USING PROC NLMIXED TO ANALYZE A TIME OF WEED REMOVAL STUDY

Many studies in weed science involve fitting a nonlinear model to experimental data. Examples of such studies include dose-response experiments and studies to determine the critical period of weed control. The experiments typically use block designs and often have additional complexity such as split-plot features. However, nonlinear models are typically fit using software such as SAS PROC NLIN that are limited to a single error term and whose ability to account for blocking is either awkward or lacking entirely. For example, Seefeldt et al. (1995) only proceeded in fitting the nonlinear model after establishing that the block effect was negligible. Issues such as multiple error terms in split-plot designs are simply not dealt with at all. In this paper, we examine a weed removal study carried out as a split-plot design with blocks and illustrate the use of SAS PROC NLMIXED to account for blocks and the two-level error structure

Early-season insect defoliation influences the critical time for weed removal in soybean

To develop more effective pest-management strategies, it is essential to understand how different pests interact with each other and the crop. Field studies were conducted in 2003 and 2004 at two Nebraska locations to determine the effects of early-season crop defoliation on the critical time for weed removal (CTWR) in narrow-row soybean. Three soybean defoliation levels were selected to simulate 0, 30, and 60% leaf tissue removal by the bean leaf beetle. Weeds were allowed to compete with the crop until V2, V4, V6, R3, and R5 growth stages. There were also season-long weedy and weed-free treatments. Results indicated that the CTWR in soybean occurred earlier as defoliation levels increased from 0 to 60%. The CTWR occurred at V3, V2, and V1 growth stage for 0, 30, and 60% defoliation levels, respectively. Overall, 60% defoliation resulted in earlier CTWR by at least 14 d. Yield losses from defoliation and weed interference were primarily associated with a reduction in number of pods per plant-1

EC02-173 Spotted and Diffuse Knapweed

Spotted knapweed (Centaure amaculosa Lam. = C. biebersteinii DC.) and diffuse knapweed (C.diffusa Lam.) are two of Nebraska’s seven noxious weeds. They are also noxious in at least 17 other states. These are closely related species that are well adapted to a variety of habitats including open forests, rangelands and pastures, Conservation Reserve Program lands, roadsides, and ditch banks. Centaurea is a large genus of over 400 species, 32 of which are common weeds of the United States and several of which [e.g., yellowstar thistle, C. solstitalis L, and Russian knapweed, C. repens L. =Acroptilon repens (L.) DC.] have been identified officially as noxious weeds in nearby western states. Other Centaurea species areused as ornamentals. The knapweeds were introduced to the United States from the grasslands of southeastern Europe and Asia. Spotted knapweed now infests more than seven million acres and diffuse knapweed more than three million acres of rangeland and pastures in the western United States

EC02-173 Spotted and Diffuse Knapweed

Spotted knapweed (Centaure amaculosa Lam. = C. biebersteinii DC.) and diffuse knapweed (C.diffusa Lam.) are two of Nebraska’s seven noxious weeds. They are also noxious in at least 17 other states. These are closely related species that are well adapted to a variety of habitats including open forests, rangelands and pastures, Conservation Reserve Program lands, roadsides, and ditch banks. Centaurea is a large genus of over 400 species, 32 of which are common weeds of the United States and several of which [e.g., yellowstar thistle, C. solstitalis L, and Russian knapweed, C. repens L. =Acroptilon repens (L.) DC.] have been identified officially as noxious weeds in nearby western states. Other Centaurea species areused as ornamentals. The knapweeds were introduced to the United States from the grasslands of southeastern Europe and Asia. Spotted knapweed now infests more than seven million acres and diffuse knapweed more than three million acres of rangeland and pastures in the western United States

Interaction of quizalofop-p-ethyl with 2,4-D choline and/or glufosinate for control of volunteer corn in corn resistant to aryloxyphenoxypropionates

Corn resistant to aryloxyphenoxypropionates (FOPs) (Enlist™ corn) enables the use of quizalofop-p-ethyl (QPE) as a selective postemergence (POST) herbicide for control of glufosinate/glyphosate-resistant corn volunteers. Growers usually mix QPE with 2,4-D choline and/or glufosinate to achieve broad-spectrum weed control in Enlist™ corn. The objectives of this study were to (1) evaluate the efficacy of QPE applied alone or mixed with 2,4-D choline and/or glufosinate for control of glufosinate/glyphosate-resistant corn volunteers in Enlist™ corn and (2) determine the impact of application time (V3 or V6 growth stage of volunteer corn) of QPE-based treatments on volunteer corn control as well as Enlist™ corn injury and yield. Field experiments were conducted at South Central Agricultural Lab, Clay Center, NE in 2021 and 2022. Quizalofop-p-ethyl (46 or 93 g ai ha‒1 ) applied at V3 or V6 growth stage controlled volunteer corn ≥ 88% and ≥ 95% at 14 and 28 d after treatment (DAT), respectively. The QPE (46 g ai ha‒1 ) mixed with 2,4-D choline (800 g ae ha‒1 ) had 33% less expected control of V3 volunteer corn in 2021, and 8% less than expected control of V6 volunteer corn in 2022 at 14 DAT. Volunteer corn control was improved by 7%-9% using the higher rate of QPE (93 g ai ha‒1 ) in a mixture with 2,4-D choline (1,060 g ae ha‒1 ). The QPE mixed with glufosinate had an additive effect and interactions in any combinations were additive beyond 28 DAT. Mixing 2,4-D choline can reduce QPE efficacy on glufosinate/glyphosate-resistant corn volunteers up to 14 DAT when applied at the V3 or V6 growth stage; however, the antagonistic interaction did not translate into corn yield loss. Increasing the rate of QPE (93 g ai ha‒1 ) while mixing with 2,4-D choline can reduce antagonism

