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Predicting Wheat Grain and Biomass Yield Using Canopy Reflectance of Booting Stage

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Abstract

Field experiment was conducted in a sandy loam soil of Indian Agricultural Research Institute, New Delhi during the year 2011–13 to see the effect of irrigation, mulch and nitrogen on canopy spectral reflectance indices and their use in predicting the grain and biomass yield of wheat. The canopy reflectances were measured using a hand held ASD FieldSpec Spectroradiometer at booting stage of wheat. Four spectral reflectance indices (SRIs) viz. RNDVI (Red Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio) and WI (Water Index) were computed using the spectral reflectance data. Out of these four indices, RNDVI, GNDVI and SR were significantly and positively related with the grain and biomass yield of wheat whereas WI was significantly and negatively related with the grain and biomass yield of wheat. Calibration with the second year data showed that among the SRIs, WI could account for respectively, 85 % and 86 % variation in grain and biomass yield of wheat with least RMSE (395 kg ha−1 (15 %) for grain yield and 1609 kg ha−1 (20 %) for biomass yield) and highest d index (0.95 for grain yield and 0.91 for biomass yield). Therefore it can be concluded that WI measured at booting stage can be successfully used for prediction of grain and biomass yield of wheat.

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References

  • Aggarwal, P. K., Kalra, N., Chander, S., & Pathak, H. (2004). Infocrop: A generic simulation model for annual crops in tropical environments. New Delhi: Division of Enviromental Science, IARI.

    Google Scholar 

  • Aparicio, N., Villegas, D., Casadesus, J., Araus, L., & Royo, C. (2000). Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal, 92, 83–91.

    Article  Google Scholar 

  • Asner, G. P. (1998). Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment, 64, 234–253.

    Article  Google Scholar 

  • Babar, M. A., Reynolds, M. P., van Ginkel, M., Klatt, A. R., Raun, W. R., & Stone, M. L. (2006). Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Science, 46, 578–588.

    Article  Google Scholar 

  • Bandyopadhyay, K. K., Misra, A. K., Ghosh, P. K., Hati, K. M., Mandal, K. G., & Mohanty, M. (2010). Effect of irrigation and nitrogen application methods on input use efficiency of wheat under limited water supply in a Vertisol of Central India. Irrigation Science, 28, 285–299.

    Article  Google Scholar 

  • Chakraborthy, D., Nagarajan, S., Aggarwal, P., Gupta, V. K., Tomar, R. K., Garg, R. N., Sahoo, R. N., Sarkar, A., Chopra, U. K., Sundara Sarma, K. S., & Kalra, N. (2008). Effect of mulching on soil and plant water status, and the growth and yield of wheat (Triticum aestivum L.) in a semi-arid environment. Agricultural Water Management, 95, 1323–1334.

    Article  Google Scholar 

  • Chang, K. W., Shen, Y., & Lo, J. C. (2005). Predicting rice yield using canopy reflectance measured at booting stage. Agronomy Journal, 97, 872–878.

    Article  Google Scholar 

  • Clevers, J. G. P. W. (1997). A simplified approach for yield prediction of sugarbeet based on optical remote sensing data. Remote Sensing of Environment, 61, 221–228.

    Article  Google Scholar 

  • Gitelson, A. A., Kaufman, Y. J., & Merzylak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289–298.

    Article  Google Scholar 

  • Gomez, K. A., & Gomez, A. A. (1984). Statistical procedures for agricultural research. New York: Wiley.

    Google Scholar 

  • Guyot, G. (1990). Optical properties of vegetation canopies. In M. D. Steven & J. A. Clark (Eds.), Applications of remote sensing in agriculture (pp. 19–43). London: Butterworths.

    Chapter  Google Scholar 

  • Hati, K. M., Mandal, K. G., Misra, A. K., Ghosh, P. K., & Acharya, C. L. (2001). Effect of irrigation regimes and nutrient management on soil water dynamics, evapotranspiration and yield of wheat in vertisols. Indian Journal of Agricultural Sciences, 71, 581–586.

    Google Scholar 

  • Huang, Y., Chen, L., Fu, B., Huang, Z., & Gong, J. (2005). The wheat yields and water use efficiency in the Loess Plateau: straw mulch and irrigation effects. Agricultural Water Management, 72(3), 209–222.

    Article  Google Scholar 

  • Jamieson, P. D., Porter, J. R., & Wilson, D. R. (1991). A test of the computer simulation model ARC-WHEAT1 on wheat crops grown in New Zealand. Field Crops Research, 27, 337–350.

    Article  Google Scholar 

  • Jat, M. L., Pal, S. S., Singh, R., Singh, D., & Gill, M. S. (2008). Effect of moisture regimes and nitrogen management options on crop and water productivity and nitrogen-use efficiency in maize (Zea mays)—wheat (Triticum aestivum) cropping system. Indian Journal of Agricultural Sciences, 78, 881–883.

