Skip to main content
Log in

The next generation models for crops and agro-ecosystems

  • Research Papers
  • Special Focus
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Growth in population, decrease in arable land area, and change in climate are endangering our food security. Precision agriculture has the potential to increase crop productivity thorough tailored agricultural practices for different growing areas. Many models of crops and agro-ecosystems capable of predicting interaction between plants and environments have been developed for precision agriculture. Currently, there are several representative categories of crop and agro-ecosystem models, including the de Wit school models, the DSSAT series models and the APSIM series models, which have contributed substantially to improvement of agricultural practices. However, these models are weak in predicting performances of crops under environmental and genetic perturbations are generally weak, which severely limits the application of these models in guiding precision agriculture. We need to develop the next generation crop and agro-ecosystems models with a high level of mechanistic basis, which can be integrated with high throughput data and can predict the heterogeneity of environmental factors inside canopy and dynamic canopy photosynthesis. In developing such a model close collaboration is inevitably required among scientists from different disciplines. The successful development and application of such models will undoubtedly advance precision agriculture through providing better agronomical practices tailored for different growing environments. These models will also form a basis to identify breeding targets for increased productivity at given location with given soil and climatic conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Conway G, Toenniessen G. Feeding the world in the twenty-first century. Nature, 1999, 402: C55–C58

    Article  Google Scholar 

  2. Peng S B, Tang Q, Zou Y. Current status and challenges of rice production in China. Plant Prod Sci, 2009, 12: 3–8

    Article  Google Scholar 

  3. Stafford J V. Implementing precision agriculture in the 21st century. J Agr Eng Res, 2002, 76: 267–275

    Article  Google Scholar 

  4. Niinemets U L O. Photosynthesis and resource distribution through plant canopies. Plant Cell Environ, 2007, 30: 1052–1071

    Article  Google Scholar 

  5. Zhu X G, Long S P, Ort D R. Improving photosynthetic efficiency for greater yield. Ann Rev Plant Biol, 2010, 61: 235–261

    Article  Google Scholar 

  6. Sage R F, Kubien D S. The temperature response of C3 and C4 photosynthesis. Plant Cell Environ, 2007, 30: 1086–1106

    Article  Google Scholar 

  7. Atkin O K, Macherel D. The crucial role of plant mitochondria in orchestrating drought tolerance. Ann Bot, 2009, 103: 581–597

    Article  Google Scholar 

  8. Cornic G, Fresneau C. Photosynthetic carbon reduction and carbon oxidation cycles are the main electron sinks for photosystem II activity during a mild drought. Ann Bot, 2002, 89: 887–894

    Article  Google Scholar 

  9. Long S P, Humphries S W, Falkowski P G. Photoinhibition of photosynthesis in nature. Ann Rev Plant Physiol Plant Mol Biol, 1994, 45: 633–662

    Article  Google Scholar 

  10. Slafer G A. Genetic basis of yield as viewed from a crop physiologist’s perspective. Ann Appl Biol, 2003, 142: 117–128

    Article  Google Scholar 

  11. Murchie E H, Pinto M, Horton P. Agriculture and the new challenges for photosynthesis research. New Phytol, 2008, 181: 532–552

    Article  Google Scholar 

  12. Gibson S. Control of plant development and gene expression by sugar signaling. Cur Opin Plant Sci, 2005, 8: 93–102

    Article  Google Scholar 

  13. Zhu X G, Long S P, Ort D R. What is the maximum efficiency with which photosynthesis can convert solar energy into biomass? Current Opin Biotech, 2008, 19: 153–159

    Article  Google Scholar 

  14. Cramer W A, Zhang H M, Yan J S, et al. Transmembrane traffic in the cytochrome b6f complex. Ann Rev Biochem, 2006, 75: 769–790

