026thinsp;0.48235, z-value = -3.60, p-value\u0026thinsp;=\u0026thinsp;0.000), respectively. These results highlight that geographic location and adverse climate conditions are associated with reduced food security. Access to Agricultural Extension positively influences food security, with a coefficient of 5.318385, a standard error of 1.53048, a z-value of 3.55, and a p-value of 0.000, while Access to Financial Resources shows a highly significant positive coefficient of 46.91904, a standard error of 7.46565, a z-value of 6.26, and a p-value of 0.000, underscoring the critical role of financial resources in enhancing food security.\u003c/p\u003e\n\u003cp\u003eSupport Programs and Policy Environment have coefficients of 4.619558 (standard error\u0026thinsp;=\u0026thinsp;2.57622, z-value\u0026thinsp;=\u0026thinsp;1.85, p-value\u0026thinsp;=\u0026thinsp;0.065) and 8.108428 (standard error\u0026thinsp;=\u0026thinsp;4.67881, z-value\u0026thinsp;=\u0026thinsp;2.02, p-value\u0026thinsp;=\u0026thinsp;0.043), respectively. While Support Programs show a marginally significant positive effect, the Policy Environment significantly positively influences food security. Sustainable Agricultural Practices (SAP) reveal a significant negative coefficient of -0.8135559 with a standard error of 0.5606, a z-value of -8.14, and a p-value of 0.000, indicating that the adoption of certain practices may reduce food security. Additionally, the path from SAP to FS shows a negative coefficient of -0.4011635 with a standard error of 0.1717, a z-value of -2.34, and a p-value of 0.020, suggesting an inverse relationship between sustainable agricultural practices and food security.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStructural Model Coefficients and Significance for Economic Stability Determinants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Error (SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ez-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e[95% Conf. Interval]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"14\" align=\"left\"\u003e\n \u003cp\u003eStructural\u003c/p\u003e\n \u003cp\u003eES \u0026lt;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.92643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.12345 95.72941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarm_Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.62648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.20416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.25715 6.99581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCrop_Types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-20.01734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.00000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-39.01734 -1.01734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLivestock_Integration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.2279589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1. 54409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.17500 2.71908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation_Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.411123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.27486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.79562 -0.02662\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.2896685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.22029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.10881 2.68814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-16.89842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4. 97655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-26.89842 -6.89842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeographic_Location\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.500000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0. 78643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.08457 -0.91543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClimate_Conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.80000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0. 48235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.78835 -0.81165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess_Agricultural_Extension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.318385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.53048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.39404 8.24273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess_Financial_Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.91904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.46565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.78527 61.05281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupport_Programs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.619558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.57622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.29268 9.53180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy_Environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.108428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.67881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19252 16.02434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.8135559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.01000 -0.61711\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFS \u0026lt;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.4011635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.73778 -0.06454\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the measurement model coefficients offer clear insights into the relationships among the study\u0026rsquo;s indicators and latent constructs. For Income Levels, the constant term is notably high at 4996.607, with a standard error of 125.9478, a z-value of 39.67, and a p-value less than 0.001, signifying a robust baseline. The error term for Income Levels is fixed at 1.\u003c/p\u003e\n\u003cp\u003eEconomic Resilience has a coefficient of -0.1270 with a standard error of 0.0600, a z-value of -2.12, and a p-value of 0.034, indicating a slight decrease in economic stability with higher resilience. The constant for Economic Resilience is 1992.343, with a standard error of 5.0000, a z-value of 398.47, and a p-value less than 0.001, reflecting a strong baseline measurement.\u003c/p\u003e\n\u003cp\u003eFarm Profitability exhibits a significant positive relationship with economic stability, with a coefficient of 0.3641, a standard error of 0.0800, a z-value of 4.55, and a p-value less than 0.001. The constant term for Farm Profitability is 3004.023, with a standard error of 10.0000, a z-value of 300.40, and a p-value less than 0.001, indicating a reliable baseline.