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Acta Agronomica Sinica ›› 2026, Vol. 52 ›› Issue (5): 1279-1290.doi: 10.3724/SP.J.1006.2026.53079

• REVIEW •     Next Articles

Strategies and prospects for large-scale crop yield improvement based on yield gap

Wang Yu-Cheng1(), Zhang Lu2(), Liu A-Kang3, Huang Jian-Liang1, Peng Shao-Bing1, Yuan Shen1,*()   

  1. 1 National Key Laboratory of Crop Genetic Improvement / Hubei Hongshan Laboratory / MARA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River / College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
    2 College of Economics and Management, Huazhong Agricultural University, Wuhan 430070, Hubei, China
    3 National Agricultural Technology Extension and Service Center, Beijing 100125, China
  • Received:2025-10-15 Accepted:2026-01-22 Online:2026-05-12 Published:2026-01-26
  • Contact: *Yuan Shen, E-mail: syuan@mail.hzau.edu.cn
  • About author:First author contact:**Contributed equally to this work
  • Supported by:
    National Natural Science Foundation of China(T2261129473);National Natural Science Foundation of China(72473049);National Key Research and Development Program of China(2023YFD1900600);Natural Science Foundation of Wuhan(2024040801020290);China Agriculture Research System of MOF and MARA(CARS-01)

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Abstract:

In the context of the “New Round of the 100-Billion-Jin Grain Production Capacity Enhancement Initiative”, large-scale yield improvement has been identified as a core strategic pathway for ensuring national food security in China. Although national grain production has remained high and stable in recent years, further yield increases through cropland expansion are increasingly constrained by limited water and soil resources and growing environmental pressures. Addressing the key scientific questions of “whether major food crops still have room for yield improvement, how large this potential is, and how it can be realized”, research on yield potential and yield gaps provide an essential theoretical and methodological foundation to quantify production potential, identify limiting factors, and design yield-enhancement strategies. This paper systematically reviews recent advances in yield potential assessment and yield gap analysis, revealing the magnitude of untapped yield potential and the dominant constraints across different crops and regions. Based on these insights, differentiated technical pathways for large-scale yield improvement are proposed. In regions with large yield gaps, priority should be given to optimizing crop management practices and resource allocation to accelerate yield gap closure. In contrast, in areas where yield gaps are relatively small, further yield gains rely on breeding innovation, key technological breakthroughs, and cropping system optimization to raise yield ceilings. In addition, this paper analyzes major bottlenecks in the dissemination and application of yield-enhancing technologies, highlighting the persistent disconnect between scientific outputs and on-farm practices that hampers large-scale adoption. Looking ahead, establishing a coordinated and closed-loop framework encompassing yield potential assessment, yield gap diagnosis, technology integration, and on-farm implementation will be critical for translating scientific advances into sustained productivity gains. Through the synergistic advancement of technological innovations and supportive policy and system arrangements, China can continue to enhance its grain production capacity under constraints of limited arable land and environmental resources, thereby strengthening food security and agricultural sustainability.

Key words: large-scale yield improvement, yield potential, yield gap, technology pathways, food security

Fig. 1

Definitions on yield potential, attainable yield, and yield gap Yield potential refers to the maximum yield a crop can achieve under optimal management conditions, free from both biotic and abiotic stresses, and is influenced by natural factors such as climate, soil quality, CO2 concentration, and others. Attainable yield represents the yield that farmers can attain under large-scale production conditions through optimized management and technological applications, typically about 80% of the yield potential. The yield gap is the difference between yield potential and actual yield, reflecting the disparity between crop yields under ideal conditions and those achieved in actual production. It indicates the extent to which the crop has not fully realized its potential, usually due to limitations in resources or technology. The exploitable yield gap refers to the difference between attainable yield and actual yield, indicating the portion of the yield gap that can be closed under large-scale production conditions through improved management practices and technological innovations [20]."

Table 1

The comparison among methods on yield potential estimation"

方法
Method		 特点
Characteristic		 优点
Advantage		 局限性
Limitation
田间高产试验
Field high-yield trials		 科研人员在试验田中优化管理
Researchers optimize crop management in experimental plots		 精度高、结果直观
High accuracy, direct results		 覆盖面窄, 代表性不足
Limited coverage, poor representativeness
高产记录
High-yield records		 示范田或高水平农户田实测
Yield measured in demonstration fields or top-performing farmers’ fields		 可操作性强、激励作用大
Highly practical, strong incentive effect		 受自然条件和投入影响大, 缺乏普遍性
Strongly affected by natural conditions and inputs, limited generalizability
高产农户产量
Top farmer yields		 调查产量排名前5%-10%的农户
Based on surveys of the top 5%-10% highest-yielding farmers		 贴近实际、反映先进管理水平
Reflects real-world practices and advanced management		 调查成本高, 受样本代表性限制
High survey cost, limited by sample
representativeness
作物模型模拟
Crop model
simulation		 输入气象、土壤、品种等参数
Uses parameters such as climate, soil, and variety		 可跨区域、跨年份模拟, 适用性广
Applicable across regions and years, wide adaptability		 依赖数据质量, 结果不确定性大
Depending on data quality, results carry high uncertainty
遥感与机器学习
Remote sensing & machine learning		 多源遥感+统计/机器学习
Integrates multi-source remote sensing with statistical or machine learning methods		 空间覆盖广、估算快速
Broad spatial coverage, rapid
estimation		 受遥感分辨率与校准限制, 算法复杂
Limited by resolution and calibration of remote sensing data, algorithmic complexity

Fig. 2

Yield gaps (expressed as percentage of yield potential) of maize, rice, wheat, and soybean in China Figures A-H show the yield gaps of the irrigated and rainfed maize [32], single-and double-season rice [33], irrigated and rainfed wheat [32], irrigated and rainfed soybean [34] systems at the agricultural climate zone level, respectively. This map is based on the standard map downloaded from the Standard Map Service of the Ministry of Natural Resources of China with the approval number GS (2024) 0650. The boundary of the base map is not modified."

Fig. 3

Crop yield increase strategies in different yield gap scenarios This figure illustrates the division of two types of regions and their corresponding prioritized technical measures. The left section represents areas with a large yield gap, where the primary objective is to reduce the yield gap and enhance actual yield through a series of measures. Key technologies include precision fertilization, efficient water management, optimization of sowing and transplanting, integrated pest and weed management, and mechanization. These measures aim to optimize existing production practices, thereby narrowing the yield gap and improving yield levels under current conditions. The right section represents areas with a smaller yield gap, where the focus is on increasing yield potential and surpassing the yield ceiling. Prioritized measures include breeding innovation and variety updates, efficient utilization of thermal and light resources, and expansion of irrigated areas. These technologies work by improving the crop’s growing environment and enhancing its yield potential [46-48]."

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