27 JAN 2026 (TUE) 16:05 - 16:35

Assessing Sustainable Yield Potential of China’s Staple Grain Systems  

Ms LU Ziqiong     

( Supervisor: Prof Peng Zhu )

Abstract:

Ensuring national food security while advancing agricultural sustainability is a critical challenge for China. Addressing this challenge requires spatially explicit quantification of crop yield, soil organic carbon (SOC), and their interactions at field-relevant scales. However, most existing studies assess crop yield and soil properties separately, lacking a unified high-resolution framework that explicitly links SOC dynamics with yield variation and yield gap diagnostics. This limitation constrains the accurate evaluation of soil-based pathways for sustainable yield improvement. This research aims to establish a national-scale, high-resolution (30 m) analytical framework to assess the sustainable yield potential of China’s major staple grains—wheat, rice, and maize—by integrating crop yield estimation, SOC mapping, and yield gap analysis. The study is structured around four interconnected objectives: (1) pixel-level yield estimation and mapping using multi-temporal remote sensing and deep learning; (2) high-resolution SOC inversion over cropland areas; (3) quantification of the contribution of SOC changes to yield variability; and (4) integration of yield and SOC information to diagnose yield gaps and evaluate soil-driven yield increase potential. 

During the probation period, methodological foundations for this framework have been established. A deep learning-based yield estimation framework has been developed and preliminarily tested using U.S. datasets, serving as a proof-of-concept for subsequent adaptation and validation under China’s agro-ecological and data conditions. In parallel, a 30 m resolution SOC mapping framework over China’s cropland has been developed as the basis for constructing a multi-temporal, spatially explicit SOC dataset. This high-resolution SOC product is designed to track SOC dynamics under different management regimes. Ongoing work focuses on integrating multi-source observational data across China to refine yield estimation and advance the coupled analysis required for quantifying SOC–yield relationships, management-driven SOC dynamics, and associated yield gaps. 

This study is expected to deliver a high-resolution diagnostic framework that jointly characterizes crop productivity and soil health across China’s staple grain systems. Scientifically, it will clarify the quantitative role of SOC in yield improvement and yield gap closure. Practically, it aims to provide spatially targeted insights to support sustainable cropland management and soil-based intensification strategies aligned with national food security goals.