Precision Agriculture

, Volume 16, Issue 4, pp 385–404 | Cite as

Representative sampling size for strip sampling and number of required samples for random sampling for soil nutrients in direct seeded fields

Original Paper


Direct seeding is a common agricultural practice in the southern Canadian prairies. The associated band application of fertilizers with minimal disturbance makes conventional soil sampling problematic, as it results in considerable micro-scale variability. Sampling via the collection of strips of soil taken from across the seed and fertilizer bands has been suggested in low disturbance direct seeded fields to help account for this variability. To obtain adequate point-based random samples across entire field areas is an alternative for reliable mean soil nutrient contents. The objective of this study was to identify the representative sampling size (RSS) for strip sampling and the number of required samples (NRS) for point-based random sampling. Soil samples of 0–10 and 10–20 cm depth increments were collected from a 4 ha portion in each of two wheat fields in the Brown soil zone in south-central Saskatchewan. One field (VF field) had a history of fertilization at a variable rate while the other field (CF field) was fertilized at a constant rate. The coefficient of variation (CV) versus sampled strip length was plotted and the RSS was defined when CV ≤10 %. The central limit theorem was used to determine the NRS with relative errors of ±5 to ±20 % at a confidence level of 95 %. The results showed that the RSSs (strip lengths) were 62 and 35 cm, respectively, for assessing available nitrogen (NO3 -N and NH4 +-N) and extractable, available P. The NRSs differed with sampled field but not with nutrient type and soil layer. With a confidence level of 95 %, about 37 and 81 random samples were needed in the VF and CF fields, respectively, to obtain mean soil nutrient contents with a relative error of ±10 %.


Representative sampling size Number of required samples Nitrogen Phosphorus Direct seeding 



A multivariate analysis of variance


Constant rate of fertilizer application


Coefficient of variation


Ammonium nitrogen


Nitrate nitrogen


Number of samples


Number of required samples


Ordinary least squares




Restricted maximum likelihood


Representative sampling size


Sample size


Variable rate of fertilizer application



The financial support of Agriculture Development Fund (ADF) is appreciated. Thanks to Cory Fatteicher, Tom King, Brett Ewen, Eric Neil, Henry W. Chau, Trent Pernitsky and Ron Urton for their assistance in sampling collection and sample analysis and to Yonge Farms for providing a field for sampling. We greatly appreciate the two anonymous reviewers for their constructive comments to improve our manuscript.


