Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality
RESEARCH ARTICLE

A modification of the arcsine–log calibration curve for analysing soil test value–relative yield relationships

Adrián A. Correndo A D , Fernando Salvagiotti B , Fernando O. García A and Flavio H. Gutiérrez-Boem C
+ Author Affiliations
- Author Affiliations

A International Plant Nutrition Institute (IPNI), Latin America Southern Cone Program, Av. Santa Fe 910, Acassuso, Buenos Aires, Argentina.

B EEA INTA Oliveros, Departamento de Agronomía, Oliveros, Santa Fe, Argentina.

C University of Buenos Aires, College of Agronomy, INBA-CONICET, Buenos Aires, Argentina.

D Corresponding author. Email: correndo@agro.uba.ar

Crop and Pasture Science 68(3) 297-304 https://doi.org/10.1071/CP16444
Submitted: 2 December 2016  Accepted: 17 February 2017   Published: 21 March 2017

Abstract

This article aims to discuss the arcsine–log calibration curve (ALCC) method designed for the Better Fertiliser Decisions for Cropping Systems (BFDC) to calibrate relationships between relative yield (RY) and soil test value (STV). Its main advantage lies in estimating confidence limits of the critical value (CSTV). Nevertheless, intervals for 95% confidence level are often too wide, and authors suggest a reduction in the confidence level to 70% in order to achieve narrower estimates. Still, this method can be further improved by modifying specific procedures. For this purpose, several datasets belonging to the BFDC were used. For any confidence level, estimates with the modified ALCC procedures were always more accurate than the original ALCC. The overestimation of confidence limits with the original ALCC was inversely related to the correlation coefficient of the dataset, which might allow a relatively simple and reliable correction of previous estimates. In addition, because the method is based on the correlation between STV and RY, the importance to test it for significance is emphasised in order to support the hypothesis of a relationship. Then, the modified ALCC approach could also allow a more reliable comparison of datasets by slopes of the bivariate linear relationship between transformed variables.

Additional keywords: bivariate model, correlation, standardised major axis regression.


References

Anderson GC, Peverill KI, Brennan RF (2013) Soil sulfur–crop response calibration relationships and criteria for field crops grown in Australia. Crop & Pasture Science 64, 523–530.
Soil sulfur–crop response calibration relationships and criteria for field crops grown in Australia.CrossRef |

Bell MJ, Moody PW, Anderson GC, Strong W (2013a) Soil phosphorus–crop response calibration relationships and criteria for oilseeds, grain legumes and summer cereal crops grown in Australia. Crop & Pasture Science 64, 499–513.
Soil phosphorus–crop response calibration relationships and criteria for oilseeds, grain legumes and summer cereal crops grown in Australia.CrossRef | 1:CAS:528:DC%2BC3sXhtlakt7rN&md5=99786096571cc670ac97a3e67e202a8eCAS |

Bell MJ, Strong W, Elliott D, Walker C (2013b) Soil nitrogen–crop response calibration relationships and criteria for winter cereal crops grown in Australia. Crop & Pasture Science 64, 442–460.
Soil nitrogen–crop response calibration relationships and criteria for winter cereal crops grown in Australia.CrossRef | 1:CAS:528:DC%2BC3sXhtlakt73I&md5=7c5f95c83e9032370dab1204c06eaca4CAS |

Bell R, Reuter D, Scott B, Sparrow L, Strong W, Chen W (2013c) Soil phosphorus–crop response calibration relationships and criteria for winter cereal crops grown in Australia. Crop & Pasture Science 64, 480–498.
Soil phosphorus–crop response calibration relationships and criteria for winter cereal crops grown in Australia.CrossRef | 1:CAS:528:DC%2BC3sXhtlakt73N&md5=80b01041c7eb8f22ccf8c993d3ae35bbCAS |

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.
Soil potassium–crop response calibration relationships and criteria for field crops grown in Australia.CrossRef | 1:CAS:528:DC%2BC3sXhtlakt73L&md5=531a15074c4f10f14af5ef1b8ff6d735CAS |

Colwell JD (1963) The estimation of phosphorus fertilizer requirements of wheat in southern New South Wales by soil analysis. Australian Journal of Experimental Agriculture and Animal Husbandry 3, 190–197.
The estimation of phosphorus fertilizer requirements of wheat in southern New South Wales by soil analysis.CrossRef | 1:CAS:528:DyaF2cXnvVOhsQ%3D%3D&md5=5cfd6220e5ad98826f6b2078bac5328fCAS |

Conyers MK, Bell MJ, Wilhelm NS, Bell R, Norton RM, Walker C (2013) Making Better Fertiliser Decisions for cropping Systems in Australia (BFDC): knowledge gaps and lessons learnt. Crop & Pasture Science 64, 539–547.
Making Better Fertiliser Decisions for cropping Systems in Australia (BFDC): knowledge gaps and lessons learnt.CrossRef |

D’Agostino RB, Belanger A, D’Agostino RB (1990) A suggestion for using powerful and informative tests of normality. The American Statistician 44, 316–321.

