Agricultural Decision Support Systems Research Articles

RIM: Anatomy of a Weed Management Decision Support System for Adaptation and Wider Application

RIM, or “Ryegrass Integrated Management,” is a model-based software allowing users to conveniently test and compare the long-term performance and profitability of numerous ryegrass control options used in Australian cropping systems. As a user-friendly decision support system that can be used by farmers, advisers, and industry professionals, RIM can aid the delivery of key recommendations among the agricultural community for broadacre cropping systems threatened by herbicide resistance. This paper provides advanced users and future developers with the keys to modify the latest version of RIM in order to facilitate future updates, modifications, and adaptations to other situations. The various components of RIM are mapped and explained, and the key principles underlying the construction of the model are explained. The implementation of RIM into a Microsoft Excel® software format is also documented, with details on how user inputs are coded and parameterized. An overview of the biological, agronomic, and economic components of the model is provided, with emphasis on the ryegrass biological characteristics most critical for its effective management. The extreme variability of these parameters and the subsequent limits of RIM are discussed. The necessary compromises were achieved by emphasizing the primary end-use of the program as a decision support system for farmers and advisors.

Assessment of Agricultural Best Management Practices Using Models: Current Issues and Future Perspectives

Best management practices (BMPs) are the most effective and practicable means to control nonpoint source (NPS) pollution at desired levels. Models are valuable tools to assess their effectiveness. Watershed managers need to choose appropriate and effective modelling methods for a given set of conditions. This paper considered state-of-the-art modelling strategies for the assessment of agricultural BMPs. Typical watershed models and specific models were analyzed in detail. Further improvements, including simplified tools, model integration, and incorporation of climate change and uncertainty analysis were also explored. This paper indicated that modelling methods are strictly scale dependent, both spatially and temporally. Despite current achievements, there is still room for future research, such as broadening the range of the pollutants considered, introducing more local BMPs, improving the representation of the functionality of BMPs, and gathering monitoring date for validation of modelled results. There is also a trend towards agricultural decision support systems (DSSs) for assessing agricultural BMPs, in which models of different scales are seamlessly integrated to bridge the scale and data gaps. This review will assist readers in model selection and development, especially those readers concerned about NPS pollution and water quality control.

A review of potential use of geo-information technologies for cotton supply chain management

In this paper, we provided an overview of the cotton supply chain management (SCM) and its processes including their inputs/ and outputs. The components of cotton SCM were discussed with their influencing factors. Additionally, we searched relevant literature on the potential use of Geographical Information Technologies (GITs) in cotton supply chain management. According to our findings, there is no study on the utilization of the web to maximize potential of GITs in all components of cotton SCM. GITs are, however, successfully used in intelligent data creation, data management, spatial analysis, agricultural decision support system and partially in supply chain management systems. We found a dire need to integrate applications of GITs in all components of cotton SCM to optimize the use of resources as agriculturalists are utilizing GITs in precision agriculture. Key words: Cotton supply chain management (CSCM), geographic information system (GIS), geo-information technologies (GITs), crop yield estimation, remote sensing (RS), agriculture.

Potential of life cycle assessment to support environmental decision making at commercial dairy farms

In this paper, we evaluate the potential of life cycle assessment (LCA) to support environmental decision making at commercial dairy farms. To achieve this, we follow a four-step method that allows converting environmental assessment results using LCA into case-specific advice for farmers. This is illustrated in a case-study involving 20 specialized Flemish dairy farms. Calculated LCA indicators are normalized into scores between 0 and 100, whereby a score of 100 is assumed optimal, to allow for a mutual comparison of indicators for different environmental impact categories. Next, major farm and management characteristics affecting environmental performance are identified using multiple regression and correlation analyses. Finally, comparing specific farm and management characteristics with those of best performing farms identifies farm-specific optimization strategies. We conclude that this approach complies with most of the identified critical success factors for the successful implementation of LCA as a decision support system for farmers. Key aspects herein are (i) the flexibility and accessibility of the model, (ii) the use of readily available farm data, (iii) farm advisors being intended model users, (iv) the identification of key farm and management characteristics affecting environmental performance and (v) the organization of discussion sessions involving farmers and farm advisors. However, attention should be paid (i) to provide sufficient training and guidance for farm advisors on the use of the applied LCA model and the interpretation of results, (ii) to evaluate the correctness of the used data and (iii) to keep the model up-to-date according to new scientific insights and knowledge concerning LCA methodology.

