Impact of mulching and planting time on spring-wheat (Triticum aestivum) growth: A combined field experiment and empirical modeling approach

Abstract

This study aimed to assess the effect of straw-mulching and sowing time on spring-wheat growth and also evaluate the suitability of nonlinear models (Logistic, Gompertz, Richards and Weibull models) in forecasting crop growth. The experiment followed a factorial design with two factors: three planting times (early, normal and late sowing times) at two different straw-mulching rates (3.75 t/ha straw [mulch] and 0 t/ha straw [no-mulch]). The following treatments were established from these factors: (1) early sowing without straw-mulch (ESW-T), (2) early sowing with straw-mulch (ESW-TS), (3) normal sowing without straw-mulch (NSW-T), (4) normal sowing with straw-mulch (NSW-TS), (5) late sowing without straw-mulch (LSW-T) and (6) late sowing with straw-mulch (LSW-TS). The results showed that, generally mulching improved soil water storage and enhanced biomass growth while early sowing combined with mulching (ESW-TS) gave the greatest results in terms of biomass growth. Furthermore, the logistic model was the most suitable for crop forecasting with a coefficient of determination (r 2) of 0.887 and a change in Akaike information criterion (∆AIC) of 0. The Gompertz model was next with r 2 = 0.884 and ∆AIC = 0.53, followed by the Weibull model (r 2 = 0.883, ∆AIC = 2.83). The Richards model showed the least performance (r 2 = 0.882, ∆AIC = 3.42). These results implied that the adoption of early sowing and straw-mulching could enhance soil water storage, improve wheat yields and improve climate resilience of agroecosystems on the Loess Plateau and similar dryland ecosystems. Furthermore, the logistic regression model can be a useful decision tool for testing the effectiveness of climate adaptation strategies.