Simulating plastic mulching effects on the soil water balance and maize yields using the modified RZ-SHAW model

Abstract

Context

Agricultural system models are practical tools for optimizing irrigation and fertilization strategies. Mulched drip irrigation (MDI) is commonly used in arid regions of China, playing a vital role in enhancing soil water and heat conditions to support agricultural production. However, current models inadequately capture the effects of MDI on land surfaces, limiting their utility in guiding irrigation and fertilization practices.

Objective

We addressed this gap by incorporating a plastic mulch module into the Simultaneous Heat and Water (SHAW) model within the RZWQM framework, creating the RZ-SHAW_-PM model.

Methods

The plastic mulch module simulates the impact of plastic film mulching on net radiation, sensible and latent heat fluxes, and rainfall interception. Field trial data from three years were used to assessed the model’s ability to simulate soil water content (SWC, cm³ cm^–3), soil evaporation (E_s, mm), crop transpiration (mm), evapotranspiration (ET, mm), leaf area index (LAI), dry matter (t ha^–1), and grain yield (t ha^–1) of maize under three treatments: no mulching (NM), full soil mulching (M0.99), and partial mulching covering 60 % of the soil surface (M0.6).

Results

The RZ-SHAW_-PM model adequately simulated SWC (RMSE: 0.006–0.018), E_s (RMSE: 0.22–0.58), and ET (RMSE: 0.28–0.67) across the three maize growing seasons. However, it slightly overestimated E_s during early growth (0 −80 days after sowing) and underestimated E_s during later growth (80 −140 days after sowing). Moreover, the model slightly overestimated ET in the NM treatment but underestimated ET during early growth and overestimated ET during later growth in the mulching treatments. Simulations were within acceptable ranges for maize LAI (RMSE: 0.15–0.22), dry matter (RMSE:

1.16–1.75), and grain yield (RMSE: 1.53–2.78), but underestimated for grain yield increases in M0.99 and M0.6 compared to field observations (9.2 % vs. 4.6 % and 17.5 % vs. 7.8 %, respectively).

Conclusions

The RZ-SHAW_-PM model is a valuable tool for simulating SWC, E_s, ET, LAI, dry matter, and grain yield under MDI. However, it falls short of capturing the slowdown in dry matter accumulation during the late growth stage of spring maize under adequate water and fertilizer supply in mulched systems. Further refinement is needed to improve the model’s accuracy and enhance its application in agronomic management within these systems.

Implications

The RZ-SHAW_-PM model offers insights for optimizing crop production under MDI, contributing to resource-efficient agronomic management in water-scarce environments.