Soybean residues sequestration affected by different tillage practices and the impacts on soil microbial characteristics and enzymatic activities

A large quantity of leaf litter was left on soil surface after soybean (Glycine max) harvest in the black soil region, northeast of China, where soybean was planted with the largest area. This paper investigated the effects of different fall tillage practices on soybean leaf litter sequestration into soil, and the subsequently durative effects on soil biological and biochemical properties during the next growing season. Two practices were investigated, fall tillage (T) and no fall tillage (NT) after soybean harvest in autumn. Results showed that the residue biomass on soil surface and in subsoil profile (0–20 cm) after soybean harvest was about 1450 kg ha^?1 and 340 kg ha^?1, respectively in October 2006. The residue biomass on soil surface and in subsoil profile was about 84 kg ha^?1, 1581 kg ha^?1 for T, and 423 kg ha^?1, 340 kg ha^?1 for NT respectively in May 2007. It was obvious that T practice can more effectively sequester leaf litter into soil compared to NT. Results also showed that T practices after soybean harvest eminently improved soil microbial carbon biomass and nitrogen biomass contents, and significantly improved soil urease and acid phosphate activities than NT. No significant difference of dehydrogenase activity was found between N and NT. The positive effects of T treatment on Soil microbial properties and soil enzymes activities among the next growing season due to soybean residues sequestration performed durative profit.     

Environmental indicators for estimating the potential soil respiration rate in alpine zone

Soil respiration is the main form of carbon flux from soil to atmosphere in the global carbon cycle. The effect of temperature on soil respiration rate is important in evaluating the potential feedback of soil organic carbon to global warming. We incubated soils from the alpine meadow zone and upper rocky zone along an altitudinal gradient (4400–5500 m a.s.l.) on the Tibetan Plateau under various temperature and soil moisture conditions. We evaluated the potential effects of temperature and soil moisture on soil respiration and its variation across altitudes. Soil respiration rates increased as the temperature increased. At 60% of soil water content, they averaged 0.21–5.33 μmol g soil⁻¹ day⁻¹ in the alpine meadow zone and 0.11–0.50 μmol g soil⁻¹ day⁻¹ in the rocky zone over the experimental temperature range. Soil respiration rates in the rocky zone did not increase between 25 and 35 °C, probably because of heat stress. Rates of decomposition of organic matter were high in the rocky zone, where the CN ratio was smaller than in the middle altitudes. Soil respiration rates also increased with increasing soil water content from 10% to 80% at 15 °C, averaging 0.04–2.00 μmol g soil⁻¹ day⁻¹ in the alpine meadow zone and 0.03–0.35 μmol g soil⁻¹ day⁻¹ in the rocky zone. Maximum respiration rates were obtained in the middle part of the alpine slope in any case of experimental temperature and soil moisture. The change patterns in soil respiration rate along altitude showed similar change pattern in soil carbon content. Although the altitude is a variable including various environmental factors, it might be used as a surrogate parameter of soil carbon content in alpine zone. Results suggest that temperature, soil moisture and altitude are used as appropriate environmental indicators for estimating the spatial distribution of potential soil respiration in alpine zone.     

Variation of soil nutrients and particle size under different vegetation types in the Yellow River Delta

