Influence of land use on the dynamics of soil organic carbon in northern Kazakhstan

Abstract

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 CO₂ emission from the soils showed a similar pattern to temperature fluctuations. Land use markedly influenced the seasonal variation of CO₂ emission, in particular fluctuations in CO₂ sensitivity to soil temperature. To estimate daily CO₂ emission, a prediction equation of CO₂ 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 CO₂ emission could be estimated. For cereal fields, the mean annual CO₂ 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.

Key words:

• carbon budget
• chernozem soil
• grain farming
• northern Kazakhstan
• summer fallow

INTRODUCTION

Land degradation poses serious economic, social and environmental problems for the transition economies of the Central Asian countries of Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan (CACs). It directly affects the livelihood of the rural population by reducing the productivity of land resources and adversely affecting the stability, functions and services derived from natural systems (Asian Development Bank 2004). The loss of soil organic carbon (SOC) is accentuated by deforestation, land-use conversion, attendant soil degradation and desertification, all of which are severe problems in the CACs (Lal 2004). In Kazakhstan, McNab (1997) reported that summer fallowing caused rapid mineralization of soil organic matter. Plowing and excessive tillage is one of the major culprits exacerbating the problem of soil degradation and reduces the SOC pool (Lal 2004).

Kazakhstan accounts for approximately 90% of the total cereal production area in the CACs (International Maize and Wheat Improvement Center (CYMMIT) 2000), making it one of the most important regions for global food production. Northern Kazakhstan alone produces 70% of the national cereal production (Nobe 1998). This area is covered with fertile Chernozem soils (Main management of geoolesy and cartography, USSR (GUGK) 1982) and these soils are also considered to store a substantial amount of organic carbon, functioning as a huge source and sink of carbon dioxide (CO₂). The Chernozem soils in northern Kazakhstan are, therefore, a vital resource from both agricultural and environmental viewpoints.

Although the Chernozem soils in northern Kazakhstan are fertile, climatic conditions are severe, with an annual precipitation of approximately 300 mm. Such low precipitation represents a major abiotic stress (Morgounov et al. 2001) and water management is a major concern for sustainable production. The summer fallow are used for conventional water management practices, which are usually rotated over a 4-year timeframe, to retain moisture in soils, decrease weed hazard and accumulate mineral nitrogen through mineralization of soil organic matter. Fields under fallow are usually mechanically harrowed several times to keep the land bare and to minimize evapotranspiration during the cropping season (Shegebaev 1998). Unfortunately, such agricultural practice accelerates organic matter decomposition in Chernozem soils (Buyanovsky et al. 1987; Karbozova-Saljnikov et al. 2004; Mikhailova et al. 2000; Srivastava and Meyer 1998). Funakawa et al. (2004a) found that the cereal farming system used in northern Kazakhstan resulted in the loss of approximately 2.9 Mg C ha⁻¹ from surface soil during periods of fallow, and concluded that fallow is disadvantageous in terms of SOC budget.

In northern Kazakhstan, three types of Chernozem soils are present: Dark Chestnut (DC; Typic Haplustolls), Southern Chernozem (SC; Typic Haplustolls) and Ordinary Chernozem (OC; Pachic Haplustolls). The main difference between DC and SC is soil color, which is related to the soil organic matter content. In general, DC has a lower organic matter content than SC. The characteristics of soils in the steppe region of northern Kazakhstan were reported by Funakawa and Kosaki (2005). Farmers apply a 4-year or 5-year crop rotation system, comprising summer fallow and 3 years or 4 years of continuous cereal cultivation. From an economic viewpoint, some farmers may consider applying a 3-year crop rotation system, including fallow, without concern for the soil environment. To assist in the application of appropriate, environmentally friendly land-use planning in northern Kazakhstan, information about the SOC budget under cereal production in the three different soil ecosystems would be of considerable importance. To date, there is, however, little such information available for this region. Therefore, the objective 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 in northern Kazakhstan.

