Agricultural Decision Support Research Articles

A Meteorological Data Service System and its Application

Meteorological conditions play an important role in agricultural practice and agricultural DSS usually takes weather data as a critical data source. A meteorological data service system was designed and implemented to provide better performance for Chinese users. Based on the service system, a web application providing online weather data retrieval and downloading was also developed. The service system was practically used in a decision support system for eucalypt management, and proved to be very feasible as an online weather data source for agricultural decision support system. Base on this service system, it is expected that agricultural researchers and decision support systems can easily obtain weather data and further improve their agricultural decision making process.

Agricultural decision support systems facilitating co-learning: a case study on environmental impacts of sugarcane production

Decision support systems (DSSs) are one of the ways in which agricultural scientists have attempted to make agricultural systems science more accessible to farmers and to foster innovation. Recently, there has been a shift towards more participatory processes in development and application of DSSs to enhance their end-user use. Apart from increasing adoption, these participatory processes are also likely to enhance co-learning resulting from development/application of DSSs. Learning is a valuable process in increasing sustainability of natural resource management, so the application of DSSs in a learning context can make a contribution to the global challenges faced by agriculture. We developed a framework, using concepts drawn from social studies of science and technology, describing the phases of the participatory DSS development/application process and its likely outcomes. We analysed experiences of participants in a case study exploring more sustainable management of nitrogen fertilizer in sugarcane production in an environmentally sensitive area of northeastern Australia. The data illustrate theoretical constructs underpinning the framework and learning processes within the case study. The framework and case study results demonstrate the value of participatory DSS development/application as a co-learning process, an outcome not traditionally valued by agricultural DSS developers and one that is likely to help address the challenges faced by agricultural sustainability.

The Theory and Method of Agricultural Expert Decision Support System

The Theory and Method of Agricultural Expert Decision Support System

A conceptual framework for guiding the participatory development of agricultural decision support systems

Scientists develop decision support systems (DSSs) to make agricultural science more accessible for farmers and extension officers. Despite the growing use of participatory approaches in agricultural DSS development, reflection on this endeavour has largely focused on the ‘doing’ of participation or the ‘problem of implementation’ when DSSs have not been adopted by stakeholders. There has been little reference to relevant theoretical approaches to the social processes involved in ‘participation’ or ‘implementation’. However, if DSS use is to reach its full potential, a more conceptually informed understanding of how stakeholders collaborate in the participatory development of DSSs is required. To contribute to this conceptualisation, we developed a framework based on three concepts drawn from the field of science and technology studies: technological frames, interpretative flexibility and boundary objects. The framework highlights the importance and value of social learning for participatory DSS development, which relies upon exploring the participating parties’ different perspectives on the agricultural system represented in the DSS. Our framework provides a broad definition of success for participatory DSS development, placing greater weight on learning during the participatory process compared with subsequent use of the DSS by farmers and/or advisors. Two case studies of stakeholder collaboration to develop an irrigation scheduling DSS for sugarcane production were used to explore the relevance of the framework. The concepts in the framework were clearly displayed during the case studies. At the conclusion of the studies there were contrasting outcomes for the DSS. One group of farmers was keen to apply it in their ongoing irrigation management, while another saw little relative advantage in use of the DSS. In both instances co-learning occurred amongst case study participants, so the participatory process was clearly a success.

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of ''inexact'' computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed.

Analysis of mealybug incidence on the cotton crop using ADSS-OLAP (Online Analytical Processing) tool

Traditionally the agriculture expert's knowledge is descriptive and experimental, therefore, it is difficult to describe it mathematically and subsequently build agriculture Decision Support Systems (DSS). Furthermore, the corresponding data may be in such a raw form that it cannot be used in a DSS. The Agriculture Decision Support System (ADSS) is a 26-month project based on the Agro-met data from 2001 to 2006 of Punjab (the bread-basket of Pakistan), its ADSS-OLAP, i.e. Online Analytical Processing tool ( www.agroict-olap.org) allows for quick analysis of all possible interesting aggregates of the ADSS data by employing drag-drop and mouse-click and is used in this paper to identify the effective pesticide groups related to the mealybug incidence. Pakistan is the world's fifth-largest producer of cotton, but the emergence of the mealybug as a new cotton pest is likely to reduce the cotton yield by 1.3 million bales. The research work reported in this paper is based on the detailed pest-scouting data of 2300+ farmers of District Multan (cotton hub of Pakistan) for the years 2005 and 2006. This paper will also provide guidelines for the design and development of similar complex systems/tools and discusses the issues of agricultural data-quality management, particularly in the field of insect-pest management.

