Relevant bibliographies by topics / Agricultural decision support systems

Academic literature on the topic 'Agricultural decision support systems'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Agricultural decision support systems"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Agricultural decision support systems"

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Meng, Chao. "Simulation-Based Decision Support For Agricultural Supply Chain Performance Improvement." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/581318.

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Grafted vegetable seedlings have been proven to possess higher seed/non-seed diseases resistance and yields compared with non-grafted ones. Owing to the seasonality of vegetable planting and labor intensiveness of grafted seedling production (e.g., grafting operation), U.S. vegetable seedling supply chains suffer from high grafted seedling cost. To make grafted seedlings affordable for vegetable growers, low-cost production systems and cost-efficient grafting capacity must be achieved via optimal design of a grafting operation system and supply chain collaboration, respectively. Toward this end, a two-level simulation-based framework is proposed in this work for improving the overall performance of the grafted seedling supply chain by supporting both the grafted seedling production system design and supply chain collaboration decisions. The considered supply chain consists of a single grafted seedling producer that produces grafted seedlings and multiple vegetable growers that seasonally purchase grafted seedlings and produce vegetables to meet price-sensitive demand from the downstream market. More specifically, the low level of the proposed framework focuses on the grafted seedling production system design by integrating discrete event simulation (DES) together with a fuzzy analytic hierarchy process (AHP) for multiple criteria (i.e. production cost, capital investment, production throughput time, resource utilization, and product quality). A Unified Modeling Language (UML)-based simulation modeling and generation approach is developed to automatically generate simulation models of various production system design alternatives. UML information models are developed to provide the system structural information for simulation model generation, production information for simulation execution, and output requirement information for defining simulation outputs. The performance of the production system design alternatives for the aforementioned criteria is evaluated via the generated simulation models, and the corresponding simulation results together with decision makers' judgments on the criteria are used to select the best system design via AHP. A best alternative search (BAS) procedure is proposed for the adopted AHP approach to search for the best system design against ranking impreciseness caused by simulation randomness. At the high level, the proposed framework focuses on the optimal supply chain decisions for early order commitment (EOC) to reduce the amortized production capacity cost. EOC is a supply chain collaboration mechanism, where the grafted seedling producer encourages the vegetable growers to commit their orders earlier than their regular ordering times by providing certain benefits (e.g., price discount). Based on the optimal design of a grafted seedling production system and the corresponding production cost obtained at the low level, we first derive analytical solutions for the grafted seedling producer's optimal capacity, vegetable grower's optimal order quantity, and ordering time under a basic supply chain structure (i.e., single-seedling producer and single-vegetable grower). We then introduce capacity competition by extending the basic structure to a multi-vegetable grower structure. The existence of the N-person game equilibrium and the corresponding relationships between the grafted seedling producer's profit and the vegetable growers' early order decisions are provided. In addition, a capacity reservation mechanism is proposed for the seedling producer to motivate the vegetable growers to release order information in advance. To identify the convergence of the vegetable growers' ordering times, a Cellular Automata simulation model is developed, where each vegetable grower is modeled as a Pavlovian or greedy agent making an ordering time decision so as to receive the higher profit over iterations. The proposed framework is demonstrated for grafted seedling supply chains in North America. The experiment results reveal the benefits of the proposed framework in reducing the grafted seedling cost, as well as in increasing the entire supply chain's profit.
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Fox, Fred Andrew 1956. "Irrigation scheduling decision support." Diss., The University of Arizona, 1997. http://hdl.handle.net/10150/288770.

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Irrigation scheduling using the soil water balance approach has been recommended to irrigators for many years. Reasonably good results are normally obtained by researchers using carefully quantified inputs. Irrigators in production agriculture may estimate inputs and then question the validity of the method when the irrigation recommendations conflict with present irrigation schedules. By associating each input with an interval representing possible bias based on the way the input was estimated, and solving the irrigation scheduling model using the intervals as inputs, the output was associated with an interval representing possible bias. This method was also used to evaluate possible bias associated with growing degree day based crop coefficient curves developed from Arizona crop consumptive use measurements. For comparison purposes, roughly estimated inputs based on irrigation system type, soil type, area weather data and available crop coefficient curves were used as default intervals. Improved input intervals consisted of observed irrigation system performance, soil property measurements, local weather data and theoretical improvements in crop coefficient curves. For surface irrigation, field observation of plant stress and soil water content showed the greatest potential to improve irrigation date predictions. For buried drip under a row crop, accuracy of the predicted daily irrigation rate was most improved by a better estimate of irrigation efficacy.
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Aladenola, Olanike. "Decision support system for irrigation water management." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=123181.

