An agent-based model of agricultural land expansion in the mountain forest of Timor Island,Indonesia

The Mutis-Timau Forest Complex,located on Timor Island,Indonesia,is a mountainous tropical forest area that gradually decreases due to deforestation and forest degradation.Previous modelling studies based on patterns indicate that deforestation primarily occurs at lower elevations and near the boundaries of forests and settlements,often associated with shifting cultivation by local farmers.This study adopts a process-based modelling approach,specifically the agent-based model,to simulate land changes,particularly farmers'expansion of agricultural land around the Mutis mountain forest.The underlying concept of this agent-based approach is the interaction between the human and environmental systems.Farmers,representing the human system,interact with the land,which represents the environmental system,through land use decision-making mechanisms.The research was conducted in the Community Forest of the Timor Tengah Utara District,one of the sites within the Mutis-Timau Forest Complex with the highest deforestation rate.Land use change simulations were performed using agent-based modelling from 1999 to 2030,considering the socio-economic conditions of farmers,spatial preferences,land use decisions,and natural transitions.The results revealed that the agricultural area increased by 14%under the Business as Usual scenario and 5%under the Reducing Emission from Deforestation and Forest Degradation scenario,compared to the initial agricultural area of 245 hectares.The probability of farmers deciding to extend agricultural activities was positively associated with the number of livestock maintained by farmers and the size of the village area.Conversely,the likelihood of farmers opting for agricultural extensification decreased with an increase in the area of private land and the farmer's age.These findings are crucial for the managers of the Mutis-Timau Forest Complex and other relevant stakeholders,as they aid in arranging actions to combat deforestation,designing proper forest-related policies,and providing support for initiatives such as reducing emissions from deforestation and forest degradation programs or further incentive schemes.

Understanding factors affecting non-participants’interest in community-supported agriculture

Community-supported agriculture(CSA)has emerged as a viable solution for addressing the agricultural challenges faced by countries like Indonesia.This study uses the wellestablished unified theory of acceptance and use of technology(UTAUT2)model to examine the interest in CSA of potential customers in Indonesia.A standardized questionnaire was distributed to 1200 respondents,and the data were analyzed using structural equation model-partial least square(SEM-PLS)in SmartPLS 4.0 software.The results capture potential CSA consumer interest and will help to improve CSA development strategies in Indonesia.The model explains 44.4%of customers’intentions,and identifies performance expectancy as the decisive factor in customers’willingness to participate in CSA.Performance expectancy(0.292),hedonic motivation(0.262),social influence(0.259),and facilitating conditions(0.086)positively influence customers’interest in participating in a CSA program.The adoption of CSA programs by both farmers and customers could be increased by implementing regulations that provide tax incentives and subsidies,offering training on sustainable farming practices,facilitating the establishment of distribution channels,and establishing guidelines for fair price and quality standards.This study shows the high potential for the implementation of CSA in Indonesia.It could also be used as a foundation for the development of new policies regarding sustainable agriculture markets in Indonesia.

The Extreme Machine Learning Actuarial Intelligent Agricultural Insurance Based Automated Underwriting Model

The paper presents an innovative approach towards agricultural insurance underwriting and risk pricing through the development of an Extreme Machine Learning (ELM) Actuarial Intelligent Model. This model integrates diverse datasets, including climate change scenarios, crop types, farm sizes, and various risk factors, to automate underwriting decisions and estimate loss reserves in agricultural insurance. The study conducts extensive exploratory data analysis, model building, feature engineering, and validation to demonstrate the effectiveness of the proposed approach. Additionally, the paper discusses the application of robust tests, stress tests, and scenario tests to assess the model’s resilience and adaptability to changing market conditions. Overall, the research contributes to advancing actuarial science in agricultural insurance by leveraging advanced machine learning techniques for enhanced risk management and decision-making.