SUSTAINABLE AGRICULTURAL DEVELOPMENT: KNOWLEDGE‐BASED DECISION SUPPORT

The article introduces the knowledge‐based agricultural decision support system. The system includes database and knowledge base as well as modules used for formation of models, optimization, simulation, decision analysis and inference. The linear programming model contains several hundreds of variables and restrictions. The farm model, using entered data, is formed according to “if‐then” type rules that are stored in the knowledge base. Having solved the task of optimization, the particular values of variables indicate what kind of crops should be grown and in what area, as well as what animals and how many of them should be kept and what resources and how much of them have to be used for achieving the biggest benefit under the environmental and other conditions. The simulation was employed to test the sensitivity of the plan to weather and market variations. Having applied a set of production rules to the given facts and modelling results within the module of decision analysis and inference, conclusions and suggestions are made. Decision support system performs the analysis of production efficiency, resource reserves and shortage, and with the help of the Internet in real time provides a farmer with conclusions and suggestions necessary to increase the efficiency of production conforming to environmental constraints. The integration of optimization calculations and knowledge management into the agricultural decisions‐support system expands its possibilities and improves the quality of solutions. Santrauka Pristatoma žiniomis grįsta žemės ūkio sprendimų paramos sistema. Ją sudaro duomenų ir žinių bazės, modelių formavimo, optimizavimo, imitavimo, sprendimų analizės ir išvadų formavimo moduliai. Žemės ūkio įmonės modelis sudaromas pagal įvestus duomenis, remiantis žinių bazėje saugomomis „jeigu–taip“ tipo taisyklėmis. Išsprendus optimizavimo uždavinį gautos kintamųjų vertės rodo, kokie javai ir kokiame plote turi būti auginami, taip pat kokie gyvuliai ir kiek jų turi būti laikoma, kokie ištekliai ir kiek jų turi būti panaudota, norint pasiekti didžiausią naudą ūkyje nurodytomis aplinkosaugos ir kitomis sąlygomis. Imitavimo būdu patikrinamas gamybos plano jautrumas orų ir rinkos pokyčiams. Išvadų formulavimo modulyje, pritaikius duotiems faktams ir modeliavimo rezultatams produkcinių taisyklių rinkinį, gaunamos išvados bei pasiūlymai. Sprendimų paramos sistema atlieka gamybos efektyvumo, išteklių rezervų bei išteklių trūkumo analizę. Ūkininkas internetu gauna išvadas bei pasiūlymus, kaip didinti gamybos efektyvumą. Optimizavimo skaičiavimų ir žinių bazės integravimas į žemės ūkio sprendimų paramos sistemą išplečia jos galimybes ir pagerina sprendimų kokybę.

A qualitative case study of the adoption and use of an agricultural decision support system in the Australian cotton industry: The socio-technical view

In response to the call for research that considers the human as well as the technical aspects of information systems implementation, the authors report on an interpretive case study which explores the adoption and use of an agricultural decision support system (DSS) CottonLOGIC in the Australian cotton industry. The study was informed through the innovation-decision model by Rogers and the technology-in-practice model by Orlikowski using a socio-technical approach. It was found that participants who achieved a high level of implementation success were reflexive and resourceful in adapting the technology to their changing needs, often in ways unanticipated by DSS builders.

Modelling daily precipitation features in the Volta Basin of West Africa

AbstractThe combination of a conventional Markov chain model (zero and first order) and a gamma distribution model are found to be applicable to derive meaningful agricultural features from precipitation in the Volta Basin (West Africa). Since the analysis of the monthly or annual precipitation amount does not provide any adequate information on rainfall timing and sufficiency of crop water requirement, rainfall modelling was performed on a daily time scale for 29 rainfall stations. The modelled rainfall features follow distinct spatial patterns, which will be presented as maps of(1) rainfall occurrence probabilities and (2) recommendations of optimal planting dates. In addition, the effective drought index (EDI) working on daily time scales is calculated in order to assess drought properties of five different rainfall regions within the Volta Basin. Apart from the common way of separately modelling the duration and intensity due to their different distributions, a copula approach is chosen in this study to construct a bivariate drought distribution. Application of the measures derived to agricultural decision support will be discussed briefly. Copyright © 2009 Royal Meteorological Society