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Abstract Variability in seasonal precipitation, potential climate change impacts, competition for water among users, rising population and increasing food demands are putting pressure on agricultural water demands. For irrigated agriculture in Canada to play a major role in addressing current and future global food supply problems, more innovative and sustainable irrigation management approaches are required. In this context a decision support system that ensured more effective irrigation water allocation, application and optimisation was developed. Crop water requirements and irrigation schedules for bell pepper (Capsicum annuum L.) were obtained from greenhouse and field studies. Greenhouse experiments were conducted to determine appropriate irrigation water applications, agronomic and physiological response to water stress for peppers grown on clay and loamy sand soils. These studies involved four irrigation levels -120% (T120), 100% (T100), 80% (T80) and 40% (T40) of pan evaporation (Epan). The results showed that highest yields and water use efficiency were obtained with 120% Epan replenishment on loamy sand compared to clay soil. The corresponding crop water stress index (CWSI) at T120 was 0.18 to 0.20 on clay, and 0.09 to 0.11 on loamy sand. The fruit total soluble solids content was highest in the T40, and least in the T120 treatments.Given that the greenhouse results were obtained under controlled conditions, it was necessary to extend the research in the field. Experiments were conducted to determine the level of available soil water at which irrigation should be applied to prevent water stress and yield loss for peppers on a clay soil. Four irrigation thresholds, as a percentage of available water content, were investigated. These were: 85% (T1), 75% (T2), 50% (T3), and 25% (T4) available water content. A control of no irrigation (T5) was implemented. The crop water stress index (CWSI) and effects of elevated CO2 on the stomatal conductance and water applied were also investigated. The three CO2 levels studied were: ambient CO2 (~400 ppm), predicted CO2 for the year 2050 (550 ppm), and predicted CO2 for the year 2100 (750 ppm). Optimum marketable yields were achieved when 50% (T3) of the available water content had been depleted with a corresponding CWSI of 0.3 to 0.4. A decrease in stomatal conductance with increasing CO2 was observed. Irrigation water requirements decreased by 6-42% under elevated CO2 of 550 ppm, and 28-58% for elevated CO2 of 750 ppm. An integrated agricultural water demand model (IAWDM) was developed using a graphical user interface (GUI) in Matlab to estimate irrigation water requirements (IWR). A pre-requisite for the model development was to ensure that solar radiation (Rs) input data were of good quality. The suitability of nine (Rs) estimation methods, and their effects on reference evapotranspiration (ETo) were evaluated using data from eight weather stations across Canada. Based on Root mean square error (RMSE) of 1-6%, the Hargreaves and Samani (H-S) method gave best results for locations that did not have reliable, long term, observed Rs and sunshine duration data. Output from the IAWDM was compared with CROPWAT simulations, and metered irrigation water-use. IWR from IAWDM deviated from field data by 7 to 28%, while CROPWAT deviated by 7 to 42%. Future IWR was estimated using Agriculture and Agri-food Canada (AAFC) generated climate change data for 2040 to 2069. Results showed that IWR of bell peppers will increase by 19 to 27% in the future. A sensitivity analysis showed that IWR is most sensitive to air temperature, reference evapotranspiration (ETo), and crop coefficients, followed by solar radiation and precipitation.Overall the findings from this study led to a more sustainable greenhouse and field production of vegetable. The improved management practices increased irrigation water use efficiency thereby leading to a more beneficial use of agricultural water.
L'imprévisibilité des présentes précipitations saisonnières et des répercussions potentielles du changement climatique, ainsi que les besoins alimentaires grandissants d'une population croissante, mènent à une compétition plus acharnée entre les utilisateurs des ressources en eau, imposant ainsi d'importantes pressions sur la demande en eau à fins agricoles. Pour que l'agriculture irriguée au Canada puisse contribuer de façon significative à la résolution de présents et futures problèmes d'approvisionnement alimentaire mondial, des modes de gestion d'irrigation plus innovateurs et durables sont nécessaires. Dans ce contexte, un système d'aide à la décision assurant une plus grande efficacité d'allocation, d'application et d'optimisation des eaux d'irrigation fut conçue. Les études en serre établirent un régime d'irrigation approprié pour les poivrons et notèrent leurs réponses agronomiques et physiologiques à des stress hydriques lorsque cultivés sur un sol argileux ou un sable loameux. Quatre niveaux d'irrigation furent évalués, soit 120% (T120), 100% (T100), 80% (T80) ou 40% (T40) de l'évaporation bac (Ebac). Un réapprovisionnement à 120% Ebac entraîna un rendement et une efficacité d'utilisation de l'eau plus élevés sur le sable loameux que sur le sol argileux. L'indice de stress hydrique (ISH) de la culture soumise au taux de réapprovisionnement de 120% fut de 0.18 à 0.20 sur le sol argileux, et de 0.09 à 0.11 sur le sable loameux. Comme les résultats en serre furent obtenus sous des conditions hautement contrôlées, il fut nécessaire d'étendre la recherche à une culture en champ. Une étude fut entreprise sur un sol argileux pour déterminer quel seuil de pourcentage d'eau disponible dans le sol (85%, 75%, 50%, ou 25%) devrait entraîner une irrigation visant à prévenir un stress hydrique du plant de poivron et la perte de rendement qui en suivrait. Un étalon n'ayant reçu aucune irrigation fut également inclus. L'indice de stress hydrique (ISH) fut suivi et l'effet de teneurs élevés en CO2 sur la conductance stomatique et la quantité d'eau devant être appliqué furent également étudiés. Les trois teneurs en CO2 évalués furent celles de l'air ambiant présent (~400 ppm), et les teneurs prédites pour 2050 et 2100 (550 et 750 ppm, respectivement). Un rendement commercialisable optimal fut obtenu avec un seuil d'irrigation représentant à une carence de 50% en eau disponible du sol, ce qui correspond à un indice de stress hydrique de 0.3 à 0.4. Par rapport aux besoins en irrigation sous la présente teneur en CO2 de l'air ambiant, ces besoins diminuèrent de 6 à 42% sous une teneur en CO2 de 550 ppm, et de 28 à 58% sous une teneur en CO2 de 750 ppm. Un modèle intégré de demande en eau pour fins agricoles (MIDEFA) permettant l'estimation des besoins en eau d'irrigation (BEI) fut élaboré en utilisant l'interface graphique de Matlab. L'élaboration du modèle nécessita des données d'entrée de radiation solaire (Rs) de haute qualité. Laquelle de neuf méthodes permettant d'estimer Rs conviendrait le mieux fut évalué en utilisant des données parvenant de huit stations météorologiques canadiennes. Avec une erreur quadratique moyenne de 1 à 6%, la méthode Hargreaves et Samani (H-S) donna les meilleurs résultats. Les données tirées du MIDEFA furent comparées à celles tirées de simulations avec CROPWAT, et aux données provenant d'un compteur d'eau utilisée à fins d'irrigation. Les différences entre le BEI mesuré au champ et ceux calculés par MIDEFA et CROPWAT furent de 7 à 28% et 7 à 42%, respectivement. De futures BEI furent estimés en utilisant des données fournies par Agriculture et Agroalimentaire Canada (AAC), reflétant le changement de climat prévu pour 2040 et 2069. Selon cette analyse, le BEI pour les poivrons augmenterait de 19 à 27% dans l'avenir.Dans l'ensemble les constats de notre étude ont mené à une production de légumes plus durable à la fois en serre et au champ.
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Mackrell, Dale Carolyn, and n/a. "Women as Farm Partners: Agricultural Decision Support Systems in the Australian Cotton Industry." Griffith University. Griffith Business School, 2006. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20070305.131533.