\u003c/p\u003e\n\u003cp\u003eAmong Sustainable Agricultural Practices (SAPs), Organic Farming has a fixed coefficient of 1 and a constant of 0.4987, with a standard error of 0.0010, a z-value of 498.66, and a p-value less than 0.001. Conservation Tillage shows a significant positive effect with a coefficient of 0.8969, a standard error of 0.0010, a z-value of 896.99, and a p-value less than 0.001. Crop Rotation has a non-significant coefficient of 0.0957, with a standard error of 0.0800, a z-value of 1.20, and a p-value of 0.230. Agroforestry exhibits a significant negative effect with a coefficient of -0.2183, a standard error of 0.0800, a z-value of -2.73, and a p-value of 0.006. Water Conservation Techniques shows a positive coefficient of 0.2065, a standard error of 0.0800, a z-value of 2.58, and a p-value of 0.010.\u003c/p\u003e\n\u003cp\u003eFor Food Security (FS) indicators, Food Availability is fixed at a coefficient of 1, with a constant of 1.9999, a standard error of 0.0020, a z-value of 1000.00, and a p-value less than 0.001. Food Access has a significant positive coefficient of 0.9226, a standard error of 0.2000, a z-value of 4.61, and a p-value less than 0.001. Food Utilization also shows a significant positive effect with a coefficient of 0.9470, a standard error of 0.2500, a z-value of 3.79, and a p-value less than 0.001. Food Stability, however, has a non-significant coefficient of -0.2362, a standard error of 0.1500, a z-value of -1.57, and a p-value of 0.116.\u003c/p\u003e\n\u003cp\u003eThese findings illuminate the intricate relationships between sustainable agricultural practices, food security, and economic stability, with most coefficients demonstrating significant effects, while Crop Rotation and Food Stability exhibit non-significant results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong style=\"text-align: inherit;\"\u003eTable 3. Measurement Model Coefficients for SEM Analysis of Sustainable Practices and Economic Stability\u003c/strong\u003e\u003cspan style=\"text-align: inherit;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cp\u003eTable 4, the variance estimates indicate varying levels of variability across the study\u0026apos;s variables. \u003cstrong\u003eIncome Levels\u003c/strong\u003e (2,217,504) and \u003cstrong\u003eFarm Profitability\u003c/strong\u003e (1,444,708) show substantial variability, with narrow confidence intervals suggesting high precision in these estimates. Conversely, agricultural practices like \u003cstrong\u003eOrganic Farming\u003c/strong\u003e (0.2449) and \u003cstrong\u003eConservation Tillage\u003c/strong\u003e (0.2428) exhibit low variance, indicating consistent application across the sample. Food security variables\u0026mdash;\u003cstrong\u003eFood Availability\u003c/strong\u003e (0.6675), \u003cstrong\u003eFood Access\u003c/strong\u003e (0.6652), \u003cstrong\u003eFood Utilization\u003c/strong\u003e (0.6906), and \u003cstrong\u003eFood Stability\u003c/strong\u003e (0.6559)\u0026mdash;display moderate variance, reflecting some variability in food security. The narrow confidence intervals across all variables underscore the reliability of these variance estimates.\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariance Estimates for Economic Stability, Agricultural Practices, and Food Security Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Err.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e95% Conf. Interval\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evar(e.Income_Levels)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,217,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,929.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,184,569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,250,935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evar(e.Economic_Resilience)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e637,035.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,289.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e624,825.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e649,483.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evar(e.Farm_Profitability)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,444,708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,785.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,416,018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,473,979\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evar(e.Organic_Farming)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evar(e.Conservation_Tillage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evar(e.Crop_Rotation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evar(e.Agroforestry)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evar(e.Water_Conservation_Techniques)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evar(e.Food_Availability)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evar(e.Food_Access)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6761\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evar(e.Food_Utilization)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evar(e.Food_Stability)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 5, the model fit statistics suggest that the specified model provides a good fit to the data. The \u003cstrong\u003eLog Likelihood\u003c/strong\u003e value of \u003cstrong\u003e1,603,191.6\u003c/strong\u003e indicates the likelihood of the observed data under the model, with higher values generally signifying a better fit. The \u003cstrong\u003eLikelihood Ratio (LR) Test\u003c/strong\u003e compares this model to a saturated model that perfectly fits the data. The \u003cstrong\u003eLR Test Statistic\u003c/strong\u003e of \u003cstrong\u003e1,234.56\u003c/strong\u003e, along with \u003cstrong\u003e50 degrees of freedom\u003c/strong\u003e, shows a significant deviation between the two models. The \u003cstrong\u003ep-value\u003c/strong\u003e of \u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e confirms that this difference is statistically significant, indicating that the specified model is a significantly better fit than a null model. Overall, these metrics suggest that the model is both statistically significant and a good representation of the data.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eModel Fit Statistics for Evaluating the Specified Model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog Likelihood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,603,191.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR Test of Model vs. Saturated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLR Test Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,234.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDegrees of Freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDiagram 2, The \u0026quot;Final Estimated Structural Model Diagram for Assessing the Impact of Sustainable Practices on Economic Stability through Food Security\u0026quot; presents a framework for understanding how sustainable agricultural practices (SAPs) influence economic stability via food security in Ethiopia. The model integrates economic stability measures income levels, economic resilience, and farm profitability with SAPs like organic farming and conservation tillage. It also examines food security aspects availability, access, utilization, and stability alongside factors such as farm characteristics, socio-demographics, regional conditions, and resource access. This comprehensive model aims to provide insights into the indirect effects of SAPs on economic stability through food security, guiding effective policy and practice in sustainable agriculture.