IMPACT OF POST EMERGENCE HERBICIDES ON WEED DYNAMICS, YIELD AND NUTRIENT UPTAKE OF MAIZE: A COMPARATIVE EVALUATION OF LINEAR AND MACHINE LEARNING APPROACH

Rayala Srinivasa Rao Jambamma B. Danam Sravanthi Srinivasa Reddy I.V. IMPACT OF POST EMERGENCE HERBICIDES ON WEED DYNAMICS, YIELD AND NUTRIENT UPTAKE OF MAIZE: A COMPARATIVE EVALUATION OF LINEAR AND MACHINE LEARNING APPROACH

Journal of Animal and Plant Sciences

JAPS

2027

37 1 1018-7081

https://doi.org/10.36899/JAPS.2027.1.0005

Effective weed management is essential for improving maize (Zea mays L.) productivity through the identification of selective and efficient post-emergence herbicides. Field experiments were conducted during 2019–2021 at the farmer field to evaluate weed control efficacy, weed dynamics, and maize yield under four herbicide treatments i.e. T1: Weedy check, T2: Tembotrione (42% S.C.) 287.5 mL ha-1 [25 DAS (days after sowing)], T3: Topramezone (33.6% S.C.)  100 mL ha-1 (25 DAS) and T4: Halosulfuron Methyl (75% WG) 90 g ha-1 (25 DAS) with data averaged across five locations for four treatments each year. The resulting dataset comprised 12 treatments-year observations with 11 agronomic variables. The machine-learning analyses were conducted as an initial exploratory assessment. Using chronological validation, 2019–2020 observations were used for training and 2021 observations for independent testing (66:34 split). Among the evaluated treatments, application of tembotrione proved the greatest broad-spectrum weed suppression and produced the highest grain yield (8,936 kg ha⁻¹), compared with the untreated control (3,897–4,324 kg ha⁻¹). Spearman correlation analysis revealed strong positive associations between grain yield and nutrient uptake of maize (ρ = 0.916) and strong negative associations with the weed index (ρ = −0.909) and broadleaf weed density (ρ = −0.825). These predictive modeling using linear regression (LR), random forest (RF), and XGBoost was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). Within the constraints of the small dataset, LR achieved the best predictive performance (R² = 0.949, RMSE = 379.58, MAE = 327.96). Ridge Regression with SHAP analysis identified total nutrient input (total NPK; +408.95) as the strongest predictor of maize yield while year had the lowest contribution (+192.78). These findings demonstrate that effective broad-spectrum weed management enhances maize productivity, while the modelling provides preliminary insights into weed control, soil fertility and yield relationships. Linear regression, maize yield, random forest, SHAP analysis, weed density, weed management, XGBoost Pakistan Agricultural Scientists Forum

https://thejaps.org.pk/AbstractView.aspx?mid=2026-JAPS-568