Search

In order to improve the effect of agricultural economic risk forecast, this paper studies the agricultural economic risk forecast combined with data mining technology and builds an intelligent agricultural economic risk forecast system. Moreover, this paper employs a dynamic factor model to estimate common factors that drive changes in target topics. In order to construct a sentiment index that can reflect the overall operating situation of the macroeconomy, this paper improves the agricultural economic risk mining algorithm and standardizes the sentiment value corresponding to the target theme. In addition, this article analyzes the sentiment changes of its individual topics one by one in combination with the specific economic environment. The simulation study shows that the agricultural economic risk forecast system based on data mining technology proposed in this paper has a good effect. [ABSTRACT FROM AUTHOR]

The "carbon peak and carbon neutral" emission target has set new requirements for the supply chain structure, exacerbated by the complexity and vulnerability of the agricultural sector and global supply chains after the COVID-19 outbreak. Under these circumstances, the resilience evaluation of the agricultural supply chain based on green transformation became an important subject. Based on this topic with outstanding problems, this paper evaluates the resilience level of the agricultural supply chain in northeast China with the goal of achieving the "double carbon" objective. By focusing on its resilience, we selected Heilongjiang, Jilin, and Liaoning provinces as research subjects and collected the agricultural supply chain-related data from 2010 to 2019. Based on the relationship between supply chain nodes and the operation system, an evaluation model containing three levels was established. The entropy weight method and TOWA operator were used to carry out secondary weighting on the evaluation system and time levels containing 12 indicators, following the evaluation of the resilience level of the agricultural supply chain in three static and dynamic dimensions using the grey correlation method. The results show that the importance of the evaluation level of "internode resilience level" has always been a priority during the decade. Therefore, it is pointed out that the resilience construction and green transformation of the agricultural supply chain should be strengthened and implemented from the following three aspects: strengthening agricultural modernization, optimizing supply chain operation, and the implementation and promotion of effective policies. [ABSTRACT FROM AUTHOR]

This paper attempts to build a performance evaluation system for agricultural financial project expenditures to make the performance evaluation of financial support for agriculture more maneuverable and provide a reference for the management of agricultural funds. Moreover, this paper combines the intelligent big data technology to construct the agricultural financial fund performance evaluation system, and describes the characteristics of the relevance of geographic elements in the entire spatial region through global spatial autocorrelation. Simultaneously, this paper uses different spatial weight matrices to examine the rationality and robustness of the results. In addition, this paper introduces covariates and removes trends through regression methods. Further, taking into account the characteristics, scientificity and operability of agricultural financial funds, the dimensions and structure of the performance evaluation of agricultural financial expenditures are drawn up. Finally, this paper verifies the effectiveness of this method through case studies, and gives several targeted suggestions. [ABSTRACT FROM AUTHOR]

Aiming at the problem of massive data storage and transaction authority in the blockchain system in the data sharing transaction scenario, this paper proposes a blockchain system for the controllable sharing transaction of massive data. By designing the blockchain system model of the consortium chain and private chain, reconstructing the data structure of the block, and introducing the InterPlanetary File System (IPFS) technology, we adopt the off-chain storage method to solve the problem of high cost caused by too much data carried by the block. The isolation control of data resource transactions is realized by introducing identity authentication and channel mechanism, which meets the needs of different subjects for confidentiality of transaction data information. From the analysis results of cold chain logistics efficiency, in the five years from 2017 to 2021, the comprehensive cold chain logistics efficiency, pure technology cold chain logistics efficiency, and scale cold chain logistics efficiency of agricultural product cold chain logistics in a certain region have all reached above 0.9. The return to scale remains unchanged, and the cold chain logistics resources have been effectively allocated and reasonably utilized. The value of comprehensive cold chain logistics efficiency, pure technology cold chain logistics efficiency, and scale cold chain logistics efficiency in a certain month is less than 1, which means that the cold chain logistics efficiency is weak or ineffective. This shows that in these years, the infrastructure investment, cold chain technical support, and management level related to the cold chain logistics of agricultural products in a certain region need to be strengthened, and it is necessary to adjust the input-output structure and allocate resources reasonably. From 2018 to 2021, the total factor productivity of agricultural product cold chain logistics in a certain region is greater than 0.85, indicating that the level of total factor productivity is relatively high during this time period, and the increase in the efficiency of technical cold chain logistics is the main reason for the increase in total factor productivity. [ABSTRACT FROM AUTHOR]

