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Decision support systems: Barriers and farmers' need for support

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Abstract

The highly complex knowledge of scientific disciplines makes nuanced analysis and modelling possible. However, the information produced often does not reach farmers because it is presented in a way that does not correspond to the way their work is carried out in practice. The decision support system Crop Protection Online is widely used by advisors and as a learning tool for students. Although the system has been validated in many field trials over the years and has shown reliable results, the number of end-users among farmers has been relatively low during the last 10 years (approximately 1000 farmers). A sociological investigation of farmers’ decision-making styles in the area of crop protection has shown that arable farmers can be divided into three major groups: (a) system-orientated farmers, (b) experience-based farmers and (c) advisory-orientated farmers. The information required by these three groups to make their decisions varies and therefore different ways of using decision support systems need to be provided. Decision support systems need to be developed in close dialogue and collaboration with user groups.

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... The program used real-time traffic inputs and data of possible roads, then analyzing them to find the best route and avoid congestion. DSS in agriculture, for example, has been used to aid farmers in choosing the right time to plant, determine the right plants for a different type of soil and the season [14,17,18]. In some countries, for example, Australia, India, the USA, Israel, and Italy DSS has been developed to help the farmer in taking decision [19]. ...
... An accurate and valid data collection is needed to build a welltargeted DSS. Moreover, DSS needs to be established in association and close discussion with enduser, hence the DSS could answer the farmers' problems [18]. ...
... Farm advisors help the farmers by demonstrating sustainable agriculture practices, providing information for farmers, and assisting the farmers throughout the agricultural practices. According to Jørgensen, Noe, Langvad, Jensen, Ørum and Rydahl [18], farmers prefer to use farm advisors rather than Crop Protection Online DSS. Farm advisors must ensure that the farmers are running their land as efficiently as possible. ...
Article
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Farmers are the vanguard of national food providers. However, they still have not yet received much serious attention and assistance. This condition is exacerbated by unpredictable climate change. Therefore, it is crucial to have an analytics tool to assist farmers in resolving production problems with some variables such as soil condition, season, and plant species. Decision support system (DSS) in agriculture helps farmers in making decisions based on previous research results. However, DSS in some countries are available, but not widely used by the farmers. This study aims to analyze the possibility of developing DSS to increase the resilience capacity of farmers in Indonesia. The method used in this research was a literature study and descriptive analysis. The results showed that there is still a long way to go in achieving the robust DSS, referring to the quality of research results so far. To elaborate on this, it is necessary to make a research roadmap in this field by understanding the current research status. Moreover, DSS will be a form of contribution from researchers in providing reliable and updated public information packages for a better agricultural management.
... In Denmark, more than 30 crops and 100 weed species were investigated by means of the DSS "IPMwise" and suggestions for herbicide applications along with recommendations for non-chemical weed control were made [23]. There is clear evidence that herbicide inputs can be decreased in major crops by applying herbicides according to the suggestion of such DSSs [24,25]. Both farmers and advisors can significantly benefit from the use of a DSS for weed control. ...
... DSSs provide knowledge to the farmer regarding the competition among weeds and crops and suggest the most appropriate herbicides against the dominant weed species at the optimum dose rate, application time and method [36]. The impact of the DSS technology on reducing herbicide use by approximately 40%, as compared to reference herbicide treatments, has been well established [25]. Efficient weed control has been recorded at over a 30% decreased herbicide use in cereals by applying herbicides according to the suggestion of a DSS, whereas in sugar beet, the advice from a DSS decreased the herbicide input up to 20% [24]. ...
... The necessary functionality of a DSS requires accurate assessments of weeds within a field as input data. However, the main targets of DSS developers are not always the exact assessments because what they want most are higher weed control levels [25]. The concept of economic thresholds is widely used in DSSs for weed management as well. ...
Article
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Decision support systems (DSS) have the potential to support farmers to make the right decisions in weed management. DSSs can select the appropriate herbicides for a given field and suggest the minimum dose rates for an herbicide application that can result in optimum weed control. Given that the adoption of DSSs may lead to decreased herbicide inputs in crop production, their potential for creating eco-friendly and profitable weed management strategies is obvious and desirable for the re-designing of farming systems on a more sustainable basis. Nevertheless, it is difficult to stimulate farmers to use DSSs as it has been noticed that farmers have different expectations of decision-making tools depending on their farming styles and usual practices. The function of DSSs requires accurate assessments of weeds within a field as input data; however, capturing the data can be problematic. The development of future DSSs should target to enhance weed management tactics which are less reliant on herbicides. DSSs should also provide information regarding weed seedbank dynamics in the soil in order to suggest management options not only within a single period but also in a rotational view. More aspects ought to be taken into account and further research is needed in order to optimize the practical use of DSSs for supporting farmers regarding weed management issues in various crops and under various soil and climatic conditions.
... Hochman et al., 2009;Carberry, Hammer, Meinke and Bange, 2000;Carter et al., 2000) and elsewhere (Jame and Cutforth, 1996;McCown, 2002a). However, overall, there has been only limited adoption of these types of decision support tools by farmers (Keating and McCown, 2001;McCown, 2002a;McCown, Hochman and Carberry, 2002;Hayman, 2003;McCown and Parton, 2006;Jørgensen, Noe, Langvad, Jensen, Ørum and Rydahl, 2007;Jakku and Thorburn, 2010;Hochman and Carberry, 2011). ...
... Low levels of adoption of decision support tools by farmers may, in part, reflect the limited capacity of these tools to incorporate (i) the range of contextual (social, economic, and environmental) factors involved in farm management decision making and (ii) existing farmer knowledge (Francis and Carter, 2001;Pannell, 2006;Matthews, Schwarz, Buchan, Rivington and Miller, 2008). Evidence of the failure of decision support systems to effectively influence farm management decisions of large numbers of farmers has led to revised thinking around the need for information to both match farmers' needs and accommodate different styles of information gathering, reasoning and decision-making (McCown, 2002b;Jørgensen et al., 2007). It has been suggested that those decision support systems which better engage with, and reflect, farmers' natural modes of learning through experience and discussion may be most effective (Nelson, Holzworth, Hammer and Hayman, 2002;McKeown, 2010). ...
... These were filmed using lifelike avatar actors, customised settings and real-world, climate-based scenarios relevant to the lives and practices of Australian sugarcane farmers (Fig. 2). To ensure an engaging integrated storyline and consistency throughout the series, back stories were created for the key characters, based on the decision-making types described by Jørgensen et al. (2007), namely: (a) system-orientated farmers, (b) experience-based farmers and (c) advisory-orientated farmers. As in real life, farmers' family members also play a part in the discussions simulated in the machinimas. ...
Article
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Climate variability represents a significant risk to farming enterprises. Effective extension of climate information may improve climate risk decision making and adaptive management responses to climate variability on farms. This paper briefly reviews current agricultural extension approaches and reports stakeholder responses to new web-based virtual world ‘discussion-support’ tools developed for the Australian sugar cane farming industry. These tools incorporate current climate science and sugar industry better management practices, while leveraging the social-learning aspects of farming, to provide a stimulus for discussion and climate risk decision making. Responses suggest that such virtual world tools may provide effective support for climate risk decision making on Australian sugar cane farms. Increasing capacity to deliver such tools online also suggests potential to engage large numbers of farmers globally.
... Technological advances have led to the development of a range of sophisticated decision support tools, with links to operations research/management science, many of which use complex biophysical modeling to derive optimal solutions to particular farm management problems [9]. However, the adoption of such tools by farmers has been limited [10,11,12]. The reasons for this may include their limited capacity to incorporate either existing farmer knowledge or the range of contextual factors involved in farm management decision making (as identified by Francis and Carter [4], above). ...
