Wild oat is a long-standing weed problem in Australian grain cropping systems, potentially reducing the yield and quality of winter grain crops significantly. The effective management of wild oat requires an integrated approach comprising diverse control techniques that suit specific crops and cropping situations. This research aimed to construct and validate a bioeconomic model that enables the simulation and integration of weed control technologies for wild oat in grain production systems. The Avena spp. integrated management (AIM) model was developed with a simple interface to provide outputs of biological and economic data (crop yields, weed control costs, emerged weeds, weed seedbank, gross margins) on wild oat management data in a cropping rotation. Uniquely, AIM was validated against real-world data on wild oat management in a wheat and sorghum cropping rotation, where the model was able to reproduce the patterns of wild oat population changes as influenced by weed control and agronomic practices. Correlation coefficients for 12 comparison scenarios ranged between 0.55 and 0.96. With accurate parameterization, AIM is thus able to make useful predictions of the effectiveness of individual and integrated weed management tactics for wild oat control in grain cropping systems.

Non-compliance with or misinterpretation of the International Regulations for Preventing Collisions at Sea (COLREGs) when assessing vessel encounters, and the lack of good communication between the vessels involved in a critical situation, are primary contributing factors in collisions. Vessels engaged in an encounter should be aware that they are part of the same scenario and situation, which can become critical. Sharing and contrasting their information about the encounter would help those responsible to take manoeuvring decisions in a consistent way. There are situations whose evaluation by the respective officers in charge of the navigational watch may diverge and lead to disagreements on the actions to be taken. If there is no proper inter-ship communication, a collision may result. This paper presents a proposal for safety communication implemented in a programmable system using common equipment (automatic identification system), and applies it to a case study of one such special situation, showing how it could help to reduce the risk of collision.

To predict weed emergence and help farmers make weed management decisions, we constructed a mathematical model of seed germination for green and redroot pigweed based on temperature and water potential (moisture) and expressing cumulative germination in terms of thermal time (degree days). Empirical observations indicated green pigweed germinated at a lower base temperature than redroot pigweed but the germination rate of redroot pigweed is much faster as mean temperature increases. Moisture limitation delayed seed germination until 23.8 C (green pigweed) or 27.9 (redroot pigweed); thereafter, germination was independent of water potential as mean temperatures approached germination optima. Our germination model, based on a cumulative normal distribution function, accounted for 80 to 95% of the variation in seed germination and accurately predicted that redroot pigweed would have a faster germination rate than green pigweed. However, the model predicted that redroot pigweed would germinate before green pigweed (in thermal time) and was generally less accurate during the early period of seed germination. The model also predicted that moisture limitation would increase, rather than delay, seed germination. These errors were related to the mathematical function chosen and analyses used, but an explicit interaction term for water potential and temperature is also needed to produce an accurate model. We also tested the effect of mean temperature on shoot elongation (emergence) and described the relationship by a linear model. Base temperatures for shoot elongation were higher than for seed germination. Shoot elongation began at 15.6 and 14.4 C for green and redroot pigweed, respectively; they increased linearly with temperature until the optimum of 27.9 C was reached. Elongation was dependent on completion of the rate-limiting step of radicle emergence and was sensitive to temperature but not moisture; hence, elongation was sensitive to a much smaller temperature range. Beyond mathematical changes, we are testing our model in the field and need to link it to ecophysiological, genetic, and spatially explicit population processes for it to be useful in decision support for weed management.

Field vegetables require large amounts of N and precision N management (PNM) may help increase their productivity, quality and profitability while reducing N leaching in the biosphere. Few studies investigated PNM for field vegetables. This may be explained by the great diversity in crops and cultivars which complicate the broad implementation of PNM discoveries. Field vegetables are often grown in histosols, which have unique properties such as quantity of N mineralized and the spatial pattern of organic soil depth. Finally, research gaps exist in the development of decision support systems adapted to field vegetable crops grown in histosols.

This paper discusses a conceptual design of a Ag Data service for the farm industry, compares it to desktops FMIS and discusses some of the main concepts this kind of system may include. Beginning with an introduction to the current situation and how the amount and size of the data is affecting the capacity to process it efficiently, on a personal computer desktop or other devices. Following with a description of the characteristics and components, presenting a case study to demonstrate the way it may function within a farm environment.

This paper presents a framework for the implementation of a decision support system that considers spatial, temporal and managerial factors in assessing the potential impact of crop amendments on the cost of a given production scenario. The proposed system includes a database and a numeric simulation model. The database is linked to previously recorded crop responses for a given agricultural input under different conditions while the numeric simulation model determines the probability of different levels of profit for each decision option. This system then determines the optimal uniform rate of application of an amendment to maximize profits, or to define the range of such rates for a case of variable rate application. Uncertainty-based treatment of each model input allows for a balance between the potential results of under-application or over-application.