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Australian farmers are supplementing traditional practices with innovative strategies in an effort to survive recent economic, environmental, and social crises in the rural sector. These innovative strategies include moving towards a technology-based farm management style. A review of past literature determines that, despite a growing awareness of the usefulness of computers for farm management, there is concern over the limited demand for computer-based agricultural decision support systems (DSS). Recent literature indicates that women are the dominant users of computers on family farms yet are hesitant to use computers for decision support, and it is also unclear what decision-making roles women assume on family farms. While past research has investigated the roles of women in the Australian rural sector, there is a dearth of research into the interaction of women cotton growers with computers. Therefore, this dissertation is an ontological study and aims to contribute to scholarly knowledge in the research domain of Australian women cotton growers, agricultural DSS, and cotton farm management. This dissertation belongs in the Information Systems (IS) stream and describes an interpretive single case study which explores the lives of Australian women cotton growers on family farms and the association of an agricultural DSS with their farm management roles. Data collection was predominantly through semi-structured interviews with women cotton growers and cotton industry professionals such as DSS developers, rural extension officers, researchers and educators, rural experimental scientists, and agronomists and consultants, all of whom advise cotton growers. The study was informed by multiple sociological theories with opposing paradigmatic assumptions: Giddens' (1984) structuration theory as a metatheory to explore the recursiveness of farm life and technology usage; Rogers' (1995) diffusion of innovations theory with a functionalist approach to objectively examine the features of the software and user, as well as the processes of technology adoption; and Connell's (2002) theory of gender relations with its radical humanist perspective to subjectively investigate the relationships between farm partners through critical enquiry. The study was enriched further by drawing on other writings of these authors (Connell 1987; Giddens 2001; Rogers 2003) as well as complementary theories by authors (Orlikowski 1992; Orlikowski 2000; Trauth 2002; Vanclay & Lawrence 1995). These theories in combination have not been used before, which is a theoretical contribution of the study. The agricultural DSS for the study was CottonLOGIC, an advanced farm management tool to aid the management of cotton production. It was developed in the late 1990s by the CSIRO and the Australian Cotton Cooperative Research Centre (CRC), with support from the Cotton Research and Development Corporation (CRDC). CottonLOGIC is a software package of decision support and record-keeping modules to assist cotton growers and their advisors in the management of cotton pests, soil nutrition, and farm operations. It enables the recording and reporting of crop inputs and yields, insect populations (heliothis, tipworm, mirids and so on), weather data, and field operations such as fertiliser and pesticide applications, as well as the running of insect density prediction (heliothis and mites) and soil nutrition models. The study found that innovative practices and sustainable solutions are an imperative in cotton farm management for generating an improved triple bottom line of economic, environmental and social outcomes. CottonLOGIC is an industry benchmark for supporting these values through the incorporation of Best Management Practices (BMP) and Integrated Pest Management (IPM) principles, although there were indications that the software is in need of restructuring as could be expected of software over five years old. The evidence from the study was that women growers are participants in strategic farm decisions but less so in operational decisions, partly due to their lack of relevant agronomic knowledge. This hindered their use of CottonLOGIC, despite creative attempts to modify it. The study endorsed the existence of gender differences and inequalities in rural Australia. Nevertheless, the study also found that the women are valued for their roles as business partners in the multidisciplinary nature of farm management. All the same, there was evidence that greater collaboration and cooperation by farm partners and advisors would improve business outcomes. On the whole, however, women cotton growers are not passive agents but take responsibility for their own futures. In particular, DSS tools such as CottonLOGIC are instrumental in enabling women cotton growers to adapt to, challenge, and influence farm management practices in the family farm enterprise, just as CottonLOGIC is itself shaped and reshaped. Hence, a practical contribution of this study is to provide non-prescriptive guidelines for the improved adoption of agricultural DSS, particularly by rural women, as well as increasing awareness of the worth of their roles as family farm business partners.
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Mackrell, Dale Carolyn. "Women as Farm Partners: Agricultural Decision Support Systems in the Australian Cotton Industry." Thesis, Griffith University, 2006. http://hdl.handle.net/10072/365290.

