Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield
Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield
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DOI：
10.3390/agronomy11020314
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发表时间：
2021-02
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通讯作者：
A. Revill;V. Myrgiotis;Anna Florence;S. Hoad;R. Rees;A. MacArthur;M. Williams
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作者：
A. Revill;V. Myrgiotis;Anna Florence;S. Hoad;R. Rees;A. MacArthur;M. Williams
气候、氮素（N）和叶面积指数（LAI）是决定作物产量的关键因素。氮肥的添加可以提高产量，但必须有效地管理，以减少污染。复杂过程模型通过模拟土壤-作物氮素相互作用来估计氮素状况，但这种模型需要大量的输入，很少可用。通过模型-数据融合（model-data fusion，EFD），我们将联合收割机气候和叶面积指数时间序列与一个中等复杂度的模型结合起来，来推断叶片氮和产量。DALEC-作物模型在英国苏格兰进行的田间试验中针对小麦叶片氮和产量进行了校准，试验范围为0至200 kg N ha−1。该模式需要逐日气象输入，模拟作物碳循环对叶面积指数、氮素和气候的响应。该模型，其中包括一个叶N-稀释功能，校准N治疗的基础上LAI的观察，并在验证地块进行测试。我们发现，一个单一的参数化变化，只有在叶氮可以模拟LAI发展和产量在所有的治疗，平均归一化根均方误差（NRMSE）的产量为10%。叶氮准确地检索模型（NRMSE = 6%）。产量也可以合理估计（NRMSE = 14%），如果叶面积指数数据可用于同化期间的典型N应用程序（4月和5月）。我们的方法产生了强大的叶片氮含量估计和及时的产量预测，可以补充现有的农业技术。此外，在高的空间和时间分辨率的EO派生的LAI产品提供了一种手段，应用我们的方法在区域。在农田上测试这种方法的产量预测是确定更广泛实用性的关键下一步。
Climate, nitrogen (N) and leaf area index (LAI) are key determinants of crop yield. N additions can enhance yield but must be managed efficiently to reduce pollution. Complex process models estimate N status by simulating soil-crop N interactions, but such models require extensive inputs that are seldom available. Through model-data fusion (MDF), we combine climate and LAI time-series with an intermediate-complexity model to infer leaf N and yield. The DALEC-Crop model was calibrated for wheat leaf N and yields across field experiments covering N applications ranging from 0 to 200 kg N ha−1 in Scotland, UK. Requiring daily meteorological inputs, this model simulates crop C cycle responses to LAI, N and climate. The model, which includes a leaf N-dilution function, was calibrated across N treatments based on LAI observations, and tested at validation plots. We showed that a single parameterization varying only in leaf N could simulate LAI development and yield across all treatments—the mean normalized root-mean-square-error (NRMSE) for yield was 10%. Leaf N was accurately retrieved by the model (NRMSE = 6%). Yield could also be reasonably estimated (NRMSE = 14%) if LAI data are available for assimilation during periods of typical N application (April and May). Our MDF approach generated robust leaf N content estimates and timely yield predictions that could complement existing agricultural technologies. Moreover, EO-derived LAI products at high spatial and temporal resolutions provides a means to apply our approach regionally. Testing yield predictions from this approach over agricultural fields is a critical next step to determine broader utility.