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Using vegetation indices from satellite remote sensing to assess corn and soybean response to controlled tile drainage

Author

Listed:
  • Cicek, H.
  • Sunohara, M.
  • Wilkes, G.
  • McNairn, H.
  • Pick, F.
  • Topp, E.
  • Lapen, D.R.

Abstract

Controlled tile drainage (CTD) is a management practice designed to retain water and nutrients in the field for crop use. CTD has shown promise for improving water quality and augmenting crop yields but findings are often restricted to field and plot scales. Remote sensing is one of the alternatives to evaluate crop responsiveness to CTD at large spatial scales. This study compared normalized and green normalized difference vegetation indices (NDVI and GNDVI) for corn (Zea mays L.) and soybean (Glycine max L.) among CTD and uncontrolled tile drainage (UCTD) fields in a ~950 ha experimental watershed setting in Ontario, Canada from 2005 to 2008. The indices were derived from Landsat-5 and SPOT-4 satellite imagery. Log-transformed NDVI and GNDVI for soybean (R3-R6 growth stage) and corn (VT to R5-R6 growth stage) crops were higher significantly (p  CTD). Log-transformed NDVI and GNDVI standard errors for CTD, relative to UCTD fields, were lower for 65% of the significant corn and 71% of the significant soybean NDVI and GNDVI comparisons for the growth stages noted above. This finding suggested overall more uniform crop growth for CTD fields relative to UCTD fields. Observed yields from a subset of commonly managed CTD and UCTD fields in the study area were not significantly different from each other (p > 0.05) with respect to tile drainage management practice; however, 87% of these paired yield comparisons indicated that CTD mean corn/soybean grain yields were greater than or equal to those for UCTD. On average, CTD observed corn and soybean grain yields were 3% and 4%, respectively, greater than those from UCTD. From observed yield and NDVI and GNDVI observations, vegetation indices vs. yield linear regression models were developed to predict grain yields over a broader land base in the experimental watershed area. Here, predicted mean yields were 0.1-11% higher for CTD corn and -5% to 4% higher for CTD soybean, relative to UCTD crops; but results varied between manured and non-manured fertilizer practices. Eighty-nine percent of the standard deviations for these yield predictions were lower for CTD relative to UCTD. The results of this study indicate that at a minimum, CTD did not adversely impact corn and soybean grain yields over the time span and field environments of the study, and based on the weight of evidence presented here, CTD shows general promise for augmenting crop performance. Finally, remote sensing derived vegetation indices such as NDVI and GNDVI can be used to assess the impact of agricultural drainage management practices on crop response and production properties.

Suggested Citation

  • Cicek, H. & Sunohara, M. & Wilkes, G. & McNairn, H. & Pick, F. & Topp, E. & Lapen, D.R., 2010. "Using vegetation indices from satellite remote sensing to assess corn and soybean response to controlled tile drainage," Agricultural Water Management, Elsevier, vol. 98(2), pages 261-270, December.
    • Handle: RePEc:eee:agiwat:v:98:y:2010:i:2:p:261-270
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    References listed on IDEAS

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    1. Mejia, M. N. & Madramootoo, C. A. & Broughton, R. S., 2000. "Influence of water table management on corn and soybean yields," Agricultural Water Management, Elsevier, vol. 46(1), pages 73-89, November.
    2. Lalonde, V. & Madramootoo, C. A. & Trenholm, L. & Broughton, R. S., 1996. "Effects of controlled drainage on nitrate concentrations in subsurface drain discharge," Agricultural Water Management, Elsevier, vol. 29(2), pages 187-199, January.
    3. Wesstrom, Ingrid & Messing, Ingmar, 2007. "Effects of controlled drainage on N and P losses and N dynamics in a loamy sand with spring crops," Agricultural Water Management, Elsevier, vol. 87(3), pages 229-240, February.
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    Cited by:

    1. Kross, Angela & Lapen, David R. & McNairn, Heather & Sunohara, Mark & Champagne, Catherine & Wilkes, Graham, 2015. "Satellite and in situ derived corn and soybean biomass and leaf area index: Response to controlled tile drainage under varying weather conditions," Agricultural Water Management, Elsevier, vol. 160(C), pages 118-131.
    2. Changwei Tan & Xinxing Zhou & Pengpeng Zhang & Zhixiang Wang & Dunliang Wang & Wenshan Guo & Fei Yun, 2020. "Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-15, March.
    3. Yuki Hamada & Colleen R. Zumpf & Jules F. Cacho & DoKyoung Lee & Cheng-Hsien Lin & Arvid Boe & Emily Heaton & Robert Mitchell & Maria Cristina Negri, 2021. "Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy," Land, MDPI, vol. 10(11), pages 1-22, November.
    4. Youssef, Mohamed A. & Strock, Jeffrey & Bagheri, Ehsan & Reinhart, Benjamin D. & Abendroth, Lori J. & Chighladze, Giorgi & Ghane, Ehsan & Shedekar, Vinayak & Fausey (Ret.), Norman R. & Frankenberger, , 2023. "Impact of controlled drainage on corn yield under varying precipitation patterns: A synthesis of studies across the U.S. Midwest and Southeast," Agricultural Water Management, Elsevier, vol. 275(C).
    5. Van Zandvoort, Alisha & Clark, Ian D. & Flemming, Corey & Craiovan, Emilia & Sunohara, Mark D. & Gottschall, Natalie & Boutz, Ronda & Lapen, David R., 2017. "Using 13C isotopic analysis to assess soil carbon pools associated with tile drainage management during drier and wetter growing seasons," Agricultural Water Management, Elsevier, vol. 192(C), pages 232-243.
    6. Ekwunife, Kosoluchukwu C. & Madramootoo, Chandra A., 2023. "Influence of seasonal climate and water table management on corn yield and nitrous oxide emissions," Agricultural Water Management, Elsevier, vol. 279(C).

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