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Australian farmers are supplementing traditional practices with innovative strategies in an effort to survive recent economic, environmental, and social crises in the rural sector. These innovative strategies include moving towards a technology-based farm management style. A review of past literature determines that, despite a growing awareness of the usefulness of computers for farm management, there is concern over the limited demand for computer-based agricultural decision support systems (DSS). Recent literature indicates that women are the dominant users of computers on family farms yet are hesitant to use computers for decision support, and it is also unclear what decision-making roles women assume on family farms. While past research has investigated the roles of women in the Australian rural sector, there is a dearth of research into the interaction of women cotton growers with computers. Therefore, this dissertation is an ontological study and aims to contribute to scholarly knowledge in the research domain of Australian women cotton growers, agricultural DSS, and cotton farm management. This dissertation belongs in the Information Systems (IS) stream and describes an interpretive single case study which explores the lives of Australian women cotton growers on family farms and the association of an agricultural DSS with their farm management roles. Data collection was predominantly through semi-structured interviews with women cotton growers and cotton industry professionals such as DSS developers, rural extension officers, researchers and educators, rural experimental scientists, and agronomists and consultants, all of whom advise cotton growers. The study was informed by multiple sociological theories with opposing paradigmatic assumptions: Giddens' (1984) structuration theory as a metatheory to explore the recursiveness of farm life and technology usage; Rogers' (1995) diffusion of innovations theory with a functionalist approach to objectively examine the features of the software and user, as well as the processes of technology adoption; and Connell's (2002) theory of gender relations with its radical humanist perspective to subjectively investigate the relationships between farm partners through critical enquiry. The study was enriched further by drawing on other writings of these authors (Connell 1987; Giddens 2001; Rogers 2003) as well as complementary theories by authors (Orlikowski 1992; Orlikowski 2000; Trauth 2002; Vanclay & Lawrence 1995). These theories in combination have not been used before, which is a theoretical contribution of the study. The agricultural DSS for the study was CottonLOGIC, an advanced farm management tool to aid the management of cotton production. It was developed in the late 1990s by the CSIRO and the Australian Cotton Cooperative Research Centre (CRC), with support from the Cotton Research and Development Corporation (CRDC). CottonLOGIC is a software package of decision support and record-keeping modules to assist cotton growers and their advisors in the management of cotton pests, soil nutrition, and farm operations. It enables the recording and reporting of crop inputs and yields, insect populations (heliothis, tipworm, mirids and so on), weather data, and field operations such as fertiliser and pesticide applications, as well as the running of insect density prediction (heliothis and mites) and soil nutrition models. The study found that innovative practices and sustainable solutions are an imperative in cotton farm management for generating an improved triple bottom line of economic, environmental and social outcomes. CottonLOGIC is an industry benchmark for supporting these values through the incorporation of Best Management Practices (BMP) and Integrated Pest Management (IPM) principles, although there were indications that the software is in need of restructuring as could be expected of software over five years old. The evidence from the study was that women growers are participants in strategic farm decisions but less so in operational decisions, partly due to their lack of relevant agronomic knowledge. This hindered their use of CottonLOGIC, despite creative attempts to modify it. The study endorsed the existence of gender differences and inequalities in rural Australia. Nevertheless, the study also found that the women are valued for their roles as business partners in the multidisciplinary nature of farm management. All the same, there was evidence that greater collaboration and cooperation by farm partners and advisors would improve business outcomes. On the whole, however, women cotton growers are not passive agents but take responsibility for their own futures. In particular, DSS tools such as CottonLOGIC are instrumental in enabling women cotton growers to adapt to, challenge, and influence farm management practices in the family farm enterprise, just as CottonLOGIC is itself shaped and reshaped. Hence, a practical contribution of this study is to provide non-prescriptive guidelines for the improved adoption of agricultural DSS, particularly by rural women, as well as increasing awareness of the worth of their roles as family farm business partners.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith Business School
Griffith Business School
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Yule, Ian J. "A decision support system for farm machinery budgeting and selection." Thesis, University of Newcastle Upon Tyne, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242352.

