Cotton thrips infestation predictor: a practical tool for predicting tobacco thrips ( Frankliniella fusca ) infestation of cotton seedlings in the south‐eastern United States (original) (raw)
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Pest Management Science, 2003
We present an overview of USDA Agricultural Research Service (ARS) computer models and databases related to pest-management science, emphasizing current developments in environmental risk assessment and management simulation models. The ARS has a unique national interdisciplinary team of researchers in surface and sub-surface hydrology, soil and plant science, systems analysis and pesticide science, who have networked to develop empirical and mechanistic computer models describing the behavior of pests, pest responses to controls and the environmental impact of pestcontrol methods. Historically, much of this work has been in support of production agriculture and in support of the conservation programs of our 'action agency' sister, the Natural Resources Conservation Service (formerly the Soil Conservation Service). Because we are a public agency, our software/database products are generally offered without cost, unless they are developed in cooperation with a private-sector cooperator. Because ARS is a basic and applied research organization, with development of new science as our highest priority, these products tend to be offered on an 'as-is' basis with limited user support except for cooperating R&D relationship with other scientists. However, rapid changes in the technology for information analysis and communication continually challenge our way of doing business. Published in
IPM: CALEW Cotton: an integrated expert system for cotton production and management
California Agriculture
CALEWCotton is a user-friendly computer program that simulates human problem-solving behavior. Growers can use this system to help manage crop production or predict the effects of any one decision on subsequent events. In 7990, more than 700 cotton producers have taken advantage of the CAL EWCotton computer program. Production of any crop requires many decisions before and during the growing season. Keepingtrackof all the information required for management or predicting what the influence of any one decision might have on subsequent decisions is difficult. Also, incorporating new knowledge and technology into existing farming practices is a slow process, requiring careful consideration to avoid any adverse side effects. Integrated expert systems are computer programs that hold promise for managing and coordinating the information required for optimal crop production. Expert systems simulate the problem-solving behavior of a human who is an expert in a narrow area. The knowledge base of agriculture is anything but narrow, and rarely is there only one solution available for a particular problem. An integrated expert decision-support system can help a grower make decisions that involve several alternatives, and can address several, often competing factors. These computer programs combine database management, regression models, simulation models, and rule bases (a rule base is like an "advisor:" it contains knowledge in a format conducive to problem solving). Components and development Monitoring for pests is a fundamental element in integrated pest management. Here, Area IPM Advisor Bill Barnett checks almond leaves for twospotted spider mites.
Role of Modelling in International Crop Research: Overview and Some Case Studies
Agronomy
Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to en...
2000
Few farmers and ranchers adopt agricultural software such as decision support systems (DSS). While numerous decision aids are available, most are too difficult for producers to use, exclude components (e.g., economic budgeting, weeds, multicriteria decision analysis) necessary for meaningful use on farms and ranches, and usually suffer from poor understanding by scientists of producer needs and how they process information. The USDA-ARS Great Plains Framework for Agricultural Resource Management (GPFARM) decision support system has been developed that integrates a graphical user interface, data from farms and ranches, soil-plant-weed-water-N-erosion simulation modules, an economic analysis package, and a multicriteria decision making (MCDM) toolbox. The purpose is to assist U.S. Great Plains producers in selecting alternative management scenarios for whole farm and ranch systems that are economically viable and environmentally sound. A major user requirement for GPFARM is to make the DSS as easy and quick to set up and use as possible. This means that plant parameters must be supplied to the user. Developing this parameter database for a large regional area differing in climate, soils, and management practices is made very difficult both by the known genotype by environment interaction (G X E) and the uncertainty in the variability (and distribution) of most parameters. This paper addresses the work, and complications, of creating a crop parameter database focusing on winter wheat (Triticum aestivum L.). One important plant parameter (thermal time from sowing to maturity) and predicting grain yield (the result of the entire parameter database) are both examined from the perspective of the G X E interaction. Some conclusions drawn from this analysis are: 1) for both thermal time and yield, the relative rankings of varieties were not consistent whether considering within or between treatments across years, showing the difficulty of simulating the G X E interaction, and 2) selected parameters must be set for at least dryland and irrigated conditions to better capture the G X E interaction.