The Yellow River Delta wetland, located at the southern coast of Bohai Gulf, provides important ecosystem services such as flood control, water purification, biodiversity conservation, nutrient removal and carbon sequestration, shoreline stabilization, tourism attraction and wetland products maintains in the Yellow River Delta. This study assessed how agricultural activities in a reclamation wetland changed soil pH, soil electric conductivity, soil nutrient and soil particle size as compared to natural vegetation by using a combination of field experiments and lab analysis. The vegetation type included adjacent alfalfa field (Medicago sativa), cotton field (Gossypium spp.), Chinese tamarisk shrub (Tamarix chinensis), and reed marsh (Phragmites sage). The results indicated that the soil pH was higher (pH > 8) in alfalfa field and cotton field, and alfalfa field and reed marsh had significant function in reducing soil salt content, soil electric conductivity of alfalfa field at 0–30 cm were 140.38 ± 14.36, 114.48 ± 14.36, 125.30 ± 11.37 μs/cm. The effect of different vegetation types on soil nutrient was significant (P < 0.05). Soil organic matter at 0–10 cm in Chinese tamarisk shrub and reed marsh was 21.66 ± 3.82 g/kg and 16.51 ± 4.60 g/kg, which was higher than that of alfalfa field (10.47 ± 2.36 g/kg) and cotton field (9.82 ± 1.27 g/kg), but soil total nitrogen content in alfalfa field was the highest, which is significantly higher than that of cotton field, Chinese tamarisk shrub and reed marsh(P < 0.05), the content of soil total nitrogen at 0–10 cm and 10–20 cm was 7.67 ± 0.38 g/kg and 5.97 ± 0.51 g/kg, respectively, while the content of available P and available K was reversed. The difference of soil particle size between layers was not significant (P > 0.05), the sand content of Chinese tamarisk shrub soils in 0–10 cm was the highest, the next was alfalfa field and cotton field, and the content of silt and clay in reed marsh was higher than the others. The correlation and significant degree between soil particle size and soil nutrient was related with vegetation types, and soil organic matter was significantly positively correlated with soil silt and clay content on the alfalfa field. The results demonstrated that wetland cultivation was one of the most important factors influencing on the nutrient fate and reserves in soil, which could lead to rapid nutrient release and slow restoration through abandon cultivation. Consequently, compared with cotton field, alfalfa field is more favorable to sustainable management of wetland cultivation in the Yellow River Delta. It should be considered in wetland restoration projects planning.     

Soil quality assessment of coastal wetlands in the Yellow River Delta of China based on the minimum data set

Little information is available to assess the dynamic changes in wetland soil quality in coastal regions, though it is essential for wetland conservation and management. Soil samples were collected in Suaeda salsa wetlands (SWs), Tamarix chinensis wetlands (TWs), Suaeda salsa–Tamarix chinensis wetlands (STWs), freshwater Phragmites australis wetlands (FPWs) and saltwater Phragmites australis wetlands (SPWs) in three sampling periods (i.e., summer and autumn of 2007 and spring of 2008). According to the flooding characteristics of these wetlands, the study area could be grouped into three sub-regions: short-term flooding region (STFR), seasonal flooding region (SFR) and tidal flooding region (TFR). Soil quality was evaluated using the soil quality index (SQI), which was calculated using the selected minimum data set (MDS) based on principal components analysis (PCA). Our results showed that soil salt content (SSC), total carbon (TC), magnesium (Mg), nitrate nitrogen (NO₃⁻-N) and total sulfur (TS) consisted of a MDS among 13 soil properties. The SQI values varied from 0.18 to 0.66 for all soil samples, of which the highest and lowest SQI values were observed in TFR. The average SQI values were significantly higher in summer (0.50 ± 0.13) than in spring (0.37 ± 0.13) and autumn (0.36 ± 0.11) in the whole study area (p < 0.05). The average SQI values followed the order STFR (0.44 ± 0.12) > TFR (0.41 ± 0.15) > SFR (0.35 ± 0.09) although no significant differences were observed among the three regions (p > 0.05). SPWs and SWs soils showed higher SQI values (0.50 ± 0.10 and 0.47 ± 0.15, respectively) than TWs (0.30 ± 0.08) soils (p < 0.05). The SSC was the dominant factor of soil quality with its proportion of 34.1% contributing to the SQI values, followed by TC (24.5%) and Mg (24.1%). Correlation analysis also showed that SQI values were significantly negatively correlated with SSC. SSC might be a characteristic indicator of wetland soil quality assessment in coastal regions. The findings of this study showed that the SQI based on MDS is a powerful tool for wetland soil quality assessment.     