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Satti, Sudheer Reddy. "GWRAPPS a GIS-based decision support system for agricultural water resources management /." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE1001180.

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Robinson, Jeffrey Brett, University of Western Sydney, of Science Technology and Environment College, and School of Environment and Agriculture. "Understanding and applying decision support systems in Australian farming systems research." THESIS_CSTE_EAG_Robinson_J.xml, 2005. http://handle.uws.edu.au:8081/1959.7/642.

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Decision support systems (DSS) are usually based on computerised models of biophysical and economic systems. Despite early expectations that such models would inform and improve management, adoption rates have been low, and implementation of DSS is now “critical” The reasons for this are unclear and the aim of this study is to learn to better design, develop and apply DSS in farming systems research (FSR). Previous studies have explored the merits of quantitative tools including DSS, and suggested changes leading to greater impact. In Australia, the changes advocated have been: Simple, flexible, low cost economic tools: Emphasis on farmer learning through soft systems approaches: Understanding the socio-cultural contexts of using and developing DSS: Farmer and researcher co-learning from simulation modelling and Increasing user participation in DSS design and implementation. Twenty-four simple criteria were distilled from these studies, and their usefulness in guiding the development and application of DSS were assessed in six FSR case studies. The case studies were also used to better understand farmer learning through models of decision making and learning. To make DSS useful complements to farmers’ existing decision-making repertoires, they should be based on: (i) a decision-oriented development process, (ii) identifying a motivated and committed audience, (iii) a thorough understanding of the decision-makers context, (iv) using learning as the yardstick of success, and (v) understanding the contrasts, contradictions and conflicts between researcher and farmer decision cultures
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Qaddoum, Kefaya. "Intelligent real-time decision support systems for tomato yield prediction management." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/58333/.

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This thesis describes the research and development of a decision support system for tomato yield prediction. Greenhouse horticulture such as tomato growing offers an interesting test bed for comparing and refining different predictive modelling techniques. The ability to accurately predict future yields, even for as little as days ahead has considerable commercial value to growers. There are several (measurable) causal variables. Some such as temperature are under the grower's control, while others are not. Modern predictive techniques, based on data mining and self-calibrating models, may be able to forecast future yields per unit area of greenhouse better than the biological causal models implicitly now used by growers. Over the past few decades, it has been possible to use the recorded daily environmental conditions in a greenhouse to predict future crop yields. Existing models fail to accurately predict the weekly fluctuations of yield, yet predicting future yields is becoming desperately required especially with weather change. This research project used data collected during seasonal tomato life cycle to develop a decision support system that would assist growers to adjust crops to meet demand, and to alter marketing strategies. The three main objectives are: firstly, to research and utilize intelligent systems techniques for analysing greenhouse environmental variables to identify the variable or variables that most effect yield fluctuations, and Secondly, to research the use of these techniques for predicting tomato yields and produce handy rules for growers to use in decision-making. Finally, to combine some existing techniques to form a hybrid technique that achieves lower prediction errors and more confident results. There are a range of intelligent systems (IS), which are used to process environment data, including artificial neural networks (ANNs), genetic algorithms (GA) and fuzzy logic (FL). A model providing more accurate yield prediction was developed and tested using industrial data from growers. The author develops and investigates the application of an intelligent decision support system for yield management, and to provide an improved prediction model using intelligent systems (IS). Using real-world data, the intelligent system employs a combination of FL, NN and GA. The thesis presents a modified hybrid adaptive neural network with revised adaptive error smoothing, which is based on genetic algorithm to build a learning system for complex problem solving in yield prediction. This system can closely predict weekly yield values of a tomato crop. The proposed learning system is constructed as an intelligent technique and then further optimized. The method is evaluated using real-world data. The results show comparatively good accuracy.Use was made of existing algorithms, such as self-organizing maps (SOMs), and principal component analysis (PCA), to analyse our datasets and identify the critical input variables. The primary conclusion from this thesis is that intelligent systems, such as artificial neural networks, genetic algorithm, and fuzzy inference systems, can be successfully applied to the creation of tomato yield predictions, these predictions were better and hence support growers’ decisions. All of these techniques are benchmarked against published existing models, such as GNMM, and RBF.
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Lynch, Teresa Ann, and t. lynch@cqu edu au. "Intelligent support systems in agriculture: A study of their adoption and use." Central Queensland University. Computing and Information Systems, 2002. http://library-resources.cqu.edu.au./thesis/adt-QCQU/public/adt-QCQU20040131.101933.

