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Delineating site-specific management zones for precision agriculture

Published online by Cambridge University Press:  08 May 2015

H. U. FARID ,
A. BAKHSH ,
N. AHMAD ,
A. AHMAD  and
Z. MAHMOOD-KHAN
H. U. FARID*
Affiliation:
Department of Agricultural Engineering, Bahauddin Zakriya University, Multan, Pakistan
A. BAKHSH
Affiliation:
Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan
N. AHMAD
Affiliation:
Per Mer Ali Shah Arid Agriculture University, Rawalpindi, Pakistan
A. AHMAD
Affiliation:
Department of Agronomy, University of Agriculture, Faisalabad, Pakistan
Z. MAHMOOD-KHAN
Affiliation:
Department of Agricultural Engineering, Bahauddin Zakriya University, Multan, Pakistan
*
*To whom all correspondence should be addressed. Email: farid_vjr@yahoo.com
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Summary

Delineating site-specific management zones within fields can be helpful in addressing spatial variability effects for adopting precision farming practices. A 3-year (2008/09 to 2010/11) field study was conducted at the Postgraduate Agricultural Research Station, University of Agriculture, Faisalabad, Pakistan, to identify the most important soil and landscape attributes influencing wheat grain yield, which can be used for delineating management zones. A total of 48 soil samples were collected from the top 300 mm of soil in 8-ha experimental field divided into regular grids of 24 × 67 m prior to sowing wheat. Soil and landscape attributes such as elevation, % of sand, silt and clay by volume, soil electrical conductivity (EC), pH, soil nitrogen (N) and soil phosphorus (P) were included in the analysis. Artificial neural network (ANN) analysis showed that % sand, % clay, elevation, soil N and soil EC were important variables for delineating management zones. Different management zone schemes ranging from three to six were developed and evaluated based on performance indicators using Management Zone Analyst (MZA V0·1) software. The fuzziness performance index (FPI) and normalized classification entropy NCE indices showed minimum values for a four management zone scheme, indicating its appropriateness for the experimental field. The coefficient of variation values of soil and landscape attributes decreased for each management zone within the four management zone scheme compared to the entire field, which showed improved homogeneity. The evaluation of the four management zone scheme using normalized wheat grain yield data showed distinct means for each management zone, verifying spatial variability effects and the need for its management. The results indicated that the approach based on ANN and MZA software analysis can be helpful in delineating management zones within the field, to promote precision farming practices effectively.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 154 , Issue 2 , March 2016 , pp. 273 - 286
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Copyright
Copyright © Cambridge University Press 2015 