Constructing a perfect urban fresh agricultural product supply system is the basic guarantee for the stability of urban life, and it is also the basic condition for supporting urbanization. This paper combines unsupervised learning algorithms to predict and analyze agricultural product consumption behaviors, determines whether to generate new neurons through the cumulative error value of the winning neurons, and gives a network model that can dynamically self-grow. Moreover, this paper constructs an agricultural product consumption behavior prediction model based on unsupervised learning and uses data to verify the performance of the algorithm in this paper. After confirming the performance test of the algorithm, it verifies the prediction effect of this method on the consumption behavior of agricultural products. Through statistical analysis of data, it can be known that the prediction method of agricultural consumption behavior based on unsupervised learning has a certain effect. [ABSTRACT FROM AUTHOR]

This paper uses agent-based modelling to study the frequent booms and busts in Chinese agricultural markets. First, an artificial agricultural commodity market consisting of heterogeneous agents, such as producers, consumers, and speculators, is built. A numerical simulation suggests that speculation can cause large price fluctuations via nonlinear price dynamics. Then, parameters are estimated by the simulated method of moments using garlic and ginger price data in China from 2006Q2 to 2018Q3. The estimation yields a statistically significant speculative behavior parameter, supporting speculators' existence. Based on the well-estimated model, a low-cost policy experiment aiming at market stabilization is carried out. The essence of this policy is to release the theoretical steady state of the estimated model as the government-guided price to producers. The guided price, even partially followed by producers, can reduce simulated price variances and weaken speculators' negative impact on market stability. Robustness tests show that the effect of policy experiment is robust under a 20% change in any parameter value or a 5% change in the guided price. [ABSTRACT FROM AUTHOR]

In the vision of smart cities, everything is highly connected with the aid of computational intelligence. Therefore, the cyber-physical society has been named a computational social system for a long time. Due to the high relation with vast populations' national livelihood, agriculture will still serve as a core industry in the national economy. As a result, this study focused on an efficient forecasting method for the agriculture economy. In recent years, the conception of deep intelligence has received overall prevalence in academia because of its excellent performance in implementing intelligent information processing tasks. Hence, this paper utilized deep intelligence driven by neural networks and managed to investigate an efficient prediction method for the agriculture economy of computational social systems. To fit the time-series forecasting scene of the long-term development of the agriculture economy, the convolutional neural network model is slightly improved by revising its parallel structure into the recurrent format. Finally, simulations on realistic datasets are carried out to evaluate the proposed forecasting method. [ABSTRACT FROM AUTHOR]

Solve the problem of agricultural product big data mining based on e-commerce platform, meet the needs of e-commerce development to agricultural products, meet the diversified needs of e-commerce platforms, and improve people's living standards and convenience. According to 1000 online questionnaires, 866 people believe that e-commerce can bring them convenience, and 134 people believe that the convenience is insufficient. Even agricultural products, as a traditional primary industry, have begun to be "involved" in the sales mode of e-commerce platforms. In the face of the increasingly huge online consumer demand market, the agricultural product economy has redisplayed a strong market vitality. Of course, the huge market base also makes the e-commerce model of agricultural products pay attention to big data mining and analysis. This paper focuses on how to carry out big data mining and analysis of agricultural products more efficiently from the technical level. Therefore, the agricultural product user data mining technology of e-commerce platform based on Hadoop is proposed. Through the intervention of association rule analysis and algorithm, the improvement of relevant algorithms and agricultural products user behavior analysis system under e-commerce platform based on Hadoop is proposed. The results show that the system can realize the analysis of commodity association degree under various agricultural products user behavior modes and can better help the e-commerce platform of agricultural products realize precision marketing. [ABSTRACT FROM AUTHOR]

