Precision Agriculture

, Volume 18, Issue 5, pp 779–800 | Cite as

Crop model- and satellite imagery-based recommendation tool for variable rate N fertilizer application for the US Corn system

  • Zhenong Jin
  • Rishi Prasad
  • John Shriver
  • Qianlai Zhuang


Precision nitrogen (N) management for corn has gained popularity due to both economic and environmental considerations. There is sufficient evidence demonstrating that N fertilizer efficiency can be improved by implementing sidedress and variable rate fertilization. In this paper, a crop model- and satellite imagery-based decision-support tool for recommending variable rate N fertilization at a high resolution of 5 m × 5 m is introduced. The sub-field management zones were delineated by overlapping the soil survey geographic (SSURGO) soil map units with wide dynamic range vegetation index (WDRVI)-derived relative productivity zones. The calibrated Agricultural Production Systems sIMulator (APSIM) was used to simulate a range of soil N processes, corn growth and N uptake by assimilating real-time weather data from the National Climate Data Center (NCDC). Sidedress N rates were estimated based on the target rate, N loss via leaching and denitrification, plant uptake and leftover N in the soil. The tool was tested on a 66 ha corn field in Illinois, USA for the growing season of 2015. Results showed that N-Prescription was able to give reasonable management zone delineation and sidedress N recommendation. The recommended sidedress N ranged from 60 to over 120 kg ha−1. Corn yield was greater in areas with higher sidedress recommendation, but the benefit from sidedress decreased with the increasing rate and plateaued above 110 kg ha−1. Sensitivity analysis suggested that soil hydraulic properties and soil organic matter content were critical to the sidedress accounting. Corn growth, and hence the cumulative N uptake, can be well simulated by calibrating the WDRVI derived leaf area index. This tool could serve as a good foundation for further development in precision N management.


Precision fertilization Sidedress Corn Agricultural Production Systems sIMulator (APSIM) Wide dynamic range vegetation index (WDRVI) SSURGO 



We thank the Backend team at FarmLogs and the Information Technology at Purdue Research Computing (RCAC) for computing support. This study is financially supported through projects funded to Q. Zhuang by the NASA Land Use and Land Cover Change program (NASA-NNX09AI26G), the NSF Division of Information and Intelligent Systems (NSF-1028291).

Supplementary material

11119_2016_9488_MOESM1_ESM.docx (368 kb)
Supplementary material 1 (DOCX 367 kb)


