A dual-model framework for irrigation performance diagnostics and water productivity benchmarking in a semi-arid sugarcane scheme

Efficient irrigation management in semi-arid sugarcane systems requires diagnostic tools that can distinguish whether changes in water productivity arise from increased consumptive water use or reduced crop response. This study applied a dual-model remote sensing framework integrating Landsat-based PySEBAL and FAO WaPOR to assess seasonal biomass production, actual evapotranspiration, and water productivity at Kasinthula Cane Growers Limited, a 1,475-ha irrigated sugarcane scheme in southern Malawi, during the 2019 and 2020 seasons. PySEBAL was used for block-scale diagnostics, while WaPOR provided an independent scheme-scale benchmark. PySEBAL-derived biomass increased from 50 t ha⁻¹ in 2019–64 t ha⁻¹ in 2020, compared with field-measured yields of 36 and 59 t ha⁻¹ , respectively. Agreement between PySEBAL and field observations was moderate to strong, with R² values of 0.74 in 2019 and 0.68 in 2020. Inter-model comparison between PySEBAL and WaPOR biomass was also strong, with R² values of 0.88 and 0.80 for 2019 and 2020, respectively. Despite higher biomass in 2020, mean water productivity declined from 5.2 to 4.1 kg m⁻³ , indicating that increased water consumption did not generate proportional yield gains. Spatial analysis further showed expansion of high actual evapotranspiration but low-water-productivity zones in 2020.The results show that integrating PySEBAL and WaPOR improves irrigation performance diagnostics by combining fine-scale spatial discrimination with broader benchmarking capacity. The framework supports irrigation scheduling refinement, identification of low-performing blocks, and prioritization of targeted interventions in water-scarce commercial sugarcane systems. • Integrated PySEBAL and WaPOR to triangulate irrigation performance. • Interannual AET increase reduced water productivity despite higher biomass. • Spatial diagnostics identified blocks with non-proportional yield–water gains. • Remote sensing distinguished productive from inefficient water use. • Framework supports scheduling optimization and water allocation decisions.