A Meta-Heuristics Based Decision Support System for Optimal Crop Planning

This paper presents a meta-heuristic based decision support system to support farmers with optimal strategic decision making in the crop planning system. The analysis and modelling of crop planning decision making process are attractive for producing formalized knowledge on cropping plans in an agricultural farm. The formalization of the decision making process is generally becoming a crucial focal point for developing decision support systems that go beyond the limitation of formerly developed prescriptive approaches. The methodology of this research strikes a balance between mathematical formulations of crop planning problems and effective implementation of crop planning decision models. The new approach used to solve this problem is applicable to both medium and large scale farming; it is also integrated a systemic framework to build better decision support systems in agriculture. DOI: 10.5901/mjss.2014.v5n20p359

Study on Counties Agricultural Economic Intelligent Decision-Making Support System (IDSS) Based on GIS and Knowledge

Aiming at the complex and diverse agricultural information resources, the article proposes a kind of intelligent and scientific agricultural economic analysis method and tool combining the various information technologies, which can improve the level of modern agricultural information management. A universal and compatible counties agricultural economic intelligent decision-making system based on GIS and knowledge is constructed to characterize and analyze agricultural economic spatial and temporal distribution of counties, which introduces multiple agricultural economic analyzing models combining the quantitative analysis of agricultural economic monitoring subsystem with the qualitative analysis of knowledge subsystem. The system utilizes C#.NET as development platform, and the secondary development technology of GIS that helps in the design of the agricultural economic intelligent decision support system, which provides efficient decision support for agricultural economic management sectors.

Addressing the implementation problem in agricultural decision support systems

A new DSS was developed for sustainable management of the vineyards.The 'implementation problem' of DSSs was specifically addressed by adopting multiple solutions.Potential end-users and stakeholde...

CommonKADS model framework for web based agricultural Decision Support System.

Increased demand of farm productions and depleting natural resources compelled the agriculture community to enhance the use of Information and Communication Technology (ICT) in various farming processes. Agricultural Decision Support System (DSS) proved useful in this regard as the agricultural systems are complex and partially known. The majority of available Agricultural DSSs are either crop or task specific. There are very less endeavors found in the direction of comprehensive DSS. The specific DSSs mainly developed with rule based or knowledge transfer based approach. These methodologies lack the ability to scale up and to support the development of large DSS. Modeling approaches are more suitable than so called transfer approaches for large and inclusive DSS. Unfortunately, it is found that that the model based knowledge engineering approach is not much utilized for the development of Agricultural DSS. The modeling approach to construct Knowledge Base Systems (KBS) becomes well accepted among the Knowledge Engineering (KE) communities due to its modular structure and ability to break down the knowledge engineering problem into smaller tasks. Modeling approach for the development of DSS offers the broad idea of structure and modules of the support system before hand. There are many modeling frameworks proposed and subsequently used by the KE communities. CommonKADS is one of the popular modeling frameworks for KBS. The paper presents the organization, agent, task, communication, knowledge and design models based on CommonKADS approach for development of scalable, broad and practically usable agricultural DSS. A web based DSS developed with multi agent CommonKADS modeling approach. The system offers decision support for irrigation scheduling and weather based disease forecasting for the popular crops of India. The proposed framework along with the required expert knowledge, provide necessary platform on which the larger DSS can built for any crop of given locations.