Methodology for the use of DSSAT models for precision agriculture decision support

A prototype decision support system (DSS) called Apollo was developed to assist researchers in using the Decision Support System for Agrotechnology Transfer (DSSAT) crop growth models to analyze precision farming datasets. Because the DSSAT models are written to simulate crop growth and development within a homogenous unit of land, the Apollo DSS has specialized functions to manage running the DSSAT models to simulate and analyze spatially variable land and management. The DSS has modules that allow the user to build model input files for spatial simulations across predefined management zones, calibrate the models to simulate historic spatial yield variability, validate the models for seasons not used for calibration, and estimate the crop response and environmental impacts of nitrogen, plant population, cultivar, and irrigation prescriptions. This paper details the functionality of Apollo, and presents the results of an example application.

Reductions in Seasonal Climate Forecast Dependability as a Result of Downscaling

This investigation addresses a practical question from an agricultural planning and management perspective: are the NOAA/CPC seasonal climate forecasts skillful enough to retain utility after they have been downscaled to field and daily scales for use in crop models to predict impacts on crop production? Utility is defined herein as net forecast dependabilities of at least 50%, where net dependability is the product of the large-scale 3-month forecast dependability with a factor accounting for losses in dependability due to the higher spatiotemporal variability of 1-month station data. This loss factor is estimated from station data by computing the frequency of matching sign (FOMS) between the direction of departures from average of 3-month forecast division values and 1-month station values, for average temperature and precipitation, over a 10-year study period and 96 stations in six regions of the U.S. The resulting FOMS does not display any consistent differences across regions, locations, or months, so is averaged across all months and stations. Average FOMS calculated in this manner are 76% for average temperature and 66% for total precipitation. The decimal FOMS are then used as the multiplicative loss factor on previously reported 3-month forecast division reliability values to produce estimates of the net reliability for downscaled forecasts at locations within each forecast division. The resulting guidance is dependent on region and forecast variable, with the forecasts for above-average temperature emerging as worthy of consideration for use in agricultural applications over the majority of the contiguous U.S. The Northeast, the Great Lakes, parts of the Northern Great Plains, interior California, and northwest Nevada are the only regions with insufficient net dependability to preclude immediate consideration. Conversely, forecasts for cooler than average temperature do not retain sufficient net dependability after downscaling to be an attractive option in any part of the contiguous U.S. at this time. Forecasts for wetter or drier than average conditions retained sufficient net dependability to encourage further development over only about 10% of the contiguous U.S., in regions well-known to experience the strongest ENSO impacts on precipitation. The forecast divisions where agricultural decision support might benefit from NOAA/CPC seasonal precipitation forecasts are located in Florida, south Texas, southwest New Mexico, Arizona, central and southern California, and parts of Oregon, Washington, Idaho, and Montana.

Wither agricultural DSS?

Computer-based agricultural decision support systems (aDSS) may be argued to have passed sequentially through phases of unbelief, euphoria and disappointment, and to be currently passing into either a phase of maturity with realistic expectations of the technology, or to abandonment. This paper appraises, in the context of the DSS development literature, our past and current efforts in decision support using simulation-models and farm-scale case-studies. The paper first reviews some of the explanations for the lack of success for aDSS including the identification of suitable roles and how best the tools may be deployed. The paper then outlines the authors’ experiences during the euphoric period of aDSS development including the undertaking of market research on the nature of the aDSS desired and their potential for commercialisation. The positive outcome of the market research was that potential end-users recognised the range of functionality that an aDSS could offer. There was, however, significant scepticism on the balance of costs and benefits. The end-user preference for aDSS delivered as software products for use in-house, when combined with the limits on the price-per-unit that the market would bear, meant that there was little commercial potential. In the light of these findings the team re-evaluated the role and development strategy for their aDSS. The paper outlines this strategy in terms of both the technical developments of the aDSS and the approach to its use with stakeholders. The paper then discusses the legacy from the euphoric period highlighting a number of socio-political and institutional barriers to the use of aDSS which remain to be overcome. The paper concludes by arguing that there is a need to think beyond technocentric solutions to overcome the barriers to wider aDSS use and that there are a number of models of best-practice for aDSS development that can ensure their relevance.

