PDF(1440 KB)
Study on Applicability of WOFOST Model in Xuchang Tobacco Production
LIWenfeng, JINShuyuan, PUTuanwei
Journal of Agriculture ›› 2026, Vol. 16 ›› Issue (5) : 101-108.
PDF(1440 KB)
PDF(1440 KB)
Study on Applicability of WOFOST Model in Xuchang Tobacco Production
To address the issue of relying on experience and lacking quantitative simulation tools in the production management of flue-cured tobacco in central Henan, in order to clarify the applicability of WOFOST model in Xuchang flue-cured tobacco production area, the parameter sensitivity analysis was carried out by OAT (one-at-a-time) method, and the localization calibration and independent verification of the model were completed by ' trial and error method '. The study was based on the field observation and meteorological data of Jian'an District and Xiangcheng County of Xuchang, Henan Province from 2021 to 2022, and the model's simulation accuracy for leaf dry weight (WLV), above-ground biomass (AGP), and leaf area index (LAI) was evaluated using the coefficient of determination (R2), consistency index (d), and normalized root mean square error (NRMSE). The main conclusions are as follows. (1) In the calibration of leaf dry weight (WLV), the simulation values from the WOFOST model showed a significant linear relationship with the observed values, indicating high accuracy in simulating leaf dry weight. The calibration accuracy for above-ground biomass (AGP) was also good. However, the calibration of the leaf area index (LAI) was less accurate, with NRMSE ranging from 20% to 30%. (2) The model was validated using data from Xuchang in 2021, Xuchang and Xiangcheng in 2022, and the simulation results were good for the most important biomass, leaf dry weight. Overall, the WOFOST model showed high accuracy in validation. The R2 and consistency index d of 90 % (8 / 9) validation data were higher than 0.8, nearly 80 % (7 / 9) were higher than 0.9, and NRMSE was between 10 % and 20 %. The correlation and consistency between simulated and measured values were good. (3) After calibration and verification, the WOFOST model could effectively simulate the growth process of tobacco in Xuchang, Henan, providing a foundation for quantitative and digital management of tobacco production. Subsequently, remote sensing data can be coupled to carry out research on crop growth monitoring and yield forecasting.
tobacco / WOFOST model / quantification / parameter localization / calibration / verification
| [1] |
郭建茂, 王琦, 施俊怡, 等. 遥感信息与作物模型结合在冬小麦区域模拟中的应用[J]. 大气科学学报, 2014, 37(2):237-242.
|
| [2] |
郑昌玲, 张蕾, 侯英雨, 等. 基于WOFOST模型的冬小麦产量动态预报方法[J]. 干旱地区农业研究, 2022, 40(6):242-250,267.
|
| [3] |
郑昌玲, 侯英雨, 吴门新, 等. 基于WOFOST模型的华北冬小麦动态长势评估指标构建[J]. 麦类作物学报, 2020, 40(6):746-753.
|
| [4] |
朱津辉, 郭建茂, 毛留喜. 基于模型的河北省保定市冬小麦最佳灌溉方案研究[J]. 气象, 2014, 40(11):1398-1407.
|
| [5] |
张阳, 王连喜, 李琪, 等. 基于WOFOST模型的吉林省中西部春玉米灌溉模拟[J]. 中国农业气象, 2018, 39(6):411-420.
|
| [6] |
张微玮. 基于WOFOST模型的春玉米生长模型精细化及其应用[D]. 沈阳: 沈阳农业大学, 2018.
|
| [7] |
王秀珍. 玉溪烤烟气象灾害风险及作物模型适用性研究[D]. 南京: 南京信息工程大学, 2021.
|
| [8] |
杨霏云, 郑秋红, 李文科, 等. 基于WOFOST模型的辽宁省春玉米干旱灾损风险评估[J]. 干旱地区农业研究, 2020, 38(6):218-225.
|
| [9] |
郑昌玲, 魏瑞江, 张蕾. 基于WOFOST模型的冬小麦长势评估和产量预报——以2021年度冬小麦为例[J]. 气象科学, 2025, 45(2):284-292.
|
| [10] |
赵晓松, 关德新, 吴家兵, 等. 长白山阔叶红松林的零平面位移和粗糙度[J]. 生态学杂志, 2004(5):84-88.
|
| [11] |
郭彩云. 基于WOFOST模型的烟叶灌溉方案及生长预测与评估研究[D]. 南京: 南京信息工程大学, 2022.
|
| [12] |
ANGSTROM A. Solar and terrestrial radiation. Report to the international commission for solar AquaCrop[J]. Agronomy journal, 2009, 101(3):477-487.
