Soil & Tillage Research最新文献

Excessive application of synthetic fertilizer has resulted in serious soil degradation and significant greenhouse gases (GHGs) fluxes in farmlands. Partial organic substitution for synthetic fertilizer was considered as a possible strategy for sustainable agricultural development, but its potential effects on soil quality, GHGs emissions, and crop productivity remain unclear. A field experiment across 3-year was conducted to evaluate the responses of soil quality, nitrous oxide (N₂O), carbon dioxide (CO₂), and methane (CH₄) emissions, and crop yields to different ratios of organic fertilizer (OF) to synthetic fertilizer (SF). Six treatments were included: non-fertilization (CK); total SF; total OF; 15 %, 30 %, and 45 % organic substitution (LO, MO, and HO). Soil cumulative N₂O emission was decreased with increasing organic substitution ratios, mainly attributing to the reducing soil NH₄⁺ content. However, organic substitution increased soil CO₂ and CH₄ emissions due to the high manure-driven C input, consequently promoting global warming potential (GWP). Meanwhile, soil organic C, total N, P, available P, K, and C-acquisition enzyme activities were increased with organic substitution, resulting the higher soil quality index (SQI) under HO and OF. HO enhanced the annual yield of wheat and maize by 7.2 % and 13.0 % compared with SF and OF, respectively. The positive relationship between crop yield and SQI indicated that the yield-enhancing effect with partial organic substitution was mainly attributed to the improved synchronization in nutrient supply and soil fertility. Overall, partial organic substitution, especially 45 % organic substitution represents a viable strategy to improve soil quality and crop productivity while mitigating N₂O emission in wheat-maize rotation systems. However, organic substitution promoted the GWP through stimulating soil CO₂ and CH₄ emissions. Further investigations of optimize fertilization managements are still needed to reduce manure-induced CO₂ and CH₄ emissions to achieve higher climate change mitigation.

Cotton (Gossypium hirsutum L.) has been a valuable economic crop in arid Xinjiang, whose cotton production accounts for 90.2 % of the grand total in China. Low phosphorus (P) bioavailability brings severe restrictions for cotton production in arid regions as a result of P precipitation caused by high soil pH and Ca²⁺ content. Therefore, how to utilize the biological potential to improve the efficiency of P utilization has become a hotspot for international research.To learn more about P bioavailability in cotton from the perspectives of root morphology, rhizosphere physiology, and mycorrhizal association, especially the synergistic effect of these three under the optimal P input on P bioavailability. A 2-year, split-plot field experiment was conducted consecutively from 2016 to 2017, in which the main plots contained three cotton varieties (XLZ57, XLZ19, and XLZ13) and the subplots were treated with five P levels (0, 75, 150, 300, and 450 kg P₂O₅ ha⁻¹). Optimal P input (P fertilizer application: 75–150 kg P₂O₅ ha⁻¹ or soil available P content in topsoil: 11–25 mg kg⁻¹) was found to not only improve the distribution of root system and mycorrhiza in soil but also promote the secretion of protons and alkaline phosphatase in the rhizosphere, leading to higher P uptake and cotton yield. Although high P input (300–450 kg P₂O₅ ha⁻¹) increased soil available P content, it inhibited root growth, mycorrhizal infection and phosphatase activity, thus reducing P uptake and product. To obtain a relatively high yield (5500–6500 kg ha⁻¹ unginned cotton) and high P accumulation (120–130 kg P₂O₅ ha⁻¹), an ideotype cotton root/rhizosphere should be characterized by high root length density (4–5 m 1000 cm⁻³), large hyphal density (15–18 m g⁻¹), and greater exudation of protons and alkaline phosphatase (60–70 μg g⁻¹ h⁻¹) in topsoil, as well as a large microbial P (MBP) value (25–28 mg kg⁻¹).Compared to mycorrhizal symbiosis (reflected by hyphal density), rhizosphere secretion of protons and alkaline phosphatase (rhizosphere physiology) and root length density (architecture) pose greater contributions to higher rhizosphere P availability and cotton P uptake. Moreover, the rhizosphere process and P use efficiency (PUE) of the P-efficient cultivar (XLZ19) were higher compared to the P-inefficient one (XLZ13).The results suggest that maximizing root/rhizosphere efficiency under optimal P input may improve cotton productivity and P uptake efficiency in mulched cotton fertigation systems in arid and semi-arid areas.