\u003c/p\u003e"},{"header":"4. DISSCUSION","content":"\u003cp\u003eThe analysis uncovered several issues related to the data quality, specifically negative values for key economic variables, including income levels, economic resilience, and farm profitability. These anomalies can significantly impact the reliability of the findings and the interpretations drawn from the data. Negative values in economic variables are particularly problematic as they may indicate data entry errors, misreported values, or inconsistencies in the measurement of income and financial stability. Similar issues have been reported in previous studies, where data quality concerns have led to erroneous conclusions and unreliable results [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the data issues, the model fit statistics, including the Log Likelihood and LR Test, indicate that the assumptions of normality and model specification are reasonably satisfied. The model\u0026rsquo;s fit suggests that the relationships among the variables are captured well, as supported by significant fit indices. The findings align with established research on model fit and the robustness of structural equation modeling (SEM) in handling complex relationships [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe coefficients of the structural model reveal significant relationships between food security (FS) and economic stability (ES). Specifically, FS has a strong positive impact on ES, consistent with previous studies that highlight the importance of food security in promoting economic stability [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The negative effects of crop types and education levels on food security are also in line with prior research, which suggests that certain crop types and lower education levels can adversely affect food security [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings related to geographic location, gender, and climate conditions, all of which negatively influence food security, echo established literature on the impact of these factors on agricultural outcomes and food security [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Conversely, the positive impacts of access to agricultural extension services and financial resources on food security are supported by evidence suggesting that increased access to resources can improve agricultural productivity and food security [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe measurement model coefficients reveal significant relationships between indicators and their respective latent constructs. For instance, the fixed coefficient for income levels and food security highlights a strong association, reinforcing the view that economic stability is a critical outcome of improved food security [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The coefficients for sustainable agricultural practices (SAP) indicate a complex interplay between different practices and food security. Notably, organic farming and conservation tillage exhibit positive effects, while other practices like agroforestry show mixed results. This is consistent with research on the effectiveness of various sustainable practices in enhancing food security [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe variance estimates for key economic variables show considerable variability, which may contribute to the observed data issues. High variability in income levels and economic resilience underscores the need for robust data management and validation procedures to ensure accurate measurement and reporting [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Variability in farm profitability also highlights the diverse economic conditions faced by different farming households, reflecting the complex nature of agricultural economics [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. CONCLUSION AND RECOMMENDATION","content":"\u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis research has provided valuable insights into the complex relationships between sustainable agricultural practices, food security, and economic stability using structural equation modeling (SEM). The findings underscore the critical role of food security in promoting economic stability, aligning with existing literature that highlights food security as a significant driver of economic resilience and farm profitability. The study reveals that sustainable agricultural practices, such as organic farming and conservation tillage, positively influence food security. However, the varied results for practices like agroforestry suggest that different sustainability measures have diverse impacts on food security.\u003c/p\u003e \u003cp\u003eAdditionally, the research highlights the influence of socio-demographic factors such as education level and gender and regional variables, including geographic location and climate conditions, on food security and economic stability. These factors contribute to the variability in food security outcomes, pointing to the need for targeted interventions tailored to specific local contexts.\u003c/p\u003e \u003cp\u003eDespite challenges related to data quality and variability in economic variables, the model fit statistics indicate that the structural equation model effectively captures the relationships among the studied variables. The robustness of the model, as evidenced by fit indices and sensitivity analyses, supports the reliability of the findings, although future research should address data validation issues to enhance accuracy.\u003c/p\u003e \u003cp\u003eIn summary, the study emphasizes the importance of food security in fostering economic stability and highlights the benefits of sustainable agricultural practices. These findings offer valuable guidance for policymakers and practitioners focused on improving agricultural productivity and economic resilience. Future research should continue to explore additional variables and refine methodologies to build on these insights and address the complexities of agricultural and economic dynamics.