SHORT COMMUNICATION Relationship between relative time of emergence of Tartary buckwheat (Fagopyrum tataricum) and yield loss of barley J. T. O Donovan 1 and A. S. McClay 2 Can. J. Plant Sci. Downloaded from www.nrcresearchpress.com by 46.3.195.229 on 02/03/18 1 Agriculture and Agri-Food Canada, Box 29, Beaverlodge, Alberta, Canada T0H 0C0 (email: O DonovanJ@agr.gc.ca); 2 Alberta Research Council, Postal Bag 4000, Vegreville, Alberta, Canada T9C 1T4. Received 8 April 2002, accepted 10 June 2002. O Donovan, J. T. and McClay, A. S. 2002. Relationship between relative time of emergence of Tartary buckwheat (Fagopyrum tataricum) and yield loss of barley. Can. J. Plant Sci. 82: 861 863. A nonlinear regression model was used to describe the relationship between Tartary buckwheat [Fagopyrum tataricum (L.) Gaertn.] density and relative time of emergence, and yield of barley (Hordeum vulgare L.). Yield loss increased the earlier the weed emerged relative to the crop. The model is being used in computerized decision support systems for weed management in western Canada. Key words: Fagopyrum tataricum, Hordeum vulgare, nonlinear regression model, relative time of emergence, decision support system. O Donovan, J. T. et McClay, A. S. 202. Lien entre le moment relatif de la levée du sarrasin de Tartarie (Fagopyrum tataricum) et la diminution du rendement de l orge. Can. J. Plant Sci. 82: 861 863. Les auteurs se sont servis d un modèle de régression non linéaire pour décrire le lien entre la densité et le moment relatif de la levée du sarrasin de Tartarie [Fagopyrum tataricum (L.) Gaertn.] et le rendement de l orge (Hordeum vulgare L.). Plus l adventice lève tôt, comparativement à la culture, plus les pertes de rendement sont élevées. Le modèle testé est utilisé dans les systèmes informatiques servant à établir quand prendre des mesures pour lutter contre les mauvaises herbes dans l ouest du Canada. Mots clés: Fagopyrum tataricum, Hordeum vulgare, modèle de régression non linéaire, moment relatif de la levée, système d aide à la décision Interest in developing integrated weed management systems with reduced dependence on herbicides is increasing due to low crop prices, weed resistance to herbicides, and environmental concerns. The use of computerized decision support systems can provide a rational approach to weed management by relating complex information on weed/crop competition to the economics of herbicide application (O Donovan 1996). To function, such systems require information on the impact of weeds on crop yield loss. Traditionally, weed density has been the variable most often used to describe the effects of weeds on crop yield. In western Canada, much weed/crop competition research has focussed on wild oat (Avena fatua), and regression models describing the effects of wild oat density on yield loss of cereal and oilseed crops have been developed (Dew 1972). A nonlinear regression model describing the effects of wild oat on barley was expanded to include relative emergence time as well as density of the weed and crop (O Donovan et al. 2001). This model has undergone considerable evaluation in growers fields in Alberta with very positive results. It is an important component of computerized decision support systems for weed management in western Canada. There has been comparatively little research conducted on the impact of annual dicot weeds on yield loss of cereal crops in western Canada. In eastern Canada, regression 861 coefficients describing the effects of lamb s-quarters (Chenopodium album) and common ragweed (Ambrosia artemisifolia) densities on yield of corn (Zea mays) and soybean (Glycine max) have been developed and incorporated in a computerized decision support system for weed management (Weaver 2001). Tartary buckwheat is recognized as a serious weed of cereal and oilseed crops in certain parts of the prairie provinces, especially Alberta and Manitoba (Sharma 1986). Linear regression analysis indicated that Tartary buckwheat was a strong competitor with cereal crops (De St. Remy et al. 1985). However, the impact of the weed on crop yield loss varied among years, and no attempt was made to incorporate a relative time of emergence parameter into the equations. This factor can be at least as important as weed density when assessing crop yield loss due to wild oats (Cousens et al. 1987) and volunteer barley (O Donovan 1992). The objective of this study was to develop a nonlinear regression equation (for use in computerized decision support systems) to describe the effects of density and relative time of emergence of Tartary buckwheat on yield loss of barley. Field experiments were conducted at Vegreville, Alberta, in 1983 and 1984 on an eluviated black silt loam soil (29% sand, 48% silt, 23% clay, 4% organic matter, and ph 8.2).
