Using process-oriented model output to enhance machine learning-based soil organic carbon prediction in space and time

Monitoring and modelling soil organic carbon (SOC) in space and time can help us to better understand soil carbon dynamics and is of key importance to support climate change research and policy. Although machine learning (ML) has attracted a lot of attention in the digital soil mapping (DSM) community for its powerful ability to learn from data and predict soil properties, such as SOC, it is better at capturing soil spatial variation than soil temporal dynamics. By contrast, process-oriented (PO) models benefit from mechanistic knowledge to express physiochemical and biological processes that govern SOC temporal changes. Therefore, integrating PO and ML models seems a promising means to represent physically plausible SOC dynamics while retaining the spatial prediction accuracy of ML models. In this study, a hybrid modelling framework was developed and tested for predicting topsoil SOC stock in space and time for a regional cropland area located in eastern China. In essence, the hybrid model uses predictions of the PO model in unsampled years as additional training data of the ML model, with a weighting parameter assigned to balance the importance of SOC values from the PO model and real measurements. The results indicated that temporal trends of SOC stock modelled by PO and ML models were largely different, while they were notably similar between the PO and hybrid models. Cross-validation showed that the hybrid model had the best performance (RMSE = 0.29 kg m-2), with a 19 % improvement compared with the ML model. We conclude that the proposed hybrid framework not only enhances space-time soil carbon mapping in terms of prediction accuracy and physical plausibility, it also provides insights for soil management and policy decisions in the face of future climate change and intensified human activities.

Soil carbon stocks and dynamics of different land uses in Italy using the LUCAS soil database

In terrestrial biosphere, soil represents the largest organic carbon pool, and a small change of soil organic carbon (SOC) can significantly affect the global carbon cycle and climate. Land use change (LUC) and soil management practices coupled with climate variables can significantly influence the soil organic carbon stocks (SOC-S) and its dynamics; however, our understanding about the responses of SOC in different LUC's (e.g., cropland, grassland and forest land) to mitigate climate change is quite limited at country level like Italy. Thus, the aims of this study were which factors are affecting SOC dynamics in three LUC's over time across Italy; and their relevance in terms of SOC-S in the superficial layer of soil that significantly contributes to the climate change mitigation, using LUCAS soil database. To calculate the SOC-S, it is necessary to have soil bulk density (BD) which is not present in the LUCAS database. Thus, we estimate the soil BD using the pedotransfer function (PTFs); and results shows that the soil BD obtained from fitting of the PTFs were reasonable to estimate the SOC-S for different land use types (R² ≥ 0.75). Overall, results showed that LUC's and soil management practices can significantly (p < 0.001) influences SOC dynamics and SOC storage from the soil and varied among LUC's but not for over time except grassland. Spatially, the mean SOC-S storage of the different LUC's was in the following order: forest land > grassland > cropland for both years 2009 and 2015. On the other hand, the SOC-S storage increased by 8.33% for cropland, 13.56% for forest land, and 29.79% for grassland during the year of 2009-2015, while SOC-S storage increased significantly (p < 0.001) in grassland over time but not for cropland and forest land which also follow the increasing trend but insignificantly. Our results also reveal that the SOC dynamics negatively correlated with MAT, and positively correlated with MAP for all land uses except forest land. Thus, this research indicates that LUC's and soil management practices coupled with climate variables can significantly influence SOC storage and its dynamics in the superficial layer of soil which have the potential capacity to mitigate climate change.

Burning increases post-fire carbon emissions in a heathland and a raised bog, but experimental manipulation of fire severity has no effect

Large amounts of carbon are stored in northern peatlands. There is concern that greater wildfire severity following projected increases in summer drought will lead to higher post-fire carbon losses. We measured soil carbon dynamics in a Calluna heathland and a raised peat bog after experimentally manipulating fire severity. A gradient of fire severity was achieved by simulating drought in 2 × 2 m plots. Ecosystem respiration (ER), net ecosystem exchange (NEE), methane (CH₄) flux and concentration of dissolved organic carbon ([DOC], measured at the raised bog only) were measured for up to two years after burning. The response of these carbon fluxes to increased fire severity in drought plots was similar to plots burnt under ambient conditions associated with traditional managed burning. Averaged across all burnt plots, burning altered mean NEE from a net carbon sink at the heathland (-0.33 μmol CO₂ m^-2 s^-1 in unburnt plots) to a carbon source (0.50 μmol m^-2 s^-1 in burnt plots) and at the raised bog (-0.38 and 0.16 μmol m^-2 s^-1, respectively). Burning also increased CH₄ flux at the raised bog (from 1.16 to 25.3 nmol m^-2 s^-1 in the summer, when it accounted for 79% of the CO₂-equivalent emission). Burning had no significant effect on soil water [DOC].

