Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management practices

Agricultural Water Management

Volumn 146, Issue , 2014, Pages 115-123

  Bandyopadhyay, K K ^a   Pradhan, S ^a   Sahoo, R N ^a   Singh, Ravender ^a   Gupta, V K ^a   Joshi, D K ^a   Sutradhar, A K ^a  

^a INDIAN AGRICULTURAL RESEARCH INSTITUTE   (India)

Author keywords

Canopy reflectance; Normalized water index; Water index; Wheat; Yield prediction

Indexed keywords

BIOMASS; FORECASTING; GRAIN GROWTH; IRRIGATION; MATHEMATICAL MODELS; MEAN SQUARE ERROR; METABOLISM; NITROGEN; REFLECTION; REGRESSION ANALYSIS; UREA;

CANOPY REFLECTANCE; CANOPY SPECTRAL REFLECTANCE; MAXIMUM CORRELATION COEFFICIENT; ROOT MEAN SQUARE ERRORS; SPECTRAL REFLECTANCES; WATER INDEX; WHEAT; YIELD PREDICTION; NITROGEN MANAGEMENT; SPECTRAL INDICES; WATER STRESS;

GRAIN (AGRICULTURAL PRODUCT);

BIOMASS; CANOPY REFLECTANCE; CROP YIELD; IRRIGATION SYSTEM; MANAGEMENT PRACTICE; NITROGEN; PREDICTION; WATER STRESS; WHEAT;

TRITICUM AESTIVUM;

EID: 84906060266     PISSN: 03783774     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.agwat.2014.07.017     Document Type: Article

References (35)

