Differences in background environment and fertilization method mediate plant response to nitrogen fertilization in alpine grasslands on the Qinghai-Tibetan Plateau

• Published in: The Science of the total environment Vol. 906; p. 167272
• Main Authors: He, Shun, Du, Jianqing, Wang, Yanfen, Cui, Lizhen, Liu, Wenjing, Xiao, Yifan, Ran, Qinwei, Li, Linfeng, Zhang, Zuopei, Tang, Li, Hu, Ronghai, Hao, Yanbin, Cui, Xiaoyong, Xue, Kai
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2024
• Subjects: Biomass; Machine learning; Plant productivity; Restoration; Threshold; MAT; AI; Biomass; QTP; N; lnR; R; Plant productivity; Restoration; w; Machine learning; AGB; BGB; MAP; Threshold
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2023.167272

Grassland degradation threatens ecosystem function and livestock production, partly induced by soil nutrient deficiency due to the lack of nutrient return to soils, which is largely ascribed to the intense grazing activities. Therefore, nitrogen (N) fertilization has been widely adopted to restore degraded Qinghai-Tibetan Plateau (QTP) grasslands. Despite numerous field manipulation studies investigating its effects on alpine grasslands, the patterns and thresholds of plant response to N fertilization remain unclear, thus hindering the prediction of its influences on the regional scale. Here, we established a random forest model to predict N fertilization effects on plant productivity based on a meta-analysis synthesizing 88 publications in QTP grasslands. Our results showed that N fertilization increased the aboveground biomass (AGB) by 46.51 %, varying wildly among plant functional groups. The positive fertilization effects intensified when the N fertilization rate increased to 272 kg ha−1 yr−1, and decreased after three years of continuous fertilization. These effects were more substantial when applying ammonium nitrate compared to urea. Further, a machine learning model was used to predict plant productivity response to N fertilization. The total explained variance and mean squared residuals ranged from 49.41 to 75.13 % and 0.011–0.058, respectively, both being the highest for grasses. The crucial predictors were identified as climatic and geographic factors, background AGB without N fertilization, and fertilization methods (i.e., rate, form, and duration). These predictors with easy access contributed 62.47 % of the prediction power of grasses' response, thus enhancing the generalizability and replicability of our model. Notably, if 30 % of yak dung is returned to soils on the QTP, the grassland productivity and plant carbon pool are predicted to increase by 5.90–6.51 % and 9.35–10.31 g C m−2 yr −1, respectively. Overall, the predictions of this study based on literature synthesis enhance our understanding of plant responses to N fertilization in QTP grasslands, thereby providing helpful information for grassland management policies. Conflict of interest: The authors declare no conflict of interest. [Display omitted] •Soil degradation in Qinghai-Tibetan Plateau is extensive from poor nutrient return.•Nonlinear patterns and response thresholds for plant response to N fertilization were observed.•Aboveground biomass is affected differently among plant functional groups.•Climate, geography, background AGB and fertilization methods mediate plant response.•Grassland management can be improved by predictions of N fertilization.