Estimation of Crop Yield Loss Due to Interference by Multiple Weed Species

Weed Science ◽  
1994 ◽  
Vol 42 (1) ◽  
pp. 103-109 ◽  
Author(s):  
Scott M. Swinton ◽  
Douglas D. Buhler ◽  
Frank Forcella ◽  
Jeffrey L. Gunsolus ◽  
Robert P. King
Keyword(s):  
Crop Yield ◽  
Yield Loss ◽  
Yield Function ◽  
Data Sets ◽  
Species Competition ◽  
Coefficient Estimates ◽  
Weed Species ◽  
Common Lambsquarters ◽  
Competition Coefficient ◽  
Crop Yield Loss

Previous efforts to model crop yield loss from multiple weed species constructed competitive indices based on yield loss from individual weed species. Our model uses a multispecies modification of Cousens’ rectangular hyperbolic yield function to estimate a nonlinear competitive index for weed-crop interference. Results from 13 Minnesota and Wisconsin data sets provide measures of the relative competitiveness of mixed green and yellow foxtails, common lambsquarters, redroot pigweed, velvetleaf, and several other weed species. Competition coefficient estimates are stable over years, but not locations.

scholarly journals Performance of WeedSOFT for Predicting Soybean Yield Loss

2006 ◽  
Vol 20 (2) ◽  
pp. 478-484 ◽  
Author(s):  
Shawn M. Hock ◽  
Stevan Z. Knezevic ◽  
Alex R. Martin ◽  
John L. Lindquist
Keyword(s):  
Crop Yield ◽  
Weed Management ◽  
Dry Matter ◽  
Yield Loss ◽  
The United States ◽  
Weed Species ◽  
Trifoliate Leaf ◽  
Soybean Yield ◽  
Weed Emergence ◽  
Crop Yield Loss

Decision support systems (DSSs) have been developed to assist producers and consultants with weed management decisions. WeedSOFT is a DSS currently used in several states in the north-central region of the United States. Accurate estimates of crop yield loss due to weed interference are required for cost-effective weed management recommendations. WeedSOFT uses competitive indices (CIs) to predict crop yield loss under multiple weed species, weed densities, and relative times of weed emergence. Performance of several WeedSOFT versions to predict soybean yield loss from weed competition was evaluated using CI values in WeedSOFT version 9.0 compared to new CI values calculated from weed dry matter, weed volume, and soybean yield loss in two soybean row spacings (19 and 76 cm) and two relative weed emergence times (at soybean emergence and first trifoliate leaf stage). Overall, new CI values improved predictions of soybean yield loss by as high as 63%. It was especially true with using new CI values based on yield loss compared to those based on weed dry matter or weed volume. However, there were inconsistencies in predictions for most weed species, suggesting that additional modifications are needed to further improve soybean yield loss predictions.

Evaluation of Imazethapyr for Weed Control in Leafy Vegetable Crops

1996 ◽  
Vol 10 (2) ◽  
pp. 253-257 ◽  
Author(s):  
Joan A. Dusky ◽  
William M. Stall
Keyword(s):  
Regression Analysis ◽  
Weed Control ◽  
Crop Yield ◽  
Relative Sensitivity ◽  
Leafy Vegetable ◽  
Field Conditions ◽  
Vegetable Crops ◽  
Weed Species ◽  
Common Lambsquarters ◽  
Common Purslane

Imazethapyr was evaluated PRE and POST in five lettuce types and chicory under Florida field conditions. The relative sensitivity of leafy crop vigor (most sensitive to most tolerant) to imazethapyr PRE, based on 20% inhibition determined using regression analysis, was as follows: Boston > bibb > crisphead > romaine > leaf > escarole > endive. Leafy crop injury increased as the rate of imazethapyr applied POST increased, with all leafy crops responding in a similar manner. Surfactant addition increased imazethapyr phytotoxicity. Imazethapyr PRE treatments at 0.067 kg ai/ha provided greater than 80% control of livid amaranth, common purslane, flatsedge, and common lambsquarters. Imazethapyr POST at 0.067 kg/ha, with surfactant provided control greater than 85% of all weed species. Greater than 85% spiny amaranth control was provided by imazethapyr POST at 0.017 kg/ha. Use of surfactant with imazethapyr did not improve spiny amaranth control over imazethapyr with no surfactant. POST treatments did not decrease leafy crop yield compared with the hand-weeded check. Imazethapyr applied PRE reduced crop yield compared to the POST treatments and the hand-weeded control.

