The Dynamic Characteristics and Influencing Factors of Soil Respiration in Different Types of Grasslands in the Barkol Lake Basin

PDF HTML阅读 XML下载 导出引用 引用提醒 旱作农田不同耕作土壤呼吸及其对水热因子的响应 DOI: 10.5846/stxb201110071460 作者: 作者单位: 中国农业科学院农业资源与农业区划研究所;北京京诚嘉宇环境科技有限公司,中国农业科学院农业资源与农业区划研究所,中国农业科学院农业资源与农业区划研究所,中国农业科学院农业资源与农业区划研究所,中国农业科学院农业资源与农业区划研究所,中国农业科学院农业资源与农业区划研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点基础研究发展"973"计划项目(2011CB100501);国家十二五"863"计划项目(2011AA100505);国际合作项目(2010DFA34420) Soil respiration and its responses to soil moisture and temperature under different tillage systems in dryland maize fields Author: Affiliation: Institute of Agricultural Resources and Regional Planning,CAAS,Institute of Agricultural Resources and Regional Planning,CAAS,Institute of Agricultural Resources and Regional Planning,CAAS,Institute of Agricultural Resources and Regional Planning,CAAS,Institute of Agricultural Resources and Regional Planning,CAAS,Institute of Agricultural Resources and Regional Planning,CAAS Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:为研究旱作农田春玉米生育期不同耕作土壤呼吸变化特征及其对水热因子的响应情况,在山西省寿阳县旱农试验基地采用红外气体分析法测定了传统耕作(CT)、少耕(RT)和免耕(NT)土壤呼吸速率,并同步测定了各土层土壤水分、温度。研究表明:在春玉米生育期内,土壤呼吸速率均呈单峰型变化趋势,峰值出现在8月;传统耕作与少耕土壤呼吸速率变化趋势基本一致,而免耕土壤与前两者相比波动幅度较大;土壤呼吸峰值与水分、温度之间无明显相关,其余时期土壤呼吸与水分、温度因子具有良好的相关性;双因子模型较单因子模型能更好的描述土壤呼吸与水分、温度之间关系,基于水热双因子(10-20 cm)的指数-幂模型能够解释土壤呼吸变化的81%-87%(P<0.01);3种耕作土壤呼吸对水热因子协同影响的敏感性表现为CT>NT>RT。 Abstract:Soil respiration and its responses to soil moisture and soil temperature under different tillage systems during the period of spring maize growth were investigated in Shouyang Dryland Farming Experimental Station, Shanxi Province, China. The soil respiration rate, soil moisture and soil temperature were determined by dynamic chamber-IRGA method, in the maize field, with three tillage practices, including conventional (CT), reduced (RT), and no-till (NT). The results showed that the changes in soil respiration rates had a single peak curve, and its peak appeared in August The seasonal variations in soil respiration rates under CT, RT and NT were 0.50-4.81, 1.11-5.44 and 0.40-5.89 μmol CO2 m-2·s-1, respectively. The trends in soil respiration between CT and RT were similar, while there was a larger fluctuation in soil respiration with NT. The regression analysis showed that soil respiration had a significant correction with soil moisture or temperature, but little at the peak values of soil respiration. Soil moisture (0-10 cm) could explain 57%-76% of seasonal variations in the soil respiration. The moisture sensitivities of soil respiration were NT>RT>CT. Soil temperature (15 cm) could explain 67%-82% of seasonal variations in the soil respiration. the Q10 was NT (2.47)>RT (2.02)>CT (1.59). The two-factor model y=aebTWc or y=a+bT+cW could better describe the relationship between soil respiration and combination of soil moisture and temperature than the one-factor model. The index-power model of combination of soil moisture and temperature (10-20 cm) y=aebTWc can explain 81%-87% of variations in soil respiration (P<0.01). The sensitivities of three tillage treatments to the combination of soil moisture and temperature were: RT>CT>NT. Soil respiration was affected differently by the hydrothermic factor or by each of the single factor. 参考文献 相似文献 引证文献