MATERIALS AND METHODS

Description of study region

The study was conducted in the Tselinogratsukaya district (51°12′N 71°06′E) east of Astana, in the Shortandy district (51°40′N 71°02′E) 60 km north of Astana, and in the Reonidovka district (53°57′N 69°32′E) 300 km north of Astana, serving as representative soil sites of DC, SC and OC, respectively. The climate of Astana is drier than that of the upper north. In the Reonidovka district, the soil color becomes progressively darker, presumably because of decreasing evapotranspiration. Monthly climatic data for the three sites during the cropping season (May–September) for the years 2002, 2004 and 2005 are shown in Fig. 1. Monthly mean air temperature reached a maximum in July, and was higher at the DC site than at the OC site.

Soil and plant sampling

In September 2003, surface soil samples were taken from 10 cereal fields and two meadow fields at both the OC and DC sites. In addition, in May 2005, surface soil samples were collected from eight and one cereal field(s) adjacent to the 2003 sampling fields at the DC and OC sites, respectively. Sampling distance was within 3 km. At the SC site, soil sampling was conducted in May 2001 (10 cereal fields and two meadow fields), May 2003 (same fields as in 2001) and May 2005 (12 cereal fields and two meadow fields). All plots were situated within a diameter of 50 km. In addition, the meadow fields contained Agropyron cristatum, which is not for grazing, and there is only one harvest in June.

Plant sampling was carried out in 2002, 2004 and 2005 at the soil sampling plots. From each cereal plot, above and below ground plants were harvested from 1 m × 0.5 m quadrats, and the above-ground biomass was threshed. In the meadow plots, above and below ground biomass was sampled in triplicate from 35 cm × 35 cm quadrats (15 cm depth) twice a year (in 2004 and 2005) in May and August. Experimental fallow fields were established at each site in 2004 and tilled by hand three times (in June, July and August). Before each tillage, plant biomass was sampled in triplicate from 50 cm × 50 cm quadrats. Total carbon content of crop residue and of weed samples collected from cereal, meadow and fallow fields was determined using the dry-combustion method (Elementar VarioMax NC analyzer Hanau, Germany). In meadow fields, input carbon content was calculated as follows:

Figure 1  Climatic condition at each site during CO2 emission monitoring period.

The SOC content was measured using the Tyurin method. Potentially mineralizable carbon (PMC) content was determined by incubation of soil samples for 19 weeks under conditions of 30°C air temperature and a soil water content at 60% of field water capacity for 15 g of soil. CO₂ was collected using an alkaline trap and titrated with 0.1 mol L⁻¹ HCl. Non-linear regression was used to describe PMC according to the following equation (SPSS Inc 1998):

where C_min is the quantity of mineralized C (mg kg⁻¹ dry soil) at time t(d), C₀ is PMC (mg kg⁻¹ dry soil), and k is a non-linear mineralization constant (i.e. fraction mineralized day⁻¹).

In situ CO₂ emission monitoring

The CO₂ emission was measured during the cropping season (May–September) 2002 (SC site only), 2004 and 2005. Chemical fertilizer was not applied to any of the cereal or fallow plots. An alkaline absorption method was used to determine CO₂ emission. Chambers were installed at the soil surface and CO₂ respired in 24 h was titrated with 0.1 mol L⁻¹ HCl. A root-impermeable mesh was buried at a depth of 15 cm to avoid the influence of root respiration. During the investigation period, respired CO₂ was measured approximately twice per month. At the time of measurement, maximum and minimum temperature at 5 cm depth was recorded using a maximum–minimum thermometer (CT-410WR, CUSTOM, Tokyo) installed at each site. In addition, volumetric water content (%) at 0–15 cm depth was recorded using a soil moisture sensor (Hydro Sense, Campbell Scientific, Queensland, Australia).

Climate monitoring and dryness factor

Air temperature, soil temperature (5 cm and 15 cm depths), soil moisture (0–15 cm) and precipitation were monitored in 2002 (SC site), 2004 (OC and SC sites) and 2005 (OC, SC and DC sites) using a micro logger (Campbell Scientific) at 30 min intervals during the cropping season. We introduced a dryness factor, derived from potential evapotranspiration (PET) and precipitation because of the economic difficulties involved in monitoring soil water content daily at each site. Daily PET was estimated using the Hargreaves–Samani equation (Hargreaves and Samani 1982; Hargreaves et al. 1985) as follows:

where PET is potential evapotranspiration (mm day⁻¹), K_c is a crop coefficient and ET₀ is reference crop evapotranspiration (mm day⁻¹).

