首页|Forecasting regional apple first flowering using the sequential model and gridded meteorological data with spatially optimized calibration
Forecasting regional apple first flowering using the sequential model and gridded meteorological data with spatially optimized calibration
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NSTL
Elsevier
? 2022China is one of the largest apple-producing countries in the world, with large orchards and diverse climates. Accurately forecasting the first-flowering time of apple trees can assist orchard managers in their deciding when to apply anti-freeze. The temperature-driven sequential model from previous studies can be used to forecast the flowering phenology of deciduous fruit trees. However, this model requires many years of observational data for calibration, so flowering forecasts based on traditional phenological models cannot be implemented in areas that lack such historical data. To overcome this problem, the present work combines a spatial rather than a temporal phenological survey method with 1-km-gridded temperature products to calibrate the chill and heat requirement parameters of the sequential model. We then use the model to forecast the first-flowering on a regional scale for Luochuan and Linyi, which are two main apple-producing areas of China. The results show that the proposed method accurately forecasts regional flowering. The root mean squared errors (RMSE) for Luochuan and Linyi were 4.7 and 4.4 days, respectively, and the normalized RMSEs were all less than 5.19%. We expect the proposed regional first-flowering forecast method to be an important aid to optimize orchard management.
Apple first-floweringChill and heat requirementGridded meteorological dataRegional-scale forecastSequential model
Zhu Y.、Yang G.、Yang H.、Xu B.、Li Z.、Han S.、Guo L.、Zhu X.、Jones G.
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Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs
College of Geological Engineering and Geomatics Chang’ an University
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Northwest A&F University
College of Resources and Environment Shandong Agricultural University
School of Natural and Environmental Sciences Newcastle University
NSTL
Elsevier