  1. Aggelopoulou, K. D., Pateras, D., Fountas, S., Gemtos, T. A., & Nanos, G. D. (2011). Soil spatial variability and site-specific fertilization maps in an apple orchard. Precision Agriculture, 12, 118–129.CrossRefGoogle Scholar
  2. Brubaker, S. C., Jones, A. J., Lewis, D. T., & Frank, K. (1993). Soil properties associated with landscape position. Soil Science Society of America Journal, 57, 235–239.CrossRefGoogle Scholar
  3. Cameron, D. R., Nyborg, M., Toogood, J. A., & Laverty, D. H. (1971). Accuracy of field sampling for soil tests. Canadian Journal of Soil Science, 51, 165–175.CrossRefGoogle Scholar
  4. Cao, C. Y., Jiang, S. Y., Ying, Z., Zhang, F. X., & Han, X. S. (2011). Spatial variability of soil nutrients and microbiological properties after the establishment of leguminous shrub Caragana microphylla Lam. plantation on sand dune in the Horqin Sandy Land of Northeast China. Ecological Engineering, 37, 1467–1475.CrossRefGoogle Scholar
  5. Cao, Y. Z., Wang, X. D., Lu, X. Y., Yan, Y., & Fan, J. H. (2013). Soil organic carbon and nutrients along an alpine grassland transect across Northern Tibet. Journal of Mountain Science, 10, 564–573.CrossRefGoogle Scholar
  6. de Gruijter, J., Brus, D., Bierkens, M. F. P., & Knotters, M. (2006). Sampling for natural resource monotoring. Heidelberg: Springer.CrossRefGoogle Scholar
  7. Donohue, S. J. (2002). Evaluation of soil nutrient variability for development of turfgrass soil test sampling methods. Communications in soil science and plant analysis, 33, 3335–3345.CrossRefGoogle Scholar
  8. Garten, C. T., Kang, S., Brice, D. J., Schadt, C. W., & Zhou, J. (2007). Variability in soil properties at different spatial scales (1 m–1 km) in a deciduous forest ecosystem. Soil Biology Biochemistry, 39, 2621–2627.CrossRefGoogle Scholar
  9. Grigal, D. F., McRoberts, R. E., & Ohmann, L. F. (1991). Spatial variation in chemical properties of forest floor and surface mineral soil in the North Central United States. Soil Science, 151, 282–290.CrossRefGoogle Scholar
  10. Han, F. P., Zheng, J. Y., Hu, W., Du, F., & Zhang, X. C. (2010). Spatial variability and distribution of soil nutrients in a catchment of the Loess Plateau in China. Acta Agriculturae Scandinavica B, 60, 48–56.CrossRefGoogle Scholar
  11. Hemingway, R. G. (1955). Soil sampling errors and advisory analysis. Journal of Agricultural Science, 46, 1–8.CrossRefGoogle Scholar
  12. Hu, W., Shao, M. A., Wan, L., & Si, B. C. (2014). Spatial variability of soil electrical conductivity in a small watershed on the Loess Plateau of China. Geoderma, 230, 212–220.CrossRefGoogle Scholar
  13. Hu, W., Shao, M. A., Wang, Q. J., & Reichardt, K. (2008). Soil water content temporal-spatial variability of the surface layer of a Loess Plateau hillside in China. Scientia Agricola, 65, 277–289.Google Scholar
  14. Ike, A. F., & Clutter, I. L. (1968). The variability of forest soils of the Georgia Blue Ridge Mountains. Soil Science Society of America Proceedings, 32, 284–288.CrossRefGoogle Scholar
  15. Ilsemann, J., Goeb, S., & Bachmann, J. (2001). How many soil samples are neccessary to obtain a reliable estimate of mean nitrate concentrations in an agricultural field? Journal of Plant Nutrition and Soil Science, 164, 585–590.CrossRefGoogle Scholar
  16. Jiang, H. L., Liu, G. S., Wang, R., Shi, H. Z., & Hu, H. C. (2012). Spatial variability of soil total nutrients in a tobacco plantation field in central China. Communications in Soil Science and Plant Analysis, 43, 1883–1896.CrossRefGoogle Scholar
  17. Kar, G., Peak, D., & Schoenau, J. J. (2012). Spatial distribution and chemical speciation of soil phosphorus in a band application. Soil Science Society of America Journal, 76, 2297–2306.CrossRefGoogle Scholar
  18. Lark, R. M., & Cullis, B. R. (2004). Model-based analysis using REML for inference from systematically sampled data on soil. European Journal of Soil Science, 55, 799–813.CrossRefGoogle Scholar
