Skip to main content
SpringerLink
Log in

Crop Stress

  • Reference work entry
  • First Online:
Encyclopedia of Remote Sensing

Part of the book series: Encyclopedia of Earth Sciences Series ((EESS))

Synonyms

Insect infestation; Nitrogen deficiency; Water deficiency; Weed infestation

Definitions

Crop stress. Crop response to environmental factors that results in suboptimal crop production.

Introduction

Crop stress is the plant response to environmental factors that ultimately results in suboptimal crop production. The environmental factors of primary interest to US corn, cotton, soybean, and wheat producers are water, nutrients, weeds, and insects. Not coincidentally, these are also the factors that are most easily managed through irrigation and applications of fertilizer, herbicides, and pesticides. Crops are generally managed to minimize crop stress within the constraints of producing a profitable yield and minimizing environmental impact. The day-to-day management decisions to achieve this delicate balance are based in part on information about the extent, duration, and cause of crop stress. The role of remote sensing in crop management is to provide such information about crop...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 329.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bibliography

  • Baez, A. D., Kiniry, J., Maas, S. J., Tiscereno, M., Macias, J., Mendoza, J., Richardson, C., Salinas, G. J., and Manjarrez, J. R., 2005. Large-area maize yield forecasting using leaf area index based yield model. Agronomy Journal, 97(2), 418–425.

    Google Scholar 

  • Brown, R. B., Steckler, J.-P. G. A., and Anderson, G. W., 1994. Remote sensing for identification of weeds in no-till corn. Transactions of ASAE, 37, 297–302.

    Google Scholar 

  • Burke, J. J., Mahan, J. R., and Hatfield, J. L., 1988. Crop-specific thermal kinetic windows in relation to wheat and cotton biomass production. Agronomy Journal, 80, 553–556.

    Google Scholar 

  • Daughtry, C. S. T., Walthall, C. L., Kim, M. S., deColstoun, E. B., and McMurtrey, J. E., 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229–239.

    Google Scholar 

  • Demotes-Mainard, S., Boumaza, R., Meyer, S., and Cerovic, Z. G., 2008. Indicators of nitrogen status for ornamental woody plants based on optical measurements of leaf epidermal polyphenol and chlorophyll contents. Scientia Horticulturae, 115, 377–385.

    Google Scholar 

  • Fitzgerald, G. J., Maas, S. J., and DeTar, W. R., 2000. Assessing spider mite damage in cotton using multispectral remote sensing. In Proceedings of the Beltwide Cotton Conference, San Antonio, TX, Vol. 2, pp. 1342–1345.

    Google Scholar 

  • Fitzgerald, G. J., Maas, S. J., and DeTar, W. R., 2001. Spying on spider mites from on high. California-Arizona-Texas Cotton Magazine. Fall, 2001, 13–15.

    Google Scholar 

  • Fitzgerald, G. J., Maas, S. J., Pinter, P. J., Hunsaker, D. J., and Clarke, T. R., 2002. Spider mite damage mapping and cotton canopy biophysical characterization using spectral mixture analysis. In Proceedings Symposium on American-Taiwan Agricultural Cooperative Projects, Taipei, Taiwan, November 15, 2005.

    Google Scholar 

  • Fitzgerald, G. J., Maas, S. J., and DeTar, W. R., 2004. Spider mite detection and canopy component mapping in cotton using hyperspectral imagery and spectral mixture analysis. Precision Agriculture, 5, 279–289.

    Google Scholar 

  • Gitelson, A. A., Keydan, G. P., and Merzlyak, M. N., 2006. Three-band model for noninvasive estimation of chlorophyll, carotenoids and anthocyanin contents in higher plant leaves. Geophysical Research Letters, 33, 111402, doi:10.1029/2006G1026457.

    Article  Google Scholar 

  • Guyot, G., Dupont, O., Joannes, H., Prieur, C., Huet, M., and Podaire, A., 1984. Investigation into the Mid-IR spectral band best suited to monitoring vegetation water content. In Proceedings of 18th International Symposium on Remote Sensing of the Environment, October 1–5, 1984, Paris, pp. 1049–1063.

    Google Scholar 

  • Hatfield, J. L., Prueger, J. H., Kustas, W., Hatfield, J. L., Prueger, J. H., and Kustas, W. P., 2004. In Ustin, S. (ed.), Remote Sensing for Natural Resources Management and Environmental Monitoring: Manual of Remote Sensing, 3rd edn. New York: Wiley, Vol. 4, pp. 531–568. Chap. 10.

    Google Scholar 

  • Hatfield, J. L., Gitelson, A. A., Schepers, J. S., Schepers, J. S., and Walthall, C. L., 2008. Application of spectral remote sensing for agronomic decisions. Agronomy Journal, 100, S-117–S-131.

    Google Scholar 

  • Jackson, R. D., Idso, S. B., Reginato, R. J., and Pinter, P. J., Jr., 1981. Canopy temperature as a crop water stress indicator. Water Resources Research, 17, 1133–1138.

    Google Scholar 

  • Jackson, T. J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doraiswamy, P., and Hunt, E. R., 2004. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment, 92, 475–482.

    Google Scholar 

  • Ko, J., Maas, S. J., Lascano, R. J., and Wanjura, D., 2005. Modification of the GRAMI model for cotton. Agronomy Journal, 97(5), 1374–1379.

    Google Scholar 

  • Ko, J., Maas, S. J., Mauget, S., Piccinni, G., and Wanjura, D., 2006. Modeling water-stressed cotton growth using within-season remote sensing data. Agronomy Journal, 98, 1600–1609.

    Google Scholar 

  • LaCapra, V. C., Melack, J. M., Gastil, M., and Valeriano, D., 1996. Remote sensing of foliar chemistry of inundated rice with imaging spectrometry. Remote Sensing of Environment, 55, 50–58.