Agriculture and forestry economy decision support system based on agent

In this paper, we focus on the common problems of current information analysis and support decision systems in agricultural and forestry economies, such as frequent manual interventions, lack of cooperation among sub‐systems, lack of support from distributed computing and group decision making. This research aims to resolve these problems by rebuilding the system with agent technology, and a prototype of the agricultural and forestry economy decision support system (AFEDSS) based on agent is designed and implemented. The AFEDSS is composed of interface agent, management agent, model agent, forecast agent, assistant decision agent, data management agent. The results show that the new system can simulate and deal with the complex processes of agricultural and forestry economies in reasonably short computational times and with less subjective uncertainty. This paper also discusses the decision processes and internal mechanisms in AFEDSS especially on cooperating with dynamic analysis, evaluation, forecast and optimisation.

Impact of China‐ASEAN Free Trade Area on China's International Agricultural Trade and Its Regional Development

AbstractThis study aims to examine the impact of the China‐ASEAN Free Trade Area (CAFTA) on China's international agricultural trade and its regional agricultural development, using the Global Trade Analysis Project model and the China Agricultural Decision Support System. Our analysis showed that: (i) CAFTA will improve resource allocation efficiencies for both China and ASEAN and will promote bilateral agricultural trade and, hence, will have positive effects on the economic development of both sides; (ii) CAFTA will accelerate China's export of the agricultural commodities in which it has comparative advantages, such as vegetables, wheat and horticultural products, but at the same time bring about a large increase in imports of commodities such as vegetable oil and sugar; and (iii) CAFTA will have significantly varying impacts on China's regional agricultural development because of large differences in the agricultural production structure in each region. Our results indicate that agriculture in the northern, northeastern and eastern regions of China will benefit from CAFTA, whereas agriculture development in southern China will suffer. Those regional specific impacts are quite different from the effects brought by multilateral free trade treaties, such as those of the WTO, which usually have positive effects on south China but negative impacts on the northern and western parts of China.

A Serially Complete U.S. Dataset of Temperature and Precipitation for Decision Support Systems

The effect of missing data can result in errors that exhibit temporal and spatial patterns in climatological and meteorological research applications. Many climate related tools perform best with a serially complete dataset (SCD). To support the National Agricultural Decision Support System (NADSS), a SCD with no missing data values for daily temperature and precipitation for the United States was developed using a self-calibrating data quality control (QC) library. The library performs two primary functions: identifies outliers and provides estimates to replace missing data values and outliers. This study presents the development of the SCD and the QC library in detail. An in-depth evaluation in terms of root mean square error (RMSE) and mean absolute error (MAE) for the SCD for the period of 1975-2004 is provided. The study shows an impressively low average RMSE in the range of 2.27 to 3.58°F for temperature and 0.07 to 0.23 inch for precipitation for the whole country for 30 years. The goal of this study is to enhance drought risk assessment and environmental risk analysis.

Evaluating the economic and environmental impact of environmental compensation payment policy under uniform and variable-rate nitrogen management

Site-specific nitrogen (N) management has been suggested as a management tool to increase N fertilizer efficiency and reduce environmental impact. Environmental laws are being implemented throughout Europe to limit N fertilization on arable land, especially to protect drinking water areas. In response to the EU legislation, the State of Baden-Württemberg (southwest Germany) passed a law to further reduce the loss of N to groundwater from agricultural sources. Farmers in governmental designated water saving regions will be paid a compensation for following N management plans that reduce N levels below a target threshold value in the soil after harvest, making an efficient use of N input crucial. Precision agriculture aims at increasing this efficiency by incorporating spatial and temporal variation into N management. Crop models can help to determine the optimum N rate across a field. The purpose of this paper was to use the CERES-Maize crop growth model and the APOLLO precision agriculture decision support system to asses the importance of accounting for spatial variation in the design of policies to control groundwater nitrate concentration under the EU legislation. The policy was evaluated for uniform and variable-rate N management on a small-scale field, which was divided into 30 grid cells. The model was calibrated using 5 years of data from 30 grid cells in the field in the Upper Rhine Valley, Germany. The model simulated yield variability in the different grid cells quite well and explained approximately 60% of the yield variability. Once the model was calibrated, optimum N rate to maximize the marginal net return considering the given target threshold value of soil N after harvest was computed for each grid cell using 28 years of historical weather data. Results indicated a spatial distribution of optimum N rates for grid cells across the field. Variable-rate management required lower amounts of N fertilizer (20–25%), achieved similar yield levels and resulted in higher net returns over the 28 years of weather data when compared to current N management. Higher marginal net return in variable-rate management was achieved because the target N level left in the soil was satisfied in most grid cells under variable-rate management resulting in a compensation payment for the farmer. This did not occur as often under current uniform management practices, especially under extreme weather conditions. These results support the relevance of managing temporal and spatial variation on fields for groundwater protection applying dynamic N management strategies.