... The reported failure of decision support systems to effectively influence farm management decisions of large numbers of farmers has also led to revised thinking of the need for information to match farmers' needs and accommodate different styles of information gathering, reasoning, and decision-making [9,12]. Close dialogue and collaboration with user groups is critical to the development of effective decision support for farmers [7,12]. ...
... The reported failure of decision support systems to effectively influence farm management decisions of large numbers of farmers has also led to revised thinking of the need for information to match farmers' needs and accommodate different styles of information gathering, reasoning, and decision-making [9,12]. Close dialogue and collaboration with user groups is critical to the development of effective decision support for farmers [7,12]. ...
Article
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In farming, the outcome of critical decisions to enhance productivity and profitability, and so ensure the viability of farming enterprises, is often influenced by seasonal conditions and weather events over the growing season. This paper reports on a project that uses cutting-edge advances in digital technologies, and their application in learning environments, to develop and evaluate a web-based virtual 'discussion-support' system for improved climate risk management in Australian sugar farming systems. Customized scripted video clips (machinima) are created in the Second Life virtual world environment. The videos use contextualized settings and lifelike avatar actors to model conversations about climate risk and key farm operational decisions relevant to the real-world lives and practices of sugarcane farmers. The tools generate new cognitive schema for farmers to access and provide stimuli for discussions around how to incorporate an understanding of climate risk into operational decision-making. They also have potential to provide cost-effective agricultural extension that simulates real world face-to-face extension services, but is accessible anytime anywhere.
... There is also a strong influence of the individual farmer's values and perceptions, which can be considered general for IWM as well as for the adoption of DSS. A Danish study on uptake of DSS showed that farmers could be grouped into three main categories: system-oriented, experienced-based and advisory-contracting (Jorgensen et al., 2007). The first group of farmers rely on many different sources of knowledge and they use this new knowledge in planning. ...
... The experienced-based farmers also seek new knowledge from a variety of sources, but only implement this new knowledge if it agrees with the personal experience of the farmer. The third group of advisory-contracting farmers typically run mixed farms and leave the decisions related to plant protection to an advisor (Jorgensen et al., 2007). Another study on end-users of DSS concluded that there are two types of situations, which call for different types of DSS: 1) guidance to improve an existing system or 2) full redesign of a system when reaching a dead-end, e.g. ...
Article
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Initiatives to reduce the reliance of agriculture on pesticides, including the European Union (EU) Directive 2009/128/EC on the sustainable use of pesticides (SUD), have yet to lead to widespread implementation of Integrated Pest Management (IPM) principles. Developments in weed management have strongly focused on increasing the efficiency of herbicides or substituting herbicides with other single tactics such as mechanical control. To increase sustainability of agricultural systems in practice, a paradigm shift in weed management is needed: from a single tactic and single growing season approach towards holistic integrated weed management (IWM) considering more than a single cropping season and focusing on management of weed communities, rather than on control of single species. To support this transition, an IWM framework for implementing a system level approach is presented. The framework consists of five pillars: diverse cropping systems, cultivar choice and establishment, field and soil management, direct control and the cross-cutting pillar monitoring and evaluation. IWM is an integral part of integrated pest management (IPM) and adopting IWM will serve as a driver for the development of sustainable agricultural systems of the future.
... After >25 years of development of methods, which have been integrated in these DSS, and validated in field experiments, these methods currently constitute a professionally recognized point of reference on IWM in Denmark among important stakeholders, which include farmers, crop advisors, agrochemical companies, schools, universities, regulatory bodies, NGOs, etc. (Jørgensen et al., 2007;Been et al., 2007). ...
... In all countries involved so far, DSS has demonstrated sufficiently robust IWM, and potentials for reducing the use of herbicides in the range of 20-40% as compared to local 'best practice' programs Reduction (Rydahl, 2004;Tørresen et al., 2004;Sønderskov et al., 2014;Sønderskov et al., 2015;Montull, 2016). However, in all countries involved, a common recognized major bottleneck for a wider exploitation of reduction potentials has been identified in terms of reluctance among farmers to conduct manual field inspections (Jørgensen et al., 2007). As the total capacity for manual weed inspection is relatively low, most herbicide applications are still relying on recommendations from crop advisors on the regional level. ...
Article
In order to exploit potentials of 20–40% reduction of herbicide use, as documented by use of Decision Support Systems (DSS), where requirements for manual field inspection constitute a major obstacle, large numbers of digital pictures of weed infestations have been collected and analysed manually by crop advisors. Results were transferred to: 1) DSS, which determined needs for control and connected, optimized options for control returned options for control and 2) convolutional, neural networks, which in this way were trained to enable automatic analysis of future pictures, which support both field- and site-specific integrated weed management.
... In addition, a range of sophisticated decision support tools has been developed, many of which use complex biophysical modeling to derive optimal solutions to particular farm management problems (McCown, 2002). However, ICTs have so far failed to deliver the anticipated increase in innovation adoption rates on farms (Chowdhury & Odame, 2013;Hayman, 2003;Jørgensen et al., 2007;McCown, Hochman, & Carberry, 2002). Sulaiman and colleagues (2012) suggest that this is, at least in part, because their use is still effectively limited to the conventional tasks of top-down information dissemination and training. ...
... The apparent failure of digital technology-based decision support tools to effectively inºuence farm management decisions of large numbers of farmers (Francis & Carter, 2001;Hayman, 2003;McCown et al., 2002) has led to revised thinking around the need for information to better match farmers' needs and to accommodate different styles of information gathering, reasoning, and decision making (Babu, Glendenning, Asenso-Okyere, & Govindarajan, 2012;Jørgensen et al., 2007;McCown, 2002). Evidence that discussion can play a critical role in effective adult learning (Kirkup, 2002) upholds calls for support systems for decision making to better engage with farmers' natural modes of learning through experience and exchanges within informal learning networks (Kroma, 2006;Nelson, Holzworth, Hammer, & Hayman, 2002). ...
Article
Full-text available
Agricultural extension that delivers timely, targeted, and cost-effective support to farmers will help ensure the sustainability and adaptive capacity of agriculture, enhancing both food security and environmental security. Lever-aging advances in agriclimate science and adult education, innovative digital technologies offer signiªcant new opportunities to engage with farmers and to support decision making. In this study, animated video clips (machinimas), developed using the Second Life TM virtual world gaming platform, model conversations around climate risk and critical on-farm decisions in the Australian sugarcane farming industry. Early evaluation indicates that this is an engaging format that promotes discussion by leveraging farmers' natural modes of information gathering and social learning. Comparison with conventional extension practices indicates that these discussion support tools may be a cost-effective addition to existing approaches. The format's ºexibility means machinimas are readily updated with new information and customized to meet the needs of different farmer groups. Rapid growth in digital access globally and the scalability of such approaches promise greater equity of access to high-value information, critical to better risk management decision making, at minimal cost, for millions of farmers.
... This excessive use of herbicides has resulted in the contamination of the groundwater and herbicideresistant weeds. In addition, it has been shown that the farmer's expenditure can be reduced by 40% if the correct herbicide is used in the right amount at the right time [34]. Moreover, most automated weed recognition techniques are only able to discriminate between crop and weed. ...
