Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-28T15:18:30.388Z Has data issue: false hasContentIssue false

ASSESSING NITROGEN NUTRITIONAL STATUS, BIOMASS AND YIELD OF COTTON WITH NDVI, SPAD AND PETIOLE SAP NITRATE CONCENTRATION

Published online by Cambridge University Press:  19 June 2017

GUISU ZHOU
Affiliation:
College of Tobacco Science, Yunnan Agricultural University, Kunming, Yunnan 650201, China Department of Plant Sciences, The University of Tennessee, 605 Airways Blvd., Jackson, TN 38301, USA
XINHUA YIN*
Affiliation:
Department of Plant Sciences, The University of Tennessee, 605 Airways Blvd., Jackson, TN 38301, USA
*
§Corresponding author. Email: [email protected]

Summary

Canopy normalized difference vegetation index (NDVI), soil plant analysis development (SPAD) reading and petiole sap NO3‒N concentration are increasingly used as quick and non-destructive methods to monitor plant N nutrition and growth status and predict yield of crops. However, little information is available on the comparisons of these three methods in assessing N nutrition, growth and yield for cotton (Gossypium hirsutum L.). Four N rates (0, 34, 67 and 101 kg N ha−1) under two cover conditions [no cover crop and hairy vetch (Vicia villosa) crop] in a 33-year long-term field trial were used to evaluate how canopy NDVI, SPAD reading (related to chlorophyll content) and petiole sap NO3‒N concentration (conventional method) are able to assess N nutrition and plant biomass and predict yield for cotton. Canopy NDVI and SPAD readings responded less sensitively to N rates than petiole sap NO3‒N. The responses of NDVI and SPAD reading to N rates were generally reduced due to the winter cover crop with hairy vetch. Significant and positive correlations existed mostly among NDVI, SPAD reading, and petiole sap NO3‒N concentration. Canopy NDVI during mid-bloom to late bloom and SPAD reading during early bloom to late bloom were effective alternative methods for assessing cotton N nutrition status. The SPAD reading at late bloom was an effective parameter to estimate cotton biomass. The NDVI at early square and SPAD reading during early square to mid-bloom were effective for cotton yield prediction.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ali, A. M., Thinda, H. S., Sharmaa, S. and Singh, V. (2014). Prediction of dry direct-seeded rice yields using chlorophyll meter, leaf color chart and GreenSeeker optical sensor in northwestern India. Field Crops Research 161:1115.Google Scholar
Al-Kaisi, M. M., Yin, X. H. and Licht, M. (2005). Soil carbon and nitrogen changes as affected by tillage systems and crop biomass in a corn-soybean rotation. Applied Soil Ecology 30:174191.CrossRefGoogle Scholar
Bally, I. S. E. and Still, L. A. (2013). Rapid monitoring of nitrogen in mango trees. Acta Horticulturae 992:107114.Google Scholar
Bauer, P. J. and Roof, M. E. (2004). Nitrogen, aldicarb, and cover crop effects on cotton yield and fiber properties. Agronomy Journal 96:369376.Google Scholar
Bronson, K. F., Booker, J. D., Keeling, J. W., Boman, R. K., Wheeler, T. A., Lascano, R. J. and Nichols, R. L. (2005). Cotton canopy reflectance at landscape scale as affected by nitrogen fertilization. Agronomy Journal 97: 654660.Google Scholar
Bronson, K. F., Chua, T. T., Booker, J. D., Keeling, J. W. and Lascano, R. J. (2003). In-season nitrogen status sensing in irrigated cotton: II. Leaf nitrogen and biomass. Soil Science Society of America Journal 67:14391448.Google Scholar
Bronson, K. F., Malapati, A., Scharf, P. C. and Nichols, R. L. (2011). Canopy reflectance-based nitrogen management strategies for subsurface drip irrigated cotton in the Texas high plains. Agronomy Journal 103:422430.CrossRefGoogle Scholar
Debaeke, P., Route, P. and Justes, E. (2006). Relationship between the normalize SPAD index and the nitrogen nutrition index: Application to durum wheat. Journal of Plant Nutrition 29:7592.Google Scholar
