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Application of information technology in plant protection in Denmark: from vision to reality
Abstract
With 2051 licences sold to farmers, ‘PC-Plant Protection’ is the most widely used PC-based farm level decision support system for control of weeds, pests and diseases in Denmark and in Europe. The system is distributed by the Danish Agricultural Advisory Centre and marketed by the local advisory centres. In order to help the local advisors promote sales at farmers meetings or agricultural exhibitions, they are provided with a video film of the field assessment methods, overhead transparencies outlining the structure of recommendation models, and sales brochures. More than 30 non-technical articles have been published in the national agricultural newspapers and magazines. National and regional TV stations have broadcast interviews with farmers on their experience with the system. A farm survey of 488 farmers who used the system in 1995 shows that the system has been well accepted by the farmers not only because of the reliable recommendations but also because of increased profits. A similar survey among crop production advisors also showed that the system has been very useful both for the direct and indirect advisory services.
... Pro-Plant Expert continues to function as an expert counseling system and covers a range of crops, pests and diseases. PC-Plant Protection, developed in Denmark [34], also uses expert scoring rules and covers control of weeds, pests and diseases in wheat, with an emphasis on reducing chemical use. EPIPRE in The Netherlands [34,35] used experimental models to relate observed disease levels to credible losses, but use of the scheme has now declined as farmers have become educated about the significance of observations. ...
... PC-Plant Protection, developed in Denmark [34], also uses expert scoring rules and covers control of weeds, pests and diseases in wheat, with an emphasis on reducing chemical use. EPIPRE in The Netherlands [34,35] used experimental models to relate observed disease levels to credible losses, but use of the scheme has now declined as farmers have become educated about the significance of observations. Predictive modelling of the outcomes resulting from actions enables a person to make a healthier decision. ...
Abstract: The challenges facing the enhancing production of agricultural sectors in spite of endowed natural resources for necessary crop production are key issues to crop yield. Information and Communication Technology (ICT) plays a central role in improving the lives of the populace in the rural areas using a computer based agro information system. The simplex algorithm support for optimization crop yield monitoring of Internet of Things in improving the current practice in agriculture. An intelligent system that will help farmers to perform their duty automatically without much manual inspection was created. The system suggest some agro information that are relevant to the crop like varieties and soil types, planting time, maturity time, temperature, distance between crops, pest, disease, pest/disease control measure , rainfall, amount of sunshine, quantity and type of fertilizer to mention but few with its geographic information system. The application will help famers to have increase productivity and the efficiency of the crop yield. The simplex algorithm using IOT will optimize the level of crop yield in the agricultural sector of Nigeria Keywords— Agriculture, IOT, Simplex Method, Soil ph, Soil Humidity, Crop Yield
... These algorithms, dedicated to the individual plants and pests, calculate the risk of occurrence of the particular threat based on above all weather data, such as temperature and humidity. In these systems, an alert appears when the calculated risk of pest occurrence exceeds the permissible value (Bajwa, et al., 2003;Di Guardo, 2017;Murali, et al., 1999). ...
... In several countries, decision support for weed control is available in the form of recommendations for herbicide selection, herbicide rate and time of application, for example Crop Protection Online (Murali et al., 1999; see also https://plantevaernonline.dlbr.dk/cp/d ocuments/InfoFactSheet2.pdf) in Denmark and Gewis (http://www.agrovision.nl/sectoren/teelt/producten_voor_ ...
p>Farmers have access to many data-intensive technologies to help them monitor and control weeds and pests. Data collection, data modelling and analysis, and data sharing have become core challenges in weed control and crop protection. We review the challenges and opportunities of Big Data in agriculture: the nature of data collected, Big Data analytics and tools to present the analyses that allow improved crop management decisions for weed control and crop protection. Big Data storage and querying incurs significant challenges, due to the need to distribute data across several machines, as well as due to constantly growing and evolving data from different sources. Semantic technologies are helpful when data from several sources are combined, which involves the challenge of detecting interactions of potential agronomic importance and establishing relationships between data items in terms of meanings and units. Data ownership is analysed using the ethical matrix method to identify the concerns of farmers, agribusiness owners, consumers and the environment. Big Data analytics models are outlined, together with numerical algorithms for training them. Advances and tools to present processed Big Data in the form of actionable information to farmers are reviewed, and a success story from the Netherlands is highlighted. Finally, it is argued that the potential utility of Big Data for weed control is large, especially for invasive, parasitic and herbicide-resistant weeds. This potential can only be realised when agricultural scientists collaborate with data scientists and when organisational, ethical and legal arrangements of data sharing are established.</p
... Besides these, with the provision of fertilizer level detectors (e.g., nitrogen sensor, soil organic matter sensor) and insects/disease detection sensors over the field, a much more efficient and site-specific application of nutrients (Lammel et al, 2001) and pesticides could be possible. For example, 'PC-Plant Protection' is the most widely used PCbased farm level decision support system to control weeds, pests and diseases in Denmark and Europe (Murali et al, 1999). This system is well accepted by the farmers as well as by crop production advisors because of increased profits. ...