862 CANADIAN JOURNAL OF PLANT SCIENCE The test areas were cultivated each year, once in the fall and twice in the spring before seeding. The experiment each year was a randomized complete block with five relative times of emergence treatments and a Tartary buckwheatfree control, each replicated four times. Prior to seeding the crops, Tartary buckwheat seed was mixed with sand and the mixture was sprinkled randomly in 2.5-cm-deep furrows created in the areas between the crop rows in each plot. The furrows in the control plots were sprinkled with sand only. The seeds were then lightly covered with soil to a depth of approximately 2.5 cm. Areas (1-m 2 ) were selected and established in each plot based on the number of Tartary buckwheat seedlings present in an effort to obtain a range of densities. This approach resulted in variable densities among the 1-m 2 areas. Actual Tartary buckwheat densities varied from 15 to 223 plants m 2 among the 1-m 2 areas in the plots over the 2 yr of the study. Seeding of the Tartary buckwheat was staggered to obtain target emergence times of 6 and 3 d before, same time as, and 3 and 6 d after the crop. Actual times of Tartary buckwheat emergence were close to those targeted, varying from 7 d before to 9 d after the crop over the 2 yr of the study. Klondike barley was seeded in rows (20 cm apart) with a double disc press drill during both years. The seeding rate was 80-kg ha 1. Plot size was 2.25 by 6 m. Each 1-m 2 area and its immediate vicinity were hand-weeded throughout the growing season to keep them free of unwanted weeds. At maturity, crop plants were hand-cut from each 1-m 2 area using sickles and dried to constant wt prior to threshing in a stationary thresher. The relationship between barley yield and both Tartary buckwheat density and relative time of emergence was described using a nonlinear regression model (Cousens et al. 1987): Y = Ywf 1 bd ct e + bd a ( ) where Y is the predicted barley yield (g m 2 ) as a function of Tartary buckwheat density and relative time of emergence, Y wf is the estimated weed-free yield, d is the Tartary buckwheat density (plants m 2 ), t is the relative time of emergence (days), e is the base of natural logarithms, a is the asymptote, and b and c are nonlinear regression coefficients for Tartary buckwheat density and relative time of emergence, respectively. Data from individual years and data pooled over both years were fitted to the model using nonlinear least squares iterative procedures. The regression curves were compared between years using an extra sum of squares F-test. Using the estimated a, b and c coefficients from model 1, percentage barley yield loss was calculated from the model: (1) Y 1 = bd/(e ct + bd/a) (2) where Y l is percentage barley yield loss. The F-test indicated that curves did not vary significantly (P = 0.22) between years. Regression coefficients are thus Table 1. Nonlinear regression coefficients ± standard errors z for barley yield regressed against relative time of emergence and density of Tartary buckwheat. n = number of observations n Y wf a b c r 2 40 493 ± 16 52.5 ± 11.1 0.32 ± 0.11 0.22 ± 0.06 0.67 z Data were fitted to model 1 (see text). Fig. 1. Relationship between percentage barley yield loss and relative time of emergence of Tartary buckwheat at various densities. Numbers on the horizontal axis refer to Tartary buckwheat emerging before ( ), at the same time as (0), and after (+) the crop. Yield loss estimates were derived from model 2 (see text). presented for the pooled model (Table 1). The coefficients were significant (P 0.05) for all variables. Assuming simultaneous emergence of Tartary buckwheat and barley, the initial slope value was 0.32 (Table 1). This compares to a value of 0.50 for wild oats in barley (Cousens et al. 1987), suggesting that Tartary buckwheat is a weaker competitor than wild oats. The relationship between predicted barley yield loss and relative time of emergence of the weed and crop at various Tartary buckwheat densities is illustrated in Fig. 1. Barley yield loss varied considerably depending on when the crop emerged