The need for coordinated regional and global electronic databases to assist prevention, early detection, rapid response, and control of biological invasions is well accepted. The Pacific Basin Information Node (PBIN), a node of the National Biological Information Infrastructure, has been increasingly engaged in the invasive species enterprise since its establishment in 2001. Since this time, PBIN has sought to support frontline efforts at combating invasions, through working with stakeholders in conservation, agriculture, forestry, health, and commerce to support joint information needs. Although initial emphasis has been on Hawaii, cooperative work with other Pacific islands and countries of the Pacific Rim is already underway and planned.

Row crop weed management decisions can be complex due to the number of available herbicide treatment options, the multispecies nature of weed infestations within fields, and the effect of soil characteristics and soil-moisture conditions on herbicide efficacy. To assist weed managers in evaluating alternative strategies and tactics, three computer programs have been developed for corn, cotton, peanut, and soybean. The programs, called HADSS (Herbicide Application Decision Support System), Pocket HERB, and WebHADSS, utilize field-specific information to estimate yield loss that may occur if no control methods are used, to eliminate herbicide treatments that are inappropriate for the specified conditions, and to calculate expected yield loss after treatment and expected net return for each available herbicide treatment. Each program has a unique interactive interface that provides recommendations to three distinct kinds of usage: desktop usage (HADSS), internet usage (WebHADSS), and on-site usage (Pocket HERB). Using WeedEd, an editing program, cooperators in several southern U.S. states have created different versions of HADSS, WebHADSS, and Pocket HERB that are tailored to conditions and weed management systems in their locations.

Remote sensing and associated spatial technologies provide tremendous opportunity to enhance weed management and improve–protect the environment through judicious use of the most efficacious control methods for a given site. They can also be invaluable assets for detection of invasions, assessment of infestation levels, monitoring rate of spread, and determining the efficacy of mitigation efforts for weed management. In combination with other technologies such as global positioning systems and geographic information systems, sampling strategies can be devised to efficiently determine the location of weed populations in agricultural and wildland situations. Maps created from remote sensing or sampling (or both) allow site-specific weed management of only the areas requiring corrective action. Potential benefits to the land managers and the ecosystem as a whole will come from reductions in inputs, reduced environmental liability from the detrimental effects of applying control measures to entire areas, crop yield increases through better management decisions, and early detection and effective management of invading species. Improvements in spatial and spectral resolution, temporal frequency, image turnaround time, and cost of image acquisition, combined with the realization of the value of the data, are enhancing the acceptance and usage of remote sensing technologies. However, remote sensing will be best used by providing accurate, site-specific data that can be converted into information used by decision support systems (DSSs). Advances in these DSSs, and their ability to incorporate remote sensing data, have been rapid and widespread in the past 10 yr. As a result, federal management and research agencies, academic institutions, and private entities have collectively developed efforts to use this information in monitoring and management efforts for invasive species in western rangelands, aquatic ecosystems and forestry, and site-specific weed management in agronomics.

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.

Decision-making processes must indicate if, how, and when weed control should be practiced. So far, Decision Support Systems (DSSs) for weed control to prevent crop yield losses can guide decisions on “if” and “how.” Experience shows that farmers need a DSS that can also guide when to treat, but this can only be done if the actual weed density observed in the field is known during the crop cycle. Emergence models allow the prediction of daily density, but precision depends on the survey date. This study focuses on the estimation of the date of the survey for the best prediction of the daily density throughout the crop cycle. The predicted daily density of each species can be used by DSSs without any further survey, saving time and money and improving the use of the DSSs. Results showed that the best date is when the actual density of each weed reaches or exceeds 50% emergence, and this is earlier than the critical point date, supporting the validity of the date estimation method. The possibility to provide specific advice for farmers considering a proper mortality rate of weed seedlings is then discussed. The ability to optimize the date of sampling can improve the reliability of decision-making tools for integrated weed management, in agreement with the European Union goal of sustainable use of pesticides and more environmentally sustainable cropping systems through the use of integrated pest management.

Decision support systems (DSSs) have been developed to assist producers and consultants with weed management decisions. WeedSOFT is a DSS currently used in several states in the north-central region of the United States. Accurate estimates of crop yield loss due to weed interference are required for cost-effective weed management recommendations. WeedSOFT uses competitive indices (CIs) to predict crop yield loss under multiple weed species, weed densities, and relative times of weed emergence. Performance of several WeedSOFT versions to predict soybean yield loss from weed competition was evaluated using CI values in WeedSOFT version 9.0 compared to new CI values calculated from weed dry matter, weed volume, and soybean yield loss in two soybean row spacings (19 and 76 cm) and two relative weed emergence times (at soybean emergence and first trifoliate leaf stage). Overall, new CI values improved predictions of soybean yield loss by as high as 63%. It was especially true with using new CI values based on yield loss compared to those based on weed dry matter or weed volume. However, there were inconsistencies in predictions for most weed species, suggesting that additional modifications are needed to further improve soybean yield loss predictions.