Retrieval of photosynthetic capability for yield gap attribution in maize via model-data fusion

Abstract

Identifying the factors driving yield gaps between the attainable and actual yields achieved by farmers is essential for agricultural improvement and water resource management. In this study, the process-based VIP (Vegetation Interface Processes) eco-hydrological model was used to estimate the yield gap of spring maize in the Hutuo River Basin (HRB), North China. To describe the realistic crop growth and attainable yield pattern, the field-sampled grain yield and aboveground biomass were used to design a scheme to retrieve the intrinsic quantum efficiency (εact) for leaf photosynthesis of C4 crop, including the statistical relationships between the intrinsic quantum efficiency, vegetation index and grain yield. The actual yields were predicted with the retrieved intrinsic quantum efficiency pattern, while the attainable yields were predicted with the average of the top 5% intrinsic quantum efficiencies. The simulated actual yields are consistent with the census data at the county level (R2 of 0.37 to 0.74 and relative RMSE of 16–29%). The average yield gap in the basin is 5246 kg ha−1, being 55% of the attainable yield. It is revealed that soil organic matter (SOM) content and depth of soil layer are the principal limiting factors in more than 80% of farmland and results in 70% of the yield gap, while the effects of SOM content is dominant in flat piedmont. Water stress is also a critical factor limiting crop yield, especially for hilly farmlands with slopes greater than 8°. Improving fertilizer management and irrigation techniques in the HRB is therefore the primary task to narrow yield gaps attributable to basin management.