Modelling the crop yield gap with a remote sensing-based process model: A case study of winter wheat in the North China Plain

Abstract

Understanding the spatial distribution of the crop yield gap (YG) is essential for improving crop yields. Recent studies have typically focused on the site scale, which may lead to considerable uncertainties when scaled to the regional scale. To mitigate this issue, this study used a process-based and remote sensing driven crop yield model for winter wheat (PRYM-Wheat), which was derived from the boreal ecosystem productivity simulator (BEPS), to simulate the YG of winter wheat in the North China Plain from 2015 to 2019. Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield (Ya). The distribution of Ya across the North China Plain showed great heterogeneity, decreasing from southeast to northwest. The remote sensing-estimated results show that the average YG of the study area was 6 400.6 kg ha−1. The YG of Jiangsu Province was the largest, at 7 307.4 kg ha−1, while the YG of Anhui Province was the smallest, at 5 842.1 kg ha−1. An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation, but there was a weak negative correlation between YG and accumulated temperature. In addition, the YG was positively correlated with elevation. In general, studying the specific features of the YG can provide directions for increasing crop yields in the future.

Keywords

• remote sensing
• PRYM-Wheat Model
• yield gap
• environmental factors
• North China Plain