Hostname: page-component-7dd5485656-g8tfn Total loading time: 0 Render date: 2025-10-31T16:36:22.036Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicting biomass partitioning to root versus shoot in corn and velvetleaf (Abutilon theophrasti)

Published online by Cambridge University Press:  20 January 2017

Kimberly D. Bonifas  and
John L. Lindquist
Kimberly D. Bonifas
Affiliation:
Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583-0817
Get access Rights & Permissions [Opens in a new window]

Abstract

Knowledge of how plants will partition their new biomass will aid in understanding competition between crops and weeds. This study determined if the amount of biomass partitioned to the root versus the shoot can be predicted from tissue carbon [C] and nitrogen [N] concentrations and the daily gain in C (GC) and N (GN) for each unit shoot and root biomass, respectively. Pots measuring 28 cm diameter and 60 cm deep were embedded in the ground, and each contained one plant of either corn or velvetleaf. Each plant received one of three nitrogen treatments: 0, 1, or 3 g of nitrogen applied as ammonium nitrate in 2001 and 0, 2, or 6 g of nitrogen in 2002. Measurements of total above- and belowground biomass and tissue [C] and [N] were made at 10 different sample dates during the growing season. Fraction of biomass partitioned to roots (Pr) was predicted from [C], [N], GC, and GN. Accurate prediction of the fraction of biomass partitioned to roots versus shoots was evaluated by comparing observed and predicted Pr across all treatments. The coordination model has potential as a reliable tool for predicting plant biomass partitioning. Normalized error values were close to zero for corn in 2001 and 2002 and for velvetleaf in 2001, indicating that biomass partitioning was correctly predicted.

Keywords

Velvetleaf, Abutilon theophrasti Medic. ABUTHcorn, Zea mays L.Biomass partitioningfunctional equilibriumontogenetic driftoptimal partitioning theoryphenology

Information

Type
Weed Biology and Ecology
Information
Weed Science , Volume 54 , Issue 1 , February 2006 , pp. 133 - 137
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.)

Article purchase

Temporarily unavailable

References

Literature Cited

Agren, G. I. and Ingestad, T. 1987. Root:shoot ratio as a balance between nitrogen productivity and photosynthesis. Plant Cell Environ 10:579–586.CrossRefGoogle Scholar
Bonifas, K. D., Walters, D. T., Cassman, K. G., and Lindquist, J. L. 2005. Nitrogen supply affects root:shoot ratio in corn and velvetleaf (Abutilon theophrasti). Weed Sci 53:670–675.CrossRefGoogle Scholar
Caton, B. P., Foin, T. C., and Hill, J. E. 1999. A plant growth model for integrated weed management in direct-seeded rice. II. Validation testing of water-depth effects and monoculture growth. Field Crops Res 62:145–155.CrossRefGoogle Scholar
Chen, J-L., Reynolds, J. F., Harley, P. C., and Tenhunen, J. D. 1993. Coordination theory of leaf nitrogen distribution in a canopy. Oecologia 93:63–69.CrossRefGoogle Scholar
Harbur, M. M. and Owen, M. D. K. 2004. Light and growth rate effects on crop and weed responses to nitrogen. Weed Sci 52:578–583.CrossRefGoogle Scholar
Hilbert, D. W. 1990. Optimization of plant root:shoot ratios and internal nitrogen concentration. Ann. Bot 66:91–99.CrossRefGoogle Scholar
Hilbert, D. W., Larigauderie, A., and Reynolds, J. F. 1991. The influence of carbon dioxide and daily photon-flux density on optimal leaf nitrogen concentration and root:shoot ratio. Ann. Bot 68:365–376.CrossRefGoogle Scholar
Hilbert, D. W. and Reynolds, J. F. 1991. A model allocating growth among leaf proteins, shoot structure, and root biomass to produce balanced activity. Ann. Bot 68:417–425.CrossRefGoogle Scholar
Kastner-Maresch, A. E. and Mooney, H. A. 1994. Modelling optimal plant biomass partitioning. Ecol. Mod 75/76:309–320.CrossRefGoogle Scholar
Levin, S. A., Mooney, H. A., and Field, C. 1989. The dependence of plant root:shoot ratios on internal nitrogen concentration. Ann. Bot 64:71–75.CrossRefGoogle Scholar
Lindquist, J. L. 2001a. Light-saturated CO2 assimilation rates of corn and velvetleaf in response to leaf nitrogen and development stage. Weed Sci 49:706–710.CrossRefGoogle Scholar
Lindquist, J. L. 2001b. Performance of INTERCOM for predicting Zea mays–Abutilon theophrasti interference across the north central USA. Weed Sci 49:195–201.CrossRefGoogle Scholar
Lindquist, J. L. and Mortensen, D. A. 1999. Ecophysiological characteristics of four corn hybrids and Abutilon theophrasti . Weed Res 39:271–285.CrossRefGoogle Scholar
Lindquist, J. L., Mortensen, D. A., Clay, S. A., Schmenk, R., Kells, J. J., Howatt, K., and Westra, P. 1996. Stability of corn (Zea mays)–velvetleaf (Abutilon theophrasti) interference relationships. Weed Sci 44:309–313.CrossRefGoogle Scholar
Littell, R. C., Milliken, G. A., Stroup, W. W., and Wolfinger, R. D. 1996. SAS® System for Mixed Models. Cary, NC: Statistical Analysis Systems Institute.Google Scholar
Mitchell, P. L. 1997. Misuse of regression for empirical validation of models. Agric. Syst 54:313–326.CrossRefGoogle Scholar
Mooney, H. A., Bloom, A. J., and Chapin, F. S. III. 1985. Resource limitation in plants-an economic analogy. Annu. Rev. Ecol. Syst 16:363–392.Google Scholar
Nye, P. H. and Tinker, P. B. 1977. Solute Movement in the Soil-Root System. Los Angeles: University of California Press. 342 p.Google Scholar
Reynolds, J. F. and Chen, J-L. 1996. Modelling whole-plant allocation in relation to carbon and nitrogen supply: coordination versus optimization: opinion. Plant Soil 185:65–74.CrossRefGoogle Scholar
Robinson, D. 1986. Compensatory changes in the partitioning of dry matter in relation to nitrogen uptake and optimal variations of growth. Ann. Bot 58:841–48.CrossRefGoogle Scholar
Tilman, E. A., Tilman, D., Crawley, M. J., and Johnston, A. E. 1999. Biological weed control via nutrient competition: potassium limitation of dandelions. Ecol. Applic 9:103–111.CrossRefGoogle Scholar
van der Werf, A. 1996. Growth analysis and photoassimilate partitioning. Pp. 1–20 in Zamski, E. and Schaffer, A. A., eds. Photoassimilate Distribution in Plants and Crops: Source-Sink Relationships. New York: Marcel Dekker.Google Scholar
Wedin, D. and Tilman, D. 1993. Competition among grasses along a nutrient gradient: initial conditions and mechanisms of competition. Ecol. Monogr 63:199–229.CrossRefGoogle Scholar