Patterns and controlling factors of soil carbon sequestration in nitrogen-limited and -rich forests in China—a meta-analysis

Soil organic carbon (SOC) management has the potential to contribute to climate change mitigation by reducing atmospheric carbon dioxide (CO2). Understanding the changes in forest nitrogen (N) deposition rates has important implications for C sequestration. We explored the effects of N enrichment on soil carbon sequestration in nitrogen-limited and nitrogen-rich Chinese forests and their controlling factors. Our findings reveal that N inputs enhanced net soil C sequestration by 5.52–18.46 kg C kg−1 N, with greater impacts in temperate forests (8.37–13.68 kg C kg−1 N), the use of NH4NO3 fertilizer (7.78 kg Ckg−1 N) at low N levels (<30 kg Ckg−1 N; 9.14 kg Ckg−1 N), and in a short period (<3 years; 12.95 kg C kg−1 N). The nitrogen use efficiency (NUE) varied between 0.24 and 13.3 (kg C kg−1 N) depending on the forest type and was significantly controlled by rainfall, fertilizer, and carbon-nitrogen ratio rates. Besides, N enrichment increased SOC concentration by an average of 7% and 2% for tropical and subtropical forests, respectively. Although soil carbon sequestration was higher in the topsoil compared to the subsoil, the relative influence indicated that nitrogen availability strongly impacts the SOC, followed by dissolved organic carbon concentration and mean annual precipitation. This study highlights the critical role of soil NUE processes in promoting soil C accumulation in a forest ecosystem

Ecological restoration stimulates environmental outcomes but exacerbates water shortage in the Loess Plateau

Restoration is the natural and intervention-assisted set of processes designed to promote and facilitate the recovery of an ecosystem that has been degraded, damaged, or destroyed. However, it can also have an adverse effect on the environment. Thus, assessing an ecological restoration project’s impact is crucial to determining its success and optimum management strategies. We performed a meta-analysis concerning the environmental outcomes during the years 2000–2015 resulting from the “Grain for Green” Project (GFGP) implementation in the Loess Plateau (LP). Data were gathered from 40 peer-reviewed English-language articles chosen from a pool of 332 articles. The results showed that, on average, GFGP increased forest coverage by 35.7% (95% CI [24.15–47.52%]), and grassland by 1.05% (95% CI [0.8–1.28%]). At the same time, GFGP has a positive impact on soil carbon (C) sequestration, net ecosystem production (NEP), and net primary production (NPP), from the years 2000 to 2015 by an average of 36% (95% CI [28.96–43.18%]), 22.7% (95% CI [9.10–36.79%]), and 13.5% (95% CI [9.44–17.354%]), respectively. Soil erosion, sediment load, runoff coefficient, and water yield were reduced by 13.3% (95% CI [0.27–25.76%]), 21.5% (95% CI [1.50–39.99%]), 22.4% (95% CI [5.28–40.45%]) and 43.3% (95% CI [27.03–82.86%]), respectively, from the years 2000 to 2015. Our results indicate that water supply decreased with the increase of vegetation coverage. Therefore, to balance the needs for green space, GFGP policies and strategies should recover, enhance, and sustain more resilient ecosystems

Low-level nitrogen and short-term addition increase soil carbon sequestration in Chinese forest ecosystems

Soil carbon (C) sequestration plays a vital role in mitigating global climate change. Human activities have vastly increased nitrogen (N) deposition rate in China, which in turn influences belowground C cycle processes. We performed a meta-analysis based on 61 published studies on N addition experiments, including 4072 observations across China, to quantify the responses of belowground soil C dynamics and sequestration to N enrichment in Chinese forest ecosystems. The results showed that, on average, N enrichment significantly enhanced C dynamics in boreal and temperate forests by an average of 24% and 10% while it reduced in subtropical and tropical forests by 11% and 19%. The response of C pools and C input to N addition were more pronounced in boreal forests with +17% and +10% while it was lower in subtropical forests with −0.4% and −19% respectively. The N enrichment enhanced soil C output by in boreal (6%) and temperate (7%) forests and a negative effect in subtropical (−30%) and tropical forests (−10%). This can be explained by the higher reduction levels in C respiration, despite the slightly lower litter and root-derived C inputs. Notably, N addition specifically increased belowground C sequestration, at low N addition rates (<30 kg N ha−1 yr−1) and during a short-term period (<1 year). These C sequestration effects were reversed at higher N deposition levels and /or during a longer time period. This implies that soil C sequestration is currently most likely not enhanced in (large) parts of China, as forests are characterized by long-term elevated N deposition levels

Modelling and mapping soil nutrient depletion in humid highlands of East Africa using ensemble machine learning : A case study from Rwanda

Soil nutrient depletion is one of the major causes of high yield gaps and nutrient deficiencies in East Africa highlands, including Rwanda. This research sought to determine the current soil nutrient balance and its spatial variation in 10 Rwandan agro-ecological zones. Soil nitrogen (N), phosphorus (P) and potassium (K) depletion in croplands were calculated using data from 455 field trials of the Optimizing Fertilizer Recommendations in Africa (OFRA) project in Rwanda. Calculated soil nutrient balances (NPK) and 15 environmental covariates were used to calibrate soil nutrient depletion models using ensemble machine learning (EML) and 10-fold cross-validation. In the 2019–2020 growing season, annual N and K depletions were 33.6 kg N ha−1 yr−1 and 71.0 kg K ha−1 yr−1, with a positive P balance of 2.30 kg P ha−1 yr−1. High soil nutrient uptake and high soil nutrient loss due to erosion and leaching were two main causes of NPK depletion. Spatial variations of NPK balance were influenced by soil nutrient stocks, soil erosion, elevation, rainfall, soil texture, and soil bulk density. The 10-fold cross-validation showed that coefficients of determination (R2) of NPK models were 62%, 58%, and 58%, respectively. Compared to single models, ensemble machine learning improved NPK model accuracy up to 5%. Our research revealed that soil nutrient depletion was highest in the northwest and lowest in the southeast of the study area. We conclude that increasing soil nutrient inputs without reducing soil nutrient loss due to soil degradation will not decrease soil nutrient depletion in Rwanda and ensemble machine learning outperforms single models in predicting soil nutrient balance. The solution to reduce high soil nutrient depletion in all agro-ecological zones of Rwanda would be to prioritize soil and water conservation measures and increase soil nutrient inputs