Canopy Measurements as Predictors of Weed-Crop Competition

Weed Science ◽  
1996 ◽  
Vol 44 (3) ◽  
pp. 511-516 ◽  
Author(s):  
J. I. Vitta ◽  
C. Fernandez Quintanilla
Keyword(s):  
Leaf Area ◽  
Crop Yield ◽  
Weed Management ◽  
Yield Losses ◽  
Weed Density ◽  
Damage Coefficient ◽  
Relative Damage ◽  
Weed Cover ◽  
Better Than ◽  
Crop Competition

The development of weed management systems requires accurate prediction of weed-crop competition. In this paper, simple regression models of crop yield losses based on weed density and weed leaf area are compared. In weed leaf area models, variations in the relative damage coefficient (q) were also analyzed. Finally, three simple methods to assess weed cover were compared: visual, photographic, and optic device assessment. Leaf area models were at least as accurate as weed density models. However, the generality of the leaf area models was restricted by changes in q, according to the date of leaf area evaluation and the year. Although all methods to assess weed cover correlated adequately with weed leaf area, visual estimates were the best to predict crop yield losses perhaps because very low levels of weed leaf area could be distinguished visually better than by other methods.

Size-Dependent Economic Thresholds for Three Broadleaf Weed Species in Soybeans

1991 ◽  
Vol 5 (3) ◽  
pp. 674-679 ◽  
Author(s):  
Susan E. Weaver
Keyword(s):  
Leaf Area ◽  
Crop Yield ◽  
Soybean Seed ◽  
Yield Losses ◽  
Weed Species ◽  
Damage Functions ◽  
Area Index ◽  
Weed Density ◽  
Size Dependent ◽  
Economic Thresholds

Soybean seed yield losses due to interference from common cocklebur, velvetleaf, and jimsonweed, with and without a PPI application of 0.42 kg ai ha-1metribuzin, were determined in 1986, 1987, and 1988. Damage functions were calculated based on weed density, weed leaf density, and relative weed leaf area index, respectively. Functions relating crop yield losses to weed density varied significantly among treatments and years for each species. Weeds which escaped soil-applied metribuzin were shorter with fewer leaves at 3 wk after planting, and caused lower crop yield losses than control plants at equal densities. Yield loss estimates based upon relative weed leaf area at 3 wk after planting showed least variation between years and treatments.

Validation of a Management Program Based on A Weed Cover Threshold Model: Effects on Herbicide Use and Weed Populations

Weed Science ◽  
2009 ◽  
Vol 57 (2) ◽  
pp. 187-193 ◽  
Author(s):  
Marie-Josée Simard ◽  
Bernard Panneton ◽  
Louis Longchamps ◽  
Claudel Lemieux ◽  
Anne Légère ◽  
...  
Keyword(s):  
Seed Production ◽  
Crop Yield ◽  
Weed Management ◽  
Crop Yields ◽  
Threshold Model ◽  
Management Program ◽  
Weed Seed ◽  
Weed Density ◽  
Weed Cover ◽  
Herbicide Use

Weed management decisions based on weed threshold models offer the opportunity to reduce herbicide use by allowing the possibility of forgoing treatment or lowering rates. Weed thresholds based on a relative leaf-cover model were tested during a 4-yr period at two locations. Two 1.62-ha fields, planted to conventional and glyphosate-resistant corn (2004, 2005, 2007) or soybean (2006), were divided in 900 m2sections. Herbicides were applied postemergence to each of these sections with either variable rates based on weed thresholds, or constant full rates. Variable herbicide rates included: no application, half rate, or full rate. Relative weed cover values of 0.2 and 0.4 (corn) or 0.1 and 0.3 (soybean) served as thresholds for incremental rates. Digital images were used to evaluate the relative weed cover. Weed density was assessed before and after herbicide application. Weed seed production was estimated for two species in 2004 and 2005. No difference in crop yield, relative weed cover, weed density, or weed seed production was observed between conventional and glyphosate-resistant cropping systems. During the first year, herbicide use reduction was obtained (−85.4%) with marginal crop yield loss (5 to 15%). In the subsequent 3 yr, preherbicide weed densities increased and concomitant increases in relative weed cover values did not allow more than a 10% overall reduction in herbicide use. This threshold model designed to maintain crop yields within a given year did not allow significant reduction in herbicide use during the following 3 yr. Residual weed populations most likely replenished the seed bank to levels that allowed weed densities to increase afterward. Increased weed density over time in plots treated with full rates of herbicide every year also indicated that a single postemergence herbicide treatment was not sufficient to contain weed populations at low levels every year in this corn–soybean rotation.

