Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
Soil Research Soil Research Society
Soil, land care and environmental research
RESEARCH ARTICLE

Spatial variation in soil organic carbon and nitrogen at two field sites under crop and pasture rotations in southern New South Wales, Australia

Mark Conyers A , Beverley Orchard A , Susan Orgill A B , Albert Oates A , Graeme Poile A , Richard Hayes A , Peter Hawkins A , Binbin Xu A , Yan Jia A , Vince van der Rijt A and Guangdi Li A
+ Author Affiliations
- Author Affiliations

A Wagga Wagga Agricultural Institute, NSW Department of Primary Industries, PMB Pine Gully Road Wagga Wagga, NSW 2650, Australia.

B Corresponding author. Email: susan.orgill@dpi.nsw.gov.au

Soil Research 56(8) 780-792 https://doi.org/10.1071/SR18174
Submitted: 19 June 2018  Accepted: 16 August 2018   Published: 1 November 2018

Abstract

Estimating the likely variance in soil organic carbon (OC) at the scale of farm fields or smaller monitoring areas is necessary for developing sampling protocols that allow temporal change to be detected. Given the relatively low anticipated soil OC sequestration rates (<0.5 Mg/ha.0.30 m/year) for dryland agriculture it is important that sampling strategies are designed to reduce any cumulative errors associated with measuring soil OC. The first purpose of this study was to evaluate the spatial variation in soil OC and nitrogen (N), in soil layers to 1.50 m depth at two monitoring sites (Wagga Wagga and Yerong Creek, 0.5 ha each) in southern New South Wales, Australia, where crop and pasture rotations are practiced. Four variogram models were tested (linear, spherical, Gaussian and exponential); however, no single model dominated across sites or depths for OC or N. At both sites, the range was smallest in surface soil, and on a scale suggesting that sowing rows (stubble) may dominate the pattern of spatial dependence, whereas the longer ranges appeared to be associated with horizon boundaries. The second purpose of the study was to obtain an estimate of the population mean with 1%, 5% and 10% levels of precision using the calculated variance. The number of soil cores required for a 1% precision in estimation of the mean soil OC or N was impractical at most depths (>500 per ha). About 30 soil cores per composite sample to 1.50 m depth, each core being at least 10 m apart, would ensure at least an average of 10% precision in the estimation of the mean soil OC at these two sites, which represent the agriculture of the region.

Additional keywords: organic matter, sampling error, soil sampling, total nitrogen, variogram.


References

Blöschl G, Sivapalan M (1995) Scale issues in hydrological modelling: a review. Hydrological Processes 9, 251–290.
Scale issues in hydrological modelling: a review.Crossref | GoogleScholarGoogle Scholar |

Chappell A, Viscarra Rossel RA (2013) The importance of sampling support for explaining change in soil organic carbon. Geoderma 193–194, 323–325.
The importance of sampling support for explaining change in soil organic carbon.Crossref | GoogleScholarGoogle Scholar |

Costa C, Papatheodorou EM, Monokrousos N, Stamou GP (2015) Spatial variability of soil organic C, inorganic N and extractable P in a Mediterranean grazed area. Land Degradation & Development 26, 103–109.
Spatial variability of soil organic C, inorganic N and extractable P in a Mediterranean grazed area.Crossref | GoogleScholarGoogle Scholar |

Hayes RC, Li GD, Sandral GA, Swan TD, Price A, Hildebrand S, Goward L, Fuller C, Peoples MB (2017) Enhancing composition and persistence of mixed pasture swards in southern New South Wales through alternative spatial configurations and improved legume performance. Crop and Pasture Science 68, 1112–1130.
Enhancing composition and persistence of mixed pasture swards in southern New South Wales through alternative spatial configurations and improved legume performance.Crossref | GoogleScholarGoogle Scholar |

Isbell RF (1996) ‘The Australian Soil Classification.’ (CSIRO Publishing: Melbourne).

Leco (1995) Instrumentation for characterisation of organic/inorganic materials and microstructural analysis. Instruction Manual FP-2000 Protein/Nitrogen Analyses, Form No. 200–558, May 1995. (Leco Corporation: St Joseph, MI)

Li GD, Hayes RC, McCormick JI, Gardner MJ, Sandral GA, Dear BS (2014) Time of sowing and the presence of cover-crop determine the productivity and persistence of perennial pastures in mixed farming systems. Crop and Pasture Science 65, 988–1001.
Time of sowing and the presence of cover-crop determine the productivity and persistence of perennial pastures in mixed farming systems.Crossref | GoogleScholarGoogle Scholar |

McBratney AB, Pringle MJ (1999) Estimating average and proportional variograms of soil properties and their potential use in precision agriculture. Precision Agriculture 1, 125–152.
Estimating average and proportional variograms of soil properties and their potential use in precision agriculture.Crossref | GoogleScholarGoogle Scholar |

Pebesma E (2004) Multivariable geostatistics in S: the gstat package. Computers & Geosciences 30, 683–691.
Multivariable geostatistics in S: the gstat package.Crossref | GoogleScholarGoogle Scholar |

Pebesma E, Cornford D, Dubois G, Heuvelink GBM, Hristopoulos D, Pilz J, Stoehlker U, Morin G, Skoien JO (2010) INTAMAP: the design and implementation of an interoperable automated interpolation web service. Computers & Geosciences 37, 343–352.
INTAMAP: the design and implementation of an interoperable automated interpolation web service.Crossref | GoogleScholarGoogle Scholar |

Pringle MJ, Allen DE, Dalal RC, Payne JE, Mayer DG, O’Reagain P, Marchant BP (2011) Soil carbon stock in the tropical rangelands of Australia: effects of soil type and grazing pressure and determination of sampling requirement. Geoderma 167–8, 261–273.
Soil carbon stock in the tropical rangelands of Australia: effects of soil type and grazing pressure and determination of sampling requirement.Crossref | GoogleScholarGoogle Scholar |

Rayment GE, Lyons DJ (2011) ‘Soil Chemical Methods – Australasia.’ (CSIRO Publishing: Melbourne).

Singh K, Murphy BW, Marchant BP (2013) Towards cost-effective estimation of soil carbon stocks at the field scale. Soil Research 50, 672–684.
Towards cost-effective estimation of soil carbon stocks at the field scale.Crossref | GoogleScholarGoogle Scholar |

Wilson BR, Barnes P, Koen TB, Ghosh S, King D (2010) Measurement and estimation of land-use effects on soil carbon and related properties for soil monitoring: a study on a basalt landscape of northern New South Wales, Australia. Soil Research 48, 421–433.
Measurement and estimation of land-use effects on soil carbon and related properties for soil monitoring: a study on a basalt landscape of northern New South Wales, Australia.Crossref | GoogleScholarGoogle Scholar |

Wright NA (1998) Soil fertility variograms from “true point sampling” on 20.0, 0.9, and 0.1 meter grids in two fields. Communications in Soil Science and Plant Analysis 29, 1649–1666.
Soil fertility variograms from “true point sampling” on 20.0, 0.9, and 0.1 meter grids in two fields.Crossref | GoogleScholarGoogle Scholar |