{"id":156610,"date":"2025-09-20T12:59:28","date_gmt":"2025-09-20T12:59:28","guid":{"rendered":"https:\/\/www.newsbeep.com\/au\/156610\/"},"modified":"2025-09-20T12:59:28","modified_gmt":"2025-09-20T12:59:28","slug":"investigating-the-impact-of-the-internet-on-managing-green-financial-innovation-and-improving-agricultural-conditions-in-water-scarce-asian-regions-using-ann-modeling","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/au\/156610\/","title":{"rendered":"Investigating the impact of the internet on managing green financial innovation and improving agricultural conditions in water-scarce Asian regions using ANN modeling"},"content":{"rendered":"

Green process innovation entails enhancing existing processes and developing new ones with the objective of recycling, reusing, and reproducing raw materials while reducing energy consumption and environmental pollution. It focuses on improving the efficiency and sustainability of internal organizational processes. Green process innovation involves implementing methods that facilitate resource conservation, waste reduction, and the adoption of environmentally friendly practices35<\/a>,36<\/a>,37<\/a>,38<\/a>,39<\/a>,40<\/a>,41<\/a>,42<\/a>,43<\/a>,44<\/a>,45<\/a>,46<\/a>,47<\/a>,48<\/a>,49<\/a>,50<\/a>,51<\/a>,52<\/a>,53<\/a>,54<\/a>,55<\/a>. In this study, a Shallow Progressive Artificial Neural Network (SPANN) was employed to predict changes in productivity within the domains of technology development, agricultural development, and support services for AGRI. The SPANN model was applied to 15 test cases, encompassing a wide range of management strategies, green innovation, and sustainability factors spanning from 0 to 70%. The neural network architecture consisted of an input layer incorporating management strategies and green innovation inputs, a hidden layer comprising 5 neurons, and an output layer representing the productivity outputs for technology development, agricultural development, and support services for AGRI. The activation function employed in the SPANN model was the nonlinear sigmoid function, known for its ability to enhance prediction accuracy and expedite network convergence due to its nonlinear nature. The optimization of the error function was achieved using the gradient descent algorithm, enabling training and estimation of results at each stage of network progression. To further enhance accuracy and convergence, the input data from Table 1<\/a> was initially normalized, and after final estimation of results, denormalization was applied to ensure reporting within an acceptable range. The accuracy of the SPANN model in predicting results was assessed by determining the network error through linear regression analysis. This involved normalizing the predicted results and creating a fit chart to compare the fitted graph, obtained through linear regression, with the ideal y\u2009=\u2009x graph representing 100% accurate estimation based on input targets from Table 1<\/a>. The deviation between the fitted graph and the y\u2009=\u2009x graph was used to quantify the error of the formed ANN. In the subsequent sections, the outcomes of the ANN formed in this study will be thoroughly examined and analyzed.<\/p>\n

Table 1 Relationship between input variables and productivity in technology development, agricultural development, and support services for AGRI35<\/a>,36<\/a>,37<\/a>,38<\/a>,39<\/a>,40<\/a>,41<\/a>,42<\/a>,43<\/a>,44<\/a>,45<\/a>,46<\/a>,47<\/a>,48<\/a>,49<\/a>,50<\/a>,51<\/a>,52<\/a>,53<\/a>,54<\/a>,55<\/a>,56<\/a>,57<\/a>,58<\/a>,59<\/a>,60<\/a>,61<\/a>,62<\/a>,63<\/a>,64<\/a>,65<\/a>,66<\/a>,67<\/a>,68<\/a>,69<\/a>,70<\/a>,71<\/a>,72<\/a>,73<\/a>,74<\/a>,75<\/a>,76<\/a>,77<\/a>,78<\/a>,79<\/a>,80<\/a>,81<\/a>,82<\/a>,83<\/a>,84<\/a>.<\/p>\n

