Interpreting spatial heterogeneity of crop yield with a process model and remote sensing

A process-based crop growth model (Vegetation Interface Processes (VIP) model) is used to estimate crop yield with remote sensing over the North China Plain. Spatial pattern of the key parameter—maximum catalytic capacity of Rubisco (V_cmax) for assimilation is retrieved from Normalized Difference of Vegetation Index (NDVI) from Terra-MODIS and statistical yield records. The regional simulation shows that the agreements between the simulated winter wheat yields and census data at county-level are quite well with R² being 0.41-0.50 during 2001-2005. Spatial variability of photosynthetic capacity and yield in irrigated regions depend greatly on nitrogen input. Due to the heavy soil salinity, the photosynthetic capacity and yield in coastal region is less than 50 μmol C m⁻² s⁻¹ and 3000 kg ha⁻¹, respectively, which are much lower than that in non-salinized region, 84.5 μmol C m⁻² s⁻¹ and 5700 kg ha⁻¹. The predicted yield for irrigated wheat ranges from 4000 to 7800 kg ha⁻¹, which is significantly larger than that of rainfed, 1500-3000 kg ha⁻¹. According to the path coefficient analysis, nitrogen significantly affects yield, by which water exerts noticeably indirect influences on yield. The effect of water on yield is regulated, to a certain extent, by crop photosynthetic capacity and nitrogen application. It is believed that photosynthetic parameters retrieved from remote sensing are reliable for regional production prediction with a process-based model.     

Modelling the impact of thermal adaptation of soil microorganisms and crop system on the dynamics of organic matter in a tropical soil under a climate change scenario

No consensus currently exists about how climate change should affect the status of soil organic matter (SOM) in the tropics. In this study, we analyse the impact of climate change on the underlying mechanisms controlling SOM dynamics in a ferralsol under two contrasting tropical crops: maize (C4 plant) and banana (C3 plant). We model the effect of microbial thermal adaptation on carbon (C) mineralisation at the crop system scale and introduce it in the model STICS, which was previously calibrated for the soil-crop systems tested in this study. Microbial thermal adaptation modelling is based on a reported theory for thermal acclimation of plant and soil respiration. The climate is simulated from 1950 to 2099 for the tropical humid conditions of Guadeloupe (French Antilles), using the ARPEGE model and the IPCC emission scenario A1B. The model predicts increases of 3.4 °C for air temperature and 1100 mm yr⁻¹ for rainfall as a response to an increase of 375 ppm for atmospheric carbon dioxide concentration in the 2090-2099 decade compared with the 1950-1959 decade. The results of the STICS model indicate that the crop affects the response of SOM to climate change by controlling the change in several variables involved in C dynamics: C input, soil temperature and soil moisture. SOM content varies little until 2020, and then it decreases faster for maize than for banana. The decrease is weakened under the hypothesis of thermal adaptation, and this effect is greater for maize (−180 kg C ha⁻¹ yr⁻¹ without adaptation and −140 kg C ha⁻¹ yr⁻¹ with adaptation) than for banana (−60 kg C ha⁻¹ yr⁻¹ and −40 kg C ha⁻¹ yr⁻¹, respectively). The greater SOM loss in maize is mainly due to the negative effect of warming on maize growth decreasing C input from residues. Climate change has a small effect on banana growth, and SOM loss is linked to its effect on C mineralisation. For both crops, annual C mineralisation increases until 2040, and then it decreases continuously. Thermal adaptation reduces the initial increase in mineralisation, but its effect is lower on the final decrease, which is mainly controlled by substrate limitation. No stabilisation in SOM status is attained at the end of the analysed period because C mineralisation is always greater than C input. Model predictions indicate that microbial thermal adaptation modifies, but does not fundamentally change the temporal pattern of SOM dynamics. The vegetation type (C3 or C4) plays a major role in SOM dynamics in this tropical soil because of the different impact of climate change on crop growth and then on C inputs.     