Given the current pressures to reduce irrigation water use, it is important to optimize the use of water in irrigated agriculture. This work was aimed at determining the optimum level of applied irrigation water (AIW) for cotton (Gossypium hirsutum L.) production in southern Spain under several climatic and agricultural policy scenarios. To generate the yield response to variations in AIW, we used the FAO crop water productivity (WP) model, AquaCrop. Model calibration and validation using four experiments conducted in the region showed that AquaCrop adequately simulated the yield response to AIW. The model was then used to determine the yield–AIW functions for different scenarios, assuming the best deficit irrigation (DI) strategy. An economic optimization procedure showed that maximum profits occurred at AIW values between 540 and 740 mm, depending on the climatic scenario. However, profits stayed close to the maximum (above 95%) for AIW levels exceeding 300–350 mm, indicating that under DI, AIW may be reduced significantly with little impact on profits. A sensitivity analysis suggested that increasing the price of water above the current level will have only a limited impact on optimum AIW, and that the current Common Agricultural Policy (CAP) of the European Union does not encourage water conservation in cotton irrigation. We conclude that AquaCrop is a useful tool to assist managers for making decisions in cotton irrigation under water supply restrictions.
|
| [13] |
和清华, 谢云. 我国太阳总辐射气候学计算方法研究[J]. 自然资源学报, 2010, 25(2):308-319.
目前太阳辐射主要通过计算获得，因此建立太阳辐射的气候学计算方法十分必要。论文利用中国54个站1961—2000年的逐日太阳总辐射和日照百分率资料，分别以天文辐射、晴天太阳辐射和理想大气太阳辐射作为三种起始值建立了各站的太阳辐射回归方程，并对其经验系数进行聚类分析，分为东部和西部两个地区。然后利用54个站1961—1990年的资料建立了3种起始值情况下，东部、西部地区和全国统一的太阳总辐射计算公式。最后利用这些站1991—2000年的观测资料对所建立的公式进行了检验。结果表明：无论是利用分区还是全国统一公式都是以天文辐射为起始值时计算结果好，其中分区公式比全国统一公式的计算结果精度略高，但差别不大。全国54个站以天文辐射为起始值的分区公式相对误差变化于2.67%~19.30%，平均为7.79%。全国统一公式的相对误差变化于3.33%~18.75%，平均为8.39%。从模拟结果的地区差异看，无论采用何种起始值，都是西部地区模拟误差小于东部地区，这与西部地区干燥、空气湿度小有密切关系。与其他研究结果相比，论文所建立的各种公式模拟结果较好，这是由于采用的资料序列长、站点多。考虑到计算天文辐射所需参数容易获得，因此建议实际应用时，采用以天文辐射为起始值的全国统一公式。
|
| [14] |
何亮, 侯英雨, 赵刚, 等. 基于全局敏感性分析和贝叶斯方法的WOFOST作物模型参数优化[J]. 农业工程学报, 2016, 32(2):169-179
|
| [15] |
何亮, 赵刚, 靳宁, 等. 不同气候区和不同产量水平下APSIM-Wheat模型的参数全局敏感性分析[J]. 农业工程学报, 2015, 31(14):148-157.
|
| [16] |
|
| [17] |
邬定荣, 欧阳竹, 赵小敏, 等. 作物生长模型WOFOST在华北平原的适用性研究[J]. 植物生态学报, 2003, 27(5):594-602.
|
| [18] |
马玉平, 王石立, 张黎. 针对华北小麦越冬的WOFOST模型改进[J]. 中国农业气象, 2005, 26(3):145-149.
|
| [19] |
|
| [20] |
|
| [21] |
胡雪琼, 徐梦莹, 买苗, 等. WOFOST模型对于云南烤烟的适用性研究[J]. 南京信息工程大学学报(自然科学版), 2015, 7(5):451-457.
|
| [22] |
|
| [23] |
姜志伟, 陈仲新, 周清波, 等. CERES-Wheat作物模型参数全局敏感性分析[J]. 农业工程学报, 2011, 27(1):236-242.
|
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