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Use of corn height to improve the relationship between active optical sensor readings and yield estimates

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Abstract

Early in-season loss of N continues to be a problem in corn (Zea mays L.). One method to improve N use efficiency is fertilizing based on in-season crop foliage sensors. The objective of this study was to evaluate two ground-based, active-optical (GBAO) sensors and explore the use of corn height with sensor readings for improving relationships with corn yield. Two GBAO sensors (GreenSeeker® (GS), Trimble, Sunnydale, CA, USA; and Holland Crop Circle (CC) ACS 470 Sensor®, Holland Scientific, Lincoln, NE, USA) were used within 30 established corn N-rate trials in North Dakota at the V6 and V12 growth stages in 2011 and 2012. Corn height was recorded manually at the date of sensor data collection. At the V6 growth stage, the GS relationship to yield and the INSEY (in-season estimate of yield) value was improved when the sensor reading was multiplied times corn height. At the V12 stage, using the GS, the INSEY relationship with yield was also generally increased when height was considered. The CC-based red/near-infrared INSEY relationship with yield was similar to the GS INSEY. The CC-based red edge/near infrared INSEY relationship was increased with height only at the first sensor date, but not with the second. The second CC-based sensor–INSEY relationship with yield was maximized using sensor reading only. Segregating the 30 site data set into sites with high clay surface textures and sites with medium texture improved all INSEY relationships compared to pooling all sites. Relationships between INSEY and corn yield at no-till sites were significant at the V12 stage in the wetter 2011 growing season, but not at the V6 stage either year, nor at the V12 stage in the very dry 2012 season. In the high clay and medium textured soils at the V6 stage, corn height improved the relationship between INSEY and yield often enough to suggest that incorporating corn height into an algorithm for yield prediction would strengthen yield prediction, and thus improve N rate decisions.

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References

  • Aase, J. K., & Tanaka, D. L. (1991). Reflectances from four wheat residue cover densities as influenced by three soil backgrounds. Agronomy Journal, 83, 753–757.

    Article  Google Scholar 

  • Biermacher, J. T., Epplin, F. M., Brorsen, B. W., Solie, J. B., & Raun, W. R. (2006). Maximum benefit of aprecise nitrogen application system for wheat. Precision Agriculture, 7, 1–12. doi:10.1007/s11119-006-9017-6.

    Google Scholar 

  • Franzen, D. W. (2010). North Dakota fertilizer recommendation tables and equations. NDSU Extension Circular SF-882. North Dakota State University Extension Service, Fargo, ND.

  • Franzen, M. (2012). Program for initial analysis of GreenSeeker® and Holland Crop Circle® Sensor raw data within Excel. Unpublished program. Fargo, ND.

  • Franzen, D. W., Wagner, G., & Sims, A. (2003). Application of a ground-based sensor to determine N credits from sugarbeet. In Sugarbeet Research and Extension Reports (Vol. 34, pp. 119–123). Sugarbeet Research and Education Board of Minnesota and North Dakota. Fargo, ND.

  • Freeman, K. W., Girma, K., Arnall, D. B., Mullen, R. W., Martin, K. L., Teal, R. K., et al. (2007). By-plant prediction of corn forage biomass and nitrogen uptake at various growth stages using remote sensing and plant height. Agronomy Journal, 99, 530–536.

    Article  CAS  Google Scholar 

  • Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90, 337–352.

    Article  Google Scholar 

  • Hoeft, R. G., Nafziger, E. D., Gonzini, L. C., Warren, J. J., Adee, E. A., Paul, L. E., et al. 1999. Strip till tillage, N placement, and starter fertilizer effects on corn growth and yield. In Illinois Fertilizer Conference Proceedings. On line at http://frec.ifca.com/1999/report4 .

  • Holland, K. H., & Schepers, J. S. (2010). Derivation of a variable rate nitrogen application method for in-season fertilization of corn. Agronomy Journal, 102, 1415–1424.