A tractor-operated sugarcane harvester was designed, constructed and tested. Results of the tests showed that the effective field capacity ranged from 0.4 to 0.5 ha/h, and decreased with increase in crop density and extent of stem lodging. In the high crop density fields, the field efficiency averaged 65.82 %. It, however, increased with decrease in crop density and stem lodging. The material capacity increased from 7.75 to 21.04 t/h as the crop yield increased from 15.48 to 53.28 t/ha. The topping unit efficiency was significantly affected by crop density and extent of stem lodging, while the base cutter efficiency was not significantly affected.
... Expert systems have been developed for many kinds of applications in agriculture, involving diagnosis , predictions, consultation, control, etc. (Gonzalez-Andujar and Recio, 1996; Knight, 1997; Carrascal and Pau, 1992). In weed science, most expert system developments have been centred mainly in weed control, particularly for herbicide selection (Castro-Tendero and García-Torres, 1995; Stigliani et al., 1993; Murali et al., 1999). Castro-Tendero and García-Torres (1995) developed SEMAGI, which predicts the potential yield reduction from multi-species weed infestation and determines the appropriate herbicide according to the different economic thresholds in sunflower and wheat. ...
Identification of weed seedlings is a difficult task. An expert system to help farmers and extension workers to identify weed species in cereals has been developed. The expert system uses a hierarchical classification and a mix of the text description, photographs and artistic pictures. The system is supported by a data base containing information about 41 weed species and 128 colour images. The expert system was evaluated following the conventional expert system evaluation methodologies. Results of the validation indicated that non-expert users were able to make identification using the expert system. A total of 149 identifications were performed and 63% were identified correctly. The erroneous identifications tended to cluster around monocot species; especially Avena sterilis, Lolium rigidum, Phalaris ssp. and Bromus sterilis were misidentified. Results of the validation process and the writing suggestions provided by the participants were used to implement improvements in the system. The program can be used as an identification tool for farmers and technicians and for educational purposes.
... Perini and Susi (2004) developed a 'systemic' approach to build decision-support systems (DSS) used in agriculture by the advisory service for pest management in Italy. Murali et al. (1999) described the impact of a DSS product -'PC -Plant Protection' on agriculture in Denmark after it had been commercialized in 1993. This system is distributed by the Danish Agricultural Advisory Centre and has been well accepted by growers because of increased profits flowing from the reliable and economically valid recommendations provided by the system. ...
The simplest definition of the term 'database' is given in Webster's dictionary as 'a comprehensive collection of related data organized for convenient access, generally in a computer' (Random House, 1996). This term appeared in the late 1960s because of the evolution of computer software and the need to distinguish the specialized computer systems for the storage and manipulation of data, called database management systems (DBMS) (Neufeld and Cornog, 1986). Today, the acronym 'DBMS' is universally understood within Information Technology (IT), just like the acronym 'Bt' for 'Bacillus thuringiensis' is in the field of biological pest control. At the present time there are numerous DBMS products available on the market. The most popular are Oracle©, dBase©, DB2©, MS SQL Server© and Access©. Access is a part of the Microsoft Office product and can be considered as a prototype of DBMS with limited functionality. These products vary in price and capacity, and therefore the budgetary constraints and the requirements of a particular database application determine their utility.
The evolution of database products has been rapid, reflecting advances in the theory of databases during the last 35-40 years. Beginning with simple data files with direct access, these database products now include very sophisticated file systems with complex interrelationships. More recently, there has been a series of new database applications named Relational Database Management Systems (RDBMS). Their development was a product of the advancement in IT, which forced DBMS to adapt. One of these advancements was the creation of distributed computer systems using local or wide-area networks (LAN/WAN) at the end of the 1980s and early 1990s (Date, 2003). These networks stimulated development of new methods for remote database connection, and the improvements of client/server technologies when databases are organized on a computer server separated from those remotely accessed and used to enter data. The uses of DBMS can be very diverse, but this chapter focuses on the application of DBMS in the field of biology, more specifically in entomology and integrated pest management (IPM).
