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Delineating productivity zones on claypan soil fields using apparent soil electrical conductivity

Authors:

S. T. Drummond,

S. Y. HongAuthors Info & Claims

Computers and Electronics in Agriculture, Volume 46, Issue 1-3

Pages 285 - 308

https://doi.org/10.1016/j.compag.2004.11.012

Published: 01 March 2005 Publication History

Abstract

Efficient and cost-effective methods are needed for delineating sub-field productivity zones to improve soil and crop site-specific management. This investigation was conducted to answer the question of whether apparent soil electrical conductivity (EC"a) and elevation could be used to delineate productivity zones (SPZ) for claypan soil fields that would agree with productivity zones delineated from yield map data (YPZ). Ten and seven years of combine-monitored yield maps were available for two Missouri claypan soil fields, designated Field 1 and Field 2, respectively. The fields were generally cropped in corn and soybean. Soil EC"a data were collected with a non-contact, electromagnetic induction-based EC"a sensor (Geonics EM38) and a coulter-based sensor (Veris model 3100). Elevation data were collected using a real-time kinematic GPS. Unsupervised fuzzy c-means clustering was independently used both on yield data to delineate three YPZ and on combinations of EC"a and/or elevation data to delineate three SPZ. Outcomes of YPZ and SPZ were matched and agreement calculated with an overall accuracy statistic and a statistical index called the Kappa coefficient. Best performing combinations of EC"a and elevation variables gave 60-70% agreement between YPZ and SPZ. We consider this level of agreement promising, especially considering that there were many other yield-limiting factors unrelated to EC"a and elevation. Generally, multiple variables of EC"a and elevation were better than a single variable for generating SPZ. The specific combinations of EC"a and/or elevation variables that gave highest agreement between YPZ and SPZ were field specific. Based on these findings, we conclude EC"a and elevation measurements can be reliably used for creating productivity zones on claypan soil fields.

References

[1]

Delineation of soil variability using geostatistics and fuzzy clustering analyses of hyperspectral data. Soil Sci. Soc. Am. J. v63. 142-150.

[2]

Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.

[3]

Identifying potential within-field management zones from cotton yield estimates. In: Stafford, J.V. (Ed.), Precision Agriculture '99. Proceedings of the Second European Conference on Precision Agriculture, SCI, London, UK. pp. 331-341.

[4]

Fuzzy classification methods for determining land suitability from soil profile observations and topography. J. Soil Sci. v43. 193-210.

[5]

Yield variability within a central Iowa field. Trans. ASAE. v40. 883-889.

[6]

Evaluation of a GIS-linked model of salt loading to groundwater. J. Environ. Qual. v28. 471-480.

[7]

Estimating depths to claypans using electromagnetic induction methods. J. Soil Water Conserv. v49. 572-575.

[8]

Evaluating farmer developed management zone maps for variable rate fertilizer application. Precision Agric. v2. 201-215.

[9]

Delineation of site-specific management zones by unsupervised classification of topographic attributes and soil electrical conductivity. Trans. ASAE. v44. 155-166.

[10]

Calibration of the Ceres-Maize model for simulating site-specific crop development and yield on claypan soils. Appl. Eng. Agric. v17. 547-556.

[11]

Evaluation of soil survey scale for zone development of site-specific nitrogen management. Agron. J. v94. 381-389.

[12]

Management Zone Analyst (MZA): software for sub-field management zone delineation. Agron. J. v96. 100-108.

[13]

Geonics Limited, 1998. EM38 Ground Conductivity Meter Operating Manual, May 1998. Mississauga, Ont., Canada.

[14]

Jamison, V.C., Smith, D.D., Thornton, J.F., 1968. Soil and water research on a claypan soil. USDA-ARS Technical Bulletin 1379, U.S. Government Printing Office, Washington, DC.

[15]

Cluster analysis of spatiotemporal corn yield patterns in an Iowa field. Agron. J. v95. 574-586.

[16]

Yield mapping by electromagnetic induction. In: Robert, P.C., Rust, R.H., Larson, W.E. (Eds.), Site-Specific Management for Agricultural Systems. Proceedings of the Second International Conference, ASA, CSSA, and SSSA, Madison, WI. pp. 383-394.

