Modelling the effects of weeds on crop production

In most quantitative studies on interplant competition, static regression models are used to describe experimental data. However, the generality of these models is limited. More mechanistic models for interplant competition, which simulate growth and production of species in mixtures on the basis of the underlying physiological processes, have been developed in the past decade. Recently, simulation models for competition between species for light and water were improved and a detailed version was developed for sugarbeet and fat hen (Chenopodium album L.). The model was validated with data sets of five field experiments, in which the effect of fat hen on sugarbeet production was analysed. About 98% of the variation in yield loss between the experiments (which ranged from –6 to 96%) could be explained with the model. Further analysis with the model showed that the period between crop and weed emergence was the main factor causing differences in yield loss between the experiments. Sensitivity analysis showed a strong interaction between the effect of the variables weed density and the period between crop and weed emergence on yield reduction. Different quantitative approaches to crop-weed competition are discussed in view of their practical applicability. Simulations of experiments, where both the weed density and the period between crop and weed emergence were varied over a wide range, showed a close relation between relative leaf cover of the weeds shortly after crop emergence and yield loss. This relation indicates that relative leaf cover of the weeds accounts for both the effect of weed density and the period between crop and weed emergence. This relation has the potential to be developed into a powerful tool for weed-control advisory systems.     

Effect of weed position on yield loss in soyabean and a comparison between relative weed cover and other regression models

The importance of the position of weeds with respect to crop rows in the determination of crop yield-weed density relationships and the usefulness of relative cover (RC) of the weeds as an explanatory variable were studied in soyabean [Glycine max (L.) Merrill] competing with two summer weeds with contrasting canopy structure (Xanthium strumarium L. ssp. italicum and Echinochloa crus-galli L.). The position of the weeds was of little importance in the relationship between yield loss and weed density. This information is important because published experiments have used different types of weed distribution (e.g. evenly distributed or sown in rows). For both weed species it was possible to obtain a single relationship between yield loss and RC for measurements made from 30 days after crop emergence to soyabean canopy closure. The competitive effect of the weeds appeared to be strictly related to RC, indicating that for weeds growing taller than the crop the main competitive factor may be the shading caused by the leaves of the weeds situated above the crop canopy.     

Comparison of empirical models depicting density of Amaranthus retroflexus L. and relative leaf area as predictors of yield loss in maize (Zea mays L.)

The outcome of crop-weed competition should be predicted as early as possible in order to allow time for weed control measures. Maize grain yield losses caused by interference from Amaranthus retroflexus L. (redroot pigweed) were determined in 1991 and 1992. The performance of three empirical models of crop-weed competition were evaluated. Damage functions were calculated based on the weed density or relative leaf area of the weed. In the yield loss-weed density model, values of I (percentage yield loss at low weed density) were relatively stable for similar emergence dates of A. retroflexus across years and locations. Estimated maximum yield loss (A) was more variable between locations and may reflect environmental variation and its effect on crop-weed competition, at least in 1991. The two-parameter yield loss-relative leaf area model, based on m (maximum yield loss caused by weeds) and q (the relative damage coefficient) gave a better fit than the single-parameter version of the model (which includes only q). In both relative leaf area models, the values of q varied between years and locations. Attempts to stabilize the value of q by using the relative growth rate of the leaves of the crop and weed were successful; however, the practical application of such relative leaf area models may still be limited owing to the lack of a method to estimate leaf area index quickly and accurately.     

