1981—2023年我国玉米产量时空演变及其驱动因素解析

doi:10.12006/j.issn.1673-1719.2026.033

气候变化研究进展 ›› 2026, Vol. 22 ›› Issue (4): 484-498.doi: 10.12006/j.issn.1673-1719.2026.033

• 气候变化科学评估及其应对专栏 • 上一篇下一篇

1981—2023年我国玉米产量时空演变及其驱动因素解析

陈典鹏¹^,², 张亮亮³, 庄慧敏¹^,², 张朝¹^,²()

¹ 北京师范大学国家安全与应急管理学院，珠海 519087
² 北京师范大学教育部巨灾模拟与系统性风险应对国际合作联合实验室，珠海 519087
³ 北京大学城市与环境学院中法地球系统模拟国际联合研究中心碳中和研究所，北京 100871

收稿日期:2026-02-02 修回日期:2026-03-09 出版日期:2026-07-30 发布日期:2026-06-26
通讯作者: 张朝，女，教授，zhangzhao@bnu.edu.cn
作者简介:陈典鹏，男，博士研究生
基金资助:
联合国教科文组织科学促进可持续发展国际十年(2024—2033);联合国教科文组织科学促进可持续发展国际十年(IDSSD);中国国家自然科学基金(42301107)

Spatiotemporal dynamics of maize yield and its driving factors in China from 1981 to 2023

CHEN Dian-Peng¹^,², ZHANG Liang-Liang³, ZHUANG Hui-Min¹^,², ZHANG Zhao¹^,²()

¹ School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
² Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
³ Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

Received:2026-02-02 Revised:2026-03-09 Online:2026-07-30 Published:2026-06-26

摘要/Abstract

摘要：

科学认识气候变化背景下中国玉米产量的时空变化特征、量化农艺管理措施（灌溉、施肥、品种）对不断加剧的气候胁迫的缓解效应，对于应对气候变化、保障粮食安全具有重要的理论与实践意义。本研究基于全国248个代表性田间试验数据，运用CERES-Maize作物模型，模拟分析1981—2023年中国玉米产量的时空格局，解析气候与技术进步对产量的贡献，并进一步采用面板阈值回归模型，量化气候因子对产量的响应阈值。结果显示：1981—2023年，全国玉米实际产量以147 kg/(hm²∙a)的速度增长，但仍有28%的站点增长停滞，甚至下降；同期潜在产量变化趋势更严峻，62%的站点呈显著下降趋势。气候变化对单产增长产生了负面影响（-38.6 kg/(hm²∙a))，而技术进步表现出积极影响（185.6 kg/(hm²∙a))，其中品种是主要贡献来源（除西北灌区受水肥主导)，贡献率达81.4%，而水肥管理的贡献率仅为18.6%。阈值分析表明，生育期日均温（阈值20.5℃）与总降雨量（阈值490.8 mm）是限制产量的关键气候因子。无水肥胁迫虽能提升产量，但气温阈值降至19.2℃；最优品种（站点上所有品种中模拟产量最高的品种）显著降低玉米对降雨的依赖，使降雨阈值降至288.0 mm，同时在高温区显著缓解高温胁迫。尽管最优品种与无水肥胁迫能有效提升气候韧性、突破部分产量停滞，但无法完全消除极端气候、尤其是高温的约束。未来构建高产生产技术体系时，突破高温限制将是持续增产面临的关键挑战。

关键词: CERES-Maize模型, 玉米产量, 水肥管理, 玉米品种, 气候阈值

Abstract:

