Green agricultural development is one of the prominent modern trends aimed at achieving a balance between economic growth, environmental protection, and social progress. As countries move towards sustainable development, the concept of green agriculture emerges as an effective solution to the challenges facing agricultural sectors, especially in densely populated countries like China. This article presents an in-depth study aimed at evaluating the efficiency of green agricultural development in specific areas of the Yangtze River Delta, using advanced analytical models. Researchers will integrate the enhanced DEA-SBM model with the BP algorithm to develop an advanced model capable of providing more accurate predictions regarding the efficiency of agricultural development. The study will also address the economic and social factors influencing this efficiency, contributing to the formulation of more effective policies to support sustainable agriculture in these vital areas. Through this research, the challenges and opportunities facing green agriculture will be clarified, contributing to the overall vision of achieving sustainable agricultural development in the future.
Green Agricultural Development: Concept and Importance
Green agricultural development (AGD) is a modern concept that has been gaining attention in recent years. This concept is based on achieving a balance between economic growth, environmental protection, and social progress. Achieving the sustainable goals of the agricultural world requires a deep understanding of three dimensions: economic, environmental, and social. The success of these dimensions contributes to enhancing agriculture’s capacity to meet global food needs while minimizing adverse environmental impacts.
The traditional agricultural model in China, which heavily relies on chemical fertilizers and pesticides, is based on high productivity standards but has not always been sustainable. For example, these practices have led to soil degradation and water pollution. Therefore, transitions towards green agriculture are essential not only for preserving the environment but also for ensuring food security. The importance of green agriculture lies in defining a responsible development path that considers the impact of agricultural activities on the environment, thereby enhancing the quality of life for farmers and their families.
Green agriculture is considered an essential part of the sustainable development strategy that seeks to narrow the gap between agriculture and industry. For example, modern applications of technology in agriculture, such as precision farming and genetically modified crops, represent an important step towards improving economic performance and reducing environmental risks. Green agriculture enhances crop productivity, which may decline due to climate change, as well as strengthens the resilience of the agricultural system and its ability to adapt to changing conditions.
DEA-SBM-BP Model: Innovation in Evaluation Techniques
The DEA-SBM-BP model for super efficiency is a qualitative leap towards improving and analyzing the performance of green agricultural development. This model combines performance evaluation methods with artificial intelligence techniques. The DEA (Dynamic Environment Analysis) model provides a precise way to measure efficiency while being enhanced using the BP (Back Propagation) algorithm to provide more accurate predictions for future performance evaluations.
The analysis results indicate that agricultural performance in the Yangtze River Delta region is characterized by relative balance at the general level, despite significant variations between different cities. The efficiency of green agriculture depends on multiple metrics, including labor quality, macroeconomic conditions, and political support. Labor quality is one of the critical factors, significantly affecting how modern agricultural methods and sustainability techniques are applied.
The new model reduces the negative influences of external factors, making it an effective tool for objective measurement of AGD performance. By predicting future trends, decision-makers can obtain valuable information to help them provide appropriate policies that enhance the transition towards green agriculture. For example, if effective policies are adopted to support research and development in the field of green agriculture, this could lead to improved productivity and reduced carbon emissions.
Challenges
Opportunities in Green Agricultural Development
Green agricultural development faces a range of challenges that hinder its progress. Among these challenges are regional gaps in resources, technological infrastructure, and government support. The capacities differ between urban and rural areas, which exacerbates the economic and social gap. For example, the Yangtze River Delta represents a notable model for investment in modern technologies, while other regions struggle with a lack of funding and technology.
However, recent developments in the digital economy and industrial integration open new horizons for enhancing green agriculture. The use of big data in precision agriculture is an example of how to improve efficiency and reduce costs. Modern technologies like drones and crop monitoring are effective tools for achieving green agriculture aspirations.
Achieving a balance between production and environmental services is also an important opportunity for promoting sustainable development. Farmers who rely on sustainable agricultural practices can enhance environmental returns by protecting natural resources and improving product quality. By adopting effective strategies like resource recycling and using ecological farming techniques, farmers can boost their productivity while reducing negative impacts on the environment.
Conclusions and Future Outlook
The current and future trends in green agricultural development indicate that the need for balanced and sustainable development has become more urgent than ever. Achieving success in AGD requires effective cooperation among all stakeholders, including government, private sector, and local communities. Government policies should include support and encouragement for innovation and reliance on modern technology.
It is also important to monitor global developments and recent trends in green agriculture to ensure that national policies align with global goals. The academic and research environment must continue to provide insights and new studies that contribute to raising awareness and finding innovative solutions. The next phase of green agricultural development requires strong commitment and investment in education and training to ensure that farmers have the knowledge and resources necessary to improve their practices.
Ultimately, achieving green agriculture will lead to a developmental model that realizes economic, environmental, and social benefits, enhancing sustainability, food security, and the well-being of people. The path to sustainable agricultural development requires strategic design and strong alliances among all stakeholders to achieve lasting and sustainable success. This step is crucial to addressing current challenges and ensuring a better future for all stakeholders in the agricultural sector.
Introduction to the Advanced Agricultural Development Assessment Model
In light of the increasing challenges facing global agriculture, countries are betting on developing advanced models for assessing agricultural development. The Advanced Agricultural Development (AGD) model refers to several strategies aimed at improving agricultural productivity efficiency, adopting modern methods such as data-driven analysis. Current studies are conducted through a range of quantitative and qualitative means, such as data analysis models and resource management efficiency, which necessitate advanced techniques like the entropy weighting method, grey relational analysis, and environmental data analysis models. However, each of these methods has specific limitations that need to be exploited to provide a comprehensive and practical assessment to achieve sustainable agricultural development goals.
Challenges and Limitations in Quantitative Assessment Methods
The main challenges facing quantitative assessment methods include high sensitivity to data. Small changes in input data can lead to significant differences in outcomes, increasing uncertainty. On the other hand, Data Envelopment Analysis (DEA) models show great flexibility in handling various types of data and input-output variables. However, DEA requires more than two to three times the number of decision-making units compared to the total input and output indicators, which limits the selection of representative indicators. Additionally, most existing assessment models are proactive analyses, lacking the predictive capabilities needed to effectively guide current production planning. Thus, there is an urgent need to develop a comprehensive assessment model that integrates systematic assessment with predictive capability.
