Skip to main content

Advertisement

Springer Nature Link
Log in

District specific, in silico evaluation of rice ideotypes improved for resistance/tolerance traits to biotic and abiotic stressors under climate change scenarios

  • Published:
Climatic Change Aims and scope Submit manuscript

Abstract

Using crop models as supporting tools for analyzing the interaction between genotype and environment represents an opportunity to identify priorities within breeding programs. This study represents the first attempt to use simulation models to define rice ideotypes improved for their resistance to biotic stressors (i.e., diseases); moreover, it extends approaches for evaluating the impact of changes in traits for tolerance to abiotic constraints (temperature shocks inducing sterility). The analysis—targeting the improvement of 34 varieties in six Italian rice districts—was focused on the impact of blast disease, and of pre-flowering cold- and heat-induced spikelet sterility. In silico ideotypes were tested at 5-km spatial resolution under current conditions and climate change scenarios centered on 2020, 2050, and 2085, derived according to the projections of two general circulation models–Hadley and NCAR–for two IPCC emission scenarios–A1B and B1. The study was performed using a dedicated simulation platform, i.e., ISIde, explicitly developed for ideotyping studies. The ideotypes improved for blast resistance obtained clear yield increases for all the combinations GCM × emission scenario × time horizon, i.e., 12.1 % average yield increase under current climate, although slightly decreasing for time windows approaching the end of the century and with a marked spatial heterogeneity in responses across districts. Concerning abiotic stressors, increasing tolerance to cold-induced sterility would lead to a substantial yield increase (+9.8 %) only for Indica-type varieties under current climate, whereas no increases are expected under future conditions and, in general, for Japonica-type varieties. Given the process-based logic behind the models used—supporting coherence of model responses under future scenarios—this study provides useful information for rice breeding programs to be realized in the medium-long term.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  • Acutis M, Donatelli M, Stöckle CO (1998) Comparing the performance of three weather generators. Proceedings of the 5th ESA Congress, Nitra, Slovak Republic, 117-118

  • Aggarwal PK, Kropff MJ, Cassman KG, Ten Berge HFM (1997) Simulating genotypic strategies for increasing rice yield potential in irrigated, tropical environments. Field Crop Res 51:5–17. doi:10.1016/S0378-4290(96)01044-1

    Article  Google Scholar 

  • Alpuerto VLEB, Norton GW, Alwang J, Ismail AM (2009) Economic impact analysis of marker-assisted breeding for tolerance to salinity and phosphorous deficiency in rice. Rev Agir Econ 31:779–792. doi:10.1111/j.1467-9353.2009.01466.x

    Article  Google Scholar 

  • Andaya VC, Mackill DJ (2003) QTLs conferring cold tolerance at the booting stage of rice using recombinant inbred lines from a japonica × indica cross. Theor Appl Genet 106:1084–1090. doi:10.1007/s00122-002-1126-7

    Google Scholar 

  • Ballini E, Morel JP, Droc G, Price A, Courtois B, Notteghem JL, Tharreau D (2008) A genome-wide meta-analysis of rice blast resistance genes and QTLs provides new insights into partial and complete resistance. Mol Plant Microbe Interact 21:859–868. doi:10.1094/MPMI-21-7-0859

    Article  Google Scholar 

  • Bastiaans L (1991) Ratio between virtual and visual lesion size as a measure to describe reduction in leaf photosynthesis of rice due to blast. Phytopathology 81:611–615

    Article  Google Scholar 

  • Battisti DS, Naylor RL (2009) Historical warnings of future food insecurity with unprecedented seasonal heat. Science 323:240–244. doi:10.1126/science.1164363

    Article  Google Scholar 

  • Bingham IJ, Topp CFE (2009) Potential contribution of selected canopy traits to the tolerance of foliar disease by spring barley. Plant Pathol 58:1010–1020

    Article  Google Scholar 

  • Boote KJ, Kropff MJ, Bindraban PS (2001) Physiology and modelling of traits in crop plants: implications for genetic improvement. Agricult Syst 70:395–420. doi:10.1016/S0308-521X(01)00053-1

