Dynamic Simulation of Jujube Tree Growth and Water Use Evaluation Based on the Calibrated WOFOST Model

doi:10.12133/j.smartag.2021.3.2.202103-SA008

Abstract

Abstract:

Irrigation schemes determined based on statistical analysis of field trials are usually only applicable to specific soils and meteorological environments. It is difficult to quantitatively analyze the impact of irrigation strategies on the growth of jujube trees. In order to realize the quantitative analysis of the influence of temperature, light and water resources on the growth of fruit trees, WOrld FOod Studies (WOFOST) model parameters were calibrated to simulate the jujube tree growth and water migration process. Firstly, the observed data obtained from field trials in 2016 and 2017 were used to calibrate the phenology development, initialization, green leaf, CO₂ assimilation, dry matter partitioning, respiration, and water use parameters of the WOFOST model. Secondly, the time series of total above-ground biomass, leaf area index (LAI) and soil moisture content in field trials were dynamically simulated, and accuracy verification and analysis were also performed. Finally, the maximum LAI, yield, actual evapotranspiration $(E T a)$ and water use efficiency (WUE) data of 55 orchards were employed to evaluate the performance of the calibrated model at the county scale. The results showed that the coefficient of determination R² of TAGP simulated in the field test area was between 0.92 and 0.98, and the normalized root mean square error (NRMSE) was between 8.7% and 20.5%, the R² of simulated LAI ranged from 0.79 to 0.97, and the NRMSE ranged from 8.3% to 21.1%. The R² of the simulated soil moisture content was between 0.29 and 0.75, and the NRMSE ranged from 4.1% and 6.1%. The model could well simulate the time series of jujube tree growth dynamics and soil moisture content changes. At the county scale, the R² between the simulated and measured maximum LAI were 0.64 and 0.78, and the NRMSE were 13.3% and 10.7% in 2016 and 2017, respectively. The simulated yield showed R² value of 0.48 and 0.60, and NRMSE of 12.1% and 11.9%, respectively. RMSE of the simulated versus measured $E T a$ were 36.1 mm (7.9%) and 30.8 mm (7.4%), respectively. The model also showed high WUE simulation accuracy (10%<NRMSE<20%) with RMSE values of 0.23 and 0.28 kg/m³in 2016 and 2017, respectively. In short, WOFOST model achieved accurate simulation of jujube tree growth and water transport at the field and county scales, which may provide new ideas for the quantitative and mechanism analysis of the coupled effects of soil, weather, irrigation management and jujube tree growth.

Key words: crop growth model, parameter calibration, WOFOST, jujube tree, water use efficiency

CLC Number:

S126

BAI Tiecheng, WANG Tao, ZHANG Nannan. Dynamic Simulation of Jujube Tree Growth and Water Use Evaluation Based on the Calibrated WOFOST Model[J]. Smart Agriculture, 2021, 3(2): 55-67.

