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Accurate estimates of grassland aboveground net primary productivity (ANPP) are essential for forage management, yet remote sensing‐based productivity models commonly assume that radiation use efficiency (RUE) is constant across vegetation types and years. This assumption may be particularly problematic in subtropical grasslands, where functionally contrasting plant communities coexist across heterogeneous edaphic and hydroclimatic conditions. We evaluated seasonal and interannual variation in RUE across three natural grassland communities in south‐central Corrientes, Argentina, over a 10‐year period (2007–2016): a C3‐dominated fine‐grass community (genus Aristida) on shallow acidic lithosols with low cation exchange capacity, a C4‐dominated short‐grass community (principally Paspalum notatum and allied species) on deep planosols with a sharp textural discontinuity between horizons, and a C4‐dominated tall tussock community (principally Andropogon lateralis and allied species) on hydromorphic brunizems with poor internal drainage . ANPP was quantified using mobile exclusion cages and sequential harvests, while absorbed photosynthetically active radiation (APAR) was estimated from MODIS NDVI‐derived fAPAR and meteorological radiation data. RUE differed markedly among communities, with the highest mean annual value in short‐grass (0.885 ± 0.133 g DM MJ −1 ) and the lowest in fine‐grass (0.576 ± 0.108 g DM MJ −1 ). Although all communities showed higher RUE during warm seasons, interannual variability and climatic sensitivity were markedly different among the three monitored communities. Fine‐grass exhibited the greatest temporal variability and negative responses to both annual temperature and precipitation, whereas tall tussock maintained comparatively stable RUE across hydroclimatic conditions. Assuming a fixed RUE introduced substantial ANPP estimation errors, exceeding 20% and reaching more than 30% in some years. These findings show that RUE cannot be treated as a uniform parameter in subtropical grassland mosaics. Incorporating community‐specific and temporally variable RUE values can improve productivity models, refine forage allowance assessments and support more reliable grazing management under increasing climatic variability.
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Maidana et al. (2026) studied this question.
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