This research article aimed to investigate the impact of Internet discovery and green finance innovation management on improving agricultural conditions for water-scarce Asian farmers. The study employed a combination of literature review and ANN modeling techniques. Through an extensive literature review of studies published between references35<\/a>,36<\/a>,37<\/a>,38<\/a>,39<\/a>,40<\/a>,41<\/a>,42<\/a>,43<\/a>,44<\/a>,45<\/a>,46<\/a>,47<\/a>,48<\/a>,49<\/a>,50<\/a>,51<\/a>,52<\/a>,53<\/a>,54<\/a>,55<\/a>,56<\/a>,57<\/a>,58<\/a>,59<\/a>,60<\/a>,61<\/a>,62<\/a>,63<\/a>,64<\/a>,65<\/a>,66<\/a>,67<\/a>,68<\/a>,69<\/a>,70<\/a>,71<\/a>,72<\/a>,73<\/a>,74<\/a>,75<\/a>,76<\/a>,77<\/a>,78<\/a>,79<\/a>,80<\/a>,81<\/a>,82<\/a>,83<\/a>,84<\/a>, relevant input and output variables were identified. Two key input variables, management strategies and green innovation and sustainability, were selected, along with three output variables: productivity of technology development, agricultural development, and support services for AGRI. Data for these variables were collected from various sources and carefully prepared for analysis. An ANN model was developed with appropriate architecture, incorporating the input and output variables. The model was trained and optimized using collected data, with the aim of predicting and optimizing the best conditions for improving agricultural outcomes. The model’s performance was evaluated using metrics such as accuracy, precision, and error rates. The results obtained from the trained ANN model were analyzed and interpreted to gain insights into the impact of Internet discovery and green finance innovation management on agricultural conditions. Limitations of the study were acknowledged, and suggestions for future research directions were provided. This research contributes to the understanding of the topic and offers valuable insights for future research and practical applications in improving agricultural conditions for water-scarce Asian farmers. To ensure the achievement of government goals in each country, comprehensive reforms are required in the agricultural, market, environmental, social, and cultural technical sectors. Without these reforms, innovation will not be sustainable. In an appropriate political environment with established rules and regulations, five key organizations need to be actively and dynamically engaged in continuous communication to facilitate the deployment of innovation: Research organizations: This includes universities, private research centers, scientific associations, national and international research centers. These organizations play a crucial role in conducting research, developing new technologies, and generating knowledge that drives innovation. Supporting organizations: This category comprises the banking and financial system, marketing and transportation structures, producer unions, marketing cooperatives, and educational and promotional systems. These organizations provide the necessary support, resources, and infrastructure for the implementation and adoption of innovative practices. Organizations involved in the supply and demand of agricultural products: This encompasses consumers and producers of agricultural products, international agricultural product markets, policy and decision-making processes, and government agencies. These organizations are responsible for facilitating efficient market dynamics, ensuring fair trade practices, and creating an enabling environment for innovation. Labor organizations: This includes farmers, cooperatives, producer unions in the agricultural transformation and processing industries, enterprises and companies involved in the distribution and supply of agricultural inputs and equipment, as well as transporters and distributors of agricultural products and exporters (see Fig.\u00a05<\/a>).<\/p>\n

Fig. 5\"figure<\/a><\/p>\n

Policy making and planning in the field of innovation.<\/p>\n

These organizations represent the workforce involved in the agricultural sector and drive the implementation of innovative practices on the ground. Intermediary organizations: this category involves advisory and promotional services provided by agricultural associations, development companies, market and production unions. These organizations act as intermediaries, providing guidance, support, and dissemination of information to facilitate the adoption of innovative technologies and practices by farmers and other stakeholders. The continuous and collaborative activity of these five organizations leads to the development of new capacities for innovation and amplifies the economic, social, and environmental significance of innovations and new technologies85<\/a>,86<\/a>,87<\/a>,88<\/a>,89<\/a>,90<\/a>,91<\/a>,92<\/a>,93<\/a>. In developing countries, the agricultural sector serves as the main driver of economic growth and development. These countries have turned to their agricultural sectors to overcome development challenges. While striving to expand agricultural production, they recognize the importance of integrating advanced technologies to achieve high productivity. However, developing countries face various obstacles such as urbanization, subsistence economies in rural areas, a weak scientific production base in agriculture, inadequate distribution networks, lack of complementary industries, low efficiency, and productivity in the agricultural sector. These factors have widened the income gap between urban and rural areas and led to significant migration. In such countries, improving agricultural activities, enhancing production productivity, and reducing the income disparity between rural and urban areas become the primary objectives. Governments often provide direct subsidies to investors in the agricultural sector to promote the adoption of new technologies, aiming to enhance productivity, reduce production costs, mitigate risks, and increase performance at the individual unit level71<\/a>,72<\/a>,73<\/a>.<\/p>\n

