Journal articles on the topic 'Agriculture Simulation Methods'
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Volk, Tinea. "Effects of agricultural policy on the development of Slovenian agriculture during the transition and the process of accession to the European Union." Journal of Agricultural Sciences, Belgrade 50, no. 1 (2005): 75–88. http://dx.doi.org/10.2298/jas0501075v.
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The thesis analyzes the development of agricultural policy and agriculture in Slovenia in the period from 1992 to 2002. The analysis is based on the classification of agricultural policy and its measures, standard indicators used for analysis of development of agricultural policy and agriculture, and specific methods for evaluating the efficiency of agricultural policy (evaluation methods, simulation methods). The results show that the transition in Slovenia caused no marked shocks for agricultural production. The development goals for agriculture were set forth early (in 1992) and were modeled on the EU standards, and they remained unchanged throughout the transition. A protectionist development concept of agricultural policy was adopted, which assured a relatively high level of support to agriculture. Under this concept, the agricultural policy was substantially reoriented during the transition, but this happened gradually and was reflected above all in the re-instrumentation of policy and changes of the structure of support to agriculture. Agricultural policy was relatively successful. It managed to achieve most of the strategic development goals of agriculture and a high degree of compatibility with the Common Agricultural Policy (CAP).
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Yamashita, Ryohei. "Dialogue with Different Fields Surrounding Agriculture through the Development of Research using Simulation Methods." Journal of Rural Problems 56, no. 1 (March 25, 2020): 11–16. http://dx.doi.org/10.7310/arfe.56.11.
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Fibriansari, Rizeki Dwi, Anggia Astuti, and Dwi Ochta Pebriyanti. "IMPROVING MC-KENZIE EXERCISE CAPABILITIES THROUGH SIMULATION METHOD IN THE AGRICULTURE AREA." Jurnal Pengabdian Masyarakat Dalam Kesehatan 4, no. 2 (December 20, 2022): 69–73. http://dx.doi.org/10.20473/jpmk.v4i2.38363.
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Introduction: Low back pain is one of the factors causing morbidity and mortality in agricultural areas, namely pain syndrome experienced by individuals caused by poor body position. This can injure soft tissue structures that include muscles and ligaments. Community service aims to improve farmers' knowledge and skills in the PTPN XII Gunung Gambir Jember Agricultural Area to prevent low back pain.
Methods: Community service activities are carried out by providing education through simulations to farmers. The reason for choosing this method is that farmers prefer real experiences compared to lectures.
Results: Simulation learning and role play will allow farmers to learn firsthand through watching, practicing, and role-playing how to do the Mc-Kenzie Exercise. Thus, it is hoped that farmers will experience more knowledge and skills in preventing low back pain.
Conclusion: Community service activities can increase farmers' knowledge of skills about Mc-Kenzie exercises to prevent low back pain without side effects.
KEYWORDS
Mc-Kenzie Workout, Low Back Pain, Simulation
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Kiani, Farzad, Amir Seyyedabbasi, Sajjad Nematzadeh, Fuat Candan, Taner Çevik, Fateme Aysin Anka, Giovanni Randazzo, Stefania Lanza, and Anselme Muzirafuti. "Adaptive Metaheuristic-Based Methods for Autonomous Robot Path Planning: Sustainable Agricultural Applications." Applied Sciences 12, no. 3 (January 18, 2022): 943. http://dx.doi.org/10.3390/app12030943.
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The increasing need for food in recent years means that environmental protection and sustainable agriculture are necessary. For this, smart agricultural systems and autonomous robots have become widespread. One of the most significant and persistent problems related to robots is 3D path planning, which is an NP-hard problem, for mobile robots. In this paper, efficient methods are proposed by two metaheuristic algorithms (Incremental Gray Wolf Optimization (I-GWO) and Expanded Gray Wolf Optimization (Ex-GWO)). The proposed methods try to find collision-free optimal paths between two points for robots without human intervention in an acceptable time with the lowest process costs and efficient use of resources in large-scale and crowded farmlands. Thanks to the methods proposed in this study, various tasks such as tracking crops can be performed efficiently by autonomous robots. The simulations are carried out using three methods, and the obtained results are compared with each other and analyzed. The relevant results show that in the proposed methods, the mobile robots avoid the obstacles successfully and obtain the optimal path cost from source to destination. According to the simulation results, the proposed method based on the Ex-GWO algorithm has a better success rate of 55.56% in optimal path cost.
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Radzajewski, Paweł. "Calculation of brake-force distribution on three-axle agricultural trailers using simulation methods." Technical Transactions 2021, no. 1 (2021): 1–18. http://dx.doi.org/10.37705/techtrans/e2021029.
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The paper presents a new methodology for calculating the optimal linear distribution of braking forces for a three-axle trailer with "walking beam" and "bogie" suspension of the rear axle assembly that will meet the requirements of the new European legislation, EU Directive 2015/68. On this basis, a computer program for selecting the linear distribution of braking forces between axles has been developed. The presented calculations and simulation results of the braking process can be used in the design process to select the parameters of the wheel braking mechanisms and then the characteristics of the pneumatic valves of the braking system. The adaptation of the braking system of agriculture trailers is a very important factor for improving the safety of the transportation systems.
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Khoruzhy, Lyudmila Ivanovna, Yury Nikolaevich Katkov, and Anastasiya Alekseevna Romanova. "Modern tools of deep analysis in the system of cost management in inter-organizational relations of agricultural formations." Buhuchet v sel'skom hozjajstve (Accounting in Agriculture), no. 6 (June 1, 2021): 6–15. http://dx.doi.org/10.33920/sel-11-2106-01.
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The article, based on the theoretical analysis and the study of accounting practices, presents the place and role of tools for deep cost analysis in the cost management system of inter-organizational interaction of partners. The features of agriculture that have a significant impact on the interorganizational cooperation of agricultural companies are revealed. The advantages of the introduction and use of modern methods of cost management, including simulation modeling in identifying deviations in business processes, are revealed.
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Klose, Steven L., and Joe L. Outlaw. "Financial and Risk Management Assistance: Decision Support for Agriculture." Journal of Agricultural and Applied Economics 37, no. 2 (August 2005): 415–23. http://dx.doi.org/10.1017/s107407080000688x.
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The Financial and Risk Management (FARM) Assistance program created by Texas Cooperative Extension is a strategic analysis service offered to farmers and ranchers in Texas. The program serves as an example of large-scale, focused programming by extension agencies, as well as the implementation of technical stochastic simulation methods for use on the farm.
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Pandey, Prateek, Shishir Kumar, and Sandeep Srivastava. "A Critical Evaluation of Computational Methods of Forecasting Based on Fuzzy Time Series." International Journal of Decision Support System Technology 5, no. 1 (January 2013): 24–39. http://dx.doi.org/10.4018/jdsst.2013010102.
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The agricultural production is a process, which being nonlinear in nature, due to various influential parameters like weather, rainfall, diseases, disaster, area of cultivation etc., is not governed by any deterministic process. Fuzzy time series forecasting is one of the approaches for predicting the future values where neither a trend is viewed nor a pattern is followed, for example, in case of sugar, Lahi and rice production. Various forecasting methods have been developed on the basis of fuzzy time series data, but accuracy has been a mercurial factor in these forecasts. In this paper, performance analysis of different fuzzy time series (FTS) models has been carried out. The analysis is applicable to any available time series data of product. In this paper performance analysis is done on the data of Indian agro products that include sugarcane, Lahi and rice. The suitability of different FTS models have been critically examined over the production data of the three agro products. The paper establishes the applicability of FTS methods also in the agriculture industry.
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Szőke, Szilvia, Lajos Nagy, Sándor Kovács, and Péter Balogh. "Examination of pig farm technology by computer simulation." Applied Studies in Agribusiness and Commerce 3, no. 5-6 (December 30, 2009): 25–29. http://dx.doi.org/10.19041/apstract/2009/5-6/4.