    Article  Google Scholar 

  • Holland, K. H., & Schepers, J. S. (2013). Use of virtual reference concept to interpret active crop canopy sensor data. Precision Agriculture, 14, 71–85.

    Article  Google Scholar 

  • Horler, D. N. H., Dockray, M., Barber, J., & Barringer, A. R. (1983). Red edge measurements for remotely sensing plant chlorophyll content. Advances in Space Research, 3, 273–277.

    Article  CAS  Google Scholar 

  • Hussain, I., Olson, K. R., & Ebelhar, S. A. (1999). Impacts of tillage and no-till on production of maize and soybean on an eroded Illinois silt loam soil. Soil Tillage Research, 52, 37–49. doi:10.1016/S0167-1987(99)00054-9.

    Article  Google Scholar 

  • Kapusta, G., Krausz, R. F., Krausz, R. F., & Matthews, J. L. (1996). Corn yield is equal in conventional, reduced, and no tillage after 20 years. Agronomy Journal, 88, 812–817. doi:10.2134/agronj1996.00021962008800050021x.

    Article  Google Scholar 

  • Katsvairo, T. W., Cox, W. J., & Van Es, H. M. (2003). Spatial growth and nitrogen uptake variability of corn at two nitrogen levels. Agronomy Journal, 95, 1000–1011. doi:10.2134/agronj2003.1000.

    Article  Google Scholar 

  • Kladivko, E. J., Griffith, D. R., & Mannering, J. V. (1986). Conservation tillage effects on soil properties and yield of corn and soya beans in Indiana. Soil Tillage Research, 8, 277–287. doi:10.1016/0167-1987(86)90340-5.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Machado, S., Bynum, E. D, Jr., Archer, T. L., Lascano, R. J., Wilson, L. T., Bordovsky, J., et al. (2002). Spatial and temporal variability of corn growth and grain yield: Implications for site-specific farming. Crop Science, 42, 1564–1576.

    Article  Google Scholar 

  • Martin, K., Raun, W., & Solie, J. (2012). By-plant prediction of corn grain yield using optical sensor readings and measured plant height. Journal of Plant Nutrition, 35, 1429–1439.

    Article  CAS  Google Scholar 

  • Nicolas, T., Bouroubi, Y. M., Bélec, C., Mullen, R. W., Kitchen, N. R., Thomason, W. E., et al. (2012). Corn response to nitrogen is influenced by soil texture and weather. Agronomy Journal, 104, 1658–1671.

    Article  Google Scholar 

  • Olsen, S. R., Cole, C. V., Watanabe, F. S., & Dean. L. A. (1954). Estimation of available phosphorus in soils by extraction with sodium bicarbonate. USDA Circular 939. U.S. Government Printing Office, Washington, D.C.

  • Payero, J. O., Neale, C. M. U., & Wright, J. L. (2004). Comparison of eleven vegetation indices for estimating plant height of alfalfa and grass. American Society of Agricultural Engineers, 20, 385–393.

    Google Scholar 

  • Raun, W. R. (2007). Reduced nitrogen and improved farm income for irrigated spring wheat in the Yaqui Valley, Mexico, using sensor based nitrogen management. Journal of Agricultural Sciences, 145, 1–8. doi:10.1017/S0021859607006995.

    Google Scholar 

  • Raun, W. R., & Schepers, J. S. (2008). Nitrogen management for improved efficiency. In J. S. Schepers & W. R. Raun (Eds.), Nitrogen in Agricultural Systems (Agronomy Monograph No. 50, pp. 675–694). ASA-CSSA-SSSA, Madison, WI.

  • Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Lukina, E. V., Thompson, W. E., et al. (2001). Agronomy Journal, 93, 131–138.

    Article  Google Scholar 

  • Raun, W. R., Solie, J. B., Stone, M. L., Martin, K. L., Freeman, K. W., Mullen, R. W., et al. (2005). Optical sensor-based algorithm for crop nitrogen fertilization. Communications in Soil Science and Plant Analysis, 36, 2759–2781.

