Salis Deris Artikanur

Population growth is increasing food demand, while watershed food systems face intense pressure from land-use/land-cover (LULC) change. Therefore, this study aimed to identify the critical threshold for food sufficiency in the Cimanuk Watershed. The methods used include dynamic analysis and prediction of LULC changes in line with four scenarios using Support Vector Machine (SVM)–Markov model, ecosystem service-based food production modeling through Crop Production module in Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, food demand estimation based on population projections, and calculation of Food Sufficiency Index (FSI) to identify the critical threshold at which the food system shifts from surplus to deficit. The results showed that paddy rice production is projected to continue meeting food demand across all scenarios through 2042. However, food demand is expected to increase as the population grows, specifically in the Accelerated Population Growth (APGS) scenario. FSI analysis indicated that the Cimanuk Watershed remained in food surplus (FSI > 1) up to 2042. The watershed may transition to a food-deficit condition after 2062 in the absence of intervention measures. Therefore, this study recommends protecting productive paddy fields, conserving forests, and managing population growth to maintain long-term food sufficiency.

One of the critical watersheds in Indonesia is the Cimanuk watershed, which is recognized as a national priority watershed. Remote sensing plays a vital role in monitoring land use/land cover (LULC) in the Cimanuk watershed. Overall Accuracy (OA) and Kappa Accuracy (KA) are commonly used as primary measures of classification accuracy. In addition to these parameters, it is essential to examine the consistency and rationality of LULC classification over time. This study aimed to compare the accuracy and temporal consistency of three Machine Learning Models: Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART) for LULC classification in the Cimanuk watershed using Sentinel-2 Multispectral Instrument (MSI) images for 2020 and 2025, processed in Google Earth Engine (GEE). The results indicate that RF has the highest accuracy, with 87.9% (OA) and 83.7% (KA) in 2025, and 83.6% (OA) and 77.9% (KA) in 2020. When examining inconsistent or irrational transitions between 2020 and 2025, CART accounted for 11.97% of these transitions, higher than RF and SVM. After applying temporal consistency correction to the 2020 LULC classification result, RF remains the best-performing classifier, achieving 90% (OA) and 86.5% (KA), followed by CART and SVM. These findings provide valuable insights into incorporating accuracy and temporal consistency assessments into time-series LULC analysis, serving as a reference for future LULC studies in watershed management and other geographic contexts.

Carbon is an essential component in the regulation of climate systems through the global biogeochemical cycle. However, changes in land use/land cover (LULC) have reduced the capacity of terrestrial ecosystems like watershed to store carbon. This shows the need for a policy framework that balances conservative objectives with agricultural demands, as watersheds are required to support carbon storage and food production. Previous studies have generally assessed carbon dynamics or LULC change separately, with limited integration of policy-driven scenarios. Therefore, this study aimed to conduct multi-scenario carbon storage modeling to evaluate LULC protection strategies in the Cimanuk Watershed, Indonesia, an area experiencing significant LULC pressures. The method used consisted of Support Vector Machine (SVM)–Markov, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), Geodetector, and Getis-Ord Gi*. A total of four scenarios were used to project LULC and carbon storage in 2042, which included Business as Usual (BAU), Paddy Field Protection (PFP), Forest Protection (FOP), and Paddy Field and Forest Protection (PFFOP). The results showed that forest area declined by 39,400 ha between 2015 and 2025, thereby reducing carbon storage. The PFFOP scenario was identified as the most viable, combining the protection of paddy fields and forests to balance agricultural production and carbon sequestration. Among the factors analyzed, slope exerted the greatest influence on carbon storage. Spatial cluster analysis showed that carbon hotspots were predominantly located in the upper Cimanuk sub-watershed. These results offered valuable insights into scenario-based sustainable watershed management to optimize carbon storage and maintain agricultural function. Furthermore, the proposed framework showed promising potential for application in other tropical watersheds, serving as a reference for decision-makers in sustainable watershed management.