    Article  Google Scholar 

  15. Nelson N, Yocum C F. Structure and function of photosystems I and II. Ann Rev Plant Biol, 2006, 57: 521–565

    Article  Google Scholar 

  16. Raines C A. The calvin cycle revisited. Photosyn Res, 2003, 75: 1–10

    Article  Google Scholar 

  17. Lawlor D W, Tezara W. Causes of decreased photosynthetic rate and metabolic capacity in water-deficient leaf cells: a critical evaluation of mechanisms and integration of processes. Ann Bot, 2009, 103: 561–579

    Article  Google Scholar 

  18. Zhu X G, De Sturler E, Long S P. Optimizing the distribution of resources between enzymes of carbon metabolism can dramatically increase photosynthetic rate: A numerical simulation using an evolutionary algorithm. Plant Physiol, 2007, 145: 513–526

    Article  Google Scholar 

  19. Rogers A, Humphries S W. A mechanistic evaluation of photosynthetic acclimation at elevated CO2. Glob Change Biol, 2000, 6: 1005–1011

    Article  Google Scholar 

  20. Lin Z H, Mo X G, Xiang Y Q. Research advances on crop growth models. Acta Agron Sin, 2003, 29: 750–758

    Google Scholar 

  21. Jones J W, Hoogenboom G, Porter C H, et al. The DSSAT cropping system model. Europ J Agron, 2003, 18: 235–265

    Article  Google Scholar 

  22. Bouman B A M, Van Keulen H, Van Laar H H, et al. The ’school of de Wit’ crop growth simulation models: a pedigree and historical overview. Agri Syst, 1996, 52: 171–198

    Article  Google Scholar 

  23. de Wit C T, Penning de Vries F W T. The simulation of photosynthetic systems. In: Prediction and Management of Photosynthetic Productivity, Proceedings of the International Biological Program/Plant Production Technical Meeting. Wageningen, 1970. 47–70

  24. de Wit C T. Simulation of assimilation, respiration and transpiration of crops. Simul Monographs, 1978

  25. Penning de Vries F W T, Laar H H. Simulation of plant growth and crop production. In: PUDOC, Wageningen, 1982, 1–308

  26. Van Keulen H, Penning de Vries F W T, Drees E M. A summary model for crop growth. In: Penning de Vries F W T, van Laar H H, eds. Simulation of Plant Growth and Crop Production. Simulation Monograph, PUDOC, Wageningen, 1982. 87–98

  27. Keulen H, Wolf J. Modelling of agricultural production: weather, soils and crops. In: PUDOC, Wageningen, 1986, 1–478

  28. Penning de Vries F W T, Jansen D M, M. Ten Berge H F M, et al. Simulation of ecophysiological processes of growth in several annual crops. In: Simulation Monograph, PUDOC, Wageningen, 1989. 1–280

  29. Spitters C J T, Schapendonk A. Evaluation of breeding strategies for drought tolerance in potato by means of crop growth simulation. Plant Soil, 1990, 123: 193–203

    Article  Google Scholar 

  30. Lal H, Hoogenboom G, Calixte J P, et al. Using crop simulation modles and GIS for regional productivity analysis. Trans ASABE, 1993, 36: 175–184

    Google Scholar 

  31. Mccown R L, Hammer G L, Hargreaves J N G, et al. APSIM: a novel software system for model development, model testing and simulation in agricultural systems research. Agr Syst, 1996, 50: 255–271

    Article  Google Scholar 

  32. Parton W J, Stewart J W B, Cole C V. Dynamics of C, N, P and S in grassland soils: a model. Biogeochemistry, 1988, 5: 109–131

    Article  Google Scholar 

  33. Humphries S W, Long S P. WIMOVAC: a software package for modelling the dynamics of plant leaf and canopy photosynthesis. Comput Appl Biosci, 1995, 11: 361–371

    Google Scholar 

  34. Farquhar G D, Von Caemmerer S, Berry J A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta, 1980, 149: 78–90

    Article  Google Scholar 

  35. Lawlor D W, Cornic G. Photosynthetic carbon assimilation and associated metabolism in relation to water deficits in higher plants. Plant Cell Environ, 2002, 25: 275–294