Development
Advanced Agriculture in the Yangtze River Delta
The Yangtze River Delta is considered one of the most influential economic regions in China and has become a leading area for the practical application of advanced agricultural development policies. This region studies various options related to environmental agriculture, technology-driven agriculture, and the integration of agricultural industries. Although these experimental areas are united for the purpose of AGD, they show clear differences in resource distribution responsible for their development. AGD embodies several dimensions that include traditional elements and emerging factors such as digital transformation in agriculture, complicating resource planning. In this context, scientific planning and reasonable resource distribution have become a major challenge where directed evaluations through DEA models provide accurate solutions to core issues.
3S-DEA-SBM-BP Model for Evaluating Agricultural Development
The 3S-DEA-SBM-BP model was established to combine the features of decision-making models without assuming output. This model focuses on addressing external environmental variables and their effects on resource distribution. Due to the limitations of traditional analyses, this model seeks to compensate by enhancing resource use efficiency and providing accurate predictions regarding AGD developments in the region. This method represents a systematic evaluative process that considers all environmental factors, making it a comprehensive solution that can be used to support strategic planning for sustainable development.
Methods and Techniques Used in Research
A variety of methods related to data analysis were employed to achieve accurate analysis. This includes the use of the SFA model for an in-depth analysis of environmental variables and their effects. This requires a thorough analysis of influencing factors and thus provides data-driven recommendations. A three-layer BP algorithm is used to train the model to reduce errors and improve predictions. The learning process relies on adjusting weights until the error is minimized. These practices highlight the importance of modern technology in agricultural development. By integrating traditional methods with modern approaches, the homeland can navigate current challenges and achieve efficient development.
Results and Future Development Trends
The results showed the significance of predictive AGD models in guiding agricultural policy and economic development in the Yangtze River Delta. It was found that the most influential areas follow notable developments, while others show the need for improvement in integration and coordination at the policy level. Valuable insights can be provided regarding the factors affecting the success of AGD policies and the level of social, economic, and environmental aspects. This study faces an important examination of those results and should help decision-makers plan more effective policies. Globally, this model serves as an example for countries seeking to achieve sustainable agricultural development through adopting innovative data-driven practices.
Learning Rate and Iteration Count Determination
In the field of machine learning, determining the learning rate and the iteration count is one of the fundamental factors for achieving effective model performance. The learning rate is a coefficient that affects the amount of information updated in each round during the training process. If the learning rate is too high, it may lead to overshooting optimal values, resulting in instability, while if it is too low, it may delay the learning process and result in a decline in overall model performance. A practical example of this is using a variable learning rate, which allows the model to adapt to different stages of training.
As for the iteration count, determining the suitable number can have a direct impact on the quality of the learning model. Increasing the number of iterations may lead to overfitting, where the model learns unnecessary details from the data, hindering its ability to handle new data. Conversely, too few iterations may lead to insufficient training, thereby reinforcing weak performance. Therefore, it is essential to achieve a delicate balance through methods such as cross-validation.
Coefficient
Gini Dagum
The Gini coefficient is considered one of the key indicators used to measure inequality in the distribution of income or wealth. In 1997, Dagum dismantled the Gini coefficient into contributions from within-group differences and net differences between groups, as well as the density of differences between groups in detail. This method allows for the identification of sources of regional disparities and provides explanations regarding issues related to the overlap among subgroups. The specific formula of the coefficient is an effective measure for analyzing regional disparities.
For instance, when the Dagum Gini coefficient is used in regional studies, we may discover that differences within groups have a significant impact and require investment in educational and employment programs to improve living conditions. These indicators can contribute to the realization of policies necessary to reduce the economic and social gap between different regions. The use of these methodologies reflects the progress in the analysis of regional gaps and helps decision-makers design effective strategies.
Moran’s Index
Moran’s Index is considered a statistical tool used to assess distributional characteristics and relationships among spatial data. Moran’s Index shows how values at neighboring locations may be dependent on each other, with this relationship decreasing as distance increases. This index enables researchers to understand patterns of geographical distribution and provides insights on how influences spread in regional data. For example, in geographical mapping studies, Moran’s Index can be used to understand how wealth or poverty may be distributed across different areas.
The spatial correlation analysis using Moran’s Index is key to understanding how environmental and economic factors influence agricultural productivity. Through time series analysis, this model allows for the identification of potential ramifications of economic and environmental changes on local communities. Thus, this index may have wide applications in regional planning, health, and public policy.
Choosing Indicators
The process of selecting indicators is a critical factor in any analysis related to performance evaluation. When considering the efficiency of agricultural development, input and output indicators must be effectively considered. Especially with the growth of the digital economy and green technology, it becomes important to understand how these factors affect the agricultural production process. This includes introducing indicators such as agricultural area, level of mechanization, and technological innovation. The use of these indicators can lead to accurate measurements to achieve sustainable development.
Furthermore, the indicators must be clear and accurately reflect the relationship between inputs and outputs. For instance, emissions of agricultural pollutants and their influencing factors are integral parts of the measurement process. The use of quantitative methods and techniques to analyze these data becomes important for understanding how to achieve effective development. Through this, researchers and policymakers can make informed decisions.
Environmental Variables and Their Impact
Environmental variables represent factors that affect production efficiency but cannot be controlled by the model. Therefore, selecting these variables requires a comprehensive understanding of the environmental and economic contexts affecting agricultural production. Analyzing multidimensional factors such as GDP and education levels can have a significant impact on understanding how to enhance productivity. Through these factors, regional progress can be measured, and fair resource distribution can be improved.
Additionally, developing indicators to support policies such as government funding can enhance efficiency in agricultural systems. Allocating budgets for agriculture can lead to improved production capacity for farmers through technical support. These environmental factors embody the vital links between policy and agricultural development, thereby enhancing overall economic functions. The combination of these variables reflects a holistic view of the challenges and opportunities available for growth.
References
Data
Effectively organizing data sources is at the heart of analytical studies. Relying on statistical yearbooks and annual city reports provides a strong foundation for understanding trends. Data needs to be accessible and easily attainable, which requires researchers to pay attention to data quality and regular updates. By processing missing data, studies allow for a more accurate understanding of export and import estimates.
