![]() The contribution rates over 0–3 months gradually decreased from positive to negative. The lag effect of temperature and precipitation on grassland productivity was approximately 3 months. Compared with the estimation using CASA, the proportion of correlation coefficients for NPP derived from the PLS regression against temperature or precipitation was over 90% at P < 0.01. The results showed that the correlation coefficient between the estimated NPP based on the CASA (Carnegie-Ames-Stanford Approach) model and the MOD17A3-NPP data reached 0.949. Based on the monthly scale of the continuous growing season (with a delay of 0–3 months), the effects of temperature and precipitation on grassland productivity of Inner Mongolia were assessed. Net primary productivity (NPP) of vegetation was used as an evaluation indicator for grassland productivity. This study developed a new analytical method to examine lag effects of climate change by integrating three techniques of time-lag effect analysis, time-lag accumulation computation and partial least-square regression. Understanding the time-lag responses of grassland productivity to climate change is crucial for revealing grassland ecosystem behavior, predicting future grassland productivity, and guiding animal husbandry practices. ![]()
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