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Agricultural production is among the riskiest production activities. Similarly to other branches of agriculture in animal breeding the finished product is the result of complex procedures. The biological technological procedure, the creation of the product is affected by an outstanding number of environmental factors which also cause uncertainties. In the North Great Plain Region of Hungary, sows, gilts and slaughter pigs are produced on a corporate farm. The reliable operation data of this company provide a stable basis for and estimating future costs and revenue and their distributions. Monte Carlo methods are one of the generally accepted tools for modeling risks. The significant independent variables, their ranges and probability distributions, and the correlation between them were inputs to the model. The values of the variables were produced using a random number generator. The computer simulation was performed using @Ris (PalisadeCorporation) software. The study concentrates on the factors affecting the number of off spring (piglets). Model inputs were the mating, mortality and farrowing rates; the costs and the income values based on these rates have been analysed as the output data of the model.
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Li, Xiaomin, Lixue Zhu, Xuan Chu, and Han Fu. "Edge Computing-Enabled Wireless Sensor Networks for Multiple Data Collection Tasks in Smart Agriculture." Journal of Sensors 2020 (February 25, 2020): 1–9. http://dx.doi.org/10.1155/2020/4398061.
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At present, precision agriculture and smart agriculture are the hot topics, which are based on the efficient data collection by using wireless sensor networks (WSNs). However, agricultural WSNs are still facing many challenges such as multitasks, data quality, and latency. In this paper, we propose an efficient solution for multiple data collection tasks exploiting edge computing-enabled wireless sensor networks in smart agriculture. First, a novel data collection framework is presented by merging WSN and edge computing. Second, the data collection process is modeled, including a plurality of sensors and tasks. Next, according to each specific task and correlation between task and sensors, on the edge computing server, a double selecting strategy is established to determine the best node and sensor network that fulfills quality of data and data collection time constraints of tasks. Furthermore, a data collection algorithm is designed, based on set values for quality of data. Finally, a simulation environment is constructed where the proposed strategy is applied, and results are analyzed and compared to the traditional methods. According to the comparison results, the proposal outperforms the traditional methods in metrics.
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Kittisuwan, Pichid. "Textural Region Denoising: Application in Agriculture." International Journal of Image and Graphics 18, no. 04 (October 2018): 1850024. http://dx.doi.org/10.1142/s0219467818500249.
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Geo-science and remote sensing technologies play enormous roles in agriculture nowadays, especially in analysis of data from aerial images such as satellite images and drone images. Most agricultural images contain more textural regions than non-textural regions. Therefore, data management in terms of textural regions is very important. Indeed, additive white Gaussian noise (AWGN) is the fundamental problem in digital image analysis. In wavelet transform, Bayesian estimation is useful in several noise reduction methods. The Bayesian technique requires a prior modeling of noise-free wavelet coefficients. In non-textural regions, the wavelet coefficients might be better modeled by super-Gaussian density such as Laplacian, Pearson type VII, Cauchy, and two-sided gamma distributions. However, the statistical model of textural regions is Gaussian model. Therefore, in agricultural images, we require flexible model between super-Gaussian and Gaussian models. In fact, the generalized Gaussian distribution (GGD) is the suitable model for this problem. Therefore, we present new maximum a posteriori (MAP) estimator for spacial case of GGD in AWGN. Here, we obtained the analytical form solution. Moreover, this research work will also describe limitations of GGD application in Bayesian estimator. The simulation results illustrate that our presented method outperforms the state-of-the-art methods qualitatively and quantitatively.
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Berzsenyi, Z. "Crop production research in multifunctional agriculture." Acta Agronomica Hungarica 51, no. 1 (April 1, 2003): 91–99. http://dx.doi.org/10.1556/aagr.51.2003.1.12.
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The research agenda for crop science in the 21st century will depend largely on whether the present conditions regarding the global food surplus continue, or whether a food scarcity recurs. Crop production research is based chiefly on small-plot field experiments, the majority of which are either long-term experiments or experiments set up to investigate the specific agronomic responses of Martonvásár maize hybrids and wheat varieties. The sustainability of crop production is examined in long-term experiments. The agronomic responses of maize hybrids and wheat varieties are studied at various levels of biological organisation. Growth analysis facilitates the exact characterisation of agronomic responses and the grouping of response effects and types using multivariable methods. Continued experimentation coupled with crop simulation models and decision support systems are an ever more useful framework for analysing the complexity of agricultural systems.
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Yao, FengMei, PengCheng Qin, JiaHua Zhang, ErDa Lin, and Vijendra Boken. "Uncertainties in assessing the effect of climate change on agriculture using model simulation and uncertainty processing methods." Chinese Science Bulletin 56, no. 8 (March 2011): 729–37. http://dx.doi.org/10.1007/s11434-011-4374-6.
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Zheng, Yi, Yaoqun Xu, and Zeguo Qiu. "Blockchain Traceability Adoption in Agricultural Supply Chain Coordination: An Evolutionary Game Analysis." Agriculture 13, no. 1 (January 11, 2023): 184. http://dx.doi.org/10.3390/agriculture13010184.
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Blockchain technology has brought about profound revolutions in supply chain management. Notably, in the agricultural sector, blockchain-based traceability has become an essential tool to maintain the safety and quality of farm commodities. However, the implementation of blockchain technology in agricultural traceability is not prevalent. In this paper, mathematical modeling and simulation methods were used to investigate the decision making regarding the adoption of blockchain traceability in agriculture, which comprises producers, processors, and governments. This paper provides further analysis of the optimal blockchain-based traceability strategies of the members of the agricultural product supply chain in different scenarios. The results reveal the following: (1) Producers and processors should manage the traceability costs for adopting blockchains to improve their brand image and gain more benefits. (2) The government should encourage supply chain agents to participate in traceability by establishing an effective reward-and-punishment mechanism. In addition, the research will help agricultural supply chain agents to design strategies to implement traceability in agriculture and create a transparent and efficient data-driven agricultural products supply chain. Furthermore, these findings provide guidance to policymakers to develop policies to accelerate the implementation of blockchain-based traceability systems to guarantee fraud-free and sustainable agricultural supply chains.
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Ivens, Sven, Gerlinde Wiese, Klaus Dittert, Oliver Mußhoff, and Monika Oberle. "Bringing Policy Decisions to the People—Education for Sustainable Development through a Digital Simulation Game." Sustainability 12, no. 20 (October 21, 2020): 8743. http://dx.doi.org/10.3390/su12208743.
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After repeated warnings by the European Commission regarding high nitrate concentrations in German waters, in 2017, Germany implemented a new fertilizer application ordinance (FO) with stricter nitrate value limits. The new regulations have severely affected agricultural regions in Germany and could lead to a high number of job losses if farmers must conform to the new regulations and do not implement new production methods. Therefore, a simulation game was developed to educate farmers and residents about the new FO and to facilitate adaptation to the new environmentally friendly legislation. The aims of the newly developed simulation game are to educate residents and farmers in affected regions about the new FO and to develop new ideas on how to comply with the new regulations. The aims of the present study are, first, to research participants’ evaluation of the simulation game and, second, to assess the effect of the simulation game on subjective knowledge, internal efficacy, and attitude towards the new FO. This pre- and post-comparison design study was based on pre-test and post-test with participants in two games (N = 90). The results were analyzed using descriptive statistics, multiple regression analyses, qualitative content analysis, and mean value comparisons. The simulation game had a positive effect on participants’ subjective knowledge (Cohen’s d 0.65) and internal efficacy (Cohen’s d 0.36), but it did not have an effect on their attitudes toward the new FO, and it was shown to slightly lower their interest in agriculture politics (Cohen’s d −0.33). The participants reported that the game made them more aware of both the difficulty and necessity of finding compromises in the field of agriculture politics. Overall, the simulation was rated very positively and was perceived as interesting and informative by the participants.
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Xu, Yuan-jun, Jia-ding Wang, Tian-feng Gu, and Jia-xu Kong. "Geological Hazards in Loess Induced by Agricultural Irrigation in Arid and Semiarid Regions of China." Advances in Civil Engineering 2020 (November 22, 2020): 1–11. http://dx.doi.org/10.1155/2020/8859166.