Drissi, R., Goutouly, J. P., Forget, D. and Gaudillere, J. P. (2009). Nondestructive measurement of grapevine leaf area by ground normalized difference vegetation index. Agronomy Journal 101:226231.Google Scholar
Errebhi, M., Rosen, C. J. and Birong, D. E. (1998). Calibration of a petiole sap nitrate test for irrigated “Russet Burbank” potato. Communications in Soil Science and Plant Analysis 29:2335.Google Scholar
Evans, J. R. (1989). Photosynthesis and nitrogen relationships in leaves of C3 plants. Oecologia 78:919.Google Scholar
Farneselli, M., Benincase, P. and Tei, F. (2010). Validation of N nutritional status tools for processing tomato. Acta Horticulturae 852:227232.Google Scholar
Gerendás, J. and Pieper, I. (2001). Suitability of the SPAD meter and the petiole nitrate test for nitrogen management in nursery potatoes. Plant Nutrition 92:716717.Google Scholar
Gutierrez, M., Norton, R., Thorp, K. R. and Wang, G. Y. (2012). Association of spectral reflectance indices with plant growth and lint yield in upland cotton. Crop Science 52:849857.CrossRefGoogle Scholar
Hardin, J. A., Smith, M. W., Weckler, P. R. and Cheary, B. S. (2012). In situ measurement of pecan leaf nitrogen concentration using a chlorophyll meter and vis-near infrared multispectral camera. HortScience 47:955960.CrossRefGoogle Scholar
Lawlor, D. W., Kontturi, M. and Young, A. T. (1989). Photosynthesis by flag leaves of wheat in relation to protein, ribulose bisphosphate carboxylase activity and nitrogen supply. Journal of Experimental Botany 40:4352.CrossRefGoogle Scholar
Littell, R. C., Milliken, G. A., Stroup, W. W. and Wolfinger, R. D. (1996). SAS System for Mixed Models. Cary, NC: SAS Inst.Google Scholar
Ma, B. L., Dwyer, L. M., Costa, C., Cober, E. R. and Morrison, M. J. (2001). Early prediction of soybean yield from canopy reflectance measurements. Agronomy Journal 93:12271234.Google Scholar
Main, C. L. (2012). Cotton Production in Tennessee. Knoxville, TN: Univ. Tennessee. http://www.utcrops.com/cotton/VarietyTestingData/2012-PB1742-CottonVarietyTests.pdf. (accessed 12 May 2014).Google Scholar
Maynard, D. N. and Barker, A. V. (1972). Nitrate content of vegetable crops. Horticultural Science 7:224226.Google Scholar
McConnell, J. S., Baker, W. H., Millerm, D. M., Frizzell, B. S. and Varil, J. J. (1993). Nitrogen fertilization of cotton cultivars of differing maturity. Agronomy Journal 85:11511156.Google Scholar
Mehlich, A. (1984). Mehlich 3 soil test extractant: A modification of Mehlich 2. Communications in Soil Science and Plant Analysis 15:14091416.Google Scholar
Oosterhuis, D. M. (1990). Growth and development of the cotton plant. In Nitrogen Nutrition in Cotton: Practical Issues. Proc. Southern Branch Workshop for Practicing Agronomists (Eds Miley, W. N. and Oosterhuis, D. M.). Madison, WI: Publ. Amer. Soc. Agron.Google Scholar
Poljak, M., Horvat, T., Majic, A., Pospisil, A. and Cosic, T. (2008). Nitrogen management for potatoes by using rapid test methods. Cereal Research Communications 36:17951798.Google Scholar
Raper, T. B., Varco, J. J. and Hubbard, K. J. (2013). Canopy-based normalized difference vegetation index sensors for monitoring cotton nitrogen status. Agronomy Journal 105:13451354.Google Scholar
Rorie, R. L., Purcell, L. C., Morteza, M., Karcher, D. E., King, C. A., Marsh, M. C. and Longer, D. E. (2011). Association of “greenness” in corn with yield and leaf nitrogen concentration. Agronomy Journal 103:529535.CrossRefGoogle Scholar
Rosolem, C. A. and Mellis, V. V. (2010). Monitoring nitrogen nutrition in cotton. Revista Brasileira de Ciencia do Solo 34:16011607.CrossRefGoogle Scholar
Sainju, U. M. and Singh, B. P. (2008). Nitrogen storage with cover crops and nitrogen fertilization in tilled and nontilled soils. Agronomy Journal 100:619627.CrossRefGoogle Scholar