The flexibility of data and analysis of results is an essential part of the research process. Through the use of advanced techniques in trend analysis, the ongoing impacts of economic and political factors on the ground can be understood. Researchers need to employ appropriate quantitative methods to ensure that estimates are accurate and allow for informed decision-making. Hence, the results can contribute to shaping the framework on which public policies for effective agricultural development are based.
3S-DEA-SBM-BP Model and Its Results
The 3S-DEA-SBM-BP model is considered an advanced step in measuring agricultural development efficiency. This method is distinguished by its accuracy in measuring agricultural performance in terms of inputs and outputs while ensuring effective resource utilization. By utilizing related algorithms, a true understanding of the efficiency of each region can be achieved based on a time series of data. This model manages the efficiency evaluation integratively and relies on accurately related information.
Results from the 3S-DEA-SBM model demonstrate effective implementation of agricultural development efficiency over the years. Additionally, clear differences in the efficiency of various regions are evident. This data can lead to significant conclusions that help establish clear strategies for improving performance and achieving balanced development. Through these results, governmental programs can be enhanced and areas that require special attention can be identified.
Analysis of Sustainable Agricultural Development Efficiency in China
Sustainable agricultural development efficiency is a vital topic that occupies the minds of researchers and policymakers in China. During the period from 2017 to 2022, the efficiency of 13 cities was evaluated according to the Data Envelopment Analysis (DEA-SBM) model, where these cities recorded an efficiency value of 1, indicating their effectiveness in resource utilization. These cities include Chengdu, Zibo, Zhu, Shanghai in Anhui Province, Jiangsu Province, and Zhejiang. These results indicate that sustainable agricultural development efficiency varies significantly across provinces; furthermore, substantial gaps in development efficiency exist between different cities.
Super Efficiency Model for Cities
To provide a more precise assessment of the 13 cities achieving DEA efficiency, the super efficiency model was employed. This model relied on measuring the efficiency of each city and inferring existing gaps. Chuzhou city in Zhejiang Province was the most efficient with a rate of 1.99, while Shucheng city in Anhui Province was the least efficient with a rate of 1.01. These differences highlight the importance of efficiency analysis, which can identify significant gaps in economic development between cities.
Analysis of Environmental Factors and Their Impact on Agricultural Efficiency
Agricultural efficiency is influenced by a number of environmental factors, including the level of overall economic development, labor quality, and policy support. Results indicate that improving the level of economic development can have a negative effect on agricultural development efficiency. In more developed areas, capital and talent are more likely to flow into more advanced sectors, thereby reducing green agricultural efficiency. Policy support is also a significant influencing factor, as research has shown that when supporting policies increase, gaps may widen in certain areas, such as the level of mechanization.
Efficiency Assessment Using BP Algorithm
To overcome the limitations of the DEA model, the artificial neural network (BP) algorithm was integrated to enhance efficiency assessment. The model relies on a comprehensive set of input indicators that cover all economic, environmental, and social aspects of agriculture. This phase represents an important step toward utilizing modern technology in agricultural efficiency assessment, thereby improving data-driven agricultural policies.
Results
Extracted from the Analysis of Agricultural Development Efficiency
The results showed a significant variation in agricultural development efficiency among cities. While some cities like Chuzhou and Zhuojiang achieved high efficiencies, others like Tongling in Anhui Province experienced a notable decline in efficiency. This reflects how environmental and economic factors affect developmental performance. Utilizing a systematic model contributes to improving the understanding of current gaps and challenges faced by cities, enabling better opportunities for achieving balance and sustainable development.
Structure of the Optical Network Model and Architectural Characteristics
The computational models used to achieve agricultural efficiency rely on the architectural structure of the optical network. The model adopted in this context includes one hidden layer with six nodes. The model uses a Logsig activation function between the input layer and the hidden layer, while the output layer relies on a Purelin function. Several important parameters were set, with the training function Trainlm chosen and a learning rate of 0.001, and a minimum training error goal of 1E-07. Additionally, the maximum number of iterations was set to 15,000. According to the results, the 3S-DEA-SBM-BP model was able to achieve matching coefficients of 0.94, 0.92, and 0.96 for the test, validation, and training sets, indicating high accuracy in predictions. For example, the efficiency values of the proposed model ranged from 0.25 to 2.14, while the actual values ranged from 0.32 to 2.00. This good convergence between expected and actual values reflects the model’s effectiveness in measuring efficiency.
Analysis of the Impact of Indicators and Weights in the Model
In the process of analyzing indicators within the model, the weights of the indicators in the neural network are effective in reflecting the impacts resulting from the input layer on the output layer. However, the weights resulting from training do not fully reflect the decision weights of the input indicators. In this context, previous research was relied upon to measure decision weights by calculating the absolute impact coefficients between the input and output indicators. Factors such as infrastructure development, agricultural digitization, and social security ranked high in their impact on sustainable agricultural efficiency in the Yangtze River Delta region, each exceeding a weight of 12%. Infrastructure development ensures the sustainability of agricultural production by reducing natural risks, while digitization promotes agricultural innovation. Conversely, traditional factors such as agricultural labor and energy resources had a weight of less than 5%, indicating a shift towards sustainable agriculture.
Analysis of Regional Imbalance and Its Impact on Agricultural Development
The analysis of agricultural efficiency results highlights significant discrepancies between cities within different regions, such as Anhui Province, which showed the highest efficiency in Chuzhou city compared to Tongling municipality. This disparity reflects the imbalance in development within the areas. The review not only shows the differences between provinces but also relies on the Gini coefficient to measure inequality. While the Gini coefficient results for the overall Yangtze Delta region remained stable around 0.3, results from Anhui Province indicated a case of greater imbalance than the average. These discrepancies lead to an increased need for comprehensive development strategies aimed at improving balance among different regions. Although the gap between Anhui Province and other provinces still exists, trends indicate that this gap is narrowing over time.
Analysis of Temporal and Spatial Developments of Agricultural Efficiency
The dynamic analysis of agricultural efficiency utilized kernel density estimation (KDE) techniques, as the developments of the efficiency curves provide a clear view of the distribution of efficiency over time. From 2017 to 2022, it was observed that the efficiency density curve remained stable, with a concentration within a certain range of values. However, significant changes were noted during 2018, where a bimodal pattern emerged indicating polarities in efficiency. Over time, until 2019, this phenomenon diminished, indicating efforts to integrate regional development. These developments suggest that performance differences highlight the need for regional cooperation and the enhancement of developmental policy integration. By utilizing global and local Moran indices, spatial relationships between agricultural efficiencies among regions can be measured, leading to a deeper understanding of interconnections and the needs for sustainable development.