  1. Abendroth, L. J. (2011). Corn growth and development. Ames, IA: Iowa State University Extension.Google Scholar
  2. Archontoulis, S. V., Miguez, F. E., & Moore, K. J. (2014). Evaluating APSIM maize, soil water, soil nitrogen, manure, and soil temperature modules in the Midwestern United States. Agronomy Journal, 106, 1025–1040.CrossRefGoogle Scholar
  3. Arthur, D., & Vassilvitskii, S. (2007). k-means++: The advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms (pp. 1027–1035). New Orleans, LS, USA: Society for Industrial and Applied Mathematics.Google Scholar
  4. Ashtekar, J. M., & Owens, P. R. (2013). Remembering knowledge: An expert knowledge based approach to digital soil mapping. Soil Horizons, 54, 1–6.CrossRefGoogle Scholar
  5. Cassman, K. G., Dobermann, A., & Walters, D. T. (2002). Agroecosystems, nitrogen-use efficiency, and nitrogen management. AMBIO: A Journal of the Human Environment, 31, 132–140.CrossRefGoogle Scholar
  6. Castaldi, F., Palombo, A., Santini, F., Pascucci, S., Pignatti, S., & Casa, R. (2016). Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon. Remote Sensing of Environment, 179, 54–65.CrossRefGoogle Scholar
  7. Chander, G., Haque, M. O., Sampath, A., Brunn, A., Trosset, G., Hoffmann, D., et al. (2013). Radiometric and geometric assessment of data from the RapidEye constellation of satellites. International Journal of Remote Sensing, 34, 5905–5925.CrossRefGoogle Scholar
  8. Chaney, N. W., Wood, E. F., McBratney, A. B., Hempel, J. W., Nauman, T. W., Brungard, C. W., et al. (2016). POLARIS: A 30-meter probabilistic soil series map of the contiguous United States. Geoderma, 274, 54–67.CrossRefGoogle Scholar
  9. Charoenhirunyingyos, S., Honda, K., Kamthonkiat, D., & Ines, A. V. (2011). Soil moisture estimation from inverse modeling using multiple criteria functions. Computers and Electronics in Agriculture, 75, 278–287.CrossRefGoogle Scholar
  10. Cicore, P., Serrano, J., Shahidian, S., Sousa, A., Costa, J. L., & da Silva, J. R. M. (2016). Assessment of the spatial variability in tall wheatgrass forage using LANDSAT 8 satellite imagery to delineate potential management zones. Environmental Monitoring and Assessment, 188, 513.CrossRefPubMedGoogle Scholar
  11. Derby, N. E., Casey, F. X. M., & Franzen, D. W. (2007). Comparison of nitrogen management zone delineation methods for corn grain yield. Agronomy Journal, 99, 405–414.CrossRefGoogle Scholar
  12. Diker, K., Heermann, D. F., & Brodahl, M. K. (2004). Frequency analysis of yield for delineating yield response zones. Precision Agriculture, 5, 435–444.CrossRefGoogle Scholar
  13. Fleming, K. L., Heermann, D. F., & Westfall, D. G. (2004). Evaluating soil color with farmer input and apparent soil electrical conductivity for management zone delineation. Agronomy Journal, 96, 1581–1587.CrossRefGoogle Scholar
  14. Gitelson, A. A. (2004). Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 161, 165–173.CrossRefPubMedGoogle Scholar
  15. Gomez, C., Viscarra Rossel, R. A., & McBratney, A. B. (2008). Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma, 146, 403–411.CrossRefGoogle Scholar
  16. Guastaferro, F., Castrignanò, A., De Benedetto, D., Sollitto, D., Troccoli, A., & Cafarelli, B. (2010). A comparison of different algorithms for the delineation of management zones. Precision Agriculture, 11, 600–620.CrossRefGoogle Scholar
  17. Hammer, G. L., Dong, Z., McLean, G., Doherty, A., Messina, C., Schussler, J., et al. (2009). Can Changes in canopy and/or root system architecture explain historical maize yield trends in the U.S. Corn Belt? Crop Science, 49, 299–312.CrossRefGoogle Scholar
  18. Hank, T. B., Bach, H., & Mauser, W. (2015). Using a remote sensing-supported hydro-agroecological model for field-scale simulation of heterogeneous crop growth and yield: Application for wheat in central Europe. Remote Sensing, 7, 3934–3965.CrossRefGoogle Scholar
  19. Holzworth, D. P., Huth, N. I., deVoil, P. G., et al. (2014). APSIM—evolution towards a new generation of agricultural systems simulation. Environmental Modelling and Software, 62, 327–350.CrossRefGoogle Scholar
  20. Honda, K., Ines, A. V., Yui, A., Witayangkurn, A., Chinnachodteeranun, R., & Teeravech, K. (2014). Agriculture information service built on geospatial data infrastructure and crop modeling. In Proceedings of the 2014 international workshop on web intelligence and smart sensing (pp. 1–9). New York, USA: Association for Computing Machinery.Google Scholar
  21. Hunt, E. R., Hively, W. D., Daughtry, C. S., McCarty, G. W., Fujikawa, S. J., Ng, T. L., et al. (2008). Remote sensing of crop leaf area index using unmanned airborne vehicles. In Proceedings of the Pecora 17 symposium. Bethesda, MD: American Society for Photogrammetry and Remote Sensing. CDROM. Accessed 31 Oct 2016.
