Modeling Soil Water Content and Crop-Growth Metrics in a Wheat Field in the North China Plain Using RZWQM2

Kun Du,Shanbao Liu,Chao Tian,Qiuying Zhang,Fadong Li,Yunfeng Qiao,Qi Li

doi:10.3390/agronomy11061245

Abstract

Soil water content (SWC) is an important factor restricting crop growth and yield in cropland ecosystems. The observation and simulation of soil moisture contribute greatly to improving water-use efficiency and crop yield. This study was conducted at the Shandong Yucheng Agro-ecosystem National Observation and Research Station in the North China Plain. The study period was across the winter wheat (Triticum aestivum L.) growth stages from 2017 to 2019. A cosmic-ray neutron probe was used to monitor the continuous daily SWC. Furthermore, the crop leaf area index (LAI), yield, and aboveground biomass of winter wheat were determined. The root zone quality model 2 (RZWQM2) was used to simulate and validate the SWC, crop LAI, yield, and aboveground biomass. The results showed that the simulation errors of SWC were minute across the wheat growth stages and mature stages in 2017–2019. The root mean square error (RMSE) and relative root mean square error (RRMSE) of the SWC simulation at the jointing stage of winter wheat were 0.0296 and 0.1605 in 2017–2018, and 0.0265 and 0.1480 in 2018–2019, respectively. During the rain-affected days, the RMSE (0.0253) and RRMSE (0.0980) for 2017–2018 were significantly lower than those of 2018–2019 (0.0301 and 0.1458, respectively), indicating that rain events decreased the model accuracy in the dry years compared to the wet years. The simulated LAIs were significantly higher than the measured values. The simulated yield value of winter wheat was 5.61% lower and 3.92% higher than the measured yield in 2017–2018 and in 2018–2019, respectively. The simulated value of aboveground biomass was significantly (45.48%) lower than the measured value in 2017–2018. This study showed that, compared with the dry and cold wheat growth period of 2018–2019, the higher precipitation and temperature in 2017–2018 led to a poorer simulation of SWC and crop-growth components. This study indicated that annual abnormal rainfall and temperature had a significant influence on the simulation of SWC and wheat growth, especially under intensive climate-change stress conditions.

Highlights

The simulated soil moisture results during the winter wheat growth period showed that in the 2017–2018 winter wheat season, the quality of soil moisture simulation was best at the jointing stage, with root mean square error (RMSE) and relative root mean square error (RRMSE) of 0.0129 and 0.0569, respectively (Table 1)
The simulation errors of Soil water content (SWC) with RZWQM2 were small in this study (Table 1), in line with the results reported earlier [2,36,37]
The dynamic simulation of SWC was presented with a variant of the growth period, and the simulation accuracy was relatively higher at the jointing and mature stages (Figure 2, Table 1), supporting our first proposed hypothesis

Summary

Introduction

Many previous studies monitored cropland SWC at various scales (local to regional) using different methods [3,5,8,9,10,11,12,13]. The cosmic-ray neutron probe (CRNP) is a reliable method for automatically measuring mean SWC at the hectometer scale without disturbing the soil. CRNP was evaluated and successfully applied in various ecosystems [14,15,16,17,18,19,20], but its application in croplands under altered weather conditions is insufficiently studied

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Conclusion