Re-analysis
Forming Agricultural Land Use Rights in the Yangtze River Delta
In 2021, China began implementing new measures for managing the transfer of agricultural land contracts, which significantly reshaped the system of agricultural land use rights. This change came as part of reforms aimed at enhancing agricultural land efficiency by allowing farmers to transfer their land use rights through leasing, contracting, and exchanges. These measures can be seen as an important step towards promoting relatively large-scale agriculture and intensive land use. However, this policy also led to a fragmentation phenomenon in land transfers, affecting the original land use pattern – instead of focusing on specific areas, the land transfer process became more fragmented.
To ensure environmental protection, the government designated areas with important and sensitive environmental functions in the Yangtze River Delta, where restrictions or bans on agricultural activities and construction were imposed, leading to a shift in agricultural production to unprotected areas. This trend contributed to changing the original agricultural agglomeration pattern, making it important to understand how these policies affect agriculture and regional planning.
Analysis of Local Spatial Correlation and Its Impact on Agricultural Development
The global analysis of Moran’s I reflects macro-level spatial agglomeration characteristics, but it does not provide details about the spatial links between individual cities. Therefore, Local Moran’s I analysis was used to study the spatial links between cities in the Yangtze River Delta. The LISA analysis reveals that about 49% of cities displayed annual spatial agglomeration between 2017 and 2022. However, this agglomeration often appeared in the form of low clusters, indicating challenges in achieving balance in agricultural development among different cities.
For instance, Changzhou and Zuo Zhan were the only two cities that exhibited high agglomeration characteristics, demonstrating varying successes and challenges. The development of agglomeration characteristics shows dramatic transformations, as the proportion of cities with local agglomeration characteristics initially increased but later declined due to regional integration policies and the commencement of agricultural land marketization. These processes reflect how agricultural policies can lead to changes in the demographic and economic composition of agricultural regions, necessitating a reconsideration of how to direct efforts towards improving agricultural development efficiency.
Forecast Model for Agricultural Development Efficiency
Within the framework of this research, a 3S-DEA-SBM-BP model was constructed, which allows for a more comprehensive assessment of agricultural development efficiency across various sectors. Thanks to this model, researchers can predict agricultural development efficiency more accurately, facilitating cities in adjusting their allocations to improve performance. The results obtained from this model show high accuracy in predicting agricultural indicators. It is evident from the derived data that the methods used were able to reflect future trends well, according to the low relative error rate.
The forecasts for agricultural development efficiency in the cities of the Yangtze River Delta present various challenges. The GM (1,1) model demonstrates the ability to predict inputs accurately, enhancing decision-makers’ capacity to address future challenges more efficiently. In this context, some cities like Hefei and Chafeng are expected to transition to a state of relative efficiency, while other cities like Chizhou may experience a decline due to low efficiency solutions. Here, the model also provides incentives for certain cities to take proactive steps to enhance performance.
Discussions on Regional Gaps in Agricultural Development Efficiency
The study results showed an upward trend in agricultural development efficiency in the Yangtze River Delta, but they are not without clear regional gaps. This disparity indicates that the gaps in agricultural efficiency are due to differences in structural and economic resources across different cities. It also suggests that average-based results at the provincial level may not be sufficient to accurately represent the regional reality.
It is worth noting that…
the emphasis on data-driven planning a crucial aspect in facilitating the transition towards intelligent agricultural practices. By leveraging advanced technologies, farmers can analyze market trends, optimize resource allocation, and enhance productivity. The integration of artificial intelligence and machine learning into agricultural practices represents a significant opportunity to revolutionize traditional farming methods, allowing for precision agriculture that maximizes yield while minimizing environmental impact.
In conclusion, the ongoing challenges in the agricultural sector highlight the necessity for comprehensive and innovative approaches that consider the unique characteristics of each region. By embracing new methodologies and fostering collaboration among stakeholders, the goal of sustainable agricultural development can be more effectively achieved. This requires a concerted effort not only from policymakers but also from the agricultural community, researchers, and technology developers to create an ecosystem that supports sustainable practices and drives economic growth in the agricultural sector.
Agricultural management tools such as remote sensing, drones, and big data analytics are technologies that must be enhanced in their applications in agriculture to achieve higher levels of efficiency and production. However, enhancing these tools will not be sufficient without effective government support and financial backing targeted towards agricultural innovation. This approach can boost the use of technology and drive incentives for better agricultural performance.
Investment in Infrastructure and Innovative Technology
Research shows the importance of investing in infrastructure development as a key tool for improving agricultural efficiency. Investment in transportation, irrigation, and energy can significantly contribute to improving agricultural productivity. For example, establishing processing and cooling facilities can increase the value of agricultural products by extending their storage life and enhancing their marketing. Opportunities for achieving technological transformation will be limited if foundational investments are overlooked.
As agriculture shifts from traditional practices to more sustainable methods, the focus on innovation and technology becomes paramount. Technology-based applications in agriculture contribute to enhancing precision and reducing waste, facilitating agricultural work with higher quality and greater efficiency. We cannot overlook the importance of access to bank financing and green financing, topics that need to be coordinated by policymakers to foster a shift towards sustainability.
The benefit of boosting investment lies in improving the ability of agricultural systems to adapt to environmental and economic changes, thus the state plays a critical role in stimulating agricultural progress through clear and effective visions aimed at raising the level of sustainability in this essential sector.
Measuring Rural Revitalization Based on Green Development
Measuring the level of rural revitalization based on green development highlights the importance of measuring and analyzing the level of sustainable development in rural areas, which is essential for achieving balanced and effective development. Rural revitalization is defined as a process that involves improving the economic, environmental, and social capacity in rural communities, heavily relying on green and clean agricultural practices that reduce the negative impacts of agricultural activities on the environment. This measurement requires collecting data on various indicators including agricultural productivity, pollution levels, water resources, and farmer well-being.