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References

REFERENCES

Abdul, R. A., Kah, J. G., Tee, B. H. & Osumanu, H. A. (2007). Transforming spatio-temporal yield maps to classified management zone maps for efficient management of oil palm. American Journal of Applied Sciences 5, 1392–1396.Google Scholar
Bansod, B. S., Pandey, O. P. & Rajesh, N. L. (2012). Analysis and delineation of spatial variability using geo-sensed apparent electrical conductivity and clustering techniques. International Journal Agriculture and Biology 14, 481–491.Google Scholar
Bakhsh, A., Jaynes, D. B., Colvin, T. S. & Kanwar, R. S. (2000). Spatio-temporal analysis of yield variability for a corn-soybean field in Iowa. Transactions of the ASAE 43, 31–38.CrossRefGoogle Scholar
Bakhsh, A. & Kanwar, R. S. (2008). Soil and landscape attributes interpret subsurface drainage clusters. Australian Journal of Soil Research 46, 735–744.CrossRefGoogle Scholar
Bakhsh, A., Kanwar, R. S. & Malone, R. W. (2007). Role of landscape and hydrologic attributes in developing and interpreting yield clusters. Geoderma 140, 235–246.CrossRefGoogle Scholar
Bianchini, A. A. & Mallarino, P. A. (2002). Soil-sampling alternatives and variable-rate liming for a Soybean-corn Rotation. Agronomy Journal 94, 1355–1366.CrossRefGoogle Scholar
Boydell, B. & McBratney, A. B. (1999). Identifying potential within-field management zone from cotton yield estimates. In Precision Agriculture ‘99. Proceedings of the 2nd European Conference on Precision Agriculture, Odense Congress Centre, Denmark, 11–15 July 1999 (Ed. Stafford, J. V.), pp. 331–341. Sheffield, UK: Sheffield Academic Press.Google Scholar
Buttafuoco, G., Castrignano, A., Colecchia, A. S. & Ricca, N. (2010). Delineation of management zones using soil properties and a multivariate geostatistical approach. Italian Journal of Agronomy 4, 323–332.CrossRefGoogle Scholar
Cahn, M. D., Hummel, J. W. & Brouer, B. H. (1994). Spatial-analysis of soil fertility for site-specific crop management. Soil Science Society of American Journal 58, 1240–1248.CrossRefGoogle Scholar
Cemek, B., Guler, M., Kilic, K., Demir, Y. & Arslan, H. (2007). Assessment of spatial variability in some soil properties as related to soil salinity and alkalinity in Bafra plain in northern Turkey. Environmental Monitoring and Assessment 124, 223–234.CrossRefGoogle ScholarPubMed
Chiericati, M., Morari, F., Sartori, L., Ortize, B., Perry, C. & Vellidis, G. (2007). Delineating management zones to apply site-specific irrigation in the Venice lagoon. In Precision Agriculture 07: Proceedings of the Sixth European Conference on Precision Agriculture (6ECPA), Skiathos, Greece (Ed. Stafford, J. V.), pp. 599–605. Wageningen, The Netherlands: Wageningen Academic Publishers.Google Scholar
Colvin, T. S., Jaynes, D. B., Karlen, D. L., Laird, D. A. & Ambuel, J. R. (1997). Yield variability within a central Iowa field. Transactions of the ASAE 40, 883–889.CrossRefGoogle Scholar
Dinners, D. L., Karlen, D. L., Jaynes, D. B., Kaspar, T. C., Hatfield, J. L., Colvin, T. S. & Cambardella, C. A. (2002). Nitrogen management strategies to reduce nitrate leaching in tile-drained Midwestern soils. Agronomy Journal 94, 153–171.CrossRefGoogle Scholar
Farid, H. U., Bakhsh, A., Ahmad, N., Ahmad, A. & Farooq, A. (2013). Spatial relationships of landscape attributes and wheat yield patterns. Journal of Agricultural Science, Canada 5, 271–294.Google Scholar
Farooq, A. (2010). Development of prediction systems using artificial neural networks for Intelligent spinning machines. Ph.D. Thesis, Institute of Textile Machinery and High Performance Material Technology, Technische University Dresden, Germany.Google Scholar
Ferguson, R. B., Hergert, G. W., Schepers, J. S., Gotway, C. A., Cahoon, J. E. & Peterson, T. A. (2002). Site-specific nitrogen management of irrigated maize: yield and soil residual nitrate effects. Soil Science Society of American Journal 66, 544–553.Google Scholar
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
Fraisse, C. W., Sudduth, K. A. & Kitchen, N. R. (2001). Delineation of site-specific management zones bu unsupervised classification of topographic attributes and soil electrical conductivity. Transactions of ASAE 44, 155–166.CrossRefGoogle Scholar
Franzen, D. W., Halvorson, A. D. & Hoffman, V. L. (2000). Management zones for soil N and P levels in the Northern Great Plains. In Proceedings of the 5th International Conference on Precision Agriculture, Bloomington, MN [CD-ROM]. 16–19 July 2000 (Eds. Robert, P. C., Rust, R. H. & Larson, W. E.), Madison, WI, USA: ASA, CSSA, and SSSA.Google Scholar
Fridgen, J., Kitchen, N. R., Sudduth, K. A., Drummond, S. T., Wiebold, W. J. & Fraisse, C. W. (2004). Management Zone Analyst (MZA): software for subfield management zone delineation. Agronomy Journal 96, 100–108.Google Scholar
Gokalp, Z., Basaran, M., Uzun, O. & Serin, Y. (2010). Spatial analysis of some physical soil properties in a saline and alkaline grassland soil of Kayseri, Turkey. African Journal of Agricultural Research 5, 1127–1137.Google Scholar
Haddad, F., Hagel, K., Mdeiwayeh, N., Natowitz, J. B., Wada, R., Xiao, B., David, C., Freslier, M. & Aichelin, J. (1997). Impact parameter determination in experimental analysis using neural network. Physical Review C (Nuclear Physics) 55, 1371–1375.CrossRefGoogle Scholar