Modeling carbon sequestration under zero-tillage at the regional scale. II. The influence of crop rotation and soil type

For policy decisions with respect to CO₂-mitigation measures in the agricultural sector, national and regional estimations of the efficiency of such measures are required. The conversion of ploughed cropland to zero-tillage is discussed as an option to reduce CO₂ emissions and promises at the same time effective soil and water conservation. Based on the upscaling of simulation results with the soil and land resources information system SLISYS-BW, estimations of CO₂-mitigation rates in relation to crop rotations and soil type have been made for the state of Baden-Württemberg (Germany). The results indicate considerable differences in the CO₂-mitigation rates between crop rotations ranging from 0.48 to 0.03 Mg C ha⁻¹ a⁻¹ for winter cereals–spring cereals–rape rotations and winter cereals–spring cereals–corn silage rotations, respectively. The efficiency of the crop rotations is strongly related to the total carbon input and in particular the amount of crop residues. Among the considered soil types, highest CO₂-mitigation rates are associated with Cumulic Anthrosols (0.62 Mg C ha⁻¹ a⁻¹) and the lowest with Gleysols (−0.01 Mg C ha⁻¹ a⁻¹). An agricultural extensification scenario with conventional plowing but conversion of the presently applied intensive crop rotations to a clover–clover–winter cereals rotation indicated a CO₂-mitigation potential of 466 Gg C a⁻¹. However, the present high market prices for cereals and increasing demand for energy production from biomass encourages an intensification of the agricultural production and an excessive removal of biomass which in future will seriously reduce the potential for carbon sequestration on cropland.     

Simulating soil organic matter with CQESTR (v. 2.0): Model description and validation against long-term experiments across North America

Soil carbon (C) models are important tools for examining complex interactions between climate, crop and soil management practices, and to evaluate the long-term effects of management practices on C-storage potential in soils. CQESTR is a process-based carbon balance model that relates crop residue additions and crop and soil management to soil organic matter (SOM) accretion or loss. This model was developed for national use in U.S and calibrated initially in the Pacific Northwest. Our objectives were: (i) to revise the model, making it more applicable for wider geographic areas including potential international application, by modifying the thermal effect and incorporating soil texture and drainage effects, and (ii) to recalibrate and validate it for an extended range of soil properties and climate conditions. The current version of CQESTR (v. 2.0) is presented with the algorithms necessary to simulate SOM at field scale. Input data for SOM calculation include crop rotation, aboveground and belowground biomass additions, tillage, weather, and the nitrogen content of crop residues and any organic amendments. The model was validated with long-term data from across North America. Regression analysis of 306 pairs of predicted and measured SOM data under diverse climate, soil texture and drainage classes, and agronomic practices at 13 agricultural sites having a range of SOM (7.3–57.9 g SOM kg⁻¹), resulted in a linear relationship with an r² of 0.95 (P < 0.0001) and a 95% confidence interval of 4.3 g SOM kg⁻¹. Using the same data the version 1.0 of CQESTR had an r² of 0.71 with a 95% confidence interval of 5.5 g SOM kg⁻¹. The model can be used as a tool to predict and evaluate SOM changes from various management practices and offers the potential to estimate C accretion required for C credits.     

A two-dimensional simulation model of phosphorus uptake including crop growth and P-response

Modelling nutrient uptake by crops implies considering and integrating the processes controlling the soil nutrient supply, the uptake by the root system and relationships between the crop growth response and the amount of nutrient absorbed. We developed a model that integrates both dynamics of maize growth and phosphorus (P) uptake. The crop part of the model was derived from Monteith's model. A complete regulation of P-uptake by the roots according to crop P-demand and soil P-supply was assumed. The soil P-supply to the roots was calculated using a diffusion equation and assuming that roots behave as zero-sinks. The actual P-uptake and crop growth were calculated at each time step by comparing phosphate and carbohydrate supply–demand ratios. Model calculations for P-uptake and crop growth were compared to field measurements on a long term P-fertilization trial. Three P-fertilization regimes (no P-fertilization, 42.8 kg P ha⁻¹ year⁻¹ and 94.3 kg P ha⁻¹ year⁻¹) have led to a range of P-supply. Our model correctly simulated both the crop development and growth for all P-treatments. P-uptake was correctly predicted for the two non-limiting P-treatments. Nevertheless, for the limiting P-treatment, P-uptake was correctly predicted during the early period of growth but it was underestimated at the last sampling date (61 day after sowing). Several arguments for under-prediction were considered. However, most of them cannot explain the observed magnitude in discrepancy. The most likely reason might be the fact that biomass allocation between shoot and root must be modelled more precisely. Despite this mismatch, the model appears to provide realistic simulations of the soil–plant dynamic of P in field conditions.     