Conference on Applied Statistics in Agriculture, 2007
This paper discusses the process of developing variable-rate treatment prescriptions and gives specifications for a prototype software system for implementing that process. The process is based on statistical analysis of data from embedded field trials, and incorporates producer preferences in determining a treatment prescription. The system can be used by researchers in agricultural research stations for developing prescriptions for commercial agricultural producers. The specifications provided are general enough to be implemented using a variety of statistical and database packages that are available to researchers. In addition to these specifications we provide online access to source code for implementing the system in SAS. We use this system to develop treatment prescriptions for a commercial cotton farming operation in northeast Louisiana. The prescriptions are based on data from a precision agriculture experiment conducted in 2006. The objective of that study was to compare the effects of five nitrogen rates on cotton lint yield across several soil types for the purpose of developing a variable-rate nitrogen treatment prescription for future use on that farm. Several possible producer preferences were incorporated with the results of the field trial to produce optional treatment prescriptions for the producer.
The role of the EMA software in integrated crop management and its commercial uptake
Pest Management Science, 2000
Integrated crop management (ICM) balances the issues of profitability and sustainability with the need for concern for the environment as a whole. As such, it requires sound decision-making based on detailed knowledge of the integrated nature of farming and how any single activity can impact on the business viability (short-and long-term) and on the environment. This paper reports the development and use of a practical software package (EMA) designed to support farmers, advisers and others in developing ICM practices. An exercise in technology transfer, the package uses a simple, but effective, technique relying on input data and information readily available on farm or that stored in the system's databases. The paper explains how EMA can support ICM, provides an analysis of the identified user-groups and an insight into how the package is being used by these different groups. It also discusses advantages and disadvantages of using software tools in ICM decision making. 1 * Originally presented as a poster presentation at 'The economic and commercial impact of Integrated Crop Management' conference, London April 2000, SCI Crop Protection Group.
Ecoinformatics for Integrated Pest Management: Expanding the Applied Insect Ecologist's Tool-Kit
Journal of Economic Entomology, 2011
Experimentation has been the cornerstone of much of integrated pest management (IPM) research. Here, we aim to open a discussion on the possible merits of expanding the use of observational studies, and in particular the use of data from farmers or private pest management consultants in "ecoinformatics" studies, as tools that might complement traditional, experimental research. The manifold advantages of experimentation are widely appreciated: experiments provide deÞnitive inferences regarding causal relationships between key variables, can produce uniform and high-quality data sets, and are highly ßexible in the treatments that can be evaluated. Perhaps less widely considered, however, are the possible disadvantages of experimental research. Using the yield-impact study to focus the discussion, we address some reasons why observational or ecoinformatics approaches might be attractive as complements to experimentation. A survey of the literature suggests that many contemporary yield-impact studies lack sufÞcient statistical power to resolve the small, but economically important, effects on crop yield that shape pest management decision-making by farmers. Ecoinformatics-based data sets can be substantially larger than experimental data sets and therefore hold out the promise of enhanced power. Ecoinformatics approaches also address problems at the spatial and temporal scales at which farming is conducted, can achieve higher levels of "external validity," and can allow researchers to efÞciently screen many variables during the initial, exploratory phases of research projects. Experimental, observational, and ecoinformatics-based approaches may, if used together, provide more efÞcient solutions to problems in pest management than can any single approach, used in isolation.
Agricultural system models in field research and technology transfer
Agricultural Systems, 2005
This book aims at presenting the state-of-the-art of science and operational use of models applied to a variety of agricultural system related activities. The 62 authors and co-authors of this book are leading international agricultural scientists with a broad knowledge with respect to the different areas of development and application of agricultural models. The operational expertise gained by several groups which have been active in the area of modelling for more than a decade is made available to the reader. Insights on the many challenges which were encountered when moving from simulation tools developed within a lab to the application in a variety of environments and interacting with stakeholders are provided. The authors cover modeling of natural resources, crop production, grazing lands and animal production systems. Also, future research needs to fill major gaps in knowledge in order to improve quality of the models and their capability of simulating different conditions, are identified.
An integrative modeling framework to evaluate wheat production systems: Fusarium head blight
This paper describes a practical, integrated, web-based and user friendly analysis tool for crop model users that provides quality control of input data, tracks user selections in model parameterization, and enables visual analysis of model outcomes using a single graphical user interface. This allows the user to undertake numerous steps in crop modeling and analysis in a seamless and integrated environment. The analysis and visualization components of the system were enabled utilizing R (pl/r) and the robustness of the underlying data structures and coupling point between crop and disease models were achieved through use of PostgreSQL database management system. The approach was tested to investigate changes in: 1) wheat yield between current climate and plausible future climate in the southern part of Brazil; and 2) climate-related risks such as Fusarium Head Blight (FHB), an important fungal disease that impacts wheat grain yield and quality. The integrative modeling framework (I...