ARTICLES

Glufosinate Application Timing and Rate Affect Peanut Yield

Authors: Eric P. Prostko , Theodore M. Webster , Michael W. Marshall , Ramon G. Leon , Timothy L. Grey , Jason A. Ferrell , Peter A. Dotray , David L. Jordan , W. James Grichar , Barry J. Brecke

  • Glufosinate Application Timing and Rate Affect Peanut Yield

    ARTICLES

    Glufosinate Application Timing and Rate Affect Peanut Yield

    Authors: , , , , , , , , ,

Abstract

Field studies were conducted at 13 locations across the US peanut belt during 2010–2012 to evaluate peanut response to postemergence applications of glufosinate. Glufosinate was applied at 0, 41, 82, 164, 328 and 656 g ai/ha 30, 60, and 90 days after planting (DAP). There was a significant interaction for peanut yield between application time and glufosinate rate; peanut yield data were regressed on rate of glufosinate and fit to a log-logistic dose response curve by application timing. At 30 DAP, peanut yield ranged from 16 to 92% of the non-treated control, with glufosinate at 266 g/ha causing an estimated 50% reduction in yield (Y50 ). At 60 DAP, peanut yield ranged from 16 to 82% of the nontreated control, with Y50  =  266 g/ha of glufosinate. Peanut yield when glufosinate was applied at 90 DAP ranged from 20 to 78% of the non-treated control; Y50  =  187 g/ha of glufosinate, which was lower than that at 30 DAP and indicated greater peanut sensitivity. Peanut plants treated at 30 DAP had more time to recover from glufosinate injury at the lower rates and/or were in a less susceptible stage of growth relative to 90 DAP. These data provide peanut growers across the US with an estimate of potential yield losses associated with mis-application, off-target movement, or sprayer contamination of glufosinate.

Keywords: Arachis hypogaea L., crop tolerance, drift, herbicide injury, sprayer contamination

How to Cite:

Prostko, E. & Webster, T. & Marshall, M. & Leon, R. & Grey, T. & Ferrell, J. & Dotray, P. & Jordan, D. & Grichar, W. & Brecke, B., (2013) “Glufosinate Application Timing and Rate Affect Peanut Yield”, Peanut Science 40(2), p.115-119. doi: https://doi.org/10.3146/PS13-4.1

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Published on
01 Jul 2013
Peer Reviewed

Introduction

The threat of glyphosate-resistant (GR) weeds, especially Palmer amaranth (Amaranthus palmeri S. Wats.), has motivated growers to consider the use of herbicides with alternative modes of action in cotton (Gossypium hirsutum L.) and soybean [Glycine max (L.) Merr.]. Glufosinate is a non-selective, broad-spectrum, postemergence herbicide that inhibits glutamine synthetase (Senseman 2007). Thus, it can be used to effectively control GR-Palmer amaranth in glufosinate-resistant crops if applied to plants less than 8 cm in height (Norsworthy et al. 2008; Wilson et al. 2007).

Glufosinate-resistant crops (Liberty-Link®) were developed by insertion of the bar gene isolated from the soil bacterium Streptomyces hygroscopicus (Duke 2005). The bar gene expresses the phosphinothricin acetyltransferase (pat) enzyme that acetylates L-phosphinothricin, and confers tolerance to glufosinate (Herouet et al. 2005; Lydon and Duke 1999). Liberty-Link® cotton cultivars were commercialized in 2004 (Duke 2005). Additionally, WideStrike® cotton cultivars, which also contain the pat gene, are commercially available but offer lower levels of resistance to glufosinate (Culpepper et al. 2009, Steckel et al. 2012). In soybean, regulatory approval for glufosinate-resistance in soybean occurred in 1996 (Duke 2005). However, wide-spread commercialization of LibertyLink® soybean cultivars did not occur until 2009. Glufosinate-resistant field corn (Zea mays L.) hybrids were commercialized in 1997 (Duke 2005).

Since peanut is grown in close proximity to field corn, cotton, and soybean in the US peanut belt, drift or sprayer contamination problems are likely to occur. Previous research has evaluated the unintentional effects of glyphosate and dicamba on peanut yield. When glyphosate was applied to peanut plants at 28 days after planting (DAP), rates of 280 g/ha or higher caused significant yield reductions (Lassiter et al. 2007). When applied between 75 to 105 DAP, glyphosate at 240, 320, and 470 g/ha reduced peanut yield by 12%, 24%, and 36%, respectively (Grey and Prostko 2010). Estimated peanut yield losses for dicamba applied at rates between 40 to 560 g/ha at 30, 60, or 90 DAP ranged between 2 and 100% (Prostko et al. 2011).