Agriculture is critical to human life. Agriculture provides a means of subsistence for a sizable portion of the world's population. Additionally, it provides a large number of work opportunities for inhabitants. Many farmers prefer traditional farming approaches, which result in low yields. Agriculture and related industries are vital to the economy's long-term growth and development. The primary issues in agricultural production include decision-making, crop selection, and supporting systems for crop yield enhancement. Agriculture forecasting is influenced by natural variables such as temperature, soil fertility, water volume, water quality, season, and crop prices. Growing advancements in agricultural automation have resulted in a flood of tools and apps for rapid knowledge acquisition. Mobile devices are rapidly being used by everyone, including farmers. This paper presents a framework for smart crop tracking and monitoring. Sensors, Internet of Things cameras, mobile applications, and big data analytics are all covered. The hardware consists of an Arduino Uno, a variety of sensors, and a Wi-Fi module. This strategy would result in the most effective use of energy and the smallest amount of agricultural waste possible. [ABSTRACT FROM AUTHOR]

The purpose of the study is to justify the use of the universal scenario method of strategic planning and forecasting the development of the agroindustrial complex of the regional rural economy. The scientific novelty of the study lies in the application of a set of theoretical and methodological provisions for scenario planning and forecasting the development of agriculture in the regions, taking into account the assessment of their existing potential and constructing a territorial planning scheme for the priority of participation in the implementation of strategic directions of rural development in agricultural production. The paper presents a territorial model of the priority of participation of the municipal regions of the Republic of Bashkortostan in the implementation of strategic areas in the areas of development of production of grain crops, sugar beet, and oilseeds. The developed scenario method, reflecting the qualitatively heterogeneous directions of the development of enterprises, is formed taking into account the achievement of the strategic goal and potential opportunities of rural areas. This allows determining strategic decisions for the further development of rural areas and integrating them into a uniform industry development strategy. The developed approach is recommended to be used as a guideline in the development of long-term programs for the development of the crop production industry, as well as for adjusting the activities of ongoing programs. [ABSTRACT FROM AUTHOR]

Building an information-based rural economic supply chain is an effective way to implement the rural revitalization strategy. The construction of China's agricultural product logistics market is still very backward, mainly including imperfect information system and high cost of intermediate circulation links. How to optimize the supply chain of agricultural products, ensure the normal supply of agricultural products quality and quantity in the market, improve people's living standards, and achieve the purpose of improving benefits is an important topic to be studied today. Aiming at the problems of traditional agricultural products supply chain, the application of Internet of Things technology in supply chain can be effectively solved. Therefore, this paper proposes the research on the environmental effect evaluation of rural economic supply chain based on Internet of Things technology and proposes to build a wireless sensor network for agricultural products for the collection and exchange of agricultural products information. At the same time, RFID (radio-frequency identification) electronic tags integrated with sensors are applied in other links of the supply chain. The experimental results show that the construction of the rural economic supply chain system based on Internet of Things technology can effectively optimize the rural economic supply chain, realize efficient information sharing, improve the quality of agricultural products, reduce the cost of each link of the supply chain, improve the competitiveness of rural supply chain, and promote the good development of rural agricultural products and rural economic supply chain environment. [ABSTRACT FROM AUTHOR]

From a macro perspective, futures index of agricultural products can reflect the trend of macroeconomy and can also have an early warning effect on the possible crisis and provide a reference for the government's economic forecast and macro control. Therefore, it is necessary to strengthen the research on early warning and prediction of agricultural futures price. For the prediction of futures price, there are two kinds of common models: one is the traditional classic time series model, and the other is the neural network model under the wave of artificial intelligence. This paper selects the 1976 closing data of agricultural futures index from January 10, 2012, to February 27, 2020, and uses the time series differential autoregressive integrated moving average model (ARIMA model) and long short-term memory model (LSTM model) to study this work, respectively, and compares the predicted effects of the two models in some metrics. Based on the predicted results of the two models, a simple trading strategy is established, and the trading effects of the two models are compared. The results show that the LSTM model has obvious advantage over ARIMA time series model in the price index prediction of agricultural futures market. [ABSTRACT FROM AUTHOR]

