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Journal articles on the topic 'Agricultural decision support systems'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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1

Laurenson, Matthew, and Seishi Ninomiya. "Successful Agricultural Decision Support Systems." Agricultural Information Research 11, no. 1 (2002): 5–25. http://dx.doi.org/10.3173/air.11.5.

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2

Jame, Y. W., and H. W. Cutforth. "Crop growth models for decision support systems." Canadian Journal of Plant Science 76, no. 1 (January 1, 1996): 9–19. http://dx.doi.org/10.4141/cjps96-003.

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Studies on crop production are traditionally carried out by using conventional experience-based agronomic research, in which crop production functions were derived from statistical analysis without referring to the underlying biological or physical principles involved. The weaknesses and disadvantages of this approach and the need for greater in-depth analysis have long been recognized. Recently, application of the knowledge-based systems approach to agricultural management has been gaining popularity because of our expanding knowledge of processes that are involved in the growth of plants, coupled with the availability of inexpensive and powerful computers. The systems approach makes use of dynamic simulation models of crop growth and of cropping systems. In the most satisfactory crop growth models, current knowledge of plant growth and development from various disciplines, such as crop physiology, agrometeorology, soil science and agronomy, is integrated in a consistent, quantitative and process-oriented manner. After proper validation, the models are used to predict crop responses to different environments that are either the result of global change or induced by agricultural management and to test alternative crop management options.Computerized decision support systems for field-level crop management are now available. The decision support systems for agrotechnology transfer (DSSAT) allows users to combine the technical knowledge contained in crop growth models with economic considerations and environmental impact evaluations to facilitate economic analysis and risk assessment of farming enterprises. Thus, DSSAT is a valuable tool to aid the development of a viable and sustainable agricultural industry. The development and validation of crop models can improve our understanding of the underlying processes, pinpoint where our understanding is inadequate, and, hence, support strategic agricultural research. The knowledge-based systems approach offers great potential to expand our ability to make good agricultural management decisions, not only for the current climatic variability, but for the anticipated climatic changes of the future. Key words: Simulation, crop growth, development, management strategy
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3

Karanikolas, Nikos, Pierre Bisquert, Patrice Buche, Christos Kaklamanis, and Rallou Thomopoulos. "A Decision Support Tool for Agricultural Applications Based on Computational Social Choice and Argumentation." International Journal of Agricultural and Environmental Information Systems 9, no. 3 (July 2018): 54–73. http://dx.doi.org/10.4018/ijaeis.2018070104.

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In the current article, the authors describe an applied procedure to support collective decision making for applications in agriculture. An extended 2-page abstract of this paper has been accepted by the EFITA WCCA congress and this manuscript is an extended version of this submission. The problem the authors are facing in this paper is how to reach the best decision regarding issues coming from agricultural engineering with the aid of Computational Social Choice (CSC) and Argumentation Framework (AF). In the literature of decision-making, several approaches from the domains of CSC and AF have been used autonomously to support decisions. It is our belief that with the combination of these two fields the authors can propose socially fair decisions which take into account both (1) the involved agents' preferences and (2) the justifications behind these preferences. Therefore, this article implements a software tool for decision-making which is composed of two main systems, i.e., the social choice system and the deliberation system. In this article, the authors describe thoroughly the social choice system of our tool and how it can be applied to different alternatives on the valorization of materials coming from agriculture. As an example, that is demonstrated an application of our tool in the context of Ecobiocap European project where several decision problems are to be addressed. These decision problems consist in finding the best solutions for questions regarding food packaging and end-of-life management.
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4

Akinfiev, Valery, and Anatoly Tsvirkun. "Decision Support Systems for Stable Development of Agricultural SMEs." IFAC-PapersOnLine 54, no. 13 (2021): 289–92. http://dx.doi.org/10.1016/j.ifacol.2021.10.461.

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5

Eastwood, B. R. "National Electronic Information Systems–Agricultural Databases for Decision Support." HortScience 32, no. 3 (June 1997): 552D—552. http://dx.doi.org/10.21273/hortsci.32.3.552d.

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A number of factors have emerged in recent years, grown in importance, and are now converging rapidly to create a window of opportunity for all of us. These factors constitute six separate, but related and important, categories: 1) Decreasing staff in the nation's Cooperative Extension System; 2) increasing complexity of agricultural production technologies; 3) increasing concerns of society; 4) opening of markets globally; 5) increased need for accountability; and 6) rapid progress in computerized information and communication technologies. These factors concurrently are causing greater sharing of expertise and resources across states, institutions, and departments; more cooperation with the private sector; improved openness and communication on issues of interest to the community; greater awareness of our role in the world; and a willingness to consider new approaches. One of these approaches involves the development of comprehensive national decision support resources for producers and those who work with producers in an educational, advisory or service role. This program, which has evolved over the past 10 years, is Agricultural Databases for Decision Support (ADDS). ADDS projects may be developed for any commodity, clientele, or major issue area. Products already available include the National Dairy Database and the National Pig Information Database. Several additional projects are underway and more will be added as interest warrants. The ADDS hallmark applies to those projects that follow the philosophy and meet the criteria agreed to by the greater community of developers and users. ADDS uses the sophisticated search and retrieval mechanism and multimedia capabilities of commercially available software. This software is applied to a cooperatively developed national resource of peer-reviewed materials that are selected by experts for their usefulness.
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6

Sultanov, Murodjon, Gayrat Ishankhodjayev, Rano Parpiyeva, and Nafisa Norboyeva. "Creation of intelligent information decision support systems." E3S Web of Conferences 365 (2023): 04031. http://dx.doi.org/10.1051/e3sconf/202336504031.