... Machado et al., 2004) and soil management operations such as weed control, herbicide selection and pest control and irrigation is nowadays well recognised (e.g. Castro-Tendero and García-Torres, 1995;Murali et al., 1999). ...
Vineyard operations for quality wines production are currently based upon costly and time-consuming manual sampling operations required to assess the maturity phases of grapevines. The ripening process however is significantly influenced by the environmental parameters which nowadays can be effectively monitored by means of ubiquitous computing technologies. Besides the possibility of gathering data, hence, suitable tools are required to support the vineyard management process. The present research concerns the development of an expert system to effectively manage the vineyard operations. The methodology is based on the analysis of the time series of indices related to the maturation phases by means of referenced growth models, and on the prediction of the achievement of maturation thresholds. The paper also reports the results of an experimental study on a Sicilian vineyard.
... Expert systems have been in fact developed for many kinds of applications in agriculture (Carrascal and Pau, 1992), and the importance of climatic conditions in scheduling harvesting (see for example Machado et al., 2004) and soil management operations such as weed control, herbicide selection and pest control and irrigation is nowadays well recognized (e.g. Castro-Tendero et al., 1995), and Murali et al., 1999). ...
... Multiple agricultural DSSs have been developed , addressing a variety of issues. These issues include measuring farm sustainability (e.g., [40,55,75,76] ), improving manure management (see overview by [31]), simplifying livestock feeding decisions (e.g., [14,23,34,35]), improving crop production systems (e.g., [11,24, 30,66,70]) or subsystems like fertilizer use (e.g., [18,22,69] ), pest management (e.g., [36,56]), irrigation (e.g., [39,71]), yield (e.g., [6,26,58]) and cultivar selection (e.g., [29,47]), improving pig production aspects like nutrition of growing pigs (e.g., [74]) and sows (e.g., [15]), sow productivity (e.g., [64]), disease control (e.g., [82]) and tail biting prevention (e.g., [2,3]). Despite the wealth of available technologies, decision support has widely failed to fulfill expectations [43,45,53,57,80]. ...
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.
... Evidence of the integration of predictive system disease warnings into the recommendations given by extension educators and private crop advisers is widely supported by reported data (see sidebar, Dissemination of Model Predictions Through Intermediaries). For example, 80% of consultants found the Danish pest management decision support system PC-Plant Protection of "average to very great usefulness" in direct advising of clients (77). Similarly, among Oregon tree fruit producers less than 31% of growers visited a decision support website during the growing season, whereas 87% of crop advisers did (13). ...
Disease predictive systems are intended to be management aids. With a few exceptions, these systems typically do not have sustained use directly by growers. Rather, their impact is mostly pedagogic and indirect, improving recommendations from farm advisers and shaping management concepts. The degree to which a system is consulted depends on its amount of perceived new, actionable information consistent with the objectives of the user. Often this involves avoiding risks associated with costly disease outbreaks. Adoption is sensitive to the correspondence between the information a system delivers and information needed to manage a particular pathosystem at an acceptable financial risk; details of the approach used to predict disease risk are less important. The continuing challenge for researchers is to construct tools relevant to farmers and their advisers that improve upon their current management skill. This goal requires an appreciation of growers decision calculus in managing disease problems and, more broadly, their overall farm enterprise management.
... Undesirable consequences of weed control failure may be reduced by using some sort of support system for decisions. During the last 15 to 20 yr, several decision-support systems for chemical weed control have been presented, and most are computer based (e.g., Hoffman et al.1999;Murali et al. 1999;Stigliani et al. 1996). Some of these systems have been validated in the field for 1 yr or more, but the validation has seldom included long-term effects on the weed flora or on crop yield in a specific field. ...