Augmented IoT Model for Smart Agriculture and Farm Irrigation Water Conservation

In Northern Nigeria, irrigation systems are operated manually. Agriculture has over the years been practiced primitively by farmers, especially in sub-Saharan Africa. This is due to the absence of intelligent technological know-how where its practice could be leveraged upon. Agricultural practice is constrained by some major challenges ranging from traditional way of farming, understating of concepts, practices, policy, environmental and financial factors. The aim of this study was to optimize an IoT-based model for smart agriculture and irrigation water management. The objectives of the study were to: design, implement, test and evaluate the performance of the optimized IoT-based model for smart agriculture and irrigation water management. The method used in the study was the prototyping model. The system was designed using balsamiq application tools. The system has a login page, dashboard, system USE-CASE diagrams, actuators page, sensor page and application interface design. Justinmind tool was used to show the flow of information in the system, which included data input and output, data stores and all the sub-processes the data moves through. The Optimized IoT model was implemented using four core platforms namely, ReactJS Frontend Application development platform, Amazon web services IoT Core backend, Arduino Development platform for developing sensor nodes and Python programming language for the actuator node based on Raspberry Pi board. When compared with the existing system, the results show that the optimized system is better than the existing system in accuracy of measurement, irrigation water management, operation node, platform access, real-time video, user friendly and efficiency. The study successfully optimized an IoT-based model for smart agriculture and irrigation water management. The study introduced the modern way of irrigation farming in the 21st century against the traditional or primitive way of irrigation farming that involved intensive human participation.

Discussion on the Current Situation and Development Model of Sightseeing Agriculture in Zhenjiang

Zhenjiang has distinct advantages to develop sightseeing agriculture as it possesses outstanding agricultural features, superior geographical locations and abundant cultural resources. At present, the situation of sightseeing agriculture in Zhenjiang is complicated. Though the development is going fast, it is quite blindfold for there is no reasonable overall plan; the production is at low level and requires more cultural connotation; the investment source is thin while the cooperation management hasn't been accomplished; a rudiment mode of varied sightseeing agriculture is forming rapidly. Based on this situation, five development modes are put forward for Zhenjiang's sightseeing agriculture, including: "enterprises as the main body of management" mode, "village committees as the main body of management" mode, "villagers as the main body of management" mode, government operating mode and joint development mode.

Low-carbon Agriculture Development Models in Southern Jiangsu Province

[Objective] To investigate the appropriate low-carbon agriculture model in Southern Jiangsu Province. [Method] Through the analysis of regional features in Southern Jiangsu and several matured low-carbon agriculture development models at present, the low-carbon agriculture development modes suitable for Southern Jiangsu were investigated, and corresponding supporting measures for the development of the models were put forward. [Result] Low-carbon agriculture is the environment- friendly agriculture which achieves low emissions, low pollution, high efficiency and high-yield through efficient recycling of energy and resources and continuous im- provements on ecological environment. With a variety of development models, the specific development model for practical use should be determined according to the local conditions, and supported by corresponding supporting measures, to achieve the rapid development of low-carbon agriculture. [Conclusion] This study laid the foundation for the development of low-carbon agriculture model in Southern Jiangsu.

Research on Comparative Advantage and Typical Models of Circular Agriculture Economy in Eastern and Mid-China Region

Developing agricultural circular economy is the important measures of relieving the pressure on agricultural resources, preserving the ecological environment and promoting the sustainable development of agriculture and rural economy. Based on the idea of circulation of agricultural research as the breakthrough point, the research firstly detailed the concept and scientific connotation of circular egdculture in an all-round way and described and analyzed the advantages and conditions of the development of circular agriculture in eastern and middle regions of China from three aspects including resources endowment, conditions of economic development and industrial base conditions. Furthermore, the research analyzed the model char- actedstics and summarized the successful ex^dence to provide a reference for promoting the models with consideration of Taihu Lake Basin in South of Jiangsu, Yi- meng mountain areas in Southeast of Shandong and hills and mountains region in northwest Henan. Finally, the reseach put forward the development orientation of agricultural circular economy and countermeasures and suggestions to further enhance the level of development.