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The use of intelligent information decision support systems implies considering the problem area's specifics. The object of study is characterized by the following set of features: - quality and efficiency of decision-making; - vagueness of goals and institutional boundaries; - the plurality of subjects involved in solving the problem; - randomness; - a plurality of mutually influencing factors; - weak formalizability, uniqueness of situations; - latency, concealment, the implicitness of information. For the efficient and reliable functioning of agricultural facilities and enterprises, it is necessary to create and implement intelligent information systems. Over the past quarter of a century, domestic information systems have undergone a progressive evolution, both in terms of developing the theoretical principles of their construction and implementing these systems. The restructuring of agriculture, the market conditions for the functioning of objects, and agriculture enterprises have their characteristics and problems. Building the structure of intelligent decision support information systems is primarily associated with building a system model, in which both traditional elements of the control system and knowledge processing models should be defined. To solve these problems, methods of system analysis were used. The key research method is the optimization of data representation structures of databases and knowledge. The following relational data representation structures have been identified: relations, attributes, and values. In the relational model, structures are not specially allocated to represent data about entity relationships. Semantic networks use a three-level representation of data on entities and a four-level representation of data on entity relationships. The conducted studies have shown that in data representation structures, entity-relationship models are a generalization and development of the structures of all traditional data models since only in this data model there are 4-level data representations of both entities and relationships. All other traditional models are some special cases of the most general entity-relationship model.
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7

Day, W., E. Audsley, and A. R. Frost. "An engineering approach to modelling, decision support and control for sustainable systems." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1491 (July 26, 2007): 527–41. http://dx.doi.org/10.1098/rstb.2007.2168.

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Engineering research and development contributes to the advance of sustainable agriculture both through innovative methods to manage and control processes, and through quantitative understanding of the operation of practical agricultural systems using decision models. This paper describes how an engineering approach, drawing on mathematical models of systems and processes, contributes new methods that support decision making at all levels from strategy and planning to tactics and real-time control. The ability to describe the system or process by a simple and robust mathematical model is critical, and the outputs range from guidance to policy makers on strategic decisions relating to land use, through intelligent decision support to farmers and on to real-time engineering control of specific processes. Precision in decision making leads to decreased use of inputs, less environmental emissions and enhanced profitability—all essential to sustainable systems.
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8

Al Manir, Mohammad Sadnan, Bruce Spencer, and Christopher J. O. Baker. "Decision Support for Agricultural Consultants With Semantic Data Federation." International Journal of Agricultural and Environmental Information Systems 9, no. 3 (July 2018): 87–99. http://dx.doi.org/10.4018/ijaeis.2018070106.

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Informational needs of agricultural consultants are increasingly complex. Advising farmers on the appropriate measures for optimizing cropping yields demands access to custom data archives and analytics tools. In line with the increasing number of archives, the expertise required of consultants goes beyond the capabilities of these non-technical agri-specialists. These end users have diverse ad-hoc query needs and require tools that provide simple access to distributed data silos and easy ways to integrate relevant information. In this article, the authors report on a pilot deployment of Semantic Automated Discovery and Integration (SADI) Web services for the federation and computation of agricultural data. A registry of 9 SADI Web services was deployed to expose data from a variety of different data resources in support of a defined set of query needs. The authors demonstrate that the deployment of these services facilitates the ad-hoc creation and execution of mission critical workflows targeting use cases in agricultural operations management. Using HYDRA, a semantic query engine for SADI Web services with a custom built graphical user interface, agricultural consultants can identify optimal crop varieties, and compute profit margins of each variety using a complex cost model.
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9

Shaffer, M. J., and M. K. Brodahl. "Rule-based management for simulation in agricultural decision support systems." Computers and Electronics in Agriculture 21, no. 2 (November 1998): 135–52. http://dx.doi.org/10.1016/s0168-1699(98)00031-3.

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10

Jacucci, Gianni, Mark Foy, and Carl Uhrik. "Developing transportable agricultural decision support systems: Part 2. An example." Computers and Electronics in Agriculture 14, no. 4 (April 1996): 301–15. http://dx.doi.org/10.1016/0168-1699(96)80778-2.

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11

Cox, P. G. "Some issues in the design of agricultural decision support systems." Agricultural Systems 52, no. 2-3 (October 1996): 355–81. http://dx.doi.org/10.1016/0308-521x(96)00063-7.

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12

Audsley, E. "Decision Support Systems for Profitable Livestock Production." Journal of Agricultural Science 121, no. 1 (August 1993): 131–34. http://dx.doi.org/10.1017/s0021859600076887.

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The Agricultural and Related Industries Study Group of the Operational Research Society held a one-day meeting on ‘Decision Support Systems for Profitable Livestock Production’ on 18 November 1992 at the Meat and Livestock Commission, Milton Keynes, UK. The Group promotes the use of the scientific method in solving management problems. The aim of the meeting was to look at the contribution of advances in modelling towards improving management and hence profitability in livestock production. The first four papers concerned feeding and the remaining two breeding. A large part of the discussion could perhaps best be summed up by the regression equation which related dry matter intake and waste output from a cow. When the output was zero, the intake was reduced by 50%!
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13

Fanelli, Rosa M. "A new classification of European Union regions: A decision support tool for policymakers." Spanish Journal of Agricultural Research 17, no. 1 (April 15, 2019): e0102. http://dx.doi.org/10.5424/sjar/2019171-13481.