[17]

Introductory digital image processing: a remote sensing perspective. Prentice-Hall, Upper Saddle River, NJ.

[18]

The DSSAT cropping system model. Eur. J. Agron. v18. 235-265.

[19]

Soil electrical conductivity as a crop productivity measure for claypan soils. J. Prod. Agric. v12. 607-617.

[20]

Soil electrical conductivity and topography related to yield for three contrasting soil-crop systems. Agron. J. v95. 483-495.

[21]

Educational needs of precision agriculture. Precision Agric. v3. 341-351.

[22]

Classification as a first step in the interpretation of temporal and spatial variation of crop yield. Ann. Appl. Biol. v130. 111-121.

[23]

Forming spatially coherent regions by classification of multivariate data: an example from the analysis of maps of crop yield. Int. J. Geogr. Inform. Sci. v12. 83-98.

[24]

Practical applications of soil electrical conductivity mapping. In: Stafford, J.V. (Ed.), Precision Agriculture '99. Proceedings of the Second European Conference on Precision Agriculture, SCI, London, UK. pp. 771-779.

[25]

Using yield and soil electrical conductivity (EC) maps to derive crop production performance information. In: Robert, P.C., Rust, R.H., Larson, W.E. (Eds.), Proceedings of the Fifth International Conference on Precision Agriculture, ASA, CSSA, and SSSA, Madison, WI.

[26]

A landform segmentation model for precision farming. In: Robert, P.C., Rust, R.H., Larson, W.E. (Eds.), Proceedings of the Fourth International Conference on Precision Agriculture, ASA, CSSA, and SSSA, Madison, WI. pp. 1335-1346.

[27]

A continuum approach to soil classification by modified fuzzy k-means with extragrades. J. Soil Sci. v43. 159-175.

[28]

Rapid accurate mapping of soil salinity by electromagnetic ground conductivity meters. Advances in Measurement of Soil Physical Properties: Bringing Theory into Practice, 1992.SSSA, Madison, WI.

[29]

Soil pattern recognition with fuzzy-c-means: application to classification and soil-landform interrelationships. Soil Sci. Soc. Am. J. v56. 505-516.

[30]

Creating spatially contiguous yield classes for site-specific management. Agron. J. v95. 1121-1131.

[31]

Geospatial measurements of soil electrical conductivity to assess soil salinity and diffuse salt loading from irrigation. In: Corwin, D.L. (Ed.), Assessment of Non-Point Source Pollution in the Vadose Zone. Geophysical Monograph 108, American Geophysical Union, Washington, DC. pp. 197-215.

[32]

Concepts of variable rate technology with considerations for fertilizer application. J. Prod. Agric. v7. 195-201.

[33]

Soil Survey Laboratory Staff, 1992. USDA-SCS National Soil Survey Center, Soil Survey Inventory Report no. 42, version 2.0, August 1992.

[34]

Accuracy issues in electromagnetic induction sensing of soil electrical conductivity for precision agriculture. Comput. Electron. Agric. v31. 239-264.

[35]

Spatial modeling of crop yield using soil and topographic data. In: Stafford, J.V. (Ed.), Precision Agriculture '97. Proceedings of the First European Conference on Precision Agriculture, vol. 1, SCI, London, UK. pp. 439-447.

[36]

Comparison of electromagnetic induction and direct sensing of soil electrical conductivity. Agron. J. v95. 472-482.

[37]

Sudduth, K.A., Kitchen, N.R., Wiebold, W.J., Batchelor, W.D., Bollero, G.A., Bullock, D.G., Clay, D.E., Palm, H.L., Pierce, F.J., Schuler, R.T., Thelen, K.D. Relating ECa to soil properties across the north-central USA. Comput. Electron. Agric., this issue.

[38]

Topsoil depth, fertility, water management, and weather influences on yield. Soil Sci. Soc. Am. J. v55. 1085-1091.

[39]

A methodology to define management units in support of an integrated, model-based approach to precision agriculture. In: Robert, P.C., Rust, R.H., Larson, W.E. (Eds.), Proceedings of the Fourth International Conference on Precision Agriculture, ASA, CSSA, and SSSA, Madison, WI. pp. 1267-1278.