As mentioned in the preceding sections, the prediction of productivity changes in technology development, agricultural development, and support services for AGRI was undertaken. The developer constructed a progressive neural network based on Table 1<\/a>, incorporating augmented management strategies, green innovation, and sustainability. The analysis encompassed a range of 0\u201370% for both management strategies and green innovation and sustainability. Figure\u00a06<\/a> shows the neural network’s predicted outcomes for the productivity reduction in technology development. It demonstrates that the productivity of technology development fluctuates, at times increasing and at times decreasing, with an overall oscillatory trend. However, there is a general upward trajectory, primarily attributed to the augmentation of management strategies.<\/p>\n

Fig. 6\"figure<\/a><\/p>\n

Results of the ANN for predicting the productivity of technology development.<\/p>\n

Figure\u00a07<\/a> illustrates the estimated results obtained by the neural network for agricultural development. It is evident that an increase in green innovation and sustainability leads to a reduction in agricultural development. Moreover, the process of agricultural development fluctuates with varying management strategies. Specifically, agricultural development declines with management strategies up to 40%, but beyond that threshold, it experiences rapid growth.<\/p>\n

Fig. 7\"figure<\/a><\/p>\n

Results of the ANN for predicting agricultural development in this study.<\/p>\n

Furthermore, Fig.\u00a08<\/a> shows the neural network’s estimated outcomes for support services in the agricultural sector. Throughout the entire process, support services for AGRI show a cosine exponential pattern. Increasing green innovation and sustainability results in fluctuating support services for Agri, with an overall declining trend. Similarly, the augmentation of management strategies leads to a reduction in support services for AGRI. Up to a 50% threshold of management strategies, the support services for Agri remain positive, but thereafter, they observe a declining trend (see Fig.\u00a09<\/a>).<\/p>\n

Fig. 8\"figure<\/a><\/p>\n

Results of the ANN for predicting support services for Agri in this study.<\/p>\n

Fig. 9\"figure<\/a><\/p>\n

Linear regression plots analyzing the error of the ANN formed in this study.<\/p>\n

for predicting the productivity of technology development, agricultural development, and support services for AGRI. The results obtained from linear regression analysis, as depicted in Fig.\u00a06<\/a>, show that the ANN achieved a high degree of accuracy, with an error of less than 1% compared to the targets specified in Table 1<\/a>, in predicting the productivity of technology development, agricultural development, and support services for AGRI. By implementing green and sustainable management strategies and innovation, the productivity of technology development, agricultural development, and support services for AGRI experienced enhancement. The highest state of productivity in technology development was observed when green innovation and sustainability were at approximately 40% and management strategies were around 30%. Similarly, for agricultural development and support services for Agri, their maximum states were attained when both management strategies and green innovation and sustainability were at their lowest levels.<\/p>\n

Types of innovations<\/p>\n

In many developing countries, considering the existing limitations in the agricultural sector, two types of innovation should be prioritized in agricultural sector programs and policies: technologies that enhance productivity and management of production: this includes technologies related to crop nutrition, pest and disease control, water resources management, water consumption optimization, restructuring of livestock and poultry farms, harvesting techniques, packaging, storage, grading, and intelligent management of production units. Technologies that increase production value and promote the market: this encompasses technologies in branding, advertising, product marketing, market information systems, knowledge of national and international market environments, virtual markets, agricultural products stock market, price transparency, and appropriate support policies aligned with production conditions. These innovations aim to improve productivity, efficiency, and marketability within the agricultural sector94<\/a>,95<\/a>,96<\/a>,97<\/a>,98<\/a>,99<\/a>,100<\/a>.<\/p>\n

Challenges in implementing agricultural technologies<\/p>\n

To effectively implement and deploy technologies in the agricultural sector and achieve significant transformation, several problems and limitations need to be addressed through appropriate policies in the government and public sector95<\/a>,96<\/a>,97<\/a>,98<\/a>,99<\/a>,100<\/a>,101<\/a>. Key challenges associated with innovation in agriculture include: insufficient participation and collaboration between the public and private sectors in developing new technologies; bureaucratic hurdles and inefficiencies within the public sector; inadequate reward structures for top technicians and experts; lack of business culture and limited commercial experience within the government; low confidence of the private sector to invest in agricultural technologies due to economic instability, weather-related risks, and uncertain income; intellectual property issues when multiple public and private entities are involved; weak negotiation skills and limited capacity of the public sector to establish strong relationships with private sector investors and technologists; lack of economic stability in technology contracts and compensation; and fluctuations in costs and cost pricing of technologies coupled with aimless budgeting by the government without considering the priority of technology deployment in the agricultural sector91<\/a>,92<\/a>,93<\/a>,94<\/a>,95<\/a>.<\/p>\n