  22. Irish, R. R. (2000). Landsat 7 automatic cloud cover assessment. In AeroSense 2000 (pp. 348–355). Bellingham, WA, USA: International Society for Optics and Photonics.Google Scholar
  23. Jin, Z., Zhuang, Q., He, J.-S., Zhu, X., & Song, W. (2015). Net exchanges of methane and carbon dioxide on the Qinghai-Tibetan Plateau from 1979 to 2100. Environmental Research Letters, 10, 085007.CrossRefGoogle Scholar
  24. Jin, Z., Zhuang, Q., Tan, Z., Dukes, J. S., Zheng, B., & Melillo, J. M. (2016). Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches. Global Change Biology, 22, 3112–3126.CrossRefPubMedGoogle Scholar
  25. Keeney, D., & Olson, R. A. (1986). Sources of nitrate to ground water. Critical Reviews in Environmental Control, 16, 257–304.CrossRefGoogle Scholar
  26. Kravchenko, A. N., & Bullock, D. G. (2000). Correlation of corn and soybean grain yield with topography and soil properties. Agronomy Journal, 92, 75–83.CrossRefGoogle Scholar
  27. Ladoni, M., Bahrami, H., Alavipanah, S., & Norouzi, A. (2010). Estimating soil organic carbon from soil reflectance: A review. Precision Agriculture, 11, 82–99.CrossRefGoogle Scholar
  28. Littleboy, M., Silburn, D. M., Freebairn, D. M., Woodruff, D. R., Hammer, G. L., & Leslie, J. K. (1992). Impact of soil erosion on production in cropping systems. I. Development and validation of a simulation model. Soil Research, 30, 757–774.CrossRefGoogle Scholar
  29. Lobell, D. B., Hammer, G. L., McLean, G., Messina, C., Roberts, M. J., & Schlenker, W. (2013). The critical role of extreme heat for maize production in the United States. Nature Climate Change, 3, 497–501.CrossRefGoogle Scholar
  30. Lobell, D. B., Thau, D., Seifert, C., Engle, E., & Little, B. (2015). A scalable satellite-based crop yield mapper. Remote Sensing of Environment, 164, 324–333.CrossRefGoogle Scholar
  31. Ma, B. L., & Biswas, D. K. (2015). Precision nitrogen management for sustainable corn production. In Sustainable agriculture reviews (pp. 33–62). Cham, Switzerland: Springer International Publishing.Google Scholar
  32. Machwitz, M., Giustarini, L., Bossung, C., Frantz, D., Schlerf, M., Lilienthal, H., et al. (2014). Enhanced biomass prediction by assimilating satellite data into a crop growth model. Environmental Modelling and Software, 62, 437–453.CrossRefGoogle Scholar
  33. Mamo, M., Malzer, G. L., Mulla, D. J., Huggins, D. R., & Strock, J. (2003). Spatial and temporal variation in economically optimum nitrogen rate for corn. Agronomy Journal, 95, 958–964.CrossRefGoogle Scholar
  34. McIsaac, G. F., David, M. B., Gertner, G. Z., & Goolsby, D. A. (2002). Relating net nitrogen input in the Mississippi River Basin to nitrate flux in the lower Mississippi River. Journal of Environmental Quality, 31, 1610–1622.CrossRefPubMedGoogle Scholar
  35. Melkonian, J. J., van Es, H. M., DeGaetano, A. T., & Joseph, T. (2008) ADAPT-N: Adaptive nitrogen management for maize using high resolution climate data and model simulations. In: R. Khosla (Ed.), Proceedings of the 9th international conference on precision agriculture. Denver, CO. 18–21 July 2010. Monticello, IL, USA: International Society of Precision Agriculture. CDROM.Google Scholar
  36. Moebius-Clune, B., Van Es, H., & Melkonian, J. (2013). Adapt-N uses models and weather data to improve nitrogen management for corn. Better Crops, 97, 7–9.Google Scholar
  37. Mulder, V. L., De Bruin, S., Schaepman, M. E., & Mayr, T. R. (2011). The use of remote sensing in soil and terrain mapping—a review. Geoderma, 162, 1–19.CrossRefGoogle Scholar
  38. Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114, 358–371.CrossRefGoogle Scholar
  39. Pappas, C., Fatichi, S., Leuzinger, S., Wolf, A., & Burlando, P. (2013). Sensitivity analysis of a process-based ecosystem model: Pinpointing parameterization and structural issues. Journal of Geophysical Research: Biogeosciences, 118, 505–528.Google Scholar
  40. Park, S., Croteau, P., Boering, K. A., Etheridge, D. M., Ferretti, D., Fraser, P. J., et al. (2012). Trends and seasonal cycles in the isotopic composition of nitrous oxide since 1940. Nature Geoscience, 5, 261–265.CrossRefGoogle Scholar
  41. Prasad, R., Hochmuth, G. J., & Boote, K. J. (2015). Estimation of nitrogen pools in irrigated potato production on sandy soil using the model SUBSTOR. PLoS ONE, 10, e0117891.CrossRefPubMedPubMedCentralGoogle Scholar