Limited studies have addressed the effects of glufosinate on peanut. In Texas, glufosinate applied at 470 to 580 g/ha provided 100% control of simulated volunteer runner peanut cultivars (Grichar and Dotray 2007). Early tests conducted in North Carolina indicated that peanut yield was reduced 14 to 74% when glufosinate was applied at rates ranging between 135 to 538 g/ha (Jordan et al. 2011). Subsequent North Carolina tests reported peanut yield was reduced 33 to 75% by 302 g/ha of glufosinate in 4 out of 4 site-years and 25% by 123 g/ha in 1 of 4 site-years (Johnson et al. 2012). However, both of these tests were conducted using only one application timing, approximately 25 to 28 DAP. Therefore, the objective of our research was to evaluate peanut yield response to glufosinate at 0, 41, 82, 164, 328, 656 g ai/ha applied 30, 60, or 90 DAP.

Materials and Methods

Small-plot field trials were conducted at 13 locations across the US peanut belt during 2010 to 2012. A complete description of these locations is presented in Table 1. Production and pest management practices were followed according to local Cooperative Extension recommendations.

Table 1.
Table 1.

Locations, cultivars, and planting dates for the peanut-glufosinate tolerance studies in 2010 to 2012.

At all locations, herbicide treatments were arranged in a split-plot design. Time of application was the main plot with glufosinate rate as the sub-plot. Application timings were 30, 60, and 90 DAP and glufosinate rates were 0, 41, 82, 164, 328, 656 g ai/ha. A logarithmic scale with a common multiplier (e.g. 2) is recommended to evaluate herbicide dose response relationships (Seefeldt et al. 1995). The typical use rate of glufosinate in cotton and and soybean is 656 g ai/ha (Anonymous 2013). Generally, peanut plants were in the R1, R4-R5, and R6 stages of growth at 30, 60, and 90 DAP timings, respectively (Boote 1982). All treatments were replicated four to six times. Glufosinate was applied using a CO2-pressurized sprayer calibrated to deliver 94 to 140 L/ha. All plot areas were maintained weed-free throughout the season using a combination of herbicides (clethodim, diclosulam, flumioxazin, imazapic, pendimethalin, and 2,4-DB) and hand-weeding. Peanut yield data were obtained by mechanical harvesting at maturity.

Data were analyzed using a mixed model ANOVA. Rate and timing of glufosinate were fixed effects, while site-years, replications, and interactions with these factors were considered random effects. Peanut yield data were regressed on rate of glufosinate and fit to a log-logistic curve (Seefeldt et al. 1995). The mathematical expression relating peanut yield to glufosinate rate was:

where c  =  the mean yield response at very high glufosinate doses, d  =  mean yield response of the nontreated control, Y50  =  the glufosinate rate causing a 50% reduction in yield, and b  =  slope of the curve around Y50 . Differences in parameter estimates from the regression models were evaluated using t-tests with an alpha of 0.05 and tcritical  =  1.96 (Glantz and Slinker 2001). Similar to Askew and Wilcut (2001) and Jasieniuk et al. (1999), the nonlinear coefficient of determination (R2 nonlinear ) was calculated as:

Results and Discussion

There were significant interactions between time of application and glufosinate rate. Therefore, yield data were regressed on glufosinate rate by time of application.

30 DAP

Peanut yield as a percentage of the non-treated control at 30 DAP ranged from to 92 to 16% at 41 and 656 g/ha, respectively (Figure 1). This supports previous findings from North Carolina, where no major reductions in peanut yield were observed when glufosinate was applied 21 days after peanut emergence at rates ≤ 67 g/ha (Jordan et al. 2011). Additionally, in North Carolina peanut yield losses ranged from 33 to 75% when applied at 302 g/ha of glufosinate (Johnson et al. 2012). At higher rates in Texas (470 to 580 g/ha glufosinate), peanut death resulted when applied to plants that were 8 to 10 cm tall (Grichar and Dotray 2007).

Fig. 1.
Fig. 1.

Peanut yield loss response to glufosinate applied 30 days after planting averaged over 13 locations. , R2 nonlinear  =  0.75.