Evaluation of three empirical models depicting Ambrosia artemisiifolia competition in white bean

The performance of three empirical models describing white bean yield loss (Y_L) from common ragweed competition was compared using field experiments from Staffa and Woodstock, both in Ontario, Canada, in 1991 and 1992. One model was based upon both weed density and relative time of emergence. The other two models described yield loss as a function of weed leaf area relative to the crop. The model based on both weed density and relative time of emergence best described the data sets. The predicted maximum yield loss (A) and the parameter for relative time of weed emergence (C) varied across locations and years whereas the yield loss at low weed density (I) was relatively more consistent across locations and years. Use of thermal time (base temperature=10^oC) rather than calendar days did not change the overall fit of the model, but reduced the value of the parameter for the relative time of weed emergence (C). The two parameter leaf area model accounting for maximum yield loss (m) gave a better fit to the data compared with the one parameter model. The relative damage coefficient (q) varied with time of leaf area assessment, location and year. Values of q calculated from relative leaf area growth rates of the crop and weed were similar to observed values. The relationship between q and accumulated thermal time was linear but varied with location and year. As management tools, models based upon relative leaf area have advantages over models based on density and relative time of emergence since the level of weed infestation needs only to be assessed once, whereas density and emergence time require frequent observations. The ability to assess accurately and quickly both the crop and weed leaf area, however, may limit the practical application of models based on leaf area. The inability of empirical models to account for year–to–year variation in environmental conditions was observed.     

Prediction of the competitive effects of weeds on crop yields based on the relative leaf area of weeds

For implementation of simple yield loss models into threshold-based weed management systems, a thorough validation is needed over a great diversity of sites. Yield losses by competition wsth Sinapis alba L. (white mustard) as a model weed, were studied in 12 experiments in sugar beet (Beta vulgaris L.) and in 11 experiments in spring wheat (Triticum aestivum L.). Most data sets were heller described by a model based on the relative leaf area of the weed than by a hyperbolic model based on weed density. This leaf area model accounted for (part of) the effect of different emerging times of the S. alba whereas the density model did not. A parameter that allows the maximum yield loss to be smaller than 100% was mostly not needed to describe the effects of weed competition. The parameter that denotes the competitiveness of the weed species with respect to the crop decreased the later the relative leaf area of the mustard was determined. This decrease could be estimated from the differences in relative growth rate of the leaf area of crop and S. alba. However, the accuracy of this estimation was poor. The parameter value of the leaf area model varied considerably between sites and years. The results strongly suggest that the predictive ability of the leaf area model needs to be improved before it can be applied in weed management systems. Such improvement would require additional information about effects of abiotic factors on plant development and morphology and the definition of a time window for predictions with an acceptable level of error.     

Modelling herbicide dose and weed density effects on crop:weed competition

The effects of a range of herbicide doses on crop:weed competition were investigated by measuring crop yield and weed seed production. Weed competitivity of wheat was greater in cv. Spark than in cv. Avalon, and decreased with increasing herbicide dose, being well described by the standard dose–response curve. A combined model was then developed by incorporating the standard dose–response curve into the rectangular hyperbola competition model to describe the effects of plant density of a model weed, Brassica napus L., and a herbicide, metsulfuron‐methyl, on crop yield and weed seed production. The model developed in this study was used to describe crop yield and weed seed production, and to estimate the herbicide dose required to restrict crop yield loss caused by weeds and weed seed production to an acceptable level. At the acceptable yield loss of 5% and the weed density of 200 B. napus plants m^–2, the model recommends 0.9 g a.i. metsulfuron‐methyl ha^–1 in Avalon and 2.0 g a.i. in Spark.     

Influence de la densité et de la période de concurrence de Solanum nigrum L. sur la tomate de semis direct, en relation avec le désherbage

Effects of density and period of competition by Solanum nigrum L. on direct seeded tomatoes in relation to weed control The effects of density and period of competition from Solanum nigrum L. were measured in direct seeded tomatoes given weed control treatments currently used in south-east France. S. nigrum emerging after a diquat treatment at the 2–3 leaf stage of the crop and thinned to low densities (<12.8 plants ha^?1) at the 5–6 leaf stage of the crop caused significant yield loss if left to compete with the crop until harvest. Yield reduction was smaller if the same weed densities were present only until the onset of flowering. The regression curves of yield on weed density differed as annual climatic variations affected sowing date and plant growth; a comparison between years was made using the relation ‘crop yield × weed biomass/crop biomass’. Significant interactions between weed density and period of competition were found with yield of both green and red fruit. For late sown crops with low densities of S. nigrum two weed control treatments at the 5–6 leaf stage and at the onset of flowering were sufficient to prevent yield loss.     