Plastic film mulching (PFM) and nitrogen (N) fertilization are two important agricultural management methods that are used to enhance crop yields in semi-arid dryland agriculture. However, the impacts of PFM and N fertilization on the temperature sensitivity (Q₁₀) of soil respiration (R_t), particularly its heterotrophic (R_h) and autotrophic (R_a) components, remain unclear. To investigate this, a trenching experiment was carried out between 2019 and 2021 in a rainfed maize-cultivated cropland that had been under cultivation for 7 years. There were four treatments: no PFM and N fertilization (control), full PFM without N fertilization (PFM), 150 kg N ha^–1 fertilization without PFM (Nfer), and full PFM with 150 kg N ha^–1 fertilization (PFM+Nfer). PFM and N fertilization not only enhanced crop yield and root biomass but also increased soil total respiration (R_t) and its components, due to improved soil hydrothermal conditions with PFM and increased N availability with N fertilization. Soil hydrothermal conditions and root biomass were identified as the most important factors influencing R_h and R_a, respectively. The greater increase in R_a (84 %–212 %) compared to R_h (9 %–29 %) resulted in a decrease in the proportion of R_h in R_t decreasing from 81.2 % in the control to 58 % under the PFM+Nfer treatment. The R_h/R_t ratio decreased in all three treatments compared to the control (p < 0.05). The increase in R_h under PFM led to a decrease in soil organic carbon (SOC) by 17 %. Specifically, the soil labile C content (i.e. LFOC 44 %) decreased more under PFM and PFM+Nfer (p < 0.05) compared to control, but not under the Nfer treatment (p > 0.05). Plastic film mulching increased the Q₁₀ of R_h (p < 0.05) through decrease the content of soil labile C, whereas N fertilization had no effect (p > 0.05). Both PFM and N fertilization increased the Q₁₀ of R_a (p < 0.05) by increasing root biomass. The impact of R_a’s Q₁₀ (0.66) on R_t’s Q₁₀ is greater compared to R_h’s Q₁₀ (0.31). To our knowledge, this is the first long-term field study to examine the response of R_t components and their Q₁₀ to PFM and N fertilization. Our results highlight that soil labile C and root biomass are the determining factors for the Q₁₀ of R_h and R_a, respectively. We emphasize the importance of accurately modeling the temperature responses of R_h and R_a when predicting R_t under climate change scenarios.

Mapping the soil organic matter (SOM) content of cultivated lands at the regional scale is of great significance for evaluating the cultivated land quality and monitoring the soil carbon cycle, especially in the fertile black-soil area of China. The large paddy fields area is one of the characteristics of the black-soil area in Northeast China. The vast differences between paddy fields and dry lands may pose a major challenge in mapping the SOM contents of local cultivated lands. In this study, the SOM of cultivated lands in Northeast China is taken as the research object, and all available Landsat-8 images from 2014 to 2022 and the main environmental covariates (climate and terrain) are obtained. By combining the random forest regression algorithm, SOM prediction models of paddy fields and dry lands are established to evaluate the optimal window period and appropriate environmental covariates for paddy fields and dry lands. Finally, the accuracy difference between the global regression and local regression results for distinguishing paddy fields and dry lands is compared. The results showed that (1) the SOM content in Northeast China increased gradually from south to north, and the average SOM content in paddy fields was approximately 0.4 % higher than that in dry lands; (2) the SOM mapping time windows in paddy fields and dry lands in Northeast China differed, with paddy fields mapped in April and dry lands mapped in May; (3) the addition of environmental covariates improved the SOM prediction accuracy, with a greater importance for mapping SOM in paddy fields than in dry lands; and (4) the local regression results based on the division of paddy fields and dry lands achieved the highest prediction accuracy, with the highest determination coefficient (R²) being 0.653 and lowest root mean square error (RMSE) being 1.144 %. This study proves that different types of arable land have a great impact on the SOM prediction accuracy. Researchers should adopt different strategies to map the SOM contents of paddy fields and dry lands.