    Article  Google Scholar 

  • Boschetti M, Stroppiana D, Briovio PA, Bocchi S (2009) Multi-year monitoring of rice corp phenology through time series analysis of MODIS images. Int J Remote Sens 30:4643–4662. doi:10.1080/01431160802632249

    Article  Google Scholar 

  • Bregaglio S, Donatelli M (2015) A set of software components for the simulation of plant airborne diseases. Environ. Modell. Softw., Resubmitted after revision

  • Bregaglio S, Titone P, Cappelli G, Donatelli M, Confalonieri R (2013) Adaptation of the Diseases platform to simulate blast impact on rice in Italy. 4th AGMIP Annual Meeting, New York, October 28th-30th, http://www.agmip.org/wp-content/uploads/2013/11/Stella_CASSANDRA_Blast-simulation.pdf [Accessed 10/03/2015]

  • Chakraborty S (2013) Migrate or evolve: options for plant pathogens under climate change. Glob Chang Biol 19:1985–2000

    Article  Google Scholar 

  • Choudhury BJ (2001) Modelling radiation- and carbon-use efficiencies of maize, sorghum, and rice. Agric For Meteorol 106:317–330. doi:10.1016/S0168-1923(00)00217-3

    Article  Google Scholar 

  • Collins WD, Hack JJ, Boville BA, Rasch PJ (2004) Description of the NCAR Community Atmosphere Model (CAM3.0). Technical note TN-464 + STR. National Center for Atmospheric Research, Boulder

    Google Scholar 

  • Confalonieri R (2012) Combining a weather generator and a standard sensitivity analysis method to quantify the relevance of weather variables on agrometeorological models outputs. Theor Appl Climatol 108:19–30. doi:10.1007/s00704-011-0510-0

    Article  Google Scholar 

  • Confalonieri R, Mariani L, Bocchi S (2005) Analysis and modelling of water and near water temperatures in flooded rice (Oryza sativa L.). Ecol Model 183:269–280. doi:10.1016/j.ecolmodel.2004.07.031

    Article  Google Scholar 

  • Confalonieri R, Rosenmund AS, Baruth B (2009) An improved model to simulate rice yield. Agron Sustain Dev 29:463–474. doi:10.1051/agro/2009005

    Article  Google Scholar 

  • Confalonieri R, Bregaglio S, Cappelli G, Francone C, Carpani M, Acutis M, El Aydam M, Niemeyer S, Balaghi R, Domng Q (2013) Wheat modelling in Morocco unexpectedly reveals predominance of photosynthesis versus leaf area expansion plant traits. Agron Sustain Dev 33:393–403. doi:10.1007/s13593-012-0104-y

    Article  Google Scholar 

  • Coppola E, Giorgi F (2010) An assessment of temperature and precipitation change projections over Italy from recent global and regional climate model simulations. Int J Clim 30:11–32. doi:10.1002/joc.1867

    Google Scholar 

  • Covey C, AchutaRao KM, Cubasch U, Jones PD, Lambert SJ, Mann ME, Phillips TJ, Taylor KE (2003) An overview of results from the Coupled Model Intercomparison Project. Glob Planet Chang 37:103–133. doi:10.1016/S0921-8181(02)00193-5

    Article  Google Scholar 

  • Donatelli M, Confalonieri R (2011) Biophysical models for cropping system simulation. In: Flichman G (ed) Bio-Economic Models applied to Agricultural Systems, Springer, pp 59-87

  • Donatelli M, Rizzoli AE (2008) A design for framework-independent model components of biophysical systems. In: Proceedings of the International Congress on Environmental Modelling and Software (iEMSs ’08), vol. 2, pp. 727-734, Barcelona, Spain

  • Dreni L, Gonzales Schain N, Pilatone A, Jagadish K, Heuer S, Colombo L, Pasquariello M, Francia E, Pucciariello C, Perata P, Pecchioni N, Kater M (2012) Thermal stress responses in rice. In: Proceeding from the International Workshop Crop Improvement in a Changing Environment: the RISINNOVA Project for sustainable rice production in Italy, Venice, Italy, pp 11

