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.     

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.     

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 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.     

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.     

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.     

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.     

Prediction of competition between oilseed rape and Stellaria media

Ten experiments have investigated competition between winter oilseed rape and Stellaria media (common chickweed). Yield losses caused by this weed were often high, but differed greatly between experiments, 5% yield loss being calculated to be caused by 1.4–328 plants m^?2. Predictions of yield loss based on relative weed dry weights [weed dry weights/(crop + weed dry weights)] in December were somewhat less variable than those based on weed density, 5% yield loss being caused by 1.4–10.6% relative weed dry weight. The variations in yield loss were related to variations in the competitiveness of the oilseed rape and the S. media, caused by weather differences between years and sites, and the long period between weed assessment and harvest (8–10 months). However, despite the lack of precise relationships, there were indications that the greater the crop dry weights in December, the lower the final yield loss. Delayed sowing of oilseed rape until late September did not clearly increase the competitive effects of the weed compared with late August/early September sowings. Weed competition was not clearly affected by reduced crop density (44–113 plants m^?2), because of the compensatory ability of the lowest density. The results of the experiments are discussed in relation to the prediction of yield loss and, thus, possible adjustment of weed control strategies to meet expected crop losses.     

Modelling the effects of sub-lethal doses of herbicide and nitrogen fertilizer on crop–weed competition

The effects of sub‐lethal dose of herbicide and nitrogen fertilizer on crop–weed competition were investigated. Biomass increases of winter wheat and a model weed, Brassica napus, at no‐herbicide treatment with increasing nitrogen were successfully described by the inverse quadratic model and the linear model respectively. Increases in weed competitivity (β₀) of the rectangular hyperbola and parameter B in the dose–response curve for weed biomass, with increasing nitrogen were also successfully described by the exponential model. New models were developed by incorporating inverse quadratic and exponential models into the combined rectangular hyperbola with the standard dose–response curve for winter wheat biomass yield and the combined standard dose—response model with the rectangular hyperbola for weed biomass, to describe the complex effects of herbicide and nitrogen on crop–weed competition. The models developed were used to predict crop yield and weed biomass and to estimate the herbicide doses required to restrict crop yield loss caused by weeds and weed biomass production to an acceptable level at a range of nitrogen levels. The model for crop yield was further modified to estimate the herbicide dose and nitrogen level to achieve a target crop biomass yield. For the target crop biomass yield of 1200 g m^?2 with an infestation of 100 B. napus plants m^?2, the model recommended various options for nitrogen and herbicide combinations: 140 and 2.9, 180 and 0.9 and 360 kg ha^?1 and 1.7 g a.i. ha^?1 of nitrogen and metsulfuron‐methyl respectively.     

Prediction of Heteranthera reniformis competition with flooded rice using day–degrees

In field studies carried out in 1992 and 1993, several durations of growth of Heteranthera reniformis Ruitz et Pavon were established in flooded rice (Oryza sativa L.) to evaluate weed influence on crop yield using day-degree predictive models for weed growth. H. reniformis was allowed to emerge at 7-day intervals from rice emergence until 49 days later. Weeds that emerged with the rice accumulated 403 day-degrees during the first 49 days. Weeds allowed to utilize 308 day-degrees had a dry weight reduction of 20% compared with plants that emerged with the rice. With the loss of all 403 day-degrees dry weight was reduced by about 95%. Maximum leaf area index (LAI) was 2.8, reached at 308 day-degrees. Weed density ranged from 48 to 5 plants m^-2 when all 403 day-degrees were accumulated or lost by the plant respectively. When the weed lost only 95 day-degrees out of the possible 403 (1 week's delay in emergence) rice yield was 34% and 39% lower in 1992 and 1993 respectively. When the weed was allowed to accumulate ail 403 day-degrees yield reduction reached 62% in 1992 and 68% in 1993, very similar to the control plot, which were maintained weedy throughout (64% and 70.5%). After an accumulation of 403 day-degrees the weed reduced the number of rice panicles by 45% in 1992 and 38% in 1993.     

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.     

Some studies on the competition between Avenu fatua L. and spring barley.II. Variation of A. fatua emergence and development and its influence on crop yield.

