A study on parameter calibration of a general crop growth model considering non-foliar green organs

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Computers and Electronics in Agriculture Pub Date : 2024-08-31 DOI:10.1016/j.compag.2024.109362
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引用次数: 0

Abstract

Accurate crop yield estimation is essential for ensuring national food security and sustainable development. The crop growth model is one of the primary methods for estimating production and has been effectively applied in simulating crop growth and development, environmental factor effects and yield formation processes. However, many related studies have focused on Gramineae crops, such as wheat, rice and maize, while there has been little research on rape and soybean yield estimation. Non-foliar green organs such as rape siliques and soybean pods make vital contributions to plant photosynthesis and influence crop output. The parameter calibration method based on the leaf area index (LAI) cannot satisfy the existing demand for high-precision yield estimation for crops with active non-foliar green organs. Therefore, a new method for crop growth model calibration was proposed, which considers non-foliar green organs and plant photosynthetic succession processes and combines them with those of leaves to construct a photosynthetic area index (PAI). With wheat, rape and soybean as the research objects, their yields were simulated via the proposed crop growth model calibration method. The results showed that using the proposed calibration method to calibrate World Food Study (WOFOST) model parameters on the basis of the PAI improved the yield estimation accuracy over that of the crop model calibration method based on the LAI. The determination coefficient (R2) of the total dry weight of storage organs (TWSO) simulation value increased from 0.73 (using the LAI) to more than 0.90 (using the PAI). The R2 values of TWSO at the rape calibration and verification points were 0.910 and 0.922, respectively, and the R2 values of TWSO at the soybean calibration and verification points were 0.741 and 0.926, respectively. The above results verified the feasibility and effectiveness of the proposed calibration method. Consequently, the application of the crop model calibration method proposed in this paper is important for accurately estimating the crop yield of plants with active non-foliar green organs, promoting the expansion of general crop models for different types of crops and achieving high-precision crop yield estimations.

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考虑非叶面绿色器官的一般作物生长模型参数校准研究
准确估算作物产量对确保国家粮食安全和可持续发展至关重要。作物生长模型是估算产量的主要方法之一,在模拟作物生长发育、环境因素影响和产量形成过程方面得到了有效应用。然而,许多相关研究都集中在小麦、水稻和玉米等禾本科作物上,而对油菜和大豆产量估算的研究却很少。非叶面绿色器官(如油菜纤毛和大豆豆荚)对植物光合作用做出了重要贡献,并影响作物产量。基于叶面积指数(LAI)的参数校准方法无法满足目前对具有活跃非叶面绿色器官的作物进行高精度产量估算的需求。因此,提出了一种新的作物生长模型校准方法,该方法考虑了非叶面绿色器官和植物光合演替过程,并将其与叶片的光合演替过程相结合,构建了光合面积指数(PAI)。以小麦、油菜和大豆为研究对象,通过提出的作物生长模型校准方法模拟了它们的产量。结果表明,与基于 LAI 的作物模型校准方法相比,利用所提出的校准方法以 PAI 为基础校准世界粮食研究(WOFOST)模型参数提高了产量估算的准确性。储藏器官总干重(TWSO)模拟值的确定系数(R2)从 0.73(使用 LAI)提高到 0.90 以上(使用 PAI)。油菜校准点和验证点的 TWSO R2 值分别为 0.910 和 0.922,大豆校准点和验证点的 TWSO R2 值分别为 0.741 和 0.926。上述结果验证了所提标定方法的可行性和有效性。因此,本文提出的作物模型校正方法的应用对于准确估算非叶面绿色器官活跃植物的作物产量,促进不同类型作物通用作物模型的扩展,实现高精度作物产量估算具有重要意义。
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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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