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The development of agriculture in the arid and semiarid regions of China mainly depends on agricultural irrigation. Until 2016, water required for agricultural irrigation has accounted for more than 90% of the total water consumption. But traditionally extensive broad irrigation causes frequent loess geological hazards in irrigation area and it threatens security of local life and property. In this paper, we selected the Heifangtai irrigation district in Yongjing County, Gansu Province, where frequent instabilities occur, as the study area. We used laboratory tests and numerical simulation to examine the mechanism of loess landslides owing to the irrigation hydrological cycle. Irrigation changes the local natural hydrogeological conditions because of the loose and macroporous structure of loess. The numerous pores and fissures constitute preferential migration pathways of irrigation water; thus, irrigation can increase the groundwater level and hydraulic gradient. Broad irrigation is the main inducing factor of geological hazards (including landslides, collapses, and soil salinization) in arid and semiarid regions, and the development of fissures and sinkholes increases the risk of landslides. New water-saving irrigation methods need to replace the traditional irrigation methods and improve the utilization of water resources. A monitoring and warning system in susceptible areas should be established to ensure the sustainability of local agriculture.
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Aghaloo, Kamaleddin, and Yie-Ru Chiu. "Identifying Optimal Sites for a Rainwater-Harvesting Agricultural Scheme in Iran Using the Best-Worst Method and Fuzzy Logic in a GIS-Based Decision Support System." Water 12, no. 7 (July 4, 2020): 1913. http://dx.doi.org/10.3390/w12071913.
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Rainwater-harvesting (RWH) agriculture has been accepted as an effective approach to easing the overexploitation of groundwater and the associated socioeconomic impacts in arid and semiarid areas. However, the stability and reliability of the traditional methods for selecting optimal sites for RWH agriculture need to be further enhanced. Based on a case study in Tehran Province, Iran, this study proposed a new decision support system (DSS) that incorporates the Best-Worst Method (BWM) and Fuzzy logic into a geographic information system (GIS) environment. The probabilistic analysis of the rainfall pattern using Monte Carlo simulation was conducted and adopted in the DSS. The results have been demonstrated using suitability maps based on three types of RWH systems, i.e., pans and ponds, percolation tanks, and check dams. Compared with traditional methods, the sensitivity analysis has verified that the proposed DSS is more stable and reliable than the traditional methods. Based on the results, a phase-wise strategy that shifts the current unsustainable agriculture to a new paradigm based on RWH agriculture has been discussed. Therefore, this DSS has enhanced the information value and thus can be accepted as a useful tool to ease the dilemma resulting from unsustainable agriculture in arid and semiarid areas.
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Jagathesan, Dr T. "Application of Mathematical Models in Agriculture- A Review." JOURNAL OF DEVELOPMENT ECONOMICS AND MANAGEMENT RESEARCH STUDIES 05, no. 05 (2020): 66–82. http://dx.doi.org/10.53422/jdms.2020.5501.
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Application of mathematical models are for solving problems in agriculture for a scientific understanding, quantitative expression and to take strategic decisions. Mathematical models include mechanistic, empirical, deterministic, and stochastic approaches. It has dynamic models with differential equations, static models with algebraic for a specific set of conditions, deterministic models suggest solutions, stochastic model deals with defined by probability functions, mechanistic model deals with theory or hypothesis, and empirical models uses existing data to explain the relationship between one or two variables. Mathematical models have been developed to investigate specific issues limited to mathematical formulation and the added complexity inherent of integrated models. Mathematical methods of resource utilization optimization have been used in practice and the first mathematical programming approaches include the method of linear programming (simplex method). Linear approach to modeling establishes the relationship between a dependent variable and one or more independent variables. In the linear equation, dependent and independent variables, coefficients, intercept or the bias coefficient and degree of freedom have been used. The application of mathematical models in agriculture portrays the main methods of various mathematical tools like analytical, simulation and empirical. This paper aims at application of Mathematical Models in agriculture.
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Yu, Danyang, Jinzhong Yang, Liangsheng Shi, Qiuru Zhang, Kai Huang, Yuanhao Fang, and Yuanyuan Zha. "On the uncertainty of initial condition and initialization approaches in variably saturated flow modeling." Hydrology and Earth System Sciences 23, no. 7 (July 12, 2019): 2897–914. http://dx.doi.org/10.5194/hess-23-2897-2019.
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Abstract. Soil water movement has direct effects on environment, agriculture and hydrology. Simulation of soil water movement requires accurate determination of model parameters as well as initial and boundary conditions. However, it is difficult to obtain the accurate initial soil moisture or matric potential profile at the beginning of simulation time, making it necessary to run the simulation model from the arbitrary initial condition until the uncertainty of the initial condition (UIC) diminishes, which is often known as “warming up”. In this paper, we compare two commonly used methods for quantifying the UIC (one is based on running a single simulation recursively across multiple hydrological years, and the other is based on Monte Carlo simulations with realization of various initial conditions) and identify the warm-up time twu (minimum time required to eliminate the UIC by warming up the model) required with different soil textures, meteorological conditions and soil profile lengths. Then we analyze the effects of different initial conditions on parameter estimation within two data assimilation frameworks (i.e., ensemble Kalman filter and iterative ensemble smoother) and assess several existing model initializing methods that use available data to retrieve the initial soil moisture profile. Our results reveal that Monte Carlo simulations and the recursive simulation over many years can both demonstrate the temporal behavior of the UIC, and a common threshold is recommended to determine twu. Moreover, the relationship between twu for variably saturated flow modeling and the model settings (soil textures, meteorological conditions and soil profile length) is quantitatively identified. In addition, we propose a warm-up period before assimilating data in order to obtain a better performance for parameter and state estimation.
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Pan, Tong, Xiao Jing Li, Hao Peng Wang, and Kai Zhao. "Research on LAI Retrieving Applied for Virtual Reality Simulation of Vegetation." Applied Mechanics and Materials 263-266 (December 2012): 1473–77. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1473.
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It is the key process for developing usability of virtual reality simulation of vegetation taking into account soil, chemistry elements of water, temperature and humidity of land surface, and lighting. Retrieving the characteristics of vegetation from remote sensing data effectively and exactly, building the relationship between remote sensing data and interfaces of VR-vegetation models are important. The paper discusses dense vegetation model, nondense vegetation model, and two-stream approximation model applied for LAI retrieving, analyzes the efficiency of the methods; presents the process and method of LAI retrieving based on FPAR. The research offers the key methods and theoretic support for virtual reality simulation of vegetation based on spatial remote sensing information. The research is the primary work of digital agriculture, and important for monitoring corn growth and estimating in northeast of China.
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Alyahya, Saleh, Waseem Ullah Khan, Salman Ahmed, Safdar Nawaz Khan Marwat, and Shabana Habib. "Cyber Secure Framework for Smart Agriculture: Robust and Tamper-Resistant Authentication Scheme for IoT Devices." Electronics 11, no. 6 (March 21, 2022): 963. http://dx.doi.org/10.3390/electronics11060963.
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Internet of Things (IoT) as refers to a network of devices that have the ability to connect, collect and exchange data with other devices over the Internet. IoT is a revolutionary technology that have tremendous applications in numerous fields of engineering and sciences such as logistics, healthcare, traffic, oil and gas industries and agriculture. In agriculture field, the farmer still used conventional agriculture methods resulting in low crop and fruit yields. The integration of IoT in conventional agriculture methods has led to significant developments in agriculture field. Different sensors and IoT devices are providing services to automate agriculture precision and to monitor crop conditions. These IoT devices are deployed in agriculture environment to increase yields production by making smart farming decisions and to collect data regarding crops temperature, humidity and irrigation systems. However, the integration of IoT and smart communication technologies in agriculture environment introduces cyber security attacks and vulnerabilities. Such cyber attacks have the capability to adversely affect the countries’ economies that are heavily reliant on agriculture. On the other hand, these IoT devices are resource constrained having limited memory and power capabilities and cannot be secured using conventional cyber security protocols. Therefore, designing robust and efficient secure framework for smart agriculture are required. In this paper, a Cyber Secured Framework for Smart Agriculture (CSFSA) is proposed. The proposed CSFSA presents a robust and tamper resistant authentication scheme for IoT devices using Constrained Application Protocol (CoAP) to ensure the data integrity and authenticity. The proposed CSFSA is demonstrated in Contiki NG simulation tool and greatly reduces packet size, communication overhead and power consumption. The performance of proposed CSFSA is computationally efficient and is resilient against various cyber security attacks i.e., replay attacks, Denial of Service (DoS) attacks, resource exhaustion.