Understanding the effects of liming plus phosphate fertilization on soil physical and chemical properties, as well as carbon stock, is critical for improving soil fertility management under conventional till (CT) and no-till (NT) systems. This study aimed to quantify changes in these soil properties resulting from incorporation (CT) or not (NT) of limestone and phosphorus (P) in a subtropical Ferralsol in southern Brazil. The experiment was conducted in Campo Mourão, Paraná State, Brazil, according to a randomized complete block design with a 6 × 4 factorial arrangement and four replications. The treatments comprised six strategies for limestone and P management and four soil depth layers (0–0.05, 0.05–0.10, 0.10–0.20 and 0.20–0.40 m), as follows: NLNT - no liming under no-till; NLCT - no liming under conventional till; LPNT - liming and P fertilization under no-till; LPCT - liming and P fertilization under conventional till; LNT - liming under no-till; and LCT - liming under conventional till. In 2012, 5.0 Mg ha⁻¹ dolomitic limestone and 53.3 kg ha⁻¹ P were applied. In 2016, dolomitic limestone was reapplied to a soybean–wheat rotation. Liming and liming plus P treatments influenced soil properties up to a depth of 0.10 m, increasing pH and decreasing Al³⁺, without significant differences between CT and NT. Higher levels of Ca²⁺ and Mg²⁺ were observed at 0–0.05 m, except in unlimed treatments. Liming and liming plus P fertilization treatments resulted in mean increments of 1.83 and 1.37 cmol_c dm⁻³ in Ca²⁺ and Mg²⁺ levels, respectively, regardless of the tillage system. Base saturation did not differ between treatments in the 0.10 m layer. However, LPCT resulted in higher base saturation in the 0.10–0.20 m (55 %) and 0.20–0.40 m (53 %) layers. P contents were affected up to 0.10 m depth, being 30 % higher in LPNT than in LPCT at 0–0.05 m. In the 0–0.05 m layer, soil bulk density was highest in NLCT and LPCT, and macroporosity was lowest in LPCT. Carbon stock was not affected by tillage practices, liming, or P fertilization. There was a positive correlation between P content and carbon stock at 0.20–0.40 m, suggesting that increased P availability at depth contributes to carbon sequestration. At 0–0.05 m, soil physical properties were negatively influenced by the combined application of liming and P fertilization under CT, indicating possible simultaneous effects on clay dispersion and pore obstruction.

Accurately mapping the spatial distribution of soil organic matter (SOM) content is critical for informed land management decisions and comprehensive climate change analyses. Remote sensing-based SOM mapping models during periods of bare soil exposure have demonstrated efficacy in various regional studies. However, integrating bare soil imagery with growing season imagery for SOM content mapping remains a complex process. We conducted a study in Youyi Farm, a representative area of black soil in Northeast China. We collected 574 soil samples (0–20 cm) with SOM content through field sampling and laboratory analysis. Additionally, cloud-free Sentinel-2 images were obtained from the Google Earth Engine (GEE) platform for both the bare soil period (April-June, October) and crop growth period (July-September) from 2019 to 2021. To assess the influence of crop growth information on SOM mapping, we incorporated remote sensing imagery during the crop growth period, considering different crop type zones (maize (Zea mays L.), soybean (Glycine max L.), and rice (Oryza sativa L.)). We conducted overall and zonal regressions using the random forest (RF) model to validate the prediction results through cross-validation. Our findings indicate that: (1) adding crop growth period images to the bare soil period images in different years can improve the accuracy of SOM mapping. For example, in the overall regression model of 2020, the highest accuracy was achieved by using the combination of May-July images, with an R² value of 0.70 and an RMSE value of 0.71 %; (2) zonal regression by differentiating crop types can effectively improve the SOM mapping accuracy. In 2019, using zonal regression, the R² of SOM mapping accuracy was improved by 0.02 and the RMSE was reduced by 0.03 % compared with the overall regression; (3) precipitation is an important factor affecting the accuracy of SOM prediction, and the lower the precipitation, the higher the accuracy of SOM prediction. In summary, the results of this study show that in the SOM remote sensing mapping of the black soil area, the growing period remote sensing information of different crop types should be comprehensively considered and combined with the image data of the years of lower precipitation, the accuracy of the SOM mapping can be effectively improved, which provides a new technological path and an application basis for the enhancement of the accuracy in remote sensing mapping with soil attributes.