Evaluation of integrating topographic wetness index with backscattering coefficient of TerraSAR-X image for soil moisture estimation in a mountainous region

The estimation of soil moisture by using the backscattering coefficient of radar in a mountainous region is a challenging task due to the complex topography, which impacts the distribution of soil moisture and changes the backscattering coefficient. Complicated terrain can disturb empirical moisture estimation models, thereby, the resulting estimates of soil moisture are very unlikely reliable. This article proposed an innovative way of integration of the topographic wetness index (TWI) and the backscattering coefficient of soil obtained from the TerraSAR-X image, which improves the accuracy of measurement of the soil moisture. The standard estimation error and the coefficient of determination from the model were used to evaluate the performance of TWI. Our results show that the standard estimation error was decreased from: (1) 4.0% to 3.3% cm³ cm⁻³ at a depth of 5 cm and (2) 4.5% to 3.9% cm³ cm⁻³ at a depth of 10 cm. The most reliable estimation was observed at a depth of 5 cm, when it was compared with those of 0–5 cm, 10 cm and 15 cm. The TWI from the digital elevation model (DEM) is useful as a constraint condition for modeling work. This article concludes that the integration of the backscattering coefficient of soil with TWI can significantly reduce the uncertainty in the estimation of soil moisture in a mountainous region.     

Optimizing the bioindication of forest soil acidity,nitrogen and mineral nutrition using plant species

Soil moisture and nutritional characteristics are frequently assessed using plant species and community bioindication, e.g., the Ellenberg system of species indicator values. This method, based on complete inventories of plant species present in plots, is time-consuming, which could prevent its general use for forest or other natural land management. Our aim was to determine the impact of a reduction in the time spent to carry out a floristic inventory on the quality of soil characteristic assessment using plant bioindication. We compared the measurements of soil pH-H₂O (pH), organic carbon to total nitrogen ratio (C:N) and base saturation (BS) in the 0–5 cm soil layer of 470 plots with the same variables estimated from floristic inventories of increasing duration, using plant indicator values (IV) from the EcoPlant database. The performance of predictions was evaluated by the square of the linear correlation coefficient between measured and predicted values (R²) and the root mean square error (RMSE) of predictions.The number rather than the percentage of total plot species used for the estimations was determinant for the prediction of soil pH quality. Performance of bioindication of pH, BS and C:N reached the maximum R² using the first 20–25 species recorded per plot, corresponding to a 14-min-long floristic inventory in comparison to a mean of 28 min spent to carry out a complete floristic inventory. A precision of prediction of 80% of the maximal precision was obtained after 4–5 min (6–12 inventoried species) for the three studied variables. These results are independent of the nutritional capability of the soils and were similar at the national and local scales. In order to estimate soil nutritional resources by plant bioindication, it is feasible to significantly reduce the time spent on floristic inventories and, thus, their cost. This is especially useful when the goal is to map the soil quality for decision-making in forest management.     

Capacities of soil water reservoirs and their better regression models by combining “merged groups PCA” in Chongqing,China

Soil water resource, together with the surface and sub-surface water resource, is essential to the regional water balance and world water cycle. A total of 90 soil samples were collected from 30 different soil profiles of dry fields throughout Chongqing, China randomly to show how soil could be a crucial part of water resources by discussing their five types of calculated soil water reservoir capacities, namely the total soil water reservoir capacity (mm) (TC), soil water storage capacity (mm) (SC), unavailable soil water reservoir capacity (mm) (UC), available soil water reservoir capacity (mm) (AC), and soil dead water storage capacity (mm) (DC) in certain layer, respectively. Overall, the total soil water reservoir capacity in 0–40 cm was about 209 mm, of which 70 mm belonged to available soil water reservoir capacity. Not all the five types of soil water reservoir capacities had significant correlations between each other. Soil structure, especially the size and quantity of soil pore was mainly determined by soil particle composition (clay, silt, and sand content). The more sand and less clay led to the more soil macropores, which provided room for soil water. Thus, clay, silt, and sand content jointly produced profound influence on soil water reservoir capacities. Nevertheless, specific water capacity and topographic factors displayed weak correlations to soil water reservoir capacities, which required further research works. Ultimately, the better regression models were achieved by multiple regression analysis coupled with “merged groups PCA” than by multiple regression analysis with “all variables PCA”. UC, SC, TC and DC could be well simulated (mostly R² > 0.70; P < 0.05) through normal multiple regression analysis using original variables as well as multiple regression analysis with “merged groups PCA”. Only regression models of TC and DC were highly significant (mostly R² > 0.70; P < 0.05) through “all variables PCA” method. And there were poor coefficients of determination (R²) for AC (mostly R² < 0.40; P < 0.05) by all the three regression methods.     