Article
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Weed management is one of the important tasks in agriculture. Weeds in rice fields are usually managed using three ways - chemical herbicides, mechanical weeders, and manual weeding. Manual weeding becomes a problem when there is a shortage of agricultural laborers. Mechanical weeders are not suitable for direct-seeded rice fields. Chemical herbicides are not advisable especially when farmers do not know about site-specific weed management. Site-specific weed management is using the right herbicide in the right amount. Therefore, this paper investigates computer vision-based deep learning techniques with transfer learning classifying three types of weeds in paddy fields, namely sedges, grasses, and broadleaved weeds so that the right herbicide is recommended to the farmers. This would reduce the broadcast application and the overuse of the herbicides, thereby limiting the negative impact of the chemical herbicides on the environment. This research work shows promising results with an accuracy around 90% and thus encourages the development of digital agriculture.
... Nonetheless, the key to success for DSS for plant disease management is meeting users' expectations by demonstrating tangible benefits (Rossi et al., 2019). Complying to user requirements in DSS design and sharing functionality (Jørgensen et al., 2007) resulted in building trust in automated decisions and removing barriers surrounding the DSS adoption (Debeljak et al., 2019). ...
Article
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Integrated pest management (IPM) practices proved to be efficient in reducing pesticide use and ensuring economic farming sustainability. Digital decision support systems (DSS) to support the adoption of IPM practices from plant protection services are required by European legislation. Available DSSs used by Italian plant protection services are heterogeneous with regards to disease forecasting models, datasets for their calibration, and level of integration in operational decision-making. This study presents the MISFITS-DSS, which has been jointly developed by a public research institution and nine regional plant protection services with the objective of harmonizing data collection and decision support for Italian farmers. Participatory approach allowed designing a predictive workflow relying on specific domain expertise, in order to explicitly match actual user needs. The DSS calibration entailed the risk of grapevine downy mildew infection (5-point scale from very low to very high), and phenological observations in 2012–2017 as reference data. Process-based models of primary and secondary infections have been implemented and tested via sensitivity analysis (Morris method) under contrasting weather conditions. Hindcast simulations of grapevine phenology, host susceptibility and disease pressure were post-processed by machine-learning classifiers to predict the reference infection risk. Results indicate that IPM principles are implemented by plant protection services since years. The accurate reproduction of grapevine phenology (RMSE = 4–14 days), which drove the dynamic of host susceptibility, and the use of weather forecasts as model inputs contributed to reliably predict the reference infection risk (88% balanced accuracy). We did a pioneering effort to homogenize the methodology to deliver decision support to Italian farmers, by involving plant protection services in the DSS definition, to foster a further adoption of IPM practices.
... Traditionally, the targeting of phytosanitary products is obtained through an air barrier or electrification of drops (Farooq et al., 2001;Jørgensen et al., 2007;Baio et al., 2016;Santinato et al., 2013). According to Balsari et al. (2007), air induction nozzles can reduce drift to an average of 6.80%, proving the accuracy of the proposed methodology, which obtains an average drift of 6.81%. ...
Article
This work presents a methodology for developing two types of equipment for precision agriculture applied in coffee handling and harvesting, based on acoustic techniques. The proposed methodology uses the similitude method to simulate induced vibration and the direction of sound pressure fields. After analyzing the scale reduction model, a prototype is built in real dimensions to validate the proposed system. Two acoustic induction systems are evaluated: (i) application of phytosanitary products and (ii) selective harvesting of fruits. In applying a phytosanitary product, there is a drift reduction and increase in leaf coverage by approximately 44.97%. In selective harvesting, the proposed method is analyzed, and approximately 40% of the fruits in the appropriate ripening stage were harvested. The presented acoustic techniques applied to agriculture are promising for developing equipment in precision coffee handling and harvesting. The results obtained indicate that using acoustic techniques in the harvest promotes the reduction of the inherent wear of the harvest in the plants.
... It is, however, important to recognize that only a small proportion of the models (including DSTs) ever developed were widely applied to real-world problems in pest management [141,143,144]. Reasons for low rates of adoption include, but are not limited to: practitioners consider models not relevant to local conditions; practitioners not having time to learn model operational procedures; models are inadequately maintained to keep pace with temporal changes in weed community structure, crop production economics, and computer software standards [141,143,144,170,171]. For DSTs, considering the challenges associated with their development and maintenance, they may be more suited for education rather than prescription. ...
Article
Full-text available
In weed science and management, models are important and can be used to better understand what has occurred in management scenarios, to predict what will happen and to evaluate the outcomes of control methods. To-date, perspectives on and the understanding of weed models have been disjointed, especially in terms of how they have been applied to advance weed science and management. This paper presents a general overview of the nature and application of a full range of simulation models on the ecology, biology, and management of arable weeds, and how they have been used to provide insights and directions for decision making when long-term weed population trajectories are impractical to be determined using field experimentation. While research on weed biology and ecology has gained momentum over the past four decades, especially for species with high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycle parameters for many agriculturally important weed species. More research efforts should be invested in filling these knowledge gaps, which will lead to better models and ultimately better inform weed management decision making.
... A last barrier for widespread uptake of DSS is that farmers have different preferences for the outcome of decision support systems and the level of detail required. A sociological survey on the use of a DSS in Denmark concluded that farmers could be assigned to three major groups characterised by different approaches to decision making and preferences for DSS [10]. The three groups were system-oriented farmers, experience-based farmers and advisory-oriented farmers. ...
Article
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Lolium multiflorum (annual Italian ryegrass) and other grass weeds are an increasing problem in cereal cropping systems in Denmark. Grass weeds are highly competitive and an increasing number of species develop resistance against the most commonly used herbicide modes of action. A diverse management strategy provides a better overall control of grass weeds and decreases the reliance on herbicides. The bio-economic decision support system, DK-RIM (Denmark-Ryegrass Integrated Management), was developed to assist integrated management of L. multiflorum in Danish cropping systems, based on the Australian RIM model. DK-RIM provides long-term estimations (10-year period) and visual outputs of L. multiflorum population development, depending on management strategies. The dynamics of L. multiflorum plants within the season and of the soil seed bank across seasons are simulated. The user can combine cultural weed control practices with chemical control options. Cultural practices include crop rotation changes, seeding density, sowing time, soil tillage system, and cover crops. Scenarios with increasing crop rotation diversity or different tillage strategies were evaluated. DK-RIM aims at being an actual support system, aiding the farmer’s decisions and encouraging discussions among stakeholders on alternative management strategies.
... However, uptake of these systems is low, especially for high value crops like potatoes (see Shtienberg and Rose et al. 7,23 for an overview; see Jørgensen and Ramseier et al. 26,27 for examples from Denmark and Switzerland). ...
Article
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BACKGROUND Precise timing of pesticide applications, as recommended by decision support systems, can ensure crop protection, while maintaining efficient use of pesticides. Yet, farmers often deviate from recommended timing strategies. Here, we assess and explain farmers’ choices to follow or not follow recommendations for the timing of fungicide applications against potato late blight in Switzerland. RESULTS Based on daily fungicide application records as well as regional application recommendations and disease pressure we found that 36% of applications take place earlier than recommended. Using regression analysis, we identified the exposure to economic risks of infection, susceptibility of the planted potato varieties to late blight infections, as well as yearly differences in disease occurrence as the most important determinants of farmers’ application decision. CONCLUSIONS Our results indicate that decisions to not follow application recommendations and apply early are linked to available information and uncertainty with respect to disease predictions. Based on our results, we make recommendations on how to account for farmers’ uncertainty with regard to the timing of pesticide applications in the design of pesticide policies and agricultural decision support systems. These include the use of new technologies and data, mandatory reporting and tailor made taxes and insurance solutions. Although the focus of this article is on late blight in Switzerland, our analysis can easily be extended to other countries and important plant diseases like powdery mildew in grapevines or Fusarium head blight in winter wheat. This article is protected by copyright. All rights reserved.
