Skip to main content

Prediction of grain yield and nitrogen uptake by basmati rice through in-season proximal sensing with a canopy reflectance sensor

Abstract

The present study was conducted to establish prediction models for grain yield and nitrogen (N) uptake using normalized difference vegetation index (NDVI) measurements with the GreenSeeker optical sensor for different cultivar groups of basmati rice (Oryza sativa L.) and to define the optimum sensing timing. Sensor readings were collected at 21, 28, 35, 42, and 49 days after transplanting (DAT) from multi-cultivar and multi-rate N fertilization experiments conducted in 2016 and 2017. Prediction model established by regressing NDVI day−1 as the determinant of plant biomass with grain yield and N uptake at maturity following exponential functions revealed that sensing the crop before or after 35 DAT (panicle initiation stage) was not accurate and did not predict satisfactorily the yield or N uptake potential. Regression analysis generated two potential and viable yield or N uptake prediction models: one for the basmati rice cultivar CSR30 (tall cultivar), and the other for a PB-PUSA (group of semi-dwarf cultivars). Validation of the prediction models using an independent experiment conducted in 2018 revealed that sensing the crop at the panicle initiation stage provide grain yield and N uptake predictions close to the observed grain yield (R2 = 0.86, RMSE = 6.1%) and N uptake (R2 = 0.75, RMSE = 8.5%). This study showed that yield and N uptake potential in basmati rice can be predicted using in-season NDVI data measured with the GreenSeeker optical sensor.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Data availability

All data generated or analysed during this study are included in this article.

References

  1. Ali, A. M., Ibrahim, S. M., & Bijay-Singh. (2019). Wheat grain yield and nitrogen uptake prediction using atLeaf and GreenSeeker portable optical sensors at jointing growth stage. Information Processing in Agriculture, 7, 375–383.

    Article  Google Scholar 

  2. Ali, A. M., Thind, H. S., Sharma, S., & Varinderpal-Singh. (2014). Prediction of dry direct-seeded rice yields using chlorophyll meter, leaf color chart and GreenSeeker optical sensor in northwestern India. Field Crops Research, 161, 11–15.

    Article  Google Scholar 

  3. Aparicio, N., Villegas, D., Araus, J. L., Casadesús, J., & Royo, C. (2002). Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Science, 42, 1547–1555. https://doi.org/10.2135/cropsci2002.1547

    Article  Google Scholar 

  4. APEDA. (2018). Basmati Survey – Report 2, Kharif 2018 (pp. 1–40). Basmati Export Development Foundation, APEDA, New Delhi. Prepared by Geotrans Technologies Pvt. Ltd.

  5. Baez-Gonzalez, A. D., Chen, P., Tiscareno-Lopez, M., & Srinivasan, R. (2002). Using satellite and field data with crop growth modelling to monitor and estimate corn yield in Mexico. Crop Science, 42, 1943–1949. https://doi.org/10.2135/cropsci2002.1943

    Article  Google Scholar 

  6. Barger, G. L. (1969). Total growing degree days. Weekly Weather Crop Bulletin, 56, 10.

    Google Scholar 

  7. Bijay-Singh, Sharma, R. K., Jaspreet-Kaur, Jat, M. L., Martin, K. L., Varinderpal-Singh, et al. (2011). Assessment of the nitrogen management strategy using an optical sensor for irrigated wheat. Agronomy for Sustainable Development, 31, 589–603. https://doi.org/10.1007/s13593-011-0005-5

    CAS  Article  Google Scholar 

  8. Bijay-Singh, V.-S., & Ali, A. M. (2020). Site-specific fertilizer nitrogen management in cereals in South Asia. Sustainable Agriculture Reviews, 39, 137–178. https://doi.org/10.1007/978-3-030-38881-2_6

    Article  Google Scholar 

  9. Bijay-Singh, Varinderpal-Singh, Purba, J., Sharma, R. K., Jat, M. L., Yadvinder-Singh, et al. (2015). Site-specific fertilizer nitrogen management in irrigated transplanted rice (Oryza sativa) using an optical sensor. Precision Agriculture, 16, 455–475. https://doi.org/10.1007/s11119-015-9389-6

    Article  Google Scholar 

  10. Bijay-Singh, Varinderpal-Singh, Yadvinder-Singh, Thind, H. S., Ajay Kumar, Choudhary, O. P., et al. (2017). Site-Specific fertilizer nitrogen management using optical sensor in irrigated wheat in the northwestern India. Agricultural Research, 6, 159–168. https://doi.org/10.1007/s40003-017-0251-0

