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Published online 17 November 2005
Published in Agron J 97:1636-1639 (2005)
DOI: 10.2134/agronj2004.0283
© 2005 American Society of Agronomy
677 S. Segoe Rd., Madison, WI 53711 USA
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Production Papers

Growth and Yield Response to Simulated Hail Damage in Guar

John W. Sija,*, Jason P. Ottb, Brian L. S. Olsonc and Todd A. Baughmana

a Texas Agric. Res. and Ext. Center, Box 1658, Vernon, TX 76385
b Texas Coop. Ext., 1506 Ave. M, Hondo, TX 78861
c Kansas State Univ. Northwest Res. and Ext. Center, 105 Experiment Farm Rd., Colby, KS 67701

* Corresponding author (jsij{at}ag.tamu.edu)

Received for publication November 18, 2004.

   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
Hail damage can result in substantial economic loss to annual spring and summer crops. Crop insurance guidelines to assess hail damage on major crops are readily available to adjusters. However, research and guidelines to assess hail damage on minor crops is lacking and may limit their expansion. A dryland field study with guar [Cyamopsis tetragonoloba (L.) Taub.] was designed to determine the effect of simulated hail damage at two growth stages and three levels of defoliation on plant regrowth and bean yield. Experiments were conducted on a Miles fine sandy loam (fine-loamy, mixed, superactive, thermic Typic Paleustalfs) near Vernon, TX, from 2001 through 2003. A commercial grass trimmer was used to simulate hail damage by flailing plants to targeted defoliations levels of 33, 66, and 90% at 6 and 12 wk after emergence (WAE). Both final plant height and yield were reduced to a greater extent when plants were damaged at 12 WAE (grain-fill period) than at 6 WAE (early flowering). Regression analysis from data combined over years showed that guar yields were reduced 50% with a 66% defoliation level at 6 WAE, but required only 42% defoliation to reduce yield levels 50% with late-season defoliation. This is the first report of using a grass trimmer to simulate hail damage by shredding plant material rather than manually removing plants and plant parts. Results from this study also provide a basis for producers and insurance adjusters to quantify and estimate economic losses in guar due to hail damage at two growth stages.

Abbreviations: WAE, weeks after emergence


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
CROP INJURY from natural disasters like hailstorms frequently results in extensive economic loss. Crop insurance offers producers protection from total loss on major crops such as cotton (Gossypium hirsutum L.), corn (Zea mays L.), soybean [Glycine max (L.) Merr.], wheat (Triticum aestivum L.), and grain sorghum [Sorghum bicolor (L.) Moench]. However, for lack of reliable data, crop insurance on minor crops may not be available and may limit production of minor crops.

Guar, a drought-tolerant summer annual legume, is grown for its galactomannan gum used in numerous food and industrial applications (Whistler and Hymowitz, 1979). Due to guar's short plant height, low branching, and low pod set, the crop is particularly susceptible to conditions that can result in reduced plant development, making machine harvesting less efficient. Guar production is limited to a small region of the semiarid, hail-prone Southern Great Plains (Stafford, 1982; Seiler and Stafford, 1985). This region is noted for unpredictable and violent weather with severe thunderstorms, high winds, and hail. Studies by Changnon and Changnon (2000), using analysis of regional weather, showed storm activity over the past 100-yr period peaking from 1976 to 1995 in the southern plains. This upward trend in storm distribution was similar to that found for hail. Crop insurance adjusters as well as producers of minor crops like guar require guidelines to assess yield reductions due to hail damage.

Surprisingly, it is not uncommon to observe yield increases due to defoliation within some genotypes at certain early growth stages (Mahapatra and Manna, 1962; Hicks et al., 1977; Crookston and Hicks, 1978; Counce et al., 1994a). Provided there is adequate time for recovery, plants compensate for stand loss and vegetative and reproductive damage in various ways. Moriondo et al. (2003) showed that sunflower (Helianthus annuus L.) compensated for defoliation damage at certain growth stages by increasing leaf area or delaying senescence. In soybean, the number of pods per plant increased due to early vegetative damage (Teigen and Vorst, 1975). However, plant damage at later growth stages, and particularly during the reproductive period, is generally associated with yield loss. Defoliation, loss of developing seed, culm bending, stand loss, and other plant damage incurred at or near the reproductive stage have all affected crop yields (Teigen and Vorst, 1975; Simmons et al., 1982; Pickle and Caviness, 1984; Counce et al., 1994a, 1994b).