ET₀ is defined and calculated using the following equation:

where T_mean, T_max and T_min are daily mean air temperature, maximum and minimum air temperature, respectively, Ra is extraterrestrial radiation (mm day⁻¹) defined as:

where G_SC is a solar constant (0.082 Mj m⁻² min⁻¹), dr is inverse relative distance from earth to sun:

Table 1 Potentially mineralizable organic carbon content and soil organic content at the CO2 monitoring plots

	Dark Chestnut			Southem Chemozem			Ordinary Chernozem
	No. plots	PMC	SOC	No. plots	PMC	SOC	No. plots	PMC	SOC
		(Mg C ha^−1)			(Mg C ha^−1)			(Mg C ha^−1)
2002
Cereal	–	–	–	9	2.65 ± 0.89^†	39.12 ± 10.3	–	–	–
Fallow	–	–	–	–	–	–	–	–	–
Meadow	–	–	–	–	–	–	–	–	–
2004
Cereal	8	1.82 ± 0.41	29.72 ± 5.01	9	2.38 ± 0.67	35.85 ± 4.66	6	1.64 ± 0.17	46.92 ± 5.43
Fallow	2	1.90	30.29	1	3.27	40.85	4	2.21 ± 0.31	45.82 ± 2.71
Meadow	2	2.47	36.92	2	3.61	41.77	2	2.41	60.70
2005
Cereal	11	2.20 ± 0.43	32.65 ± 3.71	17	2.80 ± 0.75	37.23 ± 4.48	10	1.87 ± 0.77	46.48 ± 4.38
Fallow	7	1.86 ± 0.44	29.95 ± 5.24	5	2.32 ± 0.26	34.64 ± 4.59	1	2.70	51.54
Meadow	2	2.47	36.92	4	3.42 ± 0.60	40.99 ± 3.74	2	2.41	60.70
^†Mean ± standard deviation. PMC, potentially mineralizable carbon; SOC, soil organic carbon; –, data not collected.

JD, day of the year, ψ is sunset hour angle (rad), ψ = arcos[−tan(ψ)tan(δ)], δ is solar declination (rad) represented by:

ψ is latitude of location (rad), and MJ m² day⁻¹ can be converted to mm day⁻¹ as: mm day⁻¹ = (MJ m² day⁻¹)/2.43.

Calculation of the crop coefficient Kc followed the method recommended by the Food and Agriculture Organization (1998). For cereal plots, length of the crop development stage was set at 120 days (seeding on 15 May). For K_C, 0.3 and 1.0 were used for fallow and meadow plots, respectively. The daily dryness factor was computed by dividing precipitation by PET.

RESULTS AND DISUCUSSION

Climate and soil status at the CO₂ emission monitoring sites

Monthly precipitation and mean air temperature during the crop growing season are shown in Fig. 1. Precipitation patterns show relatively high annual variability at the SC and OC sites. In 2004, there was a severe drought from May to July at the SC site and from August to September at the OC site. In contrast, in 2005 there was a wet period from May to August at the SC and OC sites. Only the DC site received normal precipitation in both years. Mean air temperature was slightly higher than usual in 2004 and 2005 at all sites. There was a new monthly precipitation record in June 2002 at the SC site.

The PMC and SOC at the CO₂ monitoring plots are shown in Table 1. At all sites, PMC and SOC were higher in meadow plots than in the cereal or fallow plots, except for PMC at the OC site. It is well known that differences between PMC and SOC among land-use types are associated with long-term differences in substrate addition (Karbozova-Saljnikov et al. 2004; Schomberg and Jones 1999). In the cereal plots, PMC and SOC ranged from 1.64 (OC) to 2.80 (SC) Mg C ha⁻¹ and 29.72 (SC) to 46.92 (OC) Mg C ha⁻¹. Clay content was approximately 40% (Funakawa and Kosaki 2005), and was the same level at all sites and for all land-use types.