Changes in soil total nitrogen (N) content would affect wetland function and the global N cycle. Determination of spatial heterogeneity controlling factors of wetland soil total N content per unit mass is essential to assess responses of ecosystem N cycle to global change. However, such information is limited in permafrost zones because few soil profiles have been acquired and methods to predict spatial distributions of wetland soil total N content in large areas are inefficient, which increase uncertainty in evaluations of N cycle at national or global scales. To determine the spatial heterogeneity of wetland soil total N content at different soil depths and frozen zones and the factors controlling wetland soil total N content in the frozen zones of Northeast China, the spatial pattern of wetland soil total N content was investigated by using a random forest method that combined field samples with environmental factors. Vertically, wetland soil total N content decreased with increasing soil depth, with the highest content in the top soil layer (0–30 cm). Spatially, wetland soil total N content decreased from northwest to southeast, with relatively high total N content in a continuous permafrost zone and relatively low total N content in a seasonally frozen zone. The overall coefficient of variation of wetland soil total N content in the frozen zones of Northeast was 29.58 %, indicating moderate variation. Land surface temperature, mean annual temperature, and mean annual humidity significantly affected total N content in 0–30 and 30–60 cm soil layers, suggesting that variations in temperature and humidity altered sequestration processes of wetland soil total N content. In the 60–100 cm soil layer, compared with other environmental factors, mean annual humidity, altitude, and mean annual precipitation had the greatest influence on the spatial distribution of wetland soil total N content. The study unravels the spatial pattern of soil total N content in frozen zones of Northeast China and reflects the direct and indirect effects of environmental factors on total N content. This provides a basis for the management and protection of wetland ecosystems.

Widespread sources of silver nanoparticles (AgNPs) might threaten soil ecosystems. Most studies on NPs have been carried out in plant-free soils, which do not represent natural conditions. Monitoring the fate and possible effects of nanoparticles (NPs) in soil-plant systems is crucial for predicting their environmental consequences. Plant root systems might respond differently to Ag types/concentrations, associated with changes in microbially-induced soil structural stability as well as soil C pools but evidence is not available. Therefore, a greenhouse experiment was conducted in a factorial arrangement of treatments within a randomized block design. The treatments included: 1) soil types (loamy sand and sandy loam), 2) root systems (non-planted, wheat with fibrous roots and safflower with taproot), 3) Ag types (no-Ag added, AgNPs of mean size 38.6 nm, and AgNO₃), and 4) Ag concentrations (50 and 100 mg kg^–1 soil). Soil samples were collected from root zone and non-planted soil 110 days after sowing. Soil quality indicators including high energy moisture characteristic (HEMC) indicators, percent of water-stable aggregates (WSA), water-dispersible clay (DC), substrate-induced respiration (SIR), microbial biomass carbon (MBC) and metabolic quotient (q_CO2) were determined. The results showed that the soil structure was improved in the presence of Ag and plants. Structural stability indicators were greater in the safflower root zone followed by the wheat root zone and the non-planted soil. A clear effect of Ag on HEMC was observed in the 100 mg AgNPs kg^–1 treatment. The stability ratio (SR, ratio of fast-wetting to slow-wetting structural indexes) of the AgNPs-treated soils (SR = 0.79) was significantly greater than that of the AgNO₃-treated soils (SR = 0.78) followed by the control (no-Ag) soils (SR = 0.74). In the AgNO₃-treated soils, the SIR was significantly lower than in the AgNPs-treated soils. The SIR of the 50 mg kg^–1 Ag treatment (232 mg CO₂-C kg^–1) was higher than the 100 mg kg^–1 (227 mg CO₂-C kg^–1). Microbial biomass was significantly affected by Ag types/concentrations and all Ag-treated soils exhibited significantly lower MBC than control. The q_CO2, the index of stress to microbial community, was significantly greater in the Ag-treated soils. Scanning electron microscope images confirmed that AgNPs altered the arrangement of particles which was greater in the higher AgNPs concentration. These results imply that multiple factors (root systems, soil texture, Ag type/concentration) may combine additively/regressively to affect soil quality indicators, which may have important consequences for soil ecosystem services.