Accurate forecasting of reference crop evapotranspiration (ET0) is vital for sustainable water resource management. In this study, four popularly used single models were selected to forecast ET0 values, including support vector regression, Bayesian linear regression, ridge regression, and lasso regression models, respectively. They all had advantages of low requirement of data input and good capability of data fitting. However, forecast errors inevitably existed in those forecasting models due to data noise or overfitting. In order to improve the forecast accuracy of models, hybrid models were proposed to integrate the advantages of the single models. Before the construction of hybrid models, each single model's weight was determined based on two weight determination methods, namely, the variance reciprocal and information entropy weighting methods. To validate the accuracy of the proposed hybrid models, 1–30 d forecast data from January 2 to February 1, 2022, were used as a test set in Xinxiang, North China Plain. The results confirmed the feasibility of the information entropy-based hybrid model. In detail, the information entropy model generated the mean absolute percentage errors of 11.9% or a decrease by 48.9% compared to the single and variance reciprocal hybrid models. Moreover, the model generated a correlation coefficient of 0.90 for 1–30 d ET0 forecasting or an increase by 13.6% compared to other models. The standard deviation and the root mean square error of the information entropy model were 1.65 mm·d−1 and 0.61 mm·d−1 or had a decrease by 16.4% and 23.7%. The maximum precision and the F1 score were 0.9618 and 0.9742 for the information entropy model. It was concluded that the information entropy-based hybrid model had the best midterm (1–30 d) ET0 forecasting performance in the North China Plain. [ABSTRACT FROM AUTHOR]

California state is among the leading producers of strawberries in the world. The value of the California strawberry crop is approximately $2.6 billion, which makes it one of the most valuable fruit crops for the state and nation’s economy. California’s weather provides ideal conditions for strawberry production and changes in weather pattern could have a significant impact on strawberry fruit production. Evaluating relationships between meteorological parameters and strawberry yield can provide valuable information and early indications of yield forecasts that growers can utilize to their advantage. Objectives of this paper were to evaluate correlations of meteorological parameters on strawberry yield for Santa Maria region and to develop meteorology based empirical yield forecasting models for strawberries. Results showed significant correlation between meteorological parameters and strawberry yield and provided a basis for yield forecasting with lead time. Results from empirical models showed that cross-validated yields were closely associated with observed yield with lead time of 2 to 5 months. Overall, this study showed great potential in developing meteorology based yield forecast using principal components. This study only looked at meteorology based yield forecasts. Skills of these models can be further improved by adding physiological parameters of strawberry to existing models for strawberry. [ABSTRACT FROM AUTHOR]

The purpose of this research is to examine the impact of rural-urban labor migration and remittances on rural agricultural productivity. A rigorous random selection process was used to pick 480 households from cross-sectional data. For this investigation, both primary and secondary data were used. Stata version 16 was used to examine both qualitative and quantitative data using descriptive (mean, standard deviation, and percentages) and econometric (three-stage least square technique) analyses. According to the descriptive data, the majority of respondents used their remittances for consumption, to acquire agricultural inputs, and to pay back their debts and tax payments. According to econometric studies, rural-urban migration has little influence on agricultural productivity. Remittances, cultivated land, livestock ownership, and extension services, on the other hand, have a positive and significant effect on agricultural productivity. In a nutshell, the link between migration, remittances, and agricultural output in agrarian and rural families is remarkable. Agriculture is the major source of income and mostly handles the liquidity issue. Agriculture production in the study region is labor intensive, and it is influenced by the amount of effort utilized in production as well as the socioeconomic characteristics of the household. As a result, it must address the major conundrum of agricultural productivity, particularly rural-urban migration and remittances. Address a knowledge gap, begin activities, and develop and implement multiple initiatives by various responsible stakeholders that are essential for the research topic. Furthermore, agricultural extension service delivery should be improved by timely recruiting, frequent agent training, and appropriate logistics. [ABSTRACT FROM AUTHOR]

