{"title":"Decomposing the total uncertainty in wheat modeling: an analysis of model structure, parameters, weather data inputs, and squared bias contributions","authors":"Jinhui Zheng , Shuai Zhang","doi":"10.1016/j.agsy.2024.104215","DOIUrl":null,"url":null,"abstract":"<div><h3>CONTEXT</h3><div>The comparison of agricultural models and the conduct of crop improvement research have garnered significant attention in recent times. One of the primary objectives in this field is to pinpoint and mitigate the uncertainties inherent in modeling the effects of climate on crop growth and productivity. Enhancing the precision and reliability of crop models has emerged as a critical concern.</div></div><div><h3>OBJECTIVE</h3><div>In this study, we calibrate and validate four wheat phenology models using wheat phenology data from 1990 to 2009. More importantly, we explain three significant sources of uncertainty in wheat phenology models, namely model structure, model parameters, and weather data inputs.</div></div><div><h3>METHODS</h3><div>This study examines four wheat models—the GLAM-Wheat model, APSIM-Wheat model, SPASS-Wheat model, and WOFOST model—to simulate phenological changes across 32 agricultural meteorological stations in the North China Plain. Additionally, the three main sources of uncertainty in the model are quantified using the Markov Chain Monte Carlo (MCMC) method.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>The results indicate that all four wheat phenological models effectively simulate the growth of wheat in the study area, with an average RMSE ranging from 4.4 to 5.2 days for the heading stage and from 4.7 to 5.6 days for the maturity stage. The uncertainty analysis encompasses parameters, squared bias, weather data inputs, and model structure. During the heading stage, the overall contributions of these uncertainties are 8.9 %, 40.8 %, 47.4 %, and 2.9 %, respectively. During the maturity stage, these contributions are 11.2 %, 51.2 %, 35.0 %, and 2.6 %, respectively. Weather data inputs are identified as the primary sources of uncertainty.</div></div><div><h3>SIGNIFICANCE</h3><div>This study quantifies the uncertainty within wheat phenology models, a critical step towards enhancing the precision and dependability of crop models. Such efforts hold substantial importance in shaping agricultural policies and refining management practices, ultimately aiding in tackling the challenges posed by impending climate change.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104215"},"PeriodicalIF":6.1000,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agricultural Systems","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0308521X24003652","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
CONTEXT
The comparison of agricultural models and the conduct of crop improvement research have garnered significant attention in recent times. One of the primary objectives in this field is to pinpoint and mitigate the uncertainties inherent in modeling the effects of climate on crop growth and productivity. Enhancing the precision and reliability of crop models has emerged as a critical concern.
OBJECTIVE
In this study, we calibrate and validate four wheat phenology models using wheat phenology data from 1990 to 2009. More importantly, we explain three significant sources of uncertainty in wheat phenology models, namely model structure, model parameters, and weather data inputs.
METHODS
This study examines four wheat models—the GLAM-Wheat model, APSIM-Wheat model, SPASS-Wheat model, and WOFOST model—to simulate phenological changes across 32 agricultural meteorological stations in the North China Plain. Additionally, the three main sources of uncertainty in the model are quantified using the Markov Chain Monte Carlo (MCMC) method.
RESULTS AND CONCLUSIONS
The results indicate that all four wheat phenological models effectively simulate the growth of wheat in the study area, with an average RMSE ranging from 4.4 to 5.2 days for the heading stage and from 4.7 to 5.6 days for the maturity stage. The uncertainty analysis encompasses parameters, squared bias, weather data inputs, and model structure. During the heading stage, the overall contributions of these uncertainties are 8.9 %, 40.8 %, 47.4 %, and 2.9 %, respectively. During the maturity stage, these contributions are 11.2 %, 51.2 %, 35.0 %, and 2.6 %, respectively. Weather data inputs are identified as the primary sources of uncertainty.
SIGNIFICANCE
This study quantifies the uncertainty within wheat phenology models, a critical step towards enhancing the precision and dependability of crop models. Such efforts hold substantial importance in shaping agricultural policies and refining management practices, ultimately aiding in tackling the challenges posed by impending climate change.
期刊介绍:
Agricultural Systems is an international journal that deals with interactions - among the components of agricultural systems, among hierarchical levels of agricultural systems, between agricultural and other land use systems, and between agricultural systems and their natural, social and economic environments.
The scope includes the development and application of systems analysis methodologies in the following areas:
Systems approaches in the sustainable intensification of agriculture; pathways for sustainable intensification; crop-livestock integration; farm-level resource allocation; quantification of benefits and trade-offs at farm to landscape levels; integrative, participatory and dynamic modelling approaches for qualitative and quantitative assessments of agricultural systems and decision making;
The interactions between agricultural and non-agricultural landscapes; the multiple services of agricultural systems; food security and the environment;
Global change and adaptation science; transformational adaptations as driven by changes in climate, policy, values and attitudes influencing the design of farming systems;
Development and application of farming systems design tools and methods for impact, scenario and case study analysis; managing the complexities of dynamic agricultural systems; innovation systems and multi stakeholder arrangements that support or promote change and (or) inform policy decisions.