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Tian, Weizhong, Liyuan Pang, Chengliang Tian, and Wei Ning. "Change Point Analysis for Kumaraswamy Distribution." Mathematics 11, no. 3 (January 20, 2023): 553. http://dx.doi.org/10.3390/math11030553.
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The Kumaraswamy distribution is a common type of bounded distribution, which is widely used in agriculture, hydrology, and other fields. In this paper, we use the methods of the likelihood ratio test, modified information criterion, and Schwarz information criterion to analyze the change point of the Kumaraswamy distribution. Simulation experiments give the performance of the three methods. The application section illustrates the feasibility of the proposed method by applying it to a real dataset.
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Madsen, Rasmus Bødker, Hyojin Kim, Anders Juhl Kallesøe, Peter B. E. Sandersen, Troels Norvin Vilhelmsen, Thomas Mejer Hansen, Anders Vest Christiansen, Ingelise Møller, and Birgitte Hansen. "3D multiple-point geostatistical simulation of joint subsurface redox and geological architectures." Hydrology and Earth System Sciences 25, no. 5 (May 25, 2021): 2759–87. http://dx.doi.org/10.5194/hess-25-2759-2021.
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Abstract. Nitrate contamination of subsurface aquifers is an ongoing environmental challenge due to nitrogen (N) leaching from intensive N fertilization and management on agricultural fields. The distribution and fate of nitrate in aquifers are primarily governed by geological, hydrological and geochemical conditions of the subsurface. Therefore, we propose a novel approach to modeling both geology and redox architectures simultaneously in high-resolution 3D (25m×25m×2m) using multiple-point geostatistical (MPS) simulation. Data consist of (1) mainly resistivities of the subsurface mapped with towed transient electromagnetic measurements (tTEM), (2) lithologies from borehole observations, (3) redox conditions from colors reported in borehole observations, and (4) chemistry analyses from water samples. Based on the collected data and supplementary surface geology maps and digital elevation models, the simulation domain was subdivided into geological elements with similar geological traits and depositional histories. The conceptual understandings of the geological and redox architectures of the study system were introduced to the simulation as training images for each geological element. On the basis of these training images and conditioning data, independent realizations were jointly simulated of geology and redox inside each geological element and stitched together into a larger model. The joint simulation of geological and redox architectures, which is one of the strengths of MPS compared to other geostatistical methods, ensures that the two architectures in general show coherent patterns. Despite the inherent subjectivity of interpretations of the training images and geological element boundaries, they enable an easy and intuitive incorporation of qualitative knowledge of geology and geochemistry in quantitative simulations of the subsurface architectures. Altogether, we conclude that our approach effectively simulates the consistent geological and redox architectures of the subsurface that can be used for hydrological modeling with nitrogen (N) transport, which may lead to a better understanding of N fate in the subsurface and to future more targeted regulation of agriculture.
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Mehrparvar, Milad, Azadeh Ahmadi, and Hamid Reza Safavi. "Resolving water allocation conflicts using WEAP simulation model and non-cooperative game theory." SIMULATION 96, no. 1 (May 7, 2019): 17–30. http://dx.doi.org/10.1177/0037549719844827.
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Disparity in water supply and demand often leads to conflicts among users over water resources in basins. Game theory is a new tool recently employed for resolving such conflicts. The present study uses non-cooperative games to resolve the conflicts in the Zayandehroud basin. The games are simulated between two stakeholders by the graph model conflict resolution (GMCR) method as one of the non-cooperative methods. The stakeholders are Isfahan Regional Water Company (IRWC) and Agriculture-Jahad Organization (AJO). Stakeholders can execute some strategies competitively, which include A: developing farming lands, B: improving irrigation efficiency, C: controlling withdrawals from aquifers, and D: selling water to the industrial sector. A and B are executed by AJO and C and D are executed by IRWC. The sustainable water allocation scenarios are obtained by GMCR and employed in a water evaluation and planning simulation model to supply consumers’ water demand. The best sustainable scenario selected based on GMCR concepts requires irrigation efficiency of agricultural lands to be improved in order to achieve a demand meeting index of 90% and, in contrast, there will be not control on water withdrawal from aquifers.
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Li, Hsin-Chi, Yi-Hua Hsiao, Chia-Wei Chang, Yung-Ming Chen, and Lee-Yaw Lin. "Agriculture Adaptation Options for Flood Impacts under Climate Change—A Simulation Analysis in the Dajia River Basin." Sustainability 13, no. 13 (June 30, 2021): 7311. http://dx.doi.org/10.3390/su13137311.
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Adaptation to climate change has become an important matter of discussion in the world in response to the growing rate of global warming. In recent years, many countries have gradually adopted adaption strategies to climate change, with the aim of reducing the impact of climate variabilities. Taiwan is in a geographical location that is prone to natural disasters and is thus very vulnerable to climate change. To explore an appropriate method for Taiwan to adapt to climate change, this study took Dajia River Basin as the simulation site to explore the potential climate change impact in the area. An impact study was conducted to identify the trend of flooding under climate change scenarios. We used the SOBEK model to simulate downstream inundation caused by the worst typhoon event of the 20th century (1979–2003) and for typhoon events that might occur at the end of the 21st century (2075–2099) in Taiwan, according to the climate change scenario of representative concentration pathways 8.5 (RCP8.5) and dynamical downscaling rainfall data. Agricultural lands were found to be the most affected areas among all land types, and the flooded area was forecast to increase by 1.89 times by the end of 21st century, when compared to the end of 20th century. In this study, upland crops, which are affected the most by flooding, were selected as the adaptation targets for this site and multiple engineering and non-engineering options were presented to reduce the potential climate change impacts. With respect to the results, we found that all adaptation options, even when considering the cost, yield higher benefits than the “do-nothing” option. Among the adaptation options presented for this site, utilizing engineering methods with non-engineering methods show the best result in effectively reducing the impact of climate change, with the benefit-to-cost ratio being around 1.16. This study attempts to explore useful and effective assessment methods for providing sound scientific and economic evidence for the selection of adequate adaption options for flood impacts in agriculture in the planning phase.
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Kobayashi*, Kent D. "Enhancing Students' Learning of High Technology in Horticulture." HortScience 39, no. 4 (July 2004): 809C—809. http://dx.doi.org/10.21273/hortsci.39.4.809c.
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How do we enhance students' learning experience and help them be aware of current and emerging technology used in horticulture? An undergraduate course on “Computer Applications, High Technology, and Robotics in Agriculture” was developed to address these needs. Its objectives were to familiarize students with the ways computers, high technology, and robotics are used in agriculture and to teach students how to design, build, and run a robot. The diverse topics included computer models and simulation, biosensors and instrumentation, graphical tracking and computer scheduling, new methods in plant ecology, automation and robotics, Web-based distance diagnostic and recommendation system, GIS and geospatial analysis, and greenhouse environmental control. An individual speaker presented one topic each week with students also visiting some speaker's labs. The students did active, hands on learning through assignments on computer simulations (STELLA simulation language) and graphical tracking (UNH FloraTrack software). They also built, programmed, and ran robots using Lego Mindstorms robotic kits. The course was evaluated using the Univ.'s CAFE system. There were also open-ended questions for student input. On a scale of 1 (strongly disagree) to 5 (strongly agree), mean scores of the 20 CAFE questions ranged from 3.71 to 4.75 with an overall mean of 4.22. When comparisons to other TPSS courses were possible, this course had a higher mean score for four out of seven questions. Course evaluations indicated this special topics course was important and valuable in helping enhance the students' learning experience.
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Kaur, Prabhjot, Shilpi Harnal, Rajeev Tiwari, Shuchi Upadhyay, Surbhi Bhatia, Arwa Mashat, and Aliaa M. Alabdali. "Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction." Sensors 22, no. 2 (January 12, 2022): 575. http://dx.doi.org/10.3390/s22020575.
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Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.
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Horváthné Petrás, Viktória. "Application of a Simulation Model in an Agricultural Vocational School Through Examples from the Livestock Sector." Regional and Business Studies 12, no. 2 (November 28, 2020): 93–107. http://dx.doi.org/10.33568/rbs.2523.
