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Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long‐term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE <390 g C m−2) relative to net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE <180 g C m−2). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long‐term network‐based monitoring of vegetation biomass, C fluxes, and SOC stocks. Plain Language Summary Rangelands play a crucial role in providing various ecosystem services, including potential climate change mitigation through increased soil organic carbon (SOC) storage. Accurate estimates of changes in carbon (C) storage are challenging due to the heterogeneous nature of rangelands and the limited availability of field observations. In this work, we leveraged remote sensing observations, tower‐based C flux measurements from over 60 rangeland sites in the Western and Midwestern US, and other environmental data sets to build the process‐based Rangeland Carbon Tracking and Management (RCTM) modeling system. The RCTM system is designed to simulate the past 20 years of rangeland C dynamics and is regionally calibrated. The RCTM system performs well in estimating spatial and temporal rangeland C fluxes as well as spatial SOC storage. Model simulation results revealed increased SOC storage and rangeland productivity driven by annual precipitation patterns. The RCTM system developed by this work can be used to generate accurate spatial and temporal estimates of SOC storage and C fluxes at fine spatial (30 m) and temporal (every 5 days) resolutions, and is well‐suited for informing rangeland C management strategies and improving broad‐scale policy making. Key Points The Rangeland Carbon Tracking and Monitoring System was calibrated to simulate vegetation type‐specific rangeland C dynamics Regional variability in carbon fluxes and soil organic carbon is well represented by a remote sensing‐driven process modeling approach Soil organic carbon stocks in Western and Midwestern US rangelands increased over the past 20 years due to increased precipitation

Information on the spatialtemporal distribution and trends in SOC stocks is limited, especially in drylands of Kenya. Understanding the spatial and temporal changes in SOC stocks in relation to Land use and land cover (LULC), and climate change is essential in advising on sustainable land use management (SLM) practices. This study assessed the spatial and temporal changes in SOC stocks from four LULC types (cropland, forested land, grassland and shrubland) in Kibwezi West, Kenya. A completely randomized design (CRD) was used, with the different LULC types as treatments, each having three sampling points with five replicates. Baseline SOC stocks were analyzed from soil samples collected from the topsoil (0–30 cm) of the four LULC types. Rothamsted carbon model (RothC) was used in simulating SOC under three land management scenarios (BAU – business-as-usual scenario/current land use, LSLM – low SLM (5% increase in carbon inputs) and HSLM – high SLM (20% increase in carbon inputs)) and two climate change scenarios (Representative Concentration Pathways (RCP) 4.5 and 8.5). The SOC stocks significantly (p < 0.05) varied across the LULC types, recording Mg C ha−1 of 20.21 in cropland, 62.13 in forested land, 53.80 in grassland and 30.80 in shrubland. Projections under different land management scenarios showed a higher SOC sequestration potential (Mg C ha−1 yr−1) under HSLM (0.71–1.63) than in LSLM (0.16–1.24) and BAU (0.13–1.12). A reduction in SOC sequestration potential was strongly (p < 0.05) influenced by warmer climate (RCP 8.5) and absence of SLM. The study has shown that HSLM practices, once adopted, could be a measure towards enhancing SOC sequestration and hence climate change mitigation. This study contributes to the body of knowledge on spatial and temporal distribution of SOC in drylands of Kenya. The results will be instrumental in guiding SLM practices and policies in Kibwezi West and other similar landscapes.