Using Estimates of Weed Pressure to Establish Crop Yield Loss Equations

1994 ◽  
Vol 8 (1) ◽  
pp. 114-118 ◽  
Author(s):  
R. Gordon Harvey ◽  
Clark R. Wagner
Keyword(s):  
Linear Regression ◽  
Crop Yield ◽  
Yield Loss ◽  
Sweet Corn ◽  
Crop Yields ◽  
Yield Reduction ◽  
Weed Species ◽  
Field Corn ◽  
Efficacy Trials ◽  
Total Crop

Herbicide efficacy trials in field corn, sweet corn, and soybean were conducted at three locations in Wisconsin over a 6-yr period. Percent weed pressure (WP) was determined by visually estimating the contribution of all weed species present to the total crop and weed volume in each plot. Crop yields in each plot were measured. Percent crop yield reduction (YLDRED) was calculated by comparing mean yields of individual treatments with those of the highest yielding treatment in each trial. Linear regression analyses of YLDRED and WP data from 1640 field corn and 138 sweet corn treatments were significant. Nonlinear regression analysis of YLDRED and WP data from all 1374 soybean treatments was significant; however, a linear regression of those 1154 soybean treatments with WP ratings of 30 or less produced a more easily interpreted regression equation.

A simulation analysis of crop yield loss due to rust disease

1977 ◽  
Vol 2 (3) ◽  
pp. 189-198 ◽  
Author(s):  
P.S. Teng ◽  
M.J. Blackie ◽  
R.C. Close
Keyword(s):  
Crop Yield ◽  
Yield Loss ◽  
Simulation Analysis ◽  
Rust Disease ◽  
Crop Yield Loss

Modeling With Limited Data: The Influence of Crop Rotation and Management on Weed Communities and Crop Yield Loss

Weed Science ◽  
2009 ◽  
Vol 57 (2) ◽  
pp. 175-186 ◽  
Author(s):  
Stephen R. Canner ◽  
L. J. Wiles ◽  
Robert H. Erskine ◽  
Gregory S. McMaster ◽  
Gale H. Dunn ◽  
...  
Keyword(s):  
Crop Rotation ◽  
Crop Yield ◽  
Weed Management ◽  
Yield Loss ◽  
Cost Effective ◽  
Crop Rotations ◽  
Alternative Crop ◽  
Weed Communities ◽  
Multiple Species ◽  
Crop Yield Loss

Theory and models of crop yield loss from weed competition have led to decision models to help growers choose cost-effective weed management. These models are available for multiple-species weed communities in a single season of several crops. Growers also rely on crop rotation for weed control, yet theory and models of weed population dynamics have not led to similar tools for planning of crop rotations for cost-effective weed management. Obstacles have been the complexity of modeling the dynamics of multiple populations of weed species compared to a single species and lack of data. We developed a method to use limited, readily observed data to simulate population dynamics and crop yield loss of multiple-species weed communities in response to crop rotation, tillage system, and specific weed management tactics. Our method is based on the general theory of density dependence of plant productivity and extensive use of rectangular hyperbolic equations for describing crop yield loss as a function of weed density. Only two density-independent parameters are required for each species to represent differences in seed bank mortality, emergence, and maximum seed production. One equation is used to model crop yield loss and density-dependent weed seed production as a function of crop and weed density, relative time of weed and crop emergence, and differences among species in competitive ability. The model has been parameterized for six crops and 15 weeds, and limited evaluation indicates predictions are accurate enough to highlight potential weed problems and solutions when comparing alternative crop rotations for a field. The model has been incorporated into a decision support tool for whole-farm management so growers in the Central Great Plains of the United States can compare alternative crop rotations and how their choice influences farm income, herbicide use, and control of weeds in their fields.

Analysis of competition between wet-seeded rice and barnyardgrass (Echinochloa crus-galli) using a response–surface model

Weed Science ◽  
2004 ◽  
Vol 52 (1) ◽  
pp. 142-146 ◽  
Author(s):  
Hanwen Ni ◽  
Keith Moody ◽  
Restituta P. Robles
Keyword(s):  
Response Surface ◽  
Crop Yield ◽  
Yield Loss ◽  
The Philippines ◽  
Response Surface Model ◽  
Surface Model ◽  
Weed Competition ◽  
Plant Type ◽  
Crop Density ◽  
Crop Yield Loss

Competition between wet-seeded rice and barnyardgrass under two distinct environments was analyzed using a two-parameter response–surface model at the International Rice Research Institute in the Philippines. The findings showed that this model could predict crop yield loss due to weed competition over a wide range of crop and weed densities. The low-tillering, new plant–type cultivar was a weaker competitor and had a higher yield loss than high-tillering cultivar ‘IR72’ and a hybrid. Increasing the crop density could reduce yield loss due to weed competition. This effect was greater for the new plant type than for IR72 and the hybrid when barnyardgrass density was low. In contrast, this effect was less for the new plant type than for IR72 and the hybrid when the weed density was high. Competitiveness of the three rice cultivars was also affected by season. Crop yield loss was higher in the wet season than in the dry season.

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