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The classification of agricultural and territorial systems is essential to improve the comparability of regions for the development programmers of the Common Agricultural Policy (CAP), to give new tools of intervention to policymakers and to increase farmers’ knowledge. Analysis of the principal characteristics of these systems is essential during a time in which the new CAP is being designed for the period 2021-2027. The research is focused on the analysis of the agricultural features of 228 regional areas (NUTS 2) of the 28 European Union (EU) countries. It considers two specific sets of environmental and socio-economic indicators provided by the Farm Accountancy Data Network (FADN). The main factors that differentiate agricultural systems in EU regions from one another were identified with the application of principal component analysis, while the classification of the same regions in homogeneous groups was carried out through hierarchical cluster analysis. The results clearly show that some groups of “homogeneous” EU regions such as the Natura 2000 area and the family-run agricultural system, which have weaker agricultural structures than the average of the 228 EU regions considered in this study, have a greater need for the restructuring of their agricultural systems than others (e.g., the professional agricultural system and the food industry system). The results confirm that policy design should not consider EU agriculture as a whole, but should take into account the environmental and structural specificities of agricultural holdings, as well as the different training levels of farm managers.
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14

Jha, Gaurav, Floyid Nicolas, Radomir Schmidt, Kosana Suvočarev, Dawson Diaz, Isaya Kisekka, Kate Scow, and Mallika A. Nocco. "Irrigation Decision Support Systems (IDSS) for California’s Water–Nutrient–Energy Nexus." Agronomy 12, no. 8 (August 19, 2022): 1962. http://dx.doi.org/10.3390/agronomy12081962.

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California has unsustainable use of agricultural water and energy, as well as problems of severe drought, nitrate pollution and groundwater salinity. As the leading producer and exporter of agricultural produce in the United States, 5.6 percent of California’s energy is currently used for pumping groundwater. These problems and new regulatory policies (e.g., Sustainable Groundwater Management Act, Irrigated Lands Regulatory Program) pressure growers to schedule, account and maintain records of water, energy and nutrients needed for crop and soil management. Growers require varying levels of decision support to integrate different irrigation strategies into farm operations. Decision support can come from the public or private sector, where there are many tradeoffs between cost, underlying science, user friendliness and overall challenges in farm integration. Thus, effective irrigation management requires clear definitions, decision support and guidelines for how to incorporate and evaluate the water–nutrient–energy nexus benefits of different practices and combinations of practices under shifting water governance. The California Energy Commission-sponsored Energy Product Evaluation Hub (Cal-EPE Hub) project has a mission of providing science-based evaluation of energy-saving technologies as a direct result of improved water management for irrigation in agriculture, including current and future irrigation decision support systems in California. This project incorporates end-user perceptions into evaluations of existing decision support tools in partnership with government, agricultural and private stakeholders. In this article, we review the policy context and science underlying the available irrigation decision support systems (IDSS), discuss the benefits/tradeoffs and report on their efficacy and ease of use for the most prevalent cropping systems in California. Finally, we identify research and knowledge-to-action gaps for incorporating irrigation decision support systems into new incentives and requirements for reporting water and energy consumption as well as salinity and nitrogen management in the state of California.
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15

PARZONKO, ANDRZEJ. "INFORMATION SYSTEMS SUPPORTING INVESTMENT DECISIONS IN DAIRY FARMS - EVALUATION IN POLAND AND GERMANY." sj-economics scientific journal 25, no. 2 (September 30, 2017): 371–82. http://dx.doi.org/10.58246/sjeconomics.v25i2.212.

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The aim of the article was to present sources of information used by dairy farms in Mazowieckie Voivodeship to make investment decisions. In addition, the article was evaluated by a system made by one of the German institutions - Das Kuratorium für Technik und Bauwesen in der Landwirtschaft (KTBL), which supports decision-making in agricultural farms. According to surveys conducted, farms that implemented investment activities used information provided by manufacturers of agricultural machinery and equipment to evaluate their effectiveness. The weakness of this type of information was their reliability. An analysis of the information system proposed by KTBL indicates that it is useful for farmers making investment decisions as well as for decision makers defining directions of financial support for agricultural farms.
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16

Rábová, I., V. Konečný, and A. Matiášová. "Decision making with support of artificial intelligence." Agricultural Economics (Zemědělská ekonomika) 51, No. 9 (February 20, 2012): 385–88. http://dx.doi.org/10.17221/5124-agricecon.

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  Development of software modules for decision support is currently a basic trend in the creation of enterprise Information Systems (IS). The IS is basically a support system of the enterprise Decision System, therefore we can regard it as a very important factor of the competition ability and enterprise prosperity. Conventional IS modules provide the enterprise managers a lot of useful information. Nevertheless, own decision process in view of difficulty, complexity or creation disability of decision process model is very often problematic. This contribution is oriented by its content to appropriate choice realization of modules for support decision processes by using of artificial intelligence methods.      
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17

Stone, Roger C., and Holger Meinke. "Operational seasonal forecasting of crop performance." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1463 (October 24, 2005): 2109–24. http://dx.doi.org/10.1098/rstb.2005.1753.

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Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.
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18

., Yogesh Sharma. "GIS Based Decision Support Systems In Agriculture." International Journal of Advanced Multidisciplinary Scientific Research 1, no. 9 (November 30, 2018): 1–9. http://dx.doi.org/10.31426/ijamsr.2018.1.9.911.

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19

Jacucci, Gianni, Mark Foy, and Carl Uhrik. "Developing transportable agricultural decision support systems: Part 1. A conceptual framework." Computers and Electronics in Agriculture 14, no. 4 (April 1996): 291–300. http://dx.doi.org/10.1016/0168-1699(95)00039-9.

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20

Eickermann, Michael, Franz Kai Ronellenfitsch, and Juergen Junk. "Developing a decision support tool to forecast the abundance of the cabbage stem weevil in winter oilseed rape." Plant Protection Science 56, No. 4 (September 18, 2020): 285–91. http://dx.doi.org/10.17221/93/2019-pps.

