Search

Agroforestry systems play an important role in sustainable agroecosystems. However, accurately and adequately quantifying the relationships between environmental factors and tree growth in these systems are still lacking. Objectives of this study were to quantify environmental factors affecting growth of four tree species and to develop functional soil maps (FSM) for each species in an agroforestry site. The diameter at breast height, absolute growth rate (AGR), and neighborhood competition index of 259 trees from four species (northern red oak [Quercus rubra], pecan [Carya illinoinensis], cottonwood [Populus deltoides], and sycamore [Platanus occidentalis]) were determined. A total of 51 topsoil samples were collected and analyzed, and 12 terrain attributes were derived from the digital elevation model. The relationships between AGR, soil, topography, and tree size were analyzed using Spearman correlation. Based on correlation analysis, FSM for each species were generated using the k-means cluster method by overlaying correlated soil and terrain attribute maps. Results showed tree size and terrain attributes were driving factors affecting tree growth rate relative to soil properties. The spatial variations in AGR among functional units were statistically compared within tree species and the areas with larger AGR were identified by the FSM. This study demonstrated that FSM could delineate areas with different AGR for the oak, cottonwood, and sycamore trees. The AGR of pecan trees did not vary among functional units. The generated FSM may allow land managers to more precisely establish and manage agroforestry systems.

Anthropogenic measures that are implemented to improve the nutrient status of soil for growing food can lead to soil and environmental contamination. The influence of agricultural measures (cultivation and fertilization) on a cropland soil can be explained and quantified by exploring the variety and distribution of elements (calcium iron, rubidium, titanium, etc) in the soil profile. In this study, a cropland soil subjected to long-term (1948–2019, 72 years) anthropogenic agricultural activity was compared with a natural, undisturbed forest soil. Results showed that the anthropogenic activity affected the correlation of different elements in the cropland soil to different degrees, with the degree of the effects ranked as follows: calcium (Ca) > iron (Fe) = rubidium (Rb) > titanium (Ti) = chromium (Cr) > potassium (K) = zirconium (Zr) = strontium (Sr) > sulfur (S) = nickel (Ni) = scandium (Sc) > manganese (Mn) > vanadium (V) = zinc (Zn) = tungsten (W) > copper (Cu) = lead (Pb) = cobalt (Co). The elements were distributed much more evenly throughout the profile of the natural forest soil than the disturbed cropland soil. Some elements, such as Fe, Ti, V, Pb, Cr, Rb, K, Ca, Mn, Sc, Sr, and Cu, can be absorbed by vegetable growth and participate in the plant physiological cycle, while others, such as K, Rb, Cr, and Ti, may be lost readily as the soil is tilled. Elements such as Pb, Sc, Zn, and Sr, gradually accumulate over time from continuous fertilizer applications. In order to maintain the health of cropland soils, both farmers and policy makers should strive to mitigate elemental pollution by carefully managing applications of micronutrient fertilizers.

Soil horizons are the manifestation of pedogenesis and contain the basic morphologic indicators of soil formation. Accurate and quantitative tools for delineating soil horizons in-situ can assist soil scientists towards rapid and dynamic soil survey information. The objective of this study was to develop a deep-learning-based, soil-profile-imaging method for identifying and delineating soil master horizons to assist in digital soil descriptions. A total of 160 soil profile images from four soil orders (Alfisols, Entisols, Inceptisols, and Mollisols) were collected from north China in the Inner Mongolia and Liaoning regions. The 160 profile images were amplified to 2400 individual images for model building using data-augmentation procedures. The augmented profile image dataset was divided into training (70%), validation (15%), and test (15%) datasets. The training and validation datasets were imported into a nested, U-net network for model building. The proposed deep-learning (DL) model classified soil profiles into A, B, and C horizons. The mean pixel accuracy of the DL model was 0.86 with the training dataset, 0.82 with the validation dataset, and 0.83 for the test dataset. Results showed that the DL model was judged to be accurate enough for identifying and delineating soil master horizons from profile images in practice. Based on the proposed DL model, a smartphone application was subsequently developed to digitalize the soil profile and assist field evaluation of soil profile horizonation. The smartphone application, for the Android operating system (version 5.0 and greater), featured a response time of

