Euphytica

, Volume 199, Issue 1–2, pp 167–181 | Cite as

Modelling concept of lettuce breeding for nutrient efficiency

  • P. J. Kerbiriou
  • T. J. Stomph
  • E. T. Lammerts van Bueren
  • P. C. Struik
Article

Abstract

Modern lettuce cultivars are bred for use under high levels of input of water and nutrients, and therefore less adapted to low-input or organic conditions in which nitrate availability varies over time and within the soil profile. To create robust cultivars it is necessary to assess which traits contribute to optimal resource capture and maximum resource use efficiency. We therefore revisited earlier published results on root growth, resource capture and resource use efficiency of lettuce exposed to localized drought and nitrate shortage in a pot experiment. Root growth in a soil profile with localized resource shortage depended on the resource that was in short supply. We conceptualized a model describing nitrogen uptake and use efficiency. We also investigated the genetic variation among 148 cultivars in resource capture over time and soil depth and in resource use efficiency in four (two locations × two planting dates) field experiments. Cultivars proved to be highly diverse in their ability to capture and use resources. This ability, however, was strongly affected by other sources of variance, stressing the need for an eco-physiological model capable of reducing the residual variance and improving the expression and evaluation of cultivar differences in relation to both resource capture and use efficiency in lettuce. We showed that genetic variation was best expressed under limiting conditions. To improve the conceptualized model further we identified issues requiring further analysis, e.g., the physiological reasons why certain cultivars are capable of quickly responding to changes in the environment to maintain optimal resource capture.

Keywords

Drought stress Modelling concept Nitrogen use efficiency Organic Root growth Resource capture 

Notes

Acknowledgments

The authors thank Peter van der Putten, Centre for Crop Systems Analysis, Wageningen University, for his assistance and guidance in trial design and coordination, and his valuable technical support in collecting and processing samples. They also thank Martin Koper, Enza Zaden BV, and Jan Velema, Marcel van Diemen and Pieter Schwegman, Vitalis Organic Seeds BV, for providing seeds, advice, and insight. The project was financially supported through the Top Institute Green Genetics (project number: 2CFD024RP).

