Interactive impacts of climate change and agricultural management on soil organic carbon sequestration potential of cropland in China over the coming decades

Re-identifying farmland carbon neutrality gap under a new carbon counting and the framework of regional interactions in China

The farmland ecosystem, with its numerous material cycles and energy flows, is an important part of the carbon cycle in terrestrial ecosystems. Focusing on the carbon neutrality of farmland is meaningful for mitigating global warming and serving national low-carbon strategies. This study enriches the carbon accounting items of farmland and establishes a new research framework to check the carbon neutrality of farmland from the aspect of regional interactions and, subsequently, the inequality among China's provinces. The results revealed that there is still a great gap in the capability of China's farmland to reach carbon neutrality, with a gap value of up to 10,503 × 104 t C. All of the provinces presented net carbon emissions, and the per unit area carbon neutrality gaps showed spatial regularity decreasing from the coastal regions to the inland areas. Anthropogenic carbon emissions on farmland played a dominant role compared with soil organic carbon. Five provinces had reduced interior-regional carbon emissions through grain trade, and the amounts were especially high for developed regions, such as Guangdong, Zhejiang, Beijing, Shanghai and Jiangsu. Sixteen provinces gained external carbon emissions through trade; these were the less developed regions located mainly in the north, such as Inner Mongolia, Hebei, Jilin, Heilongjiang and Xinjiang. Under regional inequality, 15 provinces added to the net amount of the carbon emissions generated in external regions, with China's megacities adding the highest percentage, especially Beijing, with 389.95 % compared with its original emissions. Inequality showed that most provinces had a moderate status. Sichuan and Hunan experienced weak advantages, and six provinces had disadvantages. Therefore, constructing compensation and trade-based rights and responsibilities traceability mechanisms is important.

Sensitivity of gross primary production to precipitation and the driving factors in China's agricultural ecosystems

Recent climate warming has significantly affected the sensitivity of Gross Primary Productivity (GPP) to precipitation within China's agricultural ecosystems. Nonetheless, the spatial and temporal nonlinear evolution patterns of GPP-precipitation sensitivity under climate change, as well as the underlying drivers and long-term trends of this sensitivity, are not well understood. This study employs correlation analysis to quantify the sensitivity between GPP and precipitation in China's agricultural ecosystems, and utilizes nonlinear detection algorithms to examine the long-term changes in this sensitivity. Advanced machine learning techniques and frameworks are subsequently applied to analyze the driving factors of GPP-precipitation sensitivity in China's agricultural ecosystems. The findings reveal that approximately 49.00 % of the analyzed pixels exhibit a significant positive correlation between GPP and precipitation. Nonlinear change analysis indicates spatial heterogeneity in GPP-precipitation sensitivity across China's agricultural ecosystems, with patterns showing initial increases followed by decreases accounting for 25.12 %, and patterns of initial decreases followed by increases at 13.27 %. Machine learning analysis identifies temperature, soil moisture, and crop water footprint as the primary factors influencing GPP-precipitation sensitivity in agricultural ecosystems. This study is the first to introduce crop water footprint as a significant factor in the analysis of GPP-precipitation sensitivity. It not only offers new insights into the temporal nonlinear changes and driving factors of GPP-precipitation sensitivity but also underscores the importance of enhancing agricultural water efficiency to maintain agricultural ecosystem health and ensure food security under climate change.

Shifts of soil archaeal nitrification and methanogenesis with elevation in water level fluctuation zone of the three Gorges Reservoir, China

The water level fluctuation zone is a unique ecological zone exposed to long-term drying and flooding and plays a critical role in the transport and transformation of carbon and nitrogen materials in reservoir-river systems. Archaea are a vital component of soil ecosystems in the water level fluctuation zones, however, the distribution and function of archaeal communities in responde to long-term wet and dry alternations are still unclear. The community structure of archaea in the drawdown areas at various elevations of the Three Gorges Reservoir was investigated by selecting surface soils (0-5 cm) of different inundation durations at three sites from upstream to downstream according to the flooding pattern. The results revealed that prolonged flooding and drying increased the community diversity of soil archaea, with ammonia-oxidizing archaea being the dominant species in non-flooded regions, while methanogenic archaea were abundant in soils that had been flooded for an extended period of time. Long-term alternation of wetting and drying increases methanogenesis but decreases nitrification. It was determined that soil pH, NO3--N, TOC and TN are significant environmental factors affecting the composition of soil archaeal communities (P = 0.02). Long-term flooding and drying changed the community composition of soil archaea by altering environmental factors, which in turn influenced nitrification and methanogenesis in soils at different elevations. These findings contribute to our understanding of soil carbon and nitrogen transport transformation processes in the water level fluctuation zone as well as the effects of long-term wet and dry alternation on soil carbon and nitrogen cycles. The results of this study can provide a basis for ecological management, environmental management, and long-term operation of reservoirs in water level fluctuation zones.

