水保所知识产出(1956---)知识库

根据陕西杨凌、合阳、长武3 个站点各2 a 玉米试验，在对玉米生长模拟模型CERES-Maize 进行调试、验证的
基础上，探索在生育期内进行动态产量预测的方法并验证。研究将目标生育期内未知气象数据分别用试验地的多年历史
同期数据代替，结合生育期实时数据对应生成多个完整的气象数据序列运行模型预测产量。随着生育期的推进，逐日在
气象数据序列中融入目标年实测的气象数据，从播种至收获动态模拟玉米产量。此外该研究使用改进前后的K-NN 算法
从历史气象年份中筛选目标年的气象相似年份进而预测产量。通过对3 种方法预测精度及预测效率对比，确定改进的
K-NN 算法最优。研究表明，玉米生育前期产量预测可靠性和准确率均较差，抽雄后预测精度迅速提高；利用改进的K-NN
算法在3 个站点全生育期预测产量的平均绝对相对误差的均值分别为9.9%、19.8%、17.9%，抽雄后预测产量的平均绝对
相对误差在0.2%～12.6%之间，相比于使用全部历史年份数据进行全生育期产量预测，模拟所需时间从61 min 缩短至25 min。
对该方法中降雨因子的筛选进一步改进可提高预报精度，未来有望达到业务应用水平。

Crop growth simulation models can simulate the processes of crop growth, development, yield formation, and its
response to environment, which provides an effective method for crop yield forecast. However, how to select suitable weather
data for the forecast is still an open question. In this study, we established a method for maize yield forecast based on maize
growth simulation model of CERES-Maize and historical weather data from the year of 1956 to 2015. Two year’s
experimental data from 3 sites of Yangling (2014 and 2015), Heyang (2009 and 2011) and Changwu (2010 and 2011) in
Shaanxi Province were used to test the reliable and accuracy of the method established. The weather data needed for model
simulation were divided into 2 different groups including the known weather data and unknown weather data during the whole
growth season of spring maize. The known weather data were obtained from local weather stations, while unknown data were
supplemented with historical weather data of multiple years in the local experimental sites. Multiple complete climatic data
series were then created and used to run the CERES-Maize model to forecast maize yield for a given year. As the advancing of
maize growth season, the daily weather data were gradually merged into the observed weather data in a target year.
Consequently, the daily maize yield was forecasted from sowing day to harvest. In addition, in order to reduce the times of
model runs and reduce the uncertainties in yield forecasts, this study compared the daily meteorological data of historical and
target years with normal K nearest neighbor (K-NN) and a modified K-NN algorithm to select several historical analogue years
whose weather data were similar to the target year. The results showed that: 1) the model was suitable for the yield simulation
since the absolute relative error was smaller than 15%; 2) the data distribution of predicted yields began to converge and the
uncertainty decreased rapidly after the tasseling stage. For example, the predicted yield after 30, 60 and 90 days (the tasseling
stage) of sowing was 3 531-14 461, 3 413-14 828 and 961-13 210 kg/hm2, respectively. But, the yield was 49 33-10 826,
8 484-10 565 kg/hm2, respectively after 100 and 130 days of sowing. The coefficient of variation had a sudden fall around the
tasseling stage; 3) Yield forecast accuracy was generally lower than expectation for the method based on all historical data and
climatic analogue years selected with historical data. The model run cost 61 min for a yield prediction during a complete
growth stage of spring maize, indicting a necessary change in the prediction method optimization; 4) Among the 3 methods,
the modified K-NN method showed a higher prediction accuracy and shorter run time than the other methods. The coefficient
of variation was 11.7%-23.8% for the modified K-NN method, 15.1%-29.1% for the historical data, and 14.7%-26.9% for the
K-NN method, respectively. To complete the yield prediction of a growth stage of spring maize, the modified K-NN method
only took 14 min, which was shorter than the normal K-NN method. Thus, the modified K-NN method in this study had a big
potential for the yield prediction by the CERES-Maize model. The study provides an effective method for selecting
precipitation factor used for the yield prediction by crop models.