Today, the aim of weed management is to keep the weed community at an acceptable level rather than to keep the crop totally free of weeds. Satisfactory control of weeds may often be obtained when herbicides are used at lower doses than normally recommended. To facilitate the decision of what is an adequate dose in a specific field, the farmer needs support. In 1988 and 1989, a total of 10 field trials in spring cereals were initiated in Sweden with the objective of studying long-term effects of herbicide application according to recommendations from guidelines: the guidelines were developed at the Swedish University of Agricultural Sciences and consisted of printed cards designed for in-field use. Treatments also included a full and a half dose and an untreated control. As an average over the experimental time, i.e., until 1997, the dose used in the guideline treatment varied at different sites between 20 and 70% of a full dose. In 1998, i.e., 1 yr after the last herbicide application, the plant densities of annual weeds in the guideline treatment, the half and the full doses were 51, 57, and 67% lower, respectively, than in the untreated control when averaged over sites. At two and four sites, the half and full doses resulted in significantly lower weed densities than where guidelines had been used. Compared with the control, the full and half doses increased the proportion of difficult-to-control weed species significantly at five and four sites by 21 and 24%, respectively. In the guideline treatment the proportion of difficult-to-control weeds was increased at one site. In 1998, weed counts were higher where guidelines had been used than in the full dose for common lambsquarters and common chickweed at three sites each and for wallflower mustard, catchweed bedstraw, field violet, Galeopsis spp., and Lamium spp. at one site each. At three sites, no significant treatment effects on crop yields were found, whereas yields at the remaining seven sites were higher where guidelines had been used than in other treatments in several years. It is concluded that application of dose rates according to recommendations from guidelines can be a fruitful way to reduce herbicide use. Nomenclature: Common lambsquarters, Chenopodium album L. CHEAL; common chickweed, Stellaria media (L.) Vill. STEME; wallflower mustard, Erysimum cheiranthoides L. ERYCH; catchweed bedstraw, Galium aparine L. GALAP; field violet, Viola arvensis Murr. VIOAR.
... Such systems are quite common in agriculture; they are developed to help growers plan agronomical practices or apply pesticides to their crops. Some examples of such systems are 'PC-Plant Protection' developed in Denmark to help growers control weeds, pests, and diseases in cereals (Murali et al., 1999); 'Habitat creation model', a decision support system designed to assess the viability of converting set-aside land into semi-natural habitat (Gilbert et al., 2000); 'AgClimate', a DSS for evaluating crop production risk under alternative climate forecasts (Fraisse et al., 2006). Another relevant example is CuRSS, a recommendation system for scheduling copper sprays for citrus disease control, developed by Albrigo et al. (2005), which is a stand-alone application that is used independently by the growers and, differently from a web-based system, cannot allow online updates of the decision model. ...
This paper presents Coptimizer, a model-driven decision support system designed to help growers to optimize and track the use of copper-based fungicides against grapevine downy mildew in European organic viticulture, where the use of only a fixed amount of copper per year per hectare is permitted.Copper is a preventive fungicide allowed in organic agriculture that is active only at the application site (i.e. it is non-systemic), so that new plant growth results in unprotected tissues. In areas where disease incidence is high, organic farmers usually apply weekly copper treatments at the risk of exceeding the fixed threshold. The capability of Coptimizer for not exceeding the copper threshold was evaluated in simulation using historical data and tested in two experiments under field conditions during 2008. The results show that by using Coptimizer growers could be able to maintain the same level of protection as that gained by traditional application schedule while applying only half the amount of the fungicide.
... Expert systems have been developed for many kinds of applications in agriculture, involving diagnosis, predictions, consultation, control, etc. (Gonzalez-Andujar and Recio, 1996;Knight, 1997;Carrascal and Pau, 1992). In weed science, most expert system developments have been centred mainly in weed control, particularly for herbicide selection (Castro-Tendero and García-Torres, 1995;Stigliani et al., 1993;Murali et al., 1999). Castro-Tendero and García-Torres (1995) developed SEMAGI, which predicts the potential yield reduction from multi-species weed infestation and determines the appropriate herbicide according to the different economic thresholds in sunflower and wheat. ...
Identification of weed seedlings is a difficult task. An expert system to help farmers and extension workers to identify weed species in cereals has been developed. The expert system uses a hierarchical classification and a mix of the text description, photographs and artistic pictures. The system is supported by a data base containing information about 41 weed species and 128 colour images. The expert system was evaluated following the conventional expert system evaluation methodologies. Results of the validation indicated that non-expert users were able to make identification using the expert system. A total of 149 identifications were performed and 63% were identified correctly. The erroneous identifications tended to cluster around monocot species; especially Avena sterilis, Lolium rigidum, Phalaris ssp. and Bromus sterilis were misidentified. Results of the validation process and the writing suggestions provided by the participants were used to implement improvements in the system. The program can be used as an identification tool for farmers and technicians and for educational purposes.
... Probably the most successful in terms of the proportion of the industry using it, due to regulatory requirements, is the Danish PC Plant Protection system (Murali et al., 1999), which has sold over 2500 licences. It covers control of weeds, pests and diseases in wheat and other crops, including barley, peas and oilseed rape with an emphasis on reducing chemical use. ...
