Location-specific vs location-agnostic machine learning metamodels for predicting pasture nitrogen response rate

Christos Pylianidis*, Val Snow, Dean Holzworth, Jeremy Bryant, Ioannis N. Athanasiadis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

1 Citation (Scopus)


In this work we compare the performance of a location-specific and a location-agnostic machine learning metamodel for crop nitrogen response rate prediction. We conduct a case study for grass-only pasture in several locations in New Zealand. We generate a large dataset of APSIM simulation outputs and train machine learning models based on that data. Initially, we examine how the models perform at the location where the location-specific model was trained. We then perform the Mann–Whitney U test to see if the difference in the predictions of the two models (i.e. location-specific and location-agnostic) is significant. We expand this procedure to other locations to investigate the generalization capability of the models. We find that there is no statistically significant difference in the predictions of the two models. This is both interesting and useful because the location-agnostic model generalizes better than the location-specific model which means that it can be applied to virgin sites with similar confidence to experienced sites.
Original languageEnglish
Title of host publicationPattern Recognition. ICPR International Workshops and Challenges.
Subtitle of host publicationICPR 2021
EditorsA. del Bimbo, R. Cucchiara, S. Sclaroff, G.M. Farinella, T. Mei, M. Bertini, H.J. Escalante, R. Vezzani
Place of PublicationCham
ISBN (Electronic)9783030687809
ISBN (Print)9783030687793
Publication statusPublished - 25 Feb 2021
Event25th International Conference in Pattern Recognition - Milano, Italy
Duration: 10 Jan 202115 Jan 2021
Conference number: 2020

Publication series

NameLecture Notes in Computer Science (LNCS)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference in Pattern Recognition
Abbreviated titleICPR
Internet address


  • Machine learning
  • Metamodels
  • Process-based simulation


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