Please wait a minute...
Journal of Integrative Agriculture  2025, Vol. 24 Issue (3): 1212-1215    DOI: 10.1016/j.jia.2024.09.038
Letter Advanced Online Publication | Current Issue | Archive | Adv Search |
Adaptation of the Hybrid-Maize Model in different maize-growing regions of China under dense planting conditions

Yahui Hua1, 2, 3, Ying Sun1, Guangzhou Liu2, Yunshan Yang2, Xiaoxia Guo2, Shaokun Li2, Dan Hu3, Wanmao Liu1#, Peng Hou1, 2#

1 School of Agriculture, Ningxia University, Yinchuan 750021, China 
2 Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
3 Xizang Agricultural and Animal Husbandry University, Linzhi 860000, China
 Highlights 
Hybrid-Maize Model’s performance under dense planting conditions were investigated across China.
Hybrid-Maize Model performed well in the simulation of maize grain yield and aboveground biomass under dense planting conditions.
Future model modifications and corrections should focus on the leaf area index dynamics and harvest index.
Download:  PDF in ScienceDirect  
Export:  BibTeX | EndNote (RIS)      
摘要  

通过作物模型准确的模拟作物生长和产量对于提高作物生产效率具有重要指导意义。Hybrid-Maize模型作为新型玉米专用过程模型得到了越来越广泛的应用,明确密植条件下Hybrid-Maize模型在中国不同区域的适应性是探索区域产量潜力及缩小产量差距的关键。本研究基于2011-2015年四大玉米产区(北方春玉米区、黄淮海夏玉米区、西南山地玉米区和西北内陆玉米区)22个试验点所收集的较高密度条件下干物质生产及产量田间实测数据,对Hybrid-Maize模型进行了区域适应性检验研究结果发现适当密植条件下(7.5×104 plants/ha模拟产量与实际产量基本吻合,西北、西南、黄淮海和北方NRMSE值分别为9.818.426.223.9%。线性拟合结果显示,在西北与西南地区产量模拟度较黄淮海与北方高。比较地上部生物量发现,模型在各区域地上生物量拟合优度均在可接受范围内,尤其在西北地区NRMSE(17.2%)低于西南地区24.8%、黄淮海22.9%)与北方地区26.2%)。黄淮海和北方地区收获指数HI)的模拟效果优于西北和西南地区此外,模型模拟叶面积指数与实际田间实测叶面积指数拟合度在区域间存在显著差异,模型黄淮海(NRMSE=28.8%)和北方(NRMSE=22.0%)的模拟精度优于西北(NRMSE=33.4%)和西南(NRMSE=44.2%)地区。总体来看,适度密植条件下Hybrid-Maize模型在我国玉米主产区产量、生物量模拟普遍尤其在西北地区拟合效果最优。而对于叶面积和收获指数,Hybrid-Maize模型在不同区域间拟合效果存在差异,尤其在西北和西南地区模型需要进一步校准。



Received: 14 June 2024   Accepted: 21 August 2024
Fund: 
This research was supported by the National Key Research and Development Program of China (2023YFD1900603), the National Natural Science Foundation of China (32172118), the China Agriculture Research System of MOF and MARA (CARS-02). 
About author:  #Correspondence Wanmao Liu, E-mail: liuwm@nxu.edu.cn; Peng Hou, E-mail: houpeng@caas.cn

Cite this article: 

Yahui Hua, Ying Sun, Guangzhou Liu, Yunshan Yang, Xiaoxia Guo, Shaokun Li, Dan Hu, Wanmao Liu, Peng Hou. 2025. Adaptation of the Hybrid-Maize Model in different maize-growing regions of China under dense planting conditions. Journal of Integrative Agriculture, 24(3): 1212-1215.

Abbas G, Ahmed M, Fatima Z, Hussain S, Kheir A M S, Ercişli S, Ahmad S. 2023. Modeling the potential impact of climate change on maize–maize cropping system in semi-arid environment and designing of adaptation options. Agricultural and Forest Meteorology341, 109674.

Abimbola O P, Franz T E, Rudnick D, Heeren D, Yang H S, Wolf A, Katimbo A, Nakabuye H N, Amori A. 2022. Improving crop modeling to better simulate maize yield variability under different irrigation managements. Agricultural Water Management262, 107429.

Erenstein O, Jaleta M, Sonder K, Mottaleb K, Prasanna B M. 2022. Global maize production, consumption and trade: Trends and R&D implications. Food Security14, 1295–1319.

Hou P, Cui Z L, Bu L D, Yang H S, Zhang F S, Li S K. 2014. Evaluation of a modified Hybrid-Maize Model incorporating a newly developed module of plastic film mulching. Crop Science54, 2796–2804.

Jin X, Kumar L, Li Z, Xu X, Yang G, Wang J. 2016. Estimation of winter wheat biomass and yield by combining the AquaCrop Model and field hyperspectral data. Remote Sensing8, 972.

Kheir A M S, Alkharabsheh H M, Seleiman M F, Al-Saif A M, Ammar K A, Attia A, Zhoghdan M G, Shabana M M A, Aboelsoud H, Schillaci C. 2021. Calibration and validation of AQUACROP and APSIM Models to optimize wheat yield and water saving in arid regions. Land10, 1375.

Liu W M, Hou P, Liu G Z, Yang Y S, Guo X X, Ming B, Xie R Z, Wang K R, Li S K. 2020. Contribution of total dry matter and harvest index to maize grain yield - A multisource data analysis. Food and Energy Security9, e256.

Liu Y, Yang S J, Li S, Chen F. 2012. Application of the Hybrid-Maize model for limits to maize productivity analysis in a semiarid environment. Scientia Agricola69, 300–307.

Meng Q F, Hou P, Wu L, Chen X P, Cui Z L, Zhang F S. 2013. Understanding production potentials and yield gaps in intensive maize production in China. Field Crops Research143, 91–97.

Schmitt J, Offermann F, Söder M, Frühauf C, Finger R. 2022. Extreme weather events cause significant crop yield losses at the farm level in German agriculture. Food Policy112, 102359.

Timsina J, Jat M L, Majumdar K. 2010. Rice–maize systems of South Asia: Current status, future prospects and research priorities for nutrient management. Plant and Soil335, 65–82.

Xu W J, Liu C W, Wang K R, Xie R Z, Ming B, Wang Y H, Zhang G Q, Liu G Z, Zhao R L, Fan P P, Li S K, Hou P. 2017. Adjusting maize plant density to different climatic conditions across a large longitudinal distance in China. Field Crops Research212, 126–134.

Yang H S, Dobermann A, Lindquist J L, Walters D T, Arkebauer T J, Cassman K G. 2004. Hybrid-maize - A maize simulation model that combines two crop modeling approaches. Field Crops Research87, 131–154.

No related articles found!
No Suggested Reading articles found!