Relationship between CO₂ emission and climatic factors

Daily CO₂ emission, air temperature and precipitation during the cropping season are shown in Figures 2, 3, 4. Fluctuations in CO₂ emission from the soils showed a similar pattern for temperature, which reached a maximum value at each site in June or July, and started to fall in September. This corresponded with the results of Rochette et al. (1991) and Singh and Shekhar (1986). Rochette et al. (1991) found maximum CO₂ emission occurred in June, but maximum growth of the plant was also in June. In addition, Buyanovsky et al. (1986) found in their investigation of carbon dynamics in winter wheat ecosystems that maximum CO₂ emission coincided with maximum air and soil temperatures.

Precipitation also had a marked influence on soil water content and CO₂ emission. For example, soil water content and CO₂ emission in June 2004 was lower than that recorded in June 2005 at all sites. As there was a severe drought in June 2004, especially at the SC site, only small amounts of CO₂ were emitted at that time. Despite air temperature being the same in 2004 and

Figure 2  Daily CO2 emission, soil water content, precipitation, daily mean air temperature and daily precipitation at DC site. Upper figure shows daily CO2 emission. Middle figure shows soil water content. Lower figure shows mean air temperature (solid line) and precipitation (rods).

2005, CO₂ emission in June 2004 was twofold to fivefold lower than that of 2005 at the SC site. A large increase in CO₂ emission has been observed under laboratory conditions when dry samples were rewetted (Orchard and Cook 1983), which resulted mainly from increases in soil microbial populations and activity. Although their study was conducted under controlled laboratory conditions, the same event might also occur under field conditions. In addition, Emmerich (2003) suggested that some of the CO₂ emission after precipitation originated from an inorganic carbon pool under the semiarid environment. However, the surface soil of this study area contains many rock fragments of limestone and low organic carbon content (8 g kg⁻¹). The possibility of this effect exists, but we propose that a small quantity of the CO₂ emission originated from an inorganic carbon pool.

Influence of land use on seasonal variation of CO₂ emission

Land use markedly affected seasonal variation in the amount of CO₂ emission. The influence of increased CO₂ emission together with increased temperature was not so clear in meadow fields as in cereal and summer fallow fields during the drought period of 2004. In contrast, in June 2005, in the wet period, maximum CO₂ emission was observed in meadow fields at the OC site. Tang et al. (2003) suggested that temperature sensitivity is relatively low in the dry season, and Xu and Qi (2001) showed that this sensitivity is correlated

Figure 3  Daily CO2 emission, soil water content, precipitation, daily mean air temperature, and daily precipitation at SC site. Upper figure shows daily CO2 emission. Middle figure shows soil water content. Lower figure shows mean air temperature (solid line) and precipitation (rods).

with soil moisture. It appears reasonable to assume that the soil profile of meadow fields was generally drier than that of other land-use sites in 2004 because of strong evapotranspiration in meadow fields. Therefore, soil microorganism activity might be lower in meadow fields than in the other land-use sites during periods of drought. In contrast, the soil profile in June and July 2005 might have contained sufficient moisture for soil microbial activity to be higher in meadow fields, where substrates such as PMC accumulate more at the OC site than for other land-use types (Table 1).

Prediction equations of CO₂ emission

Stepwise multiple regression of the Arrehenius model was used to estimate daily CO₂ emission using the following parameters: soil temperature, dryness factor, precipitation, PMC and SOC by soil type and by land-use type. To avoid multicollinearity, a precipitation or dryness factor was used in the regression analysis, and we used one, which had a high R² value. The Arrehenius equation together with an extensive form of estimating CO₂ emission can be stated as follows:

where CO₂ is daily CO₂ emission, A is a rate constant, OM is PMC, W is precipitation or dryness factor over several days (for which we sought the most valid accumulative days from 2 to 15 days with the highest R²), b is order of

Figure 4  Daily CO2 emission, soil water content, precipitation, daily mean air temperature, and daily precipitation at OC site. Upper figure shows daily CO2 emission. Middle figure shows soil water content. Lower figure shows mean air temperature (solid line) and precipitation (rods).

reaction with respect to OM, c is order of reaction with respect to W, E is activation energy, R is gas constant (8.314), K is Kelvin temperature (273 + T), and T is mean soil temperature.