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Reducing the use of pesticides in agricultural systems is a prerequisite for sustainable agriculture and, therefore, knowledge on the factors that influence the regional insect pest densities is necessary. Based on multi-site and multi-annual observations of the cabbage stem weevil [Ceutorhynchus pallidactylus (Marsham, 1802)] in winter oilseed rape (Brassica napus Linnaeus) and the corresponding meteorological measurements, a statistical relationship for forecasting the abundance was derived. The model explains 84% of the variation of the data set. The remaining 16% might be explained by the landscape effects and agricultural practices, such as crop protection. Based on the statistical relationship between the mean winter air temperature and the abundance of the cabbage stem weevil in the winter oilseed rape, risk maps were derived as a forecast tool for practical farming.
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21

Sánchez Céspedes, Juan Manuel, Juan Pablo Rodríguez Miranda, and Olga Lucia Ramos Sandoval. "Decision Support Systems (DSS) Applied to the Formulation of Agricultural Public Policies." Tecnura 24, no. 66 (October 1, 2020): 95–108. http://dx.doi.org/10.14483/22487638.15768.

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Context: The process of formulating agricultural public policies is very complex due to the large number of variables involved in the process. That is why the development of decision support systems (DSS) help to improve this process. The article reviews the developments that have been made regarding the subject. Method: The method was to conduct a bibliographic review in several scientific databases, looking for developments of DSS systems applied to the process of formulating agricultural policies. When determining which DSS systems have been developed, a qualitative and descriptive analysis of the systems was carried out.
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22

Kabildjanov, Alexander, Charos Okhunboboyeva, and Sarvarbek Ismailov. "Intelligent decision support in the optimization of irrigation systems in agriculture." E3S Web of Conferences 365 (2023): 01013. http://dx.doi.org/10.1051/e3sconf/202336501013.

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The issues of determining the optimal values of the regulatory parameters of irrigation systems engaged in the cultivation of agricultural crops are considered. Following the requirements of a market economy, the main emphasis is placed on taking into account two types of criteria: maximizing the yield of agricultural crops and minimizing monetary costs. The proposed method for solving the multi-criteria optimization problem is based on the combination of the minimax criterion and the medium-step convolution, which makes it possible to scalarize the vector optimality criterion with access to smooth optimization methods. Concerning the case of priority uncertainty according to particular optimality criteria, an intelligent algorithm is proposed based on the approximation of the preference function of the decision-maker by the fuzzy Mamdani model. The multi-criteria optimization of the irrigation system used for growing cotton results differ favorably from the average values. The one hectare yield in the republic- increased by 2%, monetary costs - reduced by 4.5%. It could be concluded that the developed methodology makes it possible to bypass the computational difficulties that arise when solving problems of multi-criteria optimization of irrigation systems engaged in the cultivation of agricultural crops and to obtain real results in conditions of certainty and uncertainty goals.
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23

Liss, Evie, and Andy Duncan. "The Role of Communicators in Agricultural Global Information Systems for Decision Support." HortScience 32, no. 6 (October 1997): 982b—982. http://dx.doi.org/10.21273/hortsci.32.6.982b.

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University communicators (writers, editors, designers, videographers, multimedia specialists, etc.) often are asked by a scientist to help her or him redesign a completed, or partially-completed, information package. This may be a rough draft of a publication that will include photos already taken, a plan for a video that will include field work already competed, a CD-ROM that will include photos, video, and sound already in hand. Communicators call these “salvage jobs.” It is like being asked to give advice on the most effective design of an experiment—when the experiment is three-fourths done. The emerging world of on-line electronic information offers support in real time to people working in a vast array of fields. It is critically important that communicators and other information professionals collaborate, at the project initiation stage, with scientists in “creation teams” to plan effective information design and delivery. Also, it is important that electronic information packages to be used for decision support be peer reviewed for communication, as well as scientific, integrity. The session's presenters will explain why.
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Matylonek, John. "The Role of Librarians in Agricultural Global Information Systems for Decision Support." HortScience 32, no. 6 (October 1997): 982c—982. http://dx.doi.org/10.21273/hortsci.32.6.982c.

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Anyone who has had an occupation that uses the personal computer can attest to the way the tool affects the fundamental way in which tasks are done. It's this qualitative nature of the changes—not whether the computer makes your work more productive or more efficient—that really is the central finding of the research of information technology and organizations. Of people in all occupations, those who deal with information as the main product and service find themselves in a peculiar position. Because the tool itself is also the product, every change in technology reorders tasks and procedures so that the new system is accommodated. Given enough incremental change in the way information is manipulated, the core skill set of the practitioner must necessarily change. This puts a strain on the legacy systems, both social and technical, they leave behind. Librarianship is one profession whose central skill set is being challenged by the march of progress in information technology. This paper examines the way some librarians have shifted emphasis of their core skill set to meet the challenge of remaining relevant within the new information infrastructure. It will then examine the ramifications of the adaptation on the customers that they serve, the legacy organizational protocol, and the administrative bureaucracies they have been managed by.
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Mir, Shabir Ahmad, and T. Padma. "Integrated Technology Acceptance Model for the Evaluation of Agricultural Decision Support Systems." Journal of Global Information Technology Management 23, no. 2 (April 2, 2020): 138–64. http://dx.doi.org/10.1080/1097198x.2020.1752083.

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Gutiérrez, Francisco, Nyi Nyi Htun, Florian Schlenz, Aikaterini Kasimati, and Katrien Verbert. "A review of visualisations in agricultural decision support systems: An HCI perspective." Computers and Electronics in Agriculture 163 (August 2019): 104844. http://dx.doi.org/10.1016/j.compag.2019.05.053.

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27

Dövényi-Nagy, Tamás. "Centre of Agri-Knowledge – a Web-Based Integration of Information and Decision Support Systems for Agriculture." Acta Agraria Debreceniensis, no. 10 (May 23, 2006): 64–66. http://dx.doi.org/10.34101/actaagrar/10/3466.