1. Aggarwal P.K., Kalra N., Chander S., Pathak H. Infocrop: A Generic Simulation Model for Annual Crops in Tropical Environments 2004, Division of Enviromental Science, IARI, New Delhi.
2. Amani I., Fischer R.A., Reynolds M.P. Canopy temperature depression association with yield of irrigated spring wheat cultivars in hot climate. J. Agron. Crop Sci. 1996, 176:119-129.
3. Anderson M.C., Neale C.M.U., Li F., Norman J.M., Kustas W.P., Jayanthi H., Chavez J. Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Remote Sens. Environ. 2004, 92:447-464.
4. Asner G.P. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ. 1998, 64:234-253.
5. Ashraf M.Y. Yield and yield components response of wheat (Triticum aestivum L.) genotypes grown under different soil water deficit conditions. Acta Agron. Hung. 1998, 46:45-51.
6. Bandyopadhyay K.K., Misra A.K., Ghosh P.K., Hati K.M., Mandal K.G., Mohanty M. Effect of irrigation and nitrogen application methods on input use efficiency of wheat under limited water supply in a Vertisol of Central India. Irrig. Sci. 2010, 28:285-299.
7. Babar M.A., Reynolds M.P., van Ginkel M., Klatt A.R., Raun W.R., Stone M.L. Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Sci. 2006, 46:578-588.
8. Boyer J.S., James R.A., Munns R., Condon T., Passoura J.B. Osmotic adjustment leads to anomalously low estimates of relative water content in wheat and barley. Funct. Plant Biol. 2008, 35:1172-1182.
9. Bukhat N.M. Studies in yield and yield associated traits of wheat (Triticum aestivum L.) genotypes under drought conditions. M.Sc. Thesis 2005, Department of Agronomy, Sindh Agriculture University, Tandojam, Pakistan.
10. Dencic S., Kastori R., Kobiljski B., Duggan B. Evaluation of grain yield and its components in wheat cultivars and land races under near optimal and drought conditions. Euphytica 2000, 1:43-52.
11. El-Shikha D.M., Waller P., Hunsaker D., Clarke T., Barnes E. Ground based remote sensing for assessing water and nitrogen status of broccoli. Agric. Water Manage. 2007, 229:170-182.
12. Entz M.H., Fowler D.B. Critical stress periods affecting productivity of no-till winter wheat in Western Canada. Agron. J. 1988, 80:987-992.
13. Fischer R.A. Developmental morphology of the inflorescene in hexaploid wheat cultivars with and without Norin 10 in their ancestry. Can. J. Plant Sci. 1973, 53:7-15.
14. Gao B. NDWI a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58:257-266.
15. Gomez K.A., Gomez A.A. Statistical Procedures for Agricultural Research 1984, John Wiley and Sons, New York, NY.
16. Gupta N.K., Gupta S., Kumar A. Effect of water stress on physiological attributes and their relationship with growth and yield of wheat cultivars at different stages. J. Agron. Crop Sci. 2001, 186:55-62.
17. Gutierrez M., Reynolds M.P., Raun W.R., Stone M.L., Klatt A.R. Spectral water indices for assessing yield in elite bread wheat genotypes in well irrigated, water stressed, and high temperature conditions. Crop Sci. 2010, 50:197-214.
18. Hati K.M., Mandal K.G., Misra A.K., Ghosh P.K., Acharya C.L. Effect of irrigation regimes and nutrient management on soil water dynamics, evapotranspiration and yield of wheat in vertisols. Indian J. Agric. Sci. 2001, 71:581-586.
19. Idso S.B., Jackson R.D., Reginato R.J. Remote sensing of crop yields. Science 1977, 196:19-25.
20. Idso S.B., Jackson R.D., Pinter P.J., Reginato R.J., Hatfield J.L. Normalizing the stress degree-day parameter for environmental variability. Agric. Meteorol. 1981, 24:45-55.
21. Joseph G. Fundamentals of Remote Sensing 2005, Universities Press (India) Private Limited, Hyderabad, AP, India.
22. Peñuelas J., Filella I., Biel C., Serrano L., Save R. The reflectance of 950-970nm region as an indicator of plant water status. Int. J. Remote Sens. 1993, 14:1887-1905.
23. Peñuelas J., Isla R., Fillela I., Araus J.L. Visible and near Infrared reflectance assessment of salinity effects on barley. Crop Sci. 1997, 37:198-202.
24. Prasad B., Carver B.F., Stone M.L., Babar M.A., Raun W.R., Klatt A.R. Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under great plains conditions. Crop Sci. 2007, 47:1426-1440.
25. Prasad R. Text book of Field Crops Production 2004, Directorate of Information and Publication of Agriculture, Indian Council of Agricultural Research, Krishi Anusandhan Bhavan, Pusa, New Delhi.
26. Reynolds M.P., Rajaram S., Sayre K.D. Physiological and genetic changes of irrigated wheat in the post green revolution period and approaches for meeting projected global demand. Crop Sci. 1999, 39:1611-1621.
27. Reynolds M.P., Dreccer F., Trethowan R. Drought adaptive traits derived from wheat wild relatives and landraces. J. Exp. Bot. 2007, 58:177-186.
28. Scholander P.F., Hammel H.T., Hemmingsen E.A., Bradstreet E.D. Hydrostatic pressure and osmotic potential in leaves of mangroves and some other plants. Proc. Nat. Acad. Sci. U.S.A. 1964, 52:119-125.
29. Sims D.A., Gamon J.A. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sens. Environ. 2003, 84:526-537.
30. Singh P.N., Joshi B.P., Singh G. Water use and yield response of wheat to irrigation and nitrogen on alluvial soil in North India. Agric. Water Manage. 1987, 12:311-321.
31. Slatyer R.O. Plant-Water Relationships 1967, Academic Press, New York, NY.
32. Stimson H.C., Breshears T.D.D., Ustin S.L., Kefauver S.C. Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma. Remote Sens. Environ. 2005, 96:108-118.
33. Tucker C.J. Remote sensing of leaf water content in near infrared. Remote Sens. Environ. 1980, 10:23-32.
34. Ustin S.L., Roberts D.A., Pinzon J., Jacqemoud S., Gardner M., Scheer G., Castaneda C.M., Palacios-Orueta A. Estimating canopy water content of chaparral shrubs using optical methods. Remote Sens. Environ. 1998, 65:280-291.
35. Zarco-Tejda P.J., Ustin S.L. Modeling canopy water content for carbon estimates from MODIS data at land EOS validation sites. Proceeding of the 2001 International Geosciences and Remote Sensing Symposium, vol. 1 2001, 342-344.