David R. Shaw

Reducing the Risks of Herbicide Resistance: Best Management Practices and Recommendations

Herbicides are the foundation of weed control in commercial crop-production systems. However, herbicide-resistant (HR) weed populations are evolving rapidly as a natural response to selection pressure imposed by modern agricultural management activities. Mitigating the evolution of herbicide resistance depends on reducing selection through diversification of weed control techniques, minimizing the spread of resistance genes and genotypes via pollen or propagule dispersal, and eliminating additions of weed seed to the soil seedbank. Effective deployment of such a multifaceted approach will require shifting from the current concept of basing weed management on single-year economic thresholds.

Weed Control from Herbicide Combinations with Glyphosate1

Greenhouse studies were initiated to evaluate glyphosate alone and tank-mixed with acifluorfen, CGA 277476, chlorimuron, cloransulam-methyl, fomesafen, imazaquin, or pyrithiobac on seedling johnsongrass, broadleaf signalgrass, pitted morningglory, and hemp sesbania. Johnsongrass and broadleaf signalgrass control by glyphosate was not affected by the selective herbicides applied in mixtures. Pitted morningglory control by glyphosate ranged from 0% with 280 g ai/ha to 67% with 840 g/ha. There was an additive effect when selective herbicides were added to 280 g/ha glyphosate 2 wk after treatment (WAT). When acifluorfen was added to 560 g/ha glyphosate, pitted morningglory control 2 WAT increased to 100% compared with 55% with glyphosate alone. Similarly, the addition of fomesafen or acifluorfen to 840 g/ha glyphosate controlled pitted morningglory 2 WAT by 90 and 98%, respectively, compared with 67% with glyphosate alone. Only tank mixtures of acifluorfen, CGA 277476, or fomesafen, and 840 g/ha glyphosate reduced fresh weight compared with glyphosate alone 4 WAT. Acifluorfen, CGA 277476, and fomesafen controlled pitted morningglory by 85 to 100% when added to 1,120 g/ha glyphosate. Both acifluorfen and fomesafen effectively controlled hemp sesbania without the addition of glyphosate 2 WAT. Chlorimuron and pyrithiobac added to 1,120 g/ha glyphosate increased hemp sesbania control to 88 and 99%, respectively, compared with 45% with glyphosate alone 2 WAT. CGA 277476, cloransulam-methyl, imazaquin, and pyrithiobac were antagonistic to hemp sesbania fresh weight reduction when compared with the expected response, but fresh weights were still reduced more than with the same rate of glyphosate alone. Nomenclature: Acifluorfen; CGA-277476, 2-[[[[(4,6-dimethyl-2-pyrimidinyl)amino]carbonyl]amino]sulfonyl]-3-oxetanyl ester; chlorimuron; cloransulam-methyl; fomesafen; glyphosate; imazaquin; pyrithiobac; broadleaf signalgrass, Brachiaria platyphylla (Griseb.) Nash. #3 ECHCG; hemp sesbania, Sesbania exaltata (Raf.) Rydb. ex. A. W. Hill # SEBEX; johnsongrass, Sorghum halepense (L.) Pers. # SORHA; pitted morningglory, Ipomoea lacunosa L. # IPOLA. Additional index words: Additive effects, herbicide antagonism, tank mixtures. Abbreviations: ALS, acetolactate synthase; WAT, weeks after treatment.

Using remote sensing to detect weed infestations in Glycine max

Abstract The objective of this research was to evaluate the accuracy of remote sensing for detecting weed infestation levels during early-season Glycine max production. Weed population estimates were collected from two G. max fields approximately 8 wk after planting during summer 1998. Seedling weed populations were sampled using a regular grid coordinate system on a 10- by 10-m grid. Two days later, multispectral digital images of the fields were recorded. Generally, infestations of Senna obtusifolia, Ipomoea lacunosa, and Solanum carolinense could be detected with remote sensing with at least 75% accuracy. Threshold populations of 10 or more S. obtusifolia or I. lacunosa plants m−2 were generally classified with at least 85% accuracy. Discriminant analysis functions formed for detecting weed populations in one field were at least 73% accurate in identifying S. obtusifolia and I. lacunosa infestations in independently collected data from another field. Due to highly variable soil conditions and their effects on the reflectance properties of the surrounding soil and vegetation, accurate classification of weed-free areas was generally much lower. Current remote sensing technology has potential for in-season weed detection; however, further advancements of the technology are needed to insure its use in future prescription weed management systems. Nomenclature: Ipomoea lacunosa L. IPOLA, pitted morningglory; Senna obtusifolia (L.) Irwin et Barnaby CASOB, sicklepod; Solanum carolinense L. SOLCA, horsenettle; Glycine max (L.) Merr., soybean.