黄淮海地区干旱变化特征及其对气候变化的响应

It is widely recognized that the frequency and intensity of extreme weather events and climate disasters have strongly increased with global warming. The area of influence of climate disasters has also increased,which has had adverse effects on sustainable social and economic development. Drought is a recurring natural phenomenon,and is associated with a deficit of water resources over a large geographic area and long duration. Drought is attracting increased attention from scholars,with a focus on its intensity,duration and areal extent in northern China within the context of global change. Investigation of the variation of drought and regional response to climate change is very important to agricultural production, and can provide a reference fordeveloping appropriate measures to reduce droughts on the Huang-Huai-Hai( 3H) Plain. At present,relevant research is more inclined to study meteorological drought itself, without consideration of drought characteristics in different phases in crop-growing seasons and the climate background of global change. In this paper,we determine drought characteristics in all four seasons and the winter wheat growing season on the 3H Plain,together with the effects of climate change. Based on data of 34 meteorological stations from 1961 to 2011,a relative moisture index was calculated to investigate the spatial pattern and temporal variability of drought characteristics on the 3H Plain. The results show varying degrees of drought in spring,winter and the winter wheat growing season. Drought frequency exceeded 90% over the past 50 years on the plain,with spring and winter the driest seasons. There were high-frequency drought areas in central and northern parts of the plain during spring,winter and the winter wheat growing season. The regional distribution of drought intensity and frequency showed an increasing tendency from south to north. A wet trend was detected on the plain in the winter wheat growing season over the last 50 years. However,the relative moisture index changed since 1978. That is to say,the index had an increasing trend from 1961 to 1980 when the plain was wetter; the index decreased from 1980 to 2011 when it was drier. Overall,although drought eased over the entire analysis period,a serious drought tendency has emerged over the last 20 years. In addition,temporal variability of the relative moisture index was significantly correlated with precipitation,solar radiation and relative humidity. This indicates that drought characteristics of the plain were more sensitive to these three climate variables. This has received increased attention in recent years with respect to addressing climate change. The results of our study indicate an arid trend,with increase of temperature in spring and summer on the 3H Plain. Therefore,relevant agencies should create an early warning system of extreme weather events and natural disasters,toward improvement of future regional agricultural scientific management and decision support systems in agricultural production. These agencies should also adapt to climate change by selecting strongly drought-resistant crop varieties and by adjusting cultivation methods and management measures,especially irrigation measures aimed at spring drought on the 3H Plain.

Addressing the implementation problem in agricultural decision support systems: the example of vite.net®

Although many Decision Support Systems (DSSs) have been developed for crop management, DSSs have contributed little to practical agriculture because of the so-called ‘problem of implementation’; under-utilisation has been ascribed to both technical limitations of the DSSs and to farmer attitude towards DSSs. A new DSS, named vite.net®, was developed for sustainable management of vineyards and is intended for the vineyard manager (the person who makes decisions about the vineyard management or suggests the proper actions to the grape-grower). The DSS has two main parts: (i) an integrated system for real-time monitoring of the vineyard components (air, soil, plants, pests, and diseases) and (ii) a web-based tool that analyses these data by using advanced modelling techniques and then provides up-to-date information for managing the vineyard in the form of alerts and decision supports. The information is tailored to a vineyard, or part of a vineyard, or a number of vineyards that are uniformly managed throughout the season. In the design and development of vite.net®, the implementation problem was specifically addressed by: (i) focusing on the important vineyard problems with a holistic approach (the DSS incorporates overall management solutions for growers); (ii) using automation and integration in data collection, and supporting flexible input efforts by the user; (iii) developing and validating fit-to-purpose, mechanistic, dynamic models; (iv) designing a user-friendly interface and providing complete and easy-to-understand information; (v) delivering the DSS through the Web and thereby enabling both continuous updating by the provider and flexible access by the user; (vi) designing the DSS with the goal of assisting the decision maker (by providing necessary information) rather than replacing the decision maker; (vii) involving potential users during vite.net® development and testing so as to obtain insight into how users make decisions; (viii) communicating the benefits of the DSS via seminars and visits to demonstration vineyards; (ix) involving chemical companies and other potential stakeholders; and (x) developing a two-way communication mode with the end-users, i.e., by combining “push” and “pull” systems. Feedback collected during development, testing, and practical use of vite.net® suggested that potential users were likely to use the DSS and that the ‘implementation problem’ had been successfully addressed.