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The University Debrecen Centre of Agricultural Sciences acts as the centre and co-ordinator of agrarian higher education and consultation in the eastern region of Hungary. New internet technologies afford new chances to accomplish the institute’s mission to develop agriculture, environment and countryside in the area. As the main framework of integrated e-learning, e-business and research in the agrifood sector the development of an internet portal, the „Centre of Agri-knowledge” (CAK) has been started in the recent past.To support our students and education, to stay continuous in touch with our agricultural engineers we consider very important to build internet communities, where students, farmers, traders and researchers can share their knowledge and experience.In Europe same as in Hungary agriportals born and disappear continuously. Although CAK is still in its early stage of development, its comprehensiveness, professional backend and the experience of the Centre of Agricultural Sciences guarantee a new standard of online services in the Hungarian agrifood sector
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Duan, Sophia Xiaoxia, Santoso Wibowo, and Josephine Chong. "A Multicriteria Analysis Approach for Evaluating the Performance of Agriculture Decision Support Systems for Sustainable Agribusiness." Mathematics 9, no. 8 (April 16, 2021): 884. http://dx.doi.org/10.3390/math9080884.

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Agriculture decision support systems (DSSs) play an important role in facilitating evidence-based agricultural decision-making for improving agribusiness productivity. Evaluating and selecting the most appropriate agriculture DSS for sustainable agribusiness is, however, challenging due to the existence of production and marketing alternatives, a variety of objective functions from economic to lifestyle to long-term sustainability, and the subjectiveness and imprecision involved in the evaluation process. To help decision makers effectively deal with these issues, this paper presents a multicriteria analysis approach for evaluating and selecting the most appropriate agriculture DSS for sustainable agribusiness. The subjective assessments of decision makers in the evaluation process are formulated using linguistic variables approximated by fuzzy numbers. The concept based on the positive and the negative ideal solutions is applied for producing a performance index value for every agriculture DSS alternative across all evaluation criteria based on which the most appropriate agriculture DSS is. An empirical study is presented for demonstrating the step-wise process for evaluating and selecting the most appropriate agriculture DSS for sustainable agribusiness. The outcome from the performance evaluation process allows agribusinesses to effectively adopt appropriate agriculture DSSs for achieving competitive advantages.
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Liu, Xiaohu, Han Li, and Hong Li. "Analyzing Relationship between Financing Constraints, Entrepreneurship, and Agricultural Company Using AI-Based Decision Support System." Scientific Programming 2022 (January 6, 2022): 1–9. http://dx.doi.org/10.1155/2022/1634677.

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Decision support technology has become a key link in modern information strategy. With the deepening of research, introduced expert systems have been introduced into decision support systems. In this way, decision support systems gradually become more uncertain and capable of handling uncertainties. The development direction of decision support system is typically based on qualitative analysis. Intelligent decision support system is a system that combines decision support system with artificial intelligence technology. This study attempts to assess in an innovative way the relationship between financing constraints, entrepreneurship, and agricultural firms. The most recently proposed intelligent decision support system, AI-assisted Intelligent Decision Support System (AIIDSS), is used to predict the impact of entrepreneurship on corporate performance. The paper constructs an entrepreneurship index from five aspects: innovation, competitiveness, human capital accumulation, management capability, and adventurous spirit. The method intends to construct the Kaplan–Zingales (KZ) index to evaluate financing constraints. Through an empirical study, it was found that entrepreneurship can significantly promote the growth of listed agricultural companies. The study can drastically reduce the difficulties involved in financing constraints normally faced by agricultural companies. The impact paths include increasing agricultural company operating cash flow, improving stock liquidity, and increasing debt financing. The research suggests that if listed agricultural companies are to improve financing constraints, entrepreneurs must improve their own competitiveness and management capabilities. This will help in reasonably controlling research and development investment besides the impulse to take risks. As the growth of an enterprise relies on considering the determinants of financing constraints, this research provides an effective investigation technique. Moreover, the findings of the study will help entrepreneurs, particularly agricultural companies, to bear most of the risks and to avail most of the opportunities.
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Jakku, E., and P. J. Thorburn. "A conceptual framework for guiding the participatory development of agricultural decision support systems." Agricultural Systems 103, no. 9 (November 2010): 675–82. http://dx.doi.org/10.1016/j.agsy.2010.08.007.

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Yu, Xin Wen, Yan Chen Yang, and Xu Zhang. "A Meteorological Data Service System and its Application." Advanced Materials Research 304 (July 2011): 310–15. http://dx.doi.org/10.4028/www.scientific.net/amr.304.310.

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Meteorological conditions play an important role in agricultural practice and agricultural DSS usually takes weather data as a critical data source. A meteorological data service system was designed and implemented to provide better performance for Chinese users. Based on the service system, a web application providing online weather data retrieval and downloading was also developed. The service system was practically used in a decision support system for eucalypt management, and proved to be very feasible as an online weather data source for agricultural decision support system. Base on this service system, it is expected that agricultural researchers and decision support systems can easily obtain weather data and further improve their agricultural decision making process.
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Zhai, Zhaoyu, José Fernán Martínez, Victoria Beltran, and Néstor Lucas Martínez. "Decision support systems for agriculture 4.0: Survey and challenges." Computers and Electronics in Agriculture 170 (March 2020): 105256. http://dx.doi.org/10.1016/j.compag.2020.105256.

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Gagliardi, Gianfranco, Antonio Igor Maria Cosma, and Francesco Marasco. "A Decision Support System for Sustainable Agriculture: The Case Study of Coconut Oil Extraction Process." Agronomy 12, no. 1 (January 12, 2022): 177. http://dx.doi.org/10.3390/agronomy12010177.