Soil respiration has become the main way of farmland ecosystem carbon emissions. Soil respiration and its responses to soil moisture and soil temperature under straw and biochar returning were investigated. Combined soil CO2 fluxes system(ACE-002/OPZ/SC) with the method of root exclusion, this study conducted a long-term field experiment in the national monitor station of soil fertility and fertilizer efficiency of purple soils. The total soil respiration and heterotrophic respiration rate and the soil hydrothermal factors were measured during the growth period of rape and maize in rape-maize rotation systems, and the difference between total soil respiration and heterotrophic respiration was calculated as the contribution of root respiration to soil respiration. There were five treatments including CK(no organic material), CS(straw), CSD(straw+microorganism), BC(biochar), CSBC(50%straw+50%biochar), which were replicated three times. The results showed that straw and biochar returning significantly affected the seasonal variations and the peak of soil respiration. In addition to BC treatment, other treatments promoted soil respiration and cumulative emissions of soil CO2. Soil respiration rate was significantly different under different treatments, the changes in soil respiration rates showed a single peak curve under all treatments, the seasonal variations in soil respiration rates under rape was 0.12-2.29 μmol·(m2·s)-1, displaying an order of CS > CSD > CSBC > CK > BC. Soil respiration was pretty complex in maize season, the seasonal variation in soil respiration rates under rape was 1.02-15.32 μmol·(m2·s)-1, displaying an order of CSD > CS > CSBC > CK > BC, the changes in soil respiration rate presented a double peak curve under CS and CSD and CSBC treatments and a single peak curve under BC and CK treatments. Heterotrophic respiration could explain 86.50%-93.94% of seasonal variations in the soil total respiration, and the contribution of root respiration(26.49%-32.86%) was significantly lower than CK treatment(53.65%).Straw and biochar returning did not change soil temperature and soil moisture. Soil temperature at 5 cm depth had significant effects on the change dynamics of soil respiration rates, but soil moisture did not. Soil temperature at 5 cm depth could explain 82%-94% of the variations in soil respiration. The values of temperature sensitivity coefficient changed from 3.28 to 4.47. Compared with CK treatment, Q10 of CS, CSD and CSBC decreased by 26.62%, 18.12%, 20.58%, respectively, while BC increased by 12.53%. There was no synergistic effect between soil temperature and soil moisture on soil respiration, the dynamic changes of soil respiration rate could be simulated by single factor index function of soil temperature. Overall, soil respiration was significantly promoted by returning of straw, straw+microorganism, straw+biochar, while it was inhibited by returning of biochar.

