Hostname: page-component-cc8bf7c57-xrnlw Total loading time: 0 Render date: 2024-12-11T22:55:47.389Z Has data issue: false hasContentIssue false
  • English
  • Français

Seedling emergence model for tropic ageratum (Ageratum conyzoides)

Published online by Cambridge University Press:  20 January 2017

Friday Ekeleme ,
Frank Forcella ,
Dave W. Archer ,
I. Okezie Akobundu  and
David Chikoye
Friday Ekeleme
Affiliation:
Department of Crop Protection, Michael Okpara University of Agriculture, Umudike, P.M.B. 7267, Umuahia, Abia State, Nigeria
Dave W. Archer
Affiliation:
USDA-ARS, North Central Soil Conservation Research Laboratory, Morris, MN 56267
I. Okezie Akobundu
Affiliation:
3 Liberty Place, Apartment #1, Windsor Mill, MD 21244-2060
David Chikoye
Affiliation:
International Institute of Tropical Agriculture, P.M.B. 5320, Ibadan, Nigeria
Get access Rights & Permissions [Opens in a new window]

Abstract

The timing of weed seedling emergence relative to the crop is important in planning and optimizing the time of weed control, but very little work has been done to predict seedling emergence of tropical weed species, especially in low-input and small-scale farms. We developed a simple model based on hydrothermal time to predict seedling emergence of tropic ageratum. Hydrothermal time at 2-cm soil depth was calculated from soil moisture and soil temperature simulated from several micrometeorological and soil physical variables. The model was developed using 5 yr of field emergence data from a continuous corn–cassava production system in southwestern Nigeria. Percentage of cumulative seedling emergence from the 5-yr data set was fitted to cumulative soil hydrothermal time using a Weibull function. The predicted cumulative emergence curve significantly matched observed field emergence (r 2 = 0.83). Model predictions were evaluated with root mean square error (RMSE) using four field emergence data sets from southeastern Nigeria (RMSE ≤ 10.1) and Los Banos, Philippines (RMSE = 8.9). RMSE values ≤ 10 indicated that predictions represented observations well. With such models, extension personnel working on tropical soils, especially in West Africa, may be able to provide additional advice to farmers on the appropriate time for the management of tropic ageratum.

Keywords

Tropic ageratum, Ageratum conyzoides L. AGECOcassava, Manihot esculenta Crantzcorn (maize), Zea mays L.Hydrothermal timephenologysimulationsoil moisturesoil temperaturetropical weed
Type
Weed Biology and Ecology
Information
Weed Science , Volume 53 , Issue 1 , February 2005 , pp. 55 - 61
Copyright
Copyright © 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

