EFFECT OF SELECTED INSECTICIDE ON WHITEFLY (Bemisia tabaci) INFESTING BRINJAL PLANTS 478 © 2025 AESS Publications. All Rights Reserved. The impact of digitalization on food security in G20 countries: An empirical assessment Ajay Kumar Singha Vikash Kumar Mishrab Dhanya C.c aDepartment of Humanities and Social Sciences, Graphic Era (Deemed to Be University), Dehradun, Uttarakhand, India. bDepartment of Humanities and Liberal Arts, DIT University, Dehradun, Uttarakhand, India. cDepartment of Economics, Arignar Anna Government Arts and Science College, Karaikal Puducherry, India.  a.k.seeku@gmail.com (Corresponding author) Article History ABSTRACT Received: 23 June 2025 Revised: 19 August 2025 Accepted: 12 September 2025 Published: 24 September 2025 Keywords Adaptation strategies Agricultural sustainability Climate change Digital technologies Digitalization Environmental technologies Food security Mitigation practices Sustainable development. This study assesses the impact of digitalization on food security (FS) and its dimensions in G20 countries. Descriptive statistical techniques are applied to create a food security index (FSI) and a digital development index (DDI). The FSI is considered as a combination of its dimensions, and the DDI is the integration of 16 indicators that foster appropriate digital development (DD). Log-linear regression models are introduced to examine the coefficient of DDI with FSI and specific variables. The study employs country-wise balanced panel data from 2010 to 2022. It reports significant variation in DD and FS across G20 countries. DD has a positive and statistically significant impact on FS and its other drivers. Furthermore, FS and its four dimensions have positive and bi-directional relationships with DD. FS and its other drivers, except for food utilization, are positively associated with forest area. Protection of ecosystem services is necessary to increase FS. Environmental technologies are also found to positively influence FS. Gross domestic savings appear useful for increasing FS. Environmental technologies, foreign direct investment, and gross savings show a positive impact on DD. Moreover, all dimensions of FS are favorable for promoting DD. The findings provide multiple policy implications for improving FS and DD in G20 countries. Contribution/Originality: This study is the first to create the Food Security Index (FSI) and the Digital Development Index (DDI), providing a comparative analysis of food security and digital development among G20 countries from 2010 to 2022. The causal relationship between FSI and its dimensions with DDI is analyzed using log-linear regression models. DOI: 10.55493/5005.v15i3.5624 ISSN(P): 2304-1455/ ISSN(E): 2224-4433 How to cite: Singh, A. K., Mishra, V. K., & C, D. (2025). The impact of digitalization on food security in G20 countries: An empirical assessment. Asian Journal of Agriculture and Rural Development, 15(3), 478–501. 10.55493/5005.v15i3.5624 © 2025 Asian Economic and Social Society. All rights reserved. 1. INTRODUCTION Nowadays, people are using different digital devices, tools, and platforms to achieve their expected tasks in various sectors. Digitalization is a transformation of various forms of information, data, text, images, materials, news, and others into digital formats (Benfica et al., 2023). It is a state in which people can apply digital technology, information & communication technology (ICT), and other digital devices to accomplish their desired tasks. Digitalization assists in achieving their multiple purposes in education, health, market, business, medical, finance, political, media, marketing, and transport sectors (Lee, Zeng, & Luo, 2023). It helps to increase the transformation of knowledge, skills, and scientific processes in digital format. Digital formats of information bring multiple benefits for improving the livelihood security of humanity. Improving advertisement through social media, online transaction of money, online Asian Journal of Agriculture and Rural Development Volume 15, Issue 3 (2025): 478-501 https://orcid.org/0000-0003-0429-0925 https://orcid.org/0000-0002-1018-2640 https://orcid.org/0009-0002-6359-0821 mailto:a.k.seeku@gmail.com https://doi.org/10.55493/5005.v15i3.5624 Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 479 © 2025 AESS Publications. All Rights Reserved. communication of consumers and producers, e-business, and sharing information through online platforms are a few examples of digitalization and digital development. Digital technologies (DTs) like mobile apps, computers, and the internet are helping people to increase their engagement in online activities and public services (World Bank Group, 2019). ICT is used to increase communication among people across the globe. Consequently, digitalization and DTs are transporting multiple benefits to large communities. Moreover, digitalization helps to increase online business, jobs, and advertisement of new products and develops entrepreneurship and business ecosystems. It, thus, is developing an important platform for foreign trade. Thereafter, digitalization is favorable for the creation of new markets and infrastructural development, and increases a competitive business ecosystem. It increases the availability of scientific information across the globe. Hence, digitalization is helping to increase technological upgradation as per the current needs of people, businessmen, and manufacturing industries. Digital technologies (DTs) enhance business processes with innovative models (Oborin, 2019). Digitalization is creating a smooth path for technology transfer and commercialization across countries. Accordingly, the manufacturing sector can reduce its use of ecosystem services and natural resources by increasing its dependency on digital technologies (DTs). DTs bring alternative ways to minimize the cost of production (World Bank Group, 2019). Accordingly, global countries are creating digital platforms and transforming towards digitalization to receive competitive gains in production activities. Digitalization seems highly effective in accomplishing various objectives of common people, farmers, businessmen, researchers, scientists, government, and producers in diversified areas. In the production sector, digitalization helps to increase productivity. Although, it is also reported that employment opportunities have declined due to digitalization. Despite that, multiple studies note that economic development and growth could be increased due to the commencement of digital infrastructure in most countries, especially in the European Union (e.g., Belgium, Denmark, Ireland). Moreover, digitalization is working as a significant driver to increase health security through improving medical facilities. Digitalization, for instance, could provide significant benefits to patients, medical doctors, academicians, and educationists during COVID-19. Digitalization is providing better economic returns to farmers, businessmen, online traders, consumers, and producers (Benfica et al., 2023; Lee et al., 2023). Thus, digitalization may support reducing poverty and improving health and well-being (Vărzaru, 2024). Agriculture and food sectors receive significant benefits from digital technologies (DTs) (Ferguson et al., 2024). These technologies enhance food security and nutritional security by improving foodgrain production, yields, and food quality (Ferguson et al., 2024). For instance, Big Data analysis can be effective in making future predictions of climate change impacts on cultivation (Herawati, Yuniningsih, & Dwimawanti, 2023; Vărzaru, 2024). Artificial intelligence (AI) brings revolution in the agricultural sector (Talaviya, Shah, Patel, Yagnik, & Shah, 2020). DTs can be used for weather forecasting and disaster response (Herawati et al., 2023). Many digital devices are also helpful in perceiving the forecasting of productivity of various crops in the presence of changing socioeconomic development and demographic patterns, ecosystem services, natural resources, and climatic factors. Food systems and their allied sectors contribute around 21-37% of GHG emissions in the environment (Anderson & Sandin, 2022). Hence, the application of digital technologies (DTs) in food sectors would be a revolution in reducing CO2 emissions and increasing environmental sustainability. Digital tools and DTs are vital for enhancing farm productivity by improving the quality of environmental and ecological services (Amirova, Safiullin, Bakhareva, & Sakhbieva, 2022; Chandio, Amin, Khan, Rehman, & Memon, 2024; Prause, Hackfort, & Lindgren, 2021). Further, it assists in increasing sustainable development by improving the sustainability of all inputs in the agriculture sector. Thus, it is expected that digitalization will improve the food value chain (Benfica et al., 2023; World Bank Group, 2019). While it is observed a complex association between digital transformation and FS (Vărzaru, 2024). Digitalization is providing multiple benefits to the agricultural sector (Chandio et al., 2024). It helps to increase farmers' understanding of selecting a suitable crop for cultivation in a specific season. DTs are beneficial for making future predictions of climatic events, which would suggest taking preventive actions in farming. Moreover, digital technologies (DTs) are increasing the agricultural marketing-related information among farmers at low cost (World Bank Group, 2019). It also creates new agri-business opportunities. Thus, it