One mechanism used to measure the level of rural revitalization is relying on data analysis tools, such as regression analysis and non-separable analysis, which can provide valuable insights into the relationship between inputs and outputs in sustainable agriculture. For instance, with the application of these tools, the efficiency of green agricultural production in different regions can be assessed, and the impact of economic, environmental, and social factors on this efficiency can be understood.
Applying this type of measurement is vital for understanding the dynamics and developments of green rural development. For example, studies show that if the level of education and training among farmers improves, it directly leads to better agricultural practices, which in turn increases productivity and reduces environmental costs. Therefore, measurement should also include aspects such as education and training in green agriculture and supporting rural communities through government programs and civil initiatives.
Stimulating Factors for Developing Green Agriculture in Rural Areas
There are several factors that play a key role in enhancing the development of green agriculture in rural areas, including government policies, investments in technology, and environmental awareness. Policies that support sustainable agriculture create an environment that encourages farmers to adopt eco-friendly agricultural techniques. For example, the government provides financial support and facilitation for farmers who engage in organic farming activities or those that reduce the use of agricultural chemicals.
Moreover,
modern technologies are considered one of the key factors in improving agricultural productivity and reducing its negative impact on the environment. For example, the effective use of digital project management in agriculture can significantly contribute to improving the efficiency of water and fertilizer use and reducing crop loss. One successful example of this technological transformation is the use of precision agriculture techniques, which allow farmers to conduct more accurate assessments of soil depths and crop needs for water and nutrients.
Additionally, involving farmers in the decision-making process helps enhance environmental awareness and community development. When farmers participate in shaping policies that affect their operations, they tend to make more sustainable choices. Moreover, collaboration between farmers and environmental institutions can help improve sustainable development projects, necessitating the existence of awareness and continuous training programs to ensure their knowledge of green practices is up to date.
Challenges Facing Green Development in Rural Areas
The challenges facing green development in rural areas are numerous and complex, as many factors intertwine to hinder the achievement of sustainable development goals. One of the biggest challenges is the lack of financial resources and necessary investments, as many rural communities find it difficult to secure the funds needed to improve agricultural techniques and expand the use of green methods.
Furthermore, there is an urgent need to improve infrastructure in rural areas, as farmers require access to new markets and improved transportation methods to ensure the continuity of production. The lack of infrastructure not only hinders the delivery of products to markets but also prevents access to information and tools that can enhance sustainable agricultural practices.
Additionally, the agricultural community faces the challenge of climate change, which affects productivity and exacerbates food security issues. Weather fluctuations, such as droughts and floods, threaten food security and drive farmers to abandon traditional agricultural practices. Therefore, effective adaptation strategies are required to address these challenges, such as developing agricultural systems capable of withstanding climate changes and environmental pressures.
All these challenges require a joint effort from the government, farmers, and civil society to develop sustainable solutions that support rural revitalization and enhance food security in the future. It is also important to provide suitable platforms for knowledge and experience exchange among farmers to enhance resilience and find innovative solutions.
Green Agricultural Development in the Yangtze River Delta
The Yangtze River Delta is considered one of the most economically advanced and culturally influential regions in China. This area has become a leader in developing precision agriculture through the application of modern agricultural technologies and the integration of excellent agricultural resources with the tourism sector. This integration contributes to the coordinated development of the agricultural economy, natural environment, and social progress. Studies on sustainable agricultural characteristics in the Yangtze River Delta are essential to achieve a deeper understanding of future directions for agricultural development in this region and beyond.
Several key aspects of these developments are highlighted, including advances in assessment models and systems used. A three-stage input-output efficiency model (3S-DEA-SBM) with a BP algorithm is utilized, contributing to improving agricultural performance evaluation and providing accurate predictions for future production levels. This is achieved through a comprehensive analysis of various performance indicators, allowing farmers and policy managers to adjust their strategies based on changes in agricultural efficiency.
Challenges of Traditional Agriculture and Its Environmental Impact
Traditional agriculture in China has long relied on chemical inputs such as fertilizers, pesticides, and water, which, despite increasing production, have led to noticeable negative environmental impacts. These impacts include soil pollution, deterioration of water quality, and over-extraction of groundwater resources, putting the local environment at risk. In light of these challenges, there is an urgent need to transition towards more sustainable agricultural models that seek to balance productivity and environmental protection.
It requires
the development of the DEA-SBM model in providing a more comprehensive understanding of production efficiency by incorporating unexpected outputs, allowing for a better evaluation of agricultural performance. This model enables researchers and policymakers to identify potential areas for improvement and to implement more effective strategies for enhancing resource use and agricultural productivity. Through detailed analysis, these methodologies can aid in identifying best practices across different regions and agricultural systems, ultimately contributing to the achievement of sustainable agricultural development goals.
The DEA model in improving the accuracy of efficiency measurement through characterizing the relationship between inputs and outputs. Mathematical relationships in the model are used to evaluate performance, where the effectiveness of the administrative unit is expressed by measuring its productivity compared to other units in the same field. When the SBM model is introduced, complex mathematical equations are employed to accurately reflect the impact of inputs and outputs, which allows for measuring gaps in efficiency and identifying factors contributing to these gaps.
Application of Super Efficiency Model and Profitability Analysis of Units
The super efficiency model, which adds additional constraints in measurement, helps in assessing the efficiency of production units by exploiting the production frontiers set by other units, meaning that efficiencies among units within the effective DEA case can be distinguished. Specific equations are used to enhance measurement accuracy, and various mathematical methods are applied to calculate the actual values and productivity of different units’ efficiency.
Research highlights how various economic and environmental factors affect productive efficiency, with models like SFA (Stochastic Frontier Analysis) providing insights into how external factors impact. The essence of this analysis lies in responding to different environmental factors such as technology, agricultural practices, and the extent of using modern production inputs. These factors are key in the process of improving efficiency and aiding in achieving sustainable development in the market.
BP Algorithm and Its Role in Enhancing Models
The BP (Back Propagation) algorithm is considered one of the essential tools in enhancing the efficiency of data analysis models, consisting of three main parts: the input layer, the hidden layer, and the output layer. Research has proven that a three-layer algorithm can approximate any nonlinear function, thereby enhancing the value of the SBM model in improving the analytical models used.
The training process in the BP algorithm operates by forwarding the signals and then adjusting the errors backward. This process has been developed to include precise calculations based on mathematical functions to determine errors and make necessary adjustments. The system adjusts weights and thresholds based on recorded errors until the minimum possible error is reached. This dynamic is crucial, as it determines the final performance of the model.