The results are given in Table 2. Soil temperature showed a high and positive contribution in all nine equations, and a significant relationship at 1% level was obtained between the CO₂ emission and the activation energy E, indicating a significant dependency of CO₂ emission on soil temperature. Such dependency was also reported for Chernozem soil in northern Kazakhstan by Funakawa et al. (2004a) and in Russia by Kudeyarov and Kurganova (1998). Based on the value of E, we estimated that Q₁₀ values between 10 and 20°C ranged between 1.55 and 3.18. Raich and Schlesinger (1992) reviewed soil respiration rates of various ecosystems and showed that Q₁₀ varied from 1.3 to 3.3, with a mean value of 2.4. Funakawa et al. (2004a) showed Q₁₀ in cereal fields at an SC site ranged from 1.3 to 2.0. In the present study, Q₁₀ values of fallow and meadow fields were higher at the OC site than at the DC site. Schinner (1982) reported that higher Q₁₀ values were observed at lower temperatures, a finding supported by the present results.

The cumulative precipitation or dryness factor also showed a positive contribution in all nine equations, and generally a significant 1% level was obtained. Precipitation was selected in cereal plots at all sites, and dryness factor was selected in DC and SC fallow plots and DC and OC meadow plots. There was a significant

Table 2 Coefficients determined by stepwise multiple regression analysis

Site	Land use	Coefficients							Q10 (10–20°C)	R^2	n
		A	OM	b	W	c	Cumulative days	E
DC	Cereal	23.05**	PMC	0.23*	Precipitation	0.09**	8	42.47**	1.81	0.62**	156
	Fallow	22.87**	–	–	Dryness	0.30**	15	41.42**	1.79	0.49**	60
	Meadow	28.94**	PMC	2.63*	Dryness	0.04	8	62.04**	2.39	0.65**	34
SC	Cereal	25.58**	PMC	0.15	Precipitation	0.19**	15	48.31**	1.97	0.40**	417
	Fallow	27.86**	PMC	0.68*	Dryness	0.08**	9	54.38**	2.14	0.52**	51
	Meadow	29.95**	–	–	Precipitation	0.17**	10	58.81**	2.28	0.65**	57
OC	Cereal	17.34**	PMC	0.33*	Precipitation	0.54**	15	31.34**	1.55	0.83**	148
	Fallow	28.54**	–	–	Precipitation	0.15	15	55.84**	2.19	0.72**	45
	Meadow	40.83**	–	–	Dryness	0.36**	15	82.53**	3.18	0.74**	38
*P < 0.05.
**P < 0.051.
CO2 = AOM^bW^c(–E/RT). CO2, daily CO2 emission (mol C ha^− ^1 day^−l); A, coefficient (mol C ha^1 day^−l); organic matter (OM) is soil organic carbon or potentially mineralizable carbon (PMC) (Mg C ha^−l); W, cumulative precipitation or dryness factor (cumulative days was from 2 to 15 days, selected by the highest R^2; E, activation energy (kJ mol^−1); R, gas constant (8.314 [J mol^−1 kg^−1]); T, soil temperature (K); –.

correlation between precipitation and dryness factor, which drastically affected soil moisture content (data not shown); it is the soil water status that governs the intra-seasonal variation of CO₂ emission (Davidson et al. 2000; Huxman et al. 2004; Kessavalou et al. 1998; Schlentner and Van Cleve 1985; Rochette et al. 1991; Tang et al. 2003). Cumulative days were selected from 8 to 15 days, and the results suggested that CO₂ emission in situ was controlled by several precipitation events that occurred during that period.