Yield losses and plant response due to hail damage have primarily been estimates based on simulated plant damage through the removal of leaves or portions of leaves at different growth stages (Womack and Thurman, 1962; Hanway, 1969; Crookston and Hicks, 1978), although one-time events of actual hail damage to corn and barley (Hordeum vulgare L.) have been reported. Dwyer et al. (1994) noted that the reduction in dry matter and grain yield caused by hail damage was related to the corn heat units required to reach physiological maturity at the time of the storm. In barley, Gilbertson and Hockett (2003) concluded that two-rowed or Manchurian type malting cultivars were highly susceptible to hail damage and producers should avoid growing these types in hail-prone areas.

Although hail damage has been simulated by manually removing plant parts at different stages of development, severe hailstorms result in shredded plant biomass and damaged reproductive structures (personal observations). The authors are not aware of any published report that compares removal of plant parts or whole plants with the kind of damaged inflicted through shredding as with hail or high winds and blowing sand. Hence, the question arises, "Can other procedures or methods of defoliation simulate the type of damage a crop receives in a hailstorm"?

Presently, yield data on hail-damaged guar does not exist, and guidelines are needed for crop insurance adjusters to determine economic losses if multi-peril insurance for this minor crop is to be made available. The objective of this study was to evaluate hail damage on guar at two growth stages and three levels of simulated hail defoliation using a commercial grass trimmer. This study was not designed to formally evaluate or compare plant damage from failing plant material with a grass trimmer and earlier methods that employed excision of various plant parts at different stages of growth. However, based on personal observations of hail damage to crops like soybean, corn, sorghum, wheat, and guar, we believe flailing most closely simulates severe hail damage. Results from this study should provide a basis for estimating yield reduction in guar due to hail damage and a means to adjust for economic loss.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 REFERENCES
 
The effect of simulated hail damage on guar was evaluated from June 2001 to October 2003 at the Texas A&M Research and Extension Center near Vernon, TX (34°05'40'' N lat; 99°21'56'' W long; 390 m elev.), on a Miles fine sandy loam. Annual precipitation at Vernon is characterized by 640 mm of rainfall that is distributed with peaks in spring and fall. The climate is classified as semiarid with episodes of violent weather. Monthly average maximum/minimum temperatures range from 37/22°C in July to 12/–4°C in January. The experimental design was a randomized complete block with four injury treatments at two growth stages, and six replications. Plots consisted of three rows on 1-m centers and a length of 7.6 m. No fertilizer was required for dryland guar production on this site.

A pre-emergence application of 0.56 kg a.i. ha–1 trifluralin ({alpha},{alpha},{alpha}-trifluoro-2,6-dinitro-N, N-diproply-p-toluidine) herbicide was applied and incorporated in April. "Kinman" guar was planted in late-May to early June in a conventional-tilled field to achieve a population of about 11 plants m–1 of row. The test location was kept weed-free using cultivation and hand hoeing.

A commercial gas-powered grass trimmer (Stihl Model FS 200; Stihl, Virginia Beach, VA) was turned on edge and operated at low speed to simulate hail injury by flailing plant vegetation. Simulated injury consisted of four targeted treatments: 0, 33, 66, and 90% removal of the aboveground biomass. Injury treatments were applied at either 6 or 12 wk after emergence (WAE). Practice runs to visually gauge the degree of plant damage were initiated outside the test area before applying final simulated hail damage treatments to the experimental plots. The amount of biomass removed for each targeted treatment is illustrated in Fig. 1 .



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  		Fig. 1. Defoliation treatments applied with a commercial grass trimmer to simulate hail damage on guar.

 
All three rows of each treatment plot were flailed to the same extent with the grass trimmer. Flailing plants results in damage typically observed following a severe hailstorm. The two outside rows served as border rows. Two days after treatment, 1 m of row from the middle row was harvested to ground level and dried. The 2-d delay following treatment allowed plant material to dry and aid hand harvesting. An additional 1 m of row of untreated plants adjacent to the flailed plot was also harvested and dried to calculate the percentage of simulated hail damage. Subtracting the difference in the amount of biomass harvested from the flailed row from that of the nonflailed row provided the estimate of biomass removed using the grass trimmer. Grain yield (adjusted to 130 g kg–1 moisture content) was calculated on a kg ha–1 basis by machine harvesting 4.6 m of the center row of each plot after the first freeze.

Year was considered a random effect. Yearly differences in absolute plant height and grain yield were overcome by expressing height and yield data relative to the untreated check. Relative plant height and yield data were arcsin transformed before analysis using the PROC MIXED procedure of SAS (SAS Institute, 1996) to examine treatment effects. Polynomial regression models with linear, quadratic, and cubic terms were fitted to the 6 WAE and 12 WAE treatments. Quadratic and cubic terms were deleted when not significant at P < 0.05. The relationship between WAE treatments and yield was further examined using heterogeneity regression analysis.