  • Drewry DT, Kumar P, Long S (2014) Simultaneous improvement in productivity, water use, and albedo through crop structural modification. Glob Chang Biol 20:1955–1967. doi:10.1111/gcb.12567

    Article  Google Scholar 

  • Dulli S, Furini S, Peron E (2009) Data Mining. Springer, Berlin

    Book  Google Scholar 

  • Duncan WG, Loomis RS, Williams WA, Hanau R (1967) A model for simulating photosynthesis in plant communities. Hilgardia 38:181–205

    Article  Google Scholar 

  • Faivre-Rampant O, Bruschi G, Abbruscato P, Cavigiolo S, Picco AM, Borgo L, Lupotto E, Piffanelli P (2011) Assessment of genetic diversity in Italian rice germplasm related to agronomic traits and blast resistance (Magnaporthe oryzae). Mol Breed 27:233–246. doi:10.1007/s11032-010-9426-0

    Article  Google Scholar 

  • Gordon C, Cooper C, Senior CA, Banks H (2000) The simulation of SST, sea ice extent and ocean heat transport in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16:147–168. doi:10.1007/s003820050010

    Article  Google Scholar 

  • Hammer GL, Kropff MJ, Sinclair TR, Porter JR (2002) Future contribution of crop modelling from heuristic and supporting decision making to understanding genetic regulation and aiding crop improvement. Eur J Agron 18:15–31. doi:10.1016/S1161-0301(02)00093-X

    Article  Google Scholar 

  • Herndl M, Shan C, Wang P, Graeff S, Claupein W (2007) A model based ideotyping approach for wheat under different environmental conditions in North China Plain. Agric Sci China 6:1426–1436. doi:10.1016/S1671-2927(08)60004-8

    Article  Google Scholar 

  • IPCC (2007) Climate Change 2007: The Physical Science Basis, Contribution of working group 1 to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press

  • IPCC (2013) Summery for Policymakers. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report Of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

  • Lobell DB, Schlenker W, Costa-Roberts J (2012) Climate trends and global crop production since 1980. Science 333:616–620

    Article  Google Scholar 

  • Matsui T, Omasa K, Horie T (2001) The difference in sterility due to high temperatures during the flowering period among japonica rice varieties. Plant Prod Sci 4:90–93

    Article  Google Scholar 

  • Oerke EC (2006) Crop losses to pests. J Agr Sci 144:31–43. doi:10.1017/S0021859605005708

    Article  Google Scholar 

  • Raza A, Friedel JK, Moghaddam A, Ardakani MR, Loiskandl W, Himmelbauer M, Bodner G (2013) Modeling growth of different lucerne cultivars and their effect on soil water dynamics. Agric Water Manag 119:100–110. doi:10.1016/j.agwat.2012.12.006

    Article  Google Scholar 

  • Roumen E, Levy M, Notteghem JL (1997) Characterization of the European pathogen population of Magnaporthe grisea by DNA fingerprinting and pathotype analysis. Eur J Plant Pathol 103:363–371. doi:10.1023/A:1008697728788

    Article  Google Scholar 

  • Russo S (1994) Breeding and genetical research in Italian rice. In: Clément G, Cocking EC (ed) FAO MedNet rice: breeding and biotechnology groups: proceedings of the workshops Ciheam-Iamm, Montpellier, pp 43–47

  • Sanchez B, Rasmussen A, Porter JR (2014) Temperatures and the growth and development of maize and rice: a review. Glob Chang Biol 20:408–417. doi:10.1111/gcb.12389

    Article  Google Scholar 

  • Semenov MA, Shewry PR (2011) Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe. Sci Rep 1:1–5. doi:10.1038/srep00066 1

    Article  Google Scholar 

  • Semenov MA, Stratonovitch P (2013) Designing high-yielding wheat ideotypes for a changing climate. Food and Energy Secur 2:185–196. doi:10.1002/fes3.34

    Article  Google Scholar 

  • Singh VK, Singh A, Singh SP, Ellura RK, Choudharyc V, Sarkelc S, Singha D, Gopala Krishnana S, Nagarajand M, Vinodd KK, Singhc UD, Rathoree R, Prashanthif SK, Agrawalg PK, Bhattg PC, Mohapatrah T, Prabhua KV, Singha AK (2012) Incorporation of blast resistance into “PRR78”, an elite Basmati rice restorer line, through marker assisted backcross breeding. Field Crop Res 128:8–16. doi:10.1016/j.fcr.2011.12.003