Incorporating agricultural practices in digital mapping improves prediction of cropland soil organic carbon content: The case of the Tuojiang River Basin

Accurate mapping of soil organic carbon (SOC) in cropland is essential for improving soil management in agriculture and assessing the potential of different strategies aiming at climate change mitigation. Cropland management practices have large impacts on agricultural soils, but have rarely been considered in previous SOC mapping work. In this study, cropland management practices including carbon input (CI), length of cultivation (LC), and irrigation (Irri) were incorporated as agricultural management covariates and integrated with natural variables to predict the spatial distribution of SOC using the Extreme Gradient Boosting (XGBoost) model. Additionally, we evaluated the performance of incorporating agricultural management practice variables in the prediction of cropland topsoil SOC. A case study was carried out in a traditional agricultural area in the Tuojiang River Basin, China. We found that CI was the most important environmental covariate for predicting cropland SOC. Adding cropland management practices to natural variables improved prediction accuracy, with the coefficient of determination (R²), the root mean squared error (RMSE) and Lin's Concordance Correlation Coefficient (LCCC) improving by 16.67%, 17.75% and 5.62%, respectively. Our results highlight the effectiveness of incorporating agricultural management practice information into SOC prediction models. We conclude that the construction of spatio-temporal database of agricultural management practices derived from inventories is a research priority to improve the reliability of SOC model prediction.

Spatiotemporal changes in greenhouse gas emissions and soil organic carbon sequestration for major cropping systems across China and their drivers over the past two decades

Chinese agricultural systems have experienced dramatic changes in crop planting area, cropping system, irrigation and fertilization managements, and crop yields in recent decades. These changes can substantially affect greenhouse gases (GHG) emissions and soil organic carbon (SOC) sequestration in croplands. However, the spatiotemporal patterns, as well as their driving factors and mechanisms, have not been well understood. Here, the Denitrification-Decomposition model is calibrated and validated to estimate nitrous oxide (N₂O) and methane (CH₄) emissions and SOC sequestration for seven major cropping systems in China during 2001-2020. The Logarithmic Mean Divisia Index method is further applied to attribute the net GHG emissions (NGEs) trend to various drivers. The results show that the total N₂O emissions, CH₄ emissions, and SOC sequestration were approximately 23.7, 182.0, and 177.6 Tg CO₂-eq/year in the croplands across China. The national average NGEs per unit area ranged from -8705 to 8431 kg CO₂-eq ha^-1 year^-1 across the major cropping systems. During 2001-2020, the trend in national annual NGEs was 0.66 kg CO₂-eq ha^-1 year^-2, ranging from -78.9 to 82.2 kg CO₂-eq ha^-1 year^-2 across the major cropping systems. The paddy lands were mainly a carbon source due to the large amount of CH₄ emissions while the uplands could be a carbon sink owing to SOC sequestration. As a whole, the cropland in China was a carbon source with the NGEs equal to 28.4 Tg CO₂-eq/year, and the NGEs increased by 0.047 Tg CO₂-eq/year² in the past 20 years. Nationally, changes in crop planting area and yields reduced the NGEs whereas changes in nitrogen use efficiency and cropping systems increased them, although the major factors and their impacts varied greatly among regions. Optimizing cropping systems and nitrogen fertilization based on the local genotype, environment and management should be the most effective method to reduce the NGEs in croplands.

Does the Application of ICTs Improve the Efficiency of Agricultural Carbon Reduction? Evidence from Broadband Adoption in Rural China

Based on the Environmental Kuznets Curve (EKC) hypothesis, this paper examines whether rural broadband adoption affects agricultural carbon reduction efficiency (ACRE), using panel data from 30 Chinese provinces from 2011 to 2019. This paper achieves a measurement of ACRE by taking the carbon sink of agricultural as one of the desired outputs and using a Slacks-Based Measure (SBM) model and the global Malmquist–Luenberger (GML) index. The results show that: (1) Rural broadband adoption has a positive effect on ACRE. The relationship between the income of rural residents and ACRE was an inverted U-shaped, which confirms the EKC hypothesis. (2) Land transfer has a significant promoting effect on the relationship between rural broadband adoption and ACRE. When the land transfer rate is high, the positive effect of broadband adoption is obvious. (3) The positive effect of broadband adoption on ACRE was more obvious when farmers invested more in production equipment, that is to say, it has a significant positive moderating effect. As farmers in many developing countries suffer from increasingly frequent and severe extreme weather events, we believe that the results of this study also have implications for the implementation of agricultural carbon reduction and smart agricultural equipment roll-out in many countries.