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Australian agriculture is one area in which a number of intelligent support systems have been developed. It appears, however, that comparatively few of these systems are widely used or have the impact the developers might have wished. In this study a possible explanation for this state of affairs was investigated. The development process for 66 systems was examined. Particular attention was paid to the nature of user involvement, if any, during development and the relationship to system success. The issue is not only whether there was user involvement but rather the nature of the involvement, that is, the degree of influence users had during development. The patterns identified in the analysis suggest user influence is an important contributor to the success of a system. These results have theoretical significance in that they add to knowledge of the role of the user in the development of intelligent support systems. The study has drawn together work from three areas: Rogers’ diffusion theory, the technology acceptance model, and theories relating to user involvement in the development of information systems. Most prior research in the information systems area has investigated one or two of the above three areas in any one study. The study synthesizes this knowledge through applying it to the field of intelligent support systems in Australian agriculture. The results have considerable practical significance, as apparently developers of intelligent support systems in Australian agriculture do not recognize the importance of user participation, and continue to develop systems with less than optimum impact.
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Books on the topic "Agricultural decision support systems"

1

Manos, Basil. Decision support systems in agriculture, food and the environment: Trends, applications and advances. Hershey, PA: Information Science Reference, 2010.

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Manos, Basil. Decision support systems in agriculture, food and the environment: Trends, applications and advances. Hershey, PA: Information Science Reference, 2010.

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Cao, Weixing. Crop Modeling and Decision Support. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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1950-, Manos Basil, ed. Decision support systems in agriculture, food and the environment: Trends, applications and advances. Hershey, PA: Information Science Reference, 2010.

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Esslemont, R. J. The development of decision support systems in agriculture: DAISY, the Dairy Information System. Reading: Farm Management Unit, University of Reading, Department of Agriculture, Department of Agricultural Economics and Management, 1993.

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Alphonce, Christian B. A practical decision support system for food security planning in low income food deficit developing countries. Dublin: University College Dublin, 1997.

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Mkomwa, Saidi, and Amir Kassam, eds. Conservation agriculture in Africa: climate smart agricultural development. Wallingford: CABI, 2022. http://dx.doi.org/10.1079/9781789245745.0000.

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Abstract This book is about Conservation Agriculture (the use of no tillage systems) to preserve soil structure and integrity. It has become an increasingly important step towards sustainable farming. This publication brings together conservation agriculture and climate smart decision making processes for the first time, focusing on Africa. This book brings to the fore scientific and empirical evidence about Conservation Agriculture in Africa, articulated by the Second Africa Congress on Conservation Agriculture (2ACCA) held in Johannesburg in 2018. It describes how farmers in Africa are successfully adopting Conservation Agriculture as an alternative to the unsustainable conventional farming practices and as a solution to loss of agricultural productivity, soil erosion and land degradation, climate change challenges and ever-increasing food insecurity. This work discusses how Conservation Agriculture can support the implementation of the African Union's Malabo Declaration and Agenda 2063 which calls for climate smart agricultural development. It provides development-oriented case studies and scientific evidence relevant to all stakeholders in the public, private and civil sectors who are engaged in building policy, institutional and human capacity to accelerate the mainstreaming of Conservation Agriculture across Africa.
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Loucks, Daniel P., and João R. da Costa, eds. Decision Support Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76048-8.

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Office, General Accounting. Computer matching: Quality of decisions and supporting analyses little affected by 1988 act : report to the Chairman, Information, Justice, Transportation and Agriculture Subcommittee, Committee on Government Operations, House of Representatives. Washington, D.C: The Office, 1993.

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Moreno-Jiménez, José María, Isabelle Linden, Fatima Dargam, and Uchitha Jayawickrama, eds. Decision Support Systems X: Cognitive Decision Support Systems and Technologies. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46224-6.

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Book chapters on the topic "Agricultural decision support systems"

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Nir, Dov. "Expert Systems for Agricultural Water Systems." In Decision Support Systems, 461–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76048-8_21.

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Jones, J. W. "Decision support systems for agricultural development." In Systems approaches for agricultural development, 459–71. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2840-7_28.

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Jones, J. W. "Decision support systems for agricultural development." In Systems approaches for agricultural development, 459–71. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2842-1_28.

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Hussain, Shahnaz Akhtar, and Papu Moni Saikia. "Smart Agricultural Monitoring and Decision Support System." In Algorithms for Intelligent Systems, 267–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4604-8_21.

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Morais, Danielle C., Andre M. Araújo, Eduarda A. Frej, and Adiel T. de Almeida. "Group Decision Process for Evaluating a Mango Variety to Be Planted in New Agricultural Farms." In Collective Decisions: Theory, Algorithms And Decision Support Systems, 247–64. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84997-9_11.

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Sarkar, Reshmi. "Decision Support Systems for Agrotechnology Transfer." In Sustainable Agriculture Reviews, 263–99. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4113-3_10.

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Rodriguez, Jenny Milena Moreno, Takanni Hannaka Abreu Kang, Eduarda Asfora Frej, and Adiel Teixeira de Almeida. "A Group Decision-Making Model for Supplier Selection: The Case of a Colombian Agricultural Research Company." In Decision Support Systems VIII: Sustainable Data-Driven and Evidence-Based Decision Support, 132–41. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90315-6_11.