Using Gini Coefficient and Facts about Regional Disparities
The Gini coefficient is one of the basic mathematical averages for estimating inequality in income or wealth distribution. Dagum presented a model to decompose the Gini coefficient into contributions of disparities between groups and disparities within groups, providing deep insights into the sources of regional inequality. This Gini coefficient model reflects the flexibility of understanding and analyzing income data within groups and addresses the foundations of political and social decisions that affect this inequality.
Measuring regional disparities is a necessary tool for economic policy planning. Mathematical coefficients can be used to analyze the profitability and efficiency of agricultural production, making it easier for decision-makers to identify areas needing improvement. This additional concept of social and economic dimensions adds a vital aspect to understanding the economic situation more deeply.
Moran’s Analysis and the Role of Indicators in Agricultural Efficiency
The Moran index is a statistical method used to assess distribution characteristics and relationships between spatial data. Examining the relationship between values at neighboring locations is based on the idea that values are influenced by geographical proximity. Equations associated with the Moran index help examine overall and local changes in agricultural efficiency, contributing to understanding how spatial factors affect productivity.
The indicators used in measuring agricultural production efficiency involve evaluating multiple variables such as arable land area, labor force, level of mechanization, and innovations in green technology. These positive indicators represent vital dimensions of modern agriculture, helping to promote sustainability and economic development through new methods of production.
Assessment of Carbon Emissions in Chinese Agriculture
Forming
carbon emissions from agriculture is one of the important aspects of studying the environmental impact of agricultural activities in China. The total carbon emissions for each city are calculated based on the use of nine energy sources, such as coal, crude oil, and various types of fuels. The IPCC method is considered a reliable gauge for estimating emissions, as carbon emissions from agriculture are estimated based on the ratio of primary industry. This indicates that not only factories but also agricultural activities can be a significant contributor to carbon emissions, necessitating effective measures to reduce them.
The analysis shows that emissions are not limited to one type of agriculture but include all agricultural activities, including crop cultivation and animal husbandry. This highlights the importance of transitioning to environmentally friendly production methods that promote the sustainability of agriculture and help in appropriately reducing carbon emissions. For example, the use of organic farming techniques and improved resource management can assist in lowering emissions.
Social and Economic Challenges in Rural Areas
Policies aimed at promoting agricultural development based on social and economic equality are a vital requirement for enhancing rural recovery. Understanding the gap between urban and rural areas and identifying suitable means to address it is an essential part of developmental strategies. The transition to sustainable agriculture not only contributes to economic growth but also helps provide new job opportunities, thereby reducing existing developmental gaps. Rural communities are often vulnerable to poverty and unemployment, so enhancing living standards through supporting diverse agricultural activities is crucial.
The use of indicators derived from economic growth in rural areas, such as steady population income, income assurance rates, and education levels, all indicate how policies affect the lives of the residents in rural areas. Statistics show that the more government policies support sustainable agriculture, the better the economic outcomes. This also depends on how gender gaps in education and employment are reduced, as enhancing the role of women in agriculture can significantly contribute to accelerating social and economic transformation in rural communities.
Environmental Dimensions in Developing Sustainable Agriculture
Sustainable agricultural development policies require taking the environmental dimension into account. Environmental factors, including the quality of natural resources such as land and water, play a pivotal role in determining agricultural production efficiency. Development strategies should focus on how to better utilize these resources without depleting them. For example, effective water management and crop diversification can lead to improved yields and reduced negative environmental impact.
Research into soil types and environmentally considerate farming methods can enhance productivity while reducing negative environmental effects. The concept of environmentally friendly agriculture empowers farmers to adopt practices that preserve biodiversity and reduce excess waste and chemicals. This approach contributes to improving soil quality and stimulates wildlife, benefiting the overall ecosystem.
Developing Indicators to Measure Agricultural Progress
Introducing new indicators for measuring the level of progress in agriculture is a necessary achievement. Indicators such as the value of agricultural production, pollutant emission index, and carbon index in agriculture are all vital tools for quantitatively and objectively assessing agricultural performance. By periodically measuring these indicators, the state can make adjustments to its policies and provide the appropriate support to areas and fields that need stimulation. For instance, improving planning and utilizing technology in agriculture can lead to increased productivity and reduced negative environmental impacts.
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It is also important to consider social indicators as part of measuring agricultural success, such as the Common Prosperity Index in rural areas, which contributes to enhancing social justice. Achieving a balance between economic, environmental, and social dimensions is the optimal way to achieve sustainable agricultural development that ensures improving the quality of life for farmers and rural communities alike.
Efficiency in Smart Agriculture: The Third Phase of Agriculture Development in the Yangtze River Delta
Efficiency in smart agriculture is considered one of the essential elements for the success of modern agricultural development, especially in the Yangtze River Delta region. This efficiency is based on a set of factors that play a complementary role in improving the agricultural process and achieving a balance between inputs and outputs. In the third phase of efficiency assessment, modified input data from the previous two phases was used to test agricultural development efficiency. The results illustrate the impact of environmental conditions and random factors on measuring agricultural efficiency. It was noted that cities classified as efficient in the first phase experienced a significant decline in their efficiency, indicating that such previous efficiency relied on favorable external environments, such as economic development and supportive policies.
Data has shown that the average agricultural efficiency in the third phase has decreased significantly compared to the first phase. For example, the efficiency of some cities has declined from levels close to perfection and operating efficiently to low values, such as the city of Tongling in Anhui, which recorded the lowest efficiency with a score of 0.22. This decline highlights the urgent need for reevaluation and adjustment to reconfigure inputs and improve resource distribution more effectively. This also indicates significant gaps in agricultural efficiency between different cities, underscoring the need to introduce development strategies tailored for each city to ensure it benefits from its local capabilities.
Application of BP Algorithm in Measuring Smart Agriculture Efficiency
The BP artificial neural network algorithm is a powerful tool for data analysis and improving smart agriculture efficiency by integrating it with the DEA model. By using this algorithm, a comprehensive model was created to assess agricultural efficiency by calculating additional inputs that include environmental, social, and economic inputs. Thus, the model is able to accurately measure the impact of all relevant factors.