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The high demand of information and communication technology (ICT) in agriculture applications has led to the introduction of the concept of smart farming. In this respect, moving from the main features of the Fourth Industrial Revolution (Industry 4.0) promoted by the European Community, new approaches have been suggested and adopted in agriculture, giving rise to the so-called Agriculture 4.0. Improvements in automation, advanced information systems and Internet technologies allow for farmers to increase the productivity and to allocate the resources reasonably. For these reasons, agricultural decision support systems (DSS) for Agriculture 4.0 have become a very interesting research topic. DSS are interactive tools that enable users to make informed decisions about unstructured problems, and can be either fully computerized, human or a combination of both. In general, a DSS analyzes and synthesizes large amounts of data to assist in decision making. This paper presents an innovative decision support system solution to address the issues faced by coconut oil producers in making strategic decisions, particularly in the comparison of different methods of oil extraction. In more detail, the adopted methodology describes how to address the problems of coconut oil extraction in order to minimize the processing time and processing cost and to obtain energy savings. To this end, the coconut oil extraction process of the Leão São Tomé and Principe Company is presented as a case study: a DSS instance that analyzes the problem of the optimal selection between two different oil coconut extraction methods (fermentation-based and standard extraction processes) is developed as a meta-heuristics with a mixed integer linear programming problem. The obtained results show that there is clearly a trade-off between the increase in cost and reliability that the decision-maker may be willing to evaluate. In this respect, the proposed model provides a tool to support the decision-maker in choosing the best combination between the two different coconut oil extraction methods. The proposed DSS has been tested in a real application context through an experimental campaign.
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Dankan Gowda, V., M. Sandeep Prabhu, M. Ramesha, Jayashree M. Kudari, and Ansuman Samal. "Smart Agriculture and Smart Farming using IoT Technology." Journal of Physics: Conference Series 2089, no. 1 (November 1, 2021): 012038. http://dx.doi.org/10.1088/1742-6596/2089/1/012038.

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Abstract It has become easier to access agriculture data in recent years as a result of a decline in digital breaches between agricultural producers and IoT technologies. These future technologies can be used to boost productivity by cultivating food more sustainably while also preserving the environment, thanks to improved water use and input and treatment optimization. The Internet of Things (IoT) enables the production of agricultural process-supporting systems. Referred to as remote monitoring systems, decision support tools, automated irrigation systems, frost protection systems, and fertilisation systems, respectively. Farmers and researchers must be provided with a detailed understanding of IoT applications in agriculture as a result of the knowledge described above. This study is about using Internet of Things (IoT) technologies and techniques to enhance agriculture. This article is meant to serve as an introduction to IoT-based applications in agriculture by identifying need for such tools and explaining how they support agriculture.
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Pilvere, Irina, Aleksejs Nipers, Agnese Krievina, Ilze Upite, and Daniels Kotovs. "LASAM Model: An Important Tool in the Decision Support System for Policymakers and Farmers." Agriculture 12, no. 5 (May 17, 2022): 705. http://dx.doi.org/10.3390/agriculture12050705.

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Today’s global food system (including production, transportation, processing, packing, storage, retail sale, consumption, losses and waste) provides income to more than a billion people all over the world and makes up a significant part of many countries’ economies. The 21st century’s food systems that bring food from “farm to fork” face various challenges, including a shortage of agricultural land and water, competition with the energy industry, changes in consumption preferences, a rising global population, negative effects of climate change, etc. Therefore, many countries are working on creating various models to function as an important decision support system tool for policymakers, farmers and other stakeholders. Various agricultural sector models see particularly extensive use in the European Union (EU), determining the impact of the Common Agricultural Policy (CAP) and helping to create future development scenarios. This is why a special model adapted to the national conditions, called LASAM (Latvian Agricultural Sector Analysis Model), was created in Latvia, making it possible to use historical data on the development of agricultural sectors, medium-term price projections for agricultural products in the EU, changes in support policy, as well as the necessity for the resources used to project the long-term (up to 2050) development of agriculture. The LASAM model covers the crop sector, the animal sector and the overall socioeconomic development, as well as the growth of organic farming and greenhouse gas (GHG) emissions. This paper discusses the main objectives achieved in developing a decision support tool and presenting the research results: LASAM was used to prepare projections of the possible development of Latvia’s principal sectors of agriculture until 2050, considering the necessity to reduce GHG emissions, made available through the LASAM web application. Given that the projection data obtained by LASAM are public, they can be used (1) for national policy making in rural business development, which affects the development of the economy as a whole; and (2) internationally, to compare the projections made in Latvia with those obtained through various agricultural sector models and projected development trends.
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Sudha, Mohan Kumar, Maharana Manorama, and Tarigoppula Aditi. "Smart Agricultural Decision Support Systems for Predicting Soil Nutrition Value Using IoT and Ridge Regression." Agris on-line Papers in Economics and Informatics 14, no. 1 (March 30, 2022): 95–106. http://dx.doi.org/10.7160/aol.2022.140108.

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Cost effective agricultural crop productivity is an everlasting demand, this predominant expedition has raised a global shift towards practicing smart agricultural methods to increase the productivity and the efficiency of the agricultural sector, using IoT. This research identified the benefits and the challenges in IoT adoption as an alternate for out-of-date agricultural practices. The proposed decision support system using IoT for Smart Soil Nutrition Prediction (SSNP) adopts IR sensors and implements diffuse reflectance infrared spectroscopy. Information is transferred using Arduino and Zigbee protocol. It has indicated precise outcomes in various studies giving a high repeatable, low cost and fast estimation of soil properties. The measure of light absorbed by a soil example is estimated, inside several particular wavebands over a scope of frequencies to yield an infrared range utilizing an IR sensor. Using the given values, the experimental analysis using the dataset and the nutrition values of the soil such as Ca, P, SOC, Sand and pH are predicted. This proposed IoT framework would enhance the farmer’s knowledge regarding the type of crops they should grow to get maximum profit from their agricultural produce.
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Harrison, S. R., P. K. Thornton, and J. B. Dent. "The IBSNAT Project and Agricultural Experimentation in Developing Countries." Experimental Agriculture 26, no. 4 (October 1990): 369–80. http://dx.doi.org/10.1017/s0014479700001290.

