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[1] Canopy fluxes of CO 2 and energy can be modeled with high fidelity using a small number of environmental variables and ecosystem parameters. Although these ecosystem parameters are critically important for modeling canopy fluxes, they typically are not measured with the same intensity as ecosystem fluxes. We developed an algorithm to estimate leaf area index (LAI), maximum carboxylation velocity (Vc max ), the Ball-Berry parameter (m), and substrate-dependent ecosystem respiration rate (b A ) by inverting a commonly used modeling paradigm of canopy CO 2 and energy fluxes. To test this algorithm, fluxes of sensible heat (H), latent heat (LE), and CO 2 (Fc) were measured with eddy covariance techniques in a pristine grassland-forb steppe site in northern Kazakhstan. We applied the algorithm to these data and identified ecosystem characteristics consistent with data across a time series of meteorological drivers from the Kazakhstan data. LAI was calculated by fitting the model to measured H + LE, Vc max and b A were solved simultaneously by fitting the model to measured CO 2 fluxes, and m was calculated by varying the partitioning of available energy between H and LE. Seasonal changes in LAI ranged from 2.0 to 2.4, Vc max declined from 20 to 5 mmol CO 2 m À2 s À1 , respiration as a percentage of assimilation ranged from 0.5 to 0.75, and m varied from 17 to 24. Our results with the Kazakhstan data showed that LAI, Vc max , ecosystem respiration, and m can be solved to accurately predict (R 2 = 80 to 95%) carbon and energy fluxes with nonsignificant bias at 20-min and daily timescales. The ecosystem characteristics calculated in our study were consistent with independent measurements of the seasonal dynamics of a shortgrass steppe in Kazakhstan and with values published in the literature. These characteristics were closely linked to mean daily fluxes of CO 2 but were not dependent on the environmental drivers for the periods they were measured. We conclude that process model inversion has potential for comparing CO 2 and energy fluxes among different ecosystems and years and for providing important ecosystem parameters for evaluating climatic influences on CO 2 and energy fluxes.

This study aimed to improve the accuracy of spatial prediction for soil organic matter, potential mineralizable carbon (PMC) and soil organic carbon (SOC), using secondary information, namely topographic and vegetation information, in northern Kazakhstan. Secondary information included elevation (ELEV), mean curvature (MEANC), compound topographic index (CTI) and slope (SLOPE) obtained from a digital elevation model, and enhanced vegetation index (VI) values obtained from a moderate resolution imaging spectroradiometer (MODIS). The prediction methods were statistical (multiple linear regression between soil organic matter and secondary information) and geostatistical algorithms (regression-kriging Model-C and simple kriging with varying local means [SKlm]). The VI, ELEV and MEANC were selected as the independent variables for predicting PMC and SOC. However, MEANC showed an opposite effect on PMC and SOC accumulation patterns. Model validity revealed that SKlm was the most appropriate method for predicting PMC and SOC spatial patterns because model validity revealed the smallest errors for this method. Maps from the kriged estimates showed that a combination of secondary information and geostatistical techniques can improve the accuracy of spatial prediction in study areas.

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The practice of monoculture land use without regard for local environmental conditions can accelerate organic matter decomposition. In the agriculturally and environmentally important soils of northern Kazakhstan, which primarily support cereal cultivation, economic rewards might encourage such monoculture practices. The purpose of this study was to clarify the influence of land use on the dynamics of soil organic carbon in situ for the three different soil classes, Dark Chestnut (DC), Southern Chernozem (SC) and Ordinary Chernozem (OC), in this region. Fluctuations in CO 2 emission from the soils showed a similar pattern to temperature fluctuations. Land use markedly influenced the seasonal variation of CO 2 emission, in particular fluctuations in CO 2 sensitivity to soil temperature. To estimate daily CO 2 emission, a prediction equation of CO 2 emission using stepwise multiple regression of the Arrehenius model was derived from environmental soil factors by soil type and land use type. Using soil environmental factors, 40-80% of the variation in CO 2 emission could be estimated. For cereal fields, the mean annual CO 2 emission was estimated to range from 0.75 (DC) to 1.14 (SC) Mg C ha −1 , and carbon input as plant residues ranged from 0.75 (DC) to 1.82 (SC) Mg C ha −1 . The annual carbon budget ranged from 0.10 to 0.35 Mg C ha −1 . In contrast, the carbon budget of summer fallow fields was approximately −0.8 Mg C ha −1 . Thus, the carbon budget of the typical 4-year crop rotation system was estimated to range from −0.42 (DC) to 0.25 (OC) Mg C ha −1 . It should be noted that carbon budgets were negative at DC and SC sites. Although the carbon budget of meadow fields ranged from 0.81 to 1.26 Mg C ha −1 , meadow management at all sites contributed to carbon sequestration. Therefore, to prevent depletion of soil organic carbon in northern Kazakhstan, we recommend that meadow management be introduced as part of the crop rotation system, especially at SC and DC sites.