Soil process models such as RothC typically assume soil organic carbon (SOC) is in equilibrium at the beginning of each simulation run. This is not likely to be true in the real world, since recalcitrant SOC pools (notably, humified material) take many decades to re-stabilize after a land use change. The equilibrium assumption stems from a spinup method in which the model is run under a single land use until all SOC pools stabilize. To overcome this, we demonstrate an alternative spinup procedure that accounts for historical land use changes. The “steady-state” and “historical” spinup methods both impute unknown C inputs such that the modeled SOC matches empirical measurements at the beginning of the simulation and set initial SOC fractions. Holding all other parameters equal, we evaluated how each spinup affects SOC projections in simulations of agricultural land use change in the U.S. state of Vermont. We found that projected SOC trajectories for all land use scenarios are sensitive to the spinup procedure. These differences are due to disparities in imputed below-ground plant-derived carbon between the two procedures. Compared to the steady-state, imputed C in the historical spinup is higher for land uses that increase SOC (e.g., adoption of regenerative practices) and lower for land uses that decrease SOC (e.g., a transition from pasture to crops), due to the time window within which land use changes are assumed to have occurred. The novel historical spinup procedure captures important dynamics commonly missing in previous studies, representing an advancement in soil process modeling.

Research on the impacts of climate change on soil organic carbon (SOC) stocks has focused on the effects of changes in average climate, but the potential effects of increased climate variability, including more frequent extreme events, remain under-examined. In this study, set in a semiarid agricultural landscape in southeastern Australia, we used the Rothamsted carbon (RothC) model to isolate the effects of interannual rainfall variability on SOC stocks over a 50 yr period. We modelled SOC trends in response to 3 scenarios that had the same 50 yr average climate but different interannual rainfall distributions: non-changing average climate, historic variability (H), and increased variability due to more frequent extreme rainfall years (XH). Relative to the non-changing average climate, RothC simulations predicted net decreases in mean SOC stocks to 50 yr of 11% under the H scenario and 13% under the XH scenario. These decreases were the result of predicted SOC decreases (and increased CO₂ emissions) in extreme wet years (ca. 0.26 Mg ha⁻¹ yr⁻¹) that were not counterbalanced by SOC increases in extreme dry years (ca. 0.11 Mg ha⁻¹ yr⁻¹). No significant difference in mean SOC stocks at 50 yr between the H and XH scenarios was likely due to an increase in both extreme wet and counterbalancing extreme dry years in the latter. Strong negative correlations were found between annual changes in SOC stocks and rainfall. Our modelled predictions indicate the potential for extreme rainfall years to influence SOC gains and losses in semiarid environments and highlight the importance of maintaining plant inputs in these environments, particularly during extreme wet years.

Modelling of the carbon dynamics in arable soils is complex and the accuracy of the predictions is unknown before the model is applied to each specific site. Objectives were (i) to test the accuracy of predictions of the carbon dynamics using the Rothamsted Carbon (RothC) Model in a field trial in Quzhou, North China Plain, using different methods for initialization and estimation of carbon input into the soil and (ii) to test the applicability of the RothC model for plots with either conventional tillage (CT) or no-tillage (NT) systems. A field trial was conducted with applications of differing amounts of N (0, 112 or 187 kg N ha⁻¹ year⁻¹), P (0, 75 or 150 kg P₂O₅ ha⁻¹ year⁻¹) and wheat straw (0, 2.25 or 4.5 t DM ha⁻¹ year⁻¹) in differing combinations with either CT or NT for 18 years. CT and NT affected stocks of soil organic carbon (SOC) similarly. Carbon inputs from crops were either estimated from published regression functions that relate C inputs to crop yield including rhizodeposition (models 1 and 2) or published root:aboveground biomass ratios (model 3). Model 1, which was not calibrated to the site conditions, was successful in predicting the carbon dynamics in seven out of nine treatments (model efficiencies EF ranged from 0.28 to 0.87), whereas for two treatments, EF (−0.35 and−2.3) indicated an unsuccessful prediction. The prediction of the C dynamics in NT experiments using model 1 was generally successful, but this may have been due to the fact that NT did not have a specific effect on SOC stocks for this trial. Model 2, which was the same as model 1 except for an optimization of the stock of inert organic matter using one treatment, predicted SOC stocks in the remaining eight treatments overall better than model 1. Model 3 was less successful than models 1 and 2 in all treatments (−19 ≤ EF ≤ 0.56). The results indicate that the RothC model may successfully predict C dynamics—for the site studied even without prior calibration as in model 1—, but care should be taken in choosing an appropriate approach for estimating C inputs into the soil.