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SUMMARYThe International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) project has established an international network of co-operating farming systems researchers. A decision support system that includes a number of crop models and databases has been developed to assist in the evaluation of new agrotechnology packages in the tropics and sub-tropics. Standards have been established for the recording of ‘minumum data sets’ from field experiments to allow the crop models to be validated for specific sites. The decision support system, still under active development, has the potential to reduce substantially the time and cost of field experimentation necessary for adequate evaluation of new cultivars and new crop management systems.
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Harrison, S. R., P. K. Thornton, and J. B. Dent. "The IBSNAT Project and Agricultural Experimentation in Developing Countries." Experimental Agriculture 26, no. 4 (October 1990): 369–80. http://dx.doi.org/10.1017/s0014479700003574.

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SUMMARYThe International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) project has established an international network of co-operating farming systems researchers. A decision support system that includes a number of crop models and databases has been developed to assist in the evaluation of new agrotechnology packages in the tropics and sub-tropics. Standards have been established for the recording of ‘minumum data sets’ from field experiments to allow the crop models to be validated for specific sites. The decision support system, still under active development, has the potential to reduce substantially the time and cost of field experimentation necessary for adequate evaluation of new cultivars and new crop management systems.
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Knapczyk, Adrian, Sławomir Francik, Marek Wróbel, Marcin Jewiarz, and Krzysztof Mudryk. "Decision support systems for scheduling tasks in Biosystems Engineering." E3S Web of Conferences 132 (2019): 01008. http://dx.doi.org/10.1051/e3sconf/201913201008.

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Modern decision support systems have many applications, including assistance in scheduling tasks. Biosystems engineering combines engineering sciences and physical sciences in order to understand and improve biological systems in agriculture, food production, environment, etc. The work reviews the decision support systems in the aspect of scheduling tasks in the field of biosystems engineering. The analysis was based on documents (articles and proceedings paper) indexed in the Web of Science Core Collection (WoS-CC) database from 1945-2018. The search has been limited to the category of WoS-CC related to agriculture, water resources, food processing, horticulture and forestry. The main research topics, areas of application and methods used were determined. In the analyzed documents, task scheduling was mainly used in irrigation and harvest scheduling. Simple and advanced optimization tools were used.
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Nasirahmadi, Abozar, and Oliver Hensel. "Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm." Sensors 22, no. 2 (January 10, 2022): 498. http://dx.doi.org/10.3390/s22020498.

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Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.
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Rossi, Vittorio, Francesca Salinari, Stefano Poni, Tito Caffi, and Tiziano Bettati. "Addressing the implementation problem in agricultural decision support systems: the example of vite.net®." Computers and Electronics in Agriculture 100 (January 2014): 88–99. http://dx.doi.org/10.1016/j.compag.2013.10.011.

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42

Wang, Wu Gong, Rong Guo Ma, Jing Wang, and Qi Dong. "Study on Information Systems Development and Structural Optimization of Agricultural Products Multimodal Transportation." Applied Mechanics and Materials 220-223 (November 2012): 2379–87. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2379.

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Achieving scientific decision-making and information sharing of agricultural products multimodal transport is critical to enhance the management level of agricultural transportation. Based on the advantages of agricultural products multimodal transport, this paper makes an analysis of the decision-making and information systems development of agricultural products multimodal transport, which leads into the embedded GIS technology to study information structural optimization of agricultural products multimodal transport. Studies have shown that embedded GIS can improve the agricultural products multimodal transport activities and its logistical support capabilities, so as to provide important quantitative basis for achieving accurate protection of agricultural products multimodal transport under conditions of informatization.
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43

Azizov, Fattoh Kh, Nabijon M. Nurboev, and Mubinjon M. Faiziboev. "STATE SUPPORT FOR DECISION-MAKING IN THE AGRICULTURAL SECTOR OF THE ECONOMY OF TAJIKISTAN." Today and Tomorrow of Russian Economy, no. 98 (2019): 33–42. http://dx.doi.org/10.26653/1993-4947-2019-98-04.

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The agrarian sector of the national economy of Tajikistan has its own special features, so they require a specific approach with government support for decision making. For the effective development of this sector of the economy, a comprehensive legislative and institutional, investment, technical, credit, tax and personnel support for decision-making is needed. Decision-making is one of the main, central problems of managing the economy of the agricultural sector. Leaders, managers and managers must have sufficient knowledge and skills to effectively manage various parts of the agricultural sector of the economy. For this, it is very important for them to know and use in practice computer and computing decision support systems.
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44

E, Susendar, and Amsaveni C. "A STUDY ON AGRICULTURAL SYSTEMS." International Journal of Engineering Applied Sciences and Technology 6, no. 11 (March 1, 2022): 80–83. http://dx.doi.org/10.33564/ijeast.2022.v06i11.017.

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Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the “next generation” models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid reinvention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of processbased bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.
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Tsapparellas, Giorgos, Nanlin Jin, Xuewu Dai, and Gerhard Fehringer. "Laplacian Scores-Based Feature Reduction in IoT Systems for Agricultural Monitoring and Decision-Making Support." Sensors 20, no. 18 (September 8, 2020): 5107. http://dx.doi.org/10.3390/s20185107.