    Article  Google Scholar 

  • Singh P, Nedumaran S, Traore PCS, Boote KJ, Rattunde HFW, Vara Prasad PV, Singh NP, Srinivas K, Bantilan MCS (2014) Quantifying potential benefits of drought and heat tolerance in rainy season sorghum for adapting to climate change. Agric For Meteorol 185:37–48. doi:10.1016/j.agrformet.2013.10.012

    Article  Google Scholar 

  • Suh JP, Jeung JU, Lee JI, Choi YH, Yea JD, Virk PS, Mackill DJ, Jena KK (2010) Identification and analysis of QTLs controlling cold tolerance at the reproductive stage and validation of effective QTLs in cold-tolerant genotypes of rice (Oryza sativa L.). Theor Appl Genet 120:985–995. doi:10.1007/s00122-009-1226-8

    Article  Google Scholar 

  • Tardieu F (2003) Virtual plants: modelling as a tool for the genomics of tolerance to water deficit. Trends Plant Sci 8:9–14. doi:10.1016/S1360-1385(02)00008-0

    Article  Google Scholar 

  • Tardieu F (2010) Why work and discuss the basic principles of plant modelling 50 years after the first crop models? J Exp Bot 61:2039–2041. doi:10.1093/jxb/erq135

    Article  Google Scholar 

Download references

Acknowledgments

This study has been partially funded under the project RISINNOVA (Integrated genetic and genomic approaches for new Italian rice breeding strategies), funded by Ager (http://risinnova.entecra.it/), and under the EU FP7 project MODEXTREME (Grant Agreement No. 613817).

Author information

Authors and Affiliations

  1. Università degli Studi di Milano, Department of Agricultural and Environmental Sciences, Cassandra Lab, via Celoria 2, 20133, Milan, Italy

    L. Paleari, G. Cappelli, S. Bregaglio, M. Acutis & R. Confalonieri

  2. Consiglio per la Ricerca in agricoltura e l’analisi dell’Economia Agraria, Centro di ricerca per le colture industriali, via di Corticella 133, 40128, Bologna, Italy

    M. Donatelli

  3. Università degli Studi di Milano, Department of Agricultural and Environmental Sciences, via Celoria 2, 20133, Milan, Italy

    G. A. Sacchi & G. Manfron

  4. Consiglio per la Ricerca in agricoltura e l’analisi dell’Economia Agraria, Dipartimento di Biologia e produzione vegetale, via Nazionale 82, 00184, Roma, Italy

    E. Lupotto

  5. National Research Council, IREA, via Bassini 15, 20133, Milan, Italy

    M. Boschetti & G. Manfron

Authors
  1. L. Paleari
    View author publications

    Search author on:PubMed Google Scholar

  2. G. Cappelli
    View author publications

    Search author on:PubMed Google Scholar

  3. S. Bregaglio
    View author publications

    Search author on:PubMed Google Scholar

  4. M. Acutis
    View author publications

    Search author on:PubMed Google Scholar

  5. M. Donatelli
    View author publications

    Search author on:PubMed Google Scholar

  6. G. A. Sacchi
    View author publications

    Search author on:PubMed Google Scholar

  7. E. Lupotto
    View author publications

    Search author on:PubMed Google Scholar

  8. M. Boschetti
    View author publications

    Search author on:PubMed Google Scholar

  9. G. Manfron
    View author publications

    Search author on:PubMed Google Scholar

  10. R. Confalonieri
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to L. Paleari.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 171 kb)

ESM 2

(DOCX 45.3 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Paleari, L., Cappelli, G., Bregaglio, S. et al. District specific, in silico evaluation of rice ideotypes improved for resistance/tolerance traits to biotic and abiotic stressors under climate change scenarios. Climatic Change 132, 661–675 (2015). https://doi.org/10.1007/s10584-015-1457-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10584-015-1457-4

Keywords