PDF HTML阅读 XML下载 导出引用 引用提醒 塔克拉玛干沙漠腹地冬季土壤呼吸及其驱动因子 DOI: 10.5846/stxb201404220795 作者: 作者单位: 1.新疆大学 资源与环境科学学院 新疆 乌鲁木齐 830046;2.中国气象局乌鲁木齐沙漠气象研究所 新疆 乌鲁木齐 830002;3.塔克拉玛干沙漠大气环境观测试验站, 新疆 塔中 841000;,2.中国气象局乌鲁木齐沙漠气象研究所, 新疆 乌鲁木齐 830002;3.塔克拉玛干沙漠大气环境观测试验站, 新疆 塔中 841000; 4.南京信息工程大学 应用气象学院, 江苏 南京 210044,2.中国气象局乌鲁木齐沙漠气象研究所, 新疆 乌鲁木齐 830002; 3.塔克拉玛干沙漠大气环境观测试验站, 新疆 塔中 841000,2.中国气象局乌鲁木齐沙漠气象研究所, 新疆 乌鲁木齐 830002; 3.塔克拉玛干沙漠大气环境观测试验站, 新疆 塔中 841000,2.中国气象局乌鲁木齐沙漠气象研究所, 新疆 乌鲁木齐 830002; 3.塔克拉玛干沙漠大气环境观测试验站, 新疆 塔中 841000 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(41175140);公益性行业(气象)科研专项(GYHY201306066) Environmental factors driving winter soil respiration in the hinterland of the Taklimakan Desert, China Author: Affiliation: 1.College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China.2. Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China;3.Taklimakan Desert Atmosphere and Environment Station, Tazhong 841000, Xinjiang, China.,2.Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China; 3.Taklimakan Desert Atmosphere and Environment Station, Tazhong 841000, Xinjiang, China; 4. College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044,China,2. Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China;3Taklimakan Desert Atmosphere and Environment Station, Tazhong 841000, Xinjiang, China,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:利用Li-8150系统测定了塔克拉玛干沙漠腹地冬季(1月)土壤呼吸,分析了环境驱动因子对极端干旱区荒漠生态系统土壤呼吸的影响。结果表明:(1)冬季土壤呼吸日变化呈现出显著的单峰曲线,土壤呼吸速率最大值出现在12:00,为0.0684μmol CO2 m-2 s-1,凌晨04:00附近出现最小值,为-0.0473μmol CO2 m-2 s-1;(2)土壤呼吸速率与各层气温,0cm地表温度均存在着极其显著或显著的线性关系,且都具有正相关性;(3)土壤呼吸速率与5cm土壤湿度存在着较为明显的线性关系,该层湿度能够解释土壤呼吸的69.5%;(4)0cm地表温度对土壤呼吸贡献最大,其次是5cm土壤湿度;(5)以0cm地表温度、5cm土壤湿度为变量,通过多元回归分析表明:土壤温度-湿度构成的多变量模型能够解释大于86.9%的土壤呼吸变化情况;(6)研究时段内土壤呼吸速率的平均值是-1.45mg CO2 m-2 h-1。 Abstract:In order to analyze the environmental drivers of soil respiration in an extreme arid desert ecosystem, we measured diurnal variation in winter soil respiration at Tazhong, a hinterland of Taklimakan Desert in northwest China. Regression analysis was performed with SPSS 21.0. We observed that:(1) Diurnal variation in winter soil respiration showed a single peak at 12:00 noon (local time), after which soil respiration began to decrease, reaching a minimum value at around 4:00 a.m. (2) Soil respiration and the air temperature at each height tested (0.5 m, 2 m) were significantly and positively correlated. Air temperature at 2 m was able to explain 67.8% of the diurnal variation in soil respiration. (3) Soil temperature at 0 cm, modeled by linear equations, was able to explain 86.3% of the diurnal variation in soil respiration, demonstrating that this process is more sensitive to temperature at 0 cm than at any other soil layer (10 cm, 20 cm, 40 cm). (4) Soil respiration exhibited a positive linear correlation with soil moisture at a depth of 5 cm. When linear regression analysis was used to model the relationship between these variables, the fitted linear model explained 69.5% of the diurnal variation in soil respiration, demonstrating that, in the extreme arid desert ecosystem, this shallow layer of moisture exerts a large effect on soil respiration. (5) The greatest contributors to soil respiration were soil temperature at a depth of 0 cm, followed by soil moisture at 5 cm. (6) Multiple regression analyses showed that a multi-variable model of temperature and soil moisture explains 86.9% of the diurnal variation in soil respiration, which is not significantly better than a single-variable model. (7) For winter soil respiration, the daily average rate of CO2 absorption was -1.45mg CO2 m-2 h-1. 参考文献 相似文献 引证文献

Examining the long-term spatiotemporal distribution of grassland types and their transitions is crucial for better understanding regional and global changes. Most research in this field has examined the spatial distribution, temporal dynamics of grasslands, and their causes as a unified entity. This study predicted the distribution of nine major grassland types in Xinjiang under three climate change scenarios from 2041 to 2100 based on 1980s grassland maps, field data in 2023, and 28 factors. The total area of the nine grassland types showed a decreasing trend from 2041 to 2100. The lowland meadow (LM), temperate meadow steppe (TMS), temperate steppe desert (TSD), temperate desert steppe (TDS), and mountain meadow (MM) expanded, while significant declines occurred in alpine meadow (AM), alpine steppe (AS), temperate desert (TD), and temperate steppe (TS). Among cumulative contribution rate of the 28 factors examined in this study, NDVI, vegetation type, slope, elevation, soil_symbol, soil_ph, Bio1, Bio5, Bio8, Bio9, Bio10, Bio12, Bio13, Bio15, and Bio18 played important roles in most grassland types. LM, TD, and AS grassland were found to be more sensitive to E (environment), while AM, TDS, and TSD were more influenced by T (temperature). The distributions of MM and TMS are significantly influenced by the combined effects of all three categories of factors. For TS, the impacts of both temperature and environmental factors are substantial. These findings provided a robust foundation for conservation planning and the sustainable management of grassland ecosystems in temperate and alpine regions.