is positive to increase sustainable agricultural development (SAD). Consequently, digitalization and DTs are worthwhile for improving most indicators, which nurture a suitable platform for attaining SDGs (Vărzaru, 2024). Accordingly, digitalization would assist in increasing FS. Preceding theoretical literature reveals that digitalization, digital technologies (DTs), digital development (DD), and digital platforms help to enhance agricultural productivity. Farmers are gaining innovative ideas, cultivation methods, and techniques to increase crop productivity due to DD. DD helps to increase agricultural sustainability in the presence of climate change and environmental degradation. Subsequently, DD is aiding in increasing social and economic change. Furthermore, DD is valuable for increasing the demand and supply management of agricultural commodities in the market. DD, thus, would positively contribute to increasing food security (FS) and its components. Although existing studies could not examine the clear association between DD and FS, there is a requirement to observe the contribution of digitalization in improving FS in global countries. Therefore, countries worldwide should be more serious about adopting effective policies to increase the usability of digitalization in the agricultural and food sectors. Hence, this study develops an empirical model for assessing the causal association of DD with FS in G20 countries. The present research is an attempt to answer the following questions: • What is the comparative performance of FS and DD in G20 countries? • Why does there exist diversity in digitalization and FS in G20 countries? Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 480 © 2025 AESS Publications. All Rights Reserved. • How does digitalization help to increase FS? • What is the process to increase FS through digitalization? • How can G20 countries improve their strength in digitalization and FS in the future? • Which components of FS will be beneficial from digitalization? • How would digitalization be positive in increasing FS in G20 countries? This study has the following objectives: • To create a food security index (FSI) as a composition of its key drivers and a digital development index (DDI) as an integration of 16 variables in G20 countries. • To examine the impact of digitalization on FS in G20 countries and vice versa. • To provide policy proposals for improving FS and DD in G20 countries.. 2. REVIEW OF LITERATURE 2.1. Importance and Perspectives of Food Security The theoretical foundation of food security (FS) is very old in academic research, Hoddinott and Yohannes (2002). Food is an essential component for increasing economic construction and development, as it maintains the physical and mental health of all people (Wang, Li, & Hu, 2023). Accordingly, FS helps to increase participation of people in social and economic activities, and it is important to increase national security (Yao & Fu, 2025). To ensure FS is the most indispensable due to rising population, increasing loss of food and natural resources (Vărzaru, 2024; World Bank Group, 2019). Around 600 million people fall seriously ill due to eating contaminated food, and 420,000 die annually worldwide due to the unavailability of food (Konfo et al., 2023). Around 821 million people are undernourished worldwide (Gouvea, Kapelianis, Li, & Terra, 2022). Undernutrition is also responsible for an increase of 45% in child deaths globally (World Health Organization (WHO), 2023). The quality of food products, including milk, dairy products, and meat products, is decreasing due to the overutilization of fertilizers and chemicals in cultivation (Zorić, Marić, Đurković-Marić, & Vukmirović, 2023). Lee et al. (2023) reported that 49 million people would be in trap of extreme poverty due to lack of food at global level (Lee et al., 2023). Around 1/3rd of food production is wasted in the world. Thus, it is necessary to reduce wasted food to increase FS (Anderson & Sandin, 2022; Mantravadi & Srai, 2023; Talaviya et al., 2020; Zorić et al., 2023). Wasted food contributes to increased food insecurity in urban areas (Mantravadi & Srai, 2023). While appropriate management of food systems is an essential driver to reduce poverty, hunger, and malnutrition (Benfica et al., 2023). 2.2. Components and Determinants of Food Security Primarily, food security (FS) has four dimensions (Gouvea et al., 2022; World Bank Group (WBG), 2025). These components are divided as physical availability of food, access to food, food utilization and food stability (World Bank Group (WBG), 2025). Hence, a single variable cannot improve all dimensions of FS. Soil fertility and agricultural inputs are crucial determinants for improving foodgrain production and FS (Zakari, Ying, & Song, 2014). While agricultural sustainability cannot be achieved without increasing sustainability in ecosystem services and natural resources, FS is negatively impacted due to increasing water scarcity (Zou & Guo, 2015). Air and water pollution have a negative impact on FS. Hence, these also have a vital impact on FS. Thereupon, family size, gender ratio, literacy rate, occupation, source of income, physical assets, livestock assets, financial capability, etc., are micro-level indicators that have crucial implications on FS (Harris-Fry et al., 2015; Zakari et al., 2014). Freedom of women and their literacy rates are positive factors in ensuring food security and nutritional security of family members. Food consumption expenditure (FCE) determines food security at the micro level (Alexandri, Luca, & Kevorchian, 2015). While FCE would improve as income, purchasing power, and employment opportunities for the people increase. Thus, financial support for farmers and the creation of jobs for people are essential to increase FS (Zou & Guo, 2015). Poverty, income inequality, urbanization, inflation, crime, government policies towards agricultural and rural development, employment opportunities, FDI, agri-business development, food production, infrastructural development, monetary and fiscal policies, and appropriate marketing determine FS at the macro level (Alexandri et al., 2015; Applanaidu, Bakar, & Baharudin, 2014; Gouvea et al., 2022; Lee et al., 2023; Wahab, Applanaidu, & Bakar, 2015; Ye et al., 2013; Zakari et al., 2014). Poverty and income inequality have caused a reduction in the food distribution pattern. Extensive urbanization has led to a diminished cropped area under food grains. Inflation negatively impacts the purchasing power of people to acquire food from the market. Increasing food prices are reducing food security (FS) (Gouvea et al., 2022). Black marketing of products increases crime in society. Moreover, FS is adversely affected due to the redistribution of land resources in China (Zou & Guo, 2015). Food supply chain efficiency is helpful to ensure food security in urban areas (Mantravadi & Srai, 2023). Thus, improving supply chains would also reduce post-harvest loss and increase food distribution channels (World Bank Group (WBG), 2025). Land ownership shows a significant impact on FS (Harris-Fry et al., 2015). Agricultural cooperative societies and agricultural research institutions (ARIs) play an active role in improving food security. ARIs discover, invent, and develop new varieties of seed, advance mechanisms of farming, and help the farming community adopt advanced cultivation techniques. Thus, factors affecting food security can be divided according to their nature and contribution to agricultural and environmental development. Food security is also divided into multiple components such as production, accessibility, affordability, distribution, utilization, and quality (Mantravadi & Srai, 2023; Zorić et al., 2023). 2.3. Agricultural and Food Security Agriculture is a sole source of food security (FS). While the growth of the agricultural sector is vulnerable due to rising population and decreasing freshwater resources (Talaviya et al., 2020). The progress of this sector is adversely Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 481 © 2025 AESS Publications. All Rights Reserved. affected due to low irrigation facilities and an increase in temperature (Talaviya et al., 2020). It is universally accepted that food security (FS) does not depend primarily on agricultural production. Most scholars have given high priority to increasing agricultural development to enhance FS. Global countries are emphasizing agricultural sustainability to ensure FS. However, the agricultural sector faces enormous pressure due to rising demand for food, fiber, and clean energy (Mondejar et al., 2021). While the growth of the agricultural sector depends on the social, economic, and financial structure of farmers, technological skills and literacy rates of farmers are positive factors for improving agricultural development. Farm management practices, high-yielding varieties of seed, fertilizer, and irrigation facilities are also useful inputs for the agricultural sector. Hence, the mentioned variables have a vital implication on FS. 2.4. Adaptation and Mitigation Strategies, and Food Security Climatic and geographical conditions play a crucial role in enhancing FS. At present, climate change has had a negative impact on the agricultural sector (Gouvea et al., 2022). It is also expected that climate change will be responsible for pushing 100 million people into extreme poverty by 2030 through declines in agricultural production and food security (World Bank Group, 2019). Climate change, thus, is the biggest challenge for the global community to ensure FS (Gouvea et al., 2022; Raheem, 2020). The use of digital technologies (DTs) is helping to increase farmers' understanding to adopt adaptation strategies in the farming sector (Vărzaru, 2024). Thus, DTs are nurturing an effective ecosystem for the agri-food sector to fight against climate change (Vărzaru, 2024). Moreover, digitalization and DTs are working as revolutionary tools to increase environmental sustainability through reducing GHGs and CO2 emissions. Digital platforms resolve different issues related to climate, environment, and economic situations (Ferguson et al., 2024). Climate change also increases agricultural pests and insects (Zakari et al., 2014). Occurrence of floods and drought lead to decrease FS (Badolo & Kinda, 2015; Mondejar et al., 2021; Zakari et al., 2014). Therefore, the farming community is applying several adaptation and mitigation practices in the farming sector. Thus, adaptation and mitigation approaches are crucial tools to enhance food security. Thereupon, digital development and digital technologies (DTs) are important to provide solutions to address the issues related to the mentioned problems in cultivation. 