The Effect of Antecedence on Empirical Model Forecasts of Crop Yield from Observations of Canopy Properties

Anna Florence, Andrew Revill, Stephen Hoad, Robert Rees and Mathew Williams
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Anna Florence: Agriculture, Horticulture and Engineering Science Department, Scotland’s Rural College, Edinburgh EH9 3JG, UK
Andrew Revill: School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh EH9 3FF, UK
Stephen Hoad: Agriculture, Horticulture and Engineering Science Department, Scotland’s Rural College, Edinburgh EH9 3JG, UK
Robert Rees: Agriculture, Horticulture and Engineering Science Department, Scotland’s Rural College, Edinburgh EH9 3JG, UK
Mathew Williams: School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh EH9 3FF, UK

Agriculture, 2021, vol. 11, issue 3, 1-16

Abstract: Identification of yield deficits early in the growing season for cereal crops (e.g., Triticum aestivum ) could help to identify more precise agronomic strategies for intervention to manage production. We investigated how effective crop canopy properties, including leaf area index (LAI), leaf chlorophyll content, and canopy height, are as predictors of winter wheat yield over various lead times. Models were calibrated and validated on fertiliser trials over two years in fields in the UK. Correlations of LAI and plant height with yield were stronger than for yield and chlorophyll content. Yield prediction models calibrated in one year and tested on another suggested that LAI and height provided the most robust outcomes. Linear models had equal or smaller validation errors than machine learning. The information content of data for yield prediction degraded strongly with time before harvest, and in application to years not included in the calibration. Thus, impact of soil and weather variation between years on crop phenotypes was critical in changing the interactions between crop variables and yield (i.e., slopes and intercepts of regression models) and was a key contributor to predictive error. These results show that canopy property data provide valuable information on crop status for yield assessment, but with important limitations.

Keywords: cereal yields; leaf area index; crop height; chlorophyll content; yield prediction; winter wheat; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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