Parameterization of a Model for Wild Chickpea Flowering Time by Transferring the Knowledge Learned from Multiple Sources

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

Building forecasting the flowering time helps researchers to create varieties with maximum efficiency and value under a changing climate. This paper proposes an algorithm for parameterization of the wild chickpea flowering time model by using machine learning through knowledge transfer to combine multiple input-target sets. The resulting model showed high accuracy based on genetic and climatic data on only the first 20 days after sowing – the average absolute error is slightly greater than 5 days, the Pearson correlation coefficient is 0.93. It was found that maximum and minimum temperatures have the strongest effect on the timing of flowering. At the same time, all weather factors by the 7–10 day from the date of sowing affect a solution of the model.

About the authors

Z. A Saranin

Peter the Great St. Petersburg Polytechnic University

Email: kozlov_kn@spbstu.ru
Saint Petersburg, 195251 Russia

M. G Samsonova

Peter the Great St. Petersburg Polytechnic University

Saint Petersburg, 195251 Russia

K. N Kozlov

Peter the Great St. Petersburg Polytechnic University

Saint Petersburg, 195251 Russia

References

  1. Varshney R. K., Song C., Saxena R. K., Azam S., Yu S., Sharpe A. G., Cannon S., Baek J., Rosen B. D., Tar'an B., Millan T., Zhang X., Ramsay L. D., Iwata A., Wang Y., Nelson W., Farmer A. D., Gaur P. M., Soderlund C., Penmetsa R. V., Xu C., Bharti A. K., He W., Winter P., Zhao S., Hane J. K., Carrasquilla-Garcia N., Condie J. A., Upadhyaya H. D., Luo M. C., Thudi M., Gowda C. L., Singh N. P., Lichtenzveig J., Gali K. K., Rubio J., Nadarajan N., Dolezel J., Bansal K. C., Xu X., Edwards D., Zhang G., Kahl G., Gil J., Singh K. B., Datta S. K., Jackson S. A., Wang J., and Cook D. R. Draft genome sequence of chickpea (Cicer arietinum) provides a resource for trait improvement. Nature Biotechnol., 31 (3), 240–246 (2013). doi: 10.1038/nbt.2491
  2. Smithson J. B., Thompson J. A., and Summerfield R. J. Chickpea (Cicer Arietinum L.). In Grain Legume Crops. Ed. by R.J. Summerfield and R.E. Roberts (Collins, London, UK, 1985), pp. 312–390.
  3. Abbo Sh., Berger J., and Turner N. C. Evolution of cultivated chickpea: four bottlenecks limit diversity and constrain adaptation. Funct. Plant Biol., 30, 1081–1087 (2003). doi: 10.1071/FP03084
  4. Kumar J. and Abbo Sh. Genetics of flowering time in chickpea and its bearing on productivity in the semi-arid environments. Adv. Agron., 72, 107–138 (2001).
  5. Roberts E. H., Hadley P., and Summerfield R. J. Effects of temperature and photoperiod on flowering in chickpeas (Cicer Arietinum L.). Ann. Botany, 55 (6), 881–892 (1985).
  6. Ellis R. H., Lawn R. J., Summerfield R. J., Qi A., Roberts E. H., Chay P. M., Brouwer J. B., Rose J. L., Yeates S. J., and Sandover S. Towards the reliable prediction of time to flowering in six annual crops. iv. cultivated and wild mung bean. Exp. Agricult., 30 (3), 271– 282 (1994).
  7. Upadhyaya H. D., Bajaj D., Das S., Saxena M. S., Badoni S., Kumar V., Tripathi S., Gowda C. L., Sharma S., Tyagi A. K., and Parida S. K. A genomescale integrated approach aids in genetic dissection of complex flowering time trait in chickpea. Plant Mol. Biol., 89 (4), 403–420 (2015). doi: 10.1007/s11103-015-0377-z
  8. Kumar V., Singh A., Mithra S. V., Krishnamurthy S. L., Parida S. K., Jain S., Tiwari K. K., Kumar P., Rao A. R., Sharma S. K., Khurana J. P., Singh N. K., and Mohapatra T. Genome-wide association mapping of salinity tolerance in rice (Oryza Sativa). DNA Res., 22 (2), 133–145 (2015). doi: 10.1093/dnares/dsu046
  9. B erger J., Milroy S., Turner N., Siddique K., Imtiaz M., and Malhotra R. Chickpea evolution has selected for contrasting phenological mechanisms among different habitats. Euphytica, 180, 1–15 (2011).
  10. Zhang X. and Cai X. Climate change impacts on global agricultural land availability. Environ. Res. Lett., 6 (1), 014014 (2011).
  11. Soltani A., Hammer G. L., Torabi B., Robertson M. J., and Zeinali E. Modeling chickpea growth and development: phenological development. Field Crops Res., 99 (1), 1–13 (2006).
  12. Soltani A., Robertson M. J., Mohammad-Nejad Y., and Rahemi-Karizaki A. Modeling chickpea growth and development: leaf production and senescence. Field Crops Res., 99 (1), 14–23 (2006b).
  13. Jones J. W., Antle J. M., Basso B., Boote K. J., Conant R. T., Foster I., Godfray H. C. J., Herrero M., Howitt R. E., Janssen S., Keating B. A., MunozCarpena R., Porter Ch. H., Rosenzweig C., and Wheeler T. R. Toward a new generation of agricultural system data, models, and knowledge products: state of agricultural systems science. Agricult. Syst., 155, 269–288 (2017). doi: 10.1016/j.agsy.2016.09.021
