{"title":"A study on parameter calibration of a general crop growth model considering non-foliar green organs","authors":"","doi":"10.1016/j.compag.2024.109362","DOIUrl":null,"url":null,"abstract":"<div><p>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 (R<sup>2</sup>) 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 R<sup>2</sup> values of TWSO at the rape calibration and verification points were 0.910 and 0.922, respectively, and the R<sup>2</sup> 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.</p></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169924007531","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
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.
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.