Simulation of Competition for Photosynthetically Active Radiation Between Common Ragweed (Ambrosia artemisiifolia) and Dry Bean (Phaseolus vulgaris)

Weed Science ◽  
1996 ◽  
Vol 44 (3) ◽  
pp. 545-554 ◽  
Author(s):  
David Chikoye ◽  
Leslie A. Hunt ◽  
Clarence J. Swanton
Keyword(s):  
Leaf Area ◽  
Crop Yield ◽  
Photosynthetically Active Radiation ◽  
Growth And Development ◽  
Field Studies ◽  
Weed Competition ◽  
Yield Losses ◽  
Dry Bean ◽  
Common Ragweed ◽  
Active Radiation

The influence of weeds on crop yield is not only dependent on weed-related factors such as density and time of emergence, but also on environmental and management factors that affect both the weed and crop through time. This study was undertaken to develop the first physiologically based dry bean model that would account for the influence of weed competition. The specific objective was to develop a model that would account for the influence of weed competition on crop yield, and to use this model to test the hypothesis that crop yield losses resulted from competition for photosynthetically active radiation (PAR). To this end, a model that simulated the growth and development of dry bean was developed. The model performed daily calculations and simulated the phenology, leaf area expansion, dry matter production and distribution, and grain yield of dry bean based on weather and management information, but assumed adequate water and nutrients. The model was calibrated without weed competition at two locations and yr, and for these situations, adequately described the growth and development of the crop. Simulations were then run for five common ragweed densities and two times of emergence. Common ragweed leaf area was read into the model from input files and used to simulate weed shading. Shading of the dry bean canopy by common ragweed accounted for about 50 to 70% of the yield losses observed in field studies when weeds emerged with the crop. Weed shading did not account for the yield reduction measured from weeds that emerged at the second trifoliate stage of crop growth. The agreement between model predictions and field studies was consistent with the hypothesis that competition for PAR was a principal factor in weed-crop interaction. The ability to account for differences in weed densities, management, and environmental conditions suggested that modeling was a useful tool for evaluating the interaction among weeds and crops.

Nitrogen Fertility and Weed Management Critical for Continuous No-Till Wheat in the Pacific Northwest

2006 ◽  
Vol 20 (3) ◽  
pp. 658-669 ◽  
Author(s):  
Frank L. Young ◽  
Mark E. Thorne ◽  
Douglas L. Young
Keyword(s):  
Winter Wheat ◽  
Pacific Northwest ◽  
Crop Yield ◽  
Spring Wheat ◽  
Weed Management ◽  
Weed Density ◽  
N Level ◽  
No Till ◽  
The Pacific ◽  
Density And Biomass

No-till cropping is an option for growers needing to reduce soil erosion in the Palouse annual-cropped region of the Pacific Northwest, which is well suited for wheat production. A 6-yr field study was conducted to determine optimum levels of fertilizer and herbicide inputs in a no-till continuous wheat crop production system. Three levels of nitrogen (N) and two weed management levels (WML) were compared in a spring wheat (SW)–winter wheat (WW)–WW rotation through two rotation cycles. The high WML reduced weed densities about 50% compared with the low WML. In general, herbicide treatments were more effective on broadleaf weeds and may have facilitated a shift toward grass weeds. The high WML reduced grass weed biomass only at the reduced N levels, whereas the high WML reduced broadleaf weed density at all N levels. Variable environmental conditions affected wheat yield; however, yield tended to be highest where winter wheat immediately followed spring wheat. Nitrogen had little effect on weed density but increased crop yield about 13% with each increased N level. Crop yield was greater at the high versus low WML at each N level, even though weed density and biomass were reduced least between WMLs at the highest N level. The highest crop yield and net returns were obtained with the highest N and WML; however, none of the N and WML combinations were profitable.