Measuring the agricultural sustainability of India:An application of Pressure-State-Response(PSR)model

Analyzing agricultural sustainability is essential for designing and assessing rural development initiatives.However,accurately measuring agricultural sustainability is complicated since it involves so many different factors.This study provides a new suite of quantitative indicators for assessing agricultural sustainability at regional and district levels,involving environmental sustainability,social security,and economic security.Combining the PressureState-Response(PSR)model and indicator approach,this study creates a composite agricultural sustainability index for the 14 mainstream agro-climatic regions of India.The results of this study show that the Trans-Gengatic Plain Region(TGPR)ranks first in agricultural sustainability among India's 14 mainstream agro-climatic regions,while the Eastern Himalayan Region(EHR)ranks last.Higher livestock ownership,cropping intensity,per capita income,irrigation intensity,share of institutional credit,food grain productivity,crop diversification,awareness of minimum support price,knowledge sharing with fellow farmers,and young and working population,as well as better transportation facilities and membership of agricultural credit societies are influencing indicators responsible for higher agricultural sustainability in TGPR compared with EHR.Although,the scores of environmental sustainability indicators of EHR are quite good,its scores of social and economic security indicators are fairly low,putting it at the bottom of the rank of agricultural sustainability index among the 14 mainstream agroclimatic regions in India.This demonstrates the need of understanding agricultural sustainability in relation to social and economic dimensions.In a nation as diverse and complicated as India,it is the social structure that determines the health of the economy and environment.Last but not least,the sustainability assessment methodology may be used in a variety of India's agro-climatic regions.

Modeling the impacts of climate change on China's agriculture

The impacts of climate change on China's agriculture are measured based on Ricardian model. By using county-level cross-sectional data on agricultural net revenue, climate, and other economic and geographical data for 1275 agriculture-dominated counties in the period of 1985-1991, we find that both higher temperature and more precipitation will have overall positive impact on China's agriculture. However, the impacts vary seasonally and regionally. Higher temperature in all seasons except spring increases agricultural net revenue while more precipitation is beneficial in winter but is harmful in summer. Applying the model to five climate scenarios in the 2020s and 2050s shows that the North, the Northeast, the Northwest, and the Qinghai-Tibet Plateau would always benefit from climate change while the South and the Southwest may be negatively affected. For the East and the Central China, most scenarios show that they may benefit from climate change. In conclusion, climate change would be beneficial to the whole China.

Production Efficiency of Two-Oriented Agriculture Based on Three-Stage DEA Model

Two-oriented agriculture was a complex organism coupling production,economics,society and ecology.Its development process was affected by various factors such as producers,nature,society,etc.In order to overcome measurement error of traditional data envelopment analysis caused by ignoring random,three-stage DEA model was studied to remove environmental factors and random effects.On the foundation of this model was two-oriented agriculture comprehensive production efficiency of 14 cities were estimated in Hunan Province in 2008,and brown forth corresponding policy proposals to promote agricultural development.

Building an interoperable space for smart agriculture

The digital transformation in agriculture introduces new challenges in terms of data,knowledge and technology adoption due to critical interoperability issues,and also challenges regarding the identification of the most suitable data sources to be exploited and the information models that must be used.DEMETER(Building an Interoperable,Data-Driven,Innovative and Sustainable European Agri-Food Sector)addresses these challenges by providing an overarching solution that integrates various heterogeneous hardware and software resources(e.g.,devices,networks,platforms)and enables the seamless sharing of data and knowledge throughout the agri-food chain.This paper introduces the main concepts of DEMETER and its reference architecture to address the data sharing and interoperability needs of farmers,which is validated via two rounds of 20 large-scale pilots along the DEMETER lifecycle.This paper elaborates on the two pilots carried out in region of Murcia in Spain,which target the arable crops sector and demonstrate the benefits of the deployed DEMETER reference architecture.