Carbon sequestration in soils of SW-Germany as affected by agricultural management—Calibration of the EPIC model for regional simulations

Global emissions trading allows for agricultural measures to be accounted for the carbon sequestration in soils. The Environmental Policy Integrated Climate (EPIC) model was tested for central European site conditions by means of agricultural extensification scenarios. Results of soil and management analyses of different management systems (cultivation with mouldboard plough, reduced tillage, and grassland/fallow establishment) on 13 representative sites in the German State Baden-Württemberg were used to calibrate the EPIC model. Calibration results were compared to those of the Intergovernmental Panel on Climate Change (IPCC) prognosis tool. The first calibration step included adjustments in (a) N depositions, (b) N₂-fixation by bacteria during fallow, and (c) nutrient content of organic fertilisers according to regional values. The mixing efficiency of implements used for reduced tillage and four crop parameters were adapted to site conditions as a second step of the iterative calibration process, which should optimise the agreement between measured and simulated humus changes. Thus, general rules were obtained for the calibration of EPIC for different criteria and regions. EPIC simulated an average increase of +0.341 Mg humus-C ha⁻¹ a⁻¹ for on average 11.3 years of reduced tillage compared to land cultivated with mouldboard plough during the same time scale. Field measurements revealed an average increase of +0.343 Mg C ha⁻¹ a⁻¹ and the IPCC prognosis tool +0.345 Mg C ha⁻¹ a⁻¹. EPIC simulated an average increase of +1.253 Mg C ha⁻¹ a⁻¹ for on average 10.6 years of grassland/fallow establishment compared to an average increase of +1.342 Mg humus-C ha⁻¹ a⁻¹ measured by field measurements and +1.254 Mg C ha⁻¹ a⁻¹ according to the IPCC prognosis tool. The comparison of simulated and measured humus C stocks was r² ≥ 0.825 for all treatments. However, on some sites deviations between simulated and measured results were considerable. The result for the simulation of yields was similar. In 49% of the cases the simulated yields differed from the surveyed ones by more than 20%. Some explanations could be found by qualitative cause analyses. Yet, for quantitative analyses the available information from farmers was not sufficient. Altogether EPIC is able to represent the expected changes by reduced tillage or grassland/fallow establishment acceptably under central European site conditions of south-western Germany.     

Estimating the potential impact of vegetation on the water cycle requires accurate soil water parameter estimation

It is well known that vegetation dynamics at the catchment scale depends on the prevailing weather and soil moisture conditions. Soil moisture, however, is not equally distributed in space due to differences in topography, weather patterns, soil properties and the type and amount of vegetation cover. To elucidate the complex interaction between vegetation and soil moisture, the dynamic vegetation model LPJ-GUESS (Smith et al., 2001), which provides estimations of vegetation dynamics, but does not consider lateral water fluxes was coupled with the hydrological TOPMODEL (cf. Beven, 2001) in order to be able to evaluate the importance of these lateral fluxes. The new model LG-TM was calibrated and validated in two climatically different mountain catchments. The estimations of runoff were good, when monthly and weekly time scales were considered, although the low flow periods at winter time were somewhat underestimated. The uncertainty in the climate induced change vegetation carbon storage caused by the uncertainty in soil parameters was up to 3-5 kg C m⁻² (depending on elevation and catchment), compared to the total change in vegetation carbon storage of 5-9 kg C m⁻². Therefore accurate estimates of the parameters influencing the water holding capacity of the soil, for example depth and porosity, are necessary when estimating future changes in vegetation carbon storage. Similarly, changes in plant transpiration due to climatic changes could be almost double as high (88 mm m⁻²) in the not calibrated model compared to the new model version (ca 50 mm m⁻² transpiration change). The uncertainties in these soil properties were found to be more important than the lateral water exchange between grid cells, even in steep topography at least for the temporal and spatial resolution used here.     

Dynamics of grass–clover mixtures—An analysis of the response to management with the PROductive GRASsland Simulator (PROGRASS)

A dynamic plot-scale model PROGRASS was developed to simulate the seasonal and inter-annual dynamics of productive, cut grass/clover mixtures in response to management, and specifically to examine the role of root development on grass/clover interactions. The model was parameterized by virtue of data for dry matter yield, leaf area index, root mass, soil mineral N uptake and biological N fixation from a long-term field trial in north-eastern Switzerland. It was tested using 5 years of independent data for yield and clover fraction from a field experiment with two management regimes carried out on the Swiss Central Plateau. The results of transient simulations indicated that under intensive fertilization grass dominance was initiated by preferential allocation of assimilates to the roots. The rapid growth of the grass root system lowered the substrate C:N ratio, favouring carbon allocation to the shoot, which eventually provided competitive advantages with respect to light interception. Under extensive management, limited N acquisition capacity of the grass root system maintained preferential allocation to the roots, limiting shoot development in the grass and leading to clover dominance. Co-existence regimes with dominance by one of the components were also found in equilibrium experiments, with a transition regime from clover to grass dominance for annual N applications in the range 100–200 kg N ha⁻¹ y⁻¹ that reflected adjustments of the root system to fertilization. It is concluded that the dynamics of grass/clover mixtures is driven by negative and positive feedbacks in the soil–plant system that are strongly controlled by root development and therefore by the allocation patterns of the grass component.     

