Assessing the accuracy and robustness of a process-based model for coffee agroforestry systems in Central America
Assessing the accuracy and robustness of a process-based model for coffee agroforestry systems in Central America
复制标题
评估中美洲咖啡农林系统基于流程的模型的准确性和稳健性
DOI：
10.1007/s10457-020-00521-6
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发表时间：
2020
影响因子：
2.2
通讯作者：
Ovalle-Rivera O
中科院分区：
文献类型：
--
作者：
Ovalle-Rivera O
咖啡通常种植在与提供不同生态系统服务的遮荫树相关的生产系统中。管理、天气和土壤条件是空间上可变的生产要素。CAF2007是一个咖啡农林复合系统的动态模型，它将这些因素作为输入，模拟田间尺度下的浆果生产过程。然而，关于工艺速率的不确定性仍然存在，需要通过校准来降低。使用马尔可夫链蒙特卡罗算法的贝叶斯统计越来越多地用于参数丰富模型的校准。然而，很少有研究采用多站点校准，其目的是同时使用多个站点的数据来减少参数不确定性。这项研究的主要目标是利用哥斯达黎加和尼加拉瓜长期试验中收集的数据来校准咖啡农林模型，并根据商业咖啡种植农场的独立数据来测试校准后的模型。改进了两个子模型：花期计算和两年生产模式建模。修改后的模型被称为CAF2014，可以从https://doi.org/10.5281/zenodo.3608877下载。校准改善了图里亚尔巴(哥斯达黎加)和Masatepe(尼加拉瓜)的模型性能(较高的R2，较低的RMSE)，包括将所有实验合并在一起。多点和单点贝叶斯校正得到相似的RMSE。对来自咖啡种植农场的新数据的验证表明，这两种校准方法都改进了产量及其两年一次的模拟。利用改进后的模型研究了氮肥和不同部位遮荫对咖啡产量的影响。
Coffee is often grown in production systems associated with shade trees that provide different ecosystem services. Management, weather and soil conditions are spatially variable production factors. CAF2007 is a dynamic model for coffee agroforestry systems that takes these factors as inputs and simulates the processes underlying berry production at the field scale. There remain, however, uncertainties about process rates that need to be reduced through calibration. Bayesian statistics using Markov chain Monte Carlo algorithms is increasingly used for calibration of parameter-rich models. However, very few studies have employed multi-site calibration, which aims to reduce parameter uncertainties using data from multiple sites simultaneously. The main objectives of this study were to calibrate the coffee agroforestry model using data gathered in long-term experiments in Costa Rica and Nicaragua, and to test the calibrated model against independent data from commercial coffee-growing farms. Two sub-models were improved: calculation of flowering date and the modelling of biennial production patterns. The modified model, referred to as CAF2014, can be downloaded at https://doi.org/10.5281/zenodo.3608877 . Calibration improved model performance (higher R2, lower RMSE) for Turrialba (Costa Rica) and Masatepe (Nicaragua), including when all experiments were pooled together. Multi-site and single-site Bayesian calibration led to similar RMSE. Validation on new data from coffee-growing farms revealed that both calibration methods improved simulation of yield and its bienniality. The thus improved model was used to test the effect of N fertilizer and shade in different locations on coffee yield.
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影响因子：
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作者：
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