Climate Change Impacts and Adaptation in Rainfed Farming Systems: A Modeling Framework for Scaling-Out Climate Smart Agriculture in Sub-Saharan Africa

Improving agricultural water productivity, under rainfed or irrigated conditions, holds significant scope for addressing climate change vulnerability. It also offers adaptation capacity needs as well as water and food security in the southern African region. In this study, evidence for climate change impacts and adaptation strategies in rainfed agricultural systems is explored through modeling predictions of crop yield, soil moisture and excess water for potential harvesting. The study specifically presents the results of climate change impacts under rainfed conditions for maize, sorghum and sunflower using soil-water-crop model simulations, integrated based on daily inputs of rainfall and evapotranspiration disaggregated from GCM scenarios. The research targets a vast farming region dominated by heavy clay soils where rainfed agriculture is a dominant practice. The potential for improving soil water productivity and improved water harvesting have been explored as ways of climate change mitigation and adaptation measures. This can be utilized to explore and design appropriate conservation agriculture and adaptation practices in similar agro-ecological environments, and create opportunities for outscaling for much wider areas. The results of this study can suggest the need for possible policy refinements towards reducing vulnerability and adaptation to climate change in rainfed farming systems.

Impact of systems modelling on agronomic research and adoption of new practices in smallholder agriculture

An analysis of the impact of simulation modelling in three diverse crop-livestock improvement projects in Agricultural Research for Development（AR4D） reveals benefits across a range of aspects including identification of objectives, design and implementation of experimental programs, effectiveness of participatory research with smallholder farmers, implementation of system change and scaling-out of results.In planning change, farmers must consider complex interactions within both biophysical and socioeconomic aspects of their crop and animal production activities.For this, whole-farm models that include household models of food, workload and financial requirements have the most utility and impact.The analysis also proposes improvements in design and implementation of AR4 D projects to improve the utility of simulation modelling for securing positive agronomic and livestock outcomes and lasting legacies.

GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture

Smart Agriculture,also known as Agricultural 5.0,is expected to be an integral part of our human lives to reduce the cost of agricultural inputs,increasing productivity and improving the quality of the final product.Indeed,the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important.To provide more comprehensive protection against potential cyber-attacks,this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture.The proposed Intrusion Detection System IDS,namely GMLPIDS,combines the feedforward neural network Multilayer Perceptron(MLP)and the Gaussian Mixture Model(GMM)that can better protect the Smart Agriculture system.GMLP-IDS is evaluated with the CIC-DDoS2019 dataset,which contains various Distributed Denial-of-Service(DDoS)attacks.The paper first uses the Pearson’s correlation coefficient approach to determine the correlation between the CIC-DDoS2019 dataset characteristics and their corresponding class labels.Then,the CIC-DDoS2019 dataset is divided randomly into two parts,i.e.,training and testing.75%of the data is used for training,and 25%is employed for testing.The performance of the newly proposed IDS has been compared to the traditional MLP model in terms of accuracy rating,loss rating,recall,and F1 score.Comparisons are handled on both binary and multi-class classification problems.The results revealed that the proposed GMLP-IDS system achieved more than 99.99%detection accuracy and a loss of 0.02%compared to traditional MLP.Furthermore,evaluation performance demonstrates that the proposed approach covers a more comprehensive range of security properties for Smart Agriculture and can be a promising solution for detecting unknown DDoS attacks.

The Industrial Cluster of the Primary Agriculture Based on "Diamond Model"

The status quo and problems of industrial cluster of the primary agriculture is discussed by dividing the cluster into primary cluster and advanced cluster and by using Michael Porter's Diamond Model from the five aspects including production factors, demand factors, relevant industry and supporting industry, the strategy and structure of enterprises and horizontal competition and opportunities and government. In the end, the countermeasures on promoting the development and expansion of industrial cluster of primary agriculture are put forward. Firstly, intensifying the training on farmers and introduce into advanced science and technology results; secondly, perfecting the construction of infrastructure, creating famous brand and widening the channels for funding; thirdly, strengthening the development of relevant industries and supporting industries; fourthly, perfecting land transfer system; improving the degree of systematization and cultivating pillar industries; fifthly, intensifying the government' support on industrial cluster.