... Across major crops in Denmark, there is an unexploited potential to achieve a 20-40% reduction in herbicide use, whilst maintaining weed control, by targeting specific weeds, in situ [1]. However, a sociological study has shown that Danish farmers are reluctant to conduct field scouting and that recognition of weeds (in various growth stages) is a major obstacle to targeted weed control [2]. Morphological plant traits such as size, number of leaves and leaf shape are affected by many factors, including genes and environmental factors (soil texture, soil humidity, nutrient availability, temperature, light, humidity). ...
Article
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This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516 images, which also varied in term of crop, soil type, image resolution and light conditions. The overall performance of this approach achieved a maximum accuracy of 78% for identifying Polygonum spp. and a minimum accuracy of 46% for blackgrass. In addition, it achieved an average 70% accuracy rate in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species.
... Increases in grain price as seen in 2007 (10 € to 20 € per dt) have a major impact on the optimum input. For fungicides in wheat, the optimum input for control of Septoria has been found to increase by 50% from 0.5 to 0.75 in susceptible cultivars, see Figure 3 (Jørgensen et al., 2007). A major problem for farmers is that they have to decide on the actual input before the grain price is known. ...
Book
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Papers from BSPP Presidential Meeting, Queen Mary, University of London 16-17 December 2008 http://www.bspp.org.uk/archives/bspp2008/docs/bspppres2008papers.pdf
... Decision Support Systems (DSS) for Integrated Weed Management (IWM) shows unexploited herbicide reduction of 30-50% in cereals [18]- [23]. However, DSS do not fit well into farmers usual practices by requiring manual field inspections and identification of weeds constituting a major obstacle [19]. For the past three years, the Danish nationally funded project, RoboWeedSupport, has sought to bridge the gap between the potential Crop Protection Online (CPO) or IPMwise based herbicide savings and the required field inspections [24]. ...
Thesis
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In recent years, precision agriculture and precision weed control have been developed aiming at optimising yield and cost while minimising environmental impact. Such solutions include robots for precise hoeing or spraying. The commercial success of robots and other precision weed control techniques has, however, been limited, partly due to a combination of a high acquisition price and low capacity compared to conventional spray booms, limiting the usage of precision weeding to high-value crops. Nonetheless, conventional spray booms are rarely used optimally. A study by Jørgensen et al. (2007) has shown that selecting the right herbicides can lead to savings by more than 40 percent in cereal fields without decreasing the crop yield when using conventional sprayers. Therefore, in order to utilise conventional spray booms optimally, a preliminary analysis of the field is necessary. This analysis should determine which weeds are present in the field and the density of those weeds so that herbicides targeting those weeds may be selected. Researchers have sought to detect and classify weeds and crops in images, but studies are limited regarding the number of plant species that can be discriminated and the flexibility of the camera setup. In the present PhD thesis, requirements for the camera set-up are loosened, allowing the use of consumer grade cameras or even cell phones for weed species localisation and identification in images from conventionally grown fields. In total 4 537 images have been collected over three growth seasons from Danish fields. In these images 31397 plants are annotated with names, from which the 17 most frequent species are selected for automated classifiiiication. The automated classification consists of two steps: Initially, weeds are located in images after which, the weeds are classified. Three types of weed localisation approaches are tested: Two approaches that perform a pixel-wise segmentation of plants, and one approach, that detects regions in images containing weeds. Common for all three approaches is that they aim at overcoming some of the challenges when working with images from fields: Namely changes in lighting, soil types, and plant stress due to lack of nutrition. The first of the suggested approaches segments plant material from the soil by using fuzzy C -means clustering combined with a threshold value for each pixel, which depends on the neighbourhood pixels, which helps to detect non-green stem regions. The second approach uses a fully convolutional neural network for segmenting pixels in three categories: Soil, weeds, and crops. The Neural Network is trained solely on modelled images but can segment weeds from maize with an intersection-over-union of between 0.69 and 0.93 for weeds and maize. Rather than segmenting images, the third approach produces region proposals that indicate weed locations in images. This method also uses a fully convolutional neural network, that enables it to detect weed instances in wheat fields despite occluding leaves. The three methods for weed segmentation and localisation solve four problems in the field of camera based weed detection: handling of changing environments, handling of non-green plant stems, segmentation of weeds and crops that are overlapping, and instance detection in cereal fields with occluding leaves. Following the detection of the weeds, the weed species are to be determined. For solving this problem, a convolutional neural network is used, which classifies the weeds with an overall accuracy of 87 percent for 17 species despite a severe degree of leaf occlusion. Because of the ability to handle weed detection and classification in natural environments, these methods can potentially reduce the investment of farmers, and thus lead to a higher adoption rate than existing precision weed control techniques, resulting in huge potential savings regarding herbicide consumption.
... One of the most practical applications of population dynamics models is by integrating them within decision support systems (DSS). DSS are computer-based systems designed to aid users to make more effective decisions (Parker, 2004). In weed science they offer an easy way to examine potential economic benefits of a wide range of weed manage ment treatments (Berti et al., 2003;Pannell et al., 2004). ...
Chapter
Seed biology is important for emergence in the field and for future weed infestations. This chapter focuses on seed biology, germination, dormancy and efforts in predicting weed emergence from seeds from a European perspective. It presents a brief overview of population dynamics in time and space, the factors influencing the dynamics and how population dynamics can be modelled. Emergence from the seed-bank starts with germination, pre-emergence growth and finally emergence. In addition to seeds, vegetatively propagated material is briefly mentioned. Dormancy influences under what conditions that germination can occur and regulates timing of germination. Population dynamics are important for understanding the whole system and are often based on the life-cycle of weeds: seed-bank, seedlings, adult plants, seed production and dispersal. Challenges in modelling emergence and population dynamics are large, due to differences between and within populations of species, variability in species response and there being many weed species in the same field with contrasting characteristics.
... There are around 18; 000 arable farmers in Denmark and around 2800 farmers with more than 100 ha [16]. In 2007, a survey among farmers was carried out to unravel some of the reasons for the relatively low number of subscribers among farmers [5]. The main conclusions were that the system did not exactly match the thinking or strategies of the majority of farmers. ...
Chapter
Crop Protection Online—Weeds (CPO-Weeds) is a decision support system for weed control developed in Denmark and later adjusted to conditions in several other countries. In Denmark, the DSS includes all major crops and available herbicides. The background for developing CPO-Weeds was a political motivation for reducing pesticide use and the concept of factor-adjusted doses. It was never the intention to build a sophisticated scientific model, but rather to design a simple user-friendly system. It is a knowledge-driven DSS, which offers herbicide dose suggestions based on a large database of the existing knowledge of herbicides and herbicide efficacies. The required weed control level in CPO-Weeds is based on expert evaluations, a herbicides dose-response model and an additive dose model to calculate possible mixtures of herbicides targeted a specific weed population. The herbicide dose model is a two parameter dose-response model, which is modified to include the effects of temperature, weed growth stage and influence of drought. The development has been driven by an ambition of offering a robust system with relatively low amounts of input variables and limited need for experimental parameter generation. CPO-Weeds offers overview and guidance for field specific spraying solutions, and the system has proved able to recommend herbicide doses with considerable reductions compared to label rates. Furthermore, CPO-Weeds offers a variety of tools that summarises knowledge of herbicides for a wide range of questions asked by practical weed managers, e.g. efficacy profiles of each herbicide, efficacy of users own herbicide mixtures, weed identification key and guidance for spraying strategy. The experiences have shown that even though CPO-Weeds are considered robust and trustworthy by both farmers and advisors there is a relatively low number of farmers subscribing to the system. A survey revealed that the DSS falls in between the strategies of many farmers; either the farmers relies completely on own experiences or advisory services or they considers the full crop rotation in their weed management. The latter is not supported by CPO-Weeds, which focus on a single season. The long term consequences of herbicide recommendations is only included in the need to limit input to soil seed bank. Another limiting factor for an increased practical use of CPO-Weeds is the need for field monitoring of weed populations, which can be a time consuming task and requires extensive weed recognising abilities of the farmer at the very early growth stages of weeds. The intention of CPO-Weeds was to provide recommendations for the full spraying season of a field, but experiences have shown that the system has several uses. Many farmers spray with a standard solution in the autumn in winter crops and then use the DSS for spring sprayings. The relatively simple input requirements also make the DSS suitable for teaching purposes and for farmers starting to grow new crops in their rotation as a learning tool.