Variation characteristics of soil respiration fluxes in four types of grassland communities under different precipitation intensity

Effects of various stocking rates on grassland soil respiration during the non-growing season

Grazing intensity alters soil respiration in an alpine meadow on the Tibetan plateau

The aims of this study were to investigate the diurnal and seasonal variations in soil respiration in different broad-leaved forest types with different degrees of human disturbance, to identify the major abiotic and biotic factors affecting the daily and seasonal patterns of soil respiration, and to evaluate the effects of different forest management types and human disturbance on soil respiration in subtropical forests. We selected four different 1 hm2 forests with different degrees of human disturbance in Gutianshan, Zhejiang Province: An old-growth forest (OF, almost no human disturbance); two secondary forests (SF1, a secondary forest regrown after clear cutting about 50 years ago, and SF2, a secondary forest regrown after clear cutting about 50 years ago and selective cutting about 20 years ago); and a plantation forest (AF, planted about 20 years ago). We measured the diurnal and seasonal changes in soil respiration rates in each plot every month from May 2011 to July 2012. We also measured soil temperature and water content of the surface soil layer during sampling. The annual means of daily accumulated soil carbon release were 1.48, 1.48, 1.51, and 0.87 g C m-2 d-1 in OF, SF1, SF2, and AF plots, respectively. The soil respiration rate in AF was significantly lower than those in the other three forest types. On the diurnal scale, there were no significant variations in soil respiration rates among the four forest types. Our results indicated that the rate of soil respiration measured any time during the day can be used to predict daily accumulated soil carbon release. Significant positive relationships were found between soil respiration and surface soil temperature ( R 2=0.88-0.94) for all four forest types. There were no significant relationships between soil respiration and soil water content. The Q10 value of OF was significantly higher than those of the other three forest types. The results showed that the soil respiration rate and its sensitivity to temperature were significantly decreased in the plantation with the most intense human disturbance (AF). The soil respiration rates and Q10 values of both SF forests were similar to those of OF forest. Soil temperature was the most important driving factor for soil respiration. Together, our results implied that different degrees of human disturbance had different effects on soil respiration in these forests. This information is important to estimate the role of human interference in regional carbon cycles. Also, the finding that there was no significant variation in the diurnal soil respiration rates is very important for guiding further research on soil respiration in related regions.

Soil respiration is an important component of the annual carbon balance of forests, but few studies have addressed interannual variation in soil respiration. The objectives of this study were to investigate the seasonal and interannual variation in soil respiration, temperature, precipitation, and soil water content in two New England forest soils and to develop and evaluate empirical models for predicting variations in soil respiration using temperature and soil moisture content. We have been measuring soil respiration, using dynamic chambers in well‐drained upland sites and poorly drained wetland sites since 1995 at the Harvard Forest, Massachusetts, and since 1996 at the Howland Forest, Maine. The upland sites had consistently greater rates of respiration than wetlands. Prolonged drought periods in 1995, 1998, and 1999 at the Harvard Forest resulted in decreased soil respiration rates in the uplands, particularly once soil moisture contents decreased below about −150 kPa. In contrast, wetland respiration increased upon drying. The interannual variation in soil respiration at the Harvard Forest, 0.23 kg C m−2 yr−1, exceeds the interannual variation in net ecosystem exchange (NEE), 0.14 kg C m−2 yr−1 previously measured for this forest, indicating that interannual variation in soil respiration can have an important influence on NEE. Interannual variation was lower at the Howland Forest, and the effects of low soil moisture content on respiration rates were more subtle. The onset of spring was variable among years at both forests, owing to variation in both temperature and precipitation, and contributed to 33–59% of the annual variability in total carbon release. At the upland sites, parameterization of empirical regression models for respiration as a function of soil temperature was inconsistent among years, indicating an important effect of interannual variation in soil water content. The negative residuals of the Harvard Forest temperature regression model were best explained by drought conditions (soil matric potentials ≤−150 kPa). This function was only applicable during severe drought and did not account for less severe dry periods that also reduced soil moisture and soil respiration. An empirical regression model for the wetlands as a function of temperature was significantly improved with the addition of a soil moisture function, which increased respiration rates under dry conditions and decreased it under wet conditions. Climatic changes resulting in drier conditions will likely decrease soil respiration rates in uplands and increase soil respiration in wetlands.

