The Effect of Antecedence on Empirical Model Forecasts of Crop Yield from Observations of Canopy Properties
The Effect of Antecedence on Empirical Model Forecasts of Crop Yield from Observations of Canopy Properties
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DOI：
10.3390/agriculture11030258
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发表时间：
2021-03
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影响因子：
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通讯作者：
Anna Florence;A. Revill;S. Hoad;R. Rees;M. Williams
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作者：
Anna Florence;A. Revill;S. Hoad;R. Rees;M. Williams
在谷类作物生长季节早期确定产量不足（例如，小麦）可以帮助确定更精确的农艺策略，干预管理生产。我们调查了如何有效的作物冠层特性，包括叶面积指数（LAI），叶片叶绿素含量，冠层高度，作为冬小麦产量的预测在不同的前置时间。模型进行了校准和验证的肥料试验超过两年在英国的领域。叶面积指数和株高与产量的相关性强于产量和叶绿素含量。产量预测模型在一年校准和另一个测试表明，叶面积指数和高度提供了最强大的结果。线性模型的验证误差等于或小于机器学习。产量预测数据的信息含量在收获前随着时间的推移而严重退化，并且在应用中不包括在校准中。因此，土壤和天气变化对作物表型的影响在改变作物变量和产量之间的相互作用方面至关重要（即，回归模型的斜率和截距），并且是预测误差的关键因素。这些结果表明，冠层特性数据提供了有价值的信息，作物状况的产量评估，但有重要的局限性。
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.