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Petersen, G. W., R. L. Day, C. T. Anthony, J. Pollack, and J. M. Russo. "Importance of Spatial Variability in Agricultural Decision Support Systems." In Proceedings of Soil Specific Crop Management, 167–79. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2015. http://dx.doi.org/10.2134/1993.soilspecificcrop.c14.

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Jones, James W., W. T. Bowen, W. G. Boggess, and J. T. Ritchie. "Decision Support Systems for Sustainable Agriculture." In Technologies for Sustainable Agriculture in the Tropics, 123–38. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, 2015. http://dx.doi.org/10.2134/asaspecpub56.c10.

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Jones, J. W., G. Y. Tsuji, G. Hoogenboom, L. A. Hunt, P. K. Thornton, P. W. Wilkens, D. T. Imamura, W. T. Bowen, and U. Singh. "Decision support system for agrotechnology transfer: DSSAT v3." In Understanding Options for Agricultural Production, 157–77. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-017-3624-4_8.

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Conference papers on the topic "Agricultural decision support systems"

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Somawardhana, W. D. R., G. A. Deegala, I. A. N. Fernando, G. C. Dassanayake, P. S. Haddela, and Ajith Perera. "Vee-Bissa: The agricultural decision support system." In 2013 IEEE 8th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2013. http://dx.doi.org/10.1109/iciinfs.2013.6732026.

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"6 Management and Decision Support Systems." In CIGR Handbook of Agricultural Engineering Volume VI: Information Technology . St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2006. http://dx.doi.org/10.13031/2013.21687.

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Devi, R., R. Hemalatha, and S. Radha. "Efficient decision support system for Agricultural application." In 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB). IEEE, 2017. http://dx.doi.org/10.1109/aeeicb.2017.7972336.

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Shepel, V. V. "Decision Support System For Modern Agricultural Enterprises." In Global Challenges and Prospects of The Modern Economic Development. European Publisher, 2021. http://dx.doi.org/10.15405/epsbs.2021.04.02.164.

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Apata, T., G. N’Guessan, K. Ayantoye, and O. Idowu. "Agricultural land-use systems and climate change among small Farmers in nigeria." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19301.

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In sub-Saharan-Africa (SSA), agriculture land-use supports the livelihoods of the majority of people. Land- use for agricultural-activity is an economic-activity that is highly dependent upon weather and climate that produce food and fibre necessary to sustain human life. Hence, land-use for agriculture is expected to be vulnerable to climate variability. This paper examines this relationship. The paper presents data and generated evidence-based decision making under risk and uncertainty as influenced by climate change and its effects on agricultural land-use/outputs. Farm-level cost-route survey of cross-sectional national-data of 800 respondents was used for analysis. Data were analyzed and presented using the tools of descriptive statistics, trans-logarithms model and multivariate probit model (MVP). The study indicated a strong relationship between efficient use of agricultural-land and adaptive-processes to climate-change. Thus, providing data and analysis that strengthen policy decisions on land-use and climate change. Hence, policies of promoting and motivating sustainable land-use management need to be entrenched.
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Kovljenić, Mina, and Jelena Nestorov Bizonj. "Resource Use and Food Security in the Republic of Serbia." In 27th International Scientific Conference Strategic Management and Decision Support Systems in Strategic Management. University of Novi Sad, Faculty of Economics in Subotica, 2022. http://dx.doi.org/10.46541/978-86-7233-406-7_221.

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Achieving food security and resources sustainability have a high priority in agrarian policy that universal for all economies. Today modern agriculture has many complex challenges, so a sustainable agriculture approach is needed. Agriculture now must produce more food, using available natural resources efficiently and sustainably, including a reduction of post-harvest losses and waste, and developing agriculture more resilient to climate change. The Republic of Serbia has a good quality of agricultural land, favorable ratio of available land per capita, and favorable climate conditions for agricultural production. However, Serbia has a large number of small farms with fragmented property, family workforce, low level of technical equipment and capital, which have a high production costs and irrational use of resources. The aim of this paper is to examine the impact ofresource use on the level of food security in the Republic of Serbia. The survey data were taken from the FAOSTAT database, World Bank, as well as the national statistics of the Republic of Serbia and hierarchical regression analysis was used. The results of the research have shown that resource supply has a statistically significant impact on the level of food security in the Republic of Serbia.
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Koob, Tonja L., and Michael E. Barber. "A Decision Support System for Agricultural Constructed Wetland Design." In Wetlands Engineering and River Restoration Conference 1998. Reston, VA: American Society of Civil Engineers, 1998. http://dx.doi.org/10.1061/40382(1998)34.

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Animas, Mark Ian, Yung-Cheol Byun, Bobby D. Gerardo, and Ma Beth Concepcion. "Decision support system for agricultural management using prediction algorithm." In 2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS). IEEE, 2013. http://dx.doi.org/10.1109/icis.2013.6607839.