Restoration of ecosystems for biodiversity and carbon sequestration: Simulating growth dynamics of brigalow vegetation communities in Australia

Restoration of abandoned and degraded ecosystems through enhanced management of mature remnant patches and naturally regenerating (regrowth) forests is currently being used in the recovery of ecosystems for biodiversity protection and carbon sequestration. Knowledge of long-term dynamics of these ecosystems is often very limited. Vegetation models that examine long-term forest growth and succession of uneven aged, mixed-species forest ecosystems are integral to the planning and assessment of the recovery process of biodiversity values and biomass accumulation. This paper examined the use of the Ecosystem Dynamics Simulator (EDS) in projecting growth dynamics of mature remnant brigalow forest communities and recovery process of regrowth brigalow thickets. We used data from 188 long-term monitored plots of remnant and regrowth forests measured between 1963 and 2010. In this study the model was parameterised for 34 tree and shrub species and tested with independent long-term measurements. The model closely approximated actual development trajectories of mature forests and regrowth thickets but some inaccuracies in estimating regeneration through asexual reproduction and mortality were noted as reflected in stem density projections of remnant plots that had a mean of absolute relative bias of 46.2 (±12.4)%. Changes in species composition in remnant forests were projected with a 10% error. Basal area values observed in all remnant plots ranged from 6 to 29 m² ha⁻¹ and EDS projections between 1966 and 2005 (39 years) were 68.2 (±10.9)% of the observed basal area. Projected live aboveground biomass of remnant plots had a mean of 93.5 (±5.9) t ha⁻¹ compared to a mean of 91.3 (±8.0) t ha⁻¹ observed in the plots. In regrowth thicket, the model produced satisfactory projections of tree density (91%), basal area (89%), height (87%) and aboveground biomass (84%) compared to the observed attributes. Basal area and biomass accumulation in 45-year-old regrowth plots was approximately similar to that in remnant forests but recovery of woody understorey was very slow. The model projected that it would take 95 years for the regrowth to thin down to similar densities observed in original or remnant brigalow forests. These results indicated that EDS can produce relatively accurate projections of growth dynamics of brigalow regrowth forests necessary for informing restoration planning and projecting biomass accumulation.     

Development and optimization of an Agro-BGC ecosystem model for C4 perennial grasses

Extrapolating simulations of bioenergy crop agro-ecosystems beyond data-rich sites requires biophysically accurate ecosystem models and careful estimation of model parameters not available in the literature. To increase biophysical accuracy we added C₄ perennial grass functionality and agricultural practices to the Biome-BGC (BioGeochemical Cycles) ecosystem model. This new model, Agro-BGC, includes enzyme-driven C₄ photosynthesis, individual live and dead leaf, stem, and root carbon and nitrogen pools, separate senescence and litter fall processes, fruit growth, optional annual seeding, flood irrigation, a growing degree day phenology with a killing frost option, and a disturbance handler that simulates nitrogen fertilization, harvest, fire, and incremental irrigation. To obtain spatially generalizable vegetation parameters we used a numerical method to optimize five unavailable parameters for Panicum virgatum (switchgrass) using biomass yield data from three sites: Mead, Nebraska, Rockspring, Pennsylvania, and Mandan, North Dakota. We then verified simulated switchgrass yields at three independent sites in Illinois (IL). Agro-BGC is more accurate than Biome-BGC in representing the physiology and dynamics of C₄ grass and management practices associated with agro-ecosystems. The simulated two-year average mature yields with single-site Rockspring optimization have Root Mean Square Errors (RMSE) of 70, 152, and 162 and biases of 43, −87, 156 g carbon m⁻² for Shabbona, Urbana, and Simpson IL, respectively. The simulated annual yields in June, August, October, December, and February have RMSEs of 114, 390, and 185 and biases of −19, −258, and 147 g carbon m⁻² for Shabbona, Urbana, and Simpson IL, respectively. These RMSE and bias values are all within the largest 90% confidence interval around respective IL site measurements. Twenty-four of twenty-six simulated annual yields with Rockspring optimization are within 95% confidence intervals of Illinois site measurements during the mature fourth and fifth years of growth. Ten of eleven simulated two-year average mature yields with Rockspring optimization are within 65% confidence intervals of Illinois site measurements and the eleventh is within the 95% confidence interval. Rockspring optimized Agro-BGC achieves accuracies comparable to those of two previously published models: Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) and Integrated Farm System Model (IFSM). Agro-BGC suffers from static vegetation parameters that can change seasonally and as plants age. Using mature plant data for optimization mitigates this deficiency. Our results suggest that a multi-site optimization scheme using mature plant data from more sites would be adequate for generating spatially generalizable vegetation parameters for simulating mature bioenergy crop agro-ecosystems with Agro-BGC.     