2.5. Technological Development and Food Security Moreover, there are several factors that determine the FS in the era of globalization, digitalization, technological change, innovation, and technology transfer and commercialization (Ye et al., 2013). Agriculture sector is using innovative technologies due to appropriate technology transfer. Application of technologies in the agricultural sector is reported to be effective and favorable to increase FS. Use of technology would be positive to improve yield and market structure (Amirova et al., 2022; Anderson & Sandin, 2022). Technology can be useful to increase food quality, increase the income of farmers, and improve their living conditions (Lioutas, Charatsari, & De Rosa, 2021). Gouvea et al. (2022) noticed the positive impact of technology on global food security in 106 countries. 2.6. Digital Innovation and Food Security Digital innovation supports increasing the digital transformation of the lives of common people (Aboagye-Darko & Mkhize, 2025; Benfica et al., 2023). It is apparent that digital innovation has brought significant changes in most sectors to improve their productivity, production, and efficiency (Konfo et al., 2023). Moreover, digital innovation and e-commerce would be effective in increasing a healthy food system (Mantravadi & Srai, 2023). Hence, digital innovation is found suitable for improving FS (Aboagye-Darko & Mkhize, 2025). ICT is a digital innovation that produces a positive impact on global food security (Gouvea et al., 2022). Chandio et al. (2024) opined that ICT has a greater impact on the agricultural production sector in the SAARC region. 2.7. Digital Technologies and Food Security Food sector should adopt digital technologies (DTs) to increase FS (Raheem, 2020). Most DTs, like remote sensors, help in monitoring crop, soil moisture, and environmental conditions for further improving crop production (Benfica et al., 2023; Vărzaru, 2024). Hence, DTs are creating new approaches for improving FS (Yao & Fu, 2025). Mantravadi and Srai (2023) examined the importance of DTs for improving accessibility of healthy food and waste reduction in the food sector. Thus, at present, FS is linked with the modernization and digitalization of the food production system (Abdullayev et al., 2023). Access to social media is improving the dissemination of knowledge across farmers (Harris-Fry et al., 2015; Vărzaru, 2024). Mobile phones and the internet have a conducive impact on grain production (Lee et al., 2023). The Internet of Things helps to increase food production in a sustainable way (Mondejar et al., 2021). Yao and Fu (2025) investigated the impact of DTs on FS in China. Prause et al. (2021) examined the role of DTs for changing the food system. DTs are increasing farmers' understanding to be better decision-makers in the agricultural sector (Benfica et al., 2023). It would also improve resource management and enhance the livelihood security of farming communities worldwide (Mondejar et al., 2021). Mobile technologies are helping farmers to increase better accessibility to seed, fertilizer, food production, and pest control in cultivation (Lee et al., 2023). Ferguson et al. (2024) examined the role of digital agricultural technologies in FS in Odisha, India. It noted a significant implication of DTs on FS. DTs are helping small-scale farmers to increase the local food system (Ferguson et al., 2024). Environmental sustainability in the food system can be improved through DTs (Benfica et al., 2023; World Bank Group, 2019). Benfica et al. (2023) highlighted the role of DTs in promoting food and nutritional security. It also emphasized that DTs such as remote sensing, artificial intelligence, and digital finance help increase the decision- Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 482 © 2025 AESS Publications. All Rights Reserved. making capacity of farmers and traders. The use of artificial intelligence aids in enhancing food production and food security in the era of a rising population and climate change (Talaviya et al., 2020). 2.8. Digital Transformation and Food Security Information communication develops an appropriate platform for digital transformation. FS improves as the use of IT increases (Amirova et al., 2022). Digital transformation of the food industry would lead to increased FS (Yao & Fu, 2025). Herawati et al. (2023) explored the impact of digital transformation on FS in Indonesia. It noted a positive trend in FS and the food sector due to digital transformation. Food industries are using digital transformation to increase FS (Dehghani, Popova, & Gheitanchi, 2022). Furthermore, digital transformation would also improve the efficiency and productivity of the food sector for improving FS (Vărzaru, 2024). Accordingly, it explained the significance of blockchain cloud technologies in various players of the food system, such as farmers, food suppliers, and investors. Blockchain cloud helps to increase efficiency and transparency in the food sector. Oborin (2019) observed the role of digitization in the industries for improving FS in the Perm region. (Vărzaru, 2024) noticed that digital transformation is useful to boost FS. FS is projected to increase as digital financial inclusion increases (Tan et al., 2024). 2.9. Digital Infrastructure, Digital Economy and Food Security Social-economic development, technological change, financial literacy, education, skills of people, technical literacy, and trust of people in digital technologies (DTs) have a crucial contribution to increasing digital infrastructure. Exports and imports of ICT, digital tools, and the discovery and invention of digital devices also nurture a proper network of digital infrastructure. The progress of digitalization and the digital economy could be improved by using ICT, the internet, cybersecurity, etc (Chandio et al., 2024). Investment in infrastructure, construction, and empowering the capacity of the digital economy would be significant for digitalization (Lee et al., 2023). Accordingly, the digital economy would be active in improving food security (FS) as it enhances overall food systems. The digital economy supports increasing production, distribution, and consumption in cultivation (Lee et al., 2023). It is, therefore, supportive to improve production, process, and logistics operations in the agricultural industries (Amirova et al., 2022). Lee et al. (2023) observed the impact of the digital economy on FS in China. 3. RESEARCH METHODS 3.1. Study Area This study includes G20 countries for assessing the impact of digital development (DD) on financial stability (FS). The G20 is a group of 19 countries, the European Union (27 countries), and the African Union (55 countries). However, the statistics for related indicators associated with DD and FS are not available for all G20 countries. Hence, 63 countries are considered appropriate for estimating DDI and FSI. Furthermore, statistics for the most suitable indicators are available during 2010–2022. Thus, this study develops DDI and FSI for 63 countries during this period (Table 1). Table 1. Region-wise segregation of selected countries. Country Region Australia, Japan, Korea Rep., Indonesia, China East Asia & Pacific Austria, Belgium, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, United Kingdom, Bulgaria, Romania, Russian Federation, Turkey Europe & Central Asia Argentina, Brazil, Mexico Latin America & Caribbean Malta, Egypt Arab Rep., Morocco, Tunisia Middle East & North Africa Canada, United States North America India South Asia Ethiopia, Gambia, Guinea, Madagascar, Mali, Rwanda, Uganda, Cameroon, Comoros, Congo Rep., Eswatini, Kenya, Lesotho, Senegal, Tanzania, Zimbabwe, Mauritius, Namibia, South Africa Sub-Saharan Africa 3.2. Sources of Data and Time Period This study includes 63 countries from the G20 region, and it is proposed to create FSI and DDI for these countries. Since the availability of statistics of FS and DI related indicators is available during 2010–2022, the study compiles panel data for these countries during the above-said period. The statistics of related variables are derived from World Development Indicators (the World Bank), OECD, and FAO. Although, the statistics for a few variables are missing in this time period. The missing values are estimated using interpolation and extrapolation techniques (Singh, Singh, & Negi, 2020). These missing values are estimated to create balanced country-wise panel data of specified variables. These techniques assume that particular variables have a linear trend in their time series. Furthermore, the observed trend for a time series does not lie outside the projected range in the mentioned techniques. Most specifically, these methods also assume that there is no sudden and extreme change in the value of a specific variable within a time period. 