An integrated approach to site-specific management zone delineation

Abstract

Dividing fields into a few relatively homogeneous management zones (MZs) is a practical and cost-effective approach to precision agriculture. There are three basic approaches to MZ delineation using soil and/or landscape properties, yield information, and both sources of information. The objective of this study is to propose an integrated approach to delineating site-specific MZ using relative elevation, organic matter, slope, electrical conductivity, yield spatial trend map, and yield temporal stability map (ROSE-YSTTS) and evaluate it against two other approaches using only soil and landscape information (ROSE) or clustering multiple year yield maps (CMYYM). The study was carried out on two no-till corn-soybean rotation fields in eastern Illinois, USA. Two years of nitrogen (N) rate experiments were conducted in Field B to evaluate the delineated MZs for site-specific N management. It was found that in general the ROSE approach was least effective in accounting for crop yield variability (8.0%–9.8%), while the CMYYM approach was least effective in accounting for soil and landscape (8.9%–38.1%), and soil nutrient and pH variability (9.4%–14.5%). The integrated ROSE-YSTTS approach was reasonably effective in accounting for the three sources of variability (38.6%–48.9%, 16.1%–17.3% and 13.2%–18.7% for soil and landscape, nutrient and pH, and yield variability, respectively), being either the best or second best approach. It was also found that the ROSE-YSTTS approach was effective in defining zones with high, medium and low economically optimum N rates. It is concluded that the integrated ROSE-YSTTS approach combining soil, landscape and yield spatial-temporal variability information can overcome the weaknesses of approaches using only soil, landscape or yield information, and is more robust for MZ delineation. It also has the potential for site-specific N management for improved economic returns. More studies are needed to further evaluate their appropriateness for precision N and crop management.

Keywords

economically optimum nitrogen rate / fuzzy cluster analysis / precision nitrogen management / site-specific management / soil landscape property / yield map

References

[1] Foley J A, Ramankutty N, Brauman K A, Cassidy E S, Gerber J S, Johnston M, Mueller N D, O’Connell C, Ray D K, West P C, Balzer C, Bennett E M, Carpenter S R, Hill J, Monfreda C, Polasky S, Rockstrom J, Sheehan J, Siebert S, Tilman D, Zaks D P M. Solutions for a cultivated planet. Nature, 2011, 478(7369): 337–342

[2] Tilman D, Balzer C, Hill J, Befort B L. Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108(50): 20260–20264

[3] West P C, Gerber J S, Engstrom P M, Mueller N D, Brauman K A, Carlson K M, Cassidy E S, Johnston M, Macdonald G K, Ray D K, Siebert S. Leverage points for improving global food security and the environment. Science, 2014, 345(6194): 325–328

[4] Gebbers R, Adamchuk V I. Precision agriculture and food security. Science, 2010, 327(5967): 828–831

[5] Bongiovanni R, Lowenberg-Deboer J. Precision agriculture and sustainability. Precision Agriculture, 2004, 5(4): 359–387

[6] Zhao G, Miao Y, Wang H, Su M, Fan M, Zhang F, Jiang R, Zhang Z, Liu C, Liu P, Ma D. A preliminary precision rice management system for increasing both grain yield and nitrogen use efficiency. Field Crops Research, 2013, 154: 23–30

[7] Robert P C. Precision agriculture: a challenge for crop nutrition management. Plant and Soil, 2002, 247(1): 143–149

[8] Mulla D J. Mapping and managing spatial patterns in soil fertility and crop yield. In: Robert P C, Rust R H, Larson W E, eds. Soil Specific Crop Management. Madison: ASA/CSSA/SSSA, 1993, 15–26

[9] Doerge T. Defining management zones for precision farming. Crop Insights, 1999, 8(21): 1–5

[10] Nawar S, Corstanje R, Halcro G, Mulla D, Mouazen A M. Delineation of soil management zones for variable rate fertilization: a review. Advances in Agronomy, 2017, 143: 175–245

[11] Delgado J A, Khosla R, Wausch W C, Westfall D G, Inman D J. Nitrogen fertilizer management based on site-specific management zones reduces potential for nitrate leaching. Journal of Soil and Water Conservation, 2005, 60(6): 402–410

[12] Mulla D, Miao Y. Precision farming. In: Thenkabail PS, ed. Land Resources Monitoring, Modeling, and Mapping with Remote Sensing.Boca Raton: CRC Press, Taylor & Francis Group, LLC,2016, 161–178

[13] Booltink H W G, van Alphen B J, Batchelor W D, Paz J O, Stoorvogel J J, Vargas R. Tools for optimizing management of spatially-variable fields. Agricultural Systems, 2001, 70(2–3): 445–476