Relationship between land use and determining factors of CO₂ emission

The mean Q₁₀ value of the three soil types for each land-use type was 1.78 for cereal, 2.04 for fallow and 2.62 for meadow. The mean Q₁₀ value of fallow was higher than that of cereal, implying that tillage alters the decomposition environment by aerating the soil, breaking up soil aggregates, and incorporating weeds into the soil profile (Beare et al. 1994; Fortin et al. 1996; Lee et al. 1996; Prior et al. 1997; Six et al. 2002). Meadow fields showed the highest Q₁₀ value at each site, indicating that the sensitivity of CO₂ fluctuation resulting from soil temperature was highest under a meadow management system. Precipitation factor was selected at all sites under the cereal cropping condition, whereas dryness factor tended to be selected for fallow and meadow fields. This result suggests that the sensitivity of fluctuation of CO₂ is more strongly influenced by dryness in fallow and meadow fields than in cereal fields.

The contribution of soil organic matter was somewhat uncertain in fallow and meadow fields, where CO₂ sampling plots were fewer than in the cereal plots. Prediction of the variance of CO₂ emission using the parameter of soil organic matter may, thus, not have been possible because of the limited number of CO₂ sampling datasets in fallow and meadow fields.

Annual carbon flow and carbon budget of crop rotation

To estimate annual CO₂ emission, we used prediction equations and the climatic data from data loggers or the meteorological station closest to the CO₂ measuring sites. Air temperature monitored by the meteorological observatory was calibrated to soil temperature at 5 cm depth at each site by a correlation analysis using monitored air temperature and soil temperature data from data loggers.

The annual carbon flow and carbon budget of the crop rotation system are summarized in Table 3. Annual CO₂ emission is the summation of daily CO₂ emission from 1 April to 31 October. Mean annual CO₂ emission of cereal fields was estimated to range from 0.75 (DC) to 1.14 (SC) Mg C ha⁻¹. This estimated value is the same level as that for the Canadian prairie (de Jong 1974), while it was lower than the value recorded for Eurasian Chernozem (Larionova et al. 1998; Vugakov and Popova 1968). According to other data obtained from agricultural experimental field stations at the SC site, approximately 2.5–3.2 Mg C ha⁻¹ is respired annually (Funakawa et al. 2004a). Comparatively, the present study showed that carbon was respired as low as approximately 1.01–1.14 Mg C ha⁻¹ annually at the SC site. This discrepancy in findings could be because of Funakawa et al.'s (2004a) use of experimental fields that were managed intensively, using such methods as snow trap management, fertilization, herbicide application and subsoil cutting, all of which are believed to magnify the carbon dynamics by increasing carbon input as well as output. Snow management is a drifting-snow trap technology that makes parallel snow-rows at certain intervals, and

Table 3 Annual (from April to October) carbon flow and carbon budget of the crop rotation systems

Site	Land use	Year	Carbon input as plant residue	Carbon output	Carbon budget	Average carbon budget (2004 and 2005)	Estimation under different fallow frequencies
							3-year rotation (F–C–C)	4-year rotation (F–C–C–C)	5-year rotation (F–C–C–C–C)
			(Mg C ha^−1)
DC	Cereal	2004	0.75 ± 0.20^†	0.75 ± 0.04	0.00	0.10	−0.51	−0.42	−0.32
		2005	0.99 ± 0.36	0.80 ± 0.03	0.19
	Fallow	2004	0.02	0.72	−0.70	−0.70
		2005	0.02	0.71 ± 0.01	−0.69
	Meadow	2004	1.56	0.61	0.95	0.81
		2005	1.39	0.68	0.66
SC	Cereal	2002	1.82 ± 0.50	1.06 ± 0.07	0.77
		2004	1.37 ± 0.03	1.01 ± 0.06	0.36	0.27	−0.30	−0.03	0.25
		2005	1.32 ± 0.29	1.14 ± 0.07	0.18
	Fallow	2004	0.02	0.92	−0.90	−0.84
		2005	0.02	0.79 ± 0.07	−0.77
	Meadow	2004	2.11	0.66	1.45	1.17
		2005	1.61	0.73 ± l.04	0.88
OC	Cereal	2004	1.31 ± 0.03	0.92 ± 0.03	0.39	0.35	−0.10	0.25	0.60
		2005	1.44 ± 0.79	1.13 ± 0.07	0.31
	Fallow	2004	0.02	0.85 ± 0.04	−0.83	−0.80
		2005	0.02	0.79	−0.77
	Meadow	2004	2.50	1.22	1.28	1.26
		2005	2.57	1.34	1.23
^†Mean ± standard deviation. C, cereal; F, fallow.

subsoil cutting in autumn is a conservation tillage used to reduce the loss of water through evaporation by decreasing capillary rise (Shegebaev 1998).