    Article  Google Scholar 

  36. Stitt M, Krapp A. The interaction between elevated carbon dioxide and nitrogen nutrition: the physiological and molecular background. Plant Cell Environ, 1999, 22: 583–621

    Article  Google Scholar 

  37. Buckley T N, Mott K A, Farquhar G D. A hydromechanical and biochemical model of stomatal conductance. Plant Cell Environ, 2003, 26: 1767–1785

    Article  Google Scholar 

  38. Morgan J A, Rhodes D. Mathematical modeling of plant metabolic pathways. Metabol Eng, 2002, 4: 80–89

    Article  Google Scholar 

  39. Rolland F, Baena-Gonzalez E, Sheen J. Sugar sensing and signaling in plants: conserved and novel mechanisms. Ann Rev Plant Biol, 2006, 57: 675–709

    Article  Google Scholar 

  40. Rolland F, Moore B, Sheen J. Sugar sensing and signaling in plants. Plant Cell, 2002, 14: S185–205

    Google Scholar 

  41. Rolland F, Sheen J. Sugar sensing and signalling networks in plants. Biochem Soc Trans, 2005, 33: 269–271

    Article  Google Scholar 

  42. Stitt M, Muller C, Matt P, et al. Steps towards an integrated view of nitrogen metabolism. J Exp Bot, 2002, 53: 959–970

    Article  Google Scholar 

  43. Scheible W R, Lauerer M, Schulze E D, et al. Accumulation of nitrate in the shoot acts as a signal to regulate shoot-root allocation in tobacco. Plant J, 1997, 11: 671–691

    Article  Google Scholar 

  44. Yin X Y, Struik P C, Van Eeuwijk F A, et al. QTL analysis and QTL-based prediction of flowering phenology in recombinant inbred lines of barley. J Exp Bot, 2005, 56: 967–976

    Article  Google Scholar 

  45. Tsukaya H. Mechanisms of leaf shape determination. Ann Rev Plant Biol, 2006, 57: 477–496

    Article  Google Scholar 

  46. Yin X Y, Struik P C, Tang J J, et al. Model analysis of flowering phenology in recombinant inbred lines of barley. J Exp Bot, 2005, 56: 959–965

    Article  Google Scholar 

  47. Yin X Y, Struik P C, Kropff M J. Role of crop physiology in predicting gene-to-phenotype relationships. Trends Plant Sci, 2004, 9: 426–432

    Article  Google Scholar 

  48. Yin X Y, Al E. Coupling estimated effects of QTLs for physiological traits to a crop growth model: predicting yield variation among recombinant inbred lines in barley. Heredity, 2000, 85: 539–549

    Article  Google Scholar 

  49. Bailey T L, Elkan C. Unsupervised learning of multiple motifs in biopolymers using expectation maximization. Mach Learn, 1995, 21: 51–80

    Google Scholar 

  50. Lawrence C E, Altschul S F, Boguski M S, et al. Detecting subtle sequence signals-A Gibbs sampling strategy for multiple alignment. Science, 1993, 262: 208–214

    Article  Google Scholar 

  51. Siddharthan R, Siggia E D, Van Nimwegen E. PhyloGibbs: A Gibbs sampling motif finder that incorporates phylogeny. Plos Comput Biol, 2005, 1: 534–556

    Article  Google Scholar 

  52. Friedman N, Linial M, Nachman I, et al. Using Bayesian networks to analyze expression data. J Comput Biol, 2000, 7: 601–620

    Article  Google Scholar 

  53. Segal E, Yelensky R, Koller D. Genome-wide discovery of transcriptional modules from DNA sequence and gene expression. Bioinformatics, 2003, 19Suppl.: i273–i282