Weed Manager is a model-based decision support system to assist arable farmers and advisers in weed control decisions on two time scales: within a single season and over several years in a rotation. The single season decision is supported by a wheat crop and annual weed growth simulation, with a multi-stage heuristic decision model. The rotational aspect uses a model of seed population dynamics, with decisions optimised using stochastic dynamic programming. Each time scale has its own user interface within a single program integrated into the ArableDS framework, which provides data sharing between several decision support modules. Weed Manager was used by about 100 farmers and consultants in the 2005–2006 and 2006–2007 seasons.
... com) continues to function as an expert advisory system and covers a range of crops, pests and diseases. PC-Plant Protection, developed in Denmark (Murali et al. 1999), also uses expert scoring rules and covers control of weeds, pests and diseases in wheat, with an emphasis on reducing chemical use. EPIPRE in The Netherlands (Zadoks 1981; Rijsdijk 1983) used empirical models to relate observed disease levels to probable losses, but use of the system has now declined as farmers have become educated about the meaning of observations. ...
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.
Maximization of olive (Olea europaea L.) productivity at the end of the growing season requires detailed knowledge on fruit growth and oil accumulation processes. To achieve this objective, in the last decade, regional agricultural services have adopted crop management strategies based on field monitoring networks, physical and chemical analyses, meteorological data sets, and internet-based services. In this work we describe the Decision Support System (DSS) implemented by our research group on the web site of ARSIA - Extension Service for Agriculture in Tuscany, Italy - for the optimisation of olive of 'Frantoio' harvesting. The DSS is based on a meteorological data set and on olive phenological and physiological parameters related to the olive maturation processes. Meteorological data are automatically collected from a network of agro-meteorological stations of ARSIA and olive analytical data (fruit physical parameters, maturity index, oil content and polyphenols) have been measured weekly or bi-weekly on olive samples collected from an olive orchards network. The meteorological and olive analytical data sets have been displayed on the online service where technicians and farmers are allowed to download data and reports on olive maturation process and to perform some basic agro-climatic elaborations.
This book presents the theory and practice for creating effective education and outreach programs for conserving biodiversity, addressing a variety of environmental issues, and living sustainably. It describes an exciting array of techniques for enhancing school resources, marketing environmental messages, using mass media, developing partnerships for conservation, and designing on-site programs for natural areas and community centers. Vivid case studies from around the world illustrate techniques and describe planning, implementation, and evaluation procedures, enabling readers to implement their own new ideas effectively.
The rapid advances in the computer and communication technologies and in the agricultural technology have been a boon to farmers. The computer has potential to make agriculture enter into an era of disciplined agricultural production processes wherein it would be possible to escape the extremes of natural vagaries and to ensure profitable and sustainable agricultural production by efficient planning, design and management of agribusiness. In this paper, an attempt has been made to focus on multi-faceted role of computers in the complex agriculture sector. The current and future usage of computers in various disciplines of the agriculture sector is succinctly described. It is demonstrated that the agriculture sector offers numerous opportunities for the use of computers in different disciplines of agriculture, various on-farm and off-farm activities as well as planning and management of agricultural systems. The increasing role of computer and communication technologies in education and rural development is also highlighted. Finally, it is emphasized that the use of new technologies and tools will help meet the daunting challenges ahead and will ensure long-term sustainability of agricultural production in an environmental friendly manner.
A Danish decision support system (DSS) on integrated pest management (including pathogens and weeds) has been developed during the past 20 years. This DSS is distributed as an integrated part of the ‘Danish Integrated Farm Management System’, which is a PC program, of which the plant protection module presently has about 2500 subscribers in Denmark. This program has been designed to propose relevant options for treatment with plant protection products according to observations from a field inspection. The new object-oriented web-based system architecture of this DSS was developed in 2001. This allows local (national) adaptation of the DSS in terms of language, crops, pests, products and features. Model complexity and DSS algorithms can also be adjusted to local conditions. The system has been implemented in a relational database structure (Microsoft SQL Server) and a dynamic web application (Java and Jscript in Active Server Pages on an Internet Information Server). The system architecture has been designed with emphasis on a high level of flexibility for future adjustments due to agronomic and legal requirements. The DSS models have been linked to a pest identification module and to a comprehensive database on label information on plant protection products.
