Vegetable allium crops are easily outcompeted by weeds, especially when directly seeded rather than grown from transplants or sets. The slow germination, low relative growth rate (RGR) and low, upright leaf canopy of alliums that never approaches total light interception, are innate features of these crops that make them weak competitors for light (see Table 4.5 and Fig.
4.1). The shallow, relatively sparsely branched root system is also ill-adapted to compete for water and nutrients against weeds (see Figs. 2.15 and 2.16). As an example, the poor competitive ability of leeks for light compared with celery, which has a higher seedling RGR and more horizontal foliage – and the consequences of these features for the suppression of groundsel (Senecio vulgaris) in these crops – is illustrated in Fig. 5.1 and Table 5.1 (Baumann et al., 2001). Ample water and nutrients, the elimination of all weeds but groundsel and the prevention of pests and diseases ensured that the effects shown in Fig.
5.1 and Table 5.1 resulted solely from light competition between the crops and this weed.
Experiments in the UK showed that the yield of spring-sown bulb onions exposed to competition from a natural arable weed flora on unweeded plots was only 3% of that on weed-free plots (Roberts, 1973). In these experiments the maximum relative growth rate recorded for onions was 0.12/day, whereas the weeds had a maximum of 0.18/day, and this occurred at an earlier and cooler stage of the growing season. The result of this, and the slower seedling emergence of the onions than some weeds, was that by early June the dry weight of weeds was 20-fold the crop dry weight.
The consequence of weed competition is, of course, low yield. Complete crop failure will occur if weed competition is not prevented in direct-sown Table 5.1. The effect of light competition from leeks or celery on the biomass of stands of the weed groundsel, Senecio vulgaris, which emerged at a range of dates (0, 10, 20, 30 and 40 days) after transplanting the crops. These weights were recorded 90 days after crop planting and are the means of three observations (SEM13) (from Baumann et al., 2001, Table 3).
Groundsel Biomass of groundsel (g/m2)
emergence day 0 10 20 30 40
Under leek 191 137 43 39.6 12.. 66
Under celery 165 62 7 0.6 0.01
Groundsel only 222 – – – –
Fig. 5.1. Fraction of light (PAR) interception by stands of leek ▫, celery ● and a leek–celery intercrop 䉭, growing from late May through to August in Switzerland.
Plant populations were 18 and 9 plants/m2for leek and celery, respectively, and the intercrop was alternating rows of the two species at the same within-row spacing as in their monocrops (from Baumann et al., 2001. Courtesy of Annals of Botany).
alliums. In addition, weeds cause severe problems at harvest for leafy crops like salad onions and leeks, and interfere with the drying and storage of bulb onions. Competition from weeds will reduce the mean diameter of onion and garlic bulbs, putting an increased portion of the crop into low-value, small-size grades. Weed competition will actually accelerate onion bulb initiation if photoperiods are approaching those required for bulbing (Shadbolt and Holm, 1956). Therefore, bulbs develop on younger and smaller plants than would otherwise be so, further reducing the size of the leaf canopy that supports bulb growth compared with that of a weed-free crop.
The acceleration of bulbing that can be caused by weed competition is probably due to decreases in the red:far-red ratio (R:FR) incident on leaves when shaded by weeds (see Fig. 4.33). The likely variation of weed competition from spot to spot within a field will contribute to increased variation in bulb or plant size at harvest. The presence of weeds within the leaf canopy of allium crops will restrict air flow through them, which is likely to increase relative humidity and prolong periods of leaf wetness, thereby increasing susceptibility to fungal leaf disease (see Table 5.5). For all these reasons, particularly those pertaining to quality and weed contamination at harvest, which will not be tolerated by the supermarket retailers that now sell the majority of vegetables in developed economies, there is generally a policy of ‘zero tolerance’ of weeds in allium vegetable crops (Grundy et al., 2003).
Researchers have investigated how long weeds can be left in allium crops without causing irretrievable losses in yield and, secondly, having controlled the weeds once, how long crops must be kept weed-free so that any subsequently emerging weeds do not lower yields. In experiments on bulb onions in the UK, weeds left until 4 weeks after crop emergence did not reduce yield but, if left longer than 6 weeks, bulb yield was reduced by 4% for every day their removal was delayed (Hewson and Roberts, 1973).
Such studies have led to the specification of a ‘critical period’ during which the crop must be kept weed-free to ensure there will not be a serious loss of yield caused by weed competition. Following a review of research on critical periods in vegetables crops, Grundy et al. (2003) concluded that the critical period after emergence or planting was 4–8 weeks in spring-drilled bulb onions, 5–7 weeks in spring-transplanted bulb onions and from 4 weeks onward in spring-drilled salad onions. These critical periods concern effects on yield but not weed impacts on harvesting or quality. If there is a high density of highly competitive weeds and temperatures are conducive to rapid growth, the critical period for drilled bulb onions may commence as soon as 2 weeks after emergence.
