LAUSR

Abstract Crop models may potentially explore alternative ways to improve agroecosystem resilience in arid regions of Middle East and North Africa. Mapping the outputs behavior as a function of the inputs and quantifying the uncertainty contribution of inputs to the variability of outputs are crucial for understanding and applying complex mathematical models to a new environment. Objectives of present research are (i) to calibrate and evaluate the Decision Support System for Agrotechnology Transfer (DSSAT) cropping system model using detailed experimental datasets on maize production in arid sandy soils (Entisol) and (ii) to determine the model’s sensitivity to soil, genotype and crop management inputs under the currently explored conditions (low fertility and water holding capacity) based on multivariate analysis and variance decomposition methods. The goodness-of-fit statistics between observed and simulated data indicated that the calibrated model reasonably well simulates maize phenology, growth and yield, evapotranspiration, soil water content, grain N concentration, and postharvest soil NO3-N in eight year site field experiments. A global sensitivity analysis using the co-inertia method was carried out to link 14 output variables and 25 soil and genotype input parameters. Maize growth and yield variables were strongly correlated with soil hydrological and fertility input parameters such as soil water upper limit (SDUL) and soil organic carbon (SOC), whereas simulation of maize phenology was largely determined by phenological genotype-specific cultivar input parameters. A strong association was also observed between the output variables of yield and soil fertility. The effect of carbon (C) related soil input parameters of initial SOC and stable SOC and crop management factors of maize residue retention and compost application under no-till system on the long-term (10 years) simulation of yield and SOC dynamics was further explored using Sobolʹ method. Simulated grain yield, water productivity, active SOC, and cumulative soil CO2 efflux were most sensitive to initial stable SOC and compost application. Maize residue retention significantly affected the simulation of cumulative N mineralization, SOC % in 0.2 m depth, and cumulative soil CO2 efflux through interactions effect, i.e. total-order sensitivity index (STi) > 0.05, with other inputs. Compost application increased grain yield by 13 %, SOC stock by 5%, and cumulative soil CO2 efflux by 95 % compared with no application. However, compost application with maize residue retained significantly reduced cumulative soil CO2 efflux by 12 % compared with compost application with maize residue removed. Therefore, the application of compost with maize residue retained under no-till system is a plausible crop management option for agronomically improved and environmentally sound maize production in arid sandy soils.