Improving crop modeling by simultaneously incorporating dynamic specific leaf area and leaf area index: A two-year experiment

IF 6.1 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Agricultural Systems Pub Date : 2025-04-29 DOI:10.1016/j.agsy.2025.104357

Yufan Zhang , Koki Homma , Liangsheng Shi , Yu Wang , Han Qiao , Yuanyuan Zha

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引用次数: 0

Abstract

CONTEXT

Specific leaf area (SLA) is an important trait for quantifying crop growth status and leaf physiological structure. It also serves as a vital crop-specific parameter in the derivation of leaf area index (LAI) and aboveground biomass within crop models.

OBJECTIVE

The simplified and empirical consideration of SLA in current models ignores its dynamics. To accurately characterize the leaf structural traits, the SLA in the model need to be modified as an updatable continuous variable.

METHODS

This study used a dual-branch neural network for processing two-view digital images to extract leaf-specific traits. A novel approach was then proposed to modify the SLA from a fixed sequence to an updatable variable by introducing the SLA dynamic function (SDF) into WOFOST model. Meanwhile, a data assimilation framework was developed based on the Ensemble Kalman Filter, enabling SLA and LAI observations to be uptaken simultaneously. A two-year drought-controlled experiment was conducted on rice to evaluate the method under varying water stress conditions.

RESULTS AND CONCLUSIONS

Results demonstrated that the incorporation of SDF and the assimilation of SLA and LAI significantly improved the accuracy of LAI, aboveground biomass, and grain yield estimations compared to the original model (R² = 0.85, RMSE = 1310.05 kg⋅ha⁻¹). The incorporation of SLAs further improves model performance and highlights the complementary roles of SLA and LAI in data assimilation.

SIGNIFICANCE

The technical route developed in this study aims to provide a concise and efficient solution for the integration of crop growth simulation and observation while highlighting the importance of considering dynamic leaf traits in crop modeling to better capture the responses to environmental stresses such as drought.

Abstract Image

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同时结合动态比叶面积和叶面积指数改进作物模型：一项为期两年的试验

特定叶面积（SLA）是量化作物生长状况和叶片生理结构的重要指标。它也是作物模型中叶面积指数（LAI）和地上生物量推导的重要作物参数。目的当前模型对SLA的简化和经验考虑忽略了其动态性。为了准确表征叶片结构特征，需要将模型中的SLA修改为一个可更新的连续变量。方法采用双分支神经网络对双视图数字图像进行处理，提取叶片特征。在此基础上，提出了一种将SLA动态函数（SDF）引入WOFOST模型的方法，将SLA从固定序列修改为可更新变量。同时，建立了基于集成卡尔曼滤波的数据同化框架，实现了对SLA和LAI观测数据的同时吸收。通过为期2年的水稻抗旱试验，对该方法在不同水分胁迫条件下的效果进行了评价。结果与结论结果表明，与原始模型相比，纳入SDF并同化SLA和LAI显著提高了LAI、地上生物量和粮食产量估算的精度（R2 = 0.85, RMSE = 1310.05 kg⋅ha−1）。SLA的加入进一步提高了模型的性能，并突出了SLA和LAI在数据同化中的互补作用。本研究开发的技术路线旨在为作物生长模拟与观测的整合提供一种简洁高效的解决方案，同时强调在作物建模中考虑动态叶片性状的重要性，以便更好地捕捉作物对干旱等环境胁迫的响应。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Agricultural Systems 农林科学-农业综合

CiteScore

13.30

自引率

7.60%

发文量

174

审稿时长

30 days

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.