Jaynes, D. B. & Hunsaker, D. J. (1989). Spatial and temporal variability of water content and infiltration on a flood irrigated field. Transactions of the ASAE 32, 1229–1238.CrossRefGoogle Scholar
Jaynes, D. B. & Colvin, T. S. (1997). Spatiotemporal variability of corn and soybean yield. Agronomy Journal 89, 30–37.CrossRefGoogle Scholar
Jaynes, D. B., Colvin, T. S. & Kaspar, T. C. (2005). Identifying potential soybean management zones from multi-year yield data. Computer and Electronics in Agriculture 46, 309–327.CrossRefGoogle Scholar
Ji, B., Sun, Y., Yang, S. & Wan, J. (2007). Artificial neural networks for rice yield prediction in mountainous regions. The Journal of Agricultural Science, Cambridge 145, 249–261.CrossRefGoogle Scholar
Jiang, H. L., Liu, G. S., Liu, S. D., Li, E. H., Wang, R., Yang, Y. F. & Hu, H. C. (2012). Delineation of site-specific management zones based on soil properties for a hillside field in central China. Archives of Agronomy and Soil Science 58, 1075–1090.CrossRefGoogle Scholar
Jiang, Q., Fu, Q. & Wang, Z. (2011). Delineating site-specific irrigation management zones. Irrigation and Drainage 60, 464–472.CrossRefGoogle Scholar
Kitchen, N. R., Sudduth, K. A. & Drummond, S. T. (1998). An evaluation of methods for determining site-specific management zones. In Proceedings of the 28th North Central Extension Industry Soil Fertility Conference, St. Louis, Missouri, 11–12 Nov. 1998 (Ed. Franzen, D. W.), pp. 133–139, Brookings, SD, USA: Potash and Phosphate Institute.Google Scholar
Lark, R. M. & Stafford, J. V. (1997). Classification as a first step in the interpretation of temporal and spatial variation of crop yield. Annals of Applied Biology 130, 111–121.CrossRefGoogle Scholar
Kitchen, N. R., Sudduth, K. A., Myers, D. B., Drummond, S. T. & Hong, S. Y. (2005). Delineation of productivity zones on claypan soil field using apparent soil electrical conductivity. Computer and Electronics in Agriculture 46, 285–308.CrossRefGoogle Scholar
Lashin, A. & Din, S. S. E. (2013). Reservoir parameters determination using artificial neural networks: Ras Fanar field, Gulf of Suez, Egypt. Arabian Journal of Geosciences Online 6, 2789–2806.CrossRefGoogle Scholar
Li, X., Pan, Y. C. & Ma, J. Y. (2007 a). Soil nutrients-based zoning for management of precision agriculture. Acta Pedologica Sinica 1, 14–19.Google Scholar
Li, Y., Shi, Z., Li, F. & Li, H. -Y. (2007 b). Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land. Computers and Electronics in Agriculture 56, 174–186.CrossRefGoogle Scholar
Ministry of Food, Agriculture and Livestock (2010). Agricultural Statistics of Pakistan, 2009–10. Islamabad, Pakistan: Ministry of Food, Agriculture and Livestock.Google Scholar
Moral, F. J., Terron, J. M. & Rebollo, F. J. (2011). Site-specific management zones based on the Rasch model and geostatistical techniques. Computers and Electronics in Agriculture 75, 223–230.CrossRefGoogle Scholar
Mulla, D. J. & Schepers, J. S. (1997). Key processes and properties for site-specific soil and crop management. In Proceedings of the State of Site Specific Management for Agriculture (Eds. Pierce, F. J. & Sadler, E. J.), pp. 1–18. Madison, WI, USA: ASA/CSSA/SSSA.Google Scholar
Mzuku, M., Khosla, R., Reich, R., Inman, D., Smith, F. & Macdonald, L. (2005). Spatial variability of measured soil properties across site-specific management zones. Soil Science Society of America Journal 69, 1572–1579.CrossRefGoogle Scholar
Noble, P. A. & Tribou, E. H. (2007). Neuroet: an easy-to-use artificial neural network for ecological and biological modeling. Ecological Modeling 203, 87–98.CrossRefGoogle Scholar
Odeh, I. O. A., McBratney, A. B. & Chittleborough, D. J. (1992). Soil pattern recognition with fuzzy-c means: application to classification and soil-landform interrelationships. Soil Science Society of America Journal 56, 505–516.CrossRefGoogle Scholar
Orteqa, R. A. & Santibanez, O. A. (2007). Determination of management zones in corn (Zea mays L.) based on soil fertility. Computers and Electronics in Agriculture 58, 49–59.CrossRefGoogle Scholar
Piotrowska, A., Dlugosz, J., Namysłowska-Wilczyńska, B. & Zamorski, R. (2011). Field-scale variability of topsoil dehydrogenase and cellulase activities as affected by variability of some physico-chemical properties. Biology and Fertility of Soils 47, 101–109.CrossRefGoogle Scholar
Saltan, M. & Terzi, S. (2005). Comparative analysis of using artificial neural network (ANN) and gene expression programming (GEP) in backcalculation of pavement layer thickness. Indian Journal of Engineering and Material Science 12, 42–50.Google Scholar
Schepers, A. R., Shanahan, J. F., Liebig, M. A., Schepers, J. S., Johnson, S. H. & Luchiari, A. J. (2004). Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agronomy Journal 96, 195–203.CrossRefGoogle Scholar
Tran, B. Q. & Nguyen, T. T. (2008). Assessment of the influence of interpolation techniques on the accuracy of digital elevation model. VNU Journal of Science, Earth Sciences 24, 176–183.Google Scholar
Wang, X. Z., Liu, G. S., Hu, H. C., Wang, Z. H. & Liu, Q. H. (2009). Determination of management zones for a tobacco field based on soil fertility. Computer and Electronics in Agriculture 65, 168–175.Google Scholar
Zhao, J. L., Xue, Y. A., Yang, H., Huang, L. S. & Zhang, D. Y. (2012). Evaluating and classifying field-scale soil nutrient status in Beijing using 3S technology. International Journal of Agriculture and Biology 14, 689–696.Google Scholar