- Title
- Matching the model to the available data to predict wheat, barley, or canola yield : a review of recently published models and data
- Creator
- Clark, Robert; Dahlhaus, Peter; Robinson, Nathan; Larkins, Jo-ann; Morse-McNabb, Elizabeth
- Date
- 2023
- Type
- Text; Journal article; Review
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/198377
- Identifier
- vital:19032
- Identifier
-
https://doi.org/10.1016/j.agsy.2023.103749
- Identifier
- ISSN:0308-521X (ISSN)
- Abstract
- CONTEXT: Continued increases in global population and rising living standards in many countries are driving a surge in demand for energy and protein-rich foods. Wheat, barley, and canola are important crops that are grown and traded globally. However, climate change, geopolitical tensions and competition from other crops threaten the ability to satisfy global demand. Accurate predictions of crop production and its spatial variation can play a significant role in their reliable and efficient production, marketing, and distribution. OBJECTIVE: This review examined recently published models and data used to predict wheat, barley, and canola yield to identify which factors produced the best yield predictions. METHODS: A literature search was conducted across the Scopus, EBSCOhost and Web of Science databases over seven years between 2015 and 2021. Data extracted from the papers identified by the literature search were investigated using graphical and quantitative analytical techniques to determine if the type of algorithm, input data, prediction timing, output scale or extent and climate variability both in isolation and in combination affected the model's predictive ability. RESULTS AND CONCLUSIONS: The literature search produced 11, 908 results which was reduced to 118 papers after applying the review criteria (peer reviewed papers focussed on models predicting yield at greater than plot scale across extensive areas using accessible data). China produced almost one third of all yield prediction models over the study period and 87% of models were used to predict wheat yield. Statistical models were the most common algorithm in most regions and in total. However, there was a surge in machine learning models after 2018. They were the most common model from 2019 to 2021, with one third developed in China. The review concluded that only the choice of modelling technique and the input data had a significant effect on model performance with the machine learning techniques Random Forest, Boosting algorithms and Deep Learning models as well as process-based Light Use Efficiency models that used a combination of remotely sensed and agrometeorological data performing best. SIGNIFICANCE: The review showed that matching the model to the available data could improve the ability to predict wheat, barley or canola yield. The use of quantitative statistical techniques in this review, should give modellers trying to predict wheat, barley or canola yield more confidence in matching their approach to the available data than previous reviews that relied on visual interpretation of data. © 2023 The Authors
- Publisher
- Elsevier Ltd
- Relation
- Agricultural Systems Vol. 211, no. (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- http://creativecommons.org/licenses/by-nc/4.0/
- Rights
- Copyright © 2023 The Authors
- Rights
- Open Access
- Subject
- 30 Agricultural, veterinary and food sciences; 41 Environmental sciences; Agrometeorological data; Model performance; Quantitative analysis; Remotely sensed data; Review; Yield prediction models
- Full Text
- Reviewed
- Funder
- Robert Clark is supported by an Australian Government Research Training Program (RTP) Stipend, a RTP Fee-Offset Scholarship through Federation University Australia , and a top-up Stipend Scholarship (project code G1719) through the Food Agility Cooperative Research Centre (CRC). The CRC Program supports industry-led collaborations between industry, researchers, and the community. In addition, Robert Clark's PhD studies are partially supported by a Grains Research and Development Corporation (GRDC) scholarship (project code G1210 ). Neither the Food Agility CRC or the GRDC had a direct role in in this research design or preparation of this manuscript.
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