Background: Drought is widely recognized as one of the most complex natural hazards due to its gradual onset and long-lasting impacts. With climate change, droughts are becoming increasingly intense, frequent, and prolonged, particularly in arid and semiarid rangelands, posing a serious threat to the sustainability of livestock systems. The objective of this study was to develop, calibrate, and validate a methodology for detecting forage droughts and monitoring their spatial and temporal patterns using satellite-derived anomalies in the Normalized Difference Vegetation Index (NDVI), hereafter NDVI anomalie (NDVIA). Methods: Forage droughts are defined as temporary reductions in forage productivity in rangelands, grasslands, and pastures caused by rainfall falling below the long-term average. These reductions are so severe that even with adaptive forage management, the forage biomass accumulated during the growing season is insufficient to sustain livestock during the vegetative rest period (forage drought model). The study focuses on the dry Chaco region of Argentina. NDVIA values were correlated with forage biomass data collected in the field at 20 sites over a 10-yr period. Using a logistic regression model, the NDVIA threshold indicating the presence or absence of forage drought was determined. Results: Results revealed a significant relationship (P < 0.05) between NDVIA and forage drought presence/absence. During the study period (2001-2023), forage droughts in the region typically recurred every 5 +/- 2 growing seasons. A cluster analysis identified two subzones with significant differences (P < 0.05) in the temporal dynamics of forage drought occurrence. Implications: The combined application of the forage drought model and this NDVI-based monitoring system could serve as a "guidebook" for implementing forage management strategies at the farm scale (e.g., adaptive forage management) and shaping public policies at the regional scale (e.g., satellite index-based insurance). This methodological approach, first-of-its-kind in region, could be adapted to other arid and semiarid ecosystems globally, enhancing the early warning and management of forage droughts. (c) 2025 The Society for Range Management. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.