Table 1.
Weather scenarios and fertilizer levels used in the greenhouse experiment.
Values for the weather variables are ranges. Tmin = minimum temperature, Tmax = maximum temperature, Umax = Maximum humidity.
Table 2.
Performance of the Cox and DeepL models based on Root mean square error (RMSE), Mean Absolute Error (MAE) and Coefficient of Agreement (C-index).
Fig 1.
Overall probability of emergence of maize seedlings as a function of time based on the Kaplan-Meier method.
The shaded area represents the confidence envelop.
Fig 2.
Variation of growth parameters (mean ± standard error) according to fertilizer types and weather scenarios: (a) Diameter in cm, (b) Total height in m, (c) Lenght leaf in cm, (d) width leaf (cm), (e) Number of leaf.
Table 3.
Effects of weather scenarios and fertilizer types on maïze growth traits: summary of the linear mixed models.
ICC = Intra class correation, DF = Degree of Freedom, F = Fisher Statistics, P = P-value, R2m = Marginal Rsquare, R2C = Conditional R square.
Fig 3.
Trend curve illustrates the evolution of each growing trait over time, depending on weather scenarios and fertilizer types: (a) Diameter in cm, (b, f) Leaf lenght in cm, (c, e) Height in cm, (d) Leaf width.
Fig 4.
Variation of yield parameters according to fertilizer types and weather scenarios: (a) Number of cob, (b) Cob width in cm, (c) Cob lenght in cm, (d) Fresh spathed weight in g, (e) Number of seed, (f) Fresh weight in g.
Table 4.
Effects of weather scenarios and fertilizer types on maïze grain yield: summary of the linear mixed models.
ICC = Intra class correation, DF = Degree of Freedom, F = Fisher Statistics, P = P-value, R2m = Marginal Rsquare, R2C = Conditional R square.