Green Foxtail (Setaria viridis) and Pale Smartweed (Polygonum lapathifolium) Interference in Field Crops

1994 ◽  
Vol 8 (2) ◽  
pp. 311-316 ◽  
Author(s):  
John T. O'Donovan
Keyword(s):  
Crop Yield ◽  
Field Experiments ◽  
Dry Weight ◽  
Limiting Factor ◽  
Yield Losses ◽  
Weed Density ◽  
Green Foxtail ◽  
Polygonum Lapathifolium ◽  
And Control ◽  
The Relationship

Field experiments were conducted at Vegreville, Alberta in 1984, 1985, 1986, and 1988 to determine the effects of green foxtail and pale smartweed on yield of wheat, barley, and canola. There was considerable variation among years in the response of crop yield to both weeds and in the relationship between weed dry weight and weed density. Mostly relationships between crop yield and either weed density or dry weight were poor, suggesting that the weeds competed weakly with the crops. Thus density or dry weight may be poor predictors of crop yield losses due to green foxtail or pale smartweed. Where the crops emerged ahead of these weeds, and where soil moisture was not a limiting factor, crop yield losses were minimal and control with herbicides probably uneconomical. In some instances, growth and development of the weeds was suppressed by the crops to the extent that little or no weed dry matter was present at crop maturity. This was most evident with barley, and where the crops emerged ahead of the weeds.

Effect of Postplant Tillage and Crop Density on Broadleaf Weed Control in Dry Pea (Pisum sativum) and Lentil (Lens culinaris)

1995 ◽  
Vol 9 (1) ◽  
pp. 99-106 ◽  
Author(s):  
Chris M. Boerboom ◽  
Frank L. Young
Keyword(s):  
Pisum Sativum ◽  
Weed Control ◽  
Crop Yield ◽  
Weed Management ◽  
Lens Culinaris ◽  
Crop Density ◽  
Growing Conditions ◽  
One Year ◽  
Favorable Conditions ◽  
Crop Competition

Increased crop densities and postplant tillage were evaluated as nonchemical methods to supplement metribuzin for improved broadleaf weed control in dry pea and lentil. The effects of 50, 100, and 150% of recommended 220 kg/ha pea and 67 kg/ha lentil seeding rates and two dates of rotary hoeing and harrowing on pea, lentil, and broadleaf weeds were studied with and without metribuzin for two years. Under favorable growing conditions, crop competition gave 72 and 99% weed control in pea and 33 and 70% weed control in lentil with the 50 and 150% seeding rates. Under less favorable conditions, control was 21 to 39% with the low and high pea and lentil seeding rates. At recommended seeding rates, metribuzin gave greater than 90% control in either crop or year. Postplant tillage 12 to 27 d after planting slightly reduced crop densities in three tillage treatments in one year, but not the second. Postplant tillage did not affect crop yield or improve weed control. In all studies, pea was similar to or more competitive than lentil in suppressing broadleaf weeds. Because neither non-chemical practice significantly improves weed control, changes are not recommended for weed management in pea and lentil.

scholarly journals Effect of weed management practices on weed cover in field pea (pisum sativum l.)

2021 ◽  
Vol 9 (1-2) ◽  
pp. 9-14
Author(s):  
István Kristó ◽  
Melinda Tar ◽  
Katalin Irmes ◽  
Marianna Vályi-Nagy ◽  
Attila Rácz ◽  
...  
Keyword(s):  
Pisum Sativum ◽  
Weed Management ◽  
Management Practices ◽  
Agricultural Research ◽  
Great Part ◽  
Growing Season ◽  
Field Pea ◽  
Weed Density ◽  
Pisum Sativum L ◽  
Weed Cover

Field pea (Pisum sativum L.) are planted on small area in Hungary, although it is a precious source of protein (22-28%), and it also plays a significant role like a component in fodder mixture and green forage. It is a great part in crop rotation as a short growing-season legume. Furthermore, it has beneficial effects of nitrogen-fixing nodules being able to obtain N derived from air. One of the most critical limiting factors is to find out weed management practise for control of weeds in field pea. Our field experiment was carried out on site of the National Agricultural Research and Innovation Centre, the Department of Field Crops Research in Öthalom for comparing weed management strategies by evaluate their efficacy and weed flora. We used 6 herbicides or herbicid combination and observed weed density in 5 times during the growing season. The most important weeds were: common chickweed (Stellaria media), wild mustard (Sinapis arvensis), branching lackspur (Consolida regalis), meldweed (Chenopodium album). Among the treatments the highest weed cover was the weedy check, followed by Stomp Super, obtained maximum weed control and long lasting effect. With the application of Basagran 480 SL and Pulsar 40 SL have a significantly lower weed density was recorded than preemergence applications. In case of Corum application, it was the lowest weed cover of all even at harvesting time. According to our experiments use of Dash does not control weeds considerably.

