The potential to increase grassland soil C stocks by extending reseeding intervals is dependent on soil texture and depth

Comparison of direct and indirect soil organic carbon prediction at farm field scale

To advance sustainable and resilient agricultural management policies, especially during land use changes, it is imperative to monitor, report, and verify soil organic carbon (SOC) content rigorously to inform its stock. However, conventional methods often entail challenging, time-consuming, and costly direct soil measurements. Integrating data from long-term experiments (LTEs) with freely available remote sensing (RS) techniques presents exciting prospects for assessing SOC temporal and spatial change. The objective of this study was to develop a low-cost, field-based statistical model that could be used as a decision-making aid to understand the temporal and spatial variation of SOC content in temperate farmland under different land use and management. A ten-year dataset from the North Wyke Farm Platform, a 20-field, LTE system established in southwestern England in 2010, was used as a case study in conjunction with an RS dataset. Linear, additive and mixed regression models were compared for predicting SOC content based upon combinations of environmental variables that are freely accessible (termed open) and those that require direct measurement or farmer questionnaires (termed closed). These included an RS-derived Ecosystem Services Provision Index (ESPI), topography (slope, aspect), weather (temperature, precipitation), soil (soil units, total nitrogen [TN], pH), and field management practices. Additive models (specifically Generalised Additive Models (GAMs)) were found to be the most effective at predicting space-time SOC variability. When the combined open and closed factors (excluding TN) were considered, significant predictors of SOC were: management related to ploughing being the most important predictor, soil unit (class), aspect, and temperature (GAM fit with a normalised RMSE = 9.1%, equivalent to 0.4% of SOC content). The relative strength of the best-fitting GAM with open data only, which included ESPI, aspect, and slope (normalised RMSE = 13.0%, equivalent to 0.6% of SOC content), suggested that this more practical and cost-effective model enables sufficiently accurate prediction of SOC.

Sustainable land management enhances ecological and economic multifunctionality under ambient and future climate

The currently dominant types of land management are threatening the multifunctionality of ecosystems, which is vital for human well-being. Here, we present a novel ecological-economic assessment of how multifunctionality of agroecosystems in Central Germany depends on land-use type and climate. Our analysis includes 14 ecosystem variables in a large-scale field experiment with five different land-use types under two different climate scenarios (ambient and future climate). We consider ecological multifunctionality measures using averaging approaches with different weights, reflecting preferences of four relevant stakeholders based on adapted survey data. Additionally, we propose an economic multifunctionality measure based on the aggregate economic value of ecosystem services. Results show that intensive management and future climate decrease ecological multifunctionality for most scenarios in both grassland and cropland. Only under a weighting based on farmers' preferences, intensively-managed grassland shows higher multifunctionality than sustainably-managed grassland. The economic multifunctionality measure is about ~1.7 to 1.9 times higher for sustainable, compared to intensive, management for both grassland and cropland. Soil biodiversity correlates positively with ecological multifunctionality and is expected to be one of its drivers. As the currently prevailing land management provides high multifunctionality for farmers, but not for society at large, we suggest to promote and economically incentivise sustainable land management that enhances both ecological and economic multifunctionality, also under future climatic conditions.

Assessment of carbon sequestration potential of mining areas under ecological restoration in China

Mining activities aggravate the ecological degradation and emission of greenhouse gases throughout the world, thereby affecting the global climate and posing a serious threat to the ecological safety. Vegetation restoration is considered to be an effective and sustainable strategy to improve the post-mining soil quality and functions. However, we still have a limited knowledge of the impact of vegetation restoration on carbon sequestration potential in mining areas. In this pursuit, the present study was envisaged to integrate the findings from studies on soil organic carbon (SOC) sequestration in mining areas under vegetation restoration with field monitoring data. The carbon sequestration potential under vegetation restoration in China's mining areas was estimated by using a machine learning model. The results showed that (1) Vegetation restoration exhibited a consistently positive impact on the changes in the SOC reserves. The carbon sequestration potential was the highest in mixed forests, followed by broad-leaved forests, coniferous forests, grassland, shrubland, and farmland; (2) The number of years of vegetation restoration and mean annual precipitation were found to be the important moderating variables affecting the SOC reserves in reclaimed soils in mining areas; (3) There were significant differences in the SOC sequestration potential under different vegetation restoration scenarios in mining areas in China. The SOC sequestration potential reached up to 9.86 million t C a-1, when the soil was restored to the initial state. Based on the meta-analysis, the maximal attainable SOC sequestration potential was found to be 4.26 million t C a-1. The SOC sequestration potential reached the highest level of 12.86 million t C a-1, when the optimal vegetation type in a given climate was restored. The results indicated the importance of vegetation restoration for improving the soil sequestration potential in mining areas. The time lag in carbon sequestration potential for different vegetation types in mining areas was also revealed. Our findings can assist the development of ecological restoration regimens in mining areas to mitigate the global climate change.

Distribution characteristics of soil carbon density and influencing factors in Qinghai-Tibet Plateau region

The Qinghai-Tibet Plateau has low anthropogenic carbon emissions and large carbon stock in its ecosystems. As a crucial region in terrestrial ecosystems responding to climate change, an accurate understanding of the distribution characteristics of soil carbon density holds significance in estimating the soil carbon storage capacity in forests and grasslands. It performs a crucial role in achieving carbon neutrality goals in China. The distribution characteristics of carbon and carbon density in the surface, middle, and deep soil layers are calculated, and the main influencing factors of soil carbon density changes are analyzed. The carbon density in the surface soil ranges from a minimum of 1.62 kg/m2 to a maximum of 52.93 kg/m2. The coefficient of variation for carbon is 46%, indicating a considerable variability in carbon distribution across different regions. There are substantial disparities, with geological background, land use types, and soil types significantly influencing soil organic carbon density. Alpine meadow soil has the highest carbon density compared with other soil types. The distribution of soil organic carbon density at three different depths is as follows: grassland > bare land > forestland > water area. The grassland systems in the Qinghai-Tibet Plateau have considerable soil carbon sink and storage potential; however, they are confronted with the risk of grassland degradation. The grassland ecosystems on the Qinghai-Tibet Plateau harbor substantial soil carbon sinks and storage potential. However, they are at risk of grassland degradation. It is imperative to enhance grassland management, implement sustainable grazing practices, and prevent the deterioration of the grassland carbon reservoirs to mitigate the exacerbation of greenhouse gas emissions and global warming. This highlights the urgency of implementing more studies to uncover the potential of existing grassland ecological engineering projects for carbon sequestration.