Wavelet analysis of hyperspectral reflectance data for detecting pitted morningglory (Ipomoea lacunosa) in soybean (Glycine max)

This research determined the potential for wavelet-based analysis of hyperspectral reflectance signals for detecting the presence of early season pitted morningglory when intermixed with soybean and soil. Ground-level hyperspectral reflectance signals were collected in a field experiment containing plots of soybean and plots containing soybean intermixed with pitted morningglory in a conventional tillage system. The collected hyperspectral signals contained mixed reflectances for vegetation and background soil in each plot. Pure reflectance signals were also collected for pitted morningglory, soybean, and bare soil so that synthetically mixed reflectance curves could be generated, evaluated, and the mixing proportions controlled. Wavelet detail coefficients were used as features in linear discriminant analysis for automated discrimination between the soil+soybean and the soil+soybean+pitted morningglory classes. A total of 36 different mother wavelets were investigated to determine the effect of mother wavelet selection on the ability to detect the presence of pitted morningglory. When the growth stage was two to four leaves, which is still controllable with herbicide, the weed could be detected with at least 87% accuracy, regardless of mother wavelet selection. Moreover, the Daubechies 3, Daubechies 5, and Coiflet 5 mother wavelets resulted in 100% classification accuracy. Most of the best wavelet coefficients, in terms of discriminating ability, were derived from the red-edge and the nearinfrared regions of the spectrum. For comparison purposes, the raw spectral bands and principal components were evaluated as possible discriminating features. For the two-leaf to four-leaf weed growth stage, the two methods resulted in classification accuracies of 83% and 81%, respectively. The wavelet-based method was shown to be very promising in detecting the presence of early season pitted morningglory in mixed hyperspectral reflectances. D 2003 Elsevier Science Inc. All rights reserved.

Weed Control from Herbicide Combinations with Three Formulations of Glyphosate1

Abstract: Greenhouse studies were conducted to evaluate weed control from various formulations of glyphosate alone and in combination with postemergence herbicides. Tank mixtures did not increase barnyardgrass control 2 wk after treatment (WAT) when compared with glyphosate alone; however, tank mixtures did reduce barnyardgrass fresh weight 4 WAT when compared with glyphosate alone in several instances. Antagonism was observed when chlorimuron was combined with all formulations of glyphosate 4 WAT, but control was not reduced when compared with glyphosate alone. Selective herbicides added to glyphosate had an additive or antagonistic effect on prickly sida fresh-weight reductions. Antagonism of pitted morningglory fresh-weight reductions occurred when glyphosate was combined with all herbicides except acifluorfen, which had an additive effect. Fomesafen or lactofen effectively controlled hemp sesbania 2 WAT without the addition of glyphosate. Acifluorfen and chlorimuron combined with glyphosate Cheminova, Monsanto, or Zeneca reduced hemp sesbania fresh weight nearly twofold more than glyphosate alone. Nomenclature: Acifluorfen; chlorimuron; fomesafen; glyphosate; lactofen; barnyardgrass, Echinochloa crus-galli (L.) Beauv. #3 ECHCG; hemp sesbania, Sesbania exaltata (Raf.) Rydb. ex. A. W. Hill # SEBEX; pitted morningglory, Ipomea lacunosa L. # IPOLA; prickly sida, Sida spinosa L. # SIDSP. Additional index words: Additive effects, antagonism, reduced rates, synergism. Abbreviations: ALS, acetolactase synthase; C, Cheminova; LSD, least significance difference; M, Monsanto; POST, postemergence; WAT, weeks after treatment; Z, Zeneca.

