Location-Specific vs Location-Agnostic Machine Learning Metamodels for Predicting Pasture Nitrogen Response Rate-Reference-Cited by-同舟云学术

Location-Specific vs Location-Agnostic Machine Learning Metamodels for Predicting Pasture Nitrogen Response Rate

Published:2021 Issue: Volume: Page:45-54
ISSN:0302-9743
Container-title:Pattern Recognition. ICPR International Workshops and Challenges
language:
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Author:

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

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-68780-9_5

Reference14 articles.

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2. Garrido Torres, J.A., Jennings, P.C., Hansen, M.H., Boes, J.R., Bligaard, T.: Low-Scaling algorithm for nudged elastic band calculations using a surrogate machine learning model. Phys. Rev. Lett. 122(15), 156001 (2019). https://doi.org/10.1103/PhysRevLett.122.156001

3. Gillingham, A.G., Morton, J.D., Gray, M.H.: Pasture responses to phosphorus and nitrogen fertilisers on east coast hill country: 2. Clover and grass production from easy slopes. N. Z. J. Agric. Res. 51(2), 85–97 (2008). https://doi.org/10.1080/00288230809510438

4. Gladish, D.W., Darnell, R., Thorburn, P.J., Haldankar, B.: Emulated multivariate global sensitivity analysis for complex computer models applied to agricultural simulators. J. Agric. Biol. Environ. Stat. 24(1), 130–153 (2018). https://doi.org/10.1007/s13253-018-00346-y

5. Goetz, J.N., Brenning, A., Petschko, H., Leopold, P.: Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput. Geosci. 81, 1–11 (2015). https://doi.org/10.1016/j.cageo.2015.04.007