Reversing resistance to tembotrione in an Amaranthus tuberculatus (var. rudis) population from Nebraska, USA with cytochrome P450 inhibitors

Background: A population of Amaranthus tuberculatus (var. rudis) was confirmed resistant to 4-hydroxyphenylpyruvate dioxygenase (HPPD)-inhibitor herbicides (mesotrione, tembotrione, and topramezone) in a seed corn/soybean rotation in Nebraska. Further investigation confirmed a non-target-site resistance mechanism in this population. The main objective of this study was to explore the role of cytochrome P450 inhibitors in restoring the efficacy of HPPD-inhibitor herbicides on the HPPD-inhibitor resistant A. tuberculatus population from Nebraska, USA (HPPD-R). Background: A population of Amaranthus tuberculatus (var. rudis) was confirmed resistant to 4-hydroxyphenylpyruvate dioxygenase (HPPD)-inhibitor herbicides (mesotrione, tembotrione, and topramezone) in a seed corn/soybean rotation in Nebraska. Further investigation confirmed a non-target-site resistance mechanism in this population. The main objective of this study was to explore the role of cytochrome P450 inhibitors in restoring the efficacy of HPPD-inhibitor herbicides on the HPPD-inhibitor resistant A. tuberculatus population from Nebraska, USA (HPPD-R). Results: Enhanced metabolism via cytochrome P450 enzymes is the mechanism of resistance in HPPD-R. Amitrole partially restored the activity of mesotrione, whereas malathion, amitrole, and piperonyl butoxide restored the activity of tembotrione and topramezone in HPPD-R. Although corn was injured through malathion followed by mesotrione application a week after treatment, the injury was transient, and the crop recovered. Includes supplementary file

EC11-101 Spring Seed Guide

Welcome to the 2011 Spring Seed Guide. Corn, soybean, sorghum, and alfalfa are included in this seed guide. This circular is a progress report of variety trials conducted by personnel of the Agronomy Department, West Central, and Northeast Extension Centers, and their associated agricultural laboratories and the associates of the University of Wyoming at SAREC

Quantification and Mapping of Surface Residue Cover for Maize and Soybean Fields in South Central Nebraska

The area cultivated under conservation tillage practices such as no-till and minimal tillage has recently increased in Midwestern states, including Nebraska. This increase, consequently, resulted in changes in some of the impacts of cropping systems on soil, such as enhancing soil and water quality, improving soil structure and infiltration, increasing water use efficiency, and promoting carbon sequestration. However, there are no methods currently available to quantify the percent crop residue cover (CRC) and the area under conservation tillage for maize and soybean at large scales on a continuous basis. This research used Landsat-7 (ETM+) and Landsat-8 (OLI) satellite data to evaluate six tillage indices [normalized difference tillage index (NDTI), normalized difference index 7 (NDI7), normalized difference index 5 (NDI5), normalized difference senescent vegetative index (NDSVI), modified CRC (ModCRC), and simple tillage index (STI)] to map CRC in eight counties in south central Nebraska. A linear regression CRC model showed that NDTI performed well in differentiating the CRC for different tillage practices at large scales, with a coefficient of determination (R2) of 0.62, 0.68, 0.78, and 0.07 for 25 March, 18 April, 28 May, and 6 June 2013 Landsat images, respectively. A minimum NDTI method was then used to spatially map the CRC on a regional scale by considering the timing of planting and tillage implementation. The measured CRC data were divided into training (calibration) and testing (validation) datasets. A CRC model was developed using the training dataset between minimum NDTI and measured CRC with an R2 of 0.89 (RMSD = 10.63%). A 3 × 3 matrix showed an overall accuracy of 0.90 with a kappa coefficient of 0.89. About 26% of the maize area and 15% of the soybean area had more than 70% CRC in south central Nebraska. This research and the procedures presented illustrate that multi-spectral Landsat images can be used to estimate and map CRC (error within 10.6%) on a regional scale and continuous basis using locally developed tillage practice versus crop residue algorithms. Further research is needed to incorporate soil and residue moisture content into the CRC versus tillage index to enhance the accuracy of the models for estimating CRC