Random Forests for Global and Regional Crop Yield Predictions

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Abstract

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.

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Most cited references 4

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Random forests for classification in ecology.

D Cutler, Thomas C. Edwards, Karen Beard … (2007)

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.

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Julian D. Olden, Joshua J. Lawler, N LeRoy Poff (2008)

Machine learning methods, a family of statistical techniques with origins in the field of artificial intelligence, are recognized as holding great promise for the advancement of understanding and prediction about ecological phenomena. These modeling techniques are flexible enough to handle complex problems with multiple interacting elements and typically outcompete traditional approaches (e.g., generalized linear models), making them ideal for modeling ecological systems. Despite their inherent advantages, a review of the literature reveals only a modest use of these approaches in ecology as compared to other disciplines. One potential explanation for this lack of interest is that machine learning techniques do not fall neatly into the class of statistical modeling approaches with which most ecologists are familiar. In this paper, we provide an introduction to three machine learning approaches that can be broadly used by ecologists: classification and regression trees, artificial neural networks, and evolutionary computation. For each approach, we provide a brief background to the methodology, give examples of its application in ecology, describe model development and implementation, discuss strengths and weaknesses, explore the availability of statistical software, and provide an illustrative example. Although the ecological application of machine learning approaches has increased, there remains considerable skepticism with respect to the role of these techniques in ecology. Our review encourages a greater understanding of machin learning approaches and promotes their future application and utilization, while also providing a basis from which ecologists can make informed decisions about whether to select or avoid these approaches in their future modeling endeavors.

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Prediction of protein-protein interactions using random decision forest framework.

Xue-Wen Chen, Mei Liu (2005)

Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Domains are the building blocks of proteins; therefore, proteins are assumed to interact as a result of their interacting domains. Many domain-based models for protein interaction prediction have been developed, and preliminary results have demonstrated their feasibility. Most of the existing domain-based methods, however, consider only single-domain pairs (one domain from one protein) and assume independence between domain-domain interactions. In this paper, we introduce a domain-based random forest of decision trees to infer protein interactions. Our proposed method is capable of exploring all possible domain interactions and making predictions based on all the protein domains. Experimental results on Saccharomyces cerevisiae dataset demonstrate that our approach can predict protein-protein interactions with higher sensitivity (79.78%) and specificity (64.38%) compared with that of the maximum likelihood approach. Furthermore, our model can be used to infer interactions not only for single-domain pairs but also for multiple domain pairs.

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Author and article information

Contributors

Jose Luis Gonzalez-Andujar: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 3 June 2016

Publication date Collection: 2016

Volume: 11

Issue: 6

Electronic Location Identifier: e0156571

Affiliations

[1 ]School of Environmental and Forest Sciences, College of the Environment, University of Washington, Box 354115, Seattle, WA 98195, United States of America

[2 ]Department of Geographical Sciences, University of Maryland, College Park, MD, United States of America

[3 ]Crop Systems and Global Change Laboratory, USDA-ARS, Beltsville, MD 20705, United States of America

[4 ]Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138, United States of America

[5 ]Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, United States of America

[6 ]Department of Forest Resources, University of Minnesota, St. Paul, MN 55108, United States of America

[7 ]Climate Change & Agroecology Division, National Institute of Agricultural Science, RDA, Suwon, Korea

[8 ]Institute on the Environment, University of Minnesota, St. Paul, MN 55108, United States of America

Instituto de Agricultura Sostenible (CSIC), SPAIN

Author notes

Competing Interests: The authors have declared that no competing interests exist.

Conceived and designed the experiments: SHK JHJ. Performed the experiments: JHJ SHK JPR NDM EEB DHF. Analyzed the data: JHJ SHK JPR NDM DHF DJT KY EEB. Contributed reagents/materials/analysis tools: SHK KMS DHF DJT JSG VRR. Wrote the paper: JHJ SHK JPR NDM DHF EEB JSG KY DJT KMS VRR.

* E-mail: soohkim@ 123456uw.edu

Author information

Soo-Hyung Kim http://orcid.org/0000-0003-3879-4080

Article

Publisher ID: PONE-D-15-24117

DOI: 10.1371/journal.pone.0156571

PMC ID: 4892571

PubMed ID: 27257967

SO-VID: 2bf0433c-f328-4cf2-b582-3c1689710c03

License:

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

History

Date received : 3 June 2015

Date accepted : 17 May 2016

Page count

Figures: 3, Tables: 2, Pages: 15

Funding

Funded by: funder-id http://dx.doi.org/10.13039/501100003627, Rural Development Administration;

Award ID: PJ01000707

Award Recipient :

ORCID: http://orcid.org/0000-0003-3879-4080

Soo-Hyung Kim

Funded by: funder-id http://dx.doi.org/10.13039/501100003627, Rural Development Administration;

Award ID: PJ01000707

Award Recipient : Kyo-Moon Shim

Funded by: funder-id http://dx.doi.org/10.13039/100000199, U.S. Department of Agriculture;

Award ID: 58-1265-1-074

Award Recipient :

ORCID: http://orcid.org/0000-0003-3879-4080

Soo-Hyung Kim

Funded by: National Institute of Food and Agriculture

Award ID: 2011-68004-30057

Award Recipient : David H. Fleisher

Funded by: USDA-ARS Headquarters Postdoctoral Research Associate Program

Award Recipient : David H. Fleisher

Funded by: funder-id http://dx.doi.org/10.13039/100005825, National Institute of Food and Agriculture;

Award ID: 2016-67012-25208

Award Recipient : Nathaniel D. Mueller

Funded by: funder-id http://dx.doi.org/10.13039/100000085, Directorate for Geosciences;

Award ID: 1521210

Award Recipient : Nathaniel D. Mueller

Funded by: funder-id http://dx.doi.org/10.13039/100000008, David and Lucile Packard Foundation;

Award Recipient : Ethan E. Butler

This study was supported by a Cooperative Research Program for Agricultural Science and Technology Development (Project No. PJ01000707), Rural Development Administration, Republic of Korea (SHK; KMS). Additional support was provided in part by a Specific Cooperative Agreement: 58-1265-1-074 between University of Washington and USDA-ARS (SHK; VRR), the USDA-ARS Headquarters Postdoctoral Research Associate Program (DHF), the USDA-NIFA-AFRI Grant no. 2011-68004-30057: Enhancing Food Security of Underserved Populations in the Northeast through Sustainable Regional Food Systems (DHF), the USDA AFRI fellowship 2016-67012-25208 (NDM), the NSF Hydrological Sciences grant 1521210 (NDM), and the Packard Foundation (EEB).

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Random Forests for Global and Regional Crop Yield Predictions

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