... Trotz der Vielzahl der entwickelten Systeme werden diese Entscheidungshilfen von Landwirten relativ selten genutzt (JØRGENSEN et al., 2007) ...
Article
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To choose herbicides and their optimal dosages is a great challenge in chemical weed control in arable crops, because the choice of possible herbicide mixtures is big and economic consequences are hardly estimated. DSSHerbicide is a computer aided decision support system calculating cost-saving weed control measures. It is adapted to German conditions from the Danish Crop Protection Online system with German herbicide and weed data. To test the prototype of the system, fourteen herbicide trials were installed at conventional winter weed fields with differing weed densities in 2011 and 2012. Apart from the herbicide selection of DSSHerbicide recommendations from private advisers, official advisory service and farmers were implemented as test variables. The different ways of decision-making were investigated with the parameters chosen herbicides, herbicide costs, treatment frequency index, weeds left after treatments and wheat yield. Neither in herbicide costs, nor in treatment frequency index nor in yield significant differences between the decision support system on the one side and the expert advices on the other side were found. Biomass of weeds after treatments was significantly higher in the DSSHerbicide plots than in the plots of the official advisory service. In tendency, but not significantly, this led to higher yields in the plots of the official advisory service. Variation in herbicide costs and treatment frequency indices between the field trials was highest in the DSSHerbicide plots. A higher correlation of weed density before control and herbicide costs in the DSSHerbicide plots than in the expert plots indicates a field specific herbicide advice by the DSSHerbicide. The prototype worked with a limited number of implemented herbicides. Since herbicide advices of the decision support system were robust over the fourteen field trials, it will be worth implementing more herbicides in the system.
... Sutherland et al., 2011). The classifications found are based on various concepts: farming styles defined by Van der Ploeg (1994) (Busck, 2002;Schmitzberger et al., 2005); goals and values (Gasson, 1973;Maybery et al., 2005); decision rationale (Pedersen et al., 2012); decision making styles (Austin et al., 1998;Jørgensen et al., 2007;Primdahl, 1999); or simply types (Boon et al., 2004, typology based on importance of ownership objectives; Kristensen et al., 2004, typology based on farmers' socio-economic characteristics; Madsen, 2003, typology based on two continua: productionnature orientation and attachment to agriculture; Valbuena et al., 2008, typology based on farmers views on expanding and diversifying production). Thus, in most cases the classifications included in these studies could not be directly compared. ...
Article
Farmer decision making models often focus on the behavioral assumptions in the representation of the decision making, applying bounded rationality theory to shift away from the generally criticized profit maximizer approach. Although complex on the behavioral side, such representations are usually simplistic with respect to the available choice options in farmer decision making and practical constraints related to farming decisions. To ascertain the relevance of modeling different facets of farmer decision making, we developed an agent-based model of farmer decision making on crop choice, fertilizer and pesticide usage using an existing economic farm optimization model. We then gradually modified the model to include practical agronomic constraints and assumptions reflecting bounded rationality, and assessed the explanatory power of the added model components. The assessments were based on comparisons to the real world data and to the results of the previous model stages, and included two model versions differing with assumptions on the farmers' rationality. Thus, we assessed the sensitivity of the model to its behavioral assumptions. The results indicated that contrary to expectations, implementation of the practical constraints improved the model performance more than the modifications in the behavioral assumptions.
... One reason for not using the WelFur results might be that the farmers did not understand or agree upon the measures that were used to assess the animals' welfare, and did therefore not see the importance of the identified welfare challenges and the relevance for the practical situation of the farm (Jorgensen et al., 2007;Vaarst et al., 2011a). March et al. (2014) used information about the current health status of the farms as basic information for the discussions in stable schools. ...
Article
The aim of the study was to explore farmers' perception of stable schools as a tool to improve management for the benefit of mink welfare. Stable schools are knowledge exchange between farmers working towards a common goal, being able to give practical advice to each other. The concept is based on farmer field schools, developed and used in developing countries. Several Danish mink farmers are familiar with erfa-groups which also are farmers meeting, often with an advisor taking part, but the stable schools with only farmers and a facilitator have never been tested on mink farms. In 2013, we therefore established two stable schools with farmers from five Danish mink farms in each group. The meetings were on the respective farms, and every farm was visited once within a year. The host-farmer presented one success story and two challenges he/she wanted to work with and get contributions to from the group. Qualitative interviews were conducted with the farmers to evaluate their perception of stable schools.Based on the results from the study, and results from other studies of stable schools, we can conclude that farmers generally are positive to the structural way of working in stable schools, and that motivation for working towards a common goal is very important for the process of common learning among the farmers. The uniform production system at mink farms gives special challenges in how to work with the different subjects to ensure farmer ownership of the process. The farmers did not see the seasonal production as any constraint, but express that they like to work with the specific problems and challenges related to the respective production periods.
... The amount of cognitive effort is also related to the time spent on making a decision; the bounded rationality theory holds that the time spent on analysing given problem is limited. A study on barriers for use of decision support systems by farmers (Jørgensen et al., 2007) shows that a large proportion of farmers face lack of time to make proper use of the decision support system in question (in this case Crop Protection Online). One can conclude that if a farmer does not even have enough time to benefit from systems designed to assist him with decisions, then he would certainly not engage himself in an optimisation process that such a support system is based on. ...
Article
Environmental and agricultural policy instruments cause changes in land-use which in turn affect habitat quality and availability for a range of species. These policies often have wildlife or biodiversity goals, but in many cases they are ineffective. The low effectiveness and the emergence of unwanted side effects of environmental and agricultural policies are caused by over-simplistic assumptions in the design of policy instruments as well as difficulties with predicting behaviours of policy subjects. When considering wildlife in agricultural landscapes, policy's performance depends both on human (farmers) actions, which the policies aim to affect, and wildlife responses to land-use and management changes imposed by farmers. Thus, in order to design effective agri-environmental policies, detailed ex-ante assessments of both of these aspects are necessary. Due to the restrictive assumptions and technical limitations, traditional agricultural economic and ecological models fall short in terms of predictions of impacts of agri-environmental measures. The feedback situation between policy, human behaviour and ecological systems behaviour can confound these approaches, which do not take systems complexity into account. Therefore, a solution that integrates both feedback interactions and the differing scales at which these interactions take place is needed. For this, we suggest developing integrated policy assessment tools comprising of simulated farmer decision making, on-farm land-use and wildlife responses in the form of spatially explicit, dynamically connected agent-based models. Although complex and necessitating true inter-disciplinarity, these approaches have matured to the point where this endeavour is now feasible.