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
REFERENCES
 
The use of a commercial grass trimmer was an effective method of simulating hail damage on guar (Fig. 1). Replication of simulated hail damage using this method was relatively consistent within treatments for each year of the study as indicated by the standard errors; however, it was somewhat difficult to achieve the precision needed to match targeted defoliation percentages with estimated biomass removed (Fig. 2a and 2b) . Generally, estimated biomass removal in 2002 was closest to the targeted levels.



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  		Fig. 2. Comparison of estimated hail damage at (a) 6 WAE and (b) 12 WAE to targeted defoliation in guar for three growing seasons. Values are means ±SE.

 
Weeks after emergence was significant (P < 0.001) for plant height and yield. There was a significant interaction (P < 0.001) between WAE and biomass removed for both grain yield and plant height. Therefore, results from the 6 and 12 WAE treatments are reported separately.

Plant height is important in machine harvesting. Seiler and Stafford (1985) using factor analysis showed the importance of plant height and number of nodes per plant in establishing the dependent relationship between morphological characteristics and yield components in guar genotypes. In the present study, end of season plant height was affected to a greater degree by late-season damage (Fig. 3a and 3b) . Regression analysis indicated that a linear model adequately defined the relationship between plant height and percentage defoliation. Heterogeneity regression analysis showed that the slopes between the 6 and 12 WAE treatments were significantly different (R2 = 0.98, P < 0.0001). The greater negative slope of the 12 WAE treatment indicated that plants grown under dryland conditions that became damaged late in the season did not have time to recover due to cooler temperatures, shorter photoperiods, and soil moisture deficits. Hence, plant productivity was impacted.



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  		Fig. 3. End of season relative plant height of guar flailed at (a) 6 WAE and (b) 12 WAE to different levels of defoliation using a grass trimmer (3 yr data).

 
Grain yields were reduced in a similar manner to that of plant height with increasing levels of defoliation (Fig. 4a and 4b) . Since guar has an indeterminate growth habit, plants defoliated at 6 WAE possess regrowth and yield potential depending on the seasonal growing conditions. The scatter in data points possibly reflects plant response to seasonal rainfall and soil moisture availability among years. The negative relationship between degree of defoliation and grain yield was much more apparent in the 12 WAE defoliation treatments (Fig. 4b). As with plant height, linear models adequately defined the relationship between yield and percentage defoliation.



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  		Fig. 4. Relative grain yield of guar flailed at (a) 6 WAE and (b) 12 WAE to different levels of defoliation using a grass trimmer (3 yr data).

 
Obviously, each crop year was unique due to differences in environmental conditions for that growing season. Environmental conditions, particularly timely rainfall, can potentially reduce the negative effects of early defoliation on guar due to accelerated regrowth of damaged branches and additional pod set. At 6 WAE plants begin to bloom and set pods. At 12 WAE the majority of the pods are well developed. Linear models presented in Fig. 4a and 4b indicated that at 6 WAE grain yield was reduced 50% with a 66% reduction in biomass. In the 12 WAE treatment (Fig. 4b), a 42% reduction in aboveground biomass resulted in a 50% reduction in grain yield. Heterogeneity regression analysis showed that the slopes between the 6 and 12 WAE treatments were significantly different (R2 = 0.99, P < 0.0001). This indicates higher sensitivity to defoliation at the later growth stage. Grain yields can be further compromised by late-season hail damage due to reduced plant height and lower pod set making machine harvest less efficient.

This is the first known study to attempt to quantify simulated hail damage and resulting grain yield losses in guar. Results from this research provide producers and insurance adjusters a means to assess hail damage in this crop. We also believe a commercial grass trimmer effectively simulates hail damage in guar.


   ACKNOWLEDGMENTS
 
Appreciation is extended to the National Crop Insurance Services for providing partial support for this research. We also extend our thanks to Dr. B.R. Min for assistance with statistical analyses.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 





This Article
Right arrow Abstract Freely available
Right arrow Figures Only
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Right arrow Citing Articles via ISI Web of Science (1)
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Right arrow Articles by Sij, J. W.
Right arrow Articles by Baughman, T. A.
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Right arrow Articles by Sij, J. W.
Right arrow Articles by Baughman, T. A.
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Right arrow Articles by Sij, J. W.
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Right arrow Plant and Environment Interactions

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