Critical periods of weed competition in cotton in Greece

Four experiments were conducted in central Greece during 1997 and 1998 to determine the late-season presence of weeds in cotton (Gossypium hirsutum L.) and the critical times for removing weeds. Experiments were conducted in natural, heavily infested cropland. The presence of weeds for more than 3 weeks after crop emergence caused significant reductions in crop growth and lint yields. However, weeds that emerged 11 weeks or more after crop emergence did not adversely impact yields. Total weed biomass increased with increasing time prior to weed removal. A weed-free period of 11 weeks after crop emergence was needed to prevent significant reductions in cotton height, biomass, number of squares, and yield. These results indicated that postemergence herbicides or other control measures should be initiated within 2 weeks after crop emergence to avoid significant yield reduction. For greater efficiency, soil-applied herbicides in cotton should provide effective weed control for at least 11 weeks. Curvilinear regression equations were derived to describe the relationship between critical periods of weed presence and cotton growth and fruit development.     

Effects of the relative time of emergence and the density of common lambsquarters (Chenopodium album) on corn (Zea mays) yield

Common lambsquarters (Chenopodium album) is one of the world's worst weeds. In order to study the competitive potential of single‐cross 704 corn (Zea mays) in competition with common lambsquarters at different relative times of emergence and density levels of the weed, an experiment was conducted in 2006 at the farm of the Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran. This experiment was designed as a split plot based on a randomized complete block design with three replications. The emergence time of the weed was considered at three levels (7 days and 14 days earlier than corn and simultaneously with corn) as the main plot, while the density of the weed was considered at six levels (0, 4, 8, 12, 16, and 20 plants per m²) as the subplot. The results showed a decrease in the grain yield and biomass of corn, as the emergence time of corn was delayed in comparison with the weed in a way that the maximum reduction was observed at the earlier emergence of the weed, compared to corn, and also at a high density of the weed. As the weed emerged earlier than corn, the rate of yield loss resulting from the first flush of weeds was not that high. However, with every few days that the weed emerged earlier than corn, the rate of yield loss became higher as the density of the weed increased to its maximum. The maximum reduction in the yield components was observed at 14 days earlier emergence of the weed, compared to corn, and at high densities, as the corn plants were overshadowed by the weed canopy and no ear was produced.     

Competition of sorghum cultivars and densities with Japanese millet (Echinochloa esculenta)

Field studies were conducted at two locations in southern Queensland, Australia during the 2003–2004 and 2004–2005 growing seasons to determine the differential competitiveness of sorghum (Sorghum bicolor L. Moench) cultivars and crop densities against weeds and the sorghum yield loss due to weeds. Weed competition was investigated by growing sorghum in the presence or absence of a model grass weed, Japanese millet (Echinochloa esculenta). The correlation analyses showed that the early growth traits (height, shoot biomass, and daily growth rate of the shoot biomass) of sorghum adversely affected the height, biomass, and seed production of millet, as measured at maturity. “MR Goldrush” and “Bonus MR” were the most competitive cultivars, resulting in reduced weed biomass, weed density, and weed seed production. The density of sorghum also had a significant effect on the crop's ability to compete with millet. When compared to the density of 4.5 plants per m², sorghum that was planted at 7.5 plants per m² suppressed the density, biomass, and seed production of millet by 22%, 27% and 38%, respectively. Millet caused a significant yield loss in comparison with the weed‐free plots. The combined weed‐suppressive effects of the competitive cultivars, such as MR Goldrush, and high crop densities minimized the yield losses from the weeds. These results indicate that sorghum competition against grass weeds can be improved by choosing competitive cultivars and by using a high crop density of >7.5 plants per m². These non‐chemical options should be included in an integrated weed management program for better weed management, particularly where the control options are limited by the evolution of herbicide resistance.     