Clarifying the spatiotemporal evolution of maize potential yield under climate change and disentangling the contributions of climatic factors versus agronomic advances are critical for global food security. China, as the world’s second-largest maize producer, faces increasing challenges from warming and precipitation volatility. To quantify these impacts, this study integrated long-term data (1981-2023) from 248 agrometeorological stations across China. We employed the CERES-Maize model to simulate maize growth under three scenarios: optimal cultivar without water-fertilizer stress (S1), optimal cultivar with actual management (S2), and actual cultivar without water-fertilizer stress (S3). A panel threshold regression model was further applied to identify the nonlinear response thresholds of yield to key climatic factors (temperature, precipitation, diurnal temperature range, and sunshine duration). The results are as followed. From 1981 to 2023, the national actual yield increased at a rate of 147 kg/(hm²∙a); however, 28% of the stations experienced yield stagnation or decline. Conversely, potential yield exhibited a significant downward trend at 62% of the stations, indicating a tightening biophysical ceiling. Attribution analysis revealed that climate change negatively impacted yield (-38.6 kg/(hm²∙a)), while technological progress contributed positively (185.6 kg/(hm²∙a)). Notably, cultivar improvement was the dominant driver (except in the Northwest irrigation zone), accounting for 81.4%, whereas water and fertilizer management contributed only 18.6%. Threshold analysis identified growing-season mean temperature (threshold: 20.5℃) and total precipitation (threshold: 490.8 mm) as the primary limiting factors under actual production conditions. Scenario comparison demonstrated that eliminating water and fertilizer stress lowered the temperature threshold to 19.2℃, implying increased heat sensitivity under high-input conditions. Adopting optimal cultivars significantly reduced dependency on rainfall (lowering the precipitation threshold to 288.0 mm) and mitigated heat stress in high-temperature regions. Although optimal cultivars and sufficient inputs can enhance climate resilience and break through partial yield stagnation, they cannot fully eliminate the constraints of extreme climates, particularly high temperatures. Future strategies for high-yield systems must prioritize breakthroughs in overcoming heat limitations, as cultivar improvement remains the most effective pathway compared to the diminishing marginal returns of water-fertilizer intensification.

Key words: CERES-Maize model, Maize yield, Water and fertilizer management, Maize cultivar, Climatic thresholds

陈典鹏, 张亮亮, 庄慧敏, 张朝. 1981—2023年我国玉米产量时空演变及其驱动因素解析[J]. 气候变化研究进展, 2026, 22(4): 484-498.

CHEN Dian-Peng, ZHANG Liang-Liang, ZHUANG Hui-Min, ZHANG Zhao. Spatiotemporal dynamics of maize yield and its driving factors in China from 1981 to 2023[J]. Climate Change Research, 2026, 22(4): 484-498.

图/表 13

图1 玉米种植站点基本情况注：基于自然资源部标准地图服务网站审图号为GS（2024）0650号的标准地图制作，底图边界无修改。

Fig. 1 Basic information of corn cultivation site

表1 CERES-Maize模型参数及其先验值

Table 1 Parameters and their prior values for the CERES-Maize

表2 CERES-Maize校验结果

Table 2 Simulation accuracy of the CERES-Maize

图2 1981—2023年中国玉米实际产量的空间分布

Fig. 2 Spatial distribution of actual yield of maize in China from 1981 to 2023

图3 1981—2023年中国玉米的实际产量和潜在产量的时序变化

Fig. 3 Temporal trends of actual yield and potential yield of maize in China from 1981 to 2023

图4 1981—2023年中国玉米潜在产量的空间分布

Fig. 4 Spatial distribution of potential yield of maize in China from 1981 to 2023

图5 1981—2023年中国玉米产量差的空间分布

Fig. 5 Spatial distribution of yield gap of maize in China from 1981 to 2023

图6 气候和技术进步对全国及各分区产量增长的贡献注：图中*代表通过了0.05的显著性检验。

Fig. 6 Contributions of climate and technological progress to the increase in yield at the national level and in different sub-regions

图7 1981—2023年中国玉米品种(a)和水肥(b)潜在产量及其变化

Fig. 7 Potential yield of maize cultivar (a) and water-fertilizer (b) in China and their changes for maize during 1981-2023

图8 不同情景下气候变量阈值效应的统计强度

Fig. 8 Statistical strength of threshold effects for climatic variables under different scenarios

表3 不同情景下不同气候因子阈值前后斜率情况

Table 3 Slopes before and after thresholds for different climatic factors under three scenarios

表4 全国不同地区施肥量、灌溉量和玉米产量的相关性

Table 4 Correlation of fertilizer application rate and irrigation amount with maize yield in different regions across China

图9 不同情景下生育期气候与产量的关系(a)降雨-产量；(b)气温-产量

Fig. 9 Relationship between growing season climate and yield under different scenarios. (a) Precipitation vs. yield; (b) temperature vs. yield

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1981—2023年我国玉米产量时空演变及其驱动因素解析

Spatiotemporal dynamics of maize yield and its driving factors in China from 1981 to 2023

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