    Article  Google Scholar 

  11. Bijay-Singh, Varinderpal-Singh, Yadvinder-Singh, Thind, H. S., Ajay-Kumar, Satinderpal-Singh, et al. (2013). Supplementing fertilizer nitrogen applications to irrigated wheat at maximum tillering stage using chlorophyll meter and optical sensor. Agricultural Research, 2, 81–89. https://doi.org/10.1007/s40003-013-0053-y

    CAS  Article  Google Scholar 

  12. Cao, Q., Miao, Y., Shen, J., Yu, W., Yuan, F., Cheng, S., et al. (2016). Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor. Precision Agriculture, 17, 136–154.

    Article  Google Scholar 

  13. Coelho, A. P., Rosalen, D. L., & de Faria, R. T. (2018). Vegetation indices in the prediction of biomass and grain yield of white oat under irrigation levels. Pesquisa Agropecuaria Tropical, 48, 109–117.

    Article  Google Scholar 

  14. Fox, R. H., & Walthall, C. L. (2015). Crop monitoring technologies to assess nitrogen status. In Schepers, J. S. & Raun, W. R. (Eds.), Agronomy monographs (Vol. 49, pp. 647–674). https://doi.org/10.2134/agronmonogr49.c16

  15. Gnyp, M. L., Miao, Y., Yuan, F., Ustin, S. L., Yu, K., Yao, Y., Huang, S., & Bareth, G. (2014). Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages. Field Crops Research, 155, 42–55. https://doi.org/10.1016/j.fcr.2013.09.023

    Article  Google Scholar 

  16. Harrell, D. L., Tubana, B. S., Walker, T. W., & Phillips, S. B. (2011). Estimating rice grain yield potential using normalized difference vegetation index. Agronomy Journal, 103, 1717–1723. https://doi.org/10.2134/agronj2011.0202

    Article  Google Scholar 

  17. Lindsay, W. L., & Norvell, W. A. (1978). Development of a DTPA soil test for zinc, iron, manganese, and copper. Soil Science Society of America Journal, 42, 421–428. https://doi.org/10.2136/sssaj1978.03615995004200030009x

    CAS  Article  Google Scholar 

  18. Lukina, E. V., Freeman, K. W., Wynn, K. J., Thomason, W. E., Mullen, R. W., Stone, M. L., et al. (2001). Nitrogen fertilization optimization algorithm based on in-season estimates of yield and plant nitrogen uptake. Journal of Plant Nutrition, 24, 885–898. https://doi.org/10.1081/PLN-100103780

    CAS  Article  Google Scholar 

  19. Ma, B. L., Dwyer, L. M., Costa, C., Cober, E. R., & Morrison, M. J. (2001). Early prediction of soybean yield from canopy reflectance measurements. Agronomy Journal, 93, 1227–1234. https://doi.org/10.2134/agronj2001.1227

    Article  Google Scholar 

  20. Merwin, H. D., & Peech, M. (1950). Exchangeability of soil potassium in sand, silt and clay fractions as influenced by the nature of complementary exchangeable cations. Soil Science Society of America Journal, 15, 125–128.

    Article  Google Scholar 

  21. Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114, 358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009

    Article  Google Scholar 

  22. Olsen, S. R., Cole, C. V., Watanabe, F. S., & Dean, L. A. (1954). Estimation of available phosphorus in soils by extraction with sodium bicarbonate. US Department of Agriculture, Washington, DC (Circular 939).

  23. PAU. (2016). Package of practices for crops of Punjab – Kharif. Punjab Agricultural University.

    Google Scholar 

  24. Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Mullen, R. W., Freeman, K. W., Thomason, W. E., & Lukina, E. V. (2002). Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agronomy Journal, 94, 815–820. https://doi.org/10.2134/agronj2002.8150

    Article  Google Scholar 

  25. Rehman, T. H., Borja Reis, A. F., Akbar, N., & Linquist, B. A. (2019). Use of normalized difference vegetation index to assess N status and predict grain yield in rice. Agronomy Journal, 111, 2889–2898. https://doi.org/10.2134/agronj2019.03.0217

    CAS  Article  Google Scholar 

  26. SPSS. (2012). IBM SPSS statistics for windows, Version 21.0. IBM Corp.

  27. Tagarakis, A. C., & Ketterings, Q. M. (2017). In-season estimation of corm yield potential using proximal sensing. Agronomy Journal, 109, 1323–1330. https://doi.org/10.2134/agronj2016.12.0732