Distribution and interspecific correlation of root biomass density in an arid Elaeagnus angustifolia–Achnatherum splendens community

Main objective of this study was to determine the interspecific relationships between two dominant species in terms of root distribution in a typical arid tree-herbage (Elaeagnus angustifolia–Achnatherum splendens) community at Xidatan, Pingluo County, Ningxia Autonomous Region, Northwest China. Eight concentric zones (namely, Z1–Z8) were set from the bases of E. angustifolia individuals to the open lands and five soil profiles were excavated in each zone. Each soil profile was divided into five layers at the depths of 0–10 cm, 10–30 cm, 30–60 cm, 60–100 cm and 100–150 cm. Roots were collected for each species, and soil water content (SWC) and soil bulk density (SBD) were measured for each layer. We found noteworthy roots layer separation in the sub-canopy zones (Z1–Z4). The soil layers with highest fine root biomass density (FRBD) of A. splendens was primarily in the 0–10 cm, which were significantly shallower than those of E. angustifolia; whereas in the inter-canopy zones (Z5–Z8), inconsistent separation, or even overlapping of highest-FRBD-layers emerged between the two dominant species. Correlation analyses showed that negative correlations of FRBD between the two species mainly occurred in those soil layers with relatively higher FRBD and lower SWC. In contrast, positive correlations corresponded with relatively lower FRBD and higher SWC.     

Characterization of spatial variability of soil physicochemical properties and its impact on Rhodes grass productivity

Characterization of soil properties is a key step in understanding the source of spatial variability in the productivity across agricultural fields. A study on a 16 ha field located in the eastern region of Saudi Arabia was undertaken to investigate the spatial variability of selected soil properties, such as soil compaction ‘SC’, electrical conductivity ‘EC’, pH (acidity or alkalinity of soil) and soil texture and its impact on the productivity of Rhodes grass (Chloris gayana L.). The productivity of Rhodes grass was investigated using the Cumulative Normalized Difference Vegetation Index (CNDVI), which was determined from Landsat-8 (OLI) images. The statistical analysis showed high spatial variability across the experimental field based on SC, clay and silt; indicated by values of the coefficient of variation (CV) of 22.08%, 21.89% and 21.02%, respectively. However, low to very low variability was observed for soil EC, sand and pH; with CV values of 13.94%, 7.20% and 0.53%, respectively. Results of the CNDVI of two successive harvests showed a relatively similar trend of Rhodes grass productivity across the experimental area (r = 0.74, p = 0.0001). Soil physicochemical layers of a considerable spatial variability (SC, clay, silt and EC) were utilized to delineate the experimental field into three management zones (MZ-1, MZ-2 and MZ-3); which covered 30.23%, 33.85% and 35.92% of the total area, respectively. The results of CNDVI indicated that the MZ-1 was the most productive zone, as its major areas of 50.28% and 45.09% were occupied by the highest CNDVI classes of 0.97–1.08 and 4.26–4.72, for the first and second harvests, respectively.     

Effects of yak dung patch dropped in cold season on soil and pasture on the Qinghai-Tibetan Plateau

To clarify how dung patches from grazing yaks affect soil and pasture in the alpine meadow of Qinghai-Tibetan Plateau, yak dung was collected, mixed and redistributed in a cold grazing season. The soil physical and chemical properties and forage growth were then monitored under the yak dung patch, and 10 cm and 50 cm from the edge of yak dung patches. The result has shown that yak dung significantly improved soil moisture, total organic matter, and soil available N and P under or close to the dung patches. The forage production at 10 cm from the dung patch (303 g/m²) was significantly higher than that at 50 cm from the dung patch (control) (284 g/m²) in the second year, while the production was similar to the control in the first and the third year. The process of yak dung decomposition was slow and yak dung remains were observed 3 years after the drop. The dung patches also formed a strong ‘shell’, very difficult for plant underneath to penetrate and grow. Therefore, almost all plants under yak dung patches died, leading to decline in forage yield in the first, second, and the third year. In practice in the Qinghai-Tibetan Plateau regions, yak dung is often collected as fuel by the local farmers. Removing yak dung from alpine meadow may on one hand lead to losses in soil nutrients, but on the other hand reduces some of the negative effects, e.g. the reduction of forage yield under yak dung patches.     