Empirical Models of Pigweed (Amaranthusspp.) Interference in Soybean (Glycine max)

Weed Science ◽  
1995 ◽  
Vol 43 (4) ◽  
pp. 612-618 ◽  
Author(s):  
Anita Dieleman ◽  
Allan S. Hamill ◽  
Stephan F. Weise ◽  
Clarence J. Swanton
Keyword(s):  
Leaf Area ◽  
Field Experiments ◽  
Yield Loss ◽  
Empirical Models ◽  
Parameter Estimates ◽  
Growth Stages ◽  
Data Set ◽  
Soybean Yield ◽  
Damage Coefficient ◽  
Relative Damage

Three empirical crop yield loss models were used to describe the interference of redroot pigweed and Powell amaranth populations with soybean. Data were obtained from field experiments conducted in 1992 and 1993. Pigweed densities of 0 to eight plants m−1were established within the soybean row. Pigweed sowing dates were selected so that weed seedling emergence coincided with VE, VC, and V2 soybean growth stages within the time frame of the critical weed-free period. The model incorporating pigweed density and time of emergence gave the best description of soybean yield loss in comparison to the two relative leaf area models. This model was fit to a combined data set of percent yield loss because parameter estimates did not differ among locations and years. Estimated soybean yield losses decreased from 16.4 to 0.5% with delayed pigweed emergence from 0 to 20 degree days. Leaf area of pigweed relative to soybean encompassed pigweed density and time of emergence. Relationship between relative leaf area and soybean yield loss was best described by the one-parameter model estimating a relative damage coefficient ‘q’ than the two-parameter model that also estimated maximum expected yield loss. The relative damage coefficient ‘q’ decreased with later times of leaf area assessment but could be predicted with one leaf area observation. Empirical models that incorporate time of weed emergence represent a step toward improving predictions of yield loss. This is important for the selection of cost-effective weed control strategies.

Effects of Weed Density and Defoliated or Undefoliated Soybeans (Glycine max) on Velvetleaf (Abutilon theophrasti) Development

Weed Science ◽  
1984 ◽  
Vol 32 (4) ◽  
pp. 511-519 ◽  
Author(s):  
Randall A. Higgins ◽  
David W. Staniforth ◽  
Larry P. Pedigo
Keyword(s):  
Glycine Max ◽  
Leaf Area ◽  
Weed Management ◽  
Dry Matter ◽  
Growing Season ◽  
Abutilon Theophrasti ◽  
Main Stem ◽  
Weed Density ◽  
Number Of Leaves

Velvetleaf (Abutilon theophrastiMedic. ♯3ABUTH) grown under monoculture consistently exceeded velvetleaf intercropped with soybeans [Glycine max(L.) Merr. var. ‘Amsoy 71′] in leaf area, nodes with fully developed leaves, canopy width, branches, and number of capsules as early as 3, 3, 4, 5, and 8 weeks, respectively, after simultaneous emergence. Velvetleaf plants without soybean competition eventually developed over nine times the dry matter of velvetleaf intercropped with soybean. The only components of velvetleaf plants sampled which sometimes increased significantly when soybean was defoliated in a manner simulating damage caused by the green cloverworm (GCW) (Plathypena scabraF.) were leaf area, number of leaves, and number of main-stem nodes. Soybeans in Iowa are attacked by the GCW late enough in the growing season that velvetleaf surviving previous weed management efforts benefited only slightly.

An Imagery-Based Weed Cover Threshold Established Using Expert Knowledge

Weed Science ◽  
2014 ◽  
Vol 62 (1) ◽  
pp. 177-185 ◽  
Author(s):  
Louis Longchamps ◽  
Bernard Panneton ◽  
Marie-Josée Simard ◽  
Gilles D. Leroux
Keyword(s):  
Weed Management ◽  
Operating Characteristic ◽  
Expert Knowledge ◽  
Characteristic Curve ◽  
Threshold Values ◽  
Economic Threshold ◽  
Weed Density ◽  
Economic Thresholds ◽  
Weed Cover ◽  
Curve Method

The implementation of site-specific weed management requires information about weed cover and decision support systems to determine weed cover thresholds and concomitant herbicide rates. Although it is possible to create accurate weed cover maps over large areas, weed cover thresholds have generally been evaluated using tedious weed density counts. To bridge this gap between weed cover obtained by machine vision and the concept of economic threshold, crop advisers specializing in weed scouting were asked to evaluate over 2,500 weed cover images (2 m by 3 m) and determine if a given image would require herbicide application or not. Using the area under the “receiver operating characteristic” curve method, an optimal weed cover threshold was established. The derived economic thresholds ranged from 0.06 to 0.31% weed cover contingent on the level of tolerance of the expert adviser. Although this threshold seems low, it is comparable with economic threshold values based on weed density.

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