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In today’s education system, it is possible to prepare a skilled workforce within the framework of public education, higher education and vocational training outside the school system. One of them young people need to be prepared for activities that require adaptability and perseverance. In contrast, there has been a level of development in agriculture that neither teacher training nor vocational training can keep up with, as a result of which it is not possible to teach in the same wayas before. Significant renewal is needed in secondary agricultural education. One of the areas of this is the renewal of technical and IT training, the improvement of the standard of practical education following technological innovations. Numerous researches show that educators need to breakaway from the “outdated” methods used so far, more emphasis should be placed on motivation and practice, as these graduates will ultimately be business leaders who will coordinate the work of their professionals (Berke and Kőmüves, 2016; Kőmüves, Berke and Póra, 2016). It is therefore important and necessary to apply and put into practice the ever-expanding innovative teaching methods. As an innovative pedagogical method, I examined the possibilities of applying the simulation model I created, as this model can point to interdisciplinary relationships that enable students to study an economic process in a broader, more complex light by broadening their knowledge and reflectiveness.
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Huffman, E. C., J. Y. Yang, S. Gameda, and R. de Jong. "Using Simulation and Budget Models to Scale-Up Nitrogen Leaching from Field to Region in Canada." Scientific World JOURNAL 1 (2001): 699–706. http://dx.doi.org/10.1100/tsw.2001.355.
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Efforts are underway at Agriculture and Agri-Food Canada (AAFC) to develop an integrated, nationally applicable, socioeconomic/biophysical modeling capability in order to predict the environmental impacts of policy and program scenarios. This paper outlines our Decision Support System (DSS), which integrates the IROWCN (Indicator of the Risk of Water Contamination by Nitrogen) index with the agricultural policy model CRAM (Canadian Regional Agricultural Model) and presents an outline of our methodology to provide independent assessments of the IROWCN results through the use of nitrogen (N) simulation models in select, data-rich areas. Three field-level models — DSSAT, N_ABLE, and EPIC — were evaluated using local measured data. The results show that all three dynamic models can be used to simulate biomass, grain yield, and soil N dynamics at the field level; but the accuracy of the models differ, suggesting that models need to be calibrated using local measured data before they are used in Canada. Further simulation of IROWCN in a maize field using N_ABLE showed that soil-mineral N levels are highly affected by the amount of fertilizer N applied and the time of year, meaning that fertilizer and manure N applications and weather data are crucial for improving IROWCN. Methods of scaling-up simulated IROWCN from field-level to soil-landscape polygons and CRAM regions are discussed.
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Rocha, Gloria Alexandra Ortiz, Maria Angelica Pichimata, and Edwin Villagran. "Research on the Microclimate of Protected Agriculture Structures Using Numerical Simulation Tools: A Technical and Bibliometric Analysis as a Contribution to the Sustainability of Under-Cover Cropping in Tropical and Subtropical Countries." Sustainability 13, no. 18 (September 18, 2021): 10433. http://dx.doi.org/10.3390/su131810433.
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The use of protected agriculture structures in tropical and subtropical countries is the main alternative for intensification of agricultural production selected by producers. In general, in these regions, passive and plastic-covered structures predominate, with natural ventilation as the only method of climate control. This phenomenon has been widely studied in different types of structures using computational fluid dynamics (CFD) simulation. Therefore, this review aimed to collect and analyze the publications generated in this field of knowledge between 2010 and 2020. The search for information included the main academic databases available on the web and the analysis was carried out using bibliometric techniques, from which it was possible to identify details inherent to the scientific production, such as countries of origin, main authors, journals, and citations. Likewise, a detailed breakdown of the relevant technical information of the three phases of numerical simulation, such as preprocessing, processing, and postprocessing, was carried out. A compilation of 118 papers published in 65 journals, written by 256 authors, originating from 24 countries was achieved, where it was evident that Mexico and Colombia were the countries with the highest scientific production in the last decade. These papers analyzed, together, a total of 17 different types of structures where polyethylene-covered greenhouses predominated, with steady state simulations, for daytime climate conditions and without the presence of crops. Within the current and future research trends, the predominance of studies analyzing passive climate control methods, new models of insect-proof mesh-house structures, and, finally, studies focused on the structural analysis of greenhouses was found.
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Hrytsiuk, Petro, and Tetyana Babych. "MODELING OF GRAIN PRODUCTION PROFITABILITY BY FUZZY LOGIC." International Journal of New Economics and Social Sciences 4, no. 2 (December 30, 2016): 42–52. http://dx.doi.org/10.5604/01.3001.0010.4538.
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Ukraine is an agrarian state. One of the most important brunches of agriculture sector is grain production. High yield of grain is a basis of Ukrainian food security. Therefore the task of developing a reliable mathematical model forecasting the grain production profitability is actually. Regression analysis and fuzzy simulation principles have been used for building of the grain production profitability depending model. The values profitability forecasting for 2015 obtained by three different methods are convergent to each other.
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Zaytsev, Sergey V. "OPERATIONS RESEARCH AND SYSTEM ANALYSIS AS TOOLS FOR OPTIMAL SOLUTIONS IN THE ECONOMY." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 8/4, no. 128 (2022): 101–7. http://dx.doi.org/10.36871/ek.up.p.r.2022.08.04.013.
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Complex systems are the subject of study, design and management, which is generally called system analysis. Its basis is game theory, programming and simulation modeling, as well as queuing theory and statistical inference, etc. System analysis is often used when planning scientific research, forecasting in industries or agriculture. The main directions of system analysis are the definition of methods of analysis and research of operations. When using research methods, attention is paid to Markov and semi-Markov processes. System analysts face their main task – to find the optimal way out of the current situation, i.e. the optimal solution.
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Ali, MH, H. Paul, and MR Haque. "Estimation of evapotranspiration using a simulation model." Journal of the Bangladesh Agricultural University 9, no. 2 (June 29, 2012): 257–66. http://dx.doi.org/10.3329/jbau.v9i2.11038.
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Practical methods for the accurate estimation of water requirement for irrigated agriculture are essential. Simulation model is a useful tool to estimate water balance in the crop field. In this study, the BUDGET model was used to evaluate its performance to simulate water balance in wheat field. The BUDGET model is composed of a set of validated subroutines describing the various processes involved in water extraction by plant roots and soil water movement in absence of a water table. The model was run to simulate evapotranspiration values with the actual observed weather, crop and soil data for three years (2002-2005), obtained from experimental Station of Bangladesh Institute of Nuclear Agricultural (BINA). The input data of model are separated into four stages and the value of Kc and root depth are different for each stage. Evaluation of model performance is done with both graphical display and statistical criteria. The simulated values fall close to 1:1 line, indicating better performance. The statistical parameters such as standard deviation (SD), standard error (SE), coefficient of variation (CV) of simulated and actual evapotranspiration values are found 21.07 and 29.23; 4.49 and 6.23; and 38.03 and 50.75, respectively. Both the standard error and coefficient of variation for simulated values are found lower than the observed values indicating stability of the model output. The coefficient of determination value (R2 =0.83) is high for this model, which indicates good simulation performance. The relative error (RE) is 23.28 percent and model efficiency (EF) is 78.95 percent which means that the simulation of actual evapotranspiration is satisfactory. The value of Index of agreement (IA) is 0.918 which indicates a very good performance of the model. The overall statistical parameters of simulation period are in satisfactory level. Therefore, the BUDGET model is able to predict actual evapotranspiration for any level of soil moisture with reasonable accuracy. The model can be used in planning, management and operation of an irrigation project for judicious use of water with the limited inputs, especially suitable for countries where modeling of crop yield is needed under water stress conditions. DOI: http://dx.doi.org/10.3329/jbau.v9i2.11038 J. Bangladesh Agril. Univ. 9(2): 257266, 2011
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Cau, P., and C. Paniconi. "Assessment of alternative land management practices using hydrological simulation and a decision support tool: Arborea agricultural region, Sardinia." Hydrology and Earth System Sciences 11, no. 6 (November 23, 2007): 1811–23. http://dx.doi.org/10.5194/hess-11-1811-2007.