Addae, P. C., Collis-George, N., and Pearson, C. J. 1991. Over-riding effects of temperature and soil strength of weed seedlings under minimum and conventional tillage. Field Crops Res 28:103–116.CrossRefGoogle Scholar
Akobundu, I. O. and Agyakwa, C. W. 1998. A Handbook of West African Weeds. Ibadan, Nigeria: International Institute of Tropical Agriculture. 162 p.Google Scholar
Akobundu, I. O., Ekeleme, F., and Chikoye, D. 1999. Influence of fallow management systems and frequency of cropping on weed growth and crop yield. Weed Res 39:241–256.CrossRefGoogle Scholar
Alan, L. R. and Wiese, A. F. 1985. Effects of degree days on weed emergence. Page 448 in Proceedings of Southern Weed Science Society, 38th Annual Meeting. Raleigh, NC: SWSS.Google Scholar
Anonymous. 2000. Upland Rice Ecosystem: Farmers' Practice of Using Salt for Weed Control in Upland Rice. Las Banos, Philippines: International Rice Research Program Rep. Pp. 56–59.Google Scholar
Banyikwa, F. F. and Rulangorang, Z. K. 1985. Growth analysis of groundnut (Arachis hypogea) in competition with Ageratum conyzoides . Turrialba 35:215–219.Google Scholar
Bewick, T. A., Binming, L. K., and Yandell, B. 1988. A degree day model for predicting the emergence of swamp dodder in cranberry. J. Am. Soc. Hortic. Sci 113:839–841.Google Scholar
Bhatt, B. P., Tomar, J. M. S., and Misra, L. K. 2001. Allelopathic effects of weeds on germination and growth of legumes and cereal crops of North Eastern Himalayas. Allelopathy J 18:225–232.Google Scholar
Bradford, K. J. 2002. Application of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Sci 50:248–260.CrossRefGoogle Scholar
Buhler, D. 1992. Population dynamics and control of annual weeds in corn (Zea mays) as influenced by tillage systems. Weed Sci 40:241–248.Google Scholar
Chikoye, D. S., Weise, S. F., and Swanton, C. J. 1995. Influence of common ragweed (Ambrosia artenissiifolia) time of emergence and density on white bean (Phaseolus vulgaris). Weed Sci 43:375–380.CrossRefGoogle Scholar
Cousens, R. and Moss, S. R. 1990. A model of the effects of cultivation on the vertical distribution of weed seeds within the soil. Weed Res 30:61–70.CrossRefGoogle Scholar
Devi, L. G., Potty, N. N., Abraham, C. T., and Thomas, G. 1993. The weed flora in sugarcane fields of Palghat District. J. Trop. Agric 31:137–139.Google Scholar
Ekeleme, F., Akobundu, I. O., Isichei, A. O., and Chikoye, D. 2000. Influence of fallow type and land-use intensity on weed seed rain in a forest/savanna transition zone. Weed Sci 48:604–612.CrossRefGoogle Scholar
Flerchinger, G. N. 2000. The Simultaneous Heat and Water (SHAW) Model: User's Manual. Technical Rep. NWRC 2000-10. Boise, ID: USDA-ARS, 24 p.Google Scholar
Forcella, F. 1998. Real-time assessment of seed dormancy and seedling growth for weed management. Seed Sci. Res 8:201–209.Google Scholar
Forcella, F. R., Benech-Arnold, L., Sanchez, R., and Ghersa, C. M. 2000. Modeling seedling emergence. Field Crops Res 67:123–139.Google Scholar
Garcia, M. A. 1995. Relationship between weed community and seed bank in a tropical agroecosystem. Agric. Ecosyst. Environ 55:139–146.Google Scholar
Grundy, A. C. 2003. Predicting weed emergence: a review of approaches and future challenges. Weed Res 43:1–11.Google Scholar
Grundy, A. C. and Mead, A. 2000. Modeling weed emergence as a function of meteorological records. Weed Sci 48:594–603.CrossRefGoogle Scholar
Ijani, A. S. M., Mabagala, R. B., and Nchimbi-Msolla, S. 2000. Root-knot nematode species associated with beans and weeds in the Morogoro region, Tanzania. Afr. Plant Prot 6:37–41.Google Scholar
Ismail, B. S., Chuah, T. S., Salmijah, S., Teng, Y. T., and Schumacher, R. W. 2002. Germination and seedling emergence of glyphosate-resistant and susceptible biotypes of goosegrass (Elusine indica [L.] Gaertn). Weed Biol. Manag 2:177–185.Google Scholar