U.S. Grower Views on Problematic Weeds and Changes in Weed Pressure in Glyphosate-Resistant Corn, Cotton, and Soybean Cropping Systems

Abstract Corn and soybean growers in Illinois, Indiana, Iowa, Mississippi, Nebraska, and North Carolina, as well as cotton growers in Mississippi and North Carolina, were surveyed about their views on changes in problematic weeds and weed pressure in cropping systems based on a glyphosate-resistant (GR) crop. No growers using a GR cropping system for more than 5 yr reported heavy weed pressure. Over all cropping systems investigated (continuous GR soybean, continuous GR cotton, GR corn/GR soybean, GR soybean/non-GR crop, and GR corn/non-GR crop), 0 to 7% of survey respondents reported greater weed pressure after implementing rotations using GR crops, whereas 31 to 57% felt weed pressure was similar and 36 to 70% indicated that weed pressure was less. Pigweed, morningglory, johnsongrass, ragweed, foxtail, and velvetleaf were mentioned as their most problematic weeds, depending on the state and cropping system. Systems using GR crops improved weed management compared with the technologies used before the adoption of GR crops. However, the long-term success of managing problematic weeds in GR cropping systems will require the development of multifaceted integrated weed management programs that include glyphosate as well as other weed management tactics. Nomenclature: Glyphosate; foxtail, Setaria spp.; johnsongrass, Sorghum halepense (L.) Pers.; morningglory, Ipomoea spp.; pigweed, Amaranthus spp.; ragweed, Ambrosia spp.; velvetleaf, Abutilon theophrasti Medik.; corn, Zea mays L.; cotton, Gossypium hirsutum L; soybean, Glycine max (L.) Merr

A Grower Survey of Herbicide Use Patterns in Glyphosate-Resistant Cropping Systems

Abstract A telephone survey was conducted with growers in Iowa, Illinois, Indiana, Nebraska, Mississippi, and North Carolina to discern the utilization of the glyphosate-resistant (GR) trait in crop rotations, weed pressure, tillage practices, herbicide use, and perception of GR weeds. This paper focuses on survey results regarding herbicide decisions made during the 2005 cropping season. Less than 20% of the respondents made fall herbicide applications. The most frequently used herbicides for fall applications were 2,4-D and glyphosate, and these herbicides were also the most frequently used for preplant burndown weed control in the spring. Atrazine and acetochlor were frequently used in rotations containing GR corn. As expected, crop rotations using a GR crop had a high percentage of respondents that made one to three POST applications of glyphosate per year. GR corn, GR cotton, and non-GR crops had the highest percentage of growers applying non-glyphosate herbicides during the 2005 growing season. A crop rotation containing GR soybean had the greatest negative impact on non-glyphosate use. Overall, glyphosate use has continued to increase, with concomitant decreases in utilization of other herbicides. Nomenclature: 2,4-D; acetochlor; atrazine; glyphosate; corn, Zea mays L.; cotton, Gossipium hirsutum L.; soybean, Glycine max (L.) Merr

Sicklepod (Senna obtusifolia) Response to Shading, Soybean (Glycine max) Row Spacing, and Population in Three Management Systems1

Abstract: Studies were conducted in 1997 and 1998 at the Northeast Mississippi Research and Extension Center to investigate the effects of row spacing (76, 38, and 19 cm), soybean population, and three weed management systems on sicklepod growth and seed production. The cultivars ‘Hartz 5088RR’ (glyphosate-tolerant) and ‘Hutcheson’ (a conventional cultivar) were used in two separate studies. The average soybean populations over cultivars and year were 245,000 (low), 481,000 (medium), and 676,000 (high) plants/ha. The three weed management systems were: no (untreated), one, and two herbicide applications. In the glyphosate-tolerant system, one or two postemergence (POST) applications of glyphosate were used, whereas in the conventional system, flumetsulam plus metolachlor preemergence was used alone (single) or followed by chlorimuron POST (sequential). Reducing soybean row spacing from 76 cm, coupled with increased soybean population, reduced sicklepod population up to 80%. Except for Hutcheson in 1998, reducing row spacing and increasing soybean population also reduced sicklepod seed production in both the untreated and the single applications. A single herbicide application reduced sicklepod population up to 68% from untreated plots. However, except for Hartz 5088RR in 1998, the sequential application did not further reduce sicklepod population. In a shading study, partial shading increased sicklepod height but reduced dry weight. However, as shading level increased from 65 to 80 and 95%, height was also reduced. Nomenclature: Chlorimuron; flumetsulam; glyphosate; metolachlor; sicklepod, Senna obtusifolia (L.) Irwin and Barnaby #3 CASOB; soybean, Glycine max (L.) Merr. Additional index words: Glyphosate-tolerant soybean, sequential herbicide applications, seed production. Abbreviations: MG, maturity group; POST, postemergence; PRE, preemergence; WAP, weeks after planting.