Development of an intelligent environmental knowledge system for sustainable agricultural decision support

The purpose of this research was to develop a knowledge recommendation architecture based on unsupervised machine learning and unified resource description framework (RDF) for integrated environmental sensory data sources. In developing this architecture, which is very useful for agricultural decision support systems, we considered web based large-scale dynamic data mining, contextual knowledge extraction, and integrated knowledge representation methods. Five different environmental data sources were considered to develop and test the proposed knowledge recommendation framework called Intelligent Environmental Knowledgebase (i-EKbase); including Bureau of Meteorology SILO, Australian Water Availability Project, Australian Soil Resource Information System, Australian National Cosmic Ray Soil Moisture Monitoring Facility, and NASA's Moderate Resolution Imaging Spectroradiometer. Unsupervised clustering techniques based on Principal Component Analysis (PCA), Fuzzy-C-Means (FCM) and Self-organizing map (SOM) were used to create a 2D colour knowledge map representing the dynamics of the i-EKbase to provide “prior knowledge” about the integrated knowledgebase. Prior availability of recommendations from the knowledge base could potentially optimize the accessibility and usability issues related to big data sets and minimize the overall application costs. RDF representation has made i-EKbase flexible enough to publish and integrate on the Linked Open Data cloud. This newly developed system was evaluated as an expert agricultural decision support for sustainable water resource management case study in Australia at Tasmania with promising results.

Ontario’s Nutrient Calculator: Overview and Focus on Sensitivity Analysis

The long-term build-up of nutrients in agricultural fields has contributed to surface and ground water quality problems in many jurisdictions. The Ontario government has required the use of a decision support system, entitled NMAN, to develop nutrient management strategies for new or expanding livestock facilities. The NMAN software adds nutrient (N and P) sources (commercial fertilizer, manure, crop residual, grazing) and subtracts nutrients lost through crop removal and runoff to calculate the assimilative capacity on a field-by-field basis. NMAN is designed to help guide nutrient management at the farm-scale and to prevent the long-term build-up of nutrients in agricultural soils. However, success ultimately rests with the farmers’ willingness to use NMAN on a continuing basis for determining nutrient application rates. While several factors are shown to influence the use agricultural decision support systems, perceived usefulness or value is key. In an effort to inform users about the NMAN software, we provide a brief description and sensitivity analysis of selected input variables. The newest version, NMAN3, is more intuitive than earlier versions, and it is easier to navigate and to modify parameters. Using a sample nutrient management strategy, we determine the sensitivity of output parameters to changes in the input variables. We primarily focus on the Phosphorus (P) Index, which measures a potential P loss from agricultural fields. The results indicate that the some parameters used in NMAN3, i.e. slope percentage, slope length, etc., need to be accurately measured, and that agency officials should be aware of these parameters during site visits and/or inspections.

Research on Development of Corn Production Decision Support System

This research was about the application of decision support system in agriculture. The subject of study was the corn cultivated in Jilin province, northeast of China. The research synthesized expertise and experience on corn cultivation, plant protection, soil and fertilizer, and synthesized agriculture ecology from experts, integrating computer technology, principle of decision support system with corn production knowledge. The research also concerned decision support system for corn fertilization and diagnosis of insect disease and weed harming, which included system concept design, database design, knowledge base design , model base design and preliminary inference engine design according to the characteristics of corn diseases and pests of weeds. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2829 Full Text: PDF

A Comprehensive Study of Application of Decision Support System in Agriculture in Indian Context

Agriculture is the mainstay in Indian Economy and only sustainable agriculture is likely to provide the long-term benefits required to achieve development and scarcity improvement. The research and advancements in agriculture have made available huge amounts of data in different areas of agriculture. It is a great challenge to extract knowledge from data and this has led to methods and techniques such as decision support system that can bridge the knowledge gap. The effectiveness of decision making in agriculture domain can be improved by integrating geospatial information and advanced information technology techniques. This paper reviews and summarizes the application of simulation based decision support system, advisory decision support system in different agriculture practices in Indian context.

A Review on Predicting Outbreak of Tungro Disease in Rice Fields Based on Epidemiological and Biophysical Factors

This paper reviews on tungro disease predictive technique in rice fields. The success of a monitoring system can be measured by its ability to provide early detection of pests for prevention of a serious outbreak of rice tungro. This paper also discusses on spectral reflectance data of epidemiological and biophysical factors that are related to tungro outbreak and also propose using these data along with an algorithm to predict probability of tungro disease occurrence. This information then could be used in agricultural decision support system for taking actions according to the level of threats that have been predicted.