Due to the combined influence of complex engineering geological conditions and environmental factors from agricultural mountainous areas, the evolution of slope deformation is complicated and nonlinear. Support vector machine (SVM) technology could effectively solve the technical problems of small sample, high dimension, and nonlinear, so it is applied to data mining of the measured slope displacement and the prediction and analysis of the slope deformation trend. In order to avoid blindness of human choice of SVM parameters and to improve the prediction accuracy and generalization ability of the model, an ACO-SVM model is built by adopting an improved ant colony algorithm (ACO) to optimize parameters in association with the rolling forecasting method of displacement time series. The model was applied to two engineering examples. The research results showed that the ACO-SVM model was correct with high accuracy. The ACO-SVM model had higher accuracy of prediction and stronger generalization ability than optimizing SVM based on the genetic algorithm or particle swarm optimization. The forecasting results were more reasonable. It has certain engineering application values for slope deformation prediction. [ABSTRACT FROM AUTHOR]

Most of the sub-Saharan African countries including Ethiopia were affected by the food insecurity issue. This study aimed to analyze the drivers of food insecurity, the choice of livelihood strategies, and factors that impact the choices of food security strategies in response to food insecurity in Abay Chomen District of Ethiopia's Oromia region, Ethiopia. The result of this study is based on primary data and obtained from 150 randomly chosen sample households and secondary data generated from various sources. As for the technique of data analysis, this study employed descriptive statistics for the food insecurity index, as well as a binary logistic model and a multinomial logit model for the choice of household livelihood techniques. The findings of the survey showed that 51.3% of the households were found to be food-insecure and 48.7% food-safe in the study area. Furthermore, the result indicated that the average calorie consumption of the households surveyed was 2008.54 kcal for each adult equivalent per day, which is below the lowest calorie necessity of 2200 kcal. The estimated logistic model outcome on the drivers of household food insecurity confirmed the oldness of the household leader, larger family holder, and off-farm income affects negatively, while the gender of the household leader, the size of the built-up area, the number of livestock holdings (except oxen), the number of oxen owned, access to credit, the participation in the sale of cattle, and others affect positively. In addition, the multinomial logit model result indicates that the educational status of the household leader, the size of livestock farming, the number of oxen possessed, access to credit, remoteness to the market, and monthly agricultural earning are the main drivers of the choice of livelihood strategies of concern for the food insecurity of households. As a result, this research attempted to produce a result of analysis with a defined scope, although many questions remain unsolved. Future studies should concentrate on presenting fundamental data on the factors that affect food security status and livelihood strategy, the social, political, natural, and environmental aspects, the descriptive information on the shopping habits of people who experience food insecurity, and the key aspects that increase the vulnerability of the rural poor to food insecurity. [ABSTRACT FROM AUTHOR]

Our recent study using historic data of wheat yield and associated plantation area, rainfall, and temperature has shown that incorporating statistics and artificial neural networks can produce highly satisfactory forecasting of wheat yield. However, no comparison has been made between the outcomes from the spatial neural network model and commonly used temporal neural network models in crop forecasting. This paper presents the latest research outcomes from using both the spatial and temporal neural network models in crop forecasting. Our simulation shows that the spatial NN model is able to predict the wheat yield with respect to a given plantation area with a high accuracy compared with the temporal NARNN and NARXNN models. However, the high accuracy of the spatial NN model in crop yield forecasting is limited to the forecasting of crop yield only within normal ranges. Users must be cautious when using either NARNN or NARXNN for crop yield forecasting due to their inconsistency between the results of training and forecasting. [ABSTRACT FROM AUTHOR]

Agribusiness employs more than 66 percent of India's rural population and is the country's economic backbone. Beat crop growth is essential for practical farming since it increases soil diversity and actual design, and it may be grown in blended frameworks. Crop growth rates, applicability, and yields have not improved significantly over time in the United States. Crops are defined by their seasonality, derived nature of demand, and relatively inelastic pricing. The general purpose of this research is to demonstrate the usefulness of price forecasting for agricultural prices and validate it for rice, which is consumed more in Indian states, for the year 2022, using time series data from 2016 to 2021. Every year, data for 50 days is collected and multiplied. The range of ten and its multiple is used for predicting. The results were obtained through the use of univariate analysis. To develop grain price estimates, researchers used Autoregressive Integrated Moving Average (ARIMA) methods, and the precision of the forecasts was examined using conventional mean square error (MSE) and mean absolute percentage error (MAPE) standards. As proven by the outcomes of ARIMA price predictions, the ARIMA model's efficacy as a tool for price forecasting was effectively demonstrated by realistic models of projected prices for 2020. Because the MSA and MAPE values were lower, the forecast was more accurate. In addition, the price forecasting in this model is dependent on government incentives. [ABSTRACT FROM AUTHOR]