Modelling soil carbon development in Swedish coniferous forest soils—An uncertainty analysis of parameters and model estimates using the GLUE method

Boreal forest soils such as those in Sweden contain a large active carbon stock. Hence, a relatively small change in this stock can have a major impact on the Swedish national CO₂ balance. Understanding of the uncertainties in the estimations of soil carbon pools is critical for accurately assessing changes in carbon stocks in the national reports to UNFCCC and the Kyoto Protocol. Our objective was to analyse the parameter uncertainties of simulated estimates of the soil organic carbon (SOC) development between 1994 and 2002 in Swedish coniferous forests with the Q model. Both the sensitivity of model parameters and the uncertainties in simulations were assessed. Data of forests with Norway spruce, Scots pine and Lodgepole pine, from the Swedish Forest Soil Inventory (SFSI) were used. Data of 12 Swedish counties were used to calibrate parameter settings; and data from another 11 counties to validate. The “limits of acceptability” within GLUE were set at the 95% confidence interval for the annual, mean measured SOC at county scale. The calibration procedure reduced the parameter uncertainties and reshaped the distributions of the parameters county-specific. The average measured and simulated SOC amounts varied from 60 t C ha⁻¹ in northern to 140 t C ha⁻¹ in the southern Sweden. The calibrated model simulated the soil carbon pool within the limits of acceptability for all calibration counties except for one county during one year. The efficiency of the calibrated model varied strongly; for five out of 12 counties the model estimates agreed well with measurements, for two counties agreement was moderate and for five counties the agreement was poor. The lack of agreement can be explained with the high inter-annual variability of the down-scaled measured SOC estimates and changes in forest areas over time. We conclude that, although we succeed in reducing the uncertainty in the model estimates, calibrating of a regional scale process-oriented model using a national scale dataset is a sensitive balance between introducing and reducing uncertainties. Parameter distributions showed to be scale sensitive and county specific. Further analysis of uncertainties in the methods used for reporting SOC changes to the UNFCCC and Kyoto protocol is recommended.     

Assessing the effect of long-term crop cultivation on distribution of Cd in the root zone

Trace elements such as cadmium (Cd) may be inadvertently added to cropland soils through application of fertilizers, irrigation water, and other amendments. These toxic trace elements pose a potentially threat to soil quality and, through the food chain transfer, to human health. A generalized soil trace element mass balance model that accounts for the interactive processes governing the reactions of trace elements in soils, and consequently removed with crop harvest and leaching out of the soil profile with irrigation water was developed in this research. The model conceptually approximates the mechanisms and kinetics of a real field cropland system. The model was used to evaluate the long-term cultivation on distribution of Cd in California croplands. Under typical California cropping practices, Cd added into the soils accumulated primarily in the plow layer while the Cd content below the plow layer was barely affected. After 100 years of continuous cultivation, the soil Cd content of the plow layer increases from the background level 0.22 mg kg⁻¹ to 0.40 mg kg⁻¹. The accumulation of Cd in the plow layer is in proportion to the external inputs and is affected by the soil and plant characteristics, and management practices. The model can be used to evaluate the environmental fates of other toxic element in soils with case specific parameters.     

