Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-27T09:21:37.889Z Has data issue: false hasContentIssue false

Simulation of Competition for Photosynthetically Active Radiation Between Common Ragweed (Ambrosia artemisiifolia) and Dry Bean (Phaseolus vulgaris)

Published online by Cambridge University Press:  12 June 2017

David Chikoye
Affiliation:
Dept. Crop Sci., Univ. Guelph, Guelph, Ontario, Canada NIG 2W1
Leslie A. Hunt
Affiliation:
Dept. Crop Sci., Univ. Guelph, Guelph, Ontario, Canada NIG 2W1
Clarence J. Swanton
Affiliation:
Dept. Crop Sci., Univ. Guelph, Guelph, Ontario, Canada NIG 2W1

Abstract

The influence of weeds on crop yield is not only dependent on weed-related factors such as density and time of emergence, but also on environmental and management factors that affect both the weed and crop through time. This study was undertaken to develop the first physiologically based dry bean model that would account for the influence of weed competition. The specific objective was to develop a model that would account for the influence of weed competition on crop yield, and to use this model to test the hypothesis that crop yield losses resulted from competition for photosynthetically active radiation (PAR). To this end, a model that simulated the growth and development of dry bean was developed. The model performed daily calculations and simulated the phenology, leaf area expansion, dry matter production and distribution, and grain yield of dry bean based on weather and management information, but assumed adequate water and nutrients. The model was calibrated without weed competition at two locations and yr, and for these situations, adequately described the growth and development of the crop. Simulations were then run for five common ragweed densities and two times of emergence. Common ragweed leaf area was read into the model from input files and used to simulate weed shading. Shading of the dry bean canopy by common ragweed accounted for about 50 to 70% of the yield losses observed in field studies when weeds emerged with the crop. Weed shading did not account for the yield reduction measured from weeds that emerged at the second trifoliate stage of crop growth. The agreement between model predictions and field studies was consistent with the hypothesis that competition for PAR was a principal factor in weed-crop interaction. The ability to account for differences in weed densities, management, and environmental conditions suggested that modeling was a useful tool for evaluating the interaction among weeds and crops.

Type
Weed Biology and Ecology
Copyright
Copyright © 1996 by the Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