  19. Liu, Y., Lv, J. S., Zhang, B., & Bi, J. (2013). Spatial multi-scale variability of soil nutrients in relation to environmental factors in a typical agricultural region, eastern China. Science of the Total Environment, 450, 108–119.PubMedCrossRefGoogle Scholar
  20. Metz, L. J., Wells, C. G., & Swindel, B. F. (1966). Sampling soil and foliage in a pine plantation. Soil Science Society of America Proceedings, 30, 397–399.CrossRefGoogle Scholar
  21. Montanari, R., Souza, G. S. A., Pereira, G. T., Marques, J., Siqueira, D. S., & Siqueira, G. M. (2012). The use of scaled semivariograms to plan soil sampling in sugarcane fields. Precision Agriculture, 13, 542–552.CrossRefGoogle Scholar
  22. Mueller, E. N., Wainwright, J., & Parsons, A. J. (2008). Spatial variability of soil and nutrient characteristics of semi-arid grasslands and shrublands, Jornada Basin, New Mexico. Ecohydrology, 1, 3–12.Google Scholar
  23. Noorbakhsh, S., Schoenau, J., Si, B., Zeleke, T., & Qian, P. (2008). Soil Properties, yield, and landscape relationships in South-Central Saskatchewan Canada. Journal of Plant Nutrition, 31, 539–556.CrossRefGoogle Scholar
  24. Paz-Gonzalez, A., & Taboada, M. T. (2000). Nutrient variability from point sampling on 2 meter grid in cultivated and adjacent forest land. Communications in Soil Science and Plant Analysis, 31, 2135–2146.CrossRefGoogle Scholar
  25. Pebesma, E. J., & Wesseling, C. G. (1998). Gstat: a program for geostatistical modeling, prediction and simulation. Computers and Geosciences, 24, 17–31.CrossRefGoogle Scholar
  26. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & the R Development Core Team. (2013). nlme: linear and nonlinear mixed effects models. R package version, 3, 1–110.Google Scholar
  27. Qian, P., Schoenau, J. J., & Karamanos, R. E. (1994). Simultaneous extraction of available phosphorus and potassium with a new soil test: a modification of Kelowna extraction. Communications in Soil Science and Plant Analysis, 25, 627–635.CrossRefGoogle Scholar
  28. Saskatchewan Soil Survey, (1989). Rural Municipality of Humboldt No. 370. Preliminary soil map and report: Saskatoon, SK, Canada Saskatchewan Institute of Pedology, Univ. of Saskatchewan.Google Scholar
  29. Troelstra, S. R., Lotz, L. A. P., Wagenaar, R., & Sluimer, L. (1990). Temporal and spatial variability in soil nutrient status of a former beach plain. Plant and Soil, 127, 1–12.CrossRefGoogle Scholar
  30. Utset, A., & Cid, G. (2001). Soil penetrometer resistance spatial variability in a Ferralsol at several soil moisture conditions. Soil and Tillage Research, 61, 193–202.CrossRefGoogle Scholar
  31. Vourlitis, G. L., Lobo, F. D., Biudes, M. S., Ortiz, C. E. R., & Nogueira, J. D. (2011). Spatial variations in soil chemistry and organic matter content across a vochysia divergens invasion front in the Brazilian Pantanal. Soil Science Society of America Journal, 75, 1554–1561.CrossRefGoogle Scholar
  32. Wang, J., Fu, B. J., Qiu, Y., & Chen, L. D. (2001). Soil nutrients in relation to land use and landscape position in the semi-arid small catchment on the loess plateau in China. Journal of Arid Environments, 48, 537–550.CrossRefGoogle Scholar
  33. Webster, R., & Oliver, M. A. (2007). Geostatistics for Environmental Scientists (2nd ed.). New York: Wiley.CrossRefGoogle Scholar
  34. Yan, X. Y., & Cai, Z. C. (2008). Number of soil profiles needed to give a reliable overall estimate of soil organic carbon storage using profile carbon density data. Soil Science and Plant Nutrition, 54, 819–825.CrossRefGoogle Scholar
  35. Zhang, X. Y., Sui, Y. Y., Zhang, X. D., Meng, K., & Herbert, S. J. (2007). Spatial variability of nutrient properties in black soil of northeast China. Pedosphere, 17, 19–29.CrossRefGoogle Scholar
  36. Zougmore, R., Mando, A., & Stroosnijder, L. (2010). Effect of farmer management strategies on spatial variability of soil fertility and crop nutrient uptake in contrasting agro-ecological zones in Zimbabwe. Nutrient Cycling in Agroecosystems, 88, 17–27.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Soil ScienceUniversity of SaskatchewanSaskatoonCanada

Personalised recommendations