Many studies of plant competition have been directed towards understanding how plants respond to density in monocultures and how the presence of weeds affects yield in crops. In this Botanical Briefing, the development and current understanding of plant competition is reviewed, with particular emphasis being placed on the theory of plant competition and the development and application of mathematical models to crop-weed competition and the dynamics of weeds in crops. By consolidating the results of past research in this manner, it is hoped to offer a context in which researchers can consider the potential directions for future research in competition studies and its application to integrated weed management.
Deen, W.; Cousens, R.; Warringa, J.; Bastiaans, L.; Carberrys, P.; Rebel, K.; Riha, S.; Murphy, C.; Benjamin, L. R.; Cloughley, C.; Cussans, J.; Forcella, F.; Hunt, T.; Jamieson, P.; Lindquist, John; and Wangs, E., "An evaluation of four crop : weed competition models using a common data set" (2003 An evaluation of four crop : weed competition models using a common data set SummaryTo date, several crop : weed competition models have been developed. Developers of the various models were invited to compare model performance using a common data set. The data set consisted of wheat and Lolium rigidum grown in monoculture and mixtures under dryland and irrigated conditions. Results from four crop : weed competition models are presented: ALMA-NAC, APSIM, CROPSIM and INTERCOM. For all models, deviations between observed and predicted values for monoculture wheat were only slightly lower than for wheat grown in competition with L. rigidum, even though the workshop participants had access to monoculture data while parameterizing models. Much of the error in simulating competition outcome was associated with difficulties in accurately simulating growth of individual species. Relatively simple competition algorithms were capable of accounting for the majority of the competition response. Increasing model complexity did not appear to dramatically improve model accuracy. Comparison of specific competition processes, such as radiation interception, was very difficult since the effects of these processes within each model could not be isolated. Algorithms for competition processes need to be modularised in such a way that exchange, evaluation and comparison across models is facilitated.
BackgroundThe importance of appropriate, accurate measurement and reporting of environmental parameters in plant sciences is a significant aspect of quality assurance for all researchers and their research. There is a clear need for ensuring research across the world can be compared, understood and where necessary replicated by fellow researchers. A common set of guidelines to educate, assist and encourage comparativeness is of great importance. On the other hand, the level of effort and attention to detail by an individual researcher should be commensurate with the particular research being conducted. For example, a researcher focusing on interactions of light and temperature should measure all relevant parameters and report a measurement summary that includes sufficient detail allowing for replication. Such detail may be less relevant when the impact of environmental parameters on plant growth and development is not the main research focus. However, it should be noted that the environmental experience of a plant during production can have significant impact when subsequent experiments investigate plants at a molecular, biochemical or genetic level or where species interactions are considered. Thus, researchers are encouraged to make a critical assessment of what parameters are of primary importance in their research and these parameters should be measured and reported.ContentThis paper brings together a collection of parameters that the authors, as members of International Committee on Controlled Environment Guidelines (ICCEG) in consultation with members of our three parent organizations, believe constitute those which should be recorded and reported when publishing scientific data from experiments in greenhouses. It provides recommendations to end users on when, how and where these parameters should be measured along with the appropriate internationally standardized units that should be used.
Weed Manager is a model-based decision support system to assist arable farmers and advisers in weed control decisions on two time scales: within a single season and over several years in a rotation. The single season decision is supported by a wheat crop and annual weed growth simulation, with a multi-stage heuristic decision model. The rotational aspect uses a model of seed population dynamics, with decisions optimised using stochastic dynamic programming. Each time scale has its own user interface within a single program integrated into the ArableDS framework, which provides data sharing between several decision support modules. Weed Manager was used by about 100 farmers and consultants in the 2005-2006 and 2006-20077 seasons.
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