Towards the application of soil organic matter as an indicator of forest ecosystem productivity: Deriving thresholds,developing monitoring systems,and evaluating practices

Soil organic matter (SOM), typically measured as soil organic carbon (SOC), has been widely recognized as a critical linkage between forest management and long-term site productivity. However, its use as an indicator of sustainable forest management practices has been limited both by difficulties in detecting changes in soil carbon due to inherent high variability and by challenges associated with determining appropriate thresholds for loss. In this study we evaluate a methodology for using field measures of total SOC (forest floor to 60 cm depth in mineral soil) in conjunction with a mechanistic forest growth model to derive threshold values for total SOC with respect to the maintenance of ecosystem productivity for a lodgepole pine (Pinus contorta) forest in the central interior region of British Columbia. We also examine the practicality of implementing a sustainable forest management (SFM) monitoring program around this measure and the potential long-term impact of alternative management scenarios on the indicator.Total SOC contents for the different site types sampled in the Quesnel region ranged from 35 to 57 t ha^?1. Long-term simulations of biomass extraction over several rotations showed a near 1:1 ratio in the relative decline of ecosystem productivity associated with relative declines in total SOC. A power analysis revealed that a mean sampling intensity of n = 12–25 and n = 8–17 would be required to detect 20% and 30% losses of total SOC, respectively, depending on the level of statistical power desired. The sampling intensity required for an effective monitoring program was significantly reduced by summing SOC for all soil layers to limit sampling error related to determination of layer boundaries. A modelling analysis of the effect of rotation length on SOC for the Quesnel forest types, suggests that rotation lengths shorter than 75 years should be avoided to prevent declines in ecosystem productivity. Our results confirm that the combination of modelling and statistical techniques can be successfully used to develop cost-efficient monitoring plans of sustainability of forest management, with SOC as a valid indicator of ecosystem productivity.     

Improving and evaluating the soil cover indicator for agricultural land in Canada

A soil cover days (SCD) model has been developed by Agriculture and Agri-Food Canada for use as an agri-environmental indicator to monitor the relationship between agricultural production activities and agri-environmental quality. The SCD indicator integrates information on crops, soils, climate, and field activities to estimate the total equivalent number of days that agricultural soils are covered by crop canopy, crop residue and snow in a given year. Daily cover fractions of plant and residue for a given crop in an ecoregion are simulated using typical crop calendar and field management practices, and the equivalent number of days that soil is covered by snow in winter is derived from long term climate normals. The equivalent SCD for a spatial unit is then derived as the area-weighted sum of different crops and different management practices within the unit. This paper presents the SCD framework, details an assessment of the accuracy of the model and outlines future improvements. Annual snow days derived from 30-year climate normals as used in the model was strongly correlated (excluding mountain areas) with that derived from satellite data (R² = 0.45, n = 48), even though the remote sensing product showed significant temporal and spatial variability. Crop residue fraction estimated by the model was strongly correlated with field data collected over major crop areas and crop types (R² = 0.74, n = 55), and modelled plant cover fraction was well correlated with that derived from remote sensing data (R² = 0.57, n = 57). Large discrepancies were observed for some samples due to deviation of the actual crop calendar from that estimated using climate normals. National map showing the change in the indicator from 1981 to 2011 reveals changes in crop and residue management practices.     