Peanut yield declined with increasing glufosinate rate, with the relationship described (R2 nonlinear  =  0.75) by a log-logistic curve. This particular function is frequently used to evaluate plant sensitivity across a range of herbicide doses, with biologically relevant parameters (Knezevic et al. 2007; Seefeldt et al. 1995). The estimate of the Y50 parameter was 266 g/ha, which is the amount of glufosinate needed to reduce peanut yield 50%. This value is often used to compare plant sensitivity to an herbicide across plant types. For instance, the rate of glufosinate needed to reduce seedling biomass 50% (GR50 ) ranged from 63 to 160 g/ha for seven different weeds, including common ragweed (Ambrosia artemisiifolia L.), common lambsquarters (Chenopodium album L.), and velvetleaf (Abutilon theophrasti Medik.) (Tharp et al. 1999). However, established common bermudagrass had a GR50 of 990 g/ha of glufosinate (Webster et al. 2004). Lassiter et al. (2007) evaluated glyphosate drift on peanut and determined that Y50 values ranged from 244 to 788 g/ha.

60 DAP

At this application timing, peanut yield ranged from to 82 to 16% of the non-treated control at 41 and 656 g/ha, respectively (Figure 2). The relationship was described by a log-logistic regression (R2 nonlinear  =  0.66). The Y50 was 266 g/ha of glufosinate, with b  =  5.48 (Table 2); based on t-tests, both values were equivalent to those parameter estimates at 30 DAP.

Fig. 2.
Fig. 2.

Peanut yield response to glufosinate applied 60 days after planting averaged over 13 locations. , R2 nonlinear  =  0.66.

Table 2.
Table 2.

Parameter estimates and the standard errors (s.e.) from the log-logistic regression equations from Figures 1, 2, and 3.

90 DAP

Peanut yield ranged from to 78 to 20% of the non-treated control at glufosinate rates of 41 and 656 g/ha, respectively (Figure 3). The Y50 from the log-logistic regression model was 187 g/ha, which was lower (t  =  2.28) than the Y50 at 30 DAP (266 g/ha). This suggests that glufosinate applied at 90 DAP was more sensitive to glufosinate than when applied at 30 DAP. The slope of the regression curve around the Y50 (b  =  3.6) at 90 DAP was less (t  =  3.02) than that at 30 DAP (b  =  8.14), with each of these similar to the slope at 60 DAP. Peanut yield at the highest glufosinate rate was between 15 and 20% of the nontreated control for all application dates. As a result, the smaller slope at 90 DAP and the convergence of peanut yield at the maximum glufosinate rates suggests that peanut at 90 DAP was more sensitive to lower glufosinate rates than those applied at 30 DAP. One potential explanation is that the 30 DAP treatment may have had more time during the growing season before harvest to recover from glufosinate injury. Additionally, there may have been differences in peanut sensitivity to glufosinate among plant growth stages at the time of application.

Fig. 3.
Fig. 3.

Peanut yield response to glufosinate applied 90 days after planting averaged over 13 locations. , R2 nonlinear  =  0.61.

Summary and Conclusions

These results provide growers across the U.S. Peanut Belt with an estimate of potential yield losses caused by a range of rates of glufosinate applied at three different growth stages. Consequently, decisions can be made to determine if a peanut crop that is unintentionally treated with glufosinate should be managed for production or terminated.

Acknowledgements

This research was partially supported through grants provided by the Georgia Peanut Commission, North Carolina Peanut Grower's Association, Texas Peanut Producer's Board, and the Florida Peanut Producer's Association. The technical support of Peter Eure, Charlie Hilton, Rand Merchant, Jesse Parker, Dewayne Johnson, and Lyndell Gilbert was greatly appreciated.

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Notes

    Author Affiliations

  1. Prof., Dept. Crop & Soil Sci., The Univ. of Georgia, Tifton, GA 31793
  2. Res. Agron., USDA-ARS, Tifton, GA, 31793
  3. Asst. Prof., Clemson Univ., Blackville, SC 29817
  4. Asst. Prof., Agron. Dept., Univ. of Florida, Jay, FL 32565
  5. Prof., Dept. Crop & Soil Sci., The Univ. of Georgia, Tifton, GA 31793
  6. Assoc. Prof., Agron. Dept., Univ. of Florida, Gainesville, FL 32611
  7. Prof., Dept. Plant & Soil Sci., Texas Tech Univ., Lubbock, TX 79409
  8. Prof., Crop Sci. Dept., North Carolina State Univ., Raleigh, NC 27695
  9. Senior. Res. Scientist, Texas A&M AgriLife Research, Beeville, TX 77802
  10. Emerit. Prof, Agron. Dept., Univ. of Florida, Jay, FL 32565
  11. * Corresponding author’s email: eprostko@uga.edu