[14] Guo C, Zhang L, Zhou X, Zhu Y, Cao W, Qiu X, Cheng T, Tian Y. Integrating remote sensing information with crop model to monitor wheat growth and yield based on simulation zone partitioning. Precision Agriculture, 2018, 19(1): 55–78 doi:10.1007/s11119-017-9498-5

[15] Miao Y, Mulla D J, Batchelor W D, Paz J O, Robert P C, Wiebers M. Evaluating management zone optimal nitrogen rates with a crop growth model. Agronomy Journal, 2006, 98(3): 545–553

[16] Franzen D W, Hopkins D H, Sweeney M D, Ulmer M K, Halvorson A D. Evaluation of soil survey scale for zone development of site-specific nitrogen management. Agronomy Journal, 2002, 94(2): 381–389

[17] Mulla D J. Using geostatistics and GIS to manage spatial patterns in soil fertility. In: Kranzler G, ed. Automated Agriculture for the 21st Century, St.Joseph: ASAE, 1991, 336–345

[18] Davatgar N, Neishabouri M R, Sepaskhah A R. Delineation of site specific nutrient management zones for a paddy cultivated area based on soil fertility using fuzzy clustering. Geoderma, 2012, 173–174: 111–118

[19] Johnson C K, Mortensen D A, Wienhold B J, Shanahan J F, Doran J W. Site-specific management zones based on soil electrical conductivity in a semiarid cropping system. Agronomy Journal, 2003, 95(2): 303–315

[20] Bhatti A U, Mulla D J, Frazier B E. Estimation of soil properties and wheat yields on complex eroded hills using geostatistics and Thematic Mapper images. Remote Sensing of Environment, 1991, 37(3): 181–191

[21] Fraisse C W, Sudduth K A, Kitchen N R, 0, 0, 0. Delineation of site-specific management zones by unsupervised classification of topographic attributes and soil electrical conductivity. Transactions of the ASAE (American Society of Agricultural Engineers), 2001, 44(1): 155–166

[22] Schepers A R, Shanahan J F, Liebig M A, Schepers J S, Johnson S H, Luchiari A Jr. Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agronomy Journal, 2004, 96(1): 195–203

[23] Vitharana U W A, Van Meirvenne M, Simpson D, Cockx L, De Baerdemaeker J. Key soil and topographic properties to delineate potential management classes for precision agriculture in the European loess area. Geoderma, 2008, 143(1–2): 206–215

[24] Blackmore S. The interpretation of trends from multiple yield maps. Computers and Electronics in Agriculture, 2000, 26(1): 37–51

[25] Lark R M, Stafford J V. Classification as a first step in the interpretation of temporal and spatial variability of crop yield. Aspects of Applied Biology, 1997, 130(1): 111–121

[26] Jaynes D B, Kaspar T C, Colvin T S, James D E. Cluster analysis of spatiotemporal corn yield patterns in an Iowa field. Agronomy Journal, 2003, 95(3): 574–586

[27] Dobermann A, Ping J L, Adamchuk V I, Simbahan G C, Ferguson R B. Classification of crop yield variability in irrigated production fields. Agronomy Journal, 2003, 95(5): 1105–1120

[28] Taylor J A, McBratney A B, Whelan B M. Establishing management classes for broadacre agriculture production. Agronomy Journal, 2007, 99(5): 1366–1376

[29] Hornung A, Khosla R, Reich R, Inman D, Westfall D G. Comparison of site-specific management zones. Agronomy Journal, 2006, 98(2): 407–415 doi:10.2134/agronj2005.0240

[30] Fridgen J J, Kitchen N R, Sudduth K A, Drummond S T, Wiebold W J, Fraisse C W. Management zone analyst (MZA). Agronomy Journal, 2004, 96(1): 100–108

[31] Miao Y, Mulla D J, Robert P C. Identifying important factors influencing corn yield and grain quality variability using artificial neural networks. Precision Agriculture, 2006, 7(2): 117–135

[32] Miao Y, Mulla D J, Hernandez J A, Wiebers M, Robert P C. Potential impact of precision nitrogen management on corn yield, protein content and test weight. Soil Science Society of America Journal, 2007, 71(5): 1490–1499

[33] Fernandez F G, Hoeft R G. Managing soil pH and crop nutrients. In: Nafziger E D, ed. Illinois Agronomy Handbook (24th ed.), Urbana-Champaign: University of Illinois Extension, 2009, 91–112

[34] Chang J, Clay D E, Carlson C G, Reese C L, Clay S A, Ellsbury M K. Defining yield goals and management zones to minimize yield and nitrogen and phosphorus fertilizer recommendation errors. Agronomy Journal, 2004, 96(3): 825–831

[35] Mulla D J, Bhatti A U. An evaluation of indicator properties affecting spatial patterns in N and P requirements for winter wheat yield. In: Stafford JV, ed. Precision Agriculture’97: Proceedings of the 1st European Conference on Precision Agriculture. Vol.1. Spatial Variability in Soil and Crop, Oxford: BIOS Scientific Publishers, 1997, 145–154