Important success factors in implementing innovations in the agricultural sector<\/p>\n

Long-term commitment from the government and industry, supported by joint protocols with the private sector and technologists; access to important resources within the national innovation system at national, regional, and local levels, while bringing together innovators from various sectors; creation of a network of innovators that encompasses all sectors; and conducting multidisciplinary research to foster comprehensive innovation. Allocation of a portion of the government’s public budget to support innovative activities in agriculture by the private sector, along with effective management and targeting of financial resources, can also encourage innovation. The experience of leading Asian countries has demonstrated that policymakers’ attention to these factors and strategic planning can expedite the establishment of innovation. The government plays four critical roles in supporting the innovation system in the agricultural sector: public mobilization to promote innovation, enhance the quality of agricultural products, and reduce government costs; implementation of innovations in the market, leveraging the capabilities of the private and public sectors, as well as research and development companies; structural support for research and development enterprises and innovations to enhance the knowledge and technology infrastructure; and provision of subsidies and financial support to innovators, facilitating the presence of new technologies in the market and promoting mass production. To drive transformation in the agricultural sector, there should be direct collaboration between the private and public sectors, where the private sector contributes through innovation, mass production, advertising, sales, social responsibility, and technology dissemination, while the public sector supports development enterprises, provides financial backing for innovation, and facilitates its dissemination.<\/p>\n

Smart farming<\/p>\n

In the realm of agricultural transformation, a significant focus is placed on digital transformation in agriculture by adopting next-generation technologies such as the Internet of Things (IoTs), cloud computing, big data analysis, and artificial intelligence. Planners, policymakers, farmers, and agriculture-related businesses should swiftly embrace digital transformation. The future of agriculture lies in digital technologies, which can bring stability and sustainability. Smart agriculture incorporates cutting-edge technologies to enhance sustainable productivity. It leverages big data, digital technologies, and data analysis for informed decision-making. Smart technologies should be tailored to the specific agricultural conditions of each country. The adoption of digital technologies correlates with a country’s stage of economic development and digital readiness102<\/a>,103<\/a>,104<\/a>,105<\/a>,106<\/a>,107<\/a>,108<\/a>,109<\/a>,110<\/a>,111<\/a>. To boost productivity in the agricultural sector and food production, governments, consulting agencies, agricultural activists, and food processing companies need to implement policies and make decisions that utilize the potential of new technologies, including digital technologies, throughout the agricultural product chains. Improving productivity in agriculture requires not only physical investment but also the development of science, technology, human capital, and social capital. Policies and institutional frameworks must be established to encourage private sector investment, particularly by farmers. Investment in equipment necessitates measures that enhance market access, ensure information flow, establish standards, and enact necessary laws and regulations. Stable policymaking and well-organized institutions are crucial to attracting private investment in the agricultural sector, and a strong correlation between government and private investment is observed to achieve agricultural growth112<\/a>,113<\/a>,114<\/a>.<\/p>\n

Digital readiness and smart agriculture in Asian countries<\/p>\n

Table 2<\/a> shows the digitization readiness scores for different countries. Based on the Table 2<\/a>, countries at the highest stage of digital readiness (strengthening) such as South Korea, Japan, Singapore, and Malaysia have an average score of 16.83. Countries in the middle stage of digital readiness (acceleration) like India, Sri Lanka, Indonesia, Philippines, and Thailand have an average score of 12.49. On the other hand, countries at the lowest stage of digital readiness (activation) including Bangladesh, Pakistan, Cambodia, and Nepal have an average score of 7.91101<\/a>,102<\/a>,103<\/a>,104<\/a>,105<\/a>,106<\/a>,107<\/a>,108<\/a>,109<\/a>.<\/p>\n