Risk prediction refers to a measure that predicts and formulates countermeasures to prevent accidents from occurring during the work process and work results before work. With the development of the global economy, fresh agricultural products, as one of the agricultural products with the largest daily consumption, will have a serious impact once a problem occurs. The emergence of Internet of Things technology makes the supply of agricultural products in more forms to the market. This article analyzed the related literature about agricultural calendar year outsourcing research; the scholars have identified factors influencing the quality of security screening and classification, common process and characteristics of domestic agricultural products outsourcing, standing in the perspective of external environment and internal processes, from the natural environment of supply chain, the production processing, transportation, circulation, and marketing chain. The factors that may affect the quality and safety of fresh agricultural products were identified, and the risk assessment index system was finally determined. Then, the risk of risk index is evaluated from the perspective of six attributes of risk; that is, the risk of each risk index is considered from the possibility of occurrence, loss caused, infectivity, mitigation, controllability, and predictability, rather than just the possibility and loss of risk. Finally, the system evaluation data shows that the stability of the system is as high as about 80, indicating that the system has a good stability. [ABSTRACT FROM AUTHOR]

China is a large developing country, and there is no doubt that it is a large developing agricultural country. Agriculture has played a vital role in the development of the national economy since ancient times, and it serves as the foundation of China's national economy. As agricultural modernization is the fundamental way of agricultural development, its source of funds must come from rural finance. The financial support of rural finance for agricultural modernization is the material basis and prerequisite for ensuring the healthy and rapid development of agricultural modernization. If there is no financial support from rural finance, the process of agricultural modernization will be hindered, and it will not benefit our country's socialist modernization. Although our country's rural financial system has been constantly changing and improving, our country's current financial system still has many shortcomings and defects. In the process of supporting agriculture, rural finance has insufficient capital supply, which has become a "bottleneck" in the development of agricultural modernization, and to a certain extent has severely restricted the development of agricultural modernization. On the basis of existing literature research, this article systematically studies the influence of rural financial market structure on the development of agricultural enterprises in central China. The content includes agricultural total factor productivity and agricultural emission reduction. When analyzing the impact of rural financial market structure on agricultural development, this article also pays attention to its specific impact on agricultural development under the conditions of different types of enterprises. [ABSTRACT FROM AUTHOR]

Farmers' physical labor and debt are reduced as a result of agricultural automation, which emphasizes efficient and effective use of various machines in farming operations with the purpose of reducing physical labor and debt. It is a revolutionary idea in agriculture to create custom hiring centers, which are intended to make it easier for like-minded farmers to embrace technology/machinery for enhanced resource management practices. The study in question examines the significance of tool renting and sharing in the workplace. Rental and sharing equipment are two approaches that might be used to enable farmers to borrow equipment at a cheaper cost than they would otherwise have to pay for it. The following is a manual pilot study of 562 farmers in India to address the numerous challenges farmers face when looking for tools and equipment, as well as to determine their strong interest in the process of renting and sharing equipment. The study was conducted to address the numerous challenges farmers face when looking for tools and equipment and to determine their strong interest in the process of renting and sharing equipment. Farmers are divided into three groups according to the results of this poll: small, moderate, and large. Training and testing splits were used on the same data set in order to get a better understanding of the target variables. The data set for the survey was standardized in order to remove ambiguity. In this research, three different machine learning models were utilized: nearest neighbors, logistic regression, and decision trees. K-nearest neighbors was the most often used model, followed by logistic regression and decision trees. In order to get the best possible result, a comparison of the aforementioned algorithm models was carried out, which revealed that the decision tree is the better model among the others in this regard. Because the decision tree model is completely reliant on a large number of input factors, such as the kind of crop, the time/month of harvest, and the type of equipment necessary for the crops, it has the potential to have a social and economic impact on farmers and their livelihoods. [ABSTRACT FROM AUTHOR]

