Land use change (LUC) alters the global carbon (C) stock, but our estimation of the alteration remains uncertain and is a major impediment to predicting the global C cycle. The uncertainty is partly due to the limited number and geographical bias of observations, and limited exploration of its predictors. Here we generated a comprehensive global database of 5,980 observations from 790 articles. The number of sites evaluated is at least seven times larger than in previous meta‐analyses. Our constrained estimates of different LUC's effects on soil organic C (SOC) and their variations across global climates reveal underestimation/overestimation in previous estimates. Converting forests and grasslands to croplands reduced SOC by 24.5% ± 1.53% (−11.03 ± 1.06 Mg ha⁻¹) and 22.7% ± 1.22% (−8.09 ± 0.67 Mg ha⁻¹), while 28.0% ± 1.56% (4.46 ± 0.42 Mg ha⁻¹) and 33.5% ± 1.68% (5.8 ± 0.38 Mg ha⁻¹) increases, respectively, were obtained in the reverse processes. Converting forests to grasslands decreased SOC by 2.1% ± 1.22% (−1.13 ± 0.44 Mg ha⁻¹), while the reverse process increased SOC by 18.6% ± 1.73% (3.31 ± 0.51 Mg ha⁻¹). Modeled relative importance of 10 drivers of LUC's impact on SOC revealed that higher initial SOC (iSOC) does not solely determine SOC loss in SOC‐negative LUC scenarios as previously proposed. Across four decades, reconverting croplands to forests and grasslands recovered only 49.5% (6.1 ± 0.51 Mg ha⁻¹) and 75.3% (7.0 ± 0.38 Mg ha⁻¹) of the iSOC, respectively, indicating the need for protecting C‐rich ecosystems. Our global data set advances information on LUC's effect on SOC and can be valuable to constrain Earth system models to reliably estimate global SOC stocks and plan climate change mitigation strategies.

The dynamics of soil phosphorus (P) control its bioavailability. Yet it remains a challenge to quantify soil P dynamics. Here we developed a soil P dynamics (SPD) model. We then assimilated eight data sets of 426‐day changes in Hedley P fractions into the SPD model, to quantify the dynamics of six major P pools in eight soil samples that are representative of a wide type of soils. The performance of our SPD model was better for labile P, secondary mineral P, and occluded P than for nonoccluded organic P (Po) and primary mineral P. All parameters describing soil P dynamics were approximately constrained by the data sets. The average turnover rates were labile P 0.040 g g⁻¹ day⁻¹, nonoccluded Po 0.051 g g⁻¹ day⁻¹, secondary mineral P 0.023 g g⁻¹ day⁻¹, primary mineral P 0.00088 g g⁻¹ day⁻¹, occluded Po 0.0066 g g⁻¹ day⁻¹, and occluded inorganic P 0.0065 g g⁻¹ day⁻¹, in the greenhouse environment studied. Labile P was transferred on average more to nonoccluded Po (transfer coefficient of 0.42) and secondary mineral P (0.38) than to plants (0.20). Soil pH and organic C concentration were the key soil properties regulating the competition for P between plants and soil secondary minerals. The turnover rate of labile P was positively correlated with that of nonoccluded Po and secondary mineral P. The pool size of labile P was most sensitive to its turnover rate. Overall, we suggest data assimilation can contribute significantly to an improved understanding of soil P dynamics.

Land ecosystems contribute to climate change mitigation by taking up approximately 30% of anthropogenically emitted carbon. However, estimates of the amount and distribution of carbon uptake across the world's ecosystems or biomes display great uncertainty. The latter hinders a full understanding of the mechanisms and drivers of land carbon uptake, and predictions of the future fate of the land carbon sink. The latter is needed as evidence to inform climate mitigation strategies such as afforestation schemes. To advance land carbon cycle modeling, we have developed a matrix approach. Land carbon cycle models use carbon balance equations to represent carbon exchanges among pools. Our approach organizes this set of equations into a single matrix equation without altering any processes of the original model. The matrix equation enables the development of a theoretical framework for understanding the general, transient behavior of the land carbon cycle. While carbon input and residence time are used to quantify carbon storage capacity at steady state, a third quantity, carbon storage potential, integrates fluxes with time to define dynamic disequilibrium of the carbon cycle under global change. The matrix approach can help address critical contemporary issues in modeling, including pinpointing sources of model uncertainty and accelerating spin‐up of land carbon cycle models by tens of times. The accelerated spin‐up liberates models from the computational burden that hinders comprehensive parameter sensitivity analysis and assimilation of observational data to improve model accuracy. Such computational efficiency offered by the matrix approach enables substantial improvement of model predictions using ever‐increasing data availability. Overall, the matrix approach offers a step change forward for understanding and modeling the land carbon cycle.