[36] Jaynes D B, Colvin T S. Spatiotemporal variability of corn and soybean yield. Agronomy Journal, 1997, 89(1): 30–37

[37] Boydell B, McBratney A B. Identifying potential within-field management zones from cotton-yield estimates. Precision Agriculture, 2002, 3(1): 9–23

[38] Wang Y P, Chen S H, Chang K W, Shen Y. Identifying and characterizing yield limiting factors in paddy rice using remote sensing yield maps. Precision Agriculture, 2012, 13(5): 553–567

[39] Burke M, Lobell D B. Satellite-based assessment of yield variation and its determinants in smallholder African systems. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114(9): 2189–2194

[40] Jin Z, Azzari G, Lobell D B. Improving the accuracy of satellite-based high-resolution yield estimation: a test of multiple scalable approaches. Agricultural and Forest Meteorology, 2017, 247: 207–220

[41] Azzari G, Jain M, Lobell D B. Towards fine resolution global maps of crop yields: testing multiple methods and satellites in three countries. Remote Sensing of Environment, 2017, 202: 129–141

[42] Ladoni M, Bahrami H A, Alavipanah S K, Norouzi A K. Estimating soil organic carbon from soil reflectance: a review. Precision Agriculture, 2010, 11(1): 82–99

[43] Adamchuk V I, Hummel J W, Morgan M T, Upadhyaya S K. On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture, 2004, 44(1): 71–91

[44] Roberts D F, Adamchuk V I, Shanahan J F, Ferguson R B, Schepers J S. Estimation of surface soil organic matter using a ground-based active sensor and aerial imagery. Precision Agriculture, 2011, 12(1): 82–102

[45] Zhang S, Huang Y, Shen C, Ye H, Du Y. Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information. Geoderma, 2012, 171–172: 35–43

[46] Corwin D L, Lesch S M. Apparent soil electrical conductivity measurements in agriculture. Computers and Electronics in Agriculture, 2005, 46(1–3): 11–43

[47] Farahani H J, Buchleiter G W. Temporal stability of soil electrical conductivity in irrigated sandy fields in Colorado. Transactions of the ASAE. American Society of Agricultural Engineers, 2004, 47(1): 79–90

[48] Liao K, Zhu Q, Doolittle J. Temporal stability of apparent soil electrical conductivity measured by electromagnetic induction techniques. Journal of Mountain Science, 2014, 11(1): 98–109 doi:10.1007/s11629-012-2630-0

[49] Batchelor W D, Basso B, Paz J O. Examples of strategies to analyze spatial and temporal yield variability using crop models. European Journal of Agronomy, 2002, 18(1–2): 141–158

[50] Yao Y, Miao Y, Huang S, Gao L, Ma X, Zhao G, Jiang R, Chen X, Zhang F, Yu K, Gnyp M L, Bareth G, Liu C, Zhao L, Yang W, Zhu H. Active crop sensor-based precision nitrogen management strategy for rice. Agronomy for Sustainable Development, 2012, 32(4): 925–933

[51] Cao Q, Miao Y, Li F, Gao X, Liu B, Lu D, Chen X. Developing a new Crop Circle active canopy sensor-based precision nitrogen management strategy for winter wheat in North China Plain. Precision Agriculture, 2017, 18(1): 2–18

[52] Xia T, Miao Y, Wu D, Shao H, Khosla R, Mi G. Active optical sensing of spring maize for in-season diagnosis of nitrogen status based on nitrogen nutrition index. Remote Sensing, 2016, 8(7): 605

[53] Zhang C, Kovacs J M. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 2012, 13(6): 693–712

[54] Huang S, Miao Y, Zhao G, Yuan F, Ma X, Tan C, Yu W, Gnyp M, Lenz-Wiedemann V, Rascher U, Bareth G. Satellite remote sensing-based in-season diagnosis of rice nitrogen status in Northeast China. Remote Sensing, 2015, 7(8): 10646–10667

Acknowledgements

This study was funded by Cargill Crop Nutrition (now Mosaic Company), Cargill Dry Corn Ingredients and Pioneer Hi-Bred International, Inc. We would like to thank Mr. Ron Olson, Mr. Dean Fairchild, Dr. Dan Frochlich, Mr. Matt Wiebers, Mr. Kirby Wuethrich and other employees from the three funding companies for constructive suggestions and assistance, Mr. Gene Barkley (local farmer) for his strong support and cooperation and USDA-NRCS in Illinois for conducting the detailed soil surveys of the study fields.

Compliance with ethics guidelines

Yuxin Miao, David J. MullA, and Pierre C. Robert declare that they have no conflicts of interest or financial conflicts to disclose.

This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

The Author(s) 2018. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)