Competition between annual weeds and vining peas grown at a range of population densities: effects on the crop

Competition between annual weeds and vining peas (Pisum sativum L.) at five target population densities between 11 and 194 plants/m² was examined by means of periodic destructive sampling of weedy and weed-free plots. A further area of each plot was cut and vined to assess yields. Weeds impaired vegetative development, particularly by reducing tillering in low density crops. This resulted in weedy plots having fewer pods per plant at harvest but a lower proportion of flat pods than weed-free plots. Weeds had no effect on numbers or weights of peas in full pods nor on tenderometer values of samples of vined peas. Adverse effects of weeds on the growth of individual crop plants decreased with increasing crop density. However, at lower crop densities many of the additional pods on weed-free plots contributed little to vined yield, while at higher densities, direct or indirect effects of weeds increased the problem of maintaining sufficient photosynthetic area during pod swelling to prevent pod abscission and poor ovule development. Regression analysis of yield on crop density and of yield on numbers of pods per plant showed that vined yield per hectare was reduced by weeds by a constant amount across the range of densities and numbers of pods examined. Vining throughput was also reduced in weedy as compared with weed-free crops, even on high density plots where little or no weed vegetation remained at harvest. In general, weed presence had effects similar to those caused by increasing crop density, but without the additional contribution to yield made by extra plants. Higher density crops suppressed weeds very effectively but were no less vulnerable to yield loss than those of lower density; they therefore merit just as much attention to effective weed control as crops suffering more visibly from competition by weeds.     

Competition between Ridolfia segetum and sunflower

Studies on competition between Ridolfia segetum Maris, and sunflower (Helianthemum annuus L.) were conducted at eight locations in southern Spain in 1990 and 1991. in order to define competition models and to estimate from these economic thresholds as affected by crop inputs and potential yields. Competition losses in sunflower crops ranged from 19% to 56% of weed–free yields. There were slightly better correlations between percentage sunflower reduction and weed density than with weed dry weight, (?0.66 and ?0.59, respectively). The weed competitive index, or sunflower crop dry weight reduction per unit dry weight of R. segetum, was 1.09. The percentage yield losses due to weed density (NPR_t) were fitted to multiple linear, quadratic, exponential and hyperbolic models. The hyperbolic equation, %RSY=100 (1+1/b*NPR_t)^?1, where b=0.14 and is the R. segetum competitive ability index, had the lowest error sum of squares (SSE), and gave the best biological explanation for the competition response. Early emergence (before mid–March) made weeds about 1.5 times more competitive than late emergence. The economic threshold to offset the cost of a shallow post–emergence tillage, assuming 70% control efficiency, ranged from about 2.5 plants m ^?2 for low–yielding crops(1200kgha^?1) to less than one plant m^?2 for higher–yielding crops (2800 kg ha^?1).     

Consequences of narrow crop row spacing and delayed Echinochloa colona and Trianthema portulacastrum emergence for weed growth and crop yield loss in maize

Echinochloa colona and Trianthema portulacastrum are weeds of maize that cause significant yield losses in the Indo‐Gangetic Plains. Field experiments were conducted in 2009 and 2010 to determine the influence of row spacing (15, 25 and 35 cm) and emergence time of E. colona and T. portulacastrum (0, 15, 25, 35, 45 and 55 days after maize emergence; DAME) on weed growth and productivity of maize. A season‐long weed‐free treatment and a weedy control were also used to estimate maize yield and weed seed production. Crop row spacing as well as weed emergence time had a significant influence on plant height, shoot biomass and seed production of both weed species and grain yield of maize in both years. Delay in emergence of weeds resulted in less plant height, shoot biomass and seed production. However, increase in productivity of maize was observed by delay in weed emergence. Likewise, growth of both weed species was less in narrow row spacing (15 cm) of maize, as compared with wider rows (25 and 35 cm). Maximum seed production of both weeds was observed in weedy control plots, where there was no competition with maize crop and weeds were in rows 35 cm apart. Nevertheless, maximum plant height, shoot biomass and seed production of both weed species were observed in 35 cm rows, when weeds emerged simultaneously with maize. Both weed species produced only 3–5 seeds per plant, when they were emerged at 55 DAME in crop rows spaced at 15 cm. Infestation of both weeds at every stage of crop led to significant crop yield loss in maize. Our results suggested that narrow row spacing and delay in weed emergence led to reduced weed growth and seed production and enhanced maize grain yield and therefore could be significant constituents of integrated weed management strategies in maize.     