Table 2 Digitization readiness score of some Asian countries.<\/p>\n

South Korea has demonstrated significant progress in the agricultural sector through various initiatives and policy changes. Some key factors contributing to this success include: changing attitudes and policies: there has been a shift in people’s attitudes towards the rural sector, with increased awareness of the multifunctional and multipurpose nature of agricultural activities. The focus has shifted from subsistence farming to production for the market and consumer preferences. The increased interactions between rural and urban areas have led to urban residents benefiting from rural development policies102<\/a>,103<\/a>,104<\/a>,105<\/a>,106<\/a>,107<\/a>,108<\/a>,109<\/a>,110<\/a>,111<\/a>. Establishment of new laws: the introduction of new laws, such as the 2000 law for rural policy reform and improvement, and the 2004 special law providing a framework for public and private sector investment planning, have played a crucial role. These laws have focused on comprehensive rural development and investment programs, prioritizing issues such as healthcare, education, research promotion, environmental preservation, and sustainability. The increased interactions between urban and rural areas have led to significant investments in these fields112<\/a>,113<\/a>,114<\/a>. Policy agenda: South Korea has implemented a five-year development plan (2018\u20132022) for rural community agriculture and the food industry. The plan aims to strengthen the network for income security in rural areas, improve rural welfare, promote sustainable agriculture, and enhance interactions between urban and rural areas114<\/a>. Regarding the methodology of this research, it falls under the field type in terms of data collection and non-experimental type in terms of variable control. The research is considered practical in terms of its purpose as the results and suggested mechanisms can be immediately applied in the organization of the agricultural technology research and development system. The research employed a single-section survey method and followed a quantitative research paradigm. The data collection tool was a researcher-made data based on a systematic review of previous research and reports35<\/a>,36<\/a>,37<\/a>,38<\/a>,39<\/a>,40<\/a>,41<\/a>,42<\/a>,43<\/a>,44<\/a>,45<\/a>,46<\/a>,47<\/a>,48<\/a>,49<\/a>,50<\/a>,51<\/a>,52<\/a>,53<\/a>,54<\/a>,55<\/a>,56<\/a>,57<\/a>,58<\/a>,59<\/a>,60<\/a>,61<\/a>,62<\/a>,63<\/a>,64<\/a>,65<\/a>,66<\/a>,67<\/a>,68<\/a>,69<\/a>,70<\/a>,71<\/a>,72<\/a>,73<\/a>,74<\/a>,75<\/a>,76<\/a>,77<\/a>,78<\/a>,79<\/a>,80<\/a>,81<\/a>,82<\/a>,83<\/a>,84<\/a>, as well as input from agricultural experts (see Table 3<\/a>). The data covered three levels: (1) development of the agricultural sector (15 items), (2) research and technology in the national innovation system (30 items), and (3) research and technology in the agricultural sector (26 items). The validity of the data was assessed by obtaining feedback from academic staff members and researchers with agricultural experience, and revisions were made accordingly. The reliability of the data was assessed using Cronbach’s alpha coefficient, which yielded values of 0.78, 0.70, and 0.82 for the three parts of the data, respectively. The statistical population of this research included academic faculty members and agricultural researchers. The sample size was determined using Cochran’s formula, resulting in a sample size of 188 people for researchers and 205 people for faculty members.<\/p>\n

Table 3 It compares the readiness of digitization in agriculture in Asian countries.<\/p>\n

$$n=\\frac{{N(t.s)}^{2}}{{Nd}^{2}+{(t.s)}^{2}}$$<\/p>\n

In order to ensure an appropriate sample size and gather data through data, a multi-stage sampling method was employed. The sampling process involved several stages. In the first stage, units were randomly selected from a prepared list that included agricultural research centers, institutes affiliated with the Agricultural Research and Education Organization, and agricultural colleges and higher education centers (faculties and universities). Within these selected units, faculty members and researchers were randomly chosen for participation, ensuring optimal representation115<\/a>,116<\/a>,117<\/a>,118<\/a>. The selected participants then completed the data. Once the data were collected, data analysis was performed using SPSS software. The values n, s, N, d, and t are commonly used in statistical calculations. “n” represents the sample size, “s” denotes the standard deviation, “N” refers to the population size, “d” signifies the desired precision, and “t” corresponds to the critical value in a confidence interval calculation (1.96 for a 95% confidence interval).<\/p>\n

