Agriculture. Genetically modified crops and farmland biodiversity

The debate about genetically modified crops that are resistant to herbicides and their effects on the environment rages on. In their Perspective, Firbank and Forcella discuss a new model (Watkinson et al.) that seeks to calculate the impact of genetically modified herbicide-resistant sugar beet on growth of the weed Chenopodium album and on the skylark, which feeds on the seeds of this weed.     

Describing Polygonum aviculare emergence in different tillage systems

Effects of four tillage systems (direct drill, subsoiler, chisel plough and mouldboard plough) on the dynamics of Polygonum aviculare populations were studied over three growing seasons. Cumulative emergence on a weekly basis was determined. Cumulative emergence from two years of chisel ploughing was used to develop an emergence model for P. aviculare based on hydrothermal time. Results showed that direct drilling, which had the highest seed yields of winter cereal crops every season, was the unique soil management system that lowered P. aviculare populations because of effective weed emergence reduction. The model accurately described seedling emergence in different tillage systems, although it failed in direct drilling, probably due to very low numbers of emerged seedlings. To better control this weed, direct drilling may be the best tillage option, but if this cannot be implemented, the hydrothermal time model is a practical tool that can describe the relative proportions of emergence and assist in the timing for management operations of P. aviculare in different tillage systems.     

Evaluating field‐scale sampling methods for the estimation of mean plant densities of weeds

The weed flora (comprising seven species) of a field continuously grown with soyabean was simulated for 4 years, using semivariograms established from previous field observations. Various sampling methods were applied and compared for accurately estimating mean plant densities, for differing weed species and years. The tested methods were based on (a) random selection wherein samples were chosen either entirely randomly, randomly with at least 10 or 20 m between samples, or randomly after stratifying the field; (b) systematic selection where samples were placed along diagonals or along zig‐zagged lines across the field; (c) predicted Setaria viridis (L.) P. Beauv seedling maps which were used to divide the field into low‐ and high‐density areas and to choose the largest sample proportion in the high‐density area. For each method, sampling was performed with 5–40 samples. Systematic methods generally resulted in the lowest estimation error, followed by the random methods and finally by the predicted‐map methods. In case of species over‐ or under‐represented along the diagonals or the zig‐zag sampling line, the systematic methods performed badly, especially with low sample numbers. In those instances, random methods were best, especially those imposing a minimal distance between samples. Even for S. viridis, the methods based on predicted S. viridis maps were not satisfactory, except with low sample numbers. The relationships between sampling error and species characteristics (mean density, variability, spatial structures) were also studied.     

Validation of predictive empirical weed emergence models of Abutilon theophrasti Medik based on intercontinental data

Good weed management relies on the proper timing of weed control practices in relation to weed emergence dynamics. Therefore, the development of models that predict the timing of emergence may help provide growers with tools to make better weed management decisions. The aim of this study was to validate and compare two previously published predictive empirical thermal time models of the emergence of Abutilon theophrasti growing in maize with data sets from the USA and Europe, and test the hypothesis that a robust and general weed emergence model can be developed for this species. Previously developed Weibull and Logistic models were validated against new data sets collected from 11 site-years, using four measures of validation. Our results indicated that predictions made with the Weibull model were more reliable than those made with the Logistic model. However, Weibull model results still contained appreciable biases that prevent its use as a general model of A. theophrasti emergence. Our findings highlight the need to develop more accurate models if the ultimate goal is to make more precise predictions of weed seedling emergence globally to provide growers with universally consistent tools to make better weed management decisions.     

An investigation to enhance understanding of the stimulation of weed seedling emergence by soil disturbance

Enhanced understanding of soil disturbance effects on weed seedling recruitment will help guide improved management approaches. Field experiments were conducted at 16 site‐years at 10 research farms across Europe and North America to (i) quantify superficial soil disturbance (SSD) effects on Chenopodium album emergence and (ii) clarify adaptive emergence behaviour in frequently disturbed environments. Each site‐year contained factorial combinations of two seed populations (local and common, with the common population studied at all site‐years) and six SSD timings [0, 50, 100, 150, 200 day‐degrees (d°C, base temperature 3°C) after first emergence from undisturbed soil]. Analytical units in this study were emergence flushes. Flush magnitudes (maximum weekly emergence per count flush) and flush frequencies (flushes year^?1) were compared between disturbed and undisturbed seedbanks. One year after burial, SSD promoted seedling emergence relative to undisturbed seedbanks by increasing flush magnitude rather than increasing flush frequency. Two years after burial, SSD promoted emergence through increased flush magnitude and flush frequency. The promotional effects of SSD on emergence were strongest within 500 d°C following SSD; however, low levels of SSD‐induced emergence were detected as late as 3000 d°C following SSD. Accordingly, stale seedbed practices that eliminate weed seedlings should occur within 500 d°C of disturbance, because few seedlings emerge after this time. However, implementation of stale seedbed practices will probably cause slight increases in weed population densities throughout the year. Compared with the common population, local populations exhibited reduced variance in total emergence measured within sites and across SSD treatments, suggesting that C. album adaptation to local pedo‐climatic conditions involves increased consistency in SSD‐induced emergence.     

United States Department of Agriculture-Agricultural Research Service research on pest biology: weeds

Over 125 permanent full-time scientists conduct research within the USDA Agricultural Research Service (ARS) on issues related to weeds. The research emphasis of most of these scientists involves ecology and management or biological control of weeds. Many scientists perform research on weed biology as components of their primary projects on weed control and integrated crop and soil management. Describing all ARS projects involved with weed biology is impossible, and consequently only research that falls within the following arbitrarily chosen topics is highlighted in this article: dormancy mechanisms; cell division; diversity of rangeland weeds; soil resources and rangeland weeds; poisonous rangeland plants; horticultural weeds; weed traits limiting chemical control; aquatic and semi-aquatic weeds; weed/transgenic wheat hybrids; seedbanks, seedling emergence and seedling populations; and weed seed production. Within these topics, and others not highlighted, the desire of ARS is that good information on weed biology currently translates or eventually will translate into practical advice for those who must manage weeds.     

Predicting field weed emergence with empirical models and soft computing techniques

Seedling emergence is one of the most important phenological processes that influence the success of weed species. Therefore, predicting weed emergence timing plays a critical role in scheduling weed management measures. Important efforts have been made in the attempt to develop models to predict seedling emergence patterns for weed species under field conditions. Empirical emergence models have been the most common tools used for this purpose. They are based mainly on the use of temperature, soil moisture and light. In this review, we present the more popular empirical models, highlight some statistical and biological limitations that could affect their predictive accuracy and, finally, we present a new generation of modelling approaches to tackle the problems of conventional empirical models, focusing mainly on soft computing techniques. We hope that this review will inspire weed modellers and that it will serve as a basis for discussion and as a frame of reference when we proceed to advance the modelling of field weed emergence.