References

1	国家统计局. 数据查询[EB/OL]. [2021-06-07]. .
2	胡家帅, 郑旭荣, 王振华, 等. 不同滴灌灌水处理对南疆沙区红枣产量及品质的影响[J]. 节水灌溉, 2016 (9): 40-43.
2	HU J, ZHENG X, WANG Z, et al. Effects of different drip irrigation treatment on yield and quality in southern Xinjiang sand jujube[J]. Water Saving Irrigation, 2016 (9): 40-43.
3	WANG J, JUN H, WU P, et al. Effects of soil managements on surface runoff and soil water content in jujube orchard under simulated rainfalls[J]. Catena, 2015, 135: 193-201.
4	WANG Z, BIAN Q, ZHANG J, et al. Optimized water and fertilizer management of mature jujube in Xinjiang arid area using drip irrigation[J]. Water (Switzerland), 2018, 10(10): ID 1467.
5	DAI Z, FEI L, HUANG D, et al. Coupling effects of irrigation and nitrogen levels on yield, water and nitrogen use efficiency of surge-root irrigated jujube in a semiarid region[J]. Agricultural Water Management, 2019, 213: 146-154.
6	王振华, 扁青永, 李文昊, 等. 南疆沙区成龄红枣水肥一体化滴灌的水肥适宜用量[J]. 农业工程学报, 2018, 34(11): 96-104.
6	WANG Z, BIAN Q, LI W. Suitable water and fertilizer amount for mature jujube with drip-irrigation under fertigation in southern Xinjiang sandy area[J]. Transactions of the CSAE, 2018, 34(11): 96-104.
7	扁青永, 王振华, 胡家帅, 等. 水肥供应对南疆沙区滴灌红枣生理、生长及产量的影响[J]. 干旱地区农业研究, 2018, 36(4): 165-171.
7	BIAN Q, WANG Z, HU J. Effects of water and fertilizer supply on physiology, growth and yield of drip-irrigation jujube in the southern Xinjiang sandy area[J]. Agricultural Research in the Arid Areas, 2018, 36(4): 165-171.
8	DE WIT A, BOOGAARD H, FUMAGALLI D, et al. 25 years of the WOFOST cropping systems model[J]. Agricultural Systems, 2019, 168: 154-167.
9	EWERT F, R?TTER R.P, BINDI M,et al. Crop modelling for integrated assessment of risk to food production from climate change[J]. Environmental Modelling and Software, 2015, 72: 287-303.
10	黄健熙, 贾世灵, 马鸿元, 等. 基于 WOFOST 模型的中国主产区冬小麦生长过程动态模拟[J]. 农业工程学报, 2017, 33(10): 222-228.
10	HUANG J, JIA S, MA H. Dynamic simulation of growth process of winter wheat in main production areas of China based on WOFOST model[J]. Transactions of the CSAE, 2017, 33(10): 222-228.
11	闫铃. 基于CERES-Wheat模型的小麦生长发育过程模拟研究[D]. 杨凌: 西北农林科技大学, 2020.
11	YAN L. Simulation of wheat growth and development process based on the CERES-Wheat model[D]. Yang ling: Northwest A&F University, 2020.
12	FISHMAN S, GéNARD M. A biophysical model of fruit growth: Simulation of seasonal and diurnal dynamics of mass[J]. Plant, Cell & Environment, 1998, 21: 739-752.
13	LOPEZ G, FAVREAU R R, SMITH C, et al. L-PEACH: A computer-based model to understand how peach trees grow[J]. Horttechnology, 2010, 20: 983-990.
14	LESCOURRET F, BLECHER N, HABIB R, et al. Development of a simulation model for studying kiwi fruit orchard management[J]. Agricultural Systems, 1999, 59: 215-239.
15	GREEN S, MCNAUGHTON K, WüNSCHE J N, et al. Modeling light interception and transpiration of apple tree canopies[J]. Agronomy Journal, 2003, 95(6): 1380-1387.
16	COSTES E, SMITH C, RENTON M. MAppleT: Simulation of apple tree development using mixed stochastic and biomechanical models[J]. Functional Plant Biology, 2008, 35: 936-950.
17	董智强, 王萌萌, 李鸿怡, 等. WOFOST模型对山东省夏玉米发育期与产量模拟的适用性评价[J]. 作物杂志, 2019(5): 159-165.
17	DONG Z, WANG M, LI H. Applicability assessment of WOFOST model of growth and yield of summer maize in Shandong province[J]. Crops, 2019(5): 159-165.
18	KULIG B, BARBARA S, AGNIESZKA K, et al. The use of the WOFOST model to simulate water-limited yield of early potato cultivars[J]. Agronomy, 2020, 10(1): ID 81.
19	BAI T, WANG T, ZHANG N, et al. Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model[J]. Journal of Integrative Agriculture, 2020, 19(3): 721-734.
20	DAI Z, FEI L, HUANG D, et al. Coupling effects of irrigation and nitrogen levels on yield, water and nitrogen use efficiency of surge-root irrigated jujube in a semiarid region[J]. Agricultural Water Management, 2019, 213: 146-154.
21	李朝阳, 王兴鹏, 杨玉辉. 滴灌带铺设模式对成龄枣树根系再分布及产量的影响[J]. 灌溉排水学报. 2021, 40(5): 1-7.
21	LI Z, WANG X, YANG Y. Location of drip irrigation tapes affects root redistribution and yield of mature jujube[J]. Journal of Irrigation and Drainage, 2021, 40(5): 1-7.
22	孙爱良. 气温变化对黄骅市冬枣生产的影响[J]. 贵州农业科学, 2019, 47(6): 88-91.
22	SUN A. Effect of temperature change on winter jujube production in Huanghua city[J]. Guizhou Agricultural Sciences, 2019, 47(6): 88-91.
23	DE WIT A, BOOGAARD H, SUPIT I. System description of the WOFOST7.2. cropping system model[EB/OL]. [2021-05-15].
24	王庆江, 温陟良, 贾彦丽. 赞皇大枣幼树叶片光合特性的研究[J]. 河北农业大学学报, 2002(S1): 120-121.
24	WANG Q, WEN S, JIA Y. Study on the photosynthetic characteristics of young Jujube[J]. Journal of Hebei Agricultural University, 2002(S1): 120-121.
25	HUANG J, JOSE L G, HUANG H, et al. Assimilation of remote sensing into crop growth models: Current status and perspectives[J]. Agricultural and Forest Meteorology, 2019, 276-277: ID 107609.
26	BAI T, WANG S, MERCATORIS BENOIT, et al. Assimilation of remotely-sensed LAI into WOFOST model with the SUBPLEX algorithm for improving the field-scale jujube yield forecasts[J]. Remote Sensing, 2019, 11(16): ID 1945.
27	HUANG J, TIAN L, LIANG S, et al. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model[J]. Agricultural and Forest Meteorology, 2015, 204: 106-121.
28	CURNEL Y, DE WIT A J W, DUVEILLER G, et al. Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS experiment[J]. Agricultural and Forest Meteorology, 2011, 151: 1843-1855.
29	BAI T, WANG S, MERCATORIS B, et al. Assimilation of remotely-sensed LAI into WOFOST model with the SUBPLEX algorithm for improving the field-scale jujube yield forecasts[J]. Remote Sensing, 2019, 11(16): ID 1945.