As the foundation of the national economy, agriculture is a high-risk, weak industry. Affected by many factors, agricultural production is subject to catastrophe risks from time to time. Agricultural production is mainly faced with two major threats, natural disaster risk and market risk. As an effective risk management tool, the production and promotion of agricultural insurance have played an essential role in guaranteeing the development of the agricultural industry in some developed countries and major agricultural countries in the world. This article combines the Internet of Things and Markov model for agricultural insurance risk management. First, we combine the structure of the Internet of Things and select relevant statistical data. Then, we build a panel data system, starting from two perspectives in different regions and analyze agricultural insurance's current development and characteristics at each stage. In addition, we use the Markov model to build a panel data model to explore the specific impact mechanisms deeply. We also study the effects of disaster risk levels in different regions on the development of agricultural insurance. After simulation verification, we believe that this model can effectively promote the balanced regional development of agricultural insurance. [ABSTRACT FROM AUTHOR]

From the level of agricultural material equipment, the level of agricultural output benefit, the level of agricultural economic structure, the level of agricultural industrialization management, the level of agricultural sustainable development, and the level of rural social and economic development, based on 15 indicators, an index system for evaluating the comprehensive capacity of modern agricultural development in Huizhou was constructed. The comprehensive evaluation of the development level of modern agriculture in Huizhou from 2012 to 2018 was measured by the entropy weight method, and the evaluation was analyzed with the average level of Guangdong Province. We find out the gap between the development advantages of Huizhou and the average level of Guangdong Province, so as to promote the development of modern agriculture in Huizhou. Based on the comprehensive evaluation index of the development level of modern agriculture in Huizhou from 2012 to 2018 obtained by the entropy weight method, a fractional cumulative grey GM (1, 1) forecasting model was established, and its development ability was predicted and analyzed. The forecast result has certain guiding significance to the development of modern agriculture in Huizhou. [ABSTRACT FROM AUTHOR]

Upholding sustainability in the use of energies for the increasing global industrial activity has been one of the priority agendas of the global leaders of the West and East. The projection of different GHGs has thus been the important policy agenda of the economies to justify the positions of their own as well as of others. Methane is one of the important components of GHGs, and its main sources of generation are the agriculture and livestock activities. Global diplomacy regarding the curtailment of the GHGs has set the target of reducing the levels of GHGs time to time, but the ground reality regarding the reduction is far away from the targets. Sometimes, the targets are fixed without the application of scientific methods. The aim of the present study is to examine sustainability of energy systems through the forecasting of the methane emission and agricultural output of the world's different income groups up to 2030 using the data for the period 1981–2012. The work is novel in two senses: the existing studies did not use both the Box–Jenkins and artificial neural network methods, and the present study covers all the major economic groups in the world which is unlike to any existing studies. Two methods are used for forecasting of the two. One is the Box–Jenkins method, where linear nature of the two variables is considered and the other is artificial neural network methods where nonlinear nature of the variables is also considered. The results show that, except the OECD group, all the remaining groups display increasing trends of methane emission, but unquestionably, all the groups display increasing trends of agricultural output, where middle- and upper middle-income groups hold the upper berths. The forecasted emission is justified to be sustainable in major groups under both methods of estimations since overall growth of agricultural output is greater than that of methane emission. [ABSTRACT FROM AUTHOR]

The output values for agriculture, forestry, animal husbandry, and fishery are important indicators of agricultural economic development. Therefore, accurately predicting the output values for agriculture, forestry, animal husbandry, and fishery can capture the developmental trend and the optimize the structure. Agriculture, forestry, animal husbandry, and fishery are typical seasonal industries, and thus their output values vary greatly among different seasons. To accurately estimate the seasonal variations in the observed sequence and obtain better prediction results, the output values for agriculture, forestry, animal husbandry, and fishery in different quarters from 2018 to 2021 are predicted and analyzed by using the grey seasonal model (GSM). The results indicated that the prediction accuracy of GSM is relatively high. The output values for the agriculture, forestry, animal husbandry, and fishery as well as their total output value will increase gradually. It is an important achievement of structural reform under the new normal economic situation. In addition, the GSM provides a new method for predicting seasonal data. [ABSTRACT FROM AUTHOR]