A comparative assessment of support vector regression,artificial neural networks,and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape

Soil organic carbon (SOC) is a key indicator of ecosystem health, with a great potential to affect climate change. This study aimed to develop, evaluate, and compare the performance of support vector regression (SVR), artificial neural network (ANN), and random forest (RF) models in predicting and mapping SOC stocks in the Eastern Mau Forest Reserve, Kenya. Auxiliary data, including soil sampling, climatic, topographic, and remotely-sensed data were used for model calibration. The calibrated models were applied to create prediction maps of SOC stocks that were validated using independent testing data. The results showed that the models overestimated SOC stocks. Random forest model with a mean error (ME) of −6.5 Mg C ha⁻¹ had the highest tendency for overestimation, while SVR model with an ME of −4.4 Mg C ha⁻¹ had the lowest tendency. Support vector regression model also had the lowest root mean squared error (RMSE) and the highest R² values (14.9 Mg C ha⁻¹ and 0.6, respectively); hence, it was the best method to predict SOC stocks. Artificial neural network predictions followed closely with RMSE, ME, and R² values of 15.5, −4.7, and 0.6, respectively. The three prediction maps broadly depicted similar spatial patterns of SOC stocks, with an increasing gradient of SOC stocks from east to west. The highest stocks were on the forest-dominated western and north-western parts, while the lowest stocks were on the cropland-dominated eastern part. The most important variable for explaining the observed spatial patterns of SOC stocks was total nitrogen concentration. Based on the close performance of SVR and ANN models, we proposed that both models should be calibrated, and then the best result applied for spatial prediction of target soil properties in other contexts.     

Long-term tillage effects on soil organic carbon and dissolved organic carbon in a purple paddy soil of Southwest China

The impact of conservation tillage practices on soil carbon has been of great interest in recent years. Conservation tillage might have the potential to enhance soil carbon accumulation and alter the depth distribution of soil carbon compared to conventional tillage based systems. Changes in the soil organic carbon (SOC) as influenced by tillage, are more noticeable under long-term rather than short-term tillage practices. The objective of this study was to determine the impacts of long-term tillage on SOC and dissolved organic carbon (DOC) status after 19 years of four tillage treatments in a Hydragric Anthrosol. In this experiment four tillage systems included conventional tillage with rotation of rice and winter fallow system (CTF), conventional tillage with rotation of rice and rape system (CTR), no-till and ridge culture with rotation of rice and rape system (NT) and tillage and ridge culture with rotation of rice and rape system (TR). Soils were sampled in the spring of 2009 and sectioned into 0–10, 10–20, 20–30, 30–40, 40–50 and 50–60 cm depth, respectively.Tillage effect on SOC was observed, and SOC concentrations were much larger under NT than the other three tillage methods in all soil depths from 0 to 60 cm. The mean SOC concentration at 0–60 cm soil depth followed the sequence: NT (22.74 g kg^?1) > CTF (14.57 g kg^?1) > TR (13.10 g kg^?1) > CTR (11.92 g kg^?1). SOC concentrations under NT were significantly higher than TR and CTR (P < 0.01), and higher than CTF treatment (P < 0.05). The SOC storage was calculated on equivalent soil mass basis. Results showed that the highest SOC storage at 0–60 cm depth presented in NT, which was 158.52 Mg C ha^?1, followed by CTF (106.74 Mg C ha^?1), TR (93.11 Mg C ha^?1) and CTR (88.60 Mg C ha^?1). Compared with conventional tillage (CTF), the total SOC storage in NT increased by 48.51%, but decreased by 16.99% and 12.77% under CTR and TR treatments, respectively. The effect of tillage on DOC was significant at 0–10 cm soil layer, and DOC concentration was much higher under CTF than the other three treatments (P < 0.01). Throughout 0–60 cm soil depth, DOC concentrations were 32.92, 32.63, 26.79 and 22.10 mg kg^?1 under NT, CTF, CTR and TR, and the differences among the four treatments were not significant (P > 0.05). In conclusion, NT increased SOC concentration and storage compared to conventional tillage operation but not for DOC.     

The effects of different urban land use patterns on soil microbial biomass nitrogen and enzyme activities in urban area of Beijing, China

