An improved method for biometric analysis of soil test – crop response data sets

Maheswaran Rohan; Mark Conyers

doi:10.1071/CP24162

RESEARCH ARTICLE (Open Access)

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An improved method for biometric analysis of soil test – crop response data sets

Maheswaran Rohan

^A ^* and Mark Conyers

^B

+ Author Affiliations

- Author Affiliations

^A Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia.

^B Retired. Formerly of: Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia.

^* Correspondence to: maheswaran.rohan@dpi.nsw.gov.au

Handling Editor: Davide Cammarano

Crop & Pasture Science 76, CP24162 https://doi.org/10.1071/CP24162

Submitted: 19 May 2024 Accepted: 11 December 2024 Published: 17 January 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

To increase cereal production, primary producers want to know the amount of fertiliser that needs to be applied to achieve high yield. To calculate the critical soil test value (CSTV) especially in Colwell-P, several models were found in the literature. The arcsine-log calibration curve has been commonly used in Australia to estimate the CSTV. However, this method has some mathematical weaknesses, which tend to give underestimated values for CSTV.

Aim

In this paper, we describe the mathematical issues and propose a model to overcome these issues. The simplified model proposed allows us to estimate the CSTV and its standard error.

Method

We have applied the regression and the delta method to the data used in the arcsine-log calibration curve (ALCC) method.

Key results

Based on the given data, a soil test value of 31.5 mg P kg⁻¹ soil is required to achieve 90% relative yield of wheat, which is the middle ground of previously published critical values between the underestimate (21.4 mg kg⁻¹) generated by the ALCC algorithm and the overestimate (40 mg kg⁻¹) generated by the conventional Mitscherlich method.

Conclusions

Advantages of this method are: (1) simple to apply to any data sets; and (2) easy to incorporate other covariates into the models. This method should be applied for computing estimates of CSTV and its standard error because it overcomes the contentious issue of the division of the y-axis by the correlation coefficient.

Implication

The proposed method should replace the ALCC algorithm and the current P values used in farming may need to be updated.

Keywords: delta method, phosphorus, soil test calibration, statistical models, wheat.

References

Alexandratos N, Bruinsma J (2012) World agriculture towards 2030/2050: the 2012 revision. In ‘ESA Working Paper No. 12-03.’ FAO, Rome. Available at https://www.fao.org/4/ap106e/ap106e.pdf

Bell R, Reuter D, Scott B, Sparrow L, Strong W, Chen the late W (2013) Soil phosphorus–crop response calibration relationships and criteria for winter cereal crops grown in Australia. Crop & Pasture Science 64(5), 480-498.
| Crossref | Google Scholar |

Brennan RF, Bell MJ (2013) Soil potassium–crop response calibration relationships and criteria for field crops grown in Australia. Crop & Pasture Science 64, 514-522.
| Crossref | Google Scholar |

Colwell JD (1963) The estimation of the phosphorus fertilizer requirements of wheat in southern New South Wales by soil analysis. Australian Journal of Experimental Agriculture and Animal Husbandry 3(10), 190-197.
| Crossref | Google Scholar |

Conyers M, Bell R, Bell M (2020) Factors influencing the soil-test calibration for Colwell P and wheat under winter-dominant rainfall. Crop & Pasture Science 71(2), 113-118.
| Crossref | Google Scholar |

Correndo AA, Salvagiotti F, García FO, Gutiérrez-Boem FH (2017) A modification of the arcsine-log calibration curve for analysing soil test value-relative yield relationships. Crop & Pasture Science 68(3), 297-304.
| Crossref | Google Scholar |

Dear BS, Helyar KR, Muller WJ, Loveland B (1992) The P fertilizer requirements of subterranean clover, and the soil P status, sorption and buffering capacities from two P analyses. Australian Journal of Soil Research 30(1), 27-43.
| Crossref | Google Scholar |

Dyson CB, Conyers MK (2013) Methodology for online biometric analysis of soil test-crop response datasets. Crop & Pasture Science 64(5), 435-441.
| Crossref | Google Scholar |

Legendre P, Legendre L (1998) Interpretation of ecological structures. In ‘Numerical ecology. Vol. 20’. 2nd English edn. (Eds P Legendre, L Legendre) pp. 497–545. (Elsevier: Amsterdam)

Mason S, McDonald G (2021) Time of sowing influences wheat responses to applied phosphorus in alkaline calcareous soils in a temperate climate. Crop & Pasture Science 72(11), 861-873.
| Crossref | Google Scholar |

Moody PW, Bolland MDA (1999) Phosphorus. In ‘Soil analysis: an interpretation manual’. (Eds KI Peverill, LA Sparrow, DJ Reuter) pp. 187–220. (CSIRO Publishing: Melbourne, Australia)

Rohan M, Sarmah AK (2023) Computation of standard error for half-life estimation using various dissipation models for regulatory purposes. Science of The Total Environment 893, 164773.
| Crossref | Google Scholar | PubMed |

Seber G, Wild C (1989) ‘Non-linear regression.’ (John Wiley & Sons: New York, NY, USA)

Speirs SD, Reuter DJ, Peverill KI, Brennan RF (2013a) Making better fertiliser decisions for cropping systems in Australia: an overview. Crop & Pasture Science 64(5), 417-423.
| Crossref | Google Scholar |

Speirs SD, Scott BJ, Moody PW, Mason SD (2013b) Soil phosphorus tests II: a comparison of soil test–crop response relationships for different soil tests and wheat. Crop & Pasture Science 64(5), 469-479.
| Crossref | Google Scholar |

Watmuff G, Reuter DJ, Speirs SD (2013) Methodologies for assembling and interrogating N, P, K, and S soil test calibrations for Australian cereal, oilseed and pulse crops. Crop & Pasture Science 64(5), 424-434.
| Crossref | Google Scholar |