... Insecticides probably are less subject to economic considerations than other pesticides because they are inexpensive (Freier et al., 2013). In addition, carrying out individual field records is a major obstacle, especially for farms with large numbers and areas of fields due to the required time effort (Pluschkell, 1997;Jørgensen et al., 2007). ...
... It may span self, networks, organisations, and context (Tichy and Bennis, 2007). Knowledge may reflect science, experience or culture (Pannell et al., 2006), and may be oriented on systems, experience or advice (Jorgensen et al., 2007). However, simply experiencing an event does not equate to experience or to knowledge. ...
... Weed management decision-making is a complex task requiring integration of many factors. One way that growers and consultants can manage the integration of these factors is through the use of decision support systems (DSS) (Gonzalez-Andujar & Recio, 1996; Parker, 2004; Gonzalez-Diaz et al., 2009). DSS can provide a structure upon which farmers can base their decisions and offer ideas for tackling weed management problems. ...
Article
Gonzalez-Andujar JL, Fernandez-Quintanilla C, Bastida F, Calvo R, Izquierdo J & Lezaun JA (2011). Assessment of a decision support system for chemical control of annual ryegrass (Lolium rigidum) in winter cereals. Weed Research51, 304–309. Field studies were conducted at five locations in North and East Spain to evaluate the agronomic, economic and environmental implications of a decision support system (LOLIUM-PC) for herbicide control of Lolium rigidum in winter cereals. The chemical control treatments evaluated were as follows: (i) LOLIUM-PC-based recommendations, (ii) full herbicide dose (standard farmer practice) and (iii) half herbicide dose. Results indicate that there were no statistically significant differences, within sites, between treatments with respect to weed density, crop yield and economic return. On average, LOLIUM-PC resulted in less herbicide application compared with the full herbicide dose treatment. This also was true in three of five localities for the half-dose treatment. The results of this research indicate that LOLIUM-PC provides a flexible tool for recommending less herbicide than other herbicide decision systems, adequate weed control and similar crop yields to those obtained with the standard farmer practice (full dose) in Spain.
... Several factors were found to illustrate some of the characteristics of the three types (Table 6). None of the three groups of farmers were prepared to carry out very detailed field investigations in order to adjust the input according to need in specific fields (Langvad and Noe 2006; Jørgensen et al. 2007b). System-orientated strategists generally do not rely on exact field assessments. ...
Article
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Plant pathologists have traditionally worked in the area of clarifying and understanding the disease cycles of specific diseases, factors influencing epidemiology, yield loss potential and host-pathogen interactions in order to be able to minimise the disease risk, build warning systems or recommend specific control thresholds in relation to the application of fungicides. The decision support system Crop Protection Online (CPO) is an example of a threshold-based system that determines economically viable fungicide strategies. The system is based on using appropriate doses aimed at minimising the overall pesticide input. CPO is used widely by advisors and many of the thresholds are generally accepted and disseminated through newsletters. The national figures for the use of fungicides in cereals have shown a major reduction during the last 20years and their use today is much in line with the level that can be achieved from using CPO as indicated from validation trials. The number of end-users among farmers has been stable at around 3% during the last 10years (800–1,000 farmers). Major hurdles in increasing the number of users are believed to be: (1) the requirements for carrying out assessments in the field, (2) farm sizes getting larger, leaving less time for decision making for individual fields, (3) lack of economic incentives to change from standard treatments, (4) the failure of decision support systems to interact with other computer-based programmes on the farm, (5) the lack of compatibility of decision support systems with farmers’ ways of making decisions on crop protection in general, (6) the need for direct interactions with advisors. A sociological investigation into the farmers’ way of making decisions in the area of crop protection has shown that arable farmers can be divided into three major groups: (a) systems-orientated farmers, (b) experienced-based farmers and (c) advisory-orientated farmers. The information required by these three groups is different and has to be looked at individually from the end-user’s perspective rather than from the scientist’s perspective. New ways of entering the decision support system where specific field inspections are omitted and where regional disease data are relied on, have been investigated and tested in field trials. The results show possibilities for further developments in that direction, which might be one way of gaining more end-users.
Article
This datasheet on Decision Support Systems for Horticulture covers Identity, Overview, Description, Further Information.
Chapter
The introduction of Decision Support Systems (DSSs) in weed management poses an attractive option for creating improved and more environmentally friendly control strategies. The aim of the current study was to present key factors affecting decision-making process that need to be taken into account before developing a DSS in terms of weed management. First, attention should be paid to the effects of environmental factors and agronomic practices on weed emergence and the composition of the weed flora in an agricultural field. If weed emergence and timing of weed emergence could be predicted, then a DSS could make accurate suggestions for weed control. Secondly, to develop any weed management program, it is essential to have a deep understanding of weed biology and ecology. The biological traits of weeds, weed growth, the impact of weed competition during crucial growth stages for the crop should be estimated in order to optimize decision-making process. Moreover, a better understanding of seed production and weed seedbank dynamics into the soil would help experts develop DSSs able to provide management strategies also in the long-term period. However, these objectives are quite complex and need to be addressed in the near future. Furthermore, carrying out field surveys, hosting workshops, and group meetings in order to communicate with farmers and help them familiarize with the adoption of DSS methodologies. This is a vital step for persuading farmers to trust the use DSSs for the management of weeds in their fields. Further research and extended experimentation are needed in order to develop effective DSSs in terms of weed management under different soil and climatic conditions, always according to the special needs of each farmer.
Article
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Weeds are unwanted plants in a farm field and have harmful effects on the crops. Sometimes rigorous weeds bring down the crop yield significantly, causing huge losses to farmers. A prevalent method of controlling weeds is the use of chemical herbicides. These herbicides are known to cause harmful effects on our environment. One of the ways to control the ill effects of herbicides is to follow the Site-Specific Weed Management (SSWM). Site-specific weed management is to use the right herbicide for the right amount on agricultural land. This paper investigates a semantic segmentation approach to classify two types of weeds in paddy fields, namely sedges and broadleaved weeds. Three semantic segmentation models such as SegNet, Pyramid Scene Parsing Network (PSPNet), and UNet were used in the segmentation of paddy crop and two types of weeds. Promising results with an accuracy over 90% has been obtained. We believe that this can be used to recommend suitable herbicide to farmers, thus contributing to site-specific weed management and sustainable agriculture.
Article
Use of Knowledge Processing in agriculture has continuously increased, since the first era of knowledge based systems. Such software systems are used for support in detailed tasks, such as determining the amount of fertilizer in precision farming, as well as on high-level decision support, such as ‘what to plant in the next growing season’. Unfortunately, these software systems often have shortcomings in software quality. Applying design patterns is a recognized means to achieve better systems in terms of efficiency, flexibility, and quality. In this paper, several software design patterns are mapped to the context of knowledge processing systems in agriculture. Furthermore, additional patterns are identified and described. The need for patterns with focus on particular aspects of knowledge processing in agriculture is addressed, and an implementation is introduced as a proof of concept.
Chapter
Site-specific application of pesticides has so far focused mainly on herbicides. The purpose of precision farming technologies in relation to herbicide use is to reduce herbicide cost and environmental impact from spraying, but at the same time to achieve acceptable weed control. Another purpose is to increase the spraying capacity, to reduce the number of sprayer refills, and finally to minimize time spent on weed monitoring. In this chapter the relevance and profitability of four precision herbicide application technologies, two weed detection technologies and a low dose decision support system (DSS) is analysed. With a low dose herbicide, cost can be reduced by 20–50%. It requires, however, proper monitoring of weeds, which can be a time-consuming task that again requires that the farmer is able to identify the dominant weed species. The current development of high-speed camera and software systems can help to detect and map individual weeds, and some systems have proved to be cost effective for certain weeds.