Therefore, the timing and duration of the critical period can vary with weed density, weed competitiveness and weather conditions, in particular temperature.
Dunan et al. (1996) took these factors into account in regression equations for the effect of duration of time before starting weeding on yields from commercial drilled bulb onion crops in irrigated fields in Colorado, USA.
Yields were expressed as relative to the maximum in each trial (weed-free plot yield), duration of competition (DC) was expressed in units of thermal time (TTUs) from sowing (see Chapter 4), thereby including effects of temperature as well as time, and weed competition was quantified as the ‘weed load’ (WL = the sum over all the species present of their plant densities multiplied by ‘their competitiveness indexes’). Different weed species were given a ‘competitiveness index’ based on the degree to which their presence had been found to lower maize yields. Up to 75% of the variation in relative yields between fields and seasons caused by weed competition was explained by regressions on DC and WL. For a weed flora of average competitiveness it took 220 TTUs (base temperature 7.2°C) before commencing weeding to cause a 5% decrease in relative yield with 20 weeds/m2, and 315 TTUs with five weeds/m2. Relative yields decreased at about these respective rates for each weed density as the thermal time before weeding was prolonged. Equation 5.1, which describes the response surface shown in Fig. 5.2 (Dunan et al., 1996, Fig. 2) was the most meaningful summary of the results:
Onion Relative Yield = 0.83 – {1.5.105 DC1.198
[WL/(1 + 0.2 WL)]} (Eqn 5.1)
Several dynamic simulation models of weed competition in allium crops have been developed (Dunan et al., 1999; Baumann et al., 2002; Grundy et al., 2005). These all model competition for light and therefore apply to the situation in crops without significant competition for water or nutrients, a reasonable assumption for irrigated crops. In these models, onions and weeds are assumed to grow as they would in the absence of competition until the leaf canopy approaches closure (i.e. when Leaf Area Index (LAI) exceeds unity).
Then, the fraction of the incident light that is apportioned to crop or weed species, and which therefore drives the subsequent growth of each species, is specified by rules considered adequate to describe the real situation. For example, in Dunan et al. (1999), growth rate in a competitive situation is the growth rate of an isolated plant at the prevailing temperature multiplied by a competition factor, cfi, appropriate to the species. The competition factor models the fraction of the total incident light that the species captures. This is determined by the leaf area index of the species (LAIi) weighted by its light extinction coefficient, ki, derived from its equivalent of Eqn 4.1, relative to the sum of such weighted LAIs for all the species in the competitive situation. Thus, for the ith species competing among a total of n species:
i=n
cfi= ki LAIi/(ki LAIi) (Eqn 5.2)
i=1
With these models it is possible to incorporate parameters that describe the growth rate of individual plants of a species (e.g. kiand the parameters that
describe relative growth rate and its response to temperature, and the partitioning of photosynthate into leaf area) into a prediction of how a competitive mixture of species will behave. These models can be used to simulate the effects of weeds or weed removal treatments on yields (see Fig. 5.3).
Models can be combined with economic information on the value of yield likely to be lost to competition from the weed species emerging in a crop and the cost of herbicide or hand-weeding treatments, thereby providing growers with a decision aid for weed control (Dunan et al., 1999). By using such a decision aid, growers can maximize their economic returns from weed control treat-ments and apply herbicides only when there is economic benefit, thus avoiding unnecessary environmental pollution.
Providing models are shown adequately to simulate reality, they can then be used to predict outcomes form a wide range of competitive scenarios much more economically than could be done by field experimentation. The predictions from Fig. 5.2. The effect of the intensity and duration of weed competition on yields of irrigated bulb-onions in Colorado, USA. The duration of competition is expressed as thermal time units (TTU) above a base temperature of 7.2°C from sowing, and weed loads were calculated from the population densities of the weeds present weighted by a competitiveness index for each species (see text). Yields were expressed relative to the maximum (weed-free) yield in each field (from Dunan et al., 1996. Courtesy of Weed Research).
these mechanistic competition models can be quite complex and may themselves need further simple descriptive models of the responses found to summarize their predictions (Baumann et al., 2002). The latter authors used their parameters for growth and light interception of leeks, celery and groundsel, grown separately, to model the consequences of growing leeks and celery as an intercrop on the yields of each crop species and on seed production by the weed. Thereby they were able to specify the optimum densities at which to interplant leeks with celery so that the stronger competitive ability of the latter could supplement the rather weak competitiveness of leeks and ensure good suppression of the weed while main-taining leek quality (i.e. ensuring leeks were of marketable size) and maximizing financial return per unit area of field. Weed suppression was expressed both in terms of the reduction in biomass of the weed and also the consequent reduction in the number of seeds shed by the weed. The fewer seeds a weed sheds the less seed it leaves in the soil weed seed-bank to infest future crops, a consideration of particular importance when managing weeds over the whole rotational cycle of crops, particularly in organic production (see below). These complex interactions are summarized graphically in Fig. 5.4.