Kashina, B. D., Mabagala, R. B., and Mpunam, A. A. 2003. First report of Ageratum conyzoides L. and Sida acuta Burm F. as new hosts of tomato yellow leaf curl-Tanzania virus. Plant Prot. Sci 39:18–22.Google Scholar
Kato-Noguchi, H. 2001. Assessment of the allelopathic potential of Ageratum . Biol. Plant 44:309–311.Google Scholar
Knezevic, S. Z., Horak, M. J., and Vanderlip, R. L. 1997. Relative time of redroot pigweed (Amaranthus retroflexus L.) emergence is critical in pigweed-sorghum [Sorghum bicolor (L.) Moench] competition. Weed Sci 45:502–508.CrossRefGoogle Scholar
Mahli, S. S. and O'Sullivan, P. A. 1990. Soil temperature, moisture and penetrometer resistance under zero and conventional tillage in Central Alberta. Soil Till. Res 7:167–172.Google Scholar
Mead, A., Grundy, A. C., and Burston, S. 1998. Predicting the movement of seeds following cultivation. Pages 91–98 in Champion, G. T., Grundy, A. C., Jones, N. E., Marshall, E.J.P., and Froud-Williams, R. J. eds. Weed Seedbanks: Determination, Dynamics, and Manipulation. Aspects of Applied Biology 51. Wellesbourne, U.K.: Association of Applied Biologists.Google Scholar
Moechnig, M. J., Stoltenberg, D. E., Boerboom, C. M., and Binning, L. K. 2003. Empirical corn yield loss estimation from common lambsquarters (Chenopodium album) and giant foxtail (Setaria faberi) in mixed communities. Weed Sci 51:386–393.Google Scholar
Mortimer, M. A. 1989. The biology of weeds. Pages 1–41 in Hane, R. J. and Holly, K. eds. Weed Control Handbook: Principles. Oxford: Blackwell Scientific.Google Scholar
Munkholm, L. J., Schjonning, P., Rasmussen, K., and Tanderup, K. 2003. Spatial and temporal effects of direct drilling on soil structure in the seedling environment. Soil Till. Res 71:163–173.Google Scholar
Ndubizu, T. O. C. 1987. Weeding regimes suitable for ‘false Horn’ plantain. Pages 118–122 in Proceedings of the Third Meeting of the International Co-operation for Effective Plantain and Banana Research. Ibadan, Nigeria: IITA.Google Scholar
Oryokot, J. O. E., Hunt, L. A., Murphy, S. D., and Swanton, C. J. 1997a. Simulation of pigweed (Amaranthus spp.) seedling emergence in different tillage systems. Weed Sci 45:684–690.CrossRefGoogle Scholar
Oryokot, J. O. E., Murphy, S. D., and Swanton, C. J. 1997b. Effect of tillage and corn on pigweed (Amaranthus spp.) seedling emergence and density. Weed Sci 45:120–126.Google Scholar
Pabitra, K., Bora, L. C., and Bhagabati, K. N. 1997. Goat weed: a host of citrus canker (Xanthomonas campestris pr. Citri). J. Mycol. Plant Pathol 27:96–97.Google Scholar
Rafey, A. and Prasad, K. 1995. Influence of weed management practices in pigeon pea (Cajanus cajan) intercropped with upland rice (Oryza sativa). Indian J. Agric. Sci 65:281–282.Google Scholar
Rodriguez, G. S. and Cepero, G. S. 1984. Number of seeds produced by some weed species. Cent. Agric 11:45–50.Google Scholar
Roman, E. S., Murphy, S. D., and Swanton, C. J. 2000. Simulation of Chenopodium album seedling emergence. Weed Sci 48:217–224.Google Scholar
[SAS] Statistical Analysis Systems. 1995. SAS User's Guide: Statistics. Cary, NC: Statistical Analysis Systems Institute. 956 p.Google Scholar
Sauerborn, J. and Koch, W. 1988. An investigation of the germination of six tropical arable weeds. Weed Res 28:47–52.CrossRefGoogle Scholar
Singh, S. K. and Saha, G. P. 2001. Effect of weed management practices on performance of prominent wet-season crops. Indian J. Agron 46:489–495.Google Scholar
Wezel, A. 2000. Weed vegetation and land use of upland maize fields in northwest Vietnam. GeoJournal 50:337–349.Google Scholar
Wopereis, M. C. S., Wösten, J. H. M., ten Berge, H. F. M., Woodhead, T., and de San Agustin, E. M. 1993. Comparing the performance of a soil-water balance model using measured and calibrated hydraulic conductivity data: a case study for dryland rice. Soil Sci 156:133–140.Google Scholar
Zimdahl, R. L., Moody, K., Lubigan, R. T., and Castin, E. M. 1988. Patterns of weed emergence in tropical soil. Weed Sci 36:603–608.Google Scholar