1. Acuna, L. G. 1976. Root studies in Phaseolus. . Crop Scicnec Department. University of Guelph. Guelph. Ontario N1G 2W1 pp. 109.Google Scholar
2. Akey, W. C., Jurik, T. W., and Dekker, J. 1990. Competition for light between velvetleaf (Abutilon theophrasti) and soybean (Glycine max). Weed Res. 30: 403411.CrossRefGoogle Scholar
3. Anonymous, 1992. Field crop recommendations. Ontario Ministry of Agriculture and Food. Publication 296. Toronto. Ontario. 96 pp.Google Scholar
4. Barbour, J. C. and Bridges, D. C. 1995. A model of competition for light between peanut (Arachis hypogaea) and broadleaf weeds. Weed Sci. 43: 247257.CrossRefGoogle Scholar
5. Begonia, G. B., Aldrich, R. J., and Salisbury, C. D. 1991. Soybean yield and yield components as influenced by canopy heights and durations of competition of velvetleaf (Abutilon theophrasti Medik). Weed Res. 31: 117124.CrossRefGoogle Scholar
6. Chikoye, D. and Swanton, C. J. 1995. Evaluation of three empirical models depicting Ambrosia artemisiifolia competition in white bean. Weed Res. 35: 421428.Google Scholar
7. Chikoye, D., Weise, S. F., and Swanton, C. J. 1995. Influence of common ragweed (Ambrosia artemisiifolia L.) time of emergence and density on white bean (Phaseolus vulgaris L.). Weed Sci. 43: 375380.Google Scholar
8. Coble, H. D., Williams, F. M., and Ritter, R. L. 1981. Common ragweed (Ambrosia artemisiifolia) interference in soybean (Glycine max). Weed Sci. 29: 339342.Google Scholar
9. Cordes, R. C. and Bauman, T. T. 1984. Field competition between ivyleaf morningglory (Ipomea hederacea) and soybean (Glycine max). Weed Sci. 32: 364370.CrossRefGoogle Scholar
10. Crookston, R. K., O'toole, J., and Ozbun, J. L. 1974. Characterization of the bean pod as a photosynthetic organ. Crop Sci. 14: 708712.CrossRefGoogle Scholar
11. Dieleman, A. J. 1994. Modeling pigweed (Amaranthus species) interference in soybean (Gylcine max(L.) Merr.) and determining decision rules for postemergence pigweed control. . Crop Science Department. University of Guelph. Guelph. Ontario N1G 2W1. pp. 123.Google Scholar
12. Gutierrez, A. P., Marriot, E. J., Cure, J. R., Wagner Riddle, C. S., Ellis, C. K., and Villacorta, A. M. 1994. A model of bean (Phaseolus vulgaris) growth of types I-III:factors affecting yield. Agric. Syst. 44: 3563.Google Scholar
13. Hoogenboom, G., Jones, J. W., White, J. W., and Boote, K. J. 1991. BEANGR Version 1.01. Dry bean crop growth and simulation model. User's guide. Universities of Florida and Georgia. Florida Agric. Exp. station J. No. N00379.Google Scholar
14. Hunt, L. A. and Pararajasingham, S. 1995. CROPSIM-WHEAT: A model describing the growth and development of wheat. Can. J. Plant Sci. 75: 619632.Google Scholar
15. Izquierdo, J. A. and Hosfied, G. L., 1983. The relationship of seed filling to yield among dry bean with differing architectural forms. .J. Am. Soc. Hort. Sci. 108: 106111.Google Scholar
16. Knezevic, S., Weise, S. F., and Swanton, C. J. 1994. Redroot pigweed (Amaranthus retroflexus) interference in corn (Zea mays). Weed Sci. 42: 568573.CrossRefGoogle Scholar
17. Kropff, M. J. 1993. Ecophysiological models for crop-weed competition. in Kropff, and van Laar, (eds). Modelling weed crop interaction. CAB International. Wallingford. pp. 83104.Google Scholar
18. Kropff, M. J. and van Laar, H. H. 1993. Modelling crop weed interactions. CAB International Wallingford. pp. 304.Google Scholar
19. Kropff, M. J., Weaver, S. E., and Smits, M. A. 1992. Use of ecophysiological models for crop weed interference: relations amongst weed density, relative time of weed emergence, relative leaf area and yield loss. Weed Sci. 40: 296301.CrossRefGoogle Scholar
20. Laing, D. R., Jones, P. G., and Davis, J.H.C. 1984. Common bean (Phaseolus vulgaris). In Goldsworthy and Fisher (eds). The Physiology of tropical field crops. John Wiley and Sons Ltd. London, pp. 305351.Google Scholar
21. Lancashire, P. D., Bleiholder, H., van den Boom, T., Langeluddeke, P., Stauss, R., Weber, E., and Witzenberger, A. 1991. A uniform decimal code for growth stages of crops and weeds. Ann. Appl. Biol. 119: 561601.Google Scholar
22. Leith, J. H. 1982. Light interception, growth dynamics and dry matter partitioning in phytotron grown snap bean (Phaseolus vulgaris). . !University of North Carolina. Raleigh, pp. 138.Google Scholar
23. Lotz, L.A.P., Kropff, M. J., Wallinga, J., Bos, H. J., and Groeneveld, R.M.W. 1994. Techniques to estimate relative leaf area and cover of weeds in crops for yield prediction. Weed Res. 34: 167175 CrossRefGoogle Scholar
24. Major, D. R. and Kiniry, J. R. 1991. Predicting day length effects on phenological processes. in Hogest, (ed.) Predicting crop phenology. CRC Press. Bacon Raton. FL. USA. pp. 1528.Google Scholar