References

  1. Barlow PW (2010) Plastic, inquisitive roots and intelligent plants in the light of some new vistas in plant biology. Plant Biosyst 144:396–407CrossRefGoogle Scholar
  2. Boriss H, Brunke H (2005) Commodity profile: lettuce. Agricultural Issues Center, Univ of California http://aic.ucdavis.edu/profiles/lettuce-2005.pdf. Accessed 3 Mar 2014
  3. Ceccarelli S, Acevedo E, Grando S (1991) Breeding for yield stability in unpredictable environments: single traits, interactions between traits, and architecture of genotypes. Euphytica 56:169–185CrossRefGoogle Scholar
  4. Chapman N, Whalley WR, Lindsey K, Miller AJ (2011) Water supply and not nitrate concentration determines primary root growth in Arabidopsis. Plant Cell Environ 34:1630–1638PubMedCrossRefGoogle Scholar
  5. Clark MS, Horwarth WR, Shennan C, Scow KM, Lantni WT, Ferris H (1999) Nitrogen, weeds and water as yield-limiting factors in conventional, low-input and organic tomato systems. Agric Ecosys Environ 73:257–270CrossRefGoogle Scholar
  6. Curtin D, Wright CE, Beare MH, McCullum FM (2006) Hot water extractable nitrogen as an indicator of soil nitrogen availability. Soil Sci Soc Am J 70:1512–1521CrossRefGoogle Scholar
  7. De Ponti T, Rijk B, Van Ittersum MK (2012) The crop yield gap between organic and conventional agriculture. Agric Sys 108:1–9CrossRefGoogle Scholar
  8. Des Marais DL, Hernandez KM, Juenger TE (2013) Genotype-by-environment interactions and plasticity: exploring genomic responses of plants to the abiotic environment. Annu Rev Ecol Evol Syst 44:5–29CrossRefGoogle Scholar
  9. Drew MC, Saker LR, Ashley TW (1973) Nutrient supply and the growth of the seminal root system in barley: I. The effect of nitrate concentration on the growth of axes and laterals. J Exp Bot 24:1189–1202Google Scholar
  10. Gu J, Yin X, Zhang C, Wang H, Struik PC (2014) Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved rice (Oryza sativa) yields under drought stress. Ann Bot (in press)Google Scholar
  11. Hammer G, Cooper M, Tardieu F, Welch S, Walsh B, van Eeuwijk F, Chapman S, Podlich D (2006) Models for navigating biological complexity in breeding improved crop plants. Trends Plant Sci 11:587–593PubMedCrossRefGoogle Scholar
  12. Hodge A (2004) The plastic plant: root responses to heterogeneous supplies of nutrients. New Phytol 162:9–24CrossRefGoogle Scholar
  13. Jackson P, Robertson M, Cooper M, Hammer G (1996) The role of physiological understanding in plant breeding, from a breeding perspective. Field Crop Res 49:1–37CrossRefGoogle Scholar
  14. Johnson WC, Jackson LE, Ochoa O, van Wijk R, Peleman J, St Clair DA, Michelmore RW (2000) Lettuce, a shallow-rooted crop, and Lactuca serriola, its wild progenitor, differ at QTL determining root architecture and deep soil water exploitation. Theor Appl Genet 101:1066–1073CrossRefGoogle Scholar
  15. Kerbiriou PJ, Stomph TJ, van der Putten PEL, Lammerts van Bueren ET, Struik PC (2013a) Shoot growth, root growth and resource capture under limiting water and N supply for two cultivars of lettuce (Lactuca sativa L.). Plant Soil 371:281–297CrossRefGoogle Scholar
  16. Kerbiriou PJ, Stomph TJ, Lammerts van Bueren ET, Struik PC (2013b) Influence of transplant size on the above- and below-ground performance of four contrasting field grown lettuce cultivars. Front in Plant Sci. 4, Article 379, 16 pp, doi:10.3389/fpls.2013.00379
  17. King J, Gay A, Bradley RS, Bingham I, Foulkes J, Gregory P, Robinson D (2003) Modelling cereal root systems for water and nitrogen capture: towards an economic optimum. Ann Bot 91:383–390Google Scholar
  18. Leogrande R, Lopedota O, Fiore A, Vitti C, Ventrelaa D (2013) Previous crops and organic fertilizers in lettuce: effects on yield and soil properties. J Plant Nut 36:1945–1962CrossRefGoogle Scholar
  19. Liao H, GE Z, Yan X (2001) Ideal root architecture for phosphorus acquisition of plants under water and phosphorus coupled stress: from simulation to application. Chin Sci Bull 46:1346–1351Google Scholar
  20. Masciandro G, Macci C, Peruzzi E, Ceccanti B, Doni S (2013) Organic matter-microorganism-plant in soil bioremediation: a synergic approach. Rev Environ Sci Biotechnol 12:399–419CrossRefGoogle Scholar
  21. Mele PM, Crowley DE (2008) Application of self-organizing maps for assessing soil biological quality. Agric Ecosys Env 126:139–152CrossRefGoogle Scholar
  22. Mou P, Jones RH, Tan Z, Bao Z, Chen H (2013) Morphological and physiological plasticity of plant roots when nutrients are both spatially and temporally heterogeneous. Plant Soil 364:373–384CrossRefGoogle Scholar
  23. Nautiyal CS, Chauhan PS, Bhatia CR (2010) Changes in soil physico-chemical properties and microbial functional diversity due to 14 years of conversion of grassland to organic agriculture in semi-arid agroecosystem. Soil Tillage Res 109:55–60CrossRefGoogle Scholar
  24. Ouzounidou G, Paschalidis C, Petropoulos D, Koriki A, Zamanidis P, Petridis A (2013) Interaction of soil moisture and excess of boron and nitrogen on lettuce growth and quality. Hort Sci 40:119–125Google Scholar
  25. Postma JA, Schurr U, Fiorani F (2014) Dynamic root growth and architecture responses to limiting nutrient availability: linking physiological models and experimentation. Biotechnol Adv 32:53–65PubMedCrossRefGoogle Scholar
  26. Pua EC, Davey MR (2007) Transgenic crops V. Springer-Verlag, Berlin HeidelbergCrossRefGoogle Scholar
  27. Sibley KJ, Astatkie T, Brewster G, Struik PC, Adsett JF, Pruski K (2009) Field-scale validation of an automated soil nitrate extraction and measurement system. Precis Agric 10:162–174CrossRefGoogle Scholar
  28. Yin X, Struik PC (2008) Applying modelling experiences from the past to shape crop systems biology: the need to converge crop physiology and functional genomics. New Phytol 179:629–642PubMedCrossRefGoogle Scholar
  29. Yin X, Struik PC (2010) Modelling the crop: from system dynamics to systems biology. J Exp Bot 61:2171–2183PubMedCrossRefGoogle Scholar
  30. Yin X, Struik PC (2012) Modelling gene-trait-crop relationships: Past experiences and future prospects. In: Weihong Luo et al. (eds), Proceedings IVth IS on HortiModel 2012. Acta Hort 957: 181–189Google Scholar
  31. Yin X, Struik PC, Kropff MJ (2004) Role of crop physiology in predicting gene-to-phenotype relationships. Trends in Plant Sci 9:426–432CrossRefGoogle Scholar
  32. Zhang K, Bruns IG, Turner MK (2008) Derivation of a dynamic model of the kinetics of nitrogen uptake throughout the growth of lettuce: calibration and validation. J Plant Nutr 31:1440–1460CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • P. J. Kerbiriou
    • 1
    • 2
  • T. J. Stomph
    • 2
  • E. T. Lammerts van Bueren
    • 1
  • P. C. Struik
    • 2
  1. 1.Wageningen UR Plant Breeding, Plant Sciences GroupWageningen UniversityWageningenThe Netherlands
  2. 2.Centre for Crop Systems Analysis, Plant Sciences GroupWageningen UniversityWageningenThe Netherlands

Personalised recommendations