  14. Jones J. W., Antle J. M., Basso B., Boote K. J., Conant R. T., Foster I., Godfray H. C. J., Herrero M., Howitt R. E., Janssen S., Keating B. A., MunozCarpena R., Porter C. H., Rosenzweig C., and Wheeler T. R. Brief history of agricultural systems modeling. Agricultural Systems, 155, 240–254 (2016). doi: 10.1016/j.agsy.2016.05.014
  15. Jones J. W., Hoogenboom G., Porter C. H., Boote K. J., Batchelor W. D., Hunt L. A., Wilkens P. W., Singh U., Gijsman A. J., and Ritchie J. T. The DSSAT cropping system model. Eur. J. Agronomy, 18 (3–4), 235–265 (2003).
  16. Boote J., Jones K. W., and Pickering N. B. Potential uses and limitations of crop models. Agronomy J., 88, 704–716 (1996).
  17. Boote K. J., Jones J. W., White J. W., Asseng S., and Lizaso J. I. Putting mechanisms into crop production models: putting mechanisms into crop production models. Plant, Cell & Environment, 36 (9), 1658–72 (2013).
  18. Keating B., Carberry P. S., Hammer G., Probert M. E., Robertson M. J., Holzworth D., Huth N. I., Hargreaves J. N. G, Meinke H., Hochman Z., McLean G., Verburg K., Snow V., Dimes J. P., Silburn M., Wang E., Brown S., Bristow K. L., Asseng S., Chapman S., McCown R. L., Freebairn D. M., and Smith C. J. An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agronomy, 18, 267–288 (2003) doi: 10.1016/S1161-0301(02)00108-9
  19. Battisti R., Sentelhas P. C., and Boote K. J. Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil. Int. J. Biometeorol., 62 (5), 823–832 (2018).
  20. Williams J. R., Jones C. A., Kiniry J. R., and Spanel D. A. The EPIC crop growth model. Transactions of the ASAE, 32 (2), 497–511 (1989).
  21. Vadez V., Soltani A., and Sinclair T. R. Crop simulation analysis of phenological adaptation of chickpea to different latitudes of India. Field Crops Res., 146, 1–9 (2013).
  22. Lal M., Singh K. K., Srinivasan G., Rathore L. S., Naidu D., and Tripathi C. N. Growth and yield responses of soybean in Madhya Pradesh, India to climate variability and change. Agricult. Forest Meteorol., 93 (1), 53–70 (1999).
  23. Chung U., Kim Y. U., Seo B. S., and Seo M. C. Evaluation of variation and uncertainty in the potential yield of soybeans in South Korea using multi-model ensemble climate change scenarios. Agrotechnology, 06 (02), 1000158 (2017). doi: 10.4172/2168-9881.1000158
  24. Mohammed A., Tana T., Singh P., Molla A., and Seid A. Identifying best crop management practices for chickpea (Cicer arietinum L.) in Northeastern Ethiopia under climate change condition. Agricult. Water Management, 194, 68–77 (2017). doi: 10.1016/j.agwat.2017.08.022
  25. Patil D. and Patel H. R. Calibration and validation of CROPGRO (DSSAT 4.6) model for chickpea under Middle Gujarat agroclimatic region. Int. J. Agricult. Sci., 9 (27), 4342–4344 (2017).
  26. Urgaya M. L. Modeling the impacts of climate change on chickpea production in Adaa Woreda (East Showa Zone) in the semi-arid Central Rift Valley of Ethiopia. J. Pet. Environ. Biotechnol., 7, 288 (2016).
  27. Ageev A., Aydogan A., Bishop-von Wettberg E., Nuzhdin S. V., Samsonova M., and Kozlov K. Simulation model for time to flowering with climatic and genetic inputs for wild chickpea. Agronomy, 11, 1389 (2021).
  28. von Wettberg E. J. B, Chang P. L., Başdemir F., Carrasquila-Garcia N., Korbu L. B., Moenga S. M., Bedada G., Greenlon A., Moriuchi K. S., Singh V., Cordeiro M. A., Noujdina N. V., Dinegde K. N., Shah Sani S. G. A., Getahun T., Vance L., Bergmann E., Lindsay D., Mamo B. E., Warschefsky E. J., DacostaCalheiros E., Marques E., Yilmaz M. A., Cakmak A., Rose J., Migneault A., Krieg C. P., Saylak S., Temel H., Friesen M. L., Siler E., Akhmetov Z., Ozcelik H., Kholova J., Can C., Gaur P., Yildirim M., Sharma H., Vadez V., Tesfaye K., Woldemedhin A. F., Tar'an B., Aydogan A., Bukun B., Penmetsa R. V., Berger J., Kahraman A., Nuzhdin S. V., and Cook D. R. Ecology and genomics of an important crop wild relative as a prelude to agricultural innovation. Nature Commun., 9 (1), 649 (2018). doi: 10.1038/s41467-018-02867-z
  29. Stackhouse P. W., Perez R., Sengupta M., Knapp K., Mikovitz J. C., Schlemmer J., Scarino B., Zhang T., and Cox S. J. An assessment of new satellite data products for the development of a long-term global solar resource at 10–100 km. In Proc. of the Solar 2016 Conf. (San Francisco, CA, USA, 2016), pp. 1–6.
  30. van der Laan M. J., Polley E. C., and Hubbard A. E. Super learner. Stat. Applic. in Genetics and Mol. Biology, 6 (1), Article 25 (2007).
  31. Vadez V., Berger J., Warkentin T., Asseng S., Ratnakumar P., Rao K., Gaur P., Munier-Jolain N., Larmure A., Voisin A.-S., Sharma H. C., Pande S., Sharma M., Krishnamurthy L., and Zaman-Allah M. Adaptation of grain legumes to climate change: a review. Agronomy for Sustainable Development, 32 (1), 31–44 (2012). doi: 10.1007/s13593-011-0020-6

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Russian Academy of Sciences