A variety of process-based models have been developed for predicting nitrogen (N) dynamics in agro-ecosystem; however, no reliable models have been validated for N leaching from soils receiving a long-term application of different types of animal manure composts. The Leaching Estimation and Chemistry Model (LEACHM) was recently modified by incorporating the basic structure of Rothamsted Carbon Model for extending its ability to describe soil organic matter decomposition and subsequent N leaching in soils rich in organic matter. We evaluate the applicability of the modified LEACHM in cropped Yellow soils receiving 10-year application of cattle or swine manure compost in addition to chemical fertilizers, where high-frequency field monitoring data of soil water contents, soil N contents and leachate N concentrations were available for the last 3 years. Particular attention was paid to determine all input parameters from independent measurements, parameterization from known soil properties or databases without optimisation to fit the measured field data. The model reasonably predicted temporal changes in the soil NH₄-N and NO₃-N contents, and inorganic N concentrations in the leachate as well as their differences due to different manure compost/chemical fertilizer applications. The simulations of leached N concentration yielded a Willmott index of agreement (IA) of 0.62–0.68, with those for soil moisture, soil nitrate content and crop N uptake all within an acceptable IA range. In view of the good performance without site-specific calibrations, the modified LEACHM appears to be a valuable tool for predicting N leaching from cropped soils receiving long-term manure compost applications.

The fertility of sandy soils in the Sahelian zone (SZ) is extremely low. This poor soil fertility is one of the limiting factors of crop production in the SZ. Therefore, it is imperative to improve or to maintain soil fertility through various agricultural management methods. Further, it is well known that soil organic matter plays an important role in improving the physico-chemical properties of these sandy infertile soils. Therefore, it is essential to develop a suitable tool for the appropriate evaluation of soil organic carbon (SOC) dynamics in the SZ. Therefore, the Rothamsted carbon model (Roth-C) was verified in 32 treatments of two long-term field experiments with and without crop residue application. These experiments were performed by ICRISAT. The performance of the model was evaluated by statistical methods using four indices (RMSE: root mean square error, LOFIT: lack of fit, r: correlation coefficient, and M: mean difference). As a result, the predicted SOC values in the case without crop residue management decreased with time in approximately 10 cultivated years. In contrast, in the case with crop residue application, the predicted SOC remained roughly equal to the initial SOC value during the term observed. Mostly, the Roth-C-modelled values agreed well with the actual value. RMSE and LOFIT, the statistical indicators of agreement between predicted and observed values, showed a significant conformity between the predicted and observed SOC values in all the 32 treatments. This fact means that Roth-C can estimate long-term SOC dynamics of several technical options that developed with short-term trials. Moreover the annual carbon requirement for SOC maintaining can be calculate if enough number of cases was estimated. And also analysis of regional carbon dynamics was made possible with using Roth-C model. It will contribute to show the sustainable development in SZ against global warming and other climatic changes.

Soil organic carbon (SOC) models have been widely used to predict SOC change with changing environmental and management conditions, but the accuracy of the prediction is often open to question. Objectives were (i) to quantify the amounts of C derived from maize in soil particle size fractions and at various depths in a long-term field experiment using 13C/12C analysis, (ii) to model changes in the organic C, and (iii) to compare measured and modelled pools of C. Maize was cultivated for 24 years on a silty Luvisol which resulted in a stock of 1.9 kg maize-derived C m-2 (36% of the total organic C) in the Ap horizon. The storage of maize-derived C in particle size fractions of the Ap horizon decreased in the order clay (0.65 kg C m-2) > fine and medium silt (0.43) > coarse silt (0.33) > fine sand (0.13) > medium sand (0.12) > coarse sand (0.06) and the turnover times of C3-derived C ranged from 26 (fine sand) to 77 years (clay). The turnover times increased with increasing soil depth. We used the Rothamsted Carbon Model to model the C dynamics and tested two model approaches: model A did not have any adjustable parameters, but included the Falloon equation for the estimation of the amount of inert organic matter (IOM) and independent estimations of C inputs into the soil. The model predicted well the changes in C3-derived C with time but overestimated the changes in maize-derived C 1.6-fold. In model B, the amounts of IOM and C inputs were optimized to match the measured C3- and C4-derived SOC stocks after 24 years of continuous maize. This model described the experimental data well, but the modelled annual maize C inputs (0.41 kg C m-2 a-1) were less than the independently estimated total input of maize litter C (0.63 kg C m-2 a-1) and even less than the annual straw C incorporated into the soil (0.46 kg C m-2 a-1). These results indicated that the prediction of the Rothamsted Carbon Model with independent parameterization served only as an approximation for this site. The total amount of organic C associated with the fraction 0-63 μm agreed well with the sum of the pools 'microbial biomass', 'humified-organic matter' and IOM of the model B. However, the amount of maize-derived C in this fraction (3.4 g kg-1) agreed only satisfactorily with the sum of maize-derived C in the pools 'microbial biomass' and 'humified organic matter' (2.6 g kg-1).