    Google Scholar 

  • Joseph, G. (2005). Fundamentals of Remote Sensing. Universities Press (India) Private Limited. Hyderabad, AP, India.

  • Latiri-Soki, K., Noitclitt, S., & Lawlor, D. W. (1998). Nitrogen fertilizer can increase dry matter, grain production and radiation and water use efficiency for durum wheat under semi-arid conditions. European Journal of Agronomy, 9, 21–34.

    Article  Google Scholar 

  • Lee, Y. J., Yang, C. M., & Chang, A. H. (2002). Changes of nitrogen and chlorophyll contents and reflectance spectral characteristics to the application of nitrogen fertilizer in rice plants. Journal of Agricultural Research in China, 51, 1–14.

    Google Scholar 

  • Lenka, S., Singh, A. K., & Lenka, N. (2009). Water and nitrogen interaction on soil profile water extraction and ET in maize-wheat cropping system. Agricultural Water Management, 96, 195–207.

    Article  Google Scholar 

  • Li, F. M., Wang, J., & Zhang, X. J. (2005). Plastic film mulch effect on spring wheat in a semiarid region. Journal of Sustainable Agriculture, 25(4), 5–17.

    Article  Google Scholar 

  • Li-Hong, X., Wei-Xing, C., & Lin-Zhang, Y. (2007). Predicting grain yield and protein content in winter wheat at different N supply levels using canopy reflectance spectra. Pedosphere, 17, 646–653.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Peñuelas, J., Filella, I., Biel, C., Serrano, L., & Save, R. (1993). The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing, 14, 1887–1905.

    Article  Google Scholar 

  • Prasad, B., Carver, B. F., Stone, M. L., Babar, M. A., Raun, W. R., & Klatt, A. R. (2007). Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under great plains conditions. Crop Science, 47, 1426–1440.

    Article  Google Scholar 

  • Rahman, M. A., Chikushi, J., Saifizzaman, M., & Lauren, J. G. (2005). Rice straw mulching and nitrogen response of no-till wheat following rice in Bangladesh. Field Crops Research, 91(1), 71–81.

    Article  Google Scholar 

  • Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Lukina, E. V., Thomason, W. E., & Schepers, J. S. (2001). In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal, 93, 131–138.

    Article  Google Scholar 

  • Reynolds, M. P., Rajaram, S., & Sayre, K. D. (1999). Physiological and genetic changes of irrigated wheat in the post-green revolution period and approaches for meeting projected global demand. Crop Science, 39, 1611–1621.

    Article  Google Scholar 

  • Sehgal, V. K., Sastry, C. V. S., Kalra, N., & Dadhwal, V. K. (2005). Farm-Level Yield mapping for precision crop management by linking remote sensing inputs and a crop simulation model. Journal of Indian Society of Remote Sensing, 33, 131–136.

    Article  Google Scholar 

  • Serrano, L., Filella, I., & Penuelas, J. (2000). Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science, 40, 723–731.

    Article  Google Scholar 

  • Shanahan, J. F., Schepers, J. S., Francis, D. D., Varvel, G. E., Wilhelm, W. W., Tringe, J. M., Schlemmer, M. R., & Major, D. J. (2001). Use of remote sensing imagery to estimate corn grain yield. Agronomy Journal, 93, 583–589.

    Article  Google Scholar 

  • Sidhu, H. S., Manpreet, S., Humphreys, E., Yadvinder-Singh, Balwinder-Singh, Dhillon, S. S., Blackwell, J., Bector, V., Malkeet, S., & Sarbjeet, S. (2007). The Happy Seeder enables direct drilling of wheat into rice stubble. Australian Journal of Experimental Agriculture, 47, 844–854.

    Article  Google Scholar 

  • Verma, M. L., & Acharya, C. L. (2004a). Soil moisture conservation, hydrothermal regime, nitrogen uptake and yield of rainfed wheat as affected by soil management practices and nitrogen levels. Journal of the Indian Society of Soil Science, 52(1), 69–73.

    Google Scholar 

  • Verma, M. L., & Acharya, C. L. (2004b). Effect of nitrogen fertilization on soil-plant—water relationships under different soil moisture conservation practices in wheat. Journal of the Indian Society of Soil Science, 52(1), 105–108.

    Google Scholar 

  • Willmott, C. J., Ackleson, S. G., Davis, R. E., Feddema, J. J., Klink, K. M., Legates, D. R., O’Donnell, J., & Rowe, C. M. (1985). Statistics for the evaluation of model performance. Journal of Geophysical Research, 90, 8995–9005.

    Article  Google Scholar 

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Correspondence to S. Pradhan.

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Pradhan, S., Bandyopadhyay, K.K., Sahoo, R.N. et al. Predicting Wheat Grain and Biomass Yield Using Canopy Reflectance of Booting Stage. J Indian Soc Remote Sens 42, 711–718 (2014). https://doi.org/10.1007/s12524-014-0372-x

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  • DOI: https://doi.org/10.1007/s12524-014-0372-x

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