This research aimed to investigate the state of agricultural research and technology within the agricultural innovation system in Asia. It identified key challenges and proposed mechanisms to enhance this system and contribute to the sustainable development of the agricultural sector.Through factor analysis, three factors related to the development of the agricultural sector were extracted. The first factor, named “Structure and Policy of Agricultural Development,” highlighted the importance of resources, infrastructure, and policies for sustainable agricultural development. The second factor, “Agricultural Development Resources and Infrastructures,” emphasized the crucial role of access to and proper utilization of resources and infrastructures for effective research and technological development119<\/a>,120<\/a>,121<\/a>,122<\/a>,123<\/a>,124<\/a>,125<\/a>,126<\/a>,127<\/a>,128<\/a>,129<\/a>,130<\/a>,131<\/a>,132<\/a>. The third factor, “Agricultural Development Support Services,” underscored the significance of support services in facilitating research and technology in the agricultural sector. Factor analysis of challenges and issues within the national innovation system yielded five factors: “Capacities and Investment in Research and Technology,” “Research and Technology Management,” “Productivity of Research and Technology Development,” “Research Culture,” and “Networking in Research and Technology.” In terms of challenges and issues within the agricultural innovation system, five factors were identified: “Agricultural Research Policy,” “Usefulness and Effectiveness of Agricultural Research and Technology,” “Integrated Management of Agricultural Research and Technology,” “Institution of Agricultural Research and Technology System,” and “Integration of Higher Education and Agricultural Research.”<\/p>\n

Many research explores various factors that can impact the livelihoods and sustainability of rural communities, particularly those in water-scarce regions of Asia133<\/a>. The studies examine the effects of health poverty alleviation initiatives, changes in agricultural labor productivity, the role of digital platform ecosystems, spatial networks and factor flows within ethnic regions, and the impact of digital transformation on enterprise-level environmental and economic outcomes134<\/a>,135<\/a>,136<\/a>,137<\/a>,138<\/a>. Many key findings highlight the importance of taking an integrated, data-driven approach to addressing the multifaceted challenges faced by rural populations139<\/a>,140<\/a>,141<\/a>,142<\/a>,143<\/a>. The research provides insights that can inform the design and implementation of policies, programs, and technological solutions to improve financial risk protection, agricultural productivity, digital platform management, regional spatial dynamics, and the leveraging of digital and green innovations for sustainable development in water-scarce agricultural areas144<\/a>,145<\/a>,146<\/a>. Compilation of a strategic document for sustainable agricultural sector development, focusing on integrated management, natural resources, value addition, competition, entrepreneurship, and knowledge-based agricultural businesses. Designing and establishing a comprehensive support system in the agricultural sector that provides technical and promotional services, insurance, market access, credit facilities, and transportation, using the capacities of the private sector, cooperatives, and trade unions147<\/a>,148<\/a>,149<\/a>. Implementation of a comprehensive scientific and technological roadmap in the agricultural sector, aligned with agricultural development policies and addressing the knowledge and technological needs of different agricultural sub-sectors. Development and communication of a research program or agenda in the agricultural sector based on needs assessment, prioritizing research topics, and incorporating the real needs of farmers in different regions. Facilitating private sector investment in agricultural research and technology through tax incentives and encouraging agricultural industries to allocate a percentage of their budget or profit to research and technology. Promotion of joint utilization of agricultural research and technology infrastructures and capacities through the establishment of a national agricultural research and laboratory network. Formation of national and provincial-level councils and committees for policy-making, coordination, and guidance of agricultural research and technology. Strengthening collaborations between researchers, agricultural research institutes, higher education institutions, and regional\/international agricultural research centers through research contracts, joint investments, technology transfer, and study opportunities. Designing mechanisms, incentives, and legal frameworks for the delivery and commercialization of agricultural research findings to promote technological entrepreneurship and academic entrepreneurship in the agricultural sector.<\/p>\n","protected":false},"excerpt":{"rendered":"Green process innovation entails enhancing existing processes and developing new ones with the objective of recycling, reusing, and…\n","protected":false},"author":2,"featured_media":156611,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18],"tags":[104422,6634,64,63,104421,5443,104420,26934,44009,104419,20088,1320,237,104418,1321,128,105],"class_list":{"0":"post-156610","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-internet","8":"tag-agricultural-development-infrastructures","9":"tag-agriculture","10":"tag-au","11":"tag-australia","12":"tag-digital-agriculture","13":"tag-engineering","14":"tag-environmental-destruction","15":"tag-environmental-sciences","16":"tag-environmental-social-sciences","17":"tag-environmentally-friendly-innovations","18":"tag-green-innovation","19":"tag-humanities-and-social-sciences","20":"tag-internet","21":"tag-management-strategies","22":"tag-multidisciplinary","23":"tag-science","24":"tag-technology"},"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts\/156610","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/comments?post=156610"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts\/156610\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/media\/156611"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/media?parent=156610"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/categories?post=156610"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/tags?post=156610"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}