[40]

The null hypothesis of precision agriculture management. Precision Agric. v2. 265-279.

[41]

Applications of a field-level geographic information system (FIS) in precision agriculture. Appl. Eng. Agric. v17. 885-892.

Cited By

Betzek NSouza EBazzi CSchenatto KGavioli A(2018)Rectification methods for optimization of management zonesComputers and Electronics in Agriculture10.1016/j.compag.2018.01.014146:C(1-11)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.compag.2018.01.014
Schenatto Kde Souza EBazzi CGavioli ABetzek NBeneduzzi H(2017)Normalization of data for delineating management zonesComputers and Electronics in Agriculture10.1016/j.compag.2017.10.017143:C(238-248)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1016/j.compag.2017.10.017
Haghverdi ALeib BWashington-Allen RAyers PBuschermohle M(2015)Perspectives on delineating management zones for variable rate irrigationComputers and Electronics in Agriculture10.1016/j.compag.2015.06.019117:C(154-167)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.compag.2015.06.019
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Delineating productivity zones on claypan soil fields using apparent soil electrical conductivity
1. Applied computing
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cover image Computers and Electronics in Agriculture

Computers and Electronics in Agriculture Volume 46, Issue 1-3

March, 2005

391 pages

ISSN:0168-1699

Issue’s Table of Contents

Copyright © © 2004.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 March 2005

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Cited By

Betzek NSouza EBazzi CSchenatto KGavioli A(2018)Rectification methods for optimization of management zonesComputers and Electronics in Agriculture10.1016/j.compag.2018.01.014146:C(1-11)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.compag.2018.01.014
Schenatto Kde Souza EBazzi CGavioli ABetzek NBeneduzzi H(2017)Normalization of data for delineating management zonesComputers and Electronics in Agriculture10.1016/j.compag.2017.10.017143:C(238-248)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1016/j.compag.2017.10.017
Haghverdi ALeib BWashington-Allen RAyers PBuschermohle M(2015)Perspectives on delineating management zones for variable rate irrigationComputers and Electronics in Agriculture10.1016/j.compag.2015.06.019117:C(154-167)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.compag.2015.06.019
Calafat CGallego AQuintanilla I(2015)Integrated geo-referenced data and statistical analysis for dividing livestock farms into geographical zones in the Valencian Community (Spain)Computers and Electronics in Agriculture10.1016/j.compag.2015.03.005114:C(58-67)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1016/j.compag.2015.03.005
Peeters AZude MKäthner JÜnlü MKanber RHetzroni AGebbers RBen-Gal A(2015)Getis-Ord's hot- and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree dataComputers and Electronics in Agriculture10.1016/j.compag.2014.12.011111:C(140-150)Online publication date: 1-Feb-2015
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Peralta NCosta JBalzarini MCastro Franco MCórdoba MBullock D(2015)Delineation of management zones to improve nitrogen management of wheatComputers and Electronics in Agriculture10.1016/j.compag.2014.10.017110:C(103-113)Online publication date: 1-Jan-2015
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Chang DZhang JZhu LGe SLi PLiu G(2014)Delineation of management zones using an active canopy sensor for a tobacco fieldComputers and Electronics in Agriculture10.1016/j.compag.2014.09.019109:C(172-178)Online publication date: 1-Nov-2014
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Aggelopooulou KCastrignanò AGemtos TBenedetto D(2013)Delineation of management zones in an apple orchard in Greece using a multivariate approachComputers and Electronics in Agriculture10.5555/2749493.275027390:C(119-130)Online publication date: 1-Jan-2013
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Peralta NCosta J(2013)Delineation of management zones with soil apparent electrical conductivity to improve nutrient managementComputers and Electronics in Agriculture10.5555/2749492.275025299:C(218-226)Online publication date: 1-Nov-2013
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Ruß GKruse R(2011)Exploratory hierarchical clustering for management zone delineation in precision agricultureProceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects10.5555/2033796.2033813(161-173)Online publication date: 30-Aug-2011
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