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

GraphPad Software Inc. (2016) ‘GraphPad Prism v7.0a for MacOSX.’ (GraphPad Software Inc.: La Jolla, CA, USA) Available at: www.graphpad.com/guides/prism/7/user-guide/index.htm

Jolicoeur P (1990) Bivariate allometry: interval estimation of the slope of the ordinary and standardized normal major axes and structural relationship. Journal of Theoretical Biology 144, 275–285.
Bivariate allometry: interval estimation of the slope of the ordinary and standardized normal major axes and structural relationship.CrossRef |

Jolicoeur P, Mosimann JE (1968) Intervalles de confiance pour la pente de l’axe majeur d’une distribution normale bidimensionnelle. Biometrie-Praximetrie 9, 121–140.

Kutner MH, Nachtsheim CJ, Neter J, Li W (2005) ‘Applied linear statistical models.’ 5th edn (McGraw-Hill: New York)

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

Mallarino AP, Blackmer AM (1992) Comparison of methods for determining critical concentrations of soil test phosphorus for corn. Agronomy Journal 84, 850–856.
Comparison of methods for determining critical concentrations of soil test phosphorus for corn.CrossRef | 1:CAS:528:DyaK3sXhtVWjsbg%3D&md5=cd5ab90765b7cde9446baa25a9064067CAS |

McArdle B (1988) The structural relationship: regression in biology. Canadian Journal of Zoology 66, 2329–2339.
The structural relationship: regression in biology.CrossRef |

Motulsky H, Christopoulos A (2004) ‘Fitting models to biological data using linear and nonlinear regression. A practical guide to curve fitting.’ (Oxford University Press: New York)

NSW DPI (2012) Making Better Fertiliser Decisions for Cropping Systems in Australia: online database, NSW DPI and the Grains Research and Development Corporation. Available at: www.bfdc.com.au (accessed 4 September 2016).

R Core Team (2016) ‘R: A language and environment for statistical computing.’ (R Foundation for Statistical Computing: Vienna) Available at: http://R-project.org

Sokal RR, Rohlf FJ (1995) ‘Biometry—The principles and practice of statistics in biological research.’ 3rd edn (W. H. Freeman: New York)

Speirs SD, Scott BJ, Moody PW, Mason SD (2013) Soil phosphorus tests II: A comparison of soil test–crop response relationships for different soil tests and wheat. Crop & Pasture Science 64, 469–479.
Soil phosphorus tests II: A comparison of soil test–crop response relationships for different soil tests and wheat.CrossRef | 1:CAS:528:DC%2BC3sXhtlakt7%2FJ&md5=3e9da3d871848adedc2065ac292d86c0CAS |

Warton DI, Weber NC (2002) Common slope tests for errors-in-variables models. Biometrical Journal. Biometrische Zeitschrift 44, 161–174.
Common slope tests for errors-in-variables models.CrossRef |

Warton DI, Wright IJ, Falster DS, Westoby M (2006) Bivariate line-fitting methods for allometry. Biological Reviews of the Cambridge Philosophical Society 81, 259–291.
Bivariate line-fitting methods for allometry.CrossRef |

Warton DI, Duursma RA, Falster DS, Taskinen S (2012) Smatr 3—an R package for estimation and inference about allometric lines. Methods in Ecology and Evolution 3, 257–259.
Smatr 3—an R package for estimation and inference about allometric lines.CrossRef |

Watmuff G, Reuter DJ, Speirs SD (2013) Methodologies for assembling and interrogating N, P, K, and soil test calibrations for Australian cereals, oilseed and pulse crops. Crop & Pasture Science 64, 424–434.
Methodologies for assembling and interrogating N, P, K, and soil test calibrations for Australian cereals, oilseed and pulse crops.CrossRef | 1:CAS:528:DC%2BC3sXhtlakt73F&md5=7cb0434de9d37e851cb21894e47bedb4CAS |

Webster R (1997) Regression and functional relations. European Journal of Soil Science 48, 557–566.
Regression and functional relations.CrossRef |

Webster R (2001) Statistics to support soil research and their presentation. European Journal of Soil Science 52, 331–340.
Statistics to support soil research and their presentation.CrossRef | 1:CAS:528:DC%2BD3MXltVKrsr8%3D&md5=426c86bcf7540ff8658fc77087715f0cCAS |



Rent Article (via Deepdyve) Export Citation