3.3. Estimation of Food Security Index (FSI) and Digital Development Index (DDI) The composite Z-score method is adopted to estimate FSI and its other components, as well as DDI for selected G20 countries. Zou and Guo (2015) created FSI using factor analysis in China. Singh et al. (2020) also preferred a similar method to develop a sustainable development index across countries. The same statistical technique is used to estimate various indexes in the areas of digitalization and FS in this research. Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 483 © 2025 AESS Publications. All Rights Reserved. 3.4. Indicators of DDI The process of digitalization cannot be explained by a specific indicator. Thus, most scholars consider diverse indicators for digitalization when assessing its impact on energy intensity, financial stability, economic growth, foreign trade, social development, environmental development, business creation, entrepreneurship ecosystem, sustainable development, and agricultural development (Benfica et al., 2023; Lee et al., 2023). Another group of scholars used a composite index of digitalization-enhancing variables for assessing the progress of digital development (DD). Vărzaru (2024) also assessed the impact of digital transformation on FS using indexes. Wang et al. (2023) exposed the significant impact on digital technologies, digital finance and human capital on the food system. Lee et al. (2023) created a Digital Economy Index to explore its implications on FS in China. Sixteen indicators are compiled to estimate the DDI in this study (Table 2). These indicators are divided into three categories, which are explained in Table 2. Table 2. Indicators of DDI. Category of indicators Indicators name Code ICT access Fixed telephone subscriptions (per 100 people) FTSPHP Mobile cellular subscriptions (per 100 people) MCSPHP Secure Internet servers (per 1 million people) SISPMP ICT goods exports (% of total goods exports) ICTGEPTGE ICT goods imports (% total goods imports) ICTGIPTGI ICT service exports (% of service exports, BoP) ICTSEPSEBOP Communications, computer, etc. (% of service exports, BoP) CCPSEBOP Communications, computers, etc. (% of service imports, BoP) CCPSIBOP ICT use Fixed broadband subscriptions (per 100 people) CI_FBSPHP Individuals using the Internet (% of population) IUIPP Automated teller machines (ATMs) (per 100,000 adults) ATMPLA Computer, communications and other services (% of commercial service exports) CCOSPCSE Computer, communications and other services (% of commercial service imports) CCOSPCSI ICT skills Adjusted savings: Education expenditure (% of GNI) EEPGNI Compulsory education, duration (years) CEDY School enrolment, secondary (% gross) SESPG 3.5. Indicators of FSI Most scholars agree that the Food Security Index (FSI) is a crucial policy tool for measuring food security at the national level. The FSI includes multiple indicators that promote food security (Gouvea et al., 2022; Herawati et al., 2023; Lee et al., 2023; Wahab et al., 2015). These indicators can be divided into three to five categories (Harris-Fry et al., 2015; Herawati et al., 2023). For instance, Ye et al. (2013) created FSI in China using supply and demand of food- associated indicators. Mantravadi and Srai (2023) considered distribution, access and affordability of food to explore the situation of urban FS. Zou and Guo (2015) develop FSI in China using 12 different indicators associated with FS. Alexandri et al. (2015) created the Berry index to measure the FS in Romania. Harris-Fry et al. (2015) developed FSI to examine the factors affecting household FS in Bangladesh. Herawati et al. (2023) analyze the FS as using availability, access and consumption of food in Indonesia. Lee et al. (2023) developed an FS evaluation index to detect the FS across provinces of China. The above-mentioned review infers that past studies could not pursue a uniform pattern to examine the FS at the macro level. The authors of this article, therefore, considered the most useful indicators associated with FS for measuring the FSI. The sufficiency of food for all people shows the FS (Herawati et al., 2023). Most significant indicators associated with FSI are finalized as per the existing studies. These indicators are divided into four categories (World Bank Group (WBG), 2025), and their enlightenment is given as: Table 3. Indicators of FSI. Indicator's category Indicators Code Source of data Food availability Crop production index (2014-2016 = 100) CPI Per capita availability of cereal production (Kg./Person) PCAOPKP WDI Food production index (2014-2016 = 100) FPI Livestock production index (2014-2016 = 100) LPI Population growth (Annual %) PGA Agricultural land (% of land area) ALPLA Fertilizer consumption (Kg//Ha. of arable land) FCKPHAL Food accessibility GDP per person employed (constant 2017 PPP $) GDPPPE Labor force participation rate, total (% of total population ages 15- 64) LFPRTPTP Inflation GDP deflator (annual %) IGDPDA GDP per capita (Constant 2015 US$) PCGDP Vulnerable employment, total (% of total employment) VETPTE Employment to population ratio (15+, total (%) EPRT Purchasing power parity PPP OECD Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 484 © 2025 AESS Publications. All Rights Reserved. Indicator's category Indicators Code Source of data Control of corruption: Number of Sources CCNS WDI Food stability Age dependency ratio (% of working-age population) ADRPWAP Cereal yield (Kg./Ha.) CYKPH Arable land (Ha./person) ALHPP Population density (people per sq. km of land area) PD Employment in agriculture (% of total employment) EAPTE Per capita land under cereal production (Hectares) PCLUCP Per capita food production variability (Constant 2014-2016 thousand int$ per capita) PCFPV FAO Food utilization Annual freshwater withdrawals, agriculture (% of total freshwater withdrawal) AFWWAPT FWW WDI Mortality rate infant (per 1,000 live births) IMRPTLB People using at least basic drinking water services (% of population) PULBDWSP P People using at least basic sanitation services (% of population) PUBSSPP Life expectancy at birth, total (Years) LEBTY Fertility rate total (births per woman) FRTBPW Renewable internal freshwater resources per capita (Cubic meters) RIFWRPC Average dietary energy requirement (kcal/cap/Day) ADER FAO Average fat supply (g/cap/day) (3-year average) AFS Average protein supply (g/cap/Day) (3-year average) APS Average supply of protein of animal origin (g/cap/day) (3-year average) ASPAO Minimum dietary energy requirement (kcal/cap/day) MDER Share of dietary energy supply derived from cereals, roots and tubers (kcal/cap/Day) (3-year average) SDESDFCR T Sex ratio at birth (male births per female births) SRB WDI Food Availability Associated Variables: Food availability cannot be sustained without an agricultural production system and livestock. Agricultural land (Badolo & Kinda, 2015), cereal production (Badolo & Kinda, 2015), and use of fertilizer help to increase crop production. The use of fertilizer is useful to increase the yield and production of crops (Chandio et al., 2024). Thus, it is positive to increase FS (Scanlan, 2001). Food production and livestock ensure the FS and nutritional security. Population pressure increases additional pressure on the agricultural sector and FS (Ye et al., 2013). High population growth causes an increase in food demand (Zou & Guo, 2015). Hence, FS is negatively affected due to increased population growth (Applanaidu et al., 2014; Badolo & Kinda, 2015; Scanlan, 2001). Thus, per capita availability of cereal production, food production index (Applanaidu et al., 2014), livestock production index, agricultural land and fertilizer consumption, crop production index and population growth (Badolo & Kinda, 2015) are integrated to develop AVFI in this study (Table 3). Food Accessibility Related Indicators: This component of FS depends on the purchasing power of the people. Per capita income (Scanlan, 2001) and purchasing power parity is positive in increasing the economic capacity of people to purchase food (Abdullayev et al., 2023). On the other hand, the economic capacity of people is expected to decline as inflation, vulnerable employment, and corruption increase. Hence, these variables have a negative impact on the accessibility of food. Accordingly, 8 indicators are composed for developing the accessibility of food index (ACFI) in this study (Table 3). Food Stability Related Indicators: Efficient use of arable land is crucial to increase FS (Abdullayev et al., 2023). Employment in the agricultural sector helps to increase agricultural production and the economic capacity of workers. Conversely, the age dependency ratio (Scanlan, 2001) and variability in food production are negatively associated with this component of FS. Thus, 7 different indicators are considered to develop the stability of food index (STFI) in this study (Table 3). Food Utilization Related Indicators: Food utilization ensures the food quality that maintains the health security of the people. Annual freshwater withdrawals and per capita renewable freshwater are supportive of increasing health security. Infant mortality rate (IMR) increases due to lack of nutritional security and basic medical facilities (World Health Organization (WHO), 2023). Food utilization has a negative impact on IMR. Lack of proper dietary patterns is also responsible for increasing IMR. Fertility rate and life expectancy increase as nutritional security and FS increase (Scanlan, 2001). There are other measures like average dietary energy requirement, average fat supply, minimum dietary energy, and dietary energy supply from cereals that are also important to increase the utilization of food. The contribution of women is essential to increase the utilization of food. Thus, the sex ratio at birth is used to examine the role of women in promoting FS. Hence, 14 different indicators are used to develop the food utilization index (UTFI) in this study (Table 3). Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 485 © 2025 AESS Publications. All Rights Reserved. 3.6. Regression Analysis Previous studies used different models to assess the factors affecting FS. Harris-Fry et al. (2015) applied a multinomial logistic regression model to examine the determinants of FS at households in Bangladesh. Using a logistic regression model, Zakari et al. (2014) observed the food availability improving factors in Niger. Vărzaru (2024) examined the impact of digital economy and society index on FS. This research is intended to examine the impact of digital development (DD) on FS and vice-versa. Hence, FSI and DDI are included as core variables in simultaneous regression equations. Gouvea et al. (2022), Lee et al. (2023), and Wang et al. (2023) also observed the interconnection of various indexes associated with FS and digitalization is analyzed. The development of environment-related technologies, inventions per capita in this area, final consumption expenditure, foreign direct investment net inflows, forest area, and gross domestic saving are included as explanatory variables. The study also examines the impact of DD on the accessibility of food index (ACFI), availability of food index (AVFI), stability of food index (STFI), and utilization of food index (UTFI). These variables are considered as dependent variables, with DDI used as the independent variable. Log-linear regression models are employed to analyze the impact of DVs on IVs. The following regression models are adopted in this article: 𝑙𝑜𝑔(𝐴𝐶𝐹𝐼)𝑐𝑡 = 𝛼0 + 𝛼1 𝑙𝑜𝑔(𝐷𝐷𝐼)𝑐𝑡 + 𝛼2 𝑙𝑜𝑔(𝐷𝐸𝑅𝑇𝑃𝐴𝑇)𝑐𝑡 + 𝛼3 𝑙𝑜𝑔(𝐷𝐸𝑅𝑇𝐼𝑃𝐶)𝑐𝑡 + 𝛼4 𝑙𝑜𝑔(𝐹𝐶𝐸𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝛼5 𝑙𝑜𝑔(𝐹𝐷𝐼𝑁𝐼𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝛼6 𝑙𝑜𝑔(𝐹𝐴𝑃𝐿𝐴)𝑐𝑡 + 𝛼7 𝑙𝑜𝑔(𝐺𝐷𝑆𝑃𝐺𝐷𝑃)𝑐𝑡 + µ𝑐𝑡 (1) 𝑙𝑜𝑔(𝐴𝑉𝐹𝐼)𝑐𝑡 = 𝛽0 + 𝛽1 𝑙𝑜𝑔(𝐷𝐷𝐼)𝑐𝑡 + 𝛽2 𝑙𝑜𝑔(𝐷𝐸𝑅𝑇𝑃𝐴𝑇)𝑐𝑡 + 𝛽3 𝑙𝑜𝑔(𝐷𝐸𝑅𝑇𝐼𝑃𝐶)𝑐𝑡 + 𝛽4 𝑙𝑜𝑔(𝐹𝐶𝐸𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝛽5 𝑙𝑜𝑔(𝐹𝐷𝐼𝑁𝐼𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝛽6 𝑙𝑜𝑔(𝐹𝐴𝑃𝐿𝐴)𝑐𝑡 + 𝛽7 𝑙𝑜𝑔(𝐺𝐷𝑆𝑃𝐺𝐷𝑃)𝑐𝑡 + έ𝑐𝑡 (2) Here, log is natural logarithm of corresponding DVs and IVs; c is cross country; t is time; α0, and β0 are constant- term; µct and έct are error-term; α1... and β1... are the unknown parameters of associated variables that are estimated through regression analysis in the above-mentioned Equations 1 and 2. The detailed description of all IVs and DVs is presented in Table 4. Accordingly, other regression equations are developed to examine the impact of DDI and other explanatory variables of STFI, UTFI, and FSI. Thereupon, regression equations that examine the impact of FS and its dimensions on DDI are also formulated in this research. The detailed explanation of these equations is given in Appendix A. Table 4. Explanation of DVs and IVs. Indicators Code Source of Data Accessibility of Food Index ACFI Estimated by authors Availability of food index AVFI Stability of food index STFI Utilization of the Food Index UTFI Food security index FSI Digital development index DDI Development of environment-related technologies (% of all technologies) DERTPAT OECD Per capita development of environment-related technologies and inventions DERTIPC Final consumption expenditure as a percentage of GDP FCEPGDP WDI Foreign direct investment net inflows as a percentage of GDP FDINIPGDP Forest area (% of land area) FAPLA Gross domestic savings as a percentage of GDP GDSPGDP 3.7. Statistical Software The proposed descriptive and regression analyses are completed using MS Excel, SPSS, and STATA statistical software in this study. 4. DESCRIPTIVE RESULTS The comparative performance of G20 countries in FS and its four different components is provided in Figure 1-5, respectively. Figure 6 infer the progress of G20 countries in digital development (DD). The performance of G20 countries in DD and FS is presented in Figure 7. The ranking of these countries in various measures of FS and digitalization is provided in Table 5. The statistical values of AVFI lie between 0.2059 to 0.5362 across G0 countries (Figure 1). It demonstrates that G20 countries could not uniformly improve their performance in the availability of food (AVF). Ireland and Denmark hold the 1st and 2nd positions in AVFI. In the European Union, many countries such as Malta and Sweden have lower values of AVFI. Consequently, these countries have the poorest positions in food availability. In the African Union, Lesotho, South Africa, and Eswatini have average positions in AVF. The remaining countries in the African Union have not established an adequate platform to sustain AVF. Figure 2 explains the comparative performance of G20 countries in food accessibility (ACF). It reports a high and significant diversity in ACF across G20 countries. Luxembourg has the highest value of ACFI. It could achieve the best position in this ACF compared to other countries. Thereafter, Ireland has the second-best performing country in ACF. Most high-income group countries could attain greater positions in ACF. Conversely, low-income group countries such as Rwanda, Gambia, Mali, Guinea, Uganda, Ethiopia, and Madagascar have the poorest performance in ACF. All income group countries (except Indonesia) could not improve their position in ACF. Most upper-income countries (except China, Mauritius, and Russia) have a poor position in ACF. European countries (excluding Romania, Bulgaria, and Croatia) demonstrate the best, medium, and average performance in ACF. Most countries (excluding Mauritius) can improve their performance in ACF by increasing food production, arable land, and cereal production. Countries Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 486 © 2025 AESS Publications. All Rights Reserved. with low rankings in food accessibility should create income-generating opportunities to enhance their performance. Senegal, India, Zimbabwe, Morocco, Egypt, Tunisia, Rwanda, Gambia, Mali, Comoros, Guinea, South Africa, Argentina, Uganda, Congo, Kenya, Cameroon, Türkiye, Ethiopia, Eswatini, Namibia, Lesotho, Mexico, Brazil, Romania, Tanzania, Bulgaria, Croatia, and Madagascar have AVFI values less than 0.40. Hence, these countries appear as the poorest performers in ACF. Figure 3 provides the comparative performance of G20 countries in the stability of food (STF). Australia and Canada have the 1st and 2nd highest values in STFI, respectively. Hence, both countries could achieve 1st and 2nd positions, respectively, in STF. Malta has the poorest performance in STF. Furthermore, the statistical values of STFI indicate high diversity in STF across G20 countries. Australia has the highest value of STFI and holds the best position in STF. Malta appears to be in the poorest position in STF. Most countries have the poorest positions in AVF within the African Union. However, many countries such as Madagascar, Guinea, Egypt, Mali, and South Africa from the African Union could achieve better positions in STF. The cross-comparison of G20 countries in the utilization of food (UTF) is given in Figure 4. Australia and Canada hold the 1st and 2nd positions, respectively, in UTF according to the estimated statistical values of UTFI. Uganda has the lowest value of UTFI. Therefore, Uganda seems unable to improve its performance in UTF. All countries can be considered as performers ranging from best, better, good, to average in STF. All European countries have better positions in UTF. Most countries in the African Union, excluding Tunisia, Egypt, Mauritius, and South Africa, have UTFI values below those of other nations. FSI is the integration of AVFI, ACFI, STFI and UTFI. Thus, those countries that have good performance in the mentioned indicators will be in good positions in food security (FS) (Figure 5). Netherlands, Finland, China, Germany, United Kingdom, Austria, Lithuania, France, Luxembourg, Canada, Ireland, United States, Denmark, and Australia are reported as the best performing countries in FS based on the statistical values of FSI. Tunisia, Egypt, Morocco, South Africa, Mexico, Malta, Mauritius, Türkiye, Brazil, Croatia, Slovak Republic, Indonesia, Japan, Cyprus, Slovenia, Korea Rep., Argentina, Czechia, Estonia, Romania, Hungary, Bulgaria, Italy, Russian Federation, Portugal, Poland, Latvia, Greece, Belgium, Spain, and Sweden have better performance in FS. Remaining countries have average performance in FS. Figure 1. Cross-comparison of countries in AVFI as per its mean value during 2010-2022. Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 487 © 2025 AESS Publications. All Rights Reserved. Figure 2. Cross-comparison of countries in ACFI as per its mean value during 2010-2022. Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 488 © 2025 AESS Publications. All Rights Reserved. Figure 3. Cross comparison of countries in STFI as per its mean value during 2010-2022. Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 489 © 2025 AESS Publications. All Rights Reserved. Figure 4. Cross comparison of countries in UTFI as per its mean value during 2010-2022. Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 490 © 2025 AESS Publications. All Rights Reserved. Figure 5. Cross comparison of countries in FSI as per its mean value during 2010-2022. Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 491 © 2025 AESS Publications. All Rights Reserved. Figure 6. Cross-comparison of countries in DII based on their mean values during 2010-2022. Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 492 © 2025 AESS Publications. All Rights Reserved. Figure 7. Progress of G20 countries in DII and FSI as per their mean value during 2010-2022. Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 493 © 2025 AESS Publications. All Rights Reserved. Table 5. Ranking of countries in various indexes. Country name Mean ACFI Mean AVFI Mean STFI Mean UTFI Mean FSI Mean DDI Value Rank Value Rank Value Rank Value Rank Value Rank Value Rank Ireland 0.597 2 0.536 1 0.477 39 0.617 19 0.557 4 0.672 1 Netherlands 0.563 6 0.379 32 0.451 49 0.624 18 0.504 14 0.664 2 Korea, Rep. 0.484 18 0.324 50 0.480 37 0.611 22 0.475 30 0.604 3 Sweden 0.580 3 0.285 57 0.483 36 0.641 11 0.497 15 0.597 4 Malta 0.535 13 0.261 61 0.333 63 0.657 9 0.446 40 0.590 5 Finland 0.538 12 0.323 52 0.497 24 0.668 5 0.507 13 0.587 6 Germany 0.547 10 0.397 25 0.487 31 0.616 20 0.512 11 0.571 7 United States 0.553 8 0.440 10 0.565 5 0.669 4 0.557 3 0.561 8 United Kingdom 0.543 11 0.425 14 0.475 40 0.609 25 0.513 10 0.557 9 Belgium 0.502 17 0.379 31 0.470 43 0.614 21 0.491 17 0.557 10 Japan 0.529 14 0.329 49 0.434 52 0.589 30 0.470 33 0.550 11 France 0.506 16 0.442 8 0.499 23 0.629 17 0.519 7 0.545 12 Hungary 0.410 31 0.462 6 0.486 33 0.576 35 0.483 25 0.545 13 Canada 0.554 7 0.376 33 0.578 2 0.677 2 0.546 5 0.529 14 Denmark 0.574 4 0.511 2 0.525 10 0.664 7 0.569 2 0.516 15 Estonia 0.470 21 0.355 41 0.472 41 0.609 23 0.476 27 0.509 16 Czechia 0.443 26 0.397 24 0.484 35 0.580 34 0.476 28 0.507 17 Austria 0.549 9 0.369 37 0.505 21 0.635 13 0.515 9 0.503 18 Brazil 0.356 40 0.371 35 0.519 13 0.589 31 0.459 37 0.476 19 Luxembourg 0.714 1 0.348 44 0.485 34 0.635 14 0.545 6 0.476 20 Portugal 0.470 20 0.385 29 0.439 51 0.661 8 0.489 21 0.472 21 Spain 0.476 19 0.424 15 0.415 58 0.665 6 0.495 16 0.469 22 Slovak Republic 0.435 29 0.401 22 0.492 28 0.548 40 0.469 35 0.465 23 Poland 0.401 33 0.446 7 0.516 16 0.593 28 0.489 20 0.462 24 China 0.404 32 0.436 11 0.569 4 0.631 15 0.510 12 0.461 25 Russian federation 0.441 27 0.343 47 0.574 3 0.592 29 0.487 22 0.460 26 Australia 0.568 5 0.441 9 0.613 1 0.684 1 0.576 1 0.460 27 Argentina 0.310 51 0.436 12 0.526 8 0.630 16 0.475 29 0.454 28 Italy 0.436 28 0.405 19 0.453 48 0.649 10 0.486 23 0.451 29 Romania 0.356 39 0.483 4 0.510 18 0.584 32 0.483 26 0.450 30 Slovenia 0.452 25 0.361 38 0.490 29 0.594 27 0.475 31 0.440 31 Latvia 0.458 23 0.419 16 0.497 25 0.583 33 0.489 19 0.435 32 Cyprus 0.510 15 0.316 54 0.429 55 0.640 12 0.474 32 0.421 33 Bulgaria 0.389 37 0.489 3 0.525 9 0.538 42 0.485 24 0.396 34 Croatia 0.393 36 0.403 21 0.494 26 0.568 36 0.464 36 0.387 35 Mexico 0.352 41 0.370 36 0.492 27 0.551 39 0.441 41 0.387 36 Mauritius 0.422 30 0.371 34 0.453 47 0.545 41 0.448 39 0.378 37 India 0.206 62 0.399 23 0.503 22 0.443 46 0.388 47 0.369 38 Lithuania 0.455 24 0.483 5 0.518 14 0.604 26 0.515 8 0.367 39 Eswatini 0.335 44 0.404 20 0.413 60 0.348 51 0.375 48 0.352 40 Greece 0.400 34 0.428 13 0.459 45 0.677 3 0.491 18 0.337 41 Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 494 © 2025 AESS Publications. All Rights Reserved. Country name Mean ACFI Mean AVFI Mean STFI Mean UTFI Mean FSI Mean DDI Value Rank Value Rank Value Rank Value Rank Value Rank Value Rank South Africa 0.306 52 0.410 18 0.509 20 0.474 45 0.425 42 0.336 42 Namibia 0.339 43 0.322 53 0.467 44 0.365 48 0.373 49 0.330 43 Morocco 0.253 60 0.390 27 0.490 30 0.531 43 0.416 43 0.323 44 Tunisia 0.280 58 0.357 40 0.429 54 0.556 37 0.406 45 0.315 45 Indonesia 0.461 22 0.396 26 0.540 6 0.478 44 0.469 34 0.309 46 Türkiye 0.321 46 0.386 28 0.511 17 0.609 24 0.457 38 0.305 47 Mali 0.295 55 0.302 55 0.510 19 0.363 50 0.367 51 0.284 48 Senegal 0.192 63 0.349 43 0.405 61 0.420 47 0.342 59 0.284 49 Congo, Rep. 0.317 49 0.244 62 0.415 57 0.322 54 0.325 63 0.259 50 Kenya 0.318 48 0.324 51 0.429 53 0.308 58 0.345 57 0.258 51 Guinea 0.300 53 0.339 48 0.522 11 0.306 59 0.367 52 0.244 52 Egypt 0.269 59 0.297 56 0.522 12 0.551 38 0.410 44 0.235 53 Lesotho 0.350 42 0.410 17 0.449 50 0.271 62 0.370 50 0.212 54 Cameroon 0.318 47 0.206 63 0.470 42 0.332 52 0.332 62 0.171 55 Zimbabwe 0.223 61 0.355 42 0.479 38 0.321 55 0.344 58 0.164 56 Comoros 0.297 54 0.360 39 0.404 62 0.325 53 0.347 56 0.151 57 Uganda 0.315 50 0.346 45 0.456 46 0.233 63 0.338 61 0.145 58 Madagascar 0.393 35 0.346 46 0.530 7 0.293 61 0.391 46 0.127 59 Rwanda 0.284 57 0.381 30 0.415 59 0.314 57 0.348 55 0.126 60 Gambia 0.289 56 0.284 58 0.416 56 0.364 49 0.338 60 0.124 61 Ethiopia 0.326 45 0.273 59 0.518 15 0.319 56 0.359 53 0.111 62 Tanzania 0.358 38 0.261 60 0.487 32 0.297 60 0.351 54 0.101 63 Table 6. Correlation coefficients among the estimated indexes and IVs. Indicators ACFI AVFI STFI UTFI FSI DDI DERTPAT DERTIPC FCEPGDP FDINIPGDP FAPLA GDSPGDP ACFI 1 0.137a 0.174a 0.674a 0.791a 0.731a -0.059b 0.574a -0.492a 0.245a 0.229a 0.511a AVFI 0.137a 1 0.245a 0.299a 0.575a 0.279a 0.00 -0.009 -0.052 -0.024 -0.137a 0.086a STFI 0.174a 0.245a 1 0.235a 0.446a 0.129a 0.013 0.031 -0.187a -0.128a 0.107a 0.151a UTFI 0.674a 0.299a 0.235a 1 0.879a 0.833a -0.060b 0.404a -0.459a 0.130a 0.195a 0.366a FSI 0.791a 0.575a 0.446a 0.879a 1 0.798a -0.049 0.420a -0.462a 0.127a 0.156a 0.431a DDI 0.731a 0.279a 0.129a 0.833a 0.798a 1 -0.031 0.563a -0.511a 0.113a 0.223a 0.476a DERTPAT -0.059b 0 0.013 -0.060b -0.049 -0.031 1 0.002 -0.008 -0.019 0.045 -0.020 DERTIPC 0.574a -0.009 0.031 0.404a 0.420a 0.563a 0.002 1 -0.285a 0.016 0.398a 0.285a FCEPGDP -0.492a -0.052 -0.187a -0.459a -0.462a -0.511a -0.008 -0.285a 1 -0.167a -0.333a -0.866a FDINIPGDP 0.245a -0.024 -0.128a 0.130a 0.127a 0.113a -0.019 0.016 -0.167a 1 -0.086a 0.174a FAPLA 0.229a -0.137a 0.107a 0.195a 0.156a 0.223a 0.045 0.398a -0.333a -0.086a 1 0.277a GDSPGDP 0.510a 0.086a 0.151a 0.366a 0.431a 0.476a -0.020 0.285a -0.866a 0.174a 0.277a 1 Note: a indicates that correlation is significant at 1% significance level and b indicates that correlation is significant at 5% significance level. Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 495 © 2025 AESS Publications. All Rights Reserved. Moreover, there is diversity in FS in the African and European Union due to variation in indicators used to create the FSI. All countries in the African Union (except Tunisia, Egypt, Morocco, South Africa, and Mauritius) have average performance in FS. As per estimated values of DDI, G20 countries are divided into best, better, good, and poorest performers in digital development (DD) (Figure 6). Korea, Republic of, Netherlands, and Ireland have 1st, 2nd, and 3rd best performing countries in digital development. Austria, Czechia, Estonia, Denmark, Canada, Hungary, France, Japan, Belgium, the United Kingdom, the United States, Germany, Finland, Malta, and Sweden are reported as better performing countries in DD. Many low-income countries have the poorest positions in DD. Cyprus, Latvia, Slovenia, Romania, Italy, Argentina, Australia, the Russian Federation, China, Poland, the Slovak Republic, Spain, Portugal, Luxembourg, and Brazil could sustain good performance in digitalization. Tanzania, Ethiopia, Gambia, Rwanda, Madagascar, Uganda, Comoros, Zimbabwe, Cameroon, Lesotho, Egypt, Guinea, Kenya, the Congo Republic, Senegal, and Mali have less than 0.30 values of DDI. Thus, the estimates suggest that these countries should create appropriate digital infrastructure through increasing R&D investment and strengthening the intellectual property rights regime (Benfica et al., 2023). All countries in the European Union created effective digital infrastructure and platforms. While none of the countries from the African Union could improve their positions in DD. Hence, most countries in the European Union are receiving relatively greater benefits from DD as compared to other countries in the African Union. South Korea is found to be the best performing country in DD among Asian countries. Other Asian countries like Indonesia, India, and China should give high priority to creating digital infrastructure to increase their positions in DD. FSI is positively correlated with AVFI, ACFI, STFI and UTFI in G20 countries (Table 6). FS cannot be separated from its other dimensions. AVFI has a positive and statistically significant link with STFI, UTFI, and FSI. ACFI also has a positive connection with other dimensions of FS. STFI is positively associated with AVFI, ACFI, UTFI, and FSI. The last component of FS, i.e., UTFI, is also positively associated with other indexes of FS. Hence, the estimates provide clarification that