Phenological patterns in an arable land weed community related to disturbance

The emergence pattern and life cycle of four major species growing in a non–irrigated almond tree grove were analysed in relation to ploughing frequencies and environmental factors. At the community level, the overall emergence pattern was found to be much the same whether or not the soil was disturbed. Nevertheless, soil disturbance in late winter and early spring produced peaks of seedling emergence and brought about an increase in germination. Winter annuals such as Lolium rigidum Gaudin and Diplotaxis erucoides (L.) DC., which emerged in the autumn, started to grow rapidly in winter and spring and were able to pre–empt the environmental resources of the habitat and suppress spring–germinating plants such as Chenopodium album L. and Amaranthus blitoides S. Watson. Late–winter and early–spring disturbances favoured the dominance of summer annuals such as C. album and A. blitoides S. Watson. The different ploughing regimes applied during the first year had effects on plant development and seed production which brought about changes in plant population size during the second year.     

Clonal growth and fragment regeneration of Rumex obtusifolius L.

Clonal growth and fragment regeneration of Rumex obtusifolius L. were analysed in two dif ferent studies. Clonal growth system was des cribed by a morphological study of underground structure of different-aged individuals, using maximal branching order in the stem system as an age estimator. Glasshouse experiments were conducted, testing the regenerative capacity of different below-ground parts and the estab lishment of above root-collar fragments planted at different depths under contrasting water regimes. Results showed the presence of a ‘phalanx’ type clonal growth system in grassland populations of Rumex. The main structure in volved in clonal growth proved to be the stem system; the region above the root collar was also the only part able to regrow after damage. Stem fragment regeneration occurred to depths of 15 cm, but was prevented in soils maintained at waterlogging and field capacity. The significance of these results in relation to nonchemical con trol of Rumex populations in grasslands is discussed.     

Population dynamics of Rumex obtusifolius under contrasting lucerne cropping systems

The population dynamics of Rumex obtusifolius L. was analysed in a lucerne:winter cereal crop rotation by means of a matrix population model that takes into consideration two crop rotation periods: the lucerne (Medicago sativa L.) cropping period and the cereal cropping period. Several transition matrices based on life-cycle stages were calculated for each cropping period using experimental data and were used in the construction of a model that analyses the population dynamics of R. obtusifolius under different harvest dates and lengths of lucerne cropping periods. Model projections showed that populations of R. obtusifolius increased during the lucerne cropping period regardless of harvest date and decreased during the cereal cropping period. Under a late harvest date, populations decreased at each crop rotation when lucerne was grown for 3 years, remained close to the equilibrium when lucerne was left to grow for 5 years, and increased for longer lucerne cropping periods. In contrast, populations of R. obtusifolius decreased even with a lucerne cropping period of 9 years under an early harvest date. The significance of these results in relation to the biology and the non-chemical control of the species is discussed.     

Modelling within‐field spatial variability of crop biomass – weed density relationships using geographically weighted regression

The objective of this study is to offer a new framework for exploring and modelling the spatial variation in crop biomass – weed density relationships, adapting geographically weighted regression (GWR) to include a non‐linear regression model. The relationship between crop biomass and weed density is usually modelled by non‐linear regression models, in which the spatial heterogeneity of the relationship is ignored, although the effect of weeds on crop can differ in relation to topographic and edaphic variability. GWR attempts to capture spatial variability by calibrating a regression model to each location in space. We show the application of the method in different cereal cropping systems, with one or two weed species. The results indicate that GWR can significantly improve model fitting over non‐linear least squares (NLS) in some situations. Furthermore, the parameter estimates can be mapped to illustrate local spatial variations in the regression relationship under study and eventually to relate the spatial variability of the model to the environmental heterogeneity. We discuss the value of the GWR for analysing the observed spatial variability and for improving model development and our understanding of spatial processes.