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Internet of things (IoT) systems generate a large volume of data all the time. How to choose and transfer which data are essential for decision-making is a challenge. This is especially important for low-cost and low-power designs, for example Long-Range Wide-Area Network (LoRaWan)-based IoT systems, where data volume and frequency are constrained by the protocols. This paper presents an unsupervised learning approach using Laplacian scores to discover which types of sensors can be reduced, without compromising the decision-making. Here, a type of sensor is a feature. An IoT system is designed and implemented for a plant-monitoring scenario. We have collected data and carried out the Laplacian scores. The analytical results help choose the most important feature. A comparative study has shown that using fewer types of sensors, the accuracy of decision-making remains at a satisfactory level.
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46

Goldstein, Anat, Lior Fink, and Gilad Ravid. "A Framework for Evaluating Agricultural Ontologies." Sustainability 13, no. 11 (June 4, 2021): 6387. http://dx.doi.org/10.3390/su13116387.

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An ontology is a formal representation of domain knowledge, which can be interpreted by machines. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision-support systems and other intelligent systems, inter alia, in the context of agriculture. A review of the existing literature on agricultural ontologies, however, reveals that most of the studies, which propose agricultural ontologies, are lacking an explicit evaluation procedure. This is undesired because without well-structured evaluation processes, it is difficult to consider the value of ontologies to research and practice. Moreover, it is difficult to rely on such ontologies and share them on the Semantic Web or between semantic-aware applications. With the growing number of ontology-based agricultural systems and the increasing popularity of the Semantic Web, it becomes essential that such evaluation methods are applied during the ontology development process. Our work contributes to the literature on agricultural ontologies by presenting a framework that guides the selection of suitable evaluation methods, which seems to be missing from most existing studies on agricultural ontologies. The framework supports the matching of appropriate evaluation methods for a given ontology based on the ontology’s purpose.
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Krepyshev, Dmitry Aleksandrovich, Aleksandr Pavlovich Ovcharov, and Valentina Romanovna Labintseva. "APPLICATION OF EXPERT SYSTEMS TO SUPPORT DECISION MAKING IN AGRICULTURE." Polythematic Online Scientific Journal of Kuban State Agrarian University, no. 164 (2020): 154–67. http://dx.doi.org/10.21515/1990-4665-164-011.

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48

AUDSLEY, E. "Operational Research, Agriculture and the Environment." Journal of Agricultural Science 133, no. 4 (December 1999): 455–58. http://dx.doi.org/10.1017/s0021859699007224.

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The Agricultural and Related Industries Study Group of the Operational Research Society held a one-day meeting on ‘Operational Research, Agriculture and the Environment’ on 27 November 1998 at Silsoe Research Institute, Bedfordshire, UK. The group promotes the use of the scientific method in solving management problems. The aim of this meeting was to look at models concerned with the effects of management of the environment in agriculture. Two looked at decision support systems – one for land use balancing different interests and one from the point of view of biodiversity. Andrew Moxey however highlighted the difficulties of interdisciplinary projects and the risk of the ‘illusion of technique’ in decision support systems. Three other papers were more specific – concerned with livestock and climate change, nitrogen fertilizer rates and slurry application.
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49

Sadłowski, Adrian. "The planned reform of the Common Agricultural Policy with particular reference to the direct support system." Agricultural Economics (Zemědělská ekonomika) 66, No. 8 (August 24, 2020): 381–90. http://dx.doi.org/10.17221/397/2019-agricecon.

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Having evaluated the situation in agriculture and rural areas, and having assessed the significance of new external and internal conditions, the European Commission has decided to reform the Common Agricultural Policy of the European Union. This article presents the essence of the part of the reform plan which affects the direct support system. The author of the article also examines the impact of the reform on the system’s capacity to achieve the set goals, and on its efficiency. The research showed that the new legal framework proposed by the Commission would not directly improve the efficiency of the system, but the increased decision-making authority it grants to Member States provides an opportunity to reduce administrative outlays on the functioning of the scheme. Likewise, the effectiveness of a reformed direct support system in achieving the set goals will largely depend on decisions made by Member States, i.e. on the appropriate choice of instruments, and the accuracy of their form, as well as the equitable distribution of the available funds.
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Oyedepo, John A., E. O. Oyedepo, A. A. Adeola, and A. M. Omotayo. "SPATIAL DECISION SUPPORT SYSTEM FOR LIVELIHOOD PROGRAMMES IN NORTHERN NIGERIA." FUDMA JOURNAL OF SCIENCES 5, no. 1 (June 25, 2021): 174–85. http://dx.doi.org/10.33003/fjs-2021-0501-551.

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The paper examines the relevance of geospatial maps and technics in execution of livelihood support projects among rural poor. Previous attempts towards driving the poor along prosperity pathways in Nigeria have failed because of incorrect deployment of interventions in time and space. Global positioning systems and Geographical information systems were employed in this study to provide insight to the challenges faced by previous livelihood support projects in the country while also providing a robust spatial decision support system for geographical targeting of interventions to vulnerable households. The study selected 1,459 households from the 42,000 households adopted for the Feed the Future Project in 3 northern States namely; Sokoto, Kebbi States and the Federal Capital Territory. Acquired survey data were converted into GIS maps so as to expose hidden trends in the characteristics of target population and to suggest the best options for interventions. The data were subjected to spatial analysis such as simple distance analysis. The outcomes revealed 86% of sampled households to be largely below the poverty line. This is mainly due to poor access to facilities like health care, markets, good roads, agricultural inputs, agricultural information and advisory services. 10 % of children’s absolute score of Mid Upper Arm Circumference (MUAC) revealed acute malnutrition. 42 % of the household however now have stronger safety nets as a result of the interventions. The study found Spatial Information technology highly useful in social intervention project as the one by the Feed the Future project
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