Using a competition model to quantify the optimal trade-off between machine vision capability and weed removal effectiveness

The algorithm of an optical detection system was first investigated for its ability to correctly classify transplanted crops and weeds during the critical early stages of crop establishment and its robustness over a range of different crop species. The trade-off was then examined between increasing the sensitivity of the detection system vs. the possibility of, in doing so, misclassifying some crop plants as weeds and inadvertently removing them. This was achieved by running a competition model using parameters derived from the image analysis and assessing the outcome of scenarios in terms of yield. The optimum parameter values to maximize the detection of the crop and the optimum parameter values to maximize the detection of the weed appeared relatively insensitive to time of image capture or weed density. They also appeared insensitive for different crop species where the crop had similar growth habit. However, competition scenarios indicated that the detection system parameter settings to achieve optimum yields were sensitive to the competitive ability of the weed species. For Veronica persica, crop yield was more sensitive to accidental crop removal than from competition. In contrast, in the presence of Tripleurospermum inodorum, yield loss was more attributable to weed competition. Importantly, linking the detection system with the competition model illustrated the principle that optimum yield may not necessarily be obtained by maximizing weed removal or minimizing crop removal. This first example of combining a detection system with a competition model presents a new opportunity to quantify the sensitivity of image classification in terms of yield.     

Estimation of thresholds for weed control in Australian cereals

A non-linear model relating crop yield to the density of weeds was fitted to nine Victorian weeds to evaluate their competitive abilities. The weeds were: Acroptilon repens (L.) DC. (creeping knapweed), Chondrilla juncea L. (Skeleton weed, Raphanus raphanistrum L. (Wild radish), lolium rigidum Gaud. (Annual ryegrass), Lithospermum arvense L. (White iron weed), Brassica tournefortii Gouan (Wild turnip), Lamium amplexicaule L. (Deadnettle), Fumaria parviflora Lam. (White fumitory) and Amsinckia hispida (Ruiz & Pav.) I.M. Johnston (Amsinckia). Where more than one experiment was available for a weed, the net return for a herbicide treatment over a range of weed densities was calculated to obtain the economic threshold density. Generally, the economic threshold densities within a weed species were the same order of magnitude, except for the perennial Chondrilla Juncea L., For this species data were collected in years of contrasting rainfall. The model used here is discussed in view of the threshold approach currently used in continental Europe.     

杂草密度与作物产量损失的预测模型

通过对国内外多个用于杂草密度和作物产量损失关系的经验模型比较分析，并对１０组不同来源的杂草与作物竞争资料进行模拟，证明模型Ｙ_Ｌ＝ｄ／（ａ＋ｂｄ）具有实际的生物学意义，能确地描述多种杂草和多种作物间的竞争关系，预测杂草竞争对作物可能造成的危害和损失。     

Modelling interactions between herbicide dose and multiple weed species interference in crop–weed competition

The effects of a range of herbicide doses on crop–multiple weed competition were investigated. Competitivity of Galium aparine was approximately six times greater than that of Matricaria perforata with no herbicide treatment. Competitivities of both weeds decreased with increasing herbicide dose, being well described by the standard dose–response curve with the competitivity of M. perforata being more sensitive than that of G. aparine to a herbicide mixture, metsulfuron‐methyl and fluroxypyr. A combined model was then developed by incorporating the standard dose–response curve into the multivariate rectangular hyperbola competition model to describe the effects of multiple infestation of G. aparine and M. perforata and the herbicide mixture on crop yield. The model developed in this study was used to predict crop yield and to estimate the herbicide dose required to restrict crop yield loss caused by weeds to an acceptable level. At the acceptable yield loss of 5% and the weed combination of 120 M. perforata plants m^?2 and 20 G. aparine plants m^?2, the model recommends a mixture of 1.2 g a.i. ha^?1 of metsulfuron‐methyl and 120 g a.i. ha^?1 of fluroxypyr.     

