Application of crop model for decision management in sugarcane (Saccharum officinarum L.) production: the case of Batangas, Philippines
2024
Roa-Oca, A.J.M.
This study applied the DSSAT [Decision Support System for Agrotechnology Transfer]-CANEGRO crop model to evaluate the effects of climate change on sugarcane production in Batangas, Philippines, providing a decision-support tool for better agricultural management. By comparing satellite-derived and ground-observed weather data, it was found that while daily data were less consistent, monthly temperature data showed high reliability with Pearson correlation coefficient approximately 0.99. Rainfall data showed weak daily correlations but moderate to high monthly correlations (0.57 to 0.94). The sugarcane cultivar VMC 84-524 was calibrated using the DSSAT-CANEGRO model, which improved the accuracy of simulated yields. Initially, simulated yields underestimated observed values, but after calibration, errors were reduced significantly, with a strong positive correlation achieved. Baseline weather data from 1981 was used to simulate yields under different climate scenarios (RCP 4.5 and RCP 8.5). Optimum planting windows shifted under future scenarios, generally moving earlier under dry conditions. The highest yield increase was 11.57% in Nasugbu under the RCP 4.5 late-century wet scenario, while the largest decrease was -20.51% in Tuy under the RCP 8.5 late-century dry scenario. Future research should focus on incorporating higher resolution weather data and localized soil samples to enhance crop models' predictive accuracy and utility in adapting to climate variability.
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