The rapid development of herbicide resistance in weeds, and environmental imperatives, have forced the consideration of non-chemical tactics such as crop competition for weed management. This review of wheat–weed competition examines the plant traits associated with wheat competitiveness, and the opportunities for plant breeding or manipulating crop agronomy to differentially favour the growth of the crop. Many studies have proven that enhancing crop competitive ability can reduce weed seed production and crop yield loss, although a number of difficulties in conducting this research are identified and suggestions are made for improvement. It remains to be seen whether crop competitiveness will be considered as a priority by farmers and plant breeders. Farmers require precise information on the reliability of agronomic factors such as increased crop seeding rate or choice of variety for enhancing crop competitive ability in different environments. Plant breeders need to know which plant traits to incorporate in varieties to increase competitive ability. A thorough analysis of the benefits and costs of enhancing wheat competitiveness is needed. Competitive wheat crops should be available as part of reliable and economical integrated weed management packages for farmers.

SU MMARYIncreasing crop competitiveness using higher seeding rates is a possible technique for weed management in low input and organic farming systems or when herbicide resistance develops in weeds. A range of wheat seeding rates were sown and resulted in crop densities between 50-400 plants/m 2 (current recommendations are 100-150 plants/m 2 ) in the presence and absence of annual ryegrass (Lolium rigidum Gaud.) in three wheat cultivars at nine experiments in southern Australia. Wheat densities of at least 200 plants/m 2 were required to suppress L. rigidum and to a lesser extent increase crop yield across a wide range of environments (seasonal rainfall between 200-420 mm) and weed densities (50-450 L. rigidum plants/m 2 ). Doubling crop density of all cultivars from 100 to 200 plants/m 2 halved L. rigidum dry weight (averaged over all experiments) from 100 g/m 2 to about 50 g/m 2 . Higher crop densities gave diminishing marginal reductions in weed biomass, while cultivar differences in weed suppression were small. Grain yields ranged from 0 . 5 t/ha to over 5 t/ha depending on site and season. Maximum yields in the weed-free plots (averaged over environments and cultivars) were at 200 crop plants/m 2 , and yield declined only slightly by 4-5 % at densities up to 425 plants/m 2 . In the weedy plots grain yield continued to increase up to the highest density but at a slower rate. The percentage yield loss from weed competition was of a smaller magnitude than the suppression of L. rigidum by wheat. For example, 100 wheat plants/m 2 led to an average 23 % yield loss compared with 17 % at 200 plants/m 2 , and the probability of reduced crop grain size and increased proportion of small seeds was negligible at these densities. Cultivar differences in yield loss from weed competition were small compared with differences due to crop density. Adoption of higher wheat seed rates as part of integrated weed management is now strongly promoted to farmers.

Changes in the weed flora of agro-ecosystems can occur as long-term changes or temporary fluctuations in species composition. The rate at which weed population shifts occur depends on the selection pressure imposed, genetic variability among weed populations, plant characteristics and environmental factors. Agronomic practices associated with cropping systems including crop rotation, tillage, herbicide use, soil amendments, and mechanization of harvesting that impart a range of selection pressures on weed populations are discussed in this review. Widespread use of herbicides has had the greatest impact on weed selection in recent years. Evolution of herbicide resistant weeds presents an enormous challenge to farmers. Development of herbicide tolerant crops has provided another tool for farmers however the selection pressure on weeds and potential impact on weed population shifts will require judicious use of this technology. Simulation models provide an excellent opportunity to predict future weed population shifts in response to management practices. Further insight into future management changes on weed selection must proceed towards an investigation of the processes rather than the outcomes. In particular, this must involve an understanding of the ecological factors and processes that are likely to determine the weed responses to particular management regimes.

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.

Density:yield loss models rely on fixed coefficients, parameterized from a particular site and season to predict the impact of weeds on crop yields. However, the empiricism of this approach and failure to incorporate environmental effects, has major biological and economic implications. In this study, seasonal variability in wheat yield loss and associated economic costs from Avena spp. were quantitated. A competition experiment at Wagga Wagga, NSW, showed large seasonal differences in wheat yield loss from densities of Avena spp. across 2 years. Gross margins, simulated over a 51-year period, decreased as Avena spp. density increased and were more variable at low crop densities and higher weed densities. For example, at a density of 200 Avena spp. plants m )2 , coefficient of variation in crop gross margin (CV) was $AUS 47 ha )1 for a crop density of 200 wheat plants m )2 compared with a CV of $AUS 75 ha )1 for a crop density of 50 wheat plants m )2 . The value of yield loss predictions will be vastly improved by making parameter values in yield loss models a function of seasonal factors such as rainfall.