Management and spatial resolution effects on yield and water balance at regional scale in crop models-猫眼课题宝

DOI：

10.1016/j.agrformet.2019.05.013

发表时间：

2019-09

期刊：

Agricultural and Forest Meteorology

影响因子：

6.2

通讯作者：

中科院分区：

农林科学1区

文献类型：

作者：

J. Constantin;H. Raynal;É. Casellas;H. Hoffmann;M. Bindi;L. Doro;H. Eckersten;T. Gaiser;B. Grosz;E. Haas;K. Kersebaum;S. Klatt;M. Kuhnert;E. Lewan;G. R. Maharjan;M. Moriondo;C. Nendel;P. Roggero;X. Specka;G. Trombi;A. Villa;E. Wang;L. Weihermüller;J. Yeluripati;Zhigan Zhao;F. Ewert;J. Bergez

随着区域和国家尺度作物模型的使用越来越频繁，空间数据输入分辨率的影响越来越受到重视。然而，人们对作物管理变化对模型输出的影响知之甚少。在模拟区域和周期内，通常考虑恒定和均匀的作物管理。本研究确定了适应气候条件的作物管理和输入数据分辨率对作物模型区域尺度产出的影响。为此，对德国北莱茵-威斯特伐利亚州（约34 083 km²）的冬小麦和玉米进行了30年的时空均匀管理或适应性管理模拟。在1)播种日期、2)施氮日期、3)施氮量和4)作物周期长度方面采用了适应当地气候条件的管理方法。因此，对每种作物采用四种不同的管理模式。输入的气候、土壤和管理数据以5种分辨率选择，网格大小从1 × 1 km到100 × 100 km。总体而言，11种作物模型用于预测区域平均作物产量、实际蒸散和排水。对于大多数模式，适应性管理对三个产出变量的30年平均值影响不大（差异<10%），并且不依赖于土壤、气候和管理分辨率。然而，与统一管理参考相比，对某些模式的影响是实质性的，对产量的影响高达31%，对蒸散的影响为27%，对排水的影响为12%。总体而言，对产量的影响大于对蒸散和排水的影响，蒸散和排水对管理方式的变化不敏感。结垢效应一般低于管理对产量和蒸散的影响，而不是对排水的影响。尽管有这种趋势，但不同模型对管理和规模的敏感性差异很大。在年度尺度上，某些年份的影响更强，特别是管理对产量的影响。这些结果表明，根据模型的不同，应该仔细选择管理的代表，特别是在模拟产量和以年为单位进行预测时。

Due to the more frequent use of crop models at regional and national scale, the effects of spatial data input resolution have gained increased attention. However, little is known about the influence of variability in crop management on model outputs. A constant and uniform crop management is often considered over the simulated area and period. This study determines the influence of crop management adapted to climatic conditions and input data resolution on regional-scale outputs of crop models. For this purpose, winter wheat and maize were simulated over 30 years with spatially and temporally uniform management or adaptive management for North Rhine-Westphalia (˜34 083 km²), Germany. Adaptive management to local climatic conditions was used for 1) sowing date, 2) N fertilization dates, 3) N amounts, and 4) crop cycle length. Therefore, the models were applied with four different management sets for each crop. Input data for climate, soil and management were selected at five resolutions, from 1 × 1 km to 100 × 100 km grid size. Overall, 11 crop models were used to predict regional mean crop yield, actual evapotranspiration, and drainage. Adaptive management had little effect (<10% difference) on the 30-year mean of the three output variables for most models and did not depend on soil, climate, and management resolution. Nevertheless, the effect was substantial for certain models, up to 31% on yield, 27% on evapotranspiration, and 12% on drainage compared to the uniform management reference. In general, effects were stronger on yield than on evapotranspiration and drainage, which had little sensitivity to changes in management. Scaling effects were generally lower than management effects on yield and evapotranspiration as opposed to drainage. Despite this trend, sensitivity to management and scaling varied greatly among the models. At the annual scale, effects were stronger in certain years, particularly the management effect on yield. These results imply that depending on the model, the representation of management should be carefully chosen, particularly when simulating yields and for predictions on annual scale.

Management and spatial resolution effects on yield and water balance at regional scale in crop models

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