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Abstract. Quantifying the impact of land use on water supply and quality is a primary focus of environmental management. In this work we apply a semidistributed hydrological model (SWAT) to predict the impact of different land management practices on water and agricultural chemical yield over a long period of time for a study site situated in the Arborea region of central Sardinia, Italy. The physical processes associated with water movement, crop growth, and nutrient cycling are directly modeled by SWAT. The model simulations are used to identify indicators that reflect critical processes related to the integrity and sustainability of the ecosystem. Specifically we focus on stream quality and quantity indicators associated with anthropogenic and natural sources of pollution. A multicriteria decision support system is then used to develop the analysis matrix where water quality and quantity indicators for the rivers, lagoons, and soil are combined with socio-economic variables. The DSS is used to assess four options involving alternative watersheds designated for intensive agriculture and dairy farming and the use or not of treated wastewater for irrigation. Our analysis suggests that of the four options, the most widely acceptable consists in the transfer of intensive agricultural practices to the larger watershed, which is less vulnerable, in tandem with wastewater reuse, which rates highly due to water scarcity in this region of the Mediterranean. More generally, the work demonstrates how both qualitative and quantitative methods and information can assist decision making in complex settings.
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Jiang, Xuehui. "Application of E-Commerce Interactive Marketing Model Based on Distributed Algorithm of Mobile Ad Hoc Network." Wireless Communications and Mobile Computing 2021 (December 2, 2021): 1–9. http://dx.doi.org/10.1155/2021/9766214.
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With the development of the mobile Internet, e-commerce has become one of the important ways of daily consumption, but how to effectively use e-commerce for interactive marketing and increase sales is an important research direction. Mobile ad hoc distributed algorithms are introduced in this paper. Through sorting out the mode of e-commerce interaction influence, process marketing is performed from two-dimensional code, short message, business district, mobile search, Bluetooth, wireless network, and other methods, and interactive marketing is tried in various industries such as education, tourism, agriculture, catering, finance, and publishing, and simulation experiments are used to verify them. The simulation experiment results show that the mobile ad hoc distributed algorithms are effective and can support the e-commerce interactive marketing model.
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Weibing, Wu. "TARGET DETECTION AND ANALYSIS OF INTELLIGENT AGRICULTURAL VEHICLE MOVEMENT OBSTACLE BASED ON PANORAMIC VISION." INMATEH Agricultural Engineering 59, no. 3 (December 20, 2019): 277–84. http://dx.doi.org/10.35633/inmateh-59-30.
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Agricultural automation and intelligence have a wide range of connotations, involving navigation, image, model, strategy and other engineering disciplines. With the development of modern agriculture are applied in many engineering areas. The operating environment of agricultural vehicles is very complex, especially as they often face obstacles, affecting the intelligent operation of agricultural vehicles. The traditional obstacle detection mostly uses the limited detection algorithm, in the case of which it is difficult to achieve the moving target detection of panoramic vision. In this paper, mean shift algorithm is selected to detect the moving obstacles of intelligent agricultural vehicles, and adaptive colour fusion is introduced to optimize the algorithm to solve the problems of mean shift. In order to verify the effect of the improvement and application of the algorithm, the video image obtained by the intelligent agricultural vehicle is selected for the simulation experiment, and the best combination (- 0.8.0.2) is obtained for the unequal spacing sampling method. In the process of colour selection, the coefficient needs to be adjusted continuously to improve the tracking accuracy of the algorithm. Further it can be seen that when using a variety of different quantitative methods for comparative analysis, the quantitative method of HIS-360 level is determined.
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Miao, Chiyuan, Qingyun Duan, Qiaohong Sun, and Jianduo Li. "Evaluation and application of Bayesian multi-model estimation in temperature simulations." Progress in Physical Geography: Earth and Environment 37, no. 6 (August 5, 2013): 727–44. http://dx.doi.org/10.1177/0309133313494961.
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Use of multi-model ensembles from global climate models to simulate the current and future climate change has flourished as a research topic during recent decades. This paper assesses the performance of multi-model ensembles in simulating global land temperature from 1960 to 1999, using Nash-Sutcliffe model efficiency and Taylor diagrams. The future trends of temperature for different scales and emission scenarios are projected based on the posterior model probabilities estimated by Bayesian methods. The results show that ensemble prediction can improve the accuracy of simulations of the spatiotemporal distribution of global temperature. The performance of Bayesian model averaging (BMA) at simulating the annual temperature dynamic is significantly better than single climate models and their simple model averaging (SMA). However, BMA simulation can demonstrate the temperature trend on the decadal scale, but its annual assessment of accuracy is relatively weak. The ensemble prediction presents dissimilarly accurate descriptions in different regions, and the best performance appears in Australia. The results also indicate that future temperatures in northern Asia rise with the greatest speed in some scenarios, and Australia is the most sensitive region for the effects of greenhouse gas emissions. In addition to the uncertainty of ensemble prediction, the impacts of climate change on agriculture production and water resources are discussed as an extension of this research.
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Xiao, Jing, Yang Liu, and Jie Zhou. "Quantum Clone Elite Genetic Algorithm-Based Evaluation Mechanism for Maximizing Network Efficiency in Soil Moisture Wireless Sensor Networks." Journal of Sensors 2021 (May 15, 2021): 1–14. http://dx.doi.org/10.1155/2021/5590472.
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In agriculture, soil moisture wireless sensor networks (SMWSNs) are used to monitor the growth of crops for obtaining higher yields. The purpose of this paper is to improve the network efficiency of SMWSNs. Therefore, we propose a novel network efficiency evaluation mechanism which is suitable for soil moisture sensors and design a sensor target allocation model (STAM) for the actual agricultural situation. After that, a quantum clone elite genetic algorithm (QCEGA) is proposed; then, QCEGA is applied to optimize the STAM for obtaining optimal results. QCEGA uses the parallel mechanism of quantum computing to encode individuals, integrates the quantum revolving gate in quantum computing and the concept of cloning in biology to avoid the algorithm from falling into local optimum, and applies the elite strategy to speed up the convergence of the algorithm. Subsequently, the proposed algorithm is compared with simulated annealing (SA) and particle swarm optimization (PSO). Under the novel network efficiency evaluation mechanism, the simulation results demonstrate that the network efficiency based on QCEGA is higher than that of SA and PSO; what is more, QCEGA has better convergence performance. In comparison with traditional wireless sensor network efficiency evaluation approaches, our methods are more in line with the development of modern agriculture and can effectively improve the efficiency of SMWSNs, thus ensuring that crops can have a better growth condition.
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Rekolainen, S., J. Grönroos, I. Bärlund, A. Nikander, and Y. Laine. "Modelling the impacts of management practices on agricultural phosphorus losses to surface waters of Finland." Water Science and Technology 39, no. 12 (June 1, 1999): 265–72. http://dx.doi.org/10.2166/wst.1999.0555.
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This paper presents the changes in cultivation practices in Finnish agriculture resulting from the Agri-Environmental Support Scheme of the Common Agricultural Policy of the European Union. Detailed data were collected by interviewing farmers in four different areas of the country. The potential impacts of changes in cultivation practices on phosphorus losses were assessed using a mathematical simulation model. The variables monitored were: fertilization, winter green cover and soil tillage methods in autumn. The use of fertilizers has decreased to meet the requirements of the support programme. Winter green cover has increased in areas to a minimum level of 30% of the cultivated area. However, the potential impacts on nutrient losses were small. There are two reasons for this: the increase in reduced tillage practices is likely to increase the loss of dissolved phosphorus in southern Finland, and the reduction of set-aside has led to slight increases in particulate phosphorus losses. However, the reduction in grassland fertilization rapidly decreased loss of dissolved phosphorus in northern Finland.
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Suryono, Hady, Heri Kuswanto, and Nur Iriawan. "Two-Phase Stratified Random Forest for Paddy Growth Phase Classification: A Case of Imbalanced Data." Sustainability 14, no. 22 (November 17, 2022): 15252. http://dx.doi.org/10.3390/su142215252.