25. Malik, V. S., Swanton, C. J., and Michaels, T. E. 1993. Interaction of white bean (Phaseolus vulgaris L.) cultivars, row spacing and seeding density with annual weeds. Weed Sci. 41: 6268.Google Scholar
26. Masaya, P. and White, J. W. 1991. Adaptation to photoperiod and temperature. in van Schoonhoven, and Voysest, (eds.) Common bean: Research for crop improvement. CAB International. Wallingford. pp. 445492.Google Scholar
27. Matthews, R. B. and Hunt, L. A. 1994. GUMCAS: A model describing the growth and development of cassava (Manihot esculenta L. Crantz). Field Crops Res. 36: 6984.Google Scholar
28. Nuland, D. S. 1989. A visual description of the common bean plant four major growth periods. Annu. Rep. Bean Improvement Coop. 32: 1617.Google Scholar
29. Ojehomon, O. O. 1966. The development of flower primordia in Phaseolus vulgaris(L.) Sani. Ann. Bot. 30: 487–92.CrossRefGoogle Scholar
30. Ojehomon, O. O., Rathjen, A. S., and Morgan, D. G. 1968. Effect of day length on the morphology and flowering of five determinate varieties of Phaseolus vulgaris L. J. Agric. Sci. 71: 209211.CrossRefGoogle Scholar
31. Ojehomon, O.O., Zehni, M.S. and Morgan, D.G. 1973. The effect of photoperiod on flower bud development in Phaseolus vulgaris . Ann. Bot. 37: 871874.Google Scholar
32. Regnier, E. E. and Stoller, E. W. 1989. The effect of soybean (Glycine max) interference on the canopy architecture of common cocklebur (Xanthium strumarium), Jimsonweed (Datura stramonium), and velvetleaf (Abutilon theophrasti). Weed Sci. 37: 187195.Google Scholar
33. Ritchie, J.M. 1991. Specifications for the ideal model for predicting of crop yield. Pages 97123 in Muchow, Russel C. and Bellamy, Jennifer A. (eds.) Climatic risk in crop production: models and management for the semiarid tropics and subtropics.Google Scholar
34. Sandoval-Avila, D. M., Michaels, T. E., Murphy, S. D., and Swanton, C. J. 1994. Effect of conservation tillage and planting pattern on performance of white bean (Phaseolus vulgaris) in Ontario. Can. J. Plant Sci. 74: 801805.CrossRefGoogle Scholar
35. Schepps, A. L. and Ashley, R. A., 1985. Weed-snap beans competition for light. Proc. Northeastern Weed Sci. Soc. 39: 7779.Google Scholar
36. Scully, B. and Waines, J. G. 1988. Germination and emergence response of common and tepary beans to controlled temperature. Agron. J. 80: 287291.CrossRefGoogle Scholar
37. Scully, B. and Waines, J. G. 1987. Germination and emergence response of common and tepary beans to controlled temperature. Agron. J. 79: 287291.Google Scholar
38. Spitters, C.T.J., and Aerts, R. 1983. Simulation of competition for light and water in crop-weed associations. Aspects Appl. Biol. 4: 467484.Google Scholar
39. Teng, P. S., Blackie, M. J., and Close, R. C. 1980. Simulation of the barley leaf rust epidermic: structure and validation of BARSIM-I. Agric. Syst. 5: 86103.Google Scholar
40. Torquebiau, E. and Akyeampong, E. 1994. Shedding some light on shade: its effects on beans, maize, and bananas. Agroforestry Today 6(4): 1415.Google Scholar
41. Van Acker, R. C., Weise, S. F., and Swanton, C. J. 1993. Influence of interference from a mixed weed species stand on soybean (Glycine max(L.) Merr.) growth. Can. J. Plant Sci. 73: 12931304.CrossRefGoogle Scholar
42. Vitta, J. I., Satorre, E. H., and Leguizamon, E. S. 1993. Using canopy attributes to evaluate competition between sorghum halepense(L.) Pers. and soybean. Weed Res. 33: 8997.Google Scholar
43. Wahuma, T.A.T. and Miller, D. A. 1977. Effects of shading on N-fixation, yield and plant composition of field grown soybean. Agron. J. 70: 387392.Google Scholar
44. Whisler, F. D., Acock, B., Baker, D. N., Fye, R. E., Hodges, H. F., Lambert, J. R., Lemmon, H. E., McKinion, J. M., and Reddy, V. R. 1986. Crop simulation models in agronomic systems. Advances in Agron. 40: 141209.Google Scholar
45. White, J. W. 1981. A quantitative analysis of the growth and development of bean plants. . Dept of Bot. University of California. Berkeley, pp 138.Google Scholar
46. While, J. W. and Izquierdo, J. 1991. Physiology of yield potential and stress tolerance. in van Schoonhoven, and Voysest, (eds.) Common beans: Research for crop improvement. CAB International. Wallingford. pp. 445492.Google Scholar
47. Wilkerson, G. G., Jones, J. W., Coble, H. D., and Gunsolus, J. L. 1989. SOYWEED: A simulation of soybean and common cocklebur growth and competition. Agron. J. 82: 10031010.Google Scholar
48. Woolley, B. L., Michaels, T. E., and Swanton, C. J. 1993. The critical period of weed control in white bean (Phaseolus vulgaris). Weed Sci. 41: 180184.Google Scholar
49. Zadoks, J. C., Chang, T. T., and Konzak, C. F. 1974. A decimal code for the growth stages of cereals. Weed Res. 14: 415421.Google Scholar