Estimating California ecosystem carbon change using process model and land cover disturbance data: 1951-2000

Land use change, natural disturbance, and climate change directly alter ecosystem productivity and carbon stock level. The estimation of ecosystem carbon dynamics depends on the quality of land cover change data and the effectiveness of the ecosystem models that represent the vegetation growth processes and disturbance effects. We used the Integrated Biosphere Simulator (IBIS) and a set of 30- to 60-m resolution fire and land cover change data to examine the carbon changes of California's forests, shrublands, and grasslands. Simulation results indicate that during 1951-2000, the net primary productivity (NPP) increased by 7%, from 72.2 to 77.1 Tg C yr⁻¹ (1 teragram = 10¹² g), mainly due to CO₂ fertilization, since the climate hardly changed during this period. Similarly, heterotrophic respiration increased by 5%, from 69.4 to 73.1 Tg C yr⁻¹, mainly due to increased forest soil carbon and temperature. Net ecosystem production (NEP) was highly variable in the 50-year period but on average equalled 3.0 Tg C yr⁻¹ (total of 149 Tg C). As with NEP, the net biome production (NBP) was also highly variable but averaged −0.55 Tg C yr⁻¹ (total of -27.3 Tg C) because NBP in the 1980s was very low (-5.34 Tg C yr⁻¹). During the study period, a total of 126 Tg carbon were removed by logging and land use change, and 50 Tg carbon were directly removed by wildland fires. For carbon pools, the estimated total living upper canopy (tree) biomass decreased from 928 to 834 Tg C, and the understory (including shrub and grass) biomass increased from 59 to 63 Tg C. Soil carbon and dead biomass carbon increased from 1136 to 1197 Tg C.Our analyses suggest that both natural and human processes have significant influence on the carbon change in California. During 1951-2000, climate interannual variability was the key driving force for the large interannual changes of ecosystem carbon source and sink at the state level, while logging and fire were the dominant driving forces for carbon balances in several specific ecoregions. From a long-term perspective, CO₂ fertilization plays a key role in maintaining higher NPP. However, our study shows that the increase in C sequestration by CO₂ fertilization is largely offset by logging/land use change and wildland fires.     

Dissolved organic carbon concentrations and fluxes in forest catchments and streams: DOC-3 model

Dissolved organic carbon (DOC) concentrations in south-western Nova Scotia streams, sampled at weekly to biweekly intervals, varied across streams from about 3 to 40 mg L⁻¹, being highest mid-summer to fall, and lowest during winter to spring. A 3-parameter model (DOC-3) was proposed to project daily stream DOC concentrations and fluxes from modelled estimates for daily soil temperature and moisture, year-round, and in relation to basin size and wetness. The parameters of this model refer to (i) a basin-specific DOC release parameter “k_DOC^”, related to the wet- and open-water area percentages per basin, (ii) the lag time “τ” between DOC production and subsequent stream DOC emergence, related to the catchment area above the stream sampling location; and (iii) the activation energy “Ea”, to deal with the temperature effect on DOC production. This model was calibrated with the 1988-2006 DOC concentration data from three streams (Pine Marten, Moosepit Brook, and the Mersey River sampled at or near Kejimkujik National Park, or KNP), and was used to interpret the biweekly 1999-2003 DOC concentrations data (stream, ground and lake water, soil lysimeters) of the Pockwock-Bowater Watershed Project near Halifax, Nova Scotia. The data and the model revealed that the DOC concentrations within the streams were not correlated to the DOC concentrations within the soil- and groundwater, but were predictable based on (i) the hydrologically inferred weather-induced changes in soil moisture and temperature next to each stream, and (ii) the topographically inferred basin area and wet- and open-water area percentages associated with each stream (R² = 0.53; RMSE = 3.5 mg L⁻¹). Model-predicted fluxes accounted 74% of the hydrometrically determined DOC exports at KNP.     

Estimating soil carbon turnover using radiocarbon data: A case-study for European Russia

Turnover rates of soil carbon for 20 soil types typical for a 3.7 million km² area of European Russia were estimated based on ¹⁴C data. The rates are corrected for bomb radiocarbon which strongly affects the topsoil ¹⁴C balance. The approach is applied for carbon stored in the organic and mineral layers of the upper 1 m of the soil profile. The turnover rates of carbon in the upper 20 cm are relatively high for forest soils (0.16–0.78% year⁻¹), intermediate for tundra soils (0.25% year⁻¹), and low for grassland soils (0.02–0.08% year⁻¹) with the exception of southern Chernozems (0.32% year⁻¹). In the soil layer of 20–100 cm depth, the turnover rates were much lower for all soil types (0.01–0.06% year⁻¹) except for peat bog soils of the southern taiga (0.14% year⁻¹). Combined with a map of soil type distribution and a dataset of several hundred soil carbon profiles, the method provides annual fluxes for the slowest components of soil carbon assuming that the latter is in equilibrium with climate and vegetation cover. The estimated carbon flux from the soil is highest for forest soils (12–147 gC/(m² year)), intermediate for tundra soils (33 gC/(m² year)), and lowest for grassland soils (1–26 gC/(m² year)). The approach does not distinguish active and recalcitrant carbon fractions and this explains the low turnover rates in the top layer. Since changes in soil types will follow changes in climate and land cover, we suggest that pedogenesis is an important factor influencing the future dynamics of soil carbon fluxes. Up to now, both the effect of soil type changes and the clear evidence from ¹⁴C measurements that most soil organic carbon has a millennial time scale, are basically neglected in the global carbon cycle models used for projections of atmospheric CO₂ in 21st century and beyond.     