Soil microbes are affected by various abiotic and biotic factors in urban ecosystem due to land use change. The effects of different land use patterns on soil microbial properties and soil quality are, however, largely unknown. This study compared soil nutrient status, microbial biomass nitrogen and enzyme activities under five different land use patterns—nature forest, park, farmland, street green, and roadside tree sites at various soil depths in Beijing, China. The results showed that soil properties were significantly affected by urban land use patterns and soil depths in the urban environment. Compared to forest sites, soil nutrients were markedly decreased in other land use patterns, except the highest soil organic matter content in roadside tree sites in 0–10 cm soil layer. Soil microbial biomass nitrogen showed the order as follows: nature forest > park > farmland > street green > roadside tree in 0–10 cm soil layer, and it decreased along with the soil depth gradient. Furthermore, urease activity was highest in nature forest and lowest in street green and roadside tree soils in each depth, while the activity of protease ranged between 0.84 and 3.94 mg g^?1 with the peak appeared in roadside tree at 30–40 cm soil layers. Nitrate reductase activity was also extremely higher in street green than other land use patterns. Correlation analyses suggested that change of soil microbial biomass and enzyme activity in different land use patterns were mainly controlled by nutrient availability and soil fertility in urban soils.     

Changes in root system structure,leaf water potential and gas exchange of maize and triticale seedlings affected by soil compaction

The physiological reasons associated with differential sensitivity of C₃ and C₄ plant species to soil compaction stress are not well explained and understood. The responses of growth characteristics, changes in leaf water potential and gas exchange in maize and triticale to a different soil compaction were investigated. In the present study seedlings of triticale and maize, representative of C₃ and C₄ plants were subjected to low (L – 1.10 g cm⁻³), moderate (M – 1.34 g cm⁻³) and severe (S – 1.58 g cm⁻³) soil compaction level. Distinct differences in distribution of roots in the soil profile were observed. Plants of treatments M or S in comparison to treatment L, showed a decrease in leaf number, dry mass of stem, leaves and roots, and an increase in the shoot to root ratio. A drastic decrease in root biomass in M and S treatments in the soil profile on depth from 15 to 40 cm was observed. Any level of soil compaction did not influence the number of seminal and seminal-adventitious roots but decreased their length. The number and total length of nodal roots decreased with compaction. Changes of growth traits in M and S treatments in comparison to the L were greater for maize than for triticale and were accompanied by daily changes in water potential (ψ) and gas exchange parameters (P_N, E, g_s). Differences between M and S treatments in daily changes in ψ for maize were in most cases statistically insignificant, whereas for triticale, they were statistically significant. Differences in the responses of maize and triticale to soil compaction were found in P_N, E and g_s in particular for the measurements taken at 12:00 and 16:00. The highest correlation coefficients were obtained for the relationship between leaf water potential and stomatal conductance, both for maize and triticale, which indicates the close association between stomata behavior and changes in leaf water status.     

Spatial distribution of soil penetration resistance as affected by soil compaction: The fractal approach

Using semivariograms and fractal dimension (D), we identified the spatial variation of penetration resistance (PR) in variously compacted silty loam in the Lublin region, South-East Poland. Four compaction treatments were as follows: zero traffic (0p), one pass of tractor (1p), three passes of tractor (3p), and (8p) eight passes. Penetration resistance was measured in a square net of 0.6 m × 0.6 m with grid density of 0.05 m, in horizontal planes at depths of 0.05, 0.15, 0.25, 0.35, 0.45 and 0.55 m in each traffic treatment. The data were analysed in 6 horizontal planes and 12 vertical planes. The vertical planes were obtained by transforming the measured data along one side of the square at every 0.05 m. Total number of penetrations was 864. Fractal dimension (D) was estimated from the slope of the log–log semivariogram plots. The semivariograms showed spatial autocorrelation of penetration resistance in the horizontal and vertical planes. Direction in space was important in this study. In the horizontal planes the differentiation of penetration resistance semivariance at different depths was considerable and not clearly related with traffic intensity. In the vertical planes the semivariograms showed spatial dependence of the PR and evident decreasing of semivariance with increasing traffic intensity. Kriging-interpolated maps revealed that the differentiation of penetration resistance was higher in the vertical than in the horizontal planes. The vertical differentiation was higher in 0p than in the remaining treatments. The overall mean fractal dimensions in the vertical planes increased with increasing compaction levels and can be a useful indicator of the compaction level. The opposite courses of fractal dimension in the vertical and horizontal planes indicate spatial anisotropy in distribution of penetration resistance.     