Cluster Development of Zhengzhou Urban Agriculture Based on Diamond Model

Based on basic theory of Diamond Model,this paper analyzes the competitive power of Zhengzhou urban agriculture from production factors,demand conditions,related and supporting industries,business strategies and structure,and horizontal competition.In line with these situations,it introduces that the cluster development is an effective approach to lifting competitive power of Zhengzhou urban agriculture.Finally,it presents following countermeasures and suggestions:optimize spatial distribution for cluster development of urban agriculture;cultivate leading enterprises and optimize organizational form of urban agriculture;energetically develop low-carbon agriculture to create favorable ecological environment for cluster development of urban agriculture.

Functional Sustainability of a Flight Dynamics Control System for Stable Hovering Flight of an Unmanned Aerial Vehicle(UAV),such as in Agricultural Applications:Mathematical Modeling and Simulation

The simulation of a control system for the longitudinal axis of the rotary or fixed-wing unmanned aerial vehicles(UAVs)is demonstrated in this study.The control unit includes design considerations of two controllers to provide robust stability,tracking of the proposed linear dynamics,an adequate set of proportional-integral-derivative(PID)controller gains,and a minimal cost function.The PID control and linear quadratic regulator(LQR)with or without full-state-observer were evaluated.An optimal control system is assumed to provide fast rise and settling time,minimize overshoot,and eliminate the steady-state error.The effectiveness of this approach was verified by a linear model of the UAV aircraft in the semi-dynamic simulation platform of Matlab/Simulink,in which the open-loop system was assessed in terms of flight robustness and reference tracking.The experimental results show that the proposed controllers effectively improve the configuration of the control system of the plant,maintain the sustainability of the dynamic flight model stability,and diminish the flight controller errors.The LQR provides robust stability,but it is not optimal in the transient phase of particular plant output.The PID control system can adjust the controller’s gains for optimal hovering(or stable slow flight)and is especially useful for the tracking system.Finally,comparing aircraft stability using PID and LQR controllers shows that the latter has less overshoot and a shorter settling time;in addition,all proposed controllers can be practically deployed as one UAV’s system,which can be handled as an exemplary model of the UAV flight management system.

A Multi-case Study of Community Supported Agriculture (CSA) Operation Model in Beijing

As one of the ways to localize foods,community supported agriculture can strengthen food safety and should be reasonably guided and developed.Based on the analysis of the spatial layout of the community supported agriculture organizations in Beijing,this paper described the development framework and course of community supported agriculture in Beijing.Combining 3 differentiated cases with the consumer's participation in community supported agriculture and the trust mechanism,it was found that the drive type has a very significant impact on the characteristics of these organizations.

Discussion on an Eco-Agriculture Model in the Semi-Arid Region of China

The semi-arid region of China covers an area of 2.2 x106km2. Water shortage in this region is the core but not the only one factor for the sustainable development of agriculture. Effective application of the energy and all kinds of resources and overall maintenance of the balance of ecological environment must be emphasized for sustainable development of the agriculture in the region. The extensive development of rural yard-economy is the only way to realize the intensive agricultural development there. A model is developed on the basis of our studies in recent years.

Unveiling the impact and mechanism of digital technology on agricultural economic resilience

Enhancing the economic resilience of agriculture is essential for promoting sustainable and high-quality agricultural development.The emergence of digital technology has created new opportunities in this field.However,existing research predominantly focuses on traditional agricultural factors and technologies.Therefore,the impact of digital technology on agricultural economic resilience within the broader context of the“production-operation-industry”system in agriculture has not been comprehensively explored.To bridge this gap,this study analyzes panel data from 30 Chinese provinces from 2011 to 2020.It employs the static Van Dorn’s law and a dynamic spatial panel model to examine how digital technology empowers agricultural resilience.The findings indicate a continuous strengthening of digital technology development in China,albeit with significant polarization and spatial imbalances.Moreover,the resilience of the agricultural economy undergoes notable fluctuations,initially narrowing and subsequently displaying an upward trend.Digital technology clearly plays a pivotal role in empowering resilience through agricultural scale operation,industrial transformation,and technological progress.Its impact,particularly on the promotion of resilience in the eastern region and non-grain-producing areas and on high-level agricultural economies,also shows regional and technological variations.