The MONICA model: Testing predictability for crop growth, soil moisture and nitrogen dynamics

A fundamentally revised version of the HERMES agro-ecosystem model, released under the name of MONICA, was calibrated and tested to predict crop growth, soil moisture and nitrogen dynamics for various experimental crop rotations across Germany, including major cereals, sugar beet and maize. The calibration procedure also included crops grown experimentally under elevated atmospheric CO₂ concentration. The calibrated MONICA simulations yielded a median normalised mean absolute error (nMAE) of 0.20 across all observed target variables (n = 42) and a median Willmott's Index of Agreement (d) of 0.91 (median modelling efficiency (ME): 0.75). Although the crop biomass, habitus and soil moisture variables were all within an acceptable range, the model often underperformed for variables related to nitrogen. Uncalibrated MONICA simulations yielded a median nMAE of 0.27 across all observed target variables (n = 85) and a median d of 0.76 (median ME: 0.30), also showing predominantly acceptable results for the crop biomass, habitus and soil moisture variables. Based on the convincing performance of the model under uncalibrated conditions, MONICA can be regarded as a suitable simulation model for use in regional applications. Furthermore, its ability to reproduce the observed crop growth results in free-air carbon enrichment experiments makes it suited to predict agro-ecosystem behaviour under expected future climate conditions.     

Spatially distributed modeling of the long-term carbon balance of a boreal landscape

Spatially and temporally distributed information on the sizes of biomass carbon (C) pools (BCPs) and soil C pools (SCPs) is vital for improving our understanding of biosphere-atmosphere C fluxes. Because the sizes of C pools result from the integrated effects of primary production, age-effects, changes in climate, atmospheric CO₂ concentration, N deposition, and disturbances, a modeling scheme that interactively considers these processes is important. We used the InTEC model, driven by various spatio-temporal datasets to simulate the long-term C-balance in a boreal landscape in eastern Canada. Our results suggested that in this boreal landscape, mature coniferous stands had stabilized their productivity and fluctuated as a weak C-sink or C-source depending on the interannual variations in hydrometeorological factors. Disturbed deciduous stands were larger C-sinks (NEP₂₀₀₄ = 150 gC m⁻² yr⁻¹) than undisturbed coniferous stands (e.g. NEP₂₀₀₄ = 8 gC m⁻² yr⁻¹). Wetlands had lower NPP but showed temporally consistent C accumulation patterns. The simulated spatio-temporal patterns of BCPs and SCPs were unique and reflected the integrated effects of climate, plant growth and atmospheric chemistry besides the inherent properties of the C pool themselves. The simulated BCPs and SCPs generally compared well with the biometric estimates (BCPs: r = 0.86, SCPs: r = 0.84). The largest BCP biases were found in recently disturbed stands and the largest SCP biases were seen in locations where moss necro-masses were abundant. Reconstructing C pools and C fluxes in the ecosystem in such a spatio-temporal manner could help reduce the uncertainties in our understanding of terrestrial C-cycle.     

Effect of insecticides alone and in combination with fungicides on nitrification and phosphatase activity in two groundnut (Arachis hypogeae L.) soils

The effect of selected pesticides, monocrotophos, chlorpyrifos alone and in combination with mancozeb and carbendazim, respectively, was tested on nitrification and phosphatase activity in two groundnut (Arachis hypogeae L.) soils. The oxidation of ammonical nitrogen was significantly enhanced under the impact of selected pesticides alone and in combinations at 2.5 kg ha⁻¹ in black soil, and furthermore, increase in concentration of pesticides decreased the rate of nitrification, whereas in the case of red soil, the nitrification was increased up to 5.0 kg ha⁻¹ after 4 weeks, and then decline phase was started gradually from 6 to 8 weeks of incubation. The activity of phosphatase was increased in soils, which received the monocrotophos alone and in combination with mancozeb up to 2.5 and 5.0 kg ha⁻¹, whereas the application of chlorpyrifos singly and in combination with carbendazim at 2.5 kg ha⁻¹ profoundly increased the phosphatase activity after 20 days of incubation, in both soils. But higher concentrations of pesticides were either innocuous or inhibitory to the phosphatase activity.     