Article
Flystrike in sheep, mostly caused by Lucilia sericata (Diptera: Calliphoridae), has been consistently identified as one of the most important sheep diseases from both a financial and welfare perspective (Bennett and others 1999, Bennett 2003, Bennett and Ijpelaar 2005, Boyne and others 2006). Infestation levels vary greatly depending on a wide range of factors related to the composition of the parasite fauna, the host, animal husbandry and control practices, climate and geography. However, it has been predicted that the season for flystrike will change (Wall and others 2011), which, anecdotally, appears to be the case (Anon 2012). Variation in the occurrence of flystrike in sheep, from year to year and area to area (Bisdorff and others 2006), means that traditional preventative programmes are often not as effective as they used to be. A report in 2013, commissioned by the pharmaceutical industry (Wall and others 2013), identified three key issues experienced by farmers: unpredictable weather patterns that make the timing of blowfly treatment difficult, increased risk of treatment resistance and the problem of treating parasites too late in the season. Early use of appropriate compounds for the prevention of flystrike aids effective control (Walters and Wall 2012). With the aim to help farmers tailor their flystrike control programmes, a simple website was developed (http://www.flystrikealert.co.uk/). The objective was for British farmers to be able to anonymously report when they encountered cases of flystrike in their flock. The basic details to be submitted are date of detection; location to postcode district level (first four characters); age group (lamb, ie, 1 year) and severity level (minor 25 per cent flock). To avoid spurious usage, the CPH (County, Parish, Holding) number has to be entered for a record to be validated, although this …
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The health, environmental and socio-economic issues related to the massive use of plant protection products are a concern for all the stakeholders involved in the agricultural sector. These stakeholders, including farmers and territorial actors, have expressed a need for decision-support tools for the management of diffuse pollution related to plant protection practices and their impacts. To meet the needs expressed by the public authorities and the territorial actors for such decision-support tools, we have developed a technical-economic model “OptiPhy” for risk mitigation based on indicators of pesticide toxicity risk to applicator health (IRSA) and to the environment (IRTE), under the constraint of suitable economic outcomes. This technical-economic optimisation model is based on linear programming techniques and offers various scenarios to help the different actors in choosing plant protection products, depending on their different levels of constraints and aspirations. The health and environmental risk indicators can be broken down into sub-indicators so that management can be tailored to the context. This model for technical-economic optimisation and management of plant protection practices can analyse scenarios for the reduction of pesticide-related risks by proposing combinations of substitution PPPs, according to criteria of efficiency, economic performance and vulnerability of the natural environment. The results of the scenarios obtained on real ITKs in different cropping systems show that it is possible to reduce the PPP pressure (TFI) and reduce toxicity risks to applicator health (IRSA) and to the environment (IRTE) by up to approximately 50 %.
Chapter
This chapter reviews how farmers in Australia gain information and make decisions about their rainfed farming systems. It examines the roles of consultants, farmer groups and decision support systems (DSS) in assisting farmers as their systems adjust in response to changes in their external environment. A specific DSS, Yield Prophet®, is discussed in terms of its development in conjunction with two farming systems groups and their consultants.
Conference Paper
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Improved climate risk decision-making and management in agriculture is critical to the well-being and long-term sustainability of farming communities and future global food security. Decision-making on farms often makes assumptions about seasonal conditions and weather events over the cropping season. Projected climate change and increasing climate variability are likely to pose increasing challenges to the productivity and profitability of farming systems. Hence, better understanding of climate information may improve farmers’ ability to plan for climate risk. Digital technologies offer an important alternative in the delivery and communication of agricultural information, complementing and expanding the reach of conventional agricultural extension services. Sophisticated digital platforms and their applications in learning environments offer new opportunities which may significantly enhance agricultural knowledge exchange. This paper reports on a project undertaken by the Australian Digital Futures Institute and the International Centre for Applied Climate Sciences, both at the University of Southern Queensland, to develop and evaluate a web-based virtual ‘discussion-support’ system that integrates climate information with practical farming operations in Australian sugar farming systems. Customized video clips (machinima), created in the Second Life virtual world environment, use lifelike avatar actors to model conversations about climate risk and key farm operational decisions relevant to sugarcane farmers. Designed to be readily available online, this innovative approach is designed to provide more equitable and cost-effective access to targeted climate information as well as improved learning and decision-making opportunities at local, regional, national and even global scales.
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The present major agricultural issues are to feed the world and reduce negative environmental impacts. To this end, organic farming appears as a promising solution. However organic farming has several drawbacks such as difficult weed management. Indeed weeds can reduce crop yields. Therefore there is a need for improved decision support tools for weed management in organic farming. An existing weed competition model actually predicts the effect of early multispecies weed density, both on organic wheat yield loss and on the weed density at flowering stage. However main existing models do not take into account the activity of end-users, e.g. farmers, during model design. Therefore we analysed weed information acquisition by farmers using the dynamic environment theory to design a decision support system that takes into account end-users. We interviewed eight French organic farmers. We analysed interview data using a coding scheme inspired by dynamic environment theory. Our results show that weed quantity was the information most frequently collected by organic farmers both for short- and long-term crop management. This information was compatible with early weed density, the main input of the previously developed models. Findings also show that procedures for gathering information and processing depended on farmer profiles. We also show that a conceptual model based on dynamic situations and a coding structure were appropriate for taking into account the information elaborated by end-users. Finally we propose further design of a decision support system for tactical organic weed management using a participatory approach.
Article
Decision-support systems (DSSs) are interactive computer-based systems that help decision makers solve unstructured problems under complex, uncertain conditions. Experimental use of DSSs has resulted in improved disease suppression and lowered risks of crop damage. In many cases, it has also led to the use of smaller quantities of active substances, as compared with standard spraying practices. Hundreds of DSSs have been developed over the years and are readily available and affordable. However, most farm managers do not use them as part of their integrated pest management (IPM) practices. Since the mid-1980s, the author of this paper, together with numerous colleagues, has developed DSSs and decision rules for the management of diseases in a variety of crops, including extensive crops, such as wheat, sunflower, and pea; semi-intesive crops, such as pear, chickpea, cotton, and tarragon; and intensive crops, such as tomato, potato, cucumber, sweet pepper, carrot, and grapevine. Some of these systems were used widely, but others were not. This experience may allow us to draw general conclusions regarding the use of DSSs and decision rules. Possible explanations for the widely varying acceptance rates are presented, and the effects of anticipated changes in the agribusiness sector on the future use of DSSs are discussed. Expected final online publication date for the Annual Review of Phytopathology Volume 51 is August 04, 2013. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
Presentation
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This paper offers a categorisation of the different approaches of crop protection in sociology and economy and is structured according to the main themes that we could find in the literature: 1. The changes in farmers’ conceptions and practices regarding crop protection; 2. The economic aspects of innovation; 3. The interactions with consumers and civil society. This review allows us to enhance important themes for further research, such as the analysis of the changes towards more sustainable agricultures in terms of learning processes and trajectories, the study of the interactions between farmers, extension services, and researchers, as well as approaches that consider the agro-food system as a whole. This paper is a proposal to categorise the different approaches of crop protection in sociology and in economy rather than a proper literature review. The paper is structured according to the main themes that we could find in the literature: 1. The changes in farmers’ conceptions and practices regarding crop protection; 2. The economic aspects of innovation; 3. Taking into account the interactions with consumers and civil society. These themes have been much more documented regarding organic farming than regarding other strategies, be it IPM or other forms of conventional agriculture. Some of the reasons for this might be that organic farming has a rich historical background as well as relatively clear definitions, established rules and specific networks. We will not refer to organic farming here though; see (Lamine and Bellon, 2008) for a recent literature review on conversion to organic farming.