This paper presents FireFight, which is a model-driven decision support system designed to help optimization of scouting efforts for Fire blight disease symptoms in an area where the disease is not yet established. Fire blight is the most destructive bacterial disease of apple and pear. The most effective method to prevent disease spreading inside a new area is the accurate monitoring of orchards to identify symptoms and destroy the infected material. Infections occur only if the pathogen is present during favorable environmental and plant conditions. Scouting for the disease is expensive and time-consuming, due to the vast areas to be monitored in a very short period of time (immediately after the end of the disease incubation period). In some areas, as in the Trentino region (northern Italy), exhaustive monitoring is almost impossible due to the large number of trees, and therefore only a random sample is examined after the infection risk period. This approach is problematic, since it may fail to identify infected plants. Fire blight warning systems are widely used to forecast disease outbreak and support treatment planning. However, these systems are known to suit the specific regional conditions for which they were developed. In order to overcome the uncertainty due to the local nature of disease prediction models, several algorithms were applied in parallel, so that a wide range of different environmental conditions could be covered. FireFight is based on three of the most widely used Fire blight forecasting models (MARYBLIGHT, BIS95, and Fire blight Control Advisory). Disease incubation period was also included to advice scouting efforts when visible infection symptoms are timed to appear. Historical weather data analysis of the 57 subareas of Trentino revealed that there is relatively little risk (average of 1.13 risky days per subarea per year) during bloom. In 2003, 2004, and 2005 there were risky conditions in 37%, 51%, and 67% of the subareas, respectively. By comparing the current sample-based scouting (where no symptoms were found even if they were present in the orchards) and the positive FireFight risk prediction in the areas where disease symptoms were actually found, we can conclude that FireFight can assist in the optimization of scouting efforts. It seems that the FireFight DSS approach can be applied in general to others plant diseases where accurate monitoring is needed in areas where disease is not yet present.
This article discusses the concept of relative potency of herbicides in bioassays where individual dose-response curves can be similar or nonsimilar, often denoted parallel and nonparallel curves, and have different upper and lower limits. The relative potency is constant for similar dose-response curves and measures the relative horizontal displacement of curves of a similar shape along the dose axis on a logarithmic scale. The concept of similar dose-response curves has been used extensively to assess results from herbicide experiments, for example, with the purpose of adjusting herbicide doses to environmental conditions, formulations, and adjuvants. However, deeming dose-response curves similar when they are not may greatly affect the calculation of the relative potency at response levels such as effective dosage (ED)90, which is relevant for effective weed control, or ED10, which is used in crop tolerance studies. We present a method for calculating relative potencies between nonsimilar dose-response curves at any response level. It also is demonstrated that if the upper, lower, or both limits among response curves are substantially different, then the ED50 or any other ED level cannot be used indiscriminately to compute the relative potency. Rather, the relative potency should be viewed as a function of the response level.
A genetic algorithm is used in a decision support system to select the combinations of chemicals and the timing of successive treatments for the optimal control of fungal diseases in winter wheat crops, using a simulation model to predict the performance of different treatments. The search space is large and discrete, making the use of conventional optimisation methods impractical. Furthermore, the user requirements specify that the method must supply lists of near-optimal solutions, which fits with the use of populations of solutions in the genetic algorithm. Substantial improvements in the performance of the algorithm were obtained by tuning the fitness, selection, reproduction and replacement methods for the optimisation of short-term and long-term decisions. These also ensured rapid convergence in the former and prevented premature convergence in the latter.The algorithm has proved to be effective at finding optimal and near optimal solutions within an acceptable time. When compared with exhaustive searches for cases where this is possible (short-term planning with restricted choices), it typically finds 5–8 of the top 10 plans and a similar number of the next 10. The results of the system in field and user trials have been good.
GESTINF is a decision tool for postemergence weed control based on the equivalent density approach. Using observed weed densities just before treatment, the program estimates the economic return from the treatment, thus indicating whether to treat or not and, if a treatment is needed, the most economical weed control solution. Each treatment is also characterized by an environmental pollution index. GESTINF has been tested in wheat and soybeans on a farm in northeastern Italy with a total cropping area of 60 ha of wheat and 40 ha of soybean. For both crops, weed control followed the suggestions of GESTINF, whereas the remaining cropped areas were treated according to standard farm weed control practices. To compare the two weed control systems, weed control efficacy, average crop yield, and the extra time required for scouting and treatments were measured. In both crops, the treatments suggested by GESTINF showed good efficacy, and yields proved to be no different from those obtained in the fields treated with standard farm weed control practices. In most cases, GESTINF selected treatments with a lower environmental effect. The most critical point was the time required to scout the weed population that, in low-value crops or when very cheap treatments were available, reduced the weed control economic return. In wheat, GESTINF indicated that fewer fields needed to be treated than did the conventional system. However, extra costs due to both scouting and more expensive treatments balanced the savings obtained from nontreated areas. For soybean, the treatments adopted by the farm were based on a combination of pre- and postemergence practices. In this case, GESTINF identified cheaper but still efficacious treatments, significantly reducing the total cost of weed control. Nomenclature: Soybean, Glycine max (L.) Merr.; wheat, Triticum aestivum L.