    Article  Google Scholar 

  28. Tagarakis, A. C., Ketterings, Q. M., Lyons, S., & Godwin, G. (2017). Proximal sensing to estimate yield of brown midrib forage sorghum. Agronomy Journal, 109, 107–114. https://doi.org/10.2134/agronj2016.07.0414

    Article  Google Scholar 

  29. Teal, R. K., Tubana, B. S., Girma, K., Freeman, K. W., Arnall, D. B., Walsh, O., & Raun, W. R. (2006). In-season prediction of corn grain yield potential using normalized difference vegetation index. Agronomy Journal, 98, 1488–1494. https://doi.org/10.2134/agronj2006.0103

    Article  Google Scholar 

  30. Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71, 158–182. https://doi.org/10.1016/S0034-4257(99)00067-X

    Article  Google Scholar 

  31. Tubaña, B. S., Arnall, D. B., Walsh, O., Chung, B., Solie, J. B., Girma, K., & Raun, W. R. (2008). Adjusting midseason nitrogen rate using a sensor-based optimization algorithm to increase use efficiency in corn. Journal of Plant Nutrition, 31, 1393–1419. https://doi.org/10.1080/01904160802208261

    CAS  Article  Google Scholar 

  32. Vergara-Diaz, O., Zaman-Allah, M. A., Masuka, B., Hornero, A., Zarco-Tejada, P., Prasanna, B. M., Cairns, J. E., & Araus, J. L. (2016). A novel remote sensing approach for prediction of maize yield under different conditions of nitrogen fertilization. Frontiers in Plant Science, 7, 666. https://doi.org/10.1007/3389%2Ffpls.2016.00666

    Article  PubMed  PubMed Central  Google Scholar 

  33. Walkley, A., & Black, I. A. (1934). An examination of DEGTJAREFF method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Science, 37, 29–38.

    CAS  Article  Google Scholar 

  34. Wallach, D., & Goffinet, B. (1989). Mean squared error of prediction as a criterion for evaluating and comparing system models. Ecological Modelling, 44, 299–306. https://doi.org/10.1016/0304-3800(89)90035-5

    Article  Google Scholar 

  35. Xue, L., Li, G., Qin, X., Yang, L., & Zhang, H. (2014). Topdressing nitrogen recommendation for early rice with an active sensor in south China. Precision Agriculture, 15, 95–110. https://doi.org/10.1007/s11119-013-9326-5

    Article  Google Scholar 

  36. Yao, Y., Miao, Y., Cao, Q., Wang, H., Gnyp, M. L., Bareth, G., et al. (2014). In-season estimation of rice nitrogen status with an active crop canopy sensor. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 4403–4413. https://doi.org/10.1109/JSTARS.2014.2322659

    Article  Google Scholar 

  37. Yao, Y., Miao, Y., Huang, S., Gao, L., Ma, X., Zhao, G., et al. (2012). Active canopy sensor-based precision N management strategy for rice. Agronomy for Sustainable Development, 32, 925–933. https://doi.org/10.1007/s13593-012-0094-9

    Article  Google Scholar 

  38. Yoshida, S., Forno, D. A., Cock, D. H., & Gomez, K. A. (1976). Laboratory manual for physiological studies of rice (3rd ed.). IRRI.

    Google Scholar 

  39. Zhang, K., Ge, X., Shen, P., Li, W., Liu, X., Cao, Q., et al. (2019). Predicting rice grain yield based on dynamic changes in vegetation indexes during early to mid-growth stages. Remote Sensing, 11, 387. https://doi.org/10.3390/rs11040387

    Article  Google Scholar 

Download references

Funding

The research was funded by the Department of Biotechnology (DBT), Govt. of India and Biotechnology and BBSRC under the international multi-institutional collaborative research project entitled Cambridge-India Network for Translational Research in Nitrogen (CINTRIN) (DBT Grant No.: BT/IN/UK-VNC/42/RG/2014-15; BBSRC Grant No.: BB/N013441/1).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Varinderpal-Singh.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Varinderpal-Singh, Kunal, Kaur, R. et al. Prediction of grain yield and nitrogen uptake by basmati rice through in-season proximal sensing with a canopy reflectance sensor. Precision Agric (2021). https://doi.org/10.1007/s11119-021-09857-0

Download citation

Keywords

  • Basmati rice
  • GreenSeeker optical sensor
  • Grain yield prediction
  • Nitrogen uptake prediction