Soil respiration, a key component of the global carbon cycle, is a major source of uncertainty when estimating terrestrial carbon budgets at ecosystem and higher levels. Rates of soil and root respiration are assumed to be dependent on soil temperature and soil moisture yet these factors often barely explain half the seasonal variation in soil respiration. We here found that soil moisture (range 16.5-27.6% of dry weight) and soil temperature (range 8-17.5 degrees C) together explained 55% of the variance (cross-validated explained variance; Q2) in soil respiration rate (range 1.0-3.4 micromol C m(-2) s(-1)) in a Norway spruce (Picea abies) forest. We hypothesised that this was due to that the two components of soil respiration, root respiration and decomposition, are governed by different factors. We therefore applied PLS (partial least squares regression) multivariate modelling in which we, together with below ground temperature and soil moisture, used the recent above ground air temperature and air humidity (vapour pressure deficit, VPD) conditions as x-variables. We found that air temperature and VPD data collected 1-4 days before respiration measurements explained 86% of the seasonal variation in the rate of soil respiration. The addition of soil moisture and soil temperature to the PLS-models increased the Q2 to 93%. delta13C analysis of soil respiration supported the hypotheses that there was a fast flux of photosynthates to root respiration and a dependence on recent above ground weather conditions. Taken together, our results suggest that shoot activities the preceding 1-6 days influence, to a large degree, the rate of root and soil respiration. We propose this above ground influence on soil respiration to be proportionally largest in the middle of the growing season and in situations when there is large day-to-day shifts in the above ground weather conditions. During such conditions soil temperature may not exert the major control on root respiration.

Biotic and abiotic factors controlling the spatial and temporal variation of soil respiration in an agricultural ecosystem

Field‐chamber measurements of soil respiration from 17 different forest and shrubland sites in Europe and North America were summarized and analyzed with the goal to develop a model describing seasonal, interannual and spatial variability of soil respiration as affected by water availability, temperature, and site properties. The analysis was performed at a daily and at a monthly time step. With the daily time step, the relative soil water content in the upper soil layer expressed as a fraction of field capacity was a good predictor of soil respiration at all sites. Among the site variables tested, those related to site productivity (e.g., leaf area index) correlated significantly with soil respiration, while carbon pool variables like standing biomass or the litter and soil carbon stocks did not show a clear relationship with soil respiration. Furthermore, it was evidenced that the effect of precipitation on soil respiration stretched beyond its direct effect via soil moisture. A general statistical nonlinear regression model was developed to describe soil respiration as dependent on soil temperature, soil water content, and site‐specific maximum leaf area index. The model explained nearly two thirds of the temporal and intersite variability of soil respiration with a mean absolute error of 0.82 μmol m−2 s−1. The parameterized model exhibits the following principal properties: (1) At a relative amount of upper‐layer soil water of 16% of field capacity, half‐maximal soil respiration rates are reached. (2) The apparent temperature sensitivity of soil respiration measured as Q10 varies between 1 and 5 depending on soil temperature and water content. (3) Soil respiration under reference moisture and temperature conditions is linearly related to maximum site leaf area index. At a monthly timescale, we employed the approach by Raich et al. [2002] that used monthly precipitation and air temperature to globally predict soil respiration (T&amp;P model). While this model was able to explain some of the month‐to‐month variability of soil respiration, it failed to capture the intersite variability, regardless of whether the original or a new optimized model parameterization was used. In both cases, the residuals were strongly related to maximum site leaf area index. Thus, for a monthly timescale, we developed a simple T&amp;P&amp;LAI model that includes leaf area index as an additional predictor of soil respiration. This extended but still simple model performed nearly as well as the more detailed time step model and explained 50% of the overall and 65% of the site‐to‐site variability. Consequently, better estimates of globally distributed soil respiration should be obtained with the new model driven by satellite estimates of leaf area index. Before application at the continental or global scale, this approach should be further tested in boreal, cold‐temperate, and tropical biomes as well as for non‐woody vegetation.