The use of scouting and economic thresholds has not been accepted as readily for managing weeds as it has been for insects, but the economic threshold concept is the basis of most weed management decision models available to growers. A World Wide Web survey was conducted to investigate perceptions of weed science professionals regarding the value of these models. Over half of the 56 respondents were involved in model development or support, and 82% thought that decision models could be beneficial for managing weeds, although more as educational rather than as decision-making tools. Some respondents indicated that models are too simple because they do not include all factors that influence weed competition or all issues a grower considers when deciding how to manage weeds. Others stated that models are too complex because many users do not have time to obtain and enter the required information or are not necessary because growers use a zero threshold or because skilled decision makers can make better and quicker recommendations. Our view is that economic threshold–based models are, and will continue to be, valuable as a means of providing growers with the knowledge and experience of many experts for field-specific decisions. Weed management decision models must be evaluated from three perspectives: biological accuracy, quality of recommendations, and ease of use. Scientists developing and supporting decision models may have hindered wide-scale acceptance by overemphasizing the capacity to determine economic thresholds, and they need to explain more clearly to potential users the tasks for which models are and are not suitable. Future use depends on finding cost-effective methods to assess weed populations, demonstrating that models use results in better decision making, and finding stable, long-term funding for maintenance and support. New technologies, including herbicide-resistant crops, will likely increase rather than decrease the need for decision support.
Agronomy, the science ruling the fields, has the privilege and the task of dealing with many aspects and related disciplines,
covering the biological and the physical sphere. Harmonizing the complex of interactions arising form the organisms, factors
and conditions involved in the process of plant growth is not an easy task, as general. Optimising plant growth and crop production,
while safeguarding the environment, often proves a harder challenge. In this light, the crops for industrial end-uses may
intrinsically be seen as crops of potential large scale, thus exerting a significant influence - both positive and negative
— on the environment.
Soil quality (SQ) assessment tools may facilitate adaptive management decisions that promote sustainable agricultural practices. However, without input from the target audience, these decision tools' potential for adoption remains unknown. In an effort to consider the end-user in SQ index development, we examined farmer reactions to index outcomes and uses for soil quality information. We calculated SQ indices for side-by-side comparisons of alternative (organic amendment) and conventional practices in the San Joaquin Valley, CA. The indices integrated chemical, biological, and physical data collected over 3 years in a participatory, on-farm demonstration project. In a focus group format, we asked the participating farmers about their perceptions of SQ in the study fields. We then asked the farmers to compare their perceptions with the calculated SQ indices by rating the amount of agreement between the two on a scale from 1 to 10, with 10 being excellent agreement. The survey results showed a mean of 8 and standard deviation=1 (N=12). When we presented all participants with a variety of output options for soil quality indicator information, they were asked to rate each for usefulness and understandability. The apparent disparity among their preferences (high ratings for the most and the least integrated data) was explained by the suggestion of several farmers that it would be most useful to have access to several forms of the information. Participants also discussed how they would most likely use the tools and what information would be needed for them to change a management practice. A demonstrated link between soil quality and economics was the most discussed need. In response to the farmers' emphasis on economic outcomes, we compared our SQ index with yield results (as one component of net revenue) for participating fields. Pearson correlation coefficients showed statistically significant correlations between yield and SQ index outcomes. Correlations were stronger within subsets of the data grouped by crop or soil suborder. With further refinement and site specificity, a SQ index that is acceptable to its target audience could become a useful adaptive management tool to help maintain or increase the efficiency of sustainable farming practices.
Thesis advisor: Saleem Ali. Thesis (A.M.)--Brown University, 2002. Includes bibliographical references (leaves 106-117).
In this paper, we describe a decision support system (DSS) to support the farmer when selecting a winter wheat variety. In Denmark, the yield characteristic has traditionally been the most important factor in variety selection. The DSS in this paper bases the selection on several characteristics and it takes future uncertain observations and decisions of relevance to the variety decision into account. We describe the decision process in detail and build a simple stochastic optimization model, representing the decision process. For each variety of winter wheat, the model calculates the expected net revenue, which is the expected gross revenue minus the expected costs for treatment of diseases and application of additional fertilization, which is allowed in certain varieties. Finally, we discuss the results from the model.