Research on Database Design of Corn Decision Support System

This research is about the application of decision support system in agriculture. The subject of study is the corn cultivated in Jilin province, northeast of China, and the research synthesizes expertise and experience on corn cultivation, plant protection, soil and fertilizer, and agriculture ecology from experts, integrating computer technology, principle of decision support system with corn production knowledge. The research also concerns decision support system for corn fertilization and diagnosis of insect disease and weed harming. Database is designed as the basis of decision support system, including conceptual design with object definition language, logic design and physical design. Further design for the relevant system is also inquired into.

A GIS-based fertilizer decision support system for farmers in Northeast China: a case study at Tong-le village

Application of computer technology such as Geographical Information Systems (GIS) to help farmers optimize fertilization, improve soil fertility and protect soil from erosion is currently of considerable interest as a topic of research. The objective of this study was to develop a GIS-based software package that can be used to help farmers select fertilizer application rates and manage soil nutrients. The ease of integration and programming in current digital spatial technology made it feasible to combine a fertilization model with a GIS platform to develop a GIS-based Fertilizer Decision Support System (FDSS) that enables farmers to determine precise fertilizer recommendations through an interactive computer interface. This paper outlines the development and test application of a GIS-based FDSS at Tong-le Village of Baiquan county, Heilongjiang province of Northeast China. The FDSS uses a farm field as the base mapping unit and incorporates data from field samples, farmer surveys and remote sensing, as well as expert knowledge in agriculture, soil science and computer science to develop a spatial database and soil/crop management system. The FDSS is a crop fertilization and management software developed on the SuperMap platform, and will help farmers and managers of agricultural production units to increase their fertilizer utilization efficiency, and thus their net profit.

Effect of a participatory approach on the successful development of agricultural decision support systems: The case of Pigs2win

This paper explores how a decision support system (DSS) can be developed that complies with the critical success factors of such systems. A participatory approach is used to develop Pigs2win, a DSS for Flemish pig farms. Pigs2win uses frontier analysis for comparative farm analysis. The participatory approach influences the selection of stakeholders, objective setting and evaluation of Pigs2win. Outcomes of the participatory approach result in features of Pigs2win that positively influence its compliance with critical success factors. Based on our experience with Pigs2win, we put forward points that need attention when a participatory approach is organized.

Distributed Resource Allocation

The growth of technological capabilities of mobile devices, the evolution of wireless communication technologies, and the maturity of embedded systems contributed to expand the Machine to machine (M2M) concept. M2M refers to data communication between machines without human intervention. The objective of this paper is to present the grand schemes of a model to be used in an agricultural Decision support System. The authors start by explaining and justifying the need for a hybrid system that uses both Multi-Agent System (MAS) and Constraint Programming (CP) paradigms. Then, the authors propose an approach for Constraint Programming and Multi-Agent System mixing based on controller agent concept. The authors present concrete constraints and agents to be used in a distributed architecture based on the proposed approach for M2M services and agricultural decision support. The platform is built in Java using general interfaces of both MAS and Constraint Satisfaction Problem (CSP) platforms and the conception is made by agent UML (AUML).

Rainwater Management Model Development for Agriculture in the Savu Island Semi-Arid Region

Savu Island is a semiarid region with few rainfalls. The meager annual rainfall of about 1,000-1,500 mm that lasts for three to five months tends to cause draught. To cope with this situation, the Author tries to develop a rainwater management model located in Daieko village. This model constitutes an infrastructure that consists of check dam series which are constructed by simulating a computerized model of decision supporting system called “Rainwater Management for Agriculture Decision Support System (RMA-DSS) model” in the research location of Daieko village. Employing a simulated RMA-DSS model; the locations for check-dam series, and dug-wells can be determined, the size of potential irrigable lands can be determined based on water balance analysis of water samples taken from simulated check dams and inundated lands. Through this model the sufficiency of water supply for agricultural purposes and the land size for cultivation area can be predicted with a high degree of certainty.