The emergence and development of A fatua were studied in 23 spring barly fields in 1972 and nine in 1973 nautarally infested with yhe weed. Emergence occurred up to the four-leaf stages of the crop. The mean number of days from drilling to 50% emergence of A fatua plants was 22 in 1972 and 36 in 1973.The majority of seeds were shed by early emerging plants in 1972, 79% and in 1973. 59% of A fatua plants had emerged by the two-leaf stage of the crop and these plants produced 97 and 89% respectively of all the seed shed. In both years, A fatua plants emerging before the crop produced five times as many seeds per plant as those emerging between the crop two and three-leaf stages. The earliest emerging plants were also the heaviest and had most stems per plant.In both years there was a significant relationship between the density of A fatua plants in the crop in spring and their mortality.A large variation occured between the sites in the number of A fatua plants that emerged by a given crop leaf stage. In 1973.insufficient numbers of sites were investigatesd to derive a relationship between time to A fatua emergence and crop yield loss, but in 1972, a given density of A fatua plants emerging at an early stage caused agreater crop yields loss than the same density emerging later. This was also evident to aleser extent in data derived from a similar series of 15 experiments in 1971.     

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.     

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).     

Predicting yield loss in maize fields and developing decision support for post-emergence herbicide applications

This work was initiated to integrate an image analysis system and a prediction equation to support decisions for post‐emergence herbicide applications under field conditions. Data were collected from 1999 to 2001 in 32 commercial fields to obtain weed cover data at the three to four leaf stage of maize (Zea mays L.), and crop yield at maturity. Relative crop yield was predicted using a non‐linear sigmoidal equation with relative weed cover as the predictor variable (P < 0.0001; R² = 0.39). The decision procedure consists of using the equation within the limits of a yield loss threshold that represents the loss one is willing to tolerate. The tolerance threshold (TT) allows determination of a weed threshold (WT). The procedure considers the variability around the prediction equation by setting the WT at the intersection between the lower 95% confidence interval of the prediction line and the TT. It also considers the variability around the weed cover estimate. For a given field, the decision is made by comparing the average weed cover corrected for sampling error, to the WT. We tested the performance of the decision procedure and found it could lead to a saving of 25% of herbicide use. We also computed a probability table showing the chances of getting relative yield above or below the TT. We suggest using the probability table in combination with the decision procedure to manage risks. The proposed approach does not offer a set ‘yes’ or ‘no’ answer but rather provides a framework to support decisions by producers who ultimately must manage the risks.     

Competition between maize and Datura stramonium in an irrigated field under semi-arid conditions

Crop growth of maize ( Zea mays L.) and Datura stramonium L. in monoculture and competition was studied over 4 years in a flood irrigated field in Zaragoza (Spain). Plant density was 8.33 m^–2 for maize and 16.66 m^–2 (1994 and 1995) and 8.33 m^–2 (1996 and 1997) for D. stramonium . Maize yield was decreased by 14–63% when competing with the weed. Yield reduction increased as the time between crop and weed emergence decreased. The development of leaf area per plant during the exponential growth phase was faster in maize primarily because the leaf area of maize seedlings at emergence time was greater than that of the weed. The faster growth of maize in leaf area and height reduced the photosynthetically active radiation received by the weed. Datura stramonium had a lower radiation use efficiency (RUE) than maize. Competition from the weed slightly decreased the maximum leaf area index (LAI) of the crop, and leaf senescence of maize was accelerated. The weed competed with the crop late in the season reducing crop growth rate, grain number per ear and grain weight. Competitive ability of D. stramonium for light was mainly due to its growth habit, with the leaves concentrated in the upper part of the canopy (more than 75% of LAI in the upper 25% of its height), its higher light extinction coefficient (0.89) and its indeterminate growth habit. The N plant content of maize was not influenced by the presence of the weed. The weed had a higher N plant content than the crop throughout the season and took up more N in monoculture.     

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.     

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.     

Assessing variability in fecundity of Amaranthus powellii using a simulation model

Estimates of weed fecundity and its variability are critical for the development of population dynamic models and for evaluating the long‐term consequences of weed management practices. The purpose of this research was to estimate Amaranthus powellii fecundity across years, seasons and competitive environments using a mechanistic model. Existing models were modified to account for weed responses to shade, and to dynamically simulate seed production among subthreshold densities of A. powellii. The model was parameterized and tested using five sets of field data in which A. powellii was grown either alone or with transplanted broccoli. The model overestimated A. powellii height, but predicted both dry weight and fecundity well. Mean simulated fecundity for A. powellii ranged from 0 to 268 000 seeds per plant depending on year, crop maturity date, relative time of emergence and location. Year‐to‐year variability in simulated fecundity was large with coefficients of variation ranging from 77 to 128 under low and high competitive environments respectively. Our results suggest that estimates of weed fecundity based on 1 or 2 years of empirical data may result in significant errors in population dynamic models, and that the use of economic optimum thresholds based on weed density alone entails considerable risks.     

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.