there is a high interdependency of FS with its other dimensions. Subsequently, G20 countries should give equal priority to all dimensions of FS to increase their positions in it. The results imply that digital development (DD) helps to enhance AVF, ACF, STF, UTF, and overall FS. Thereupon, it is not perceived as a uniform association of external variables with estimated indexes. Despite that, per capita environmental technology, FDINI, forest area, and gross domestic saving are positively associated with AVFI, UTFI, FSI, and DDI. Hence, these variables are found useful for improving AVF, UTF, FS, and DD. 5. EMPIRICAL RESULTS The impact of digital development and explanatory variables on ACF, AVF, STF, UTF and FS are presented in Table 7. While comprehensive results which estimate the impact of DD on ACF, AVF, STF, UTF, and FS are given in Appendix B (Table B1-B5). The regression coefficients of DDI with ACFI, AVFI, STFI, UTFI, and FSI are reported as positive. The estimates demonstrate that digital development (DD) positively influences FS and its other dimensions. Per capita environmental technology and the share of environmental technologies show a positive impact on ACF, AVF, UTF, and FS. However, STF appears to decline due to increases in per capita environmental technology and the share of environmental technologies. The impact of final consumption expenditure on FS and its drivers is not consistent. Although it shows a negative impact on ACF and UTF, AVF, STF, and FS are likely to increase as final consumption expenditure rises. The impact of FDINI on ACF, UTF, and FS is positive and statistically significant. Conversely, AVF and STF are expected to decline as FDINI increases. Forest area supports ecosystem services and helps reduce temperature impacts on Earth. Therefore, it is proposed that forest area would positively influence FS. Our results also demonstrate that forest areas have a positive impact on ACF, AVF, and STF, while forest area is not found to be favorable for promoting UTF and FS. Table 7. Impact of DD on FS and its dimensions. Number of obs. 819 819 819 819 819 Number of groups 63 63 63 63 63 R-squared 0.6156 0.1154 0.0958 0.741 0.6888 Wald Chi2 5214.32* 200.02* 353.84* 5932.05* 8082.29* DVs/IVs log(ACFI) log(AVFI) log(STFI) log(UTFI) log(FSI) Reg. Coef. Reg. Coef. Reg. Coef. Reg. Coef. Reg. Coef. log(DDI) 0.0957* 0.1790* 0.0152** 0.3988* 0.1661* log(DERTPAT) 0.0247* 0.006 0.0120* 0.0411* 0.0223* log(DERTIPC) 0.0705* 0.0003 -0.0049* 0.0225* 0.0218* log(FCEPGDP) -0.062 0.3567* 0.0024 -0.0321 0.0496*** log(FDINIPGDP) 0.0302* -0.0120*** -0.0129* 0.0035 0.0038*** log(FAPLA) 0.0042 0.0031 0.0111* -0.0103* -0.0021 log(GDSPGDP) 0.0679* 0.0680* 0.0298* 0.0105 0.0424* Constant Coefficient -0.8869* -2.5969* -0.8600* -0.2316 -1.0381* Note: *, ** and *** infer that coefficients are statistically significant at 1%, 5% and 10% significance level, respectively. Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 496 © 2025 AESS Publications. All Rights Reserved. Table 8. Impact of FS and its dimensions on DD. Number of obs. 819 819 819 819 819 Number of groups 63 63 63 63 63 R-squared 0.6895 0.4031 0.0906 0.0188 0.7165 Wald Chi2 3230.02* 1229.58* 24.8* 72.21* 5234.68* DVs/IVs log(DDI) log(DDI) log(DDI) log(DDI) log(DDI) Reg. Coef. Reg. Coef. Reg. Coef. Reg. Coef. Reg. Coef. log(FSI) 1.4627* log(DERTPAT) 0.0921* log(DERTIPC) 0.0639* log(FCEPGDP) -0.2799* log(FDINIPGDP) 0.0007 log(FAPLA) -0.0189* log(GDSPGDP) 0.0126 log(ACFI) 1.0723* log(AVFI) 0.5282* log(STFI) 0.6074* log(UTFI) 1.4166* Constant Coefficient 1.1689* -0.0408 -0.4927* -0.5849* -0.0662* Note: * infer that coefficient is statistically significant at 1% significance level. The impact of FS, ACF, AVF, STF and UTF on digital development (DD) is included in Table 8. The impact of FS, accessibility of food, availability of food, stability of food, and utilization of food on DD are provided in Table C1,- C5, respectively, in Appendix C. The positive coefficients of FS and other components are detected as positive and statistically significant. Thus, the results indicate that DD is expected to improve as AVF, ACF, STF, UTF, and FS increase. Moreover, FDINI and gross domestic savings are reported as positive factors for increasing digitalization. In contrast, forest area and final consumption expenditure showed negative implications on DD. 6. CONCLUSION This study estimated various indexes in the areas of food security (FS) and digital development (DD) for selected G20 countries. The results highlighted that G20 countries exhibit high diversity in DD and FS. The variation in DD and FS is due to significant differences in digital infrastructure and FS-enhancing factors. Additionally, there is considerable diversity in AVF, ACF, STF, and UTF across G20 countries. High diversity in FS and DD-promoting indicators could pose challenges for low-ranking countries in achieving SDGs in the near future. For example, most countries in the African Union have the poorest performance in FS and DD. A few countries in the European Union also need to implement conducive policies to improve their performance in DD and FS. Therefore, low-ranking countries should prioritize FS and its drivers equally, along with activities that promote digital infrastructure development. The correlation results also imply that FS can be achieved by increasing AVF, ACF, STF, and UTF. Moreover, FS is reported positively to increase its other drivers. Thus, FS and its drivers have a positive and significant interconnection with each other. ACF, AVF, STF, UTF, and FS are positively associated with DD. FS cannot be sustainable without improving its other components and creating an appropriate digital infrastructure (Scanlan, 2001). Also, ACF, AVF, STF, UTF, and FS are also reported positive for further improving DD in G20 countries. Furthermore, the regression results reveal a positive impact of DD on FS and its other drivers. FS and its other components showed a positive impact on DD. FS and its drivers have a positive and bi-directional relationship with DD. Thereupon, DD also has a positive and bi-directional association with FS, AVF, ACF, STF, and UTF. 7. POLICY IMPLICATIONS The results suggested several policy proposals that can support increasing FS in G20 countries. FS cannot be attained without improving its other components. Hence, it is necessary to give priority to boosting the availability, stability, accessibility, and utilization of food. Irrigation facilities, water conservation practices, organic farming, high- yielding seeds, and appropriate technologies should be improved to increase food availability (Abdullayev et al., 2023). More creation of employment, non-farming income, and price stability of food products would be conducive to increasing food accessibility (Abdullayev et al., 2023). Thereafter, protection of arable land and sustainable yield of crops would enhance STF. Appropriate food supply management must be improved to reduce food inequality in rural and urban areas. Application of clean and green technologies, minimum quantities of fertilizer, chemicals, and pesticides in cultivation would maintain food quality and food utilization. R&D investment in this sector is necessary to make better returns (World Bank Group (WBG), 2025). Monitoring and controlling pests and insects in cultivation would be positive for increasing food utilization. Farming communities should move towards agricultural digitalization to increase FS for the present and growing population (Lioutas et al., 2021). Thereupon, FDI, level of employment, inflation, technological change, environmental technologies, and per capita CO2 emissions showed a significant impact on FS. The process of DD is determined by technological change, per capita income, FDI, and inflation. Hence, these indicators must be considered in policy implementation to enhance FS and to create DD. These countries should develop sustainable agricultural management policies to give more scope of digital technologies to increase FS. Land protection mechanisms must be promoted to Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 497 © 2025 AESS Publications. All Rights Reserved. ensure the FS of the growing population (Zou & Guo, 2015). Extensive investment in infrastructure, mobile connectivity and digital skills would cultivate a best DD (Anderson & Sandin, 2022). Use of digital technologies would enhance improvement in the food system (Wang et al., 2023). Farmers should also move towards digital agriculture to increase FS (Wang et al., 2023). 8. SIGNIFICANCE AND INNOVATION The world's countries are growing very fast. Accordingly, per capita income and social welfare also increase at unprecedented rates in most countries. Despite that, a significant share of global countries is still unable to ensure their food security (FS). The World Food Program (WFP) reported that around 45% of the population is food insecure worldwide. Additionally, chronic poverty, income inequality, corruption, black marketing, and ineffective government policies are increasing hunger, food and health insecurity, and malnutrition in many countries. Many G20 countries also have lower positions in FS and digitalization. Therefore, this study aims to attract the attention of policymakers in G20 countries to adopt effective policies to improve their standings in FS and digital development (DD). Subsequently, the variables considered can be used in further policy implications to enhance FS and DD in G20 countries. The article also provides directions for future research with specific questions. 