Article
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Gonzalez-Andujar JL, Fernandez-Quintanilla C, Bastida F, Calvo R, Gonzalez-Diaz L, Izquierdo J, Lezaun JA, Perea F, Sanchez Del Arco MJ & Urbano JM (2010). Field evaluation of a decision support system for herbicidal control of Avena sterilis ssp. ludoviciana in winter wheat. Weed Research50, 83–88. Two field studies were conducted in Central and Northern Spain over a total of five seasons to assess the usefulness of a decision support system (AVENA-PC) from agronomic, economic and environmental points of view on herbicidal control of Avena sterilis ssp. ludoviciana in winter wheat. The control treatments evaluated were: (i) AVENA-PC-based recommendations, (ii) full herbicide dose (standard farmer practice), (iii) half herbicide dose and (iv) no herbicide. The herbicide rates used in the AVENA-PC treatment averaged 65% and 30% lower than the full and half dose treatments respectively. AVENA-PC implementation controlled A. ludoviciana with similar efficacy as standard herbicide treatments. Nevertheless, it did support a reduction in relation to the non-herbicide treatment. Yields obtained with AVENA-PC were, in general, not statistically different to those obtained with herbicide treatments and were on average 69% higher than those in the no herbicide application strategy. Comparing AVENA-PC economic performance with the other treatments there were, in general, no significant statistical differences in Central Spain. In Northern Spain, all herbicide treatments had similar net returns, with there being no statistical differences between AVENA-PC and the herbicide treatments. However, there were differences recorded with the non-herbicide treatment. The results of this research indicate that AVENA-PC, due to its flexibility, may recommend less herbicide than the standard farmer practice, providing clear environmental benefits and adequate weed control with maintained crop yield and net returns similar to standard farmer practice.
Article
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A survey has been conducted of decisions support systems (DSS's) for weed control in Europe. 9 DSS's representing 7 countries were studied. These were all targeted at farmers, but they differed in decisions supported, in number of crops covered and in demonstrated impact. At a workshop in Denmark in March 2008, a set of 'best parts' / 'building blocks' from these DSS's suitable for unification of a European level was identified. These could form the basis for building and validating DSS's on a European level that meets requirements for robustness in production lines and which hold some potential for reducing dependency and / or use of herbicides. When some robust and potent DSS concepts have been identified, the production and exchange of data that support integrated decision algorithms and calculation models of such DSS should be co-ordinated on a European level.
Article
Use of low quality water for irrigation of food crops is an important option to secure crop productivity in dry regions, alleviate water scarcity and recycle nutrients, but it requires assessment of adverse effects on health and environment. In the EU-project "SAFIR1" a model system was developed that combines irrigation management with risk evaluation, building on research findings from the different research groups in the SAFIR project. The system applies to field scale irrigation management and aims at assisting users in identifying safe modes of irrigation when applying low quality water. The cornerstone in the model system is the deterministic "Plant-Soil-Atmosphere" model DAISY, which simulates crop growth, water and nitrogen dynamics and if required heavy metals and pathogen fate in the soil. The irrigation and fertigation module calculates irrigation and fertigation requirements based on DAISY's water and nitrogen demands. A Water Source Administration module keeps track of water sources available and their water quality, as well as water treatments, storage, and criteria for selection between different sources. At harvest, the soil concentrations of heavy metals and pathogens are evaluated and the risk to consumers and farmers assessed. Crop profits are calculated, considering fixed and variable costs of input and output. The user can run multiple "what-if" scenarios that include access to different water sources (including wastewater), water treatments, irrigation methods and irrigation and fertilization strategies and evaluate model results in terms of crop yield, water use, fertilizer use, heavy metal accumulation, pathogen exposure and expected profit. The management model system can be used for analysis prior to investments or when preparing a strategy for the season.
Chapter
The present paper presents the rationale for the use of pathogen surveys, inoculated and non-inoculated disease nurseries and varietal resistance characteristics in an integrated approach to control wheat yellow rust in Denmark. The non-inoculated disease observation plots, which gave valuable information about yellow rust at the year, site and variety level, served as the primary sample source for the pathogen survey revealing pathogen virulence dynamics. This survey was also the main source for isolates of new pathotypes, a prerequisite for the assessment of the resistance characteristics of varieties and breeding lines in inoculated nurseries, and the postulation of race-specific resistance genes. A simple grouping of varieties into four categories with respect to resistance to the current yellow rust population proved robust, and this grouping was used as a determinant in a web-based decision support system for pesticide applications in cereals, Crop Protection On-line (CPO). The interplay between the different research and survey activities in the integrated pest management (IPM) approach demonstrated the need for a coherent and long-term involvement at all stages from plant breeding to the official variety approval system, extension service and research in disease epidemiology and resistance genetics.
Article
In Norway, the new web-based warning system called VIPS aims to give open access to all the information needed for farmers to reduce their reliance on plant protection products. VIPS calculates warnings for 70 weather stations for several pests in selected fruits, vegetables and cereals. Registered users may adjust the climatic data used in the models and record field observations to get private warnings. They may also use the system to record farm practices. VIPS is unique in several aspects: (1) it has a general user interface for all crops and pests, and the user gets a quick overview of which pests to look out for; (2) all warnings are site-specific and linked to an authorized weather station which supplies validated meteorological data. The extension service supplies the validated biological data necessary to run the models; (3) the presentation is layered under the weather stations of each county. The first level gives information of danger (red), possible danger (yellow) and no danger (green) for each model (past 5 days/coming 5 days). The next three levels give information of the specific model, historical data and exact values of the input parameters used in the models for the calculations.
Article
A Danish decision support system (DSS) named Crop Protection Online (CPO) for integrated management of weeds in cereals and beet has been developed during the past 20 years. CPO is based on a model that runs in three main steps: model step 1 quantifies the level of weed control needed on a field level, model step 2 selects candidate herbicides and calculates dose rates to meet the need, and model step 3 calculates tank mixtures of herbicides with two to four mixing components, if advantageous. CPO has been developed in cereals and beet, and various prototype versions have been validated in 1679 field tests. CPO secured yield potentials, and the level of residual weeds was not increased when compared with reference treatments. The potential of CPO to reduce herbicide use has been observed in all model crops, but the potential was greatest in cereals. In spring cereal field trials highly infested with weeds, the present version of CPO suggested 35% of one full herbicide dose on average and in winter cereals CPO suggested 44% on average of one full dose. The results from validation trials demonstrate that CPO is capable of suggesting robust treatment options with a low input of herbicides. The system architecture of CPO has been exported to Poland and the Baltic countries, and the system is expected to be suitable for export to other countries too.
Article
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.
Twenty years' experience with reduced agrochemical inputs
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Twenty years' experience with reduced agrochemical inputs. HGCA R & D Conference on Arable Crop Protection in the Balance Profit and the Environment , Lincolnshire (GB)
  • Ln Jørgensen
  • P Kudsk
Jørgensen LN & Kudsk P (2006) Twenty years' experience with reduced agrochemical inputs. HGCA R & D Conference on Arable Crop Protection in the Balance Profit and the Environment, Lincolnshire (GB). http:// www.hgca.com/publications/documents/cropresearch/ Paper_16_Lise_Jorgensen.pdf [accessed on 14 June 2007].