A simple model of crop loss by weed competition from early observations on relative leaf area of the weeds

A new simple empirical model for early prediction of crop losses by weed competition was introduced. This model relates yield loss to relative leaf area of the weeds shortly after crop emergence using the relative damage coefficient q as the single model parameter. The model is derived from the hyperbolic yield density relationship and therefore accounts for the effects of weed density. It is shown that the model also accounts for the effect of different relative times of weed emergence. A strong advantage of the approach is that it can be used when weeds emerge in separate flushes. The regression model described experimental data on sugar-beet – lambsquarters (Beta vulgaris L. –Chenopodium album L.) and maize-barnyard grass (Zea mays L. –Echinochloa crus-galli L.) competition precisely. The model describes a single relationship between crop yield loss and relative leaf area of the weeds over a wide range of weed densities and relative times of weed emergence. Possibilities for scientific and practical application of the model are discussed.     

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.     

Increased density and spatial uniformity increase weed suppression by spring wheat

It has been hypothesized that increased crop density and spatial uniformity can increase weed suppression and thereby play a role in weed management. Field experiments were performed over 2 years to investigate the effects of the density and spatial arrangement of spring wheat (Triticum aestivum) on weed biomass and wheat yield in weed-infested fields. We used three crop spatial patterns (normal rows, random and uniform) and three densities (204, 449 and 721 seeds m⁻²), plus a fourth density (1000 seeds m⁻²) in the random pattern. Increased crop density reduced weed biomass in all three patterns. Weed biomass was lower and crop biomass higher in wheat sown in the random and uniform patterns than in normal rows in both years. At 449 seeds m⁻², weed biomass was 38% lower in the uniform and 27% lower in the random pattern than in rows. There was evidence of decreasing grain yield due to intraspecific competition only at 1000 seeds m⁻². The results not only confirm that increasing density and increasing crop spatial uniformity increase the suppression of weeds, but also suggest that a very high degree of spatial uniformity may not be necessary to achieve a major increase in weed suppression by cereal crops. Rows represent a very high degree of spatial aggregation. Decreasing this aggregation increased weed suppression almost as much as sowing the crop in a highly uniform spatial pattern. While the random pattern produced as much crop biomass and suppressed weeds almost as well as the uniform pattern, the uniform pattern gave the highest yield.     

Anoda cristata control with glyphosate in narrow- and wide-row soyabean

Experiments evaluated the effect of glyphosate rate and Anoda cristata density, on crop and weed biomass and weed seed production in wide (70 cm) and narrow rows (35 cm) glyphosate‐resistant soyabean (Glycine max). Soyabean density was higher at 35 cm row spacing as an increase in planting rate in narrow‐row soyabean is recommended for producers in Argentina. Soyabean biomass at growth stage V4 (four nodes on the main stem with fully developed leaves beginning with the unifoliate leaves) was higher when grown on narrow than in wide‐rows but was not affected by the presence of A. cristata. At growth stage R5 (seed initiation – seed 3 mm long in a pod at one of the four uppermost nodes on the main stem, with a fully developed leaf and full canopy development), crop biomass was greater in narrow rows compared with wide rows with 12 plants m^?2 of A. cristata. In narrow‐row soyabean, a single application of a reduced rate of glyphosate maintained soyabean biomass at R5 and provided excellent weed control regardless of weed density. In wide‐row soyabean control was reduced at the high weed density. Regardless of row spacing, A. cristata biomass and seed production were severely reduced by half of the recommended dose rate of glyphosate but the relationship between biomass and seed production was not altered. Glyphosate rates as low as 67.5 g a.e. ha^?1 in narrow rows or 540 g a.e. ha^?1 in wide rows provided excellent control of A. cristata. To minimize glyphosate use, planting narrow‐row soyabean are effective where A. cristata density is low.     

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.     

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.     

Weed and fertility management using maize/soyabean intercropping in the north‐western Himalayas

Abstract

In a field experiment conducted on a moderately fertile silty clay loam soil in the north‐western Himalayas, growing one or two rows of soyabean (Glycine max L.) as an intercrop in maize (Zea mays L.) reduced weed numbers and weights significantly and increased maize yields. Sowing two rows of soyabean had a better effect than one row, with maize at a constant sowing density. The maize equivalent yield increased significantly with intercropping in the ratio 1:2 (maize:soyabean rows) as compared to maize alone or maize + soyabean in alternate rows, when all these trials were supplied with the full recommended amount of fertilizer. The maize yield in trials with half the recommended fertilizer amount in the intercropped system was similar to that of maize alone supplied with the full fertilizer dose. The weeds were effectively controlled and yields of maize and soyabean were increased with pre‐emergence application of alachlor at the rate of 2.5 kg a.i./ha or with three hand weedings.     

