Sugarcane

The experiment explored AET seasonal dynamics of a sugarcane field at the Navsari Agricultural University (NAU) in Navsari using the high-resolution remote sensing to address the deficiency of ground-based micrometeorological instruments, e.g. lysimeters or eddy covariance towers. Surface Energy Balance Algorithm on Land (SEBAL) was run in a Python-GRASS GIS setting that combined Landsat 8 satellite data on land with local weather data. The outputs of SEBAL were checked by comparing the estimates with the cloud-based METRIC-EEFlux algorithm. Phenological measurements using NDVI time-series data monitored the sugarcane growth cycle with the greatest vegetative vigor in June and senescence during the months of November to December. Spatial analysis showed that seasonal AET calculated using SEBAL was between 1459 and 1496 mm. An effective rainfall of 676 mm was taken into consideration to estimate the total irrigation water requirements (IWR) of 783 to 820 mm. Conversely METRIC-EEFlux always gave higher values of AET, with the values falling between 1627 and 1698 mm. The pixel-by-pixel comparison of the two models each day showed a moderate correlation (R2 = 0.63) with a Root Mean square error (RMSE) of 1.40mm/day. SEBAL had a mean error (MBE) of bias of -1.17 mm/day, which shows that it was generally underestimated compared to METRIC-EEFlux. The results notwithstanding these differences in the algorithms, SEBAL is a powerful, well-adjusted tool to measure crop water needs and support sound water management decisions in data deficient areas.

The study aims to assess the impact of climate change on sugarcane yield attributes in South Gujarat region using bias-corrected General Circulation Model (GCM) projections under SSP245 and SSP585 scenarios. These models were selected based on their accuracy in representing historical climate data and their applicability for future climate projections in the study region. Under SSP585, maximum temperature is projected to rise by 2.4 °C and minimum temperature by over 5.6 °C by the end of the century. Rainfall projections suggest a potential increase of up to 14.50% by 2090. Yield simulations using CANEGRO indicate moderate yield declines (-1% to -2.1%) under SSP245 but substantial reductions (-14% to -15%) under SSP585 due to heat and water stress. Sucrose content also exhibited sharper declines, underscoring the adverse effects of high-emission scenarios. These findings highlight the necessity for climate adaptation and mitigation strategies in sugarcane cultivation.