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Wang, Jianxiong, and Fuxia Zhang. "Research on spatial data mining technology in agricultural spatial decision support system." In International Conference on Transportation Systems and Intelligent Control. Southampton, UK: WIT Press, 2015. http://dx.doi.org/10.2495/ictsic140101.

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Li, Meng-Ying, Chih-Hong Sun, Min-Fang Lien, and Tsun-Kuo Chang. "A design of spatial decision support system to enhance decision progress in agricultural actions." In 2014 Third International Conference on Agro-Geoinformatics. IEEE, 2014. http://dx.doi.org/10.1109/agro-geoinformatics.2014.6910613.

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Reports on the topic "Agricultural decision support systems"

1

Walsh, Margaret, Peter Backlund, Lawrence Buja, Arthur DeGaetano, Rachel Melnick, Linda Prokopy, Eugene Takle, Dennis Todey, and Lewis Ziska. Climate Indicators for Agriculture. United States. Department of Agriculture. Climate Change Program Office, July 2020. http://dx.doi.org/10.32747/2020.7201760.ch.

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The Climate Indicators for Agriculture report presents 20 indicators of climate change, carefully selected across multiple agricultural production types and food system elements in the United States. Together, they represent an overall view of how climate change is influencing U.S. agriculture and food systems. Individually, they provide useful information to support management decisions for a variety of crop and livestock production systems. The report includes multiple categories of indicators, including physical indicators (e.g., temperature, precipitation), crop and livestock (e.g., animal heat stress), biological indicators (e.g., pests), phenological indicators (e.g. seasonality), and socioeconomic indicators (e.g., total factor productivity).
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Sorensen, H. B., John S. Park, and Jr. Instructional Systems Development Decision Support. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada228052.

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Grevet, Jean-Louis M., and Alexander H. Levis. Coordination in Organizations with Decision Support Systems. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada197951.

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Gurney, John O., Marsh Jr., Wauchope Elaine, and Kenneth. Focus of Attention in Decision Support Systems. Fort Belvoir, VA: Defense Technical Information Center, May 1995. http://dx.doi.org/10.21236/ada294037.

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Bostick, K. V. Decision support system to select cover systems. Office of Scientific and Technical Information (OSTI), February 1995. http://dx.doi.org/10.2172/10116819.

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Hawgood, John. Formal 'Systems Languages' in Decision Support Systems for Military Commanders. Fort Belvoir, VA: Defense Technical Information Center, June 1986. http://dx.doi.org/10.21236/ada169673.

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Kennedy, John C., Piyush Sabharwall, Shannon M. Bragg-Sitton, Konor L. Frick, Patrick McClure, and D. V. Rao. Special Purpose Application Reactors: Systems Integration Decision Support. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1475413.

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Hirsch, Gary B., Jack Homer, Brooke N. Chenoweth, George A. Backus, and David R. Strip. Behavior-aware decision support systems : LDRD final report. Office of Scientific and Technical Information (OSTI), November 2007. http://dx.doi.org/10.2172/934860.

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Savani, Manu, and Alastair Stewart. Making Market Systems Work for Women Dairy Farmers in Bangladesh: A final evaluation of Oxfam's Gendered Enterprise and Markets programme in Bangladesh. Oxfam GB, December 2019. http://dx.doi.org/10.21201/2019.5365.

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Gendered Enterprise and Markets (GEM) is Oxfam GB’s approach to market systems development. The GEM approach facilitates change in market systems and social norms, with the aim of ensuring more sustainable livelihood opportunities for marginalized women and men. The GEM DFID AidMatch Programme (June 2014–February 2018) worked within the soya, milk and vegetable value chains targeting women smallholder farmers in areas of poverty. The programme aimed to benefit 63,600 people (10,600 smallholder households) living in Zambia, Tajikistan and Bangladesh through increases in household income, women having greater influence over key livelihood decisions within their households and communities, and engaging in livelihoods more resilient to shocks, such as natural disasters and market volatility. The GEM programme in Bangladesh was implemented under Oxfam Bangladesh’s flagship REE-CALL programme (Resilience, through Economic Empowerment, Climate Adaptation, Leadership and Learning). GEM operated in seven districts across Bangladesh, with the project activities implemented by seven local partners. The project aimed to establish 84 producer groups for smallholder dairy farmers, and this was achieved during the first year. Building on these local networks, GEM aimed to deliver a suite of training and support covering assertiveness, rights and leadership skills, agricultural practice and disaster risk management. The evaluation was designed to investigate if and how the GEM programme might have contributed to its intended outcomes – not only in the lives of individual women smallholder farmers targeted by the programme but also in changes in their communities and the larger market system. It also sought to capture any potential unintended outcomes of the programme.
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Mowrer, H. Todd, Klaus Barber, Joe Campbell, Nick Crookston, Cathy Dahms, John Day, Jim Laacke, et al. Decision support systems for ecosystem management: An evaluation of existing systems. Ft. Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1997. http://dx.doi.org/10.2737/rm-gtr-296.

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