Krotovinas, burrows of small mammals, occur widely in the soils of the steppes. The distribution and effect of krotovinas on soil organic carbon (SOC) stocks were studied in a deep profile (450 cm) at an archaeological site of the Palaeolithic period at Shenyang Agricultural University of China. It was found krotovinas occurred at all depths, and there was variation in their distribution and size. The density of krotovinas increased with depth, and was the highest (10/m 2 ) at 250 cm depth. The average area of krotovinas' sections in most part of the profile ranged from 100 to 160 cm 2 . Krotovinas covered an area of 7 to 17% of the profile wall. Average SOC concentration in the krotovinas was 5.3 g/kg, whereas it was 4.5 g/kg in the surrounding soil matrix. SOC stocks at 100 to 450 cm accounted for 72% of total SOC stocks at 0 to 450 cm depth. Total SOC stock in the whole profile (0 to 450 cm) was 295 t/ha considering the presence of krotovinas, whereas it was 274 t/ha when the krotovinas were excluded. This study shows how krotovinas can be quantified, and that krotovinas can affect SOC concentration and change patterns of SOC distribution. The amount of SOC stored in deep soils should be taken into consideration for evaluating SOC stocks.

Accounting for soil organic carbon (SOC) in stony soils is critical for estimating global SOC stocks. However, accurate and cost-effective assessments of SOC stocks in stony soil profiles remain challenging due to the difficulties in accurately determining the rock fragment volume. The objective of this study was to develop a stereological profile imaging method for rock fragment volume fraction estimation and SOC stock assessment in stony soil profiles. Three soil profiles with different rock fragment lithologies, concentrations, and distributions were imaged and quantitatively sampled. The stereological profile imaging method reproduced consistent estimation of the 3-dimensional rock fragment number density distribution and volume fraction compared to the direct measurement. The SOC stocks of the three stony profiles estimated by visual estimation, profile imaging, and stereological profile imaging methods were also assessed and compared to direct measurement. The average difference in total SOC stock between direct measurement and visual estimation, profile imaging, and stereological profile imaging methods was 0.32, 0.17, and 0.09 kg m−2, respectively. The total profile (0–100 cm) SOC stock for profile 2 differed among the visual estimation and profile imaging methods, but estimated SOC stock from the proposed method did not differ from direct measurement for the three profiles. Results also showed that the differences in SOC stock assessment between rock fragment estimation methods increased with rock fragment concentration. The proposed method was less affected by rock fragment concentration and was more stable compared to the visual and profile imaging methods. The stereological profile imaging method was demonstrated to be a reasonably accurate method for the quantification of rock fragment number density distribution, volume fraction, and SOC stock in stony soil profiles.

Tephra layers can serve as ideal datable horizons to provide invaluable datums for supplementing research on loess-paleosol sequences. The objective of this research was to evaluate a typical loess profile interbedded with a macroscopic tephra in northeastern China to better understand its formation to determine the potential impact of volcanism on climate change, as evidenced by loess pedogenesis. Geochemical and physical properties of the profile were determined and compared with loess materials from other areas to address its characteristics and potential significance for understanding climate variability. Results showed the tephra layers with overlying loess horizons were uniform in parent material, as inferred from evidence such as uniform Ti/Zr ratios with depth, similar rare earth element distribution patterns, and major elemental distributions with depth. The volcanic properties in the bottom of the profile, including morphology, high phosphate adsorption capacity (PAC) and pH (NaF) values, further supported the speculation that the depositions of aeolian dust and volcanic materials were synchronous with pedogenic processes. According to the comparison of magnetic susceptibility of the typical profile with the global δ18O curve, the local paleoclimate showed an increase in temperature in warmth and wetness, which may have been affected by volcanism. Research on the loess profile interbedded with a macroscopic tephra could improve our knowledge of loess formation and deepen our understanding of the impact of volcanism on climate changes.

“Black soils” recently proposed by the FAO Global Soil Partnership, include fertile soils, characterized by a thick, dark-colored soil horizon rich in organic matter. This study addressed the proposed concept of “black soils”, in terms of taxonomic relationship of their corresponding soil types of existing soil classifications. Similarity studies were based on the calculation of taxonomic distances between selected soil groups with organic matter–enriched horizons of the Russian Soil Classification System, the United States Soil Taxonomy and the World Reference Base for Soil Resources. In the process, we used the so called ”centroids”, which are the calculated mean values of several soil properties for each of the soil groups using legacy data derived from national and international databases. The results indicated that the Great Groups of the Mollisol Order in the US Soil Taxonomy had small taxonomic distances within the order, except some soils with shallow depth to the hardpan or permafrost. Dark-colored Vertisