Soil & Tillage Research最新文献

Conservation agriculture and sustainable agronomic principles include several management practices such as cover cropping, no-till, and alternative fertilization rates. Each of these practices can result in changes among agricultural productivity, sustainability for future farming, and protections for the environment. These management practices are important concepts that can be applied in the production of maize (Zea mays L., corn). The aim of this three-year study (2018–2020) was to compare maize health across two fields located in Riesel, TX under varying management approaches and precipitation conditions. The first field utilized ‘business as usual’ operations characterized by the implementation of tillage, fertilization at a maximum rate (10.06 Mg/ha), and no cover crops. The second field utilized ‘aspirational’ cultivation techniques categorized by no tillage, cover crops, and an alternative method for rate of fertilization. Each field was subject to satellite-based remote sensing methods incorporating Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Red Edge (NDRE). Indices were subject to sensitivity analyses to determine the most sensitive index for maize under various managements and precipitation conditions. The most sensitive index (EVI) served as a proxy for time series analysis for maize health under the varying managements and rainfall conditions. The results suggest improvements to maize health are experienced over time when aspirational managements are employed, even though business as usual management resulted in higher yields. However, studies with greater duration could point to these perceived benefits over a long-term implementation. Nevertheless, productivity comparisons considering amount of input (i.e. fertilizer) suggests an increase in efficiency each year for the aspirational management. These findings suggest factors such as improved soil health from implementation of no-till and cover crops contribute to field health, efficiencies, and resiliency across varying precipitation conditions.

In this study, we developed a new general approach to estimate the macroscopic capillary length (λ_c) using different hydraulic function models and related shape parameters, along with the Haverkamp infiltration model constant. We next applied this new approach to the van Genuchten model coupled with a Burdine condition (vGB) to estimate λ_c. Then, we applied the new λ_c computation to three different methods for estimating saturated hydraulic conductivity (K_s), and analyzed two sets of constant infiltration data: 1) an analytically generated Beerkan-type dataset and 2) constant head and Beerkan-type infiltration tests performed at the Ambarköprü Experimental Station of Blacksea Agricultural Research Institute in Samsun, Turkey. Our new approach provided accurate K_s estimates when applied to the analytical Beerkan infiltration data. The highest error was observed for a silt soil, with 30 % error for one formulation versus <15 % for the others. For synthetic coarse-textured soils such loamy sand and sandy loam, the error was <10 %. For the field data and Beerkan-type experiments, the new approach gave consistent estimates of K_s regardless of analytical interpretation. However, ANOVA analysis revealed that K_s varied between different infiltration test types, with constant head infiltrometry with 5 cm of applied water head having greater K_s values than the Beerkan tests (p < 0.05). Estimated K_s values also differed between land use types (p < 0.01), with a maize field having significantly greater K_s compared to a soybean field. Overall, we conclude that the proposed approach represents an efficient and appropriate method for characterizing point-scale saturated hydraulic conductivity, so long as experimental artifacts such as ring insertion deep and preferential flows are considered.

Conservation tillage (CS) is a widely implemented and sustainable agricultural practice. Nevertheless, there is substantial controversy regarding its influence on crop yield and the underlying factors that contribute to these effects. We conducted a comprehensive meta-analysis incorporating 5191 comparisons from 551 studies to assess the global crop yield response to CS. The overall findings indicate that CS resulted in a modest reduction in yield, approximately 1.35 % (P<0.05), compared to conventional tillage (CT). However, this result varied, with no significant yield difference (P>0.05) between CS and CT when strictly following the three principles of CS (no-till, straw mulching, and crop rotation). It should be acknowledged that the relative importance of these three principles varies depending on natural conditions. For example, straw mulching had a greater positive effect in arid regions than no-till and crop rotation. A random forest model analysis identified several influential factors on the relative yield of CS: seasonal precipitation, temperature, soil pH, and no-till duration. For example, CS had negative benefits when seasonal precipitation exceeded 400 mm. Conversely, implementing CS in alkaline soils had significant positive effects (4 %, P<0.05). Additionally, the no-till duration did not always yield absolute positive results; no-till durations exceeding 20 years significantly decreased CS yields (P<0.05). Prolonged no-till may lead to undesirable consequences such as increased soil bulk density, weed infestation, pest outbreaks, and disease, all of which can adversely affect crop yields; therefore, it is recommended that no-tillage be rotated with conventional tillage to minimize the negative effects of prolonged and sustained no-tillage on yields. Furthermore, CS had greater potential for increasing production in tropical regions. In conclusion, adopting regionally adapted CS practices can minimize the risk of yield reduction. Implementing adaptive CS techniques in specific locations can promote global food security and achieve sustainable agricultural development.

Soil structure governs the functions of soil in many ecosystems, including those dominated by agriculture, such as water and carbon storage, biomass production, and physical stability. Specifically in tropical and subtropical soils, the long-term impacts of different fertilizers on soil functionality and stability in no-till crops are poorly understood. Under subtropical climate conditions, we evaluated how 17 years of continuous fertilizer application (organic vs. inorganic) on no-till crops affected structure of a sandy loam texture, in terms of the pore size distribution and pore functionality for water storage and aeration, and the intra-aggregate pore geometry. The investigated long-term experiment was implemented in a randomized block design with four repetitions in Santa Maria, Brazil. Treatments were pig slurry (PS), cattle slurry (CS), pig deep litter (pig manure with rice husk; PDL), mineral fertilizer (MF), and an unfertilized control (CL) applied in a no-till system. Soil sampling was done in two depths (0–5 and 5–15 cm) to analyze (i) soil pore size distribution, soil water retention, air permeability and pore continuity indices in core samples (± 98 cm³); and (ii) the intra-aggregate pore system using X-ray computed tomography in macroaggregate samples (± 5 cm³). The treatments had different impacts on soil pore functionality and intra-aggregate pore geometry. Only PDL application increased field capacity by around 34 % and the plant available water by about 36 % (compared to all other treatments). Soil air-filled porosity was not affected by fertilizer management in any of the layers. However, in the 0–5 cm layer, fertilizer management had a significant effect on soil air permeability which increased at −6, −10, and −33 kPa matric potential from 6.6, 14.4, and 16.1 µm² in CL treatment to 29.5, 34.2, and 43.6 µm² in PDL, respectively. The PS and PDL treatments increased the intra-aggregate porosity and provided a continuous, connected pore network. These fertilizers provided increased biomass productivity (PS and PDL) and soil organic matter content (higher in PDL only). Therefore, continuous application of fertilizer with higher carbon input, such as PDL, improved soil structural conditions and crop yield of sandy soil under subtropical climate.

Soil shrinkage during the drying process (water stress) is one of the main issues in expansive soils of paddy fields. It occurs due to decrease in soil water content, resulting in changes in soil volume and the geometry of pores, leading to the formation of cracks and higher water loss. The aim of this study was to assess the shrinkage characteristic curve and pore size of paddy soils to determine the shrinkage -swelling behavior in Guilan province, Iran. 120 soil samples were collected from the study area. Pore size was determined using soil moisture retention curve (SMRC). It was established by plotting the soil water content (θ) versus the corresponding matric suction (h), and the shrinkage curve by plotting the void ratio (e) against the moisture ratio (υ). The suction-pore relationships were also determined. Furthermore, the geometric factors indicating the change in vertical (subsidence) and horizontal (crack) volume of the soils were determined and varied from 1.23 to 2.53, indicating that the vertical change in soil volume is predominant. The zero, residual and proportional shrinkage phases accounted for less than 2 %, 8–38 %, and 61–91 % of the total soil volume change, respectively. The shrinkage capacity of the soils ranged from 0.52 to 1.37. Cation exchange capacity and clay content were identified as the most important factors affecting soil shrinkage properties. In general, the studied paddy soils have great potential for swelling- shrinkage and cracking during the drying process due to the large percentage of expandable clays and the medium to fine pores. The resultant cracks negatively affect crop yield by damaging plant roots and increasing water losses through the soil profiles.

Soil Water Retention Curve (SWRC) provides crucial information for understanding soil moisture retention, essential for agriculture, hydrology, engineering and environmental science applications. Many SWRC fitting models in the literature are based on empirical equations without a direct physical meaning. However, SWRC data is physically related to the soil’s porous structure and its interactions with the wetting fluid. Hence, the curve’s behavior reflects the porous complexity. Non-physical model equations might even be able to fit the data to be used in several applications; however, the search for physically fitting models representing the SWRC data as a smooth continuous distribution function can reflect new insights and information about this heterogeneous porous media. In this regard, the well-established physically-based Kosugi model is based on the assumption of lognormal pore size distributions. However, a general approach for any modality and distribution shape could be interesting. This paper proposes applying the mathematical method known as “Inverse Laplace Transform” (ILT) to fit the Soil Water Retention Curve using a weighted superposition of exponential decays. This multi-exponential approach involves working with two physically related parameters, the amplitude and its respective characteristic matric potential, which are physically interpreted as the amount of pores that empty at that suction head. The ILT-EXP method proposed was implemented in Python software to fit the curves, and it is now available in an online web app. The evaluation of the ILT-EXP model to fit SWRC data is discussed, presenting its potential to estimate soil pore size distribution of multimodal samples. One advantage of ILT-EXP over other multimodal models is that it does not need to know how many modal components are present in the SWRC data, being automatically determined by the method. Finally, a statistical fitting comparison of 439 SWRC data, with six other classical models is discussed. The results indicate that fitting with the ILT-EXP model demonstrates strong potential, making it a powerful method for handling multimodal curves. This approach represents a novel and robust method for estimating a smooth, continuous soil pore size distribution.

Across the Brazilian Cerrado, the land area under soybean-maize (double cropping) and maize-brachiaria (intercropping) systems has been expanding. In this study, we evaluated the efficiency of P fertilization and the response of residual P compartments to i) soybean monoculture in conventional (CT) or no-tillage (NT), ii) soybean in rotation systems including the maize+brachiaria consortium; and iii) soybean in succession systems including soybean-maize double cropping. These factors were combined into eight treatments in an experiment conducted at Itiquira, Mato Grosso, Brazil. Our study was laid out following a completely randomized block design with four replicates. We determined the apparent efficiency of P fertilization using the balance method for 12 years, after which residual P was evaluated in soil samples submitted to a soil P fractionation scheme using CaCl₂, Mehlich-3, NaOH (inorganic-Pi and organic-Po) and HCl. P occluded was estimated as the difference between total soil P and the sum of the extractable P fractions. Our data showed apparent efficiency of P fertilization about 78.3 % under soybean-fallow (CT or NT) and about 93.0–94 % under the soybean-successions. Under the rotations, the apparent efficiency of P fertilizations was about 100–123.0 % coupled to some depletion of P-Mehlich-3 and Pi-NaOH. We found positive correlations between P-Mehlich-3 and Pi-NaOH, whereas both Po-NaOH and P-Mehlich-3 showed strong negative correlations with P occluded. Overall, the rotation systems evaluated in this research appear to benefit from the transfer of P between P-Mehlich-3 and Pi-NaOH pools, coupled to the formation of Po-NaOH limiting the accumulation of P occluded. These characteristics allow reconciling productive crop grain systems with efficient P fertilizations and recycling of P in soils with high residual P pools in tropical agroecosystems.

Changes in soil pores and aggregate stability due to freeze-thaw cycles (FTCs) are important causes of increased soil erosion during snowmelt. Soil erosion causes spatial redistribution of soils, enhancing soil heterogeneity and potentially impacting soil responses to FTCs. Nonetheless, there is minimal knowledge of the responses of soils subjected to different degrees of erosion to seasonal FTCs. To reveal the impact of seasonal FTCs, the dynamic variations of pore characteristics and aggregates of soils with four different degrees of erosion (original, degraded, deposited and parent soil) were measured, and the connections between influencing factors and soil properties were analyzed. The results showed that FTCs altered the structure of the soils and weakened their resistance to erosion and that soils with different degrees of erosion responded differently to FTCs. After seasonal FTCs, soil porosity increased (0.4 %-11.9 %) to some extent in all soils, with greater changes observed in the more eroded soils. Notably, capillary porosity exhibited a complex changing trend compared to total porosity. Degraded and parent soils showed a stable bulk density, while the original soil showed a decrease (2.1 %) in bulk density and the deposited soil showed an increase (18.4 %) in bulk density. With the increase of FTCs, the field capacity of original, degraded, and deposited soils exhibited a gradual decrease (15.1 %-18.5 %), while that of the parent soil slightly increased (0.9 %). After seasonal FTCs, the saturated hydraulic conductivity decreased for original and deposited soils (19.5 %-41.5 %), while it increased for degraded and parent soils (29.2 %-41.6 %). Throughout the FTCs, the proportion of the large aggregates decreased and the small aggregates increased, and the transformation was greater on the less eroded soils. The mean weight diameter and geometric mean diameter of the soils gradually decreased with increasing FTCs, while the change was smaller for the more eroded soils. After seasonal FTCs, the less eroded soils were at greater risk of erosion. Our results demonstrated that the number of FTCs had a more significant impact on soil physical properties compared to the temperature difference and soil water content. Overall, freeze-thaw action reinforced the spatial heterogeneity of soil properties, potentially intensifying soil erosion. These findings help reveal the effects of FTCs on the physical properties of soils with different degrees of erosion and deepen the understanding of the mechanism of FTCs on soil erosion processes.

Soil aggregate size, which is a proxy used to guide agricultural decisions and tillage management, can be estimated using optical remote sensing techniques. However, limited investigation has been conducted into the potential of using a physical model to retrieve soil aggregate size (< 2 mm) from different types of soil. This study is based on the multi-angular spectral measurements of seven soil samples from five soil types with 14 aggregate size fractions collected in Northeast China, three versions of the Hapke model were inverted using the Bayesian method. The findings confirmed that all three versions of the Hapke model can well characterize the angular reflection characteristics of all soil samples with different aggregate sizes. The inverted photometric parameters such as single scattering albedo ω, shape parameter b, and asymmetry parameter c were found to be sensitive to soil aggregate size, but the relationships rely on soil types because of the dependence of parameters related to soil composition. In order to obtain a general model that can be applied to different types of soil, the ratio of parameters (RoP) = (b + c)/ω, which is controlled by the external structure of soil aggregates, was proposed to retrieve the aggregate size from different soil types. Results show that the RoP can robustly capture the aggregate size of the soil with high accuracy and is insensitive to soil types. The combination of photometric parameters related to soil aggregate size provides a new method for retrieving the structural properties of the soil by eliminating interfering factors.

Continued mining operations have resulted in substantial soil degradation, necessitating the effective restoration of ecological functions in soils. Accurate and rapid measurement of soil organic matter (SOM) is essential for boosting soil fertility, supporting ecological restoration, and facilitating effective environmental management. Combining visible to near-infrared spectroscopy with machine learning algorithms is a promising technique for quantitative analysis of SOM. Firstly, the paper utilized a spectral pre-processing method that integrates fractional order differentiation transformation (FOD) and optimal band combination (OBC) algorithm. OBC algorithm was used to construct six three-band spectral indices to obtain optimal spectral combination parameters. Then, the HOVD-TELM model was constructed based on the hybrid model of two-hidden-layer extreme learning machine and Harris hawk optimizer. The opposition-based learning, vertical crossover operator and disruption operator were added to prevent the model from converging prematurely. The experimental results showed that: (1) compared with the pre-processing methods such as integer order differentiation and two-band spectral index, the FOD and OBC methods used in this paper obtained more ideal spectral pre-processing effects. (2) compared with models such as Partial least square regression and Extreme gradient boosting, the HOVD-TELM model proposed in this paper obtained the optimal prediction performance, with the minimum RMSE of 6.7874 g·kg⁻¹ and the maximum R² of 0.9209, indicating good prediction reliability. In summary, this paper proposed a fast and accurate method for detecting soil organic matter content and improves the estimation accuracy of SOM content.