An integrated assessment approach to conduct analyses of climate change impacts on whole-farm systems

This paper argues that an integrated assessment (IA) approach, combining simulation modelling with deliberative processes involving decision makers and other stakeholders, has the potential to generate credible and relevant assessments of climate change impacts on farming systems. The justification for the approach proposed is that while simulation modelling provides an effective way of exploring the range of possible impacts of climate change and a means of testing the consequences of possible management or policy interventions, the interpretation of the outputs is highly dependent on the point of view of the stakeholder. Inevitably, whatever the responses to climate change, there will be trade-offs between the benefits and costs to a range of stakeholders. The use of a deliberative process that includes stakeholders, both in defining the topics addressed and in debating the interpretations of the outcomes, addresses many of the limitations that have been previously identified in the use of computer-based tools for agricultural decision support. The paper further argues that the concepts of resilience and adaptive capacity are useful for the assessment of climate change impacts as they provide an underpinning theory for processes of change in land use systems. The integrated modelling framework (IMF) developed for the simulation of whole-farm systems is detailed, including components for crop and soil processes, livestock systems and a tool for scheduling of resource use within management plans. The use of the IMF for assessing climate change impacts is then outlined to demonstrate the range of analyses possible. The paper concludes with a critique of the IA approach and notes that issues of quantification and communication of uncertainty are central to the success of the methodology.

A software architecture and framework for Web-based distributed Decision Support Systems

This paper presents an integrated method to help design and implement a Web-based Decision Support Systems (DSS) in a distributed environment. First, a layered software architecture is presented to assist in the design of a Web-based DSS. The layered software architecture can provide a formal and hierarchical view of the Web-based DSS at the design stage. Next, a component-based framework is presented to implement the Web-based DSS in a distributed environment. Finally, an instance of the layered software architecture and 3CoFramework applied to the Web-based National Agricultural Decision Support System (NADSS) is presented.

"We Work as a Team Really": Gender Homophily on Australian Cotton Farms

This paper is based on an ongoing study that looks at farm management practices by Australian women cotton growers using farm management software, most particularly an agricultural decision support system, CottonLOGIC. The study is informed through a theoretical framework of structuration theory as a metatheory for probing the recursiveness of farm management and technology usage, and diffusion of innovations theory as a lower-level theory for analysing software adoption characteristics. Empirical research indicates that effective information exchange flows from homophilous communication. In this paper, the principles of homophily and heterophily in communication networks were initially drawn from diffusion theory. The findings suggest that despite apparent gender disparities, the presence of empathy and shared goals between farming partners overrides ‘gender heterophily’ to become gender homophily. Therefore cotton growers are informed of scientific research through homophilous communication, influencing the construction and reconstruction of innovative software usage and existing farm management practices.

Make Policy Science to Promote Agriculture Modernization; The Open Laboratory of National Agriculture Policy Formally Startup

on October 16, 2003. The national agriculture policy open laboratory formally startup in the Institute of Agriculture Economics, Chinese Academy of Agriculture Sciences. The item to be formally established by China Department of Agriculture, Ministry of Finance and World Bank. The first help and support by the fourth technical assistance of World Bank ,which has offered one million USD. The item will organize researchers of the Institute of Agriculture Economics, Chinese Academy of Agriculture Sciences .It will retain international and national famous agriculture economist and young agriculture policy analysis personnel, to form the high level of research team, set up the first agriculture economy and policy simulated laboratory in the world, and found a nation class agriculture policy analysis platform and decision support system