Respiration was measured at daytime during the growing seasons (May–October) of 2011 and 2012 in a young Pinus tabulaeformis plantation with heavy, medium and light intensity thinning and unthinned control plots in Shanxi province in northern China. Soil temperature, moisture, fine root biomass, amounts of soil organic C and litterfall biomass were also measured. We found that immediately following thinning treatments, soil respiration increased by 8 %–21 % compared with the unthinned control plots during both growing seasons. Thinning significantly affected soil respiration and soil temperature with different thinning intensities, while there were no significant differences in soil moisture among the various treatments. During the growing seasons, the soil respiration rates were positively correlated with the soil moisture: the 19.4 %–54.0 % variation in soil respiration rates in the four thinning regimes are explained by the changes in soil moisture. Meanwhile, a positive correlation was found between soil temperature and soil respiration rates at all sites. The best fitting model with temperature and moisture explained 44.3 % of the variation in soil respiration in the high thinning treatment, 27.6 % in the light thinning treatment, 18.6 % in medium thinning and in the control sites during the measuring periods. Overall, soil respiration is better predicted by soil moisture, soil organic C, live fine root biomass and soil temperature when data are pooled for all thinning treatments over the two growing seasons. The best regression model explained 74.7 % of the total variation in soil respiration over the different thinning intensities for the two sampling periods.

Many cities are currently interested in becoming climate-neutral (European Commission 2022), which has increased the need to understand the biogenic carbon cycle in urban areas; a topic still lacking a comprehensive, measurement-based understanding. Soil respiration, i.e. the biogenic carbon flux through soil surface as a result of belowground plant and microbial activity, is a key part of the carbon cycle (Ryan and Law 2005). Its magnitude is principally regulated by soil temperature, soil moisture, soil microbial community, and decomposable substrate availability, as well as the dominant vegetation and its metabolism. Microclimatic variability is likely to induce fine-scale spatial variation in soil respiration through various pathways, as all of these drivers are either part of the naturally varying microclimate (soil moisture, soil temperature), or subjected to it (substrate availability, plant and microbial communities and their metabolism) (Bramer et al. 2018). To study the connection of microclimate and soil respiration especially in urban areas, we performed a field measurement campaign over two consecutive growing seasons (2023-2024) in Kumpula botanic garden in Helsinki, Finland. A network of 46 microclimate stations was established to continuously measure soil moisture, soil temperature, and near-surface air temperature in high temporal resolution and dense spatial coverage across the garden. Manual chamber measurements of soil respiration were conducted bi-monthly in June-August at 33 measurement points divided between six measurement sites within the garden, together with manual measurements of soil moisture and temperature. The soil respiration measurement sites were situated under tree canopies: three sites under conifers and three sites under broadleaved trees. Stand characteristics were inventoried at all sites, and soil organic carbon and nitrogen contents were determined from soil samples. We aimed to quantify: how much of the observed variation in soil respiration was attributed to variation in soil temperature and soil moisture and how large the role of the site-specific soil and stand characteristics was. how much of the observed variation in soil respiration was attributed to variation in soil temperature and soil moisture and how large the role of the site-specific soil and stand characteristics was. Based on this, we estimate whether the microclimatic data collected by the logger network could be utilised for reliable spatial upscaling of the manual soil respiration measurements to cover the entire garden area. Overall, our results shed light on urban soil respiration characteristics and help in establishing a more comprehensive understanding of the biogenic carbon cycle in urban green space.

Investigating the seasonal variations in soil respiration in different crop fields and their relationships with crop productivity is crucial to understanding the key biotic controls of soil respiration. A field experiment was performed during the 2020‒2021 winter wheat‒soybean, canola‒maize and broad bean‒sweet potato growing seasons. The seasonal variations in soil respiration, soil temperature and moisture were measured. The variables that were associated with crop productivity were also determined. The results showed that crop types significantly (p < .05) affected the mean seasonal soil respiration. Most variables that were associated with crop productivity exhibited obvious seasonal variation patterns. The soil temperature, moisture and crop productivity comprehensively influenced the soil respiration, and models based on these potential controlling factors explained 45.7%‒59.2% (R 2 = 0.457‒0.592) of the variation in soil respiration in the different crop rotation fields. A model based on the mean seasonal soil temperature, moisture, leaf area index, and root carbon content explained 68.3% (R 2 = 0.683) of the variation in the mean seasonal soil respiration across the different crop fields. We demonstrated the potential of effectively characterizing the variations in soil respiration in agroecosystems using temperature, moisture and crop productivity.