Using a Grower Survey to Assess The Benefits and Challenges of Glyphosate-Resistant Cropping Systems for Weed Management in U.S. Corn, Cotton, and Soybean

Abstract Over 175 growers in each of six states (Illinois, Indiana, Iowa, Mississippi, Nebraska, and North Carolina) were surveyed by telephone to assess their perceptions of the benefits of utilizing the glyphosate-resistant (GR) crop trait in corn, cotton, and soybean. The survey was also used to determine the weed management challenges growers were facing after using this trait for a minimum of 4 yr. This survey allowed the development of baseline information on how weed management and crop production practices have changed since the introduction of the trait. It provided useful information on common weed management issues that should be addressed through applied research and extension efforts. The survey also allowed an assessment of the perceived levels of concern among growers about glyphosate resistance in weeds and whether they believed they had experienced glyphosate resistance on their farms. Across the six states surveyed, producers reported 38, 97, and 96% of their corn, cotton, and soybean hectarage planted in a GR cultivar. The most widely adopted GR cropping system was a GR soybean/non-GR crop rotation system; second most common was a GR soybean/GR corn crop rotation system. The non-GR crop component varied widely, with the most common crops being non-GR corn or rice. A large range in farm size for the respondents was observed, with North Carolina having the smallest farms in all three crops. A large majority of corn and soybean growers reported using some type of crop rotation system, whereas very few cotton growers rotated out of cotton. Overall, rotations were much more common in Midwestern states than in Southern states. This is important information as weed scientists assist growers in developing and using best management practices to minimize the development of glyphosate resistance. Nomenclature: Glyphosate; corn, Zea mays L.; cotton, Gossipium hirsutum L.; rice, Oryza sativa L.; soybean, Glycine max (L.) Merr

Utility of Remote Sensing in Predicting Crop and Soil Characteristics

Remote sensing during the production season can provide visual indications of crop growth along with the geographic locations of those areas. A grid coordinate system was used to sample cotton and soybean fields to determine the relationship between spectral radiance, soil parameters, and cotton and soybean yield. During the 2 years of this study, mid- to late-season correlation coefficients between spectral radiance and yield generally ranged from 0.52 to 0.87. These correlation coefficients were obtained using the green–red ratio and a vegetation index similar to the normalized difference vegetation index (NDVI) using the green and red bands. After 102 days after planting (DAP), the ratio vegetation index (RVI), difference vegetation index (DVI), NDVI, and soil-adjusted vegetation index (SAVI) generally provided correlation coefficients from 0.54 to 0.87. Correlation coefficients for cotton plant height measurements taken 57 and 66 DAP during 2000 ranged from 0.51 to 0.76 for all bands, ratios, and indices examined, with the exception of Band 4 (720 nm). The most consistent correlation coefficients for soybean yield were obtained 89–93 DAP, corresponding to peak vegetative production and early pod set, using RVI, DVI, NDVI, and SAVI. Correlation coefficients generally ranged from 0.52 to 0.86. When the topographic features and soil nutrient data were analyzed using principal component analysis (PCA), the interaction between the crop canopy, topographic features, and soil parameters captured in the imagery allowed the formation of predictive models, indicating soil factors were influencing crop growth and could be observed by the imagery. The optimum time during 1999 and 2000 for explaining the largest amount of variability for cotton growth occurred during the period from first bloom to first open boll, with R values ranging from 0.28 to 0.70. When the PCA-stepwise regression analysis was performed on the soybean fields, R2 values were obtained ranging from 0.43 to 0.82, 15 DAP, and ranged from 0.27 to 0.78, 55–130 DAP. The use of individual bands located in the green, red, and NIR, ratios such as RVI and DVI, indices such as NDVI, and stepwise regression procedures performed on the cotton and soybean fields performed well during the cotton and soybean production season, though none of these single bands, ratios, or indices was consistent in the ability to correlate well with crop and soil characteristics over multiple dates within a production season. More research needs to be conducted to determine whether a certain image analysis method will be needed on a field-by-field basis, or whether multiple analysis procedures will need to be performed for each imagery date in order to provide reliable estimates of crop and soil characteristics.