    Article  Google Scholar 

  54. Segal E, Shapira M, Regev A, et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet, 2003, 34: 166–176

    Article  Google Scholar 

  55. Lee T I, Rinaldi N J, Robert F, et al. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 2002, 298: 799–804

    Article  Google Scholar 

  56. Bar-Joseph Z, Gerber G K, Lee T I, et al. Computational discovery of gene modules and regulatory networks. Nat Biotech, 2003, 21: 1337–1342

    Article  Google Scholar 

  57. Harbison C T, Gordon D B, Lee T I, et al. Transcriptional regulatory code of a eukaryotic genome. Nature, 2004, 431: 99–104

    Article  Google Scholar 

  58. Jin V X, Rabinovich A, Squazzo S L, et al. A computational genomics approach to identify cis-regulatory modules from chromatin immunoprecipitation microarray data-A case study using E2F1. Genome Res, 2006, 16: 1585–1595

    Article  Google Scholar 

  59. Niinemets U, Valladares F. Photosynthetic acclimation to simultaneous and interacting environmental stresses along natural light gradients: Optimality and constraints. Plant Biol, 2004, 6: 254–268

    Article  Google Scholar 

  60. Long S P, Zhu X G, Naidu S L, et al. Can improvement in photosynthesis increase crop yields? Plant Cell Environ, 2006, 29: 315–330

    Article  Google Scholar 

  61. Leakey A D B, Scholes J D, Press M C. Physiological and ecological significance of sunflecks for dipterocarp seedlings. J Exp Bot, 2005, 56: 469–482

    Article  Google Scholar 

  62. Pearcy R W, Roden J S, Gamon J A. Sunfleck dynamics in relation to canopy structure in a soybean (Glycine max (L.) Merr) canopy. Agr Forest Meteorol, 1990, 52: 359–372

    Article  Google Scholar 

  63. Pearcy R W, Yang W M. A three-dimensional crown architecture model for assessment of light capture and carbon gain by understory plants. Oecologia, 1996, 108: 1–12

    Article  Google Scholar 

  64. Espana M L, Baret F, Aries F, et al. Modeling maize canopy 3D architecture-Application to reflectance simulation. Ecol Model, 1999, 122: 25–43

    Article  Google Scholar 

  65. Fischer R A. Understanding the physiological basis of yield potential in wheat. J Agr Sci, 2007, 145: 99–113

    Article  Google Scholar 

  66. Reynolds M, Calderini D, Condon A, et al. Association of source/sink traits with yield, biomass and radiation use efficiency among random sister lines from three wheat crosses in a high-yield environment. J Agr Sci, 2007, 145: 3–16

    Article  Google Scholar 

  67. Martha G J, Kay D, Alan M M. Starch synthesis in t he cereal endosperm. Curr Opin Plant Biol, 2003, 6: 215–222

    Article  Google Scholar 

  68. Geigenberger P, Stitt M, Fernie A R. Metabolic control analysis and regulation of the conversion of sucrose to starch in growing potato tubers. Plant Cell Environ, 2004, 27: 655–673

    Article  Google Scholar 

  69. Clifford P E, Offler C E, Patrick J W. Growth-regulators have rapid effects on photosynthate unloading from seed coats of phaseolus-vulgaris L. Plant Physiol, 1986, 80: 635–637

    Article  Google Scholar 

  70. Jones R J, Brenner M L. Distribution of abscisic-acid in maize kernel during grain filling. Plant Physiol, 1987, 83: 905–909

    Article  Google Scholar 

  71. Jang J C, Leon P, Zhou L, et al. Hexokinase as a sugar sensor in higher plants. Plant Cell, 1997, 9: 5–19

    Article  Google Scholar 

  72. Sheen J. Feedback control of gene expression. Photosynth Res, 1994, 39: 427–438

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to XinGuang Zhu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhu, X., Zhang, G., Tholen, D. et al. The next generation models for crops and agro-ecosystems. Sci. China Inf. Sci. 54, 589–597 (2011). https://doi.org/10.1007/s11432-011-4197-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-011-4197-8

Keywords

Navigation