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The United Nations Sustainable Development Goals (SDGs) have had a considerable impact on Indonesia’s national development policies for the period 2015 to 2030. The agricultural industry is one of the world’s most important industries, and it is critical to the achievement of the SDGs. The second major aspect of the SDGs, i.e., zero hunger, addresses food security (SDG 2). To measure the status of food security, accurate statistics on paddy production must be accessible. Paddy phenological classification is a way to determine a food plant’s growth phase. Imbalanced data are a common occurrence in agricultural data, and machine learning is frequently utilized as a technique for classification issues. The current trend in agriculture is to use remote sensing data to classify crops. This paper proposes a new approach—one that uses two phases in the bootstrap stage of the random forest method—called a two-phase stratified random forest (TPSRF). The simulation scenario shows that the proposed TPSRF outperforms CART, SVM, and RF. Furthermore, in its application to paddy growth phase data for 2019 in Lamongan Regency, East Java, Indonesia, the proposed TPSRF showed higher overall accuracy (OA) than the compared methods.
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Ahmed, A. A. Masrur, Ekta Sharma, S. Janifer Jabin Jui, Ravinesh C. Deo, Thong Nguyen-Huy, and Mumtaz Ali. "Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors." Remote Sensing 14, no. 5 (February 25, 2022): 1136. http://dx.doi.org/10.3390/rs14051136.
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Wheat dominates the Australian grain production market and accounts for 10–15% of the world’s 100 million tonnes annual global wheat trade. Accurate wheat yield prediction is critical to satisfying local consumption and increasing exports regionally and globally to meet human food security. This paper incorporates remote satellite-based information in a wheat-growing region in South Australia to estimate the yield by integrating the kernel ridge regression (KRR) method coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the grey wolf optimisation (GWO). The hybrid model, ‘GWO-CEEMDAN-KRR,’ employing an initial pool of 23 different satellite-based predictors, is seen to outperform all the benchmark models and all the feature selection (ant colony, atom search, and particle swarm optimisation) methods that are implemented using a set of carefully screened satellite variables and a feature decomposition or CEEMDAN approach. A suite of statistical metrics and infographics comparing the predicted and measured yield shows a model prediction error that can be reduced by ~20% by employing the proposed GWO-CEEMDAN-KRR model. With the metrics verifying the accuracy of simulations, we also show that it is possible to optimise the wheat yield to achieve agricultural profits by quantifying and including the effects of satellite variables on potential yield. With further improvements in the proposed methodology, the GWO-CEEMDAN-KRR model can be adopted in agricultural yield simulation that requires remote sensing data to establish the relationships between crop health, yield, and other productivity features to support precision agriculture.
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Deng, Jianxun. "Analysis and Recognition Based on Citrus Color Grading Model considering Computer Vision Technology." Advances in Multimedia 2021 (December 8, 2021): 1–7. http://dx.doi.org/10.1155/2021/6426163.
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With the continuous advancement of smart agriculture, the introduction of robots for intelligent harvesting in modern agriculture is one of the crucial methods for the picking of fruits, vegetables, and melons. In this paper, three different illuminations, including front lighting, normal lighting, and back lighting, are first applied to citrus based on the computer vision technology. Secondly, the image data of the fruits, fruit stems, and leaves of the citrus are collected. The color component distributions of citrus based on different color models are analyzed according to the corresponding characteristic values, and an exploratory data analysis process for the image data of citrus is established. In addition, 300 citrus images are selected, and the citrus fruits are segmented from the background through the simulation experiment. The results of the study indicate that the recognition rate for the maturity of citrus has exceeded 98%, which has proved the effectiveness of the method proposed in this paper.
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Roberts, Wayne S., and Scott M. Swinton. "Economic methods for comparing alternative crop production systems: A review of the literature." American Journal of Alternative Agriculture 11, no. 1 (March 1996): 10–17. http://dx.doi.org/10.1017/s0889189300006652.
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AbstractNew crop production technologies developed in response to growing concern over environmental contamination from agriculture may be neither more profitable nor higher yielding than the systems they replace, but they often reduce environmental contamination or improve soil and water quality. Systems designed with environmental objectives cannot be evaluated fairly just by productivity, which is what often is done in economic studies of alternative systems. We review 58 recent studies comparing alternative crop production systems to identify the key criteria for system comparisons, the system characteristics important in designing the analysis, and the methods most suited for comparing alternative systems.The four key criteria we looked for in system comparisons are expected profit, stability of profits, expected environmental impacts, and stability of environmental impacts. Most economic studies of crop production focus exclusively on profitability, and incorporate neither environmental criteria nor the dynamic characteristics inherent in alternative systems. We identify promising new approaches that take account of specific environmental characteristics and attempt to balance the objectives of profitability and environmental risk management. Balanced environmental-economic analysis is most likely to be achieved by integrating biophysical simulation models with economic optimization methods to model the trade-offs among profitability, environmental impact, and system stability (both financial and environmental).
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Liu, Chengwen, Guoliang Liu, and Haitao Zhang. "Optimized Design for Reliability of Pointer Irrigation Machine Components for Intelligent Computing." International Transactions on Electrical Energy Systems 2022 (September 9, 2022): 1–11. http://dx.doi.org/10.1155/2022/7114934.
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The application of intelligent technology has realized the transformation of people’s production and lifestyle, and also promoted the development and transformation of the agricultural field. At present, the application of agricultural intelligence is getting stronger and stronger; using its intelligent advanced methods and technologies, this paper aimed to achieve the optimization of sprinkler irrigation machine parts in the intelligent network environment to promote the rapid development of agriculture, and proposed the use of the NSGA-II algorithm in intelligent computing to guide the integration of artificial intelligence and pointer sprinkler parts, which helps to analyze and solve the objective problem of machine failure and parts damage in agriculture. In the study of the sprinkler gear system, from the perspective of gear efficiency, since it is optimized according to the minimum efficiency point of the fourth gear of the gear reducer, compared with the gear efficiency of 49.05% before this point, the efficiency of this point after optimization is 59.45%, and the minimum efficiency point will be increased by 21.2%. And because the energy loss unrelated to the power loss load will be greatly reduced, these energy losses have a greater relationship with the structure of the gearbox. In terms of each gear, compared with the previous period, the efficiency of the first gear was increased by 8.5% to 15.9%; the efficiency of the second gear increased by 8.7% to 17.4%; the efficiency of the third gear increased by 9.4% to 18.7%; and the efficiency of the fourth gear increased by 10.1% to 21.2%. Therefore, it is currently necessary to optimize the components of the sprinkler irrigation machine.
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Erciulescu, Andreea L., and Wayne A. Fuller. "Bootstrap Prediction Intervals for Small Area Means from Unit-Level Nonlinear Models." Journal of Survey Statistics and Methodology 7, no. 3 (June 16, 2018): 309–33. http://dx.doi.org/10.1093/jssam/smy014.
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Abstract For analyses based on nonlinear models, agencies and policy makers are often interested in prediction intervals for small area means. We give statistics for small area predictions that can be used to construct prediction intervals in the same way that standard errors and degrees of freedom are used to construct prediction intervals based on the Student-t distribution. In a simulation study, the new parametric bootstrap prediction interval has good coverage properties and much better coverage than the bootstrap percentile prediction interval. The methods are applied in a study of soil erosion and water runoff conducted by the US Department of Agriculture.
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Souza, Juliana Mio de, Paulo Morgado, Eduarda Marques da Costa, and Luiz Fernando de Novaes Vianna. "Predictive Scenarios of LULC Changes Supporting Public Policies: The Case of Chapecó River Ecological Corridor, Santa Catarina/Brazil." Land 12, no. 1 (January 5, 2023): 181. http://dx.doi.org/10.3390/land12010181.
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The studies of spatial-temporal land use and land cover (LULC) change patterns, supported by future scenarios and simulation methods based on the assumption of natural socio-economic and territorial driving forces, allow us to go beyond an accurate diagnosis of the dynamics that have occurred so far, providing a picture of possible alternative futures, and are fundamental in assisting with the planning and policy-making in the territory. In this paper, we use LULC maps and explanatory variables aggregated in five dimensions (physical/natural, economic, sociocultural, technological, and demographic) to identify which are the main drinving forces in the evolution process and the simulation of LULC dynamics for 2036, using as a case study the Chapecó River ecological corridor (Chapecó EC) area. The Chapecó EC was created by the state government in 2010 with the goal of combining nature conservation with local and regional development. In this region, in the last two decades, the loss of areas of natural grassland and forest was on average five times higher than the average recorded in the state. Based on scenario-building methods using artificial neural networks, six predictive scenarios were elaborated, based on three socioeconomic scenarios (current conditions, growth, and socioeconomic recession) and two territorial intervention options (actions). This includes an action based on maintaining the current LULC, and another action of a conservationist nature with the recovery of forest and natural grassland areas to the proportions of areas found in 1990. The results indicate that if the current LULC is maintained, forest, pasture and agriculture areas tend to increase, while silviculture and natural grassland areas decrease, driven by economic and physical/natural driving forces. If there is a conservationist action, natural grassland and pasture areas tend to increase and silviculture and agriculture tend to lose area due to economic, technological, and physical/natural driving forces. These trends have revealed that the natural grassland preservation/restoration, the encouragement of conservationist agricultural practices combined with economic strategies, and the technological development of the rural sector seem to form the basis of economic development combined with biodiversity conservation.
