An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty

Newlands, Nathaniel K. and Zamar, David S. and Kouadio, Louis A. and Zhang, Yinsuo and Chipanshi, Aston and Potgieter, Andries and Toure, Souleymane and Hill, Harvey S. J. (2014) An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty. Frontiers in Environmental Science, 2 (art. 17). pp. 1-21. ISSN 2296-665X

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Abstract

We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotely-sensed indices. The method devises a multivariate statistical model to compute bias and uncertainty in forecasted yield at the Census of Agricultural Region (CAR) scale across the Canadian Prairies. The method uses robust variable-selection to select the best predictors within spatial subregions. Markov-Chain Monte Carlo (MCMC) simulation and random forest-tree machine learning techniques are then integrated to generate sequential forecasts through the growing season. Cross-validation of the model was performed by hindcasting/backcasting and comparing forecasts against available historical data (1987–2011) for spring wheat (Triticum aestivum L.). The model was also validated for the 2012 growing season by comparing forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were under-estimated by 1–4% in mid-season and over-estimated by 1% at the end of the growing season. This integrated methodology offers a consistent, generalizable approach for sequentially forecasting crop yield at the regional-scale. It provides a statistically robust, yet flexible way to concurrently adjust to data-rich and data-sparse situations, adaptively select different predictors of yield to changing levels of environmental uncertainty, and to update forecasts sequentially so as to incorporate new data as it becomes available. This integrated method also provides additional statistical support for assessing the accuracy and reliability of model-based crop yield forecasts in time and space.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2014 Newlands, Zamar, Kouadio, Zhang, Chipanshi, Potgieter, Toure and Hill. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Nathaniel K. Newlands, Science and Technology Branch, Agriculture and Agri-Food Canada, Lethbridge Research Centre, 5403 1st. Ave. S., P.O. Box 3000, Lethbridge, AB, T1J 4B1, Canada e-mail: nathaniel.newlands@agr.gc.ca
Faculty / Department / School: No Faculty
Date Deposited: 27 Dec 2014 12:59
Last Modified: 18 Dec 2017 06:02
Uncontrolled Keywords: agriculture; forecasting; Bayesian; uncertainty; climate; regional; crop yield; seasonal variation
Fields of Research : 07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070103 Agricultural Production Systems Simulation
07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070104 Agricultural Spatial Analysis and Modelling
07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070105 Agricultural Systems Analysis and Modelling
Socio-Economic Objective: D Environment > 96 Environment > 9603 Climate and Climate Change > 960307 Effects of Climate Change and Variability on Australia (excl. Social Impacts)
Identification Number or DOI: 10.3389/fenvs.2014.00017
URI: http://eprints.usq.edu.au/id/eprint/26188

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