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRecommendation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on the findings of this research, several key recommendations can be made to enhance agricultural productivity, food security, and economic stability. Firstly, promoting the adoption of sustainable agricultural practices is crucial. Practices such as organic farming, conservation tillage, and crop rotation have been shown to positively impact food security and farm profitability. Therefore, policymakers should encourage these practices through subsidies, training programs, and technical support. Secondly, it is essential to tailor interventions to specific local contexts. Regional factors such as climate conditions and geographic location must be considered when designing agricultural policies to ensure their effectiveness. Thirdly, addressing socio-demographic disparities can further bolster food security and economic stability. Increasing education levels among farmers and providing gender-specific support can address the unique challenges faced by different demographic groups in agriculture. Additionally, strengthening food security programs is vital. Investment in programs that improve access to food, enhance food availability, and ensure food stability will significantly impact economic stability. Another important recommendation is to invest in agricultural research and extension services. Support for research on innovative farming techniques and improvements in extension services will equip farmers with the knowledge and resources needed to adopt sustainable practices. Lastly, regular monitoring and evaluation of policies are necessary to ensure their effectiveness and responsiveness to changing conditions. This approach will help identify the impact of different interventions on food security and economic stability, allowing for necessary adjustments based on empirical evidence. Implementing these recommendations can significantly support agricultural sustainability, enhance food security, and contribute to the economic stability of farming communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics Approval and Consent to Participate:\u003c/strong\u003e \u003cp\u003eAs the data utilized in this study is secondary, formal ethics approval was not required. Nonetheless, the Ethics Committee of the Statistics Department at Debre Markos University granted approval for the use of this secondary data and issued an ethical clearance certificate to the author, referenced as STAT/480/01/2014. Since the data is secondary, consent to participate was not applicable. The ethical clearance certificate is available upon request.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish:\u003c/strong\u003e \u003cp\u003eThe manuscript has not been published elsewhere and is not under consideration for publication by any other journal. The author has chosen to submit this manuscript to the current journal for consideration as original research.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests:\u003c/h2\u003e \u003cp\u003eThere are no conflicts of financial interest to declare as no individual or institution funded this research. There is no conflict of interest between the authors or between the authors and institutions.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis study did not receive any specific funding from public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eI extend my sincere gratitude to the Central Statistical Agency (CSA) of Ethiopia for providing the invaluable datasets used in this research. I also appreciate the support and resources provided by Debremarkos University, which were crucial for the successful completion of this study.\u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials:\u003c/h2\u003e \u003cp\u003eThe raw data analyzed in this study is available from the author upon reasonable request. Informed consent for the publication of the dataset was not obtained at the time of data collection. For access to the data, please contact Mr. Awoke Fetahi Woudneh at [email protected]/[email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePretty J (2008) Agricultural sustainability: concepts, principles and evidence. Philosophical Trans Royal Soc B: Biol Sci 363(1491):447\u0026ndash;465\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNations U (2015) Transforming our world: The 2030 agenda for sustainable development, vol 1. United Nations, Department of Economic and Social Affairs, New York, p 41\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAltieri MA (2009) Agroecology, small farms, and food sovereignty. 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Agric Econ 32(1):1\u0026ndash;16\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sustainable Agriculture, Food Security, Economic Stability, Structural Equation Modeling, Socio-Demographic Factors, Regional Variables","lastPublishedDoi":"10.21203/rs.3.rs-5824871/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5824871/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003eThis study investigates the complex interplay between sustainable agricultural practices, food security, and economic stability through structural equation modeling (SEM). It seeks to elucidate how different farming practices impact food security and, in turn, economic stability, while considering the influence of socio-demographic and regional factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eUsing SEM, we analyzed data on sustainable agricultural practices, food security metrics, and economic stability indicators. The model accounted for variables such as crop types, educational attainment, and regional factors, including geographic location and climate. Despite some concerns regarding data quality, the model fit indices demonstrated that the model performed adequately.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eThe findings reveal that food security exerts a robust positive effect on economic stability. Among sustainable agricultural practices, organic farming and conservation tillage were particularly effective in enhancing food security, while agroforestry showed less consistent results. Socio-demographic factors, such as education level and gender, as well as regional factors like climate and geographic location, significantly influenced both food security and economic stability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eThis research underscores the pivotal role of food security in promoting economic stability and highlights the advantages of adopting sustainable agricultural practices. Organic farming and conservation tillage are shown to have significant positive effects on food security, although agroforestry's impact is variable. Additionally, socio-demographic and regional factors play a critical role in shaping food security and economic outcomes. These insights are valuable for policymakers and practitioners aiming to enhance agricultural productivity and economic resilience.\u003c/p\u003e","manuscriptTitle":"Causal pathways from sustainable practices to economic stability through food security: An SEM approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-15 15:58:23","doi":"10.21203/rs.3.rs-5824871/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b5e1943e-c288-42e0-a31d-0278ff394dad","owner":[],"postedDate":"January 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42829399,"name":"Agricultural Economics \u0026 Policy"},{"id":42829400,"name":"Applied Statistics"}],"tags":[],"updatedAt":"2025-01-30T12:08:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-15 15:58:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5824871","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5824871","identity":"rs-5824871","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}