Inter-annual variability of ecosystem production in boreal jack pine forests (1975-2004) estimated from tree-ring data using CBM-CFS3

We describe and apply a method of using tree-ring data and an ecosystem model to reconstruct past annual rates of ecosystem production. Annual data on merchantable wood volume increment and mortality obtained by dendrochronological stand reconstruction were used as input to the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) to estimate net ecosystem production (NEP), net primary production (NPP), and heterotrophic respiration (Rh) annually from 1975 to 2004 at 10 boreal jack pine (Pinus banksiana Lamb.) stands in Saskatchewan and Manitoba, Canada. From 1975 (when sites aged 41-60 years) to 2004 (when they aged 70-89 years), all sites were moderate C sinks except during some warmer than average years where estimated Rh increased. Across all sites and years, estimated annual NEP averaged 57 g Cm⁻² yr⁻¹ (range −31 to 176 g Cm⁻² yr⁻¹), NPP 244 g Cm⁻² yr⁻¹ (147-376 g Cm⁻² yr⁻¹), and Rh 187 g Cm⁻² yr⁻¹ (124-270 g Cm⁻² yr⁻¹). Across all sites, NPP was related to stand age and density, which are proxies for successional changes in leaf area. Regionally, warm spring temperature increased NPP and defoliation by jack pine budworm 1 year previously reduced NPP. Our estimates of NPP, Rh, and NEP were plausible when compared to regional eddy covariance and carbon stock measurements. Inter-annual variability in ecosystem productivity contributes uncertainty to inventory-based assessments of regional forest C budgets that use yield curves predicting averaged growth over time. Our method could expand the spatial and temporal coverage of annual forest productivity estimates, providing additional data for the development of empirical models accounting for factors not presently considered by these models.     

Dealing with microtopography of an ombrotrophic bog for simulating ecosystem-level CO2 exchanges

Peatlands contain approximately 25% of the global soil carbon (C), despite covering only 3% of the earth's land surface. In order to evaluate the role of peatlands in global C cycling, models of ecosystem biogeochemistry are required, but peatland ecosystems present a number of unique challenges, particularly how to deal with the large variability that occurs at scales of one to several metres. In models, spatial variability is considered either explicitly for each individual unit and the outputs averaged, referred to as flux upscaling, or implicitly by weighting model parameters by the fractional occurrence of the individual units, referred to as parameter upscaling. The advantage of parameter upscaling is that it is much more computationally efficient: a requirement for hemispheric scale simulations. In this study we determined the differences between modelling a raised bog peatland with hummock-hollow microtopography using flux and parameter upscaling. We used the McGill Wetland Model (MWM), a process-based ecosystem C model for peatlands, configured for hummocks and hollows separately and then a weighted mixture of both. The simulated output based on flux and parameter upscaling was compared with eddy-covariance tower measurements. We found that net ecosystem production (NEP) for hollows was much larger than that for hummocks because total ecosystem respiration (TER) for hummocks was greater while gross primary production (GPP) did not differ significantly between the two topographic features. However, despite differences in components of NEP between hummocks and hollows, there was no statistically significant difference between the NEP based on flux and parameter upscaling using the MWM. Both flux and parameter upscaling show equivalent capability to capture the magnitude, direction, seasonality and inter-annual variability. The root-mean-square-errors (RMSE) are 0.66, 0.45, and 0.49 g C m⁻² day⁻¹, respectively for GPP, TER and NEP based on the flux upscaling, while 0.67, 0.44, and 0.48 g C m⁻² day⁻¹, respectively based on the parameter upscaling. The degree of agreement (d*) is 0.96, 0.97, and 0.88, respectively for GPP, TER and NEP based on the flux upscaling, while 0.96, 0.97, and 0.89, respectively based on the parameter upscaling. This result suggests that differences in processes caused by peatland microtopography scale linearly, which means an ecosystem-level model set-up (i.e. parameter upscaling scheme), is sufficient to simulate the C cycling.