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Yang, Yanxiang, Xiangyin Zhang, Jiayi Zhou, Bo Li, and Kaiyu Qin. "A Relative Coordinate-Based Topology Shaping Method for UAV Swarm with Low Computational Complexity." Applied Sciences 12, no. 5 (March 3, 2022): 2631. http://dx.doi.org/10.3390/app12052631.
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Functional topology shaping is crucial for unmanned aerial vehicles (UAVs) swarm applications, such as remote sensing, precision agriculture, and emergency wireless communication. However, the current research on topology shaping is mostly based on the assumption that the target positions of the nodes are known or have been pre-defined. Moreover, the computational complexity of existing shaping methods is still high. In this paper, a topology shaping method based on a relative coordinate system is proposed to solve the problem of UAV swarm topology shaping with no external source of localization information. Based on the relative coordinates of nodes and target topology shape of the swarm, the topology shaping is transformed into a problem of optimal coordinate mapping from initial relative coordinates to target relative coordinates of nodes with minimized global energy consumption. The Jonker–Volgenant algorithm is employed to solve the optimization problem. As verified by simulations, the proposed method can achieve UAV swarm topology shaping with no external localization information. Furthermore, simulation results show that the proposed method has an average reduction in computation time of 94% in the case of 1000 nodes compared with existing methods with the same level of global energy consumption.
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Qureshi, Kashif Naseer, Muhammad Umair Bashir, Jaime Lloret, and Antonio Leon. "Optimized Cluster-Based Dynamic Energy-Aware Routing Protocol for Wireless Sensor Networks in Agriculture Precision." Journal of Sensors 2020 (January 31, 2020): 1–19. http://dx.doi.org/10.1155/2020/9040395.
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Wireless sensor networks (WSNs) are becoming one of the demanding platforms, where sensor nodes are sensing and monitoring the physical or environmental conditions and transmit the data to the base station via multihop routing. Agriculture sector also adopted these networks to promote innovations for environmental friendly farming methods, lower the management cost, and achieve scientific cultivation. Due to limited capabilities, the sensor nodes have suffered with energy issues and complex routing processes and lead to data transmission failure and delay in the sensor-based agriculture fields. Due to these limitations, the sensor nodes near the base station are always relaying on it and cause extra burden on base station or going into useless state. To address these issues, this study proposes a Gateway Clustering Energy-Efficient Centroid- (GCEEC-) based routing protocol where cluster head is selected from the centroid position and gateway nodes are selected from each cluster. Gateway node reduces the data load from cluster head nodes and forwards the data towards the base station. Simulation has performed to evaluate the proposed protocol with state-of-the-art protocols. The experimental results indicated the better performance of proposed protocol and provide more feasible WSN-based monitoring for temperature, humidity, and illumination in agriculture sector.
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Li, Yanping, Zhenhua Li, Zhe Zhang, Liang Chen, Sopan Kurkute, Lucia Scaff, and Xicai Pan. "High-resolution regional climate modeling and projection over western Canada using a weather research forecasting model with a pseudo-global warming approach." Hydrology and Earth System Sciences 23, no. 11 (November 18, 2019): 4635–59. http://dx.doi.org/10.5194/hess-23-4635-2019.
Full textAbstract:
Abstract. Climate change poses great risks to western Canada's ecosystem and socioeconomical development. To assess these hydroclimatic risks under high-end emission scenario RCP8.5, this study used the Weather Research Forecasting (WRF) model at a convection-permitting (CP) 4 km resolution to dynamically downscale the mean projection of a 19-member CMIP5 ensemble by the end of the 21st century. The CP simulations include a retrospective simulation (CTL, 2000–2015) for verification forced by ERA-Interim and a pseudo-global warming (PGW) for climate change projection forced with climate change forcing (2071–2100 to 1976–2005) from CMIP5 ensemble added on ERA-Interim. The retrospective WRF-CTL's surface air temperature simulation was evaluated against Canadian daily analysis ANUSPLIN, showing good agreements in the geographical distribution with cold biases east of the Canadian Rockies, especially in spring. WRF-CTL captures the main pattern of observed precipitation distribution from CaPA and ANUSPLIN but shows a wet bias near the British Columbia coast in winter and over the immediate region on the lee side of the Canadian Rockies. The WRF-PGW simulation shows significant warming relative to CTL, especially over the polar region in the northeast during the cold season, and in daily minimum temperature. Precipitation changes in PGW over CTL vary with the seasons: in spring and late autumn precipitation increases in most areas, whereas in summer in the Saskatchewan River basin and southern Canadian Prairies, the precipitation change is negligible or decreased slightly. With almost no increase in precipitation and much more evapotranspiration in the future, the water availability during the growing season will be challenging for the Canadian Prairies. The WRF-PGW projected warming is less than that by the CMIP5 ensemble in all seasons. The CMIP5 ensemble projects a 10 %–20 % decrease in summer precipitation over the Canadian Prairies and generally agrees with WRF-PGW except for regions with significant terrain. This difference may be due to the much higher resolution of WRF being able to more faithfully represent small-scale summer convection and orographic lifting due to steep terrain. WRF-PGW shows an increase in high-intensity precipitation events and shifts the distribution of precipitation events toward more extremely intensive events in all seasons. Due to this shift in precipitation intensity to the higher end in the PGW simulation, the seemingly moderate increase in the total amount of precipitation in summer east of the Canadian Rockies may underestimate the increase in flooding risk and water shortage for agriculture. The change in the probability distribution of precipitation intensity also calls for innovative bias-correction methods to be developed for the application of the dataset when bias correction is required. High-quality meteorological observation over the region is needed for both forcing high-resolution climate simulation and conducting verification. The high-resolution downscaled climate simulations provide abundant opportunities both for investigating local-scale atmospheric dynamics and for studying climate impacts on hydrology, agriculture, and ecosystems.
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Elmokadem, Taha, and Andrey V. Savkin. "Computationally-Efficient Distributed Algorithms of Navigation of Teams of Autonomous UAVs for 3D Coverage and Flocking." Drones 5, no. 4 (October 25, 2021): 124. http://dx.doi.org/10.3390/drones5040124.
Full textAbstract:
This paper proposes novel distributed control methods to address coverage and flocking problems in three-dimensional (3D) environments using multiple unmanned aerial vehicles (UAVs). Two classes of coverage problems are considered in this work, namely barrier and sweep problems. Additionally, the approach is also applied to general 3D flocking problems for advanced swarm behavior. The proposed control strategies adopt a region-based control approach based on Voronoi partitions to ensure collision-free self-deployment and coordinated movement of all vehicles within a 3D region. It provides robustness for the multi-vehicle system against vehicles’ failure. It is also computationally-efficient to ensure scalability, and it handles obstacle avoidance on a higher level to avoid conflicts in control with the inter-vehicle collision avoidance objective. The problem formulation is rather general considering mobile robots navigating in 3D spaces, which makes the proposed approach applicable to different UAV types and autonomous underwater vehicles (AUVs). However, implementation details have also been shown considering quadrotor-type UAVs for an example application in precision agriculture. Validation of the proposed methods have been performed using several simulations considering different simulation platforms such as MATLAB and Gazebo. Software-in-the-loop simulations were carried out to asses the real-time computational performance of the methods showing the actual implementation with quadrotors using C++ and the Robot Operating System (ROS) framework. Good results were obtained validating the performance of the suggested methods for coverage and flocking scenarios in 3D using systems with different sizes up to 100 vehicles. Some scenarios considering obstacle avoidance and robustness against vehicles’ failure were also used.