    Article  CAS  Google Scholar 

  • Schulte, E. E., & Hopkins. B. G. (1996). Estimation of soil organic matter by weight-loss-on ignition. In F. R. Magdoff et al. (Eds.), Soil Organic Matter: Analysis and Interpretation (Chap. 3, pp. 21–31). SSSA Special Publication 46. Madison, WI: SSSA.

  • Shrestha, D. S., Steward, B. L., Birrell, S. J., & Kaspar, T. C. (2002). Plant height estimation using two sensing systems. St. Joseph, MI: ASAE. CD-ROM.

    Google Scholar 

  • Sogbedji, J. M., van Es, H. M., Klausner, S. D., Bouldin, D. R., & Cox, W. J. (2001). Spatial and temporal processes affecting nitrogen availability at the landscape scale. Soil and Tillage Research, 58, 233–244.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Thomas, G. W. (1982). Exchangeable cations. In A. L. Page et al. (Eds.). Methods of soil analysis. Part 2. (Agronomy Monographs 9, pp. 159–165). Madison, WI: ASA and SSSA.

  • Tubana, B. S., Tubana, B. S., Arnall, D. B., Walsh, O., Chung, B., Solie, J. B., et al. (2008). Adjusting midseason nitrogen rate using a sensor-based optimization algorithm to increase use efficiency in corn. Journal of Plant Nutrition, 31, 1393–1419. doi:10.1080/01904160802208261.

    Article  CAS  Google Scholar 

  • UCLA-IDRE. 2013. SAS FAQ: How do I compare regression coefficients between two groups? http://www.ats.ucla.edu/stat/sas/faq/compreg2.htm .

  • van Es, H. M., Yang, C. L., & Geohring, L. D. (2005). Maize nitrogen response as affected by soil type and drainage variability. Precision Agriculture, 6, 281–295.

    Article  Google Scholar 

  • Vanderlip, R. L., & D. L. Fjell. 1994. Use of growing degree units in corn production. In Corn Production Handbook (pp. 6-8). Manhattan, KS: Kansas State University Agricultural Experiment Station and Cooperative Extension Service C-560.

  • Watson, D., & Brown. J. R. (1998). pH and lime requirement. In J. R. Brown (Ed.) Recommended Chemical Soil Test Procedure for the North Central Region (p. 13). North Central Regional Res. Pub. No. 221 (revised). Missouri Agric. Exp. Stat. SB 1001, Univ. of Missouri, Columbia.

  • Whitmore, A. P., & Whalley, W. R. (2013). Physical effects of soil drying on roots and crop growth. Journal of Experimental Botany, 60, 2845–2857.

    Article  Google Scholar 

  • Wilhelm, W., Ruwe, K., & Schlemmer, M. R. 2000. Comparison of three leaf area index meters in a corn canopy. Publication from USDA-ARS/UNL Faculty Paper 71. http://digitalcommons.unl.edu/usdaarsfacpug/71.

  • Yin, X., Jaja, N., McClure, M. A., & Hayes, R. M. (2011a). Comparison of models in assessing relationship of corn yield with plant height measured during early- to mid-season. Journal of Agricultural Science, 3, 14–24.

    Article  Google Scholar 

  • Yin, X., McClure, M. A., Jaja, N., Tyler, D. D., & Hayes, R. M. (2011b). In-season prediction of corn yield using plant height under major production systems. Agronomy Journal, 103, 923–929.

    Article  Google Scholar 

Download references

Acknowledgments

Thanks to the North Dakota Corn Council, International Plant Nutrition Institute and Pioneer Hi-Bred International for their financial support of this Project.

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Correspondence to D. W. Franzen.

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Sharma, L.K., Franzen, D.W. Use of corn height to improve the relationship between active optical sensor readings and yield estimates. Precision Agric 15, 331–345 (2014). https://doi.org/10.1007/s11119-013-9330-9

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