Predicting the competitive effects of weed and crop density on weed biomass, weed seed production and crop yield in wheat

Competition between winter-sown wheat and Viola arvensis Murray or Papaver rhoeas L. was studied in two experiments in two successive years. The effects of varying crop and weed density were modelled in terms of weed biomass over time, weed seed production and crop yield. Biomass model parameters, representing maximum weed biomass and intra- and interspecific competition, were obtained for different assessment dates, enabling biomass levels to be predicted during the two growing seasons. Weed biomass declined, and its maximum level was reached earlier, with increasing crop density. Intraspecific competition was higher in the absence than in the presence of crop, increasing with time and with weed density. Halving the wheat population increased June biomass of V. arvensis by 74% and of P. rhoeas by 63%. Crop yield losses with increasing weed density were greater with low than with medium and high crop populations. P. rhoeas was significantly more competitive than V. arvensis in both years. Weed biomass in 1989 responded more to reductions in crop density following the milder winter of 1988/89 than in the previous year; however crop yields were less affected in 1989 due to summer drought, restricting late weed growth and competition. Weed seed production was related to weed biomass; the progressive lowering of crop density increased seed production, and both species were very prolific in the absence of crop. By combining models, seed production could be derived for a given competitive effect on the crop. Threshold weed populations, based on low weed levels that are not economic to control, could then be equated with the accompanying weed seed production.     

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.     

Influence of sowing density and spatial pattern of spring wheat (Triticum aestivum) on the suppression of different weed species

To better understand the potential for improving weed management in cereal crops with increased crop density and spatial uniformity, we conducted field experiments over two years with spring wheat ( Triticum aestivum ) and four weed species: lambsquarters ( Chenopodium album ) , Italian ryegrass ( Lolium multiflorum ), white mustard ( Sinapis alba ), and chickweed ( Stellaria media ). The crops were sown at three densities (204, 449, and 721 seeds m⁻²) and in two spatial patterns (normal rows and a highly uniform pattern), and the weeds were sown in a random pattern at a high density. In most cases, the sown weeds dominated the weed community but, in other cases, naturally occurring weeds were also important. There were strong and significant effects regarding the weed species sown, the crop density, and the spatial distribution on the weed biomass in both years. The weed biomass decreased with increased crop density in 29 out of 30 cases. On average, the weed biomass was lower and the grain yield was higher in the uniform compared to the row pattern in both 2001 and 2002. Despite the differences in weed biomass, the responses of L. multiflorum , S. media , and C. album populations to crop density and spatial uniformity were very similar, as were their effects on the grain yield. Sinapis alba was by far the strongest competitor and it responded somewhat differently. Our results suggest that a combination of increased crop density and a more uniform spatial pattern can contribute to a reduction in weed biomass and yield loss, but the effects are smaller if the weeds are taller than the crop when crop–weed competition becomes intense.     

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.     

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.     

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.     

Cover crops and mulches influence weed management and weed flora composition in strip‐tilled tomato (Solanum lycopersicum)

This study was conducted in the Mediterranean environment of Central Italy from 2011 to 2013 with the aim of evaluating the effects of winter cover crops and their residues on weed composition in a cover crop‐tomato sequence. Treatments consisted of five soil managements (three cover crop species ‐ hairy vetch, phacelia, white mustard, winter fallow mulched with barley straw before tomato transplanting and conventionally tilled soil), two nitrogen fertilisation levels (0 and 100 kg N ha^?1) and two weed management levels (weed free and weedy) on tomato. Cover crop residues were arranged in strips on the soil surface and then used as beds for transplanting the tomato seedlings in paired rows. Rotary hoeing was performed in the bare strips between paired tomato rows. At tomato harvesting, the weed aboveground biomass and density was higher in nitrogen‐fertilised tomato than unfertilised tomato, except in hairy vetch and barley straw that showed similar values. Hairy vetch used as a cover crop and dead mulch was the most suppressive species with the highest production of residues, while phacelia and mustard were not suitable for controlling weeds. The tomato yield was high in nitrogen fertilised and weed‐free treatments, except in barley straw mulch, which showed similar values among the weed management treatments. The mulch strips caused variations in weed species composition that was mainly composed of perennial ruderal weeds, while in tilled soil, the weed flora was dominated by annual photoblastic weeds.     