relative to the weed. For example, at 80 Tartary buckwheat plants m 2, barley yield loss was only 6% when barley emerged 6 d ahead of the weed but increased to 34% when the weed emerged 6 d ahead of barley. This suggests that annual dicot weeds emerging several days after barley may have a minimal impact on yield even when present at high densities, and is consistent with results obtained in previous studies on wild oat (Cousens et al. 1987) and volunteer barley competition (O Donovan 1992). Early emerging weeds are in a better position to access moisture, nutrients, and light, thus impacting crop yield more than late emerging weeds. This suggests that control of weeds that emerge early is much more critical than control of those that emerge late, and concurs with conclusions drawn from a recent study on field pea (Pisum sativum) where optimal yields were
O DONOVAN AND McCLAY EFFECTS OF TARTARY BUCKWHEAT ON BARLEY 863 Can. J. Plant Sci. Downloaded from www.nrcresearchpress.com by 46.3.195.229 on 02/03/18 achieved when Tartary buckwheat was controlled very early in the pea life cycle (Harker et al. 2001). Growers can maximize crop yield and minimize financial losses and weed seed production by striving to ensure that crops emerge as early as possible ahead of weeds. Early crop emergence can be promoted by planting high quality seed at relatively shallow depths as soon as possible after a tillage operation or pre-seed herbicide application. Otherwise weed seed present in the soil may begin germinating even before the crop is planted. The results of the study indicate that the effects of Tartary buckwheat infestations on yield loss of barley will vary considerably depending on when the weed emerges relative to the crop. Thus, using regression models in computerized decision support systems that include a coefficient to describe relative time of emergence should provide more reliable estimates of potential crop yield loss than models that focus on weed density only. The model has been incorporated into two decision support systems for weed management that are presently in use in western Canada. Studies are currently underway to determine the relative competitiveness of Tartary buckwheat with other annual dicot weeds common in cereal fields in western Canada in an effort to broaden the scope and applicability of the yield loss model. Cousens, R., Brain P., O Donovan, J. T. and O Sullivan, P. A. 1987. The use of biologically realistic equations to describe the effects of weed density and relative time of emergence on crop yield. Weed Sci. 35: 720 725. De St. Remy, E. A., O Donovan, J. T., O Sullivan, P. A., Sharma, M. P., Tong, A. K. W. and Dew, D. A. 1985. Influence of Tartary buckwheat (Fagopyrum tataricum) density on yield loss of barley (Hordeum vulgare) and wheat (Triticum aestivum). Weed Sci. 33: 521 523. Dew, D. A. 1972. An index of competition for estimating crop loss due to weeds. Can. J. Plant Sci. 52: 921 927. Harker, K. N., Blackshaw, R. E. and Clayton, G. W. 2001. Timing weed removal in field pea (Pisum sativum). Weed Technol. 15: 277 283. O Donovan, J. T. 1992. Seed yields of canola and volunteer barley as influenced by their relative times of emergence. Can. J. Plant Sci. 72: 263 267. O Donovan, J. T. 1996. Computerised decision support systems: aids to rational and sustainable weed management. Can. J. Plant Sci. 76: 3 7. O Donovan, J. T., Harker, K. N., Clayton, G. W., Blackshaw, R. E., Robinson, D. and Maurice, D. 2001. Evaluation of a yield loss model based on wild oat and barley density and relative time of emergence. Proc. British Crop Protection Conf. Weeds. pp. 639 644. Sharma, M. P. 1986. The biology of Canadian weeds. 74. Fagopyrum tataricum (L.) Gaertn. Can. J. Plant Sci. 66: 381 393. Weaver, S. E. 2001. Impact of lamb s-quarters, common ragweed and green foxtail on yield of corn and soybean in Ontario. Can. J. Plant Sci. 81: 821 828.
This article has been cited by: 1. Derek W. Lewis, Robert H. Gulden. 2014. Effect of Kochia (Kochia scoparia) Interference on Sunflower (Helianthus annuus) Yield. Weed Science 62:01, 158-165. [Crossref]