    Google Scholar 

  • Liu, C., Muchhal, U. S., and Raghothama, K. G., 1997. Differential expression of TPS-11, a phosphate starvation-induced gene in tomato. Plant Molecular Biology, 33, 867–874.

    Google Scholar 

  • Maas, S. J., 1998. Remote sensing: value in a bird’s-eye view. In Wrona, A. F. (ed.), Cotton Physiology Today, 9th edn. Memphis: National Cotton Council, Vol. 2, pp. 13–15.

    Google Scholar 

  • Maas, S. J., 2003. Irrigation scheduling by remote sensing technologies. In Stewart, B. A., and Howell, T. A. (eds.), Encyclopedia of Water Science. New York: Marcel Dekker, pp. 523–527.

    Google Scholar 

  • Maas, S. J., 2005. An overview of interfacing models with remote sensing. In Proceedings of the 35th Biological Systems Simulation Conference. Phoenix, AZ, pp. 31–33.

    Google Scholar 

  • Maas, S. J., and Rajan, N., 2008. Estimating ground cover of field crops using medium-resolution multispectral satellite imagery. Agronomy Journal, 100(2), 320–327.

    Google Scholar 

  • Maas, S., Lascano, R., Cooke, D., Richardson, C., Upchurch, D., Wanjura, D., Krieg, D., Mengel, S., Ko, J., Paynem, W., Rush, C., Brightbill, J., Bronson, K., Guo, W., and Rajapakse, S., 2004. Within-season estimation of evapotranspiration and soil moisture in the high plains using YieldTracker. In Proceedings of the 2004 High Plains Groundwater Resources Conference. Lubbock, TX, pp. 219–226.

    Google Scholar 

  • Medlin, C. R., Shaw, D. R., Gerard, P. D., and LaMastus, F. E., 2000. Using remote sensing to detect weed infestations in Glycine max. Weed Science, 48, 393–398.

    Google Scholar 

  • Meyer, S., Cerovic, Z. G., Goulas, Y., Montpied, P., Demotes-Mainard, S., Bidel, L. P. R., Moya, I., and Dreyer, E., 2006. Relationships between optically assessed polyphenols and chlorophyll contents, and leaf mass per area ratio in woody plants: a signature of the carbon-nitrogen balance within leaves? Plant, Cell & Environment, 29, 1338–1348.

    Google Scholar 

  • Moran, M. S., Maas, S. J., Vanderbilt, V. C., Barnes, E. M., Miller, S. N., and Clarke, T. R., 2004. Application of image-based remote sensing to irrigated agriculture. In Ustin, S. (ed.), Remote Sensing for Natural Resources Management and Environmental Monitoring: Manual of Remote Sensing, 3rd edn. New York: Wiley, Vol. 4, pp. 617–676. Chap. 12.

    Google Scholar 

  • Peñuelas, J., and Filella, I., 1998. Visible and near infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science, 3(4), 151–156.

    Google Scholar 

  • Pérez, A. J., López, F., Benlloch, J. V., and Christensen, S., 2000. Colour and shape analysis techniques for weed detection in cereal fields. Computers and Electronics in Agriculture, 25, 197–212.

    Google Scholar 

  • Rosenthal, W., Arkin, G., Shouse, P., and Jordan, W., 1987. Water deficit effects on transpiration and leaf growth. Agronomy Journal, 79, 1019–1026.

    Google Scholar 

  • Schepers, J. S., Blackmer, T. M., Wilhelm, W. W., and Resende, M., 1996. Transmittance and reflectance measurements of corn leaves from plants with different nitrogen and water supply. Journal of Plant Physiology, 148, 523–529.

    Google Scholar 

  • Sikkema, P. H., and Dekker, J., 1987. Use of infrared thermometry in determining critical stress periods induced by quackgrass (Agropyron repens) in Soybeans (Glycine max). Weed Science, 35, 784–791.

    Google Scholar 

  • Thomas, J. R., and Oerther, G. F., 1972. Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agronomy Journal, 64, 11–13.

    Google Scholar 

  • Tremblay, N., Wang, Z., and Belec, C., 2007. Evaluation of the Dualex for the assessment of corn nitrogen status. Journal of Plant Nutrition, 30, 1355–1369.

    Google Scholar 

  • Turner, N. C., 1977. Drought resistance and adaptation to water deficits in crop plants. In Mussell, H., and Staples, R. C. (eds.), Stress Physiology in Crop Plants. New York: Wiley, pp. 344–372.

    Google Scholar 

  • Wiles, L. J., Oliver, G. W., York, A. C., Gold, H. J., and Wilkerson, G. G., 1992. Spatial distribution of broadleaf weeds in North Carolina soybean (Glycine max) fields. Weed Science, 40, 554–557.

    Google Scholar 

  • Willers, J. L., Seal, M. R., and Luttrell, R. G., 1999. Remote sensing, line-intercept sampling for tarnished plant bugs (Heteroptera: Miridae) in mid-South cotton. Journal of Cotton Science, 3, 160–170.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. USDA ARS Southwest Watershed Research Center, 2000 E. Allen Rd, 85719, Tucson, AZ, USA

    Susan Moran

Authors
  1. Susan Moran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Susan Moran .

Editor information

Editors and Affiliations

  1. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA

    Eni G. Njoku

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this entry

Cite this entry

Moran, S. (2014). Crop Stress. In: Njoku, E.G. (eds) Encyclopedia of Remote Sensing. Encyclopedia of Earth Sciences Series. Springer, New York, NY. https://doi.org/10.1007/978-0-387-36699-9_23

Download citation

Publish with us

Policies and ethics

Search

Navigation