The BP algorithm training process was conducted on a dataset divided into three groups: a training group, a validation group, and a testing group. Different criteria were used to improve model accuracy, such as utilizing a single-layer neural network and applying advanced activation functions. The results showed that the model achieved fit coefficients exceeding 0.94, indicating high predictive accuracy from the algorithm. This accuracy means that the model can predict actual changes in agricultural efficiency in the future based on previous data patterns.
For instance, the difference between the predicted values and the actual values in the model results indicates the success of the prediction approach, reinforcing the importance of using artificial intelligence techniques in advancing agriculture. Through further analysis, greater focus can be directed towards the most impactful inputs like infrastructure development and digital technology in agriculture, making it easier for farmers to make data-driven decisions to enhance their productivity and achieve sustainable goals.
Analysis of Input Impact and Factors Influencing Agricultural Efficiency
The analysis revealed the weight of inputs concerning agricultural efficiency in the Yangtze River Delta region. Elements such as infrastructure development, digital transformation in agriculture, and social safety are among the most influential. Data suggests that these factors can enhance agricultural productivity by creating a favorable environment that facilitates innovation and improves traditional farming methods. For example, farmers can optimize their techniques by employing precision agriculture technologies that rely on big data and analysis to deduce the best field practices.
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indicates that investing in environmental investment is crucial for enhancing agricultural efficiency. With increasing awareness of the importance of environmental preservation, the demand for sustainable farming that contributes to protecting natural resources is growing. Therefore, environmental investments can be seen as a contributing factor to achieving greater efficiency and increasing returns. It is essential to align environmental policies with agricultural development strategies to ensure achieving shared environmental and economic benefits.
In the end, it is clear that traditional factors such as human resources, energy, and agriculture still play a major role, but the impact arising from digital transformation and modern technologies is what may lead agriculture to new levels of efficiency. By understanding the relationship between these factors, more effective strategies can be developed to advance agriculture in the Yangtze River Delta region and ensure sustainability.
Analysis of Agricultural Development Efficiency in the Yangtze River Delta
Agricultural Development Efficiency (AGD) is one of the key indicators for measuring the effectiveness of agricultural activities in the Yangtze River Delta region. Analyses have shown that efficiency has gradually improved in this area, although there are notable disparities among cities within provinces. For instance, Chuzhou city in Anhui Province demonstrated the highest level of efficiency at 0.87, while Tongling city had an efficiency of 0.22, reflecting a clear difference in development within the province.
This disparity in efficiency serves as an indicator of the gradual transition from intensive traditional agriculture to sustainable agriculture. The Dagum Gini coefficient model was used to measure agricultural inequality, with results indicating that the imbalance within provinces is a vital factor affecting balanced development in the region. In short, transitioning to sustainable agriculture requires a comprehensive reassessment of the efficiency of traditional agriculture and the application of modern and innovative methods to ensure desired outcomes.
Regional Analysis of Disparities in Agricultural Development Efficiency
Regional disparities in agricultural development efficiency are an important issue that requires thorough examination to determine the underlying causes of this variation. Using the Dagum Gini coefficient, the discrepancies among different provinces were analyzed, noting that Anhui Province suffers from a high level of imbalance, negatively impacting overall development. This factor poses a significant challenge for the local government to achieve sustainable development strategies that consider regional differences.
When comparing provinces like Jiangsu, Zhejiang, and Shanghai, it appears that these areas have achieved strong cooperation in attaining agricultural development efficiency. However, the gap between Anhui and the rest remains substantial, reflecting the necessity of improving efficiency in Anhui to narrow this gap.
It can be said that enhancing agricultural efficiency in poorly performing provinces is a fundamental requirement for achieving economic and agricultural integration in the region, which will help bring about significant social and economic benefits. It is noteworthy that active regional integration among these cities may contribute to improving responses to the economic and environmental challenges facing the area.
Analysis of Temporal and Spatial Characteristics of Development
Temporal and spatial studies reflect the development of agricultural development efficiency through the analysis of data over time. Analyses have shown that the kernel density estimate (KDE) curve for AGD efficiency between 2017 and 2022 has generally remained stable, but some important changes were observed, particularly in 2018, which saw a dual peak pattern indicating increased variation in efficiency across major cities. The decline in the secondary peak after 2019 reflects the impact of the surrounding economic affairs on the developmental integration process.
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The results showed that changes in policies, such as the launch of the Yangtze River Delta development integration strategy, directly affect the characteristics of AGD. These transformations require comprehensive studies of temporal and spatial characteristics to ensure that the policies adopted align with the economic and social realities of the region.
Spatial Correlation Analysis and Lack of Balance
Studying spatial correlation within the Yangtze River Delta requires the use of indicators such as the local Moran’s I index, which reveals the dynamics of correlation between cities. The results indicate that there is a clear positive spatial clustering in the distribution of AGD efficiency among cities, highlighting the need to work on enhancing coordination among local policies to improve efficiency in the weaker areas.
The Jiangsu and Zhejiang regions are more distinguished in performance compared to Anhui, which necessitates taking sustainable initiatives that help improve agricultural efficiency in the weaker areas through innovative solutions and effective resource management. Through collective efforts and government support, gaps can be overcome and balanced economic growth achieved that benefits everyone.
Future Prospects for Agricultural Development Efficiency
The 3S-DEA-SBM-BP model was used to forecast agricultural development efficiency, providing a comprehensive means to evaluate the efficiency of the agricultural sector. These forecasts guide future planning and determine the input levels needed to achieve optimal performance.
Although data for planning beyond 2023 is not yet available, using the GM (1,1) model is an effective step to analyze expected inputs. The focus on forecasts highlights the importance of continuous innovation in the agricultural sector to ensure the development of effective sustainable strategies…
The 3S-DEA-SBM-BP Model and Forecasting Agricultural Development Efficiency in Anqing
The 3S-DEA-SBM-BP model was applied to forecast agricultural development efficiency (AGD) in Anqing, Anhui Province, focusing on analyzing future trends of that efficiency over the next three years (2023-2025). This model incorporates advanced methods in data analysis, achieving high accuracy in predicting various inputs, with an average relative error consistently around 3.03%, indicating the model’s superior capability in reflecting future trends of various agricultural inputs. The relevant inputs vary, including agricultural chemicals, working hours, and crop time, and these results are expected to align with achieving coordination between traditional agricultural methods and modern technology.