Effects of grassland conversion to cropland and forest on soil organic carbon and dissolved organic carbon in the farming-pastoral ecotone of Inner Mongolia

Effects of grassland conversion to cropland and forest on soil organic carbon (SOC), dissolved organic carbon (DOC) in the farming-pastoral ecotone of Inner Mongolia were investigated by direct field sampling. SOC content and DOC content in soil decreased after grassland were shifted to forest or cropland, in the sequence of grassland soil > forest soil > cropland soil. SOC stock declined by 18% after grassland shifted from to forest. Reclamation of cropland for 10 years, 15 years and 20 years lost SOC in 0–30 cm soil layer, by 34%, 14% and 18%, respectively, compared with that of grassland. DOC in 3 soil layers was within 21.1–26.5 mg/L in grassland, 12.1–14.6 mg/L in forest soil, and 8.0–14.0 mg/L in cropland soil. Correlation analysis indicated that SOC content and DOC content were positively dependent on total nitrogen content (p < 0.05), but negatively on bulk density or land use type (p < 0.05). DOC was positively correlated SOC (p < 0.01). Moreover, SOC content could be quantitatively described by a linear combination of land use types (p = 0.000, r² = 0.712), and DOC content by a linear combination of two soil-related variables, land use types and SOC (p = 0.000, r² = 0.861).     

Potential impacts of climate warming on active soil organic carbon contents along natural altitudinal forest transect of Changbai Mountain

Understanding soil carbon fractions and their responses to the global warming is important for improving soil carbon management of natural altitudinal forest ecosystem. In this study, the contents of soil total organic carbon (SOC), soil labile organic carbon (LOC), and microbial biomass carbon (MBC) in soil upper layers (0–20 cm) were measured along a natural altitudinal transect in the north slope of Changbai Mountain. The results showed that under natural conditions the contents of SOC and LOC were largest in Betula ermanii forest (altitude 1996 m), moderate in spruce-fir forest (altitude 1350 m), and smallest in Korean pine mixed broad-leaf tree forest (altitude 740 m). MBC contents in different forest ecosystems decreased in the order of Betula ermanii forest, Korean pine mixed broad-leaf tree forest, and dark coniferous forest. In addition, the responses of SOC, LOC, and MBC to soil warming were conducted by relocating intact soil cores from high- to low-elevation forests for one year. As expected, the soil core relocation caused significant increase in soil temperature but made no significant effect on soil moisture. After one year incubation, soil relocation significantly decreased SOC contents, whereas the contents of LOC, MBC, and the ratios of LOC to SOC and MBC to SOC increased.     

Modeling WEPP erodibility parameters in calcareous soils in northwest Iran

Modeling soil detachment rates at the regional scale is important for better understanding of the processes of erosion and the development of erosion models. Soil erodibility is an important factor for predicting soil loss, but its direct measurement at the watershed scale is difficult, time-consuming and costly. This study used stepwise multiple-linear regression (MLR) and artificial neural networks (ANNs) to model Water Erosion Prediction Project (WEPP) soil erodibility parameters, including the baseline inter-rill erodibility (K_ib), baseline rill erodibility (K_rb) and critical shear stress (τ_cb) of cropland conditions in calcareous soils of northwest Iran. Simulated inter-rill and rill erosion experiments were conducted at 100 locations with three replications. K_ib, K_rb and τ_cb and basic soil properties were measured at each location. Auxiliary variables related to soil erodibility were derived from a Landsat 7 satellite image and a 30 m × 30 m digital elevation model (DEM). MLR and ANN models were employed to predict K_ib, K_rb and τ_cb using two groups of input variables: i) more easily measurable basic soil properties (pedo-transfer functions (PTFs)) and ii) more easily measurable basic soil properties and auxiliary data (soil spatial prediction functions (SSPFs)). The results indicated that the WEPP models performed poorly in comparison to the derived models. PTFs and SSPFs generated from ANN models provided more reliable predictions than the MLR models. ANN-based SSPF models yielded the best results (with the highest R² and lowest RMSE values) for predicting Kib and Krb. ANN-based PTF model performed reasonably well for predicting τ_cb. These results show that information from terrain attributes and remote sensing data are potential auxiliary variables for improving prediction of soil erodibility parameters.