Agronomic evaluation of precise mechanical hoeing and chemical weed control in sugar beet

Most herbicide applications to sugar beet (Beta vulgaris L.) are made to the whole crop area, but there is the opportunity to restrict applications to the crop row, decreasing the usage of herbicide by up to 70%. However, this would require greater use of mechanical weed control between rows. Experiments were performed in two seasons to evaluate the weed control performance of a novel, vision‐guided, inter‐row hoe in sugar beet crops grown on a peat fen soil. Hoe lateral placement was within ±30 mm. A precise hoeing and band spraying treatment was compared with overall herbicide use, and with treatments in which the herbicide applications were replaced by hand weeding to minimize competition between crop and weeds. Two hoe passes were made in each season, at crop growth stages of two and 10–12 true leaves in the first season and four and eight true leaves in the second season. Plant population density was not affected by treatment, indicating that none of the treatments caused crop plant loss. Use of the guided hoe controlled weeds better than overall spraying. Crop yields were not significantly different between treatments, indicating that weed control prevented competition with the crop in all treatments.     

Effects of density and sowing pattern on weed suppression and grain yield in three varieties of maize under high weed pressure

We tested the hypothesis that improved weed suppression by maize can be achieved through increased crop density and spatial uniformity. Field experiments on three varieties of maize sown at three densities (5, 7 and 10.5 seeds m^?2) and in two spatial patterns (grid pattern and rows) under very high weed pressure from Brachiaria brizantha were performed in 2012 and 2013. We measured weed biomass 1 month after sowing and at harvest, and grain yield at harvest. Density, variety and sowing pattern all had strong and significant effects on both weed biomass and yield. On average, weed biomass was reduced (by 72% in the first year and 58% in the second year), and grain yield was increased (by 48% and 44%) at the highest density in the grid pattern compared with standard sowing practices (medium density, row pattern). There was a significant density × variety interaction, which is evidence for genetic differences in the response of the varieties to density in characteristics that influence weed suppression. The variety that suppressed weeds best at high density had the lowest variation in the angle of insertion of the oldest living leaf at harvest (leaf 6), supporting the hypothesis that reduced phenotypic plasticity may be advantageous for weed suppression under high density and spatial uniformity. Increased density and uniformity can contribute to weed management in maize in many cases, potentially reducing the need for herbicides or mechanical weed control.     

Effect of crop density on competition by wheat and barley with Agrostemma githago and other weeds

The effect of Agrostemma githago L. and other naturally occurring weeds on biomass production and grain yield was studied in winter wheat and winter barley. Naturally occurring weeds had only a negligible effect on barley, but reduced wheat grain yield by 10% at a quarter of normal crop density. The interaction between the cereals and A. githago was studied in additive series employing different crop densities. Growth of this weed species was strongly dependent on crop density, which was more important for controlling weed growth than it was for obtaining a normal grain yield. Wheat and especially barley had a better competitive ability than A. githago. Wheat and A. githago utilized resources for growth better when grown in mixture than when grown in pure stands as the relative yield totals were significantly larger than unity.     

Peanut (Arachis hypogaea) response to weed and disease management in northern Ghana

Weeds and diseases can reduce peanut (Arachis hypogaea L.) yield or increase cost of production to maintain acceptable yield. While herbicides and fungicides have limited availability in many areas of Ghana and currently are too expensive for resource-poor farmers, control by these pesticides can have a major positive impact on peanut yield. Field experiments were conducted during the rainy seasons of 2009 and 2010 in northern Ghana to determine the effects of herbicide and hand weeding in combination with fungicides on pest management and peanut yield. Peanut pod yield was often more highly correlated with disease severity and canopy defoliation resulting from early leaf spot (caused by Cercospora arachidicola Hori) and late leaf spot (caused by Cercosporidium personatum (Berk. & M.A. Curtis) Deighton) than weed biomass. In some instances, less disease and canopy defoliation were observed when weeds were not controlled effectively compared with increased weed management through hand weeding or herbicide. Two hand weedings or applying pendimethalin preemergence with one hand weeding in combination with 4 applications of triadimefon and chlorothalonil resulted in the lowest weed density and canopy defoliation and often resulted in the highest peanut yield.     

Competition between safflower and weeds as influenced by crop genotype and sowing time

The effects of crop genotype and sowing time on competition between safflower (Carthamus tinctorius L.) and weeds were investigated in a 2-year field study. Each year, safflower was grown as a pure stand and in mixture with a natural weed infestation, mostly represented by Polygonum aviculare L., Fallopia convolvulus (L.) A. Löve and Chenopodium album L., in an additive design including weed stands grown without the crop. Grain yield reduction ranged from about 50% to 80% depending on crop genotype, and was higher under spring sowing (mid-March) than under winter sowing (mid-February). In general, those genotypes incurring the least yield reduction gave the greatest suppression of weed biomass. More competitive genotypes also tended to reduce the proportion of C. album in the weed biomass at harvest, particularly in 1994 (the wetter growing season). The competitive ability of the crop was mainly correlated with its biomass at early growth stages, but was not related to its grain-yielding ability in the absence of weeds. Results showed that more competitive crops may be obtained by sowing the most competitive genotypes early. Selecting for higher competitive ability in safflower does not seem to imply a reduction in grain-yielding ability.     

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.