Forecasting Agricultural Development Efficiency in the Yangtze River Delta
In the next three years, it is predicted that the agricultural development efficiency of the Yangtze River Delta will remain relatively stable. Although Anhui Province lags behind Jiangsu and Zhejiang provinces, there is a noticeable trend towards improvement. Some cities, such as Hefei and Xuancheng in Anhui, are expected to transition to a higher efficiency state, indicating the government’s interest in moving forward with agricultural infrastructure modernization and enhancing sustainable practices. However, it remains essential to address regional disparities and improve resources to achieve more viable outcomes for rural economies.
Regional Differences in Agricultural Development Efficiency
The study reveals significant regional disparities in agricultural development efficiency among cities in Anhui Province. While statistical values indicate improvement, disparities remain the prevailing phenomenon. Differences in geographical distribution and economic formations lead to inequities in achieving efficiency, reflecting the impact of public policies on the direction of this distribution and how tailored strategies can mitigate existing gaps. By focusing on enhancing local agricultural capacities, farmers can achieve better outcomes through the use of modern agricultural techniques.
Changes in Agricultural Dynamics and Influential Factors
Analyses show that traditional factors such as the level of mechanization and agricultural chemicals have seen a decline in their impact on agricultural production efficiency. Instead, factors such as infrastructure improvement and a shift towards agricultural digitalization have begun to emerge as key influential factors. These differences indicate that modern agriculture requires a diverse array of strategies, including the activation of new technologies such as big data analysis, which in turn may improve decision-making processes for farmers, raising the overall efficiency and sustainability of the agricultural system.
Recommendations
For the Future of Sustainable Agricultural Development
To enhance the efficiency of agricultural development in various regions of China, deep planning and regional coordination of smart production operations are recommended. By organizing agricultural activities in accordance with market demands and the comparative advantages of each region, a more effective utilization of agricultural resources can be achieved. Furthermore, the government should support the establishment of agricultural information systems that provide accurate and up-to-date information regarding climatic conditions, soil, and market trends, aiding farmers in making informed decisions. Focusing on infrastructure investments and employing modern technology represents a key step towards achieving sustainable and effective agricultural development.
Transition to Smart Agriculture and Modern Technology
The transition from traditional agriculture to smart agriculture refers to the significant changes occurring in the ways food is produced and distributed. With the use of advanced technology, farmers can improve the management of their farms and increase their productivity efficiency. Smart agriculture includes the use of big data, the Internet of Things, and remote sensing technologies, which help in crop planning and resource distribution more effectively. For example, using sensors provides accurate information about soil and water conditions, enabling farmers to make decisions based on real data to boost productivity. Modern technology can also contribute to reducing waste, as water and pesticide usage can be monitored accurately and reduced without impacting production.
For instance, farm management software can assist farmers in tracking agricultural practices, ensuring that every operation is executed timely and in a manner that yields optimal results. This type of data can also aid in predicting harvests and analyzing markets, providing farmers with valuable information to plan for their future. As investments in infrastructure and technology increase, we may witness a significant transformation in how food is produced sustainably, benefiting both farmers and consumers.
The Role of Infrastructure in Agricultural Improvement
Agricultural infrastructure plays a crucial role in enhancing productivity and efficiency in farming. This infrastructure includes works such as roads, transport, irrigation systems, and electricity. Improving this infrastructure is a vital step for farmers towards a more efficient and effective farming model. For example, when roads are improved, the transportation of agricultural products to markets is facilitated, helping to reduce transport costs and increase farmers’ profitability.
Additionally, building storage facilities such as agricultural product processing plants and cold logistics systems can help reduce waste and increase product value. These systems not only improve product quality but also enhance the sustainability of agricultural processes. For example, the expansion of insulated and refrigerated storage systems preserves agricultural products for longer periods, thus opening new markets for both local and international sales.
Investing more in these systems can create new job opportunities, boost economic growth, and enable farmers to achieve higher income due to better market access. By improving infrastructure, agricultural trade can be facilitated, and sustainable agricultural practices can be promoted, contributing to the achievement of global sustainable development goals.
Digitization and Economic Transformation in Agriculture
Digitization constitutes one of the essential elements in developing the modern agriculture sector. Digital technology represents new tools for how this sector operates, providing innovative solutions to traditional challenges. Digitization enhances the competitiveness of agriculture through more efficient management processes and big data analysis. Agricultural applications are used to provide instant information on crop status, weather forecasts, and market prices, empowering farmers to make informed decisions.
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Therefore, financial technology platforms enable farmers to access loans and investments more easily, allowing them to purchase modern equipment or expand their crops. These digital credits provide financial resources that help enhance productivity and sustainability. By offering irrigation services and electronic payment facilities, digitization overcomes traditional financing barriers and allows farmers to significantly increase their production.
Digitization can also be considered an important mechanism to enhance transparency and monitor the agricultural supply chain. Through product tracking systems, consumers and farmers can know the source and history of products, thereby enhancing trust among stakeholders. These systems also help reduce manipulation and increase the quality of agricultural products available in the market, thus promoting sustainability in the agricultural system as a whole.
Enhancing Farmers’ Income Through Modern Technologies
Farmers’ income plays a vital role in the sustainability of the agricultural and economic system. With the emergence of modern technology and digital tools, new techniques are emerging to help enhance farmers’ income and increase profits. By improving product quality and increasing efficiency, farmers can sell their products at higher prices and move to new markets, such as international markets.
By implementing smart agriculture practices, farmers can reduce waste and increase yield per acre. For example, multiple cropping has been used, allowing several types of crops to be grown at the same time, to increase productivity and achieve better diversity. Additionally, some technologies such as vertical farming and hydroponics are used to improve the efficiency of using limited spaces, thereby increasing returns.
Value-added services are also an effective means to increase farmers’ income. By processing agricultural products locally, farmers can obtain greater value from their products, such as turning tomatoes into paste or fruits into juices. This strategy not only increases profits but also enhances the sustainability of the local economy and creates job opportunities.
Governments and companies need to invest more in this technology and provide necessary support to farmers to ensure success in agricultural fields. The integration of these technologies will enable farmers to better face challenges, increase their income, and thus achieve a lasting balance between economic development and environmental protection.
Source link: https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2024.1502824/full
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