9. LIMITATIONS AND SCOPE OF FURTHER RESEARCH Estimations of FSI and DDI are limited for selected G20 countries during 2010–2022. The statistical values of FSI and DDI may not remain the same when considering or excluding any variables or countries in their estimations. The ranking of the undertaken countries in FS and DD may not be comparable with existing studies that have estimated FSI and DDI using different indicators and methods. The findings of this research are feasible for selected G20 countries. These countries exhibit diversity in many indicators. Existing researchers, therefore, can include the above- mentioned issues in further studies. Further research can also examine the impact of digitalization on the availability, stability, accessibility, and utilization of food. Additional studies can replicate this research using similar empirical exercises for individual countries to verify its validity. There are many areas such as the food supply chain, exports and food, food quality, transportation of foodstuffs, and quality measures of food where DD has a significant contribution. Therefore, further studies can incorporate these aspects into empirical analysis. Additionally, future research can explore the role of DD in controlling food waste in countries worldwide (Mantravadi & Srai, 2023). Existing researchers can also examine the roles of globalization, foreign trade, technology transfer, intellectual property rights, exports and imports of high-tech products, financial development, and the green economy on DD and FS in G20 countries. Funding: This study received no specific financial support. Institutional Review Board Statement: Not applicable. Transparency: The authors state that the manuscript is honest, truthful, and transparent, that no key aspects of the investigation have been omitted, and that any differences from the study as planned have been clarified. This study followed all writing ethics. Competing Interests: The authors declare that they have no competing interests. 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Appendix A: Overview of regression equations. 𝑙𝑜𝑔(𝑆𝑇𝐹𝐼)𝑐𝑡 = 𝛾0 + 𝛾1 𝑙𝑜𝑔(𝐷𝐷𝐼)𝑐𝑡 + 𝛾2 𝑙𝑜𝑔(𝐷𝐸𝑅𝑇𝑃𝐴𝑇)𝑐𝑡 + 𝛾3 𝑙𝑜𝑔(𝐷𝐸𝑅𝑇𝐼𝑃𝐶)𝑐𝑡 + 𝛾4 𝑙𝑜𝑔(𝐹𝐶𝐸𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝛾5 𝑙𝑜𝑔(𝐹𝐷𝐼𝑁𝐼𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝛾6 𝑙𝑜𝑔(𝐹𝐴𝑃𝐿𝐴)𝑐𝑡 + 𝛾7 𝑙𝑜𝑔(𝐺𝐷𝑆𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝛿𝑐𝑡 (1) 𝑙𝑜𝑔(𝑈𝑇𝐹𝐼)𝑐𝑡 = 𝜉0 + 𝜉1 𝑙𝑜𝑔(𝐷𝐷𝐼)𝑐𝑡 + 𝜉2 𝑙𝑜𝑔(𝐷𝐸𝑅𝑇𝑃𝐴𝑇)𝑐𝑡 + 𝜉3 𝑙𝑜𝑔(𝐷𝐸𝑅𝑇𝐼𝑃𝐶)𝑐𝑡 + 𝜉4 𝑙𝑜𝑔(𝐹𝐶𝐸𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝜉5 𝑙𝑜𝑔(𝐹𝐷𝐼𝑁𝐼𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝜉6 𝑙𝑜𝑔(𝐹𝐴𝑃𝐿𝐴)𝑐𝑡 + 𝜉7 𝑙𝑜𝑔(𝐺𝐷𝑆𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝜆𝑐𝑡 (2) 𝑙𝑜𝑔(𝐹𝑆𝐼)𝑐𝑡 = 𝜒0 + 𝜒1 𝑙𝑜𝑔(𝐷𝐷𝐼)𝑐𝑡 + 𝜒2 𝑙𝑜𝑔(𝐷𝐸𝑅𝑇𝑃𝐴𝑇)𝑐𝑡 + 𝜒3 𝑙𝑜𝑔(𝐷𝐸𝑅𝑇𝐼𝑃𝐶)𝑐𝑡 + 𝜒4 𝑙𝑜𝑔(𝐹𝐶𝐸𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝜒5 𝑙𝑜𝑔(𝐹𝐷𝐼𝑁𝐼𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝜒6 𝑙𝑜𝑔(𝐹𝐴𝑃𝐿𝐴)𝑐𝑡 + 𝜒7 𝑙𝑜𝑔(𝐺𝐷𝑆𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝜂𝑐𝑡 (3) 𝑙𝑜𝑔(𝐷𝐷𝐼)𝑐𝑡 = 𝛹0 + 𝛹1 𝑙𝑜𝑔(𝐹𝑆𝐼)𝑐𝑡 + 𝛹2 𝑙𝑜𝑔(𝐷𝐸𝑅𝑇𝑃𝐴𝑇)𝑐𝑡 + 𝛹3 𝑙𝑜𝑔(𝐷𝐸𝑅𝑇𝐼𝑃𝐶)𝑐𝑡 + 𝛹4 𝑙𝑜𝑔(𝐹𝐶𝐸𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝛹5 𝑙𝑜𝑔(𝐹𝐷𝐼𝑁𝐼𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝛹6 𝑙𝑜𝑔(𝐹𝐴𝑃𝐿𝐴)𝑐𝑡 + 𝛹7 𝑙𝑜𝑔(𝐺𝐷𝑆𝑃𝐺𝐷𝑃)𝑐𝑡 + 𝜃𝑐𝑡 (4) 𝑙𝑜𝑔(𝐷𝐷𝐼)𝑐𝑡 = 𝜉0 + 𝜉1 𝑙𝑜𝑔(𝐴𝐶𝐹𝐼)𝑐𝑡 + £𝑐𝑡 (5) 𝑙𝑜𝑔(𝐷𝐷𝐼)𝑐𝑡 = 𝜙0 + 𝜙1 𝑙𝑜𝑔(𝐴𝑉𝐹𝐼)𝑐𝑡 + ¥𝑐𝑡 (6) 𝑙𝑜𝑔(𝐷𝐷𝐼)𝑐𝑡 = 𝛺0 + 𝛺1 𝑙𝑜𝑔(𝑆𝑇𝐹)𝑐𝑡 + §𝑐𝑡 (7) 𝑙𝑜𝑔(𝐷𝐷𝐼)𝑐𝑡 = 𝜑0 + 𝜑1 𝑙𝑜𝑔(𝑈𝑇𝐹)𝑐𝑡 + 𝛹𝑐𝑡 (8) Here, log is natural logarithm of corresponding DVs and IVs; c is cross country; t is time; γ0, ξ0, χ0, 𝛹0, ξ0, ϕ0, 𝛺0, and φ0 are constant-term; δct, 𝜆ct, 𝜂ct, θct, £ct, ¥ct, §ct and Ψct are error-term; γ0..., ξ0..., χ0..., 𝛹0..., ξ1, ϕ1, 𝛺1, and φ1 are the unknown parameter of associated variables that are estimated through regression analysis in the above-mentioned equations (1), (2), ...., (8), respectively. The detailed description of all IVs and DVs is also provided in Table 4. Appendix B. Impact of DD on FS and its other dimensions. Table B1. Impact of DD on accessibility of food (ACF). Number of obs. 819 Number of groups 63 R-squared 0.6156 Wald Chi2 5214.32 log(ACFI)=[DV] Coef. Std. Err. z P>|z| [95% Conf. Interval] log(DDI) 0.0957 0.0238 4.020 0.000 0.0490 0.1424 log(DERTPAT) 0.0247 0.0066 3.710 0.000 0.0116 0.0377 log(DERTIPC) 0.0705 0.0060 11.800 0.000 0.0588 0.0823 log(FCEPGDP) -0.0620 0.0554 -1.120 0.263 -0.1705 0.0466 log(FDINIPGDP) 0.0302 0.0054 5.590 0.000 0.0196 0.0408 log(FAPLA) 0.0042 0.0053 0.790 0.430 -0.0063 0.0147 log(GDSPGDP) 0.0679 0.0136 5.000 0.000 0.0413 0.0945 Constant Coefficient -0.8869 0.3008 -2.950 0.003 -1.4765 -0.2972 Table B2. Impact of DD on availability of food (AVF). Number of obs. 819 Number of groups 63 R-squared 0.1154 Wald Chi2 200.02 log(AVFI)=[DV] Coef. Std. Err. z P>|z| [95% Conf. Interval] log(DDI) 0.1790 0.0261 6.870 0.000 0.1280 0.2301 log(DERTPAT) 0.0060 0.0129 0.460 0.645 -0.0194 0.0313 log(DERTIPC) 0.0003 0.0099 0.030 0.978 -0.0191 0.0196 log(FCEPGDP) 0.3567 0.0588 6.060 0.000 0.2414 0.4720 log(FDINIPGDP) -0.0120 0.0072 -1.670 0.096 -0.0262 0.0021 log(FAPLA) 0.0031 0.0110 0.280 0.778 -0.0184 0.0246 log(GDSPGDP) 0.0680 0.0200 3.400 0.001 0.0287 0.1072 Constant Coefficient -2.5969 0.2976 -8.730 0.000 -3.1802 -2.0137 Table B3. Impact of DD on stability of food (STF). Number of obs. 819 Number of groups 63 R-squared 0.0958 Wald Chi2 353.84 log(STFI)=[DV] Coef. Std. Err. z P>|z| [95% Conf. Interval] log(DDI) 0.0152 0.0075 2.010 0.044 0.000 0.030 log(DERTPAT) 0.0120 0.0046 2.600 0.009 0.003 0.021 log(DERTIPC) -0.0049* 0.0018 -2.740 0.006 -0.008 -0.001 log(FCEPGDP) 0.0024 0.0215 0.110 0.911 -0.040 0.045 log(FDINIPGDP) -0.0129 0.0032 -4.080 0.000 -0.019 -0.007 log(FAPLA) 0.0111 0.0015 7.310 0.000 0.008 0.014 log(GDSPGDP) 0.0298 0.0086 3.450 0.001 0.013 0.047 Constant Coefficient -0.8600 0.1113 -7.730 0.000 -1.078 -0.642 Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 500 © 2025 AESS Publications. All Rights Reserved. Table B4. Impact of DD on utilization of food (UTF). Number of obs. 819 Number of groups 63 R-squared 0.741 Wald Chi2 5932.05 log(UTFI)=[DV] Coef. Std. Err. z P>|z| [95% Conf. Interval] log(DDI) 0.3988 0.0190 20.940 0.000 0.3615 0.4362 log(DERTPAT) 0.0411 0.0127 3.230 0.001 0.0162 0.0661 log(DERTIPC) 0.0225 0.0026 8.730 0.000 0.0175 0.0276 log(FCEPGDP) -0.0321 0.0358 -0.900 0.370 -0.1022 0.0381 log(FDINIPGDP) 0.0035 0.0034 1.040 0.298 -0.0031 0.0102 log(FAPLA) -0.0103 0.0024 -4.200 0.000 -0.0150 -0.0055 log(GDSPGDP) 0.0105 0.0133 0.800 0.427 -0.0155 0.0365 Constant Coefficient -0.2316 0.2000 -1.160 0.247 -0.6236 0.1605 Table B5. Impact of DD on food security (FS). Number of obs. 819 Number of groups 63 R-squared 0.6888 Wald Chi2 8082.29 log(FSI)=[DV] Coef. Std. Err. z P>|z| [95% Conf. Interval] log(DDI) 0.1661 0.0098 16.880 0.000 0.1468 0.1854 log(DERTPAT) 0.0223 0.0064 3.480 0.000 0.0098 0.0349 log(DERTIPC) 0.0218 0.0029 7.450 0.000 0.0161 0.0276 log(FCEPGDP) 0.0496 0.0263 1.890 0.059 -0.0019 0.1011 log(FDINIPGDP) 0.0038 0.0023 1.660 0.096 -0.0007 0.0082 log(FAPLA) -0.0021 0.0027 -0.780 0.435 -0.0073 0.0031 log(GDSPGDP) 0.0424 0.0073 5.790 0.000 0.0281 0.0567 Constant Coefficient -1.0381 0.1407 -7.380 0.000 -1.3139 -0.7623 Appendix C. Impact of FS and its dimensions on DD. Table C1. Impact of food security on DD. Number of obs. 819 Number of groups 63 R-squared 0.6895 Wald Chi2 3230.02 log(DDI)=[DV] Coef. Std. Err. z P>|z| [95% Conf. Interval] log(FSI) 1.4627 0.0820 17.840 0.000 1.3020 1.6234 log(DERTPAT) 0.0921 0.0161 5.730 0.000 0.0606 0.1235 log(DERTIPC) 0.0639 0.0052 12.370 0.000 0.0538 0.0740 log(FCEPGDP) -0.2799 0.0710 -3.940 0.000 -0.4190 -0.1408 log(FDINIPGDP) 0.0007 0.0061 0.110 0.911 -0.0113 0.0126 log(FAPLA) -0.0189 0.0044 -4.320 0.000 -0.0275 -0.0103 log(GDSPGDP) 0.0126 0.0239 0.530 0.598 -0.0343 0.0595 Constant Coefficient 1.1689 0.3687 3.170 0.002 0.4463 1.8916 Table C2. Impact of accessibility of food on DD. Number of obs. 819 Number of groups 63 R-squared 0.4031 Wald Chi2 1229.58 log(DDI)=[DV] Coef. Std. Err. z P>|z| [95% Conf. Interval] log(ACFI) 1.0723 0.0306 35.070 0.000 1.0124 1.1323 Constant Coefficient -0.0408 0.0286 -1.420 0.154 -0.0969 0.0153 Table C3. Impact of availability of food on DD. Number of obs. 819 Number of groups 63 R-squared 0.0906 Wald Chi2 24.8 log(DDI)=[DV] Coef. Std. Err. z P>|z| [95% Conf. Interval] log(AVFI) 0.5282 0.1061 4.980 0.000 0.3204 0.7361 Constant Coefficient -0.4927 0.1095 -4.500 0.000 -0.7074 -0.2780 Asian Journal of Agriculture and Rural Development, 15(3) 2025: 478-501 501 © 2025 AESS Publications. All Rights Reserved. Table C4. Impact of stability of food on DD. Number of obs. 819 Number of groups 63 R-squared 0.0188 Wald Chi2 72.21 log(DDI)=[DV] Coef. Std. Err. z P>|z| [95% Conf. Interval] log(STFI) 0.6074 0.0715 8.500 0.000 0.4673 0.7475 Constant Coefficient -0.5849 0.0519 -11.280 0.000 -0.6866 -0.4833 Table C5. Impact of utilization of food on DD. Number of obs. 819 Number of groups 63 R-squared 0.7165 Wald Chi2 5234.68 log(DDI)=DV Coef. Std. Err. z P>|z| [95% Conf. Interval] log(UTFI) 1.4166 0.0196 72.350 0.000 1.3782 1.4549 Constant Coefficient -0.0662 0.0155 -4.270 0.000 -0.0966 -0.0358 Views and opinions expressed in this article are the views and opinions of the author(s), Asian Journal of Agriculture and Rural Development shall not be responsible or answerable for any loss, damage or liability etc. caused in relation to/arising out of the use of the content.