Effects of Conventional Tillage and No-Tillage Systems on Maize (Zea mays L.) Growth and Yield, Soil Structure, and Water in Loess Plateau of China: Field Experiment and Modeling Studies

Abstract

Cropping system models can be useful tools for assessing tillage systems, which are both economically and environmentally viable. The objectives of this study were to evaluate the decision support system for agrotechnology transfer (DSSAT) CERES-Maize model’s ability to predict maize growth and yield, as well as soil water dynamics, and to apply the evaluated model to predict evapotranspiration processes under conventional tillage (CT) and no-tillage (NT) systems in a semi-arid loess plateau area of China from 2014 to 2016. The field experiment results showed that NT increased the surface soil bulk density and water-holding capacity but decreased the total porosity for the surface soil and the maize grain yield. Model calibration for maize cultivar was achieved using grain yield measurements from 2014 to 2016 for CT, and model evaluation was achieved using soil and crop measurements from both CT and NT for the same 3 yr period. Good agreement was reached for CT grain yields for model calibration (nRMSE = 4.02%; d = 0.87), indicating that the model was successfully calibrated. Overall, the results of model evaluation were acceptable, with good agreement for NT grain yields (nRMSE = 4.26%; d = 0.86); the agreement for LAI ranged from good to moderate (RMSE = 0.30‒0.31; d = 0.84‒0.85); the agreement for soil water content was good for NT (RMSE = 0.03‒0.08; d = 0.81‒0.95), but ranged from good to poor for CT (RMSE = 0.06‒0.09; d = 0.42‒0.88); the overall agreement between measured and simulated soil water varied from poor to good depending on soil depth and tillage. It was concluded that the DSSAT CERES-Maize model provided generally good-to-moderate simulations of continuous maize production (yield and LAI) for a short-term tillage experiment in the loess plateau, China, but generally good-to-poor simulations of soil water content.