A model for the turnover of organic matter in soil, ROTHC-26.3, can be used to calculate how much organic C needs to enter a soil annually in order to maintain a specified stock of soil organic C. The annual return of organic C thus calculated, plus the amount of organic C removed annually from the site by harvesting, burning, etc., provides an estimate of the Net Primary Production (NPP) of that site, averaged over many years. The new method was used to calculate NPP for two adjacent savanna sites in the Nairobi National Park in Kenya, one grazed and one not, and for a dry Miombo woodland site in Zambia. Both the Kenyan and Zambian sites are taken to be at equilibrium, with soil organic C levels at steady state. Soils from the three sites were analyzed by layer for organic C, δ14C, δ13C, soil microbial biomass C, total N, pH, and clay content. Radiocarbon measurements were >100% modern in the surface layers (0-15 cm) of the Kenyan soils (both Vertisols) and in all three layers (0-15, 15-30 and 30-50 cm) of the Zambian soil (an Oxisol), presumably because of14C coming from the testing of thermonuclear bombs. The 15-30 cm layer of the Kenyan soils dated at ∼ 500 yr and the 30-50 cm layer at ∼ 900 yr. The14C data were consistent with the presence of a small inert fraction of organic C that accounted for an increasing proportion of total organic C with increasing soil depth. The13C data indicated that the Kenyan soils had developed under C4vegetation, whereas the Zambian soils had developed under vegetation dominated by C3plants. From these results the annual input of C to soil from the ungrazed Kenyan site was calculated to be 388 g C· m-2· yr-1, to the grazed site 380 g C· m-2· yr-1, and to the Zambian soil 373 g C· m-2· yr-1. Taking the loss of C from the Kenyan sites by burning to be 40 g C· m-2· yr-1, the mean NPP for both Kenyan sites is 424 g C· m-2· yr-1. This value for NPP is compatible with earlier estimates of NPP by botanical methods from the same site in Kenya. Wood-taking is thought to be minimal in the protected Zambian woodland, so that here the annual input of C to the soil can be taken as the NPP without great error. This new method provides a long-term, integrated measure of NPP that should complement and enhance productivity measurements made by harvest methods over shorter periods.

Classification of soil potential productivity, the classification criteria, and standards for evaluating the soil fertility in Japan are introduced in this chapter. Increasing the carbon sequestration in soil during the process of biomass production is of great importance to improve the soil fertility for producing more biomass. The modified Rothamsted Carbon model that has been improved for evaluating major arable soils in Japan, i.e. Andosols and paddy soils, will also be included. Soil microbes play crucial roles in soil functions, especially nutrient cycling, and thus contribute significantly to sustainable crop production. General aspects of soil microbes are discussed in order to provide an understanding of the role that soil microbes play in crop production. Because soils with higher nitrogen-supplying capacity, expressed as the rate of microbial mineralization of soil organic matter, are considered to have higher soil fertility; microbial mediation to enhance soil fertility is also discussed.