Predicting the growth and competitive effects of annual weeds in wheat

The growth and competitiveness of 12 annual weed species were studied in crops of winter wheat, in which weeds were sown to give a wide range of plant densities. Weed growth patterns were identified; early species which senesced in mid-summer were less competitive than those with a growth pattern similar to that of the crop. Most species had little effect on crop yield in 1987, and this was attributed to a high crop den sity. Crop yield-weed density relationships for all species in 1988 and for Galium aparine in 1987 were well described by a rectangular hyperbola. Species were listed in the following competitive order based on the percentage yield loss per weed m^?2: Avena fatua > Matricaria perforata > Galium aparine > Myosotis arvenis > Poa trivialis > Alopecurus myosuroides > Stellaria media > Papaver rhoeas > Lamiumpur-pureum > Veronica persica > Veronica hederi-folia > Viola arvensis. Prediction of yield loss is discussed. The assumptions inherent in using Crop Equivalents (based on relative weights of weed and crop plants), are challenged; with intense competition, weed biomass at harvest failed to replace lost crop biomass, and harvest index was reduced. It is concluded that a competi tive index, derived from yield density relation ships, and expressed as the percentage yield loss per weed m^?2, is more likely to reflect the com petitive ability of a species than an index obtained from plant weights in the growing crop.     

A model of the cost of delay in spraying weeds in cereals

Using a previous model as a base, data derived in field trials in wheat ( Triticum aestivuort L.) and bailey ( Hordeum vulgare L.) were assessed to estimate the yield penalty, and resulting reduction in economic benefit, from progressive spraying of herbicides up to late crop tillering. Compared with early post-emergence application, yield penalties from delayed spraying began at early tillering. For median values of weed-free yield and weed density in the data set. the loss in potential yield increase when spraying at late tillering compared with early post-emergence was 71%, Crops with higher weed-free yield potential and with greater initial weed density showed a proportionately (as well as absolutely) larger yield penalty from a moderate delay in spraying, indicating an earlier and more intense onset of competition with weeds.     

Influence of intra-row cruciferous surrogate weed growth on crop yield in organic spring cereals

In Northern Europe, inter-row hoeing has become a popular tactic for controlling weeds in organic cereals. Hoeing is highly effective and can be implemented from crop emergence until stem elongation to maintain a nearly weed-free inter-row zone. However, hoeing has a lesser effect on weeds growing in the intra-row zone, where crop–weed proximity results in heightened competition. In the hoed cereal system, it is investigated whether tall-growing, competitive, cruciferous weeds in the intra-row zone affect crop biomass, yield and thousand kernel weight (TKW). An additive experimental design is employed to enable the fitting of rectangular hyperbolas, describing and quantifying the effects of increasing intra-row surrogate weed density on crop growth parameters. Regressions were studied under the influence of crop (spring barley and spring wheat), row spacing (narrow [12.5 or 15.0 cm] and wide [25.0 cm]) and nitrogen rate (50 and 100 kg NH₄-N/ha). Cruciferous surrogate weeds were found to impact crop yield and quality severely. For example, ten intra-row plants/m² of surrogate weed Sinapis alba reduced grains yields by 7%–14% in spring barley and by 7%–32% in spring wheat with yield losses becoming markedly greater in wheat compared to barley as weed density increases. Compared to wheat, barley limited yield and quality losses and suppressed intra-row weed growth more. Row spacing did not have a consistent effect on crop or weed parameters; in one of six experiments, the 25 cm row spacing reduced yields and increased intra-row weed biomass in wheat. Nitrogen rate did not affect crop or weed parameters. Results warrant the implementation of additional tactics to control intra-row weeds and limit crop losses.