The DOI matches its Crossref record; 119 of the 119 references it cites were found in a registry or an index.
Seasonal crop yield forecast: Methods, applications, and accuracies
Abstract
Abstract The perfect knowledge of yield before harvest has been a wish puzzling human being since the beginning of agriculture because seasonal forecast of crop yield plays a critical role in decision making for different stakeholders—from farmers to policy makers to governments for food security, to commodities traders. Different methods have been used to forecast yield with different levels of granularity, accuracy and timing. This chapter presents a critical review of the current seasonal crop yield forecasting methods found in the scientific literature. Extensive research has been conducted on crop yield forecast, particularly for wheat, maize, rice, barley, and soybean. Yield forecast are mainly based on field surveys, statistical regressions between historical yield and in-season variables (agrometeorological, or remotely sensed data), crop simulation models, or on integration between statistical modeling with dynamic process-based crop simulation models. A low number of studies rely on field surveys as a means to forecast yield, but they remain the main methods of yield forecast and estimation in several countries (i.e., USA). This chapter aims to report results found in peer-review journals for different crops, methods, geographies, and accuracies, and to end with a critical perspective on the advantage and disadvantage of the different methods currently employed by researchers and stakeholders.
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Cites
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Cited by
Rashid, M., Bari, B. S., Yusup, Y., Kamaruddin, M. A., & Khan, N. (2021) · IEEE Access · doi:10.1109/access.2021.3075159 · Cited by 369
Machine learning for large-scale crop yield forecasting
Paudel, D., Boogaard, H., de Wit, A., Janssen, S., Osinga, S., Pylianidis, C., & Athanasiadis, I. N. (2021) · Agricultural Systems · doi:10.1016/j.agsy.2020.103016 · Cited by 357
Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt
Archontoulis, S. V., Castellano, M. J., Licht, M. A., Nichols, V., Baum, M., Huber, I., Martinez‐Feria, R., Puntel, L., Ordóñez, R. A., Iqbal, J., et al. (2020) · Crop Science · doi:10.1002/csc2.20039 · Cited by 166
Metadata
- Authors
- Basso, B., & Liu, L.
- Venue
- Advances in Agronomy
- Publisher
- Elsevier
- Published
- 2019
- Pages
- 201-255
- ISSN
- 0065-2113 (ISSN-L)
- OpenAlex
- W2905983018
- Cited by
- 320
- References
- 119
- FWCI
- 37.53
- Type
- book chapter
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