Saleh, B. (2012). Effect of salt stress on growth and chlorophyll content of some cultivated cotton varieties grown in Syria. Communications in Soil Science and Plant Analysis. 43:19761983.Google Scholar
Seemann, J. R., Sharkey, T. D., Wang, J. and Osmond, C. B. (1987). Environmental effects on photosynthesis, nitrogen-use efficiency, and metabolite pools in leaves of sun and shade plants. Plant Physiology 84:796802.Google Scholar
Sexton, P. and Carroll, J. (2002). Comparison of SPAD chlorophyll meter readings vs. petiole nitrate concentration in sugarbeet. Journal of Plant Nutrition 25:19751986.Google Scholar
Smith, J. H., Silvertooth, J. C. and Norton, E. R. (1998). Comparison of the two methods for the analysis of petiole nitrate nitrogen concentration in irrigated cotton. Cotton Report 1006:476479.Google Scholar
Stone, M. L., Solie, J. B., Raun, W. R., Whitney, R. W., Taylor, S. L. and Ringer, J. D. (1996). Use of spectral radiance for correcting in-season fertilizer nitrogen deficiencies in winter wheat. Transactions of the ASAE. 39:16231631.Google Scholar
Teal, R. K., Tubana, B. S., Girma, K., Freeman, K. W., Arnall, D. B., Walsh, O. and Raun, W. R. (2006). In-season prediction of corn grain yield potential using normalized difference vegetation index. Agronomy Journal 98:14881494.Google Scholar
Varvel, G. E., Schepers, J. S. and Francis, D. D. (1997). Chlorophyll meter and stalk nitrate techniques as complementary indices for residual nitrogen. Journal of Production Agriculture 10:147151.CrossRefGoogle Scholar
Vollmann, J., Walter, H., Sato, T. and Schweiger, P. (2011). Digital image analysis and chlorophyll metering for phenotyping the nodulation in soybean. Computers and Electronics in Agriculture 75:190195.Google Scholar
Vos, J. and Bom, M. (1993). Hand-held chlorophyll meter: A promising tool to assess the nitrogen status of potato foliage. Potato Research 36:301308.Google Scholar
Wang, Y. W., Dunn, B. L. and Arnall, D. B. (2012). Assessing nitrogen status in potted geranium through discriminant analysis of ground-based spectral reflectance data. HortScience 47:343348.Google Scholar
Warner, D. A. and Burke, J. J. (1993). Cool night temperatures alter leaf starch and photosystem II chlorophyll fluorescence in cotton. Agronomy Journal 85:836840.Google Scholar
Westcott, M. P. and Wraith, J. M. (1995). Correlation of leaf chlorophyll readings and stem nitrate concentrations in peppermint. Communications in Soil Science and Plant Analysis 26:14811490.Google Scholar
Woodson, W. R. and Boodley, J. W. (1983). Petioles nitrate concentration as an indicator of geranium nitrogen status. Communications in Soil Science and Plant Analysis 14:363372.Google Scholar
Wu, J. D., Wang, D., Rosen, C. J. and Bauer, M. E. (2007). Comparison of petiole nitrate concentration, SPAD chlorophyll readings, and QuickBird satellite imagery in detecting nitrogen status of potato canopies. Field Crops Research 101:96103.Google Scholar
Yin, X. H. and McClure, M. A. (2013). Relationship of corn yield, biomass, and leaf nitrogen with normalized difference vegetation index and plant height. Agronomy Journal 105:10051016.Google Scholar
Yin, X., Jaja, N., McClure, M. A. and Hayes, R. M. (2011). Comparison of models in assessing relationship of corn yield with plant height during early to mid-season. Journal of Agriculture Science 3:1424.Google Scholar
Zhao, D. L., Reddy, K. R., Kakani, V. G., Read, J. J. and Koti, S. (2005). Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field–grown cotton. Agronomy Journal 97:8998.Google Scholar
Zhou, G. S. and Yin, X. H. (2014). Relationship of cotton nitrogen and yield with normalized difference vegetation index and plant height. Nutrient Cycling in Agroecosystems 100:147160.Google Scholar
Supplementary material: File

Zhou and Yin supplementary material

Zhou and Yin supplementary material 1

Download Zhou and Yin supplementary material(File)
File 1.7 MB