Mean annual CO₂ emission of fallow and meadow fields was estimated to range from 0.71 (DC) to 0.92 (SC) Mg C ha⁻¹ and from 0.61 (DC) to 1.34 (OC) Mg C ha⁻¹, respectively. Annual CO₂ emission of cereal and meadow fields in 2005 was slightly higher than in 2004 at each site because precipitation in 2005 was higher than that in 2004, which greatly affected water availability for soil microbes.

Average carbon input as plant residues 2004–2005 in cereal fields ranged from 0.87 to 1.38 Mg C ha⁻¹, with the highest value at the OC site, followed by the SC and the DC sites. In 2002, there was extremely high carbon input at the SC site. The high carbon input as plant residues in 2002 might have resulted from new a precipitation record in June at the SC site. Thus, the high carbon input at the SC site is highly unusual. The annual carbon budget was calculated by subtracting annual CO₂ emission from carbon input as plant residues, and was found to vary between 0.00 and 0.77 Mg C ha⁻¹ in cereal fields. It is noteworthy that cereal cropping fields at all sites contributed to carbon sequestration. Plant residue burning is an occasional practice in this study area, and it has agronomic advantages linked to soil management, such as the tillage. In addition, disease control effects, derived from residue burning, could be an agronomic advantage in this study area. However, the advantages of not burning, especially the long-term increase in soil organic matter and its impact on aggregation, have been reported in detail (Crovetto 1996). Therefore, the profit of plant residues, such as carbon sequestration, should be paid more attention. The carbon budget of summer fallow fields, however, ranged from −0.69 to −0.90 Mg C ha⁻¹. To obtain the representative carbon budget data, 2004 and 2005 data were used for calculating the average carbon budget at all sites. The carbon budget of a 3-year crop rotation system was estimated to range from −0.51 (DC) to −0.10 (OC) Mg C ha⁻¹. It should be noted that the carbon budget was negative at all sites. This result indicates that a 3-year crop rotation system contributes to a marked decrease in soil organic carbon at all sites. The carbon budget of a 5-year crop rotation system, however, was estimated to range from −0.32 to 0.60 Mg C ha⁻¹, with only the DC site contributing as a carbon source. In meadow fields, the 2004–2005 carbon budget ranged from 0.8 to 1.3 Mg C ha⁻¹ and meadow management at all sites contributed to carbon sequestration.

To further reduce the loss of soil organic matter, extensive use of summer fallow should be reconsidered. Yanai et al. (2005) reported that spatial dependency of PMC and carbon input as plant residue were well developed in this region, and suggested that the spatial patterns of the soil organic matter dynamics under grain farming are markedly affected by the topography. Funakawa et al. (2004b) recommended intensive snow management as an alternative approach for improving the soil moisture conditions at some topographical locations. Diversification of cropping systems is also important for eliminating summer fallow, especially legumes as pea and chickpea and/or oilseed as sunflower and mustard are alternative crops (Sulemenov et al. 2005). Given the findings of the present study, we recommend meadow management be introduced as a part of the crop rotation system, especially at DC sites and at some SC or OC sites depending on topographical locations. Obviously, as the most appropriate management would vary regionally depending on both environmental and socio-economic conditions (Paustian et al. 1997), the nature of site-specific management should be carefully selected depending on local conditions. The general conclusion reached in the present study is that land use influences the dynamics of soil organic carbon because of temperature sensitivity of soil CO₂ emission, precipitation or dryness factor sensitivity of intra-seasonal variation of CO₂ emission, and carbon input as plant residue. These findings will support decision making for environmentally friendly land-use planning of cereal production in northern Kazakhstan.

ACKNOWLEDGMENTS

The authors thank Messrs Koishibai Erzhanov, Kairat Kuitenov, Ansagan Bolatbek and Daniel Shorppack for their collaboration during the field research, and Mrs Sholpan Akshalov for her constant assistance during our long-term stay in Shortandy, Kazakhstan.