  42. Randall, G. W., Vetsch, J. A., & Huffman, J. R. (2003). Nitrate losses in subsurface drainage from a corn-soybean rotation as affected by time of nitrogen application and use of nitrapyrin. Journal of Environmental Quality, 32, 1764–1772.CrossRefPubMedGoogle Scholar
  43. Sakamoto, T., Gitelson, A. A., & Arkebauer, T. J. (2014). Near real-time prediction of US corn yields based on time-series MODIS data. Remote Sensing of Environment, 147, 219–231.CrossRefGoogle Scholar
  44. Saxton, K. E., & Rawls, W. J. (2006). Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Science Society of America Journal, 70, 1569–1578.CrossRefGoogle Scholar
  45. Saxton, K. E., Rawls, W. J., Romberger, J. S., & Papendick, R. I. (1986). Estimating generalized soil-water characteristics from texture. Soil Science Society of America Journal, 50, 1031–1036.CrossRefGoogle Scholar
  46. Scharf, P. C. (2015) Managing nitrogen. In: Managing nitrogen in crop production (pp. 25–76). Madison, WI, USA: American Society of Agronomy, Inc., Crop Science Society of America, Inc., and Soil Science Society of America, Inc.Google Scholar
  47. Sela, S., van Es, H. M., Moebius-Clune, B. N., Marjerison, R., Melkonian, J., Moebius-Clune, D., et al. (2016). Adapt-N outperforms grower-selected nitrogen rates in Northeast and Midwestern United States strip trials. Agronomy Journal, 103(108), 1726–1734.CrossRefGoogle Scholar
  48. Setiyono, T. D., Yang, H., Walters, D. T., Dobermann, A., Ferguson, R. B., Roberts, D. F., et al. (2011). Maize-N: A Decision tool for nitrogen management in maize. Agronomy Journal, 103, 1276–1283.CrossRefGoogle Scholar
  49. Shaddad, S. M., Madrau, S., Castrignanò, A., & Mouazen, A. M. (2016). Data fusion techniques for delineation of site-specific management zones in a field in UK. Precision Agriculture, 17, 200–217.CrossRefGoogle Scholar
  50. Shahandeh, H., Wright, A. L., & Hons, F. M. (2011). Use of soil nitrogen parameters and texture for spatially-variable nitrogen fertilization. Precision Agriculture, 12, 146–163.CrossRefGoogle Scholar
  51. Sibley, A. M., Grassini, P., Thomas, N. E., Cassman, K. G., & Lobell, D. B. (2014). Testing remote sensing approaches for assessing yield variability among maize fields. Agronomy Journal, 106, 24–32.CrossRefGoogle Scholar
  52. Sinclair, T. R., & Muchow, R. C. (1995). Effect of nitrogen supply on maize yield: I. Modeling physiological responses. Agronomy Journal, 87, 632–641.CrossRefGoogle Scholar
  53. Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Retrieved Octobor 31, 2016 from
  54. Solie, J. B., Monroe, A. D., Raun, W. R., & Stone, M. L. (2012). Generalized algorithm for variable-rate nitrogen application in cereal grains. Agronomy Journal, 104, 378–387.CrossRefGoogle Scholar
  55. Song, X., Wang, J., Huang, W., Liu, L., Yan, G., & Pu, R. (2009). The delineation of agricultural management zones with high resolution remotely sensed data. Precision Agriculture, 10, 471–487.CrossRefGoogle Scholar
  56. Thompson, L. J., Ferguson, R. B., Kitchen, N., Frazen, D. W., Mamo, M., Yang, H., et al. (2015). Model and sensor-based recommendation approaches for in-season nitrogen management in corn. Agronomy Journal, 107, 2020–2030.CrossRefGoogle Scholar
  57. Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63, 411–423.CrossRefGoogle Scholar
  58. Tremblay, N., 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.CrossRefGoogle Scholar
  59. Viña, A., Gitelson, A. A., Nguy-Robertson, A. L., & Peng, Y. (2011). Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment, 115, 3468–3478.CrossRefGoogle Scholar
  60. Wilson, D. R., Muchow, R. C., & Murgatroyd, C. J. (1995). Model analysis of temperature and solar radiation limitations to maize potential productivity in a cool climate. Field crops research, 43, 1–18.CrossRefGoogle Scholar
  61. Yang, H., Dobermann, A., Cassman, K. G., & Walters, D. T. (2006). Features, applications, and limitations of the Hybrid-Maize simulation model. Agronomy Journal, 98, 737–748.CrossRefGoogle Scholar
  62. Zhang, X., Shi, L., Jia, X., Seielstad, G., & Helgason, C. (2010). Zone mapping application for precision-farming: A decision support tool for variable rate application. Precision Agriculture, 11, 103–114.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Zhenong Jin
    • 1
    • 2
  • Rishi Prasad
    • 3
  • John Shriver
    • 3
  • Qianlai Zhuang
    • 1
    • 4
  1. 1.Department of Earth, Atmospheric and Planetary SciencePurdue UniversityWest LafayetteUSA
  2. 2.Department of Earth System Science and Center on Food Security and the EnvironmentStanford UniversityStanfordUSA
  3. 3.FarmlogsAnn ArborUSA
  4. 4.Department of AgronomyPurdue UniversityWest LafayetteUSA

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