Thymus sp. is one of the widely known spicy aromatic and medicinal plants widely used in medicine, cooking and perfumery. Biochar is an amendment to improve soil quality attributes. However, the interactive effect of Thymus cultivation and biochar application rate (BAR) on soil quality indicators in field conditions is not documented yet. This study aimed to investigate the effect of biochar of apple tree branches (BAR of 0, 1 and 2 %), two Thymus species (TS, T. daenensis and T. migricus), and soil sampling zone (SSZ, rhizosphere and bulk soil) on structural stability and water repellency of a silty clay loam soil in the field conditions. The experiment was conducted in a randomized complete block design with a factorial arrangement of the 12 treatments (i.e., 3 levels of BAR × 2 levels of TS × 2 levels of SSZ) with three replicates in Absard plain research site with semiarid climate affiliated with Research Institute of Forests and Rangelands, east of the Tehran. After plant harvest, the soil aggregate stability was characterized by high energy moisture characteristic (HEMC) and percent of water-stable aggregates (WSA), and the soil water repellency was measured by the sorptivity method. Biochar affected soil structural stability indices in both rhizosphere of the two Thymus species and bulk soil. The results showed that an increase in the BAR from 0 to 1 and 2 % enhanced the soil organic carbon (SOC) by 22 and 48 % and consequently increased the HEMC stability ratio from 0.616 to 0.667 and 0.859, respectively. Water holding capacity in the biochar treatments was higher in all matric suctions, therefore, greater HEMC aggregate-structure stability indices were measured in the biochar-amended soils. The rhizosphere soil had significantly greater mean of organic carbon storage (6.74 g kg⁻¹), stability ratio (0.762) and water repellency index (4.08) compared to those in the bulk soil (5.23 g kg⁻¹, 0.666 and 2.17, respectively). The rhizosphere of T. migricus (with greater root development in the upper layers) had significantly greater WSA (59.6 %) compared to that of T. daenensis (51.2 %). However, there was no significant difference between the two plant species for SOC in the rhizosphere. This finding implies that quality (not quantity) and fractions of organic matter might be different in the rhizosphere of the plant species. Water repellency could partly explain the structural stability differences in the rhizosphere of T. daenensis and T. migricus species. Overall, the combination of wood biochar application with a rate of 1 % and T. migricus cultivation is recommended for improving soil physical quality in the region and similar zones.

Saturated, K_s, and near-saturated, K, soil hydraulic conductivity control many hydrological processes but they are difficult to measure. Comparing methods to determine K_s and K is a means to establish how and why these soil hydrodynamic properties vary with the applied method. A comparison was established between the K_s and K values of a sandy-loam soil obtained, in the field, with the BEST (Beerkan Estimation of Soil Transfer parameters) method of soil hydraulic characterization and an unconfined MDI (mini-disk infiltrometer) experiment and, in the laboratory, with a confined MDI experiment and the CHP (constant-head permeameter) method. Using for the BEST calculations the soil porosity instead of the saturated soil water content yielded 1.4–1.1 times higher estimates of K_s and K, depending on the pressure head, and differences decreased in more unsaturated soil conditions. The confined MDI experiment yielded 22 % - 77 % higher K values than the unconfined MDI experiment, depending on the established pressure head, h₀, and differences were not significant for h₀ = −1 cm. In the close to saturation region, the soil hydraulic conductivity function predicted with BEST did not generally agree well with the K_s and K values obtained in the laboratory by a direct application of the Darcy’s law. In particular, BEST yielded a 5.6 times smaller K_s value than the CHP method and up to an 8.1 times higher K value than the MDI. Overall, i) the two application methods of the MDI yielded relatively similar results, especially close to saturation, and ii) there was not a satisfactory agreement between the field (BEST) and the laboratory (MDI plus CHP) determination of soil hydraulic conductivity close to saturation, unless a comparison was made with the same soil water content. The detected differences were probably attributable to soil spatial variability, overestimation of K_s in the laboratory due to preferential flow phenomena, underestimation of K_s in the field due to air entrapment in the soil and infiltration surface disturbance, inability of BEST to describe the actual soil hydraulic conductivity function at the sampled field site. Testing BEST predictions of K_s and K in other soils appears advisable and combining the MDI and CHP methods appears a rather simple means to make these checks. These additional investigations could improve interpretation of the differences between methods, which is an important step for properly selecting a method yielding K_s and K data appropriate for an intended use.