The Danish Integrated Farm Management System is a PC-based system developed by the Danish agricultural advisory service covering all management and control aspects of a farm. It consists of a joint module linking a number of sector modules (cattle production, plant production, economy, pig production and buildings/machinery). The sector module for plant production provides facilities for detailed planning, decision support and control, including all subjects of a complete cultivation plan for each individual field. The fertilizer plan and the crop protection plan provide detailed specifications within the overall cultivation plan. For decision support there are advanced sub-modules for fertilization, crop protection and grassland management. PC-Plant Protection is an optional and fully integrated sub-module of The Integrated Farm Management System, or a self-contained program product.
Unwanted side-effects of plant protection products and related problems have become a major issue in the public discussion in Denmark. It is expected that side-effects can be reduced if knowledge about the risks for non-target organisms and the environment are made available to farmers, thus enabling them to choose products which in a particular situation will minimize the overall risk. It is important that the farmers can identify their own situation when such a risk assessment is used at the farm level. The Danish decision-support system PC-Plant Protection will be improved with a module which provides farmers with environmentally relevant information about plant protection products. The module will present users with estimated relative risk quotients for bees, aphid-predating insects, earthworms, birds, mammals and aquatic organisms, risk of ground-water contamination and operator exposure, together with general information. The risk quotients are calculated by dividing the toxicity by an estimated PIEC value (Predicted Initial Environmental Concentration). PIEC is calculated from the application rate recommended by PC-Plant Protection, the growth stage of the crop, expected yield and an estimated leaf-area index. The farmer can select the risk quotients he considers relevant when planning plant protection applications during the winter and in each individual spraying situation.
Field experiments were conducted in two winter wheat fields to assess weed control and quantify the amount of herbicide applied with a GIS map-based patch sprayer. Site specific weed management decisions were based on previously acquired weed distribution maps and an economic weed threshold model. A weed treatment map (on-off) was generated and georeferenced to control the sprayer. A global positioning system was used to locate the sprayer in the field. In both fields, Alopecurus myos-uroides was observed at low infestation levels, Galium aparine at medium infestation levels and the mean density for other broadleaf species exceeded 40 weed seedlings va- in both fields. Herbicide application was recommended for 58.3% of the first field and 54.6% of the second field. With the patch sprayer used for this study, 21% less herbicides were sprayed compared to an uniform broadcast application. Additional assessments of weed density in the first field 3 and 6 weeks after site specific weed control showed that almost the entire field was weed free. The results of this study demonstrated that site specific weed control was technically feasible but further investigations are needed to verify and evaluate site specific weed control methods.
At the Danish Institute of Plant and Soil Science, models for estimating the risks of development of cereal pests and diseases have been developed and field tested. Models are based on the weather data collected from field weather stations. A MicroSoft Windows 95 program has been developed using Borland's Delphi RAD and Paradox DBMS to implement the models for use by agricultural advisors and farmers. Model procedures and parameters are accessible through the database and thus facilitate easy modification of parameter values and addition of pests and diseases.
The decision in 1986, on an action plan to reduce pesticide use in Denmark by 50% led to increased research on the potential of reducing dosages. A decision support system (PC-Plant Protection), developed by The Danish Institute for Plant and Soil Science, implements this research. It combines a detailed use of threshold values to support decisions on treatment need, choice of pesticides and the appropriate dosage for actual problems in cereals. The pest and disease module within the system has been available commercially since 1993 and up to the end of April 1995, 2000 licences have been issued for its use at agricultural schools and by advisers and farmers. The recommendation model for pest and disease control has been validated in field trials since 1990. The validation has shown that the model is able to provide recommendations for the control of pests and diseases to a satisfactory level, without affecting farmers' gross margins. The model was able to adjust pesticide use to large yearly variations and the average amount of pesticides in the plots treated according to the model was well below that in the reference plots and in the commonly used strategies in Denmark.
Survey on European Crop Protection Decision Support Systems Danish Institute of Plant and Soil Science, 8830 Foulum, Denmark SP-report no Field validation of a decision support system for control of pests and diseases in cereals in Denmark
- B J M Secher
- E Bouma
Secher, B.J.M., Bouma, E., 1996. Survey on European Crop Protection Decision Support Systems. Danish Institute of Plant and Soil Science, 8830 Foulum, Denmark SP-report no. 16 1996, 95 pp. Secher, B.J.M., Jørgensen, L.N., Murali, N.S., Boll, P.S., 1995. Field validation of a decision support system for control of pests and diseases in cereals in Denmark. Pestic. Sci. 45, 195 – 199.
An information system for plant protection: II. Recommendation models structure and performance
- Secher
Secher, B.J.M., 1991. An information system for plant protection: II. Recommendation models structure and performance. Ann. ANPP 2, 153 – 159.
Site specific control of diseases in winter wheat
- Secher