Long-term impact of conservation agriculture and diversified maize rotations on carbon pools and stocks, mineral nitrogen fractions and nitrous oxide fluxes in inceptisol of India

Given the increasing scarcity of production resources such as water, energy and labour coupled with growing climatic risks, maize-based production systems could be potential alternatives to intensive rice-wheat (RW) rotation in western Indo-Gangetic Plains (IGP). Conservation agriculture (CA) in maize systems has been widely promoted for minimizing soil degradation and ensuring sustainability under emerging climate change scenario. Such practices are also believed to provide mitigation co-benefits through reduced GHG emission and increased soil carbon sequestration. However, the combined effects of diversified crop rotations and CA-based management on GHG mitigation potential and other co-benefits are generally over looked and hence warrant greater attention. A field trial was conducted for 5-years to assess the changes in soil organic carbon fractions, mineral-N, N₂O emission and global warming potential (GWP) of maize-based production systems under different tillage & crop establishment methods. Four diversified cropping systems i.e. maize-wheat-mungbean (MWMb), maize-chickpea-Sesbania (MCS), maize-mustard-mungbean (MMuMb) and maize-maize-Sesbania (MMS) were factorially combined with three tillage & crop establishment methods i.e. zero tilled permanent beds (PB), zero-tillage flat (ZT) and conventional tillage (CT) in a split-plot design. After 5-years of continued experimentation, we recorded that across the soil depths, SOC content, its pools and mineral-N fractions were greatly affected by tillage & crop establishment methods and cropping systems. ZT and PB increased SOC stock (0-30 cm depth) by 7.22-7.23 Mg C ha^-1 whereas CT system increased it only by 0.88 Mg C ha^-1as compared to initial value. Several researchers reported that SOC & mineral-N fraction contents in the top 30 cm soil depth are correlated with N₂O-N emission. In our study, global warming potential (GWP) under CT system was higher by 18.1 and 17.4%, compared to CA-based ZT and PB, respectively. Among various maize systems, GWP of MMS were higher by 11.2, 6.7 and 6.6%, compared that of MWMb (1212 kg CO₂-eq. ha^-1), MCS (1274 kg CO₂-eq. ha^-1) and MMuMb (1275 kg CO₂-eq. ha^-1), respectively. The results of our study suggest that CA and diversified crop rotations should be promoted in north-western IGP and other similar agro-ecologies across the globe for ensuring food security, restoration of soil health and climate change mitigation, the key sustainable development goals (SDGs).

Soil respiration and carbon loss relationship with temperature and land use conversion in freeze-thaw agricultural area

Soil respiration (Rs) was hypothesized to have a special response pattern to soil temperature and land use conversion in the freeze-thaw area. The Rs differences of eight types of land use conversions during agricultural development were observed and the impacts of Rs on soil organic carbon (SOC) loss were assessed. The land use conversions during last three decades were categorized into eight types, and the 141 SOC sampling sites were grouped by conversion type. The typical soil sampling sites were subsequently selected for monitoring of soil temperature and Rs of each land use conversion types. The Rs correlations with temperature at difference depths and different conversion types were identified with statistical analysis. The empirical mean error model and the biophysical theoretical model with Arrhenius equation about the Rs sensitivity to temperature were both analyzed and shared the similar patterns. The temperature dependence of soil respiration (Q10) analysis further demonstrated that the averaged value of eight types of land use in this freeze-thaw agricultural area ranged from 1.15 to 1.73, which was lower than the other cold areas. The temperature dependence analysis demonstrated that the Rs in the top layer of natural land covers was more sensitive to temperature and experienced a large vertical difference. The natural land covers exhibited smaller Rs and the farmlands had the bigger value due to tillage practices. The positive relationships between SOC loss and Rs were identified, which demonstrated that Rs was the key chain for SOC loss during land use conversion. The spatial-vertical distributions of SOC concentration with the 1.5-km grid sampling showed that the more SOC loss in the farmland, which was coincided with the higher Rs in farmlands. The analysis of Rs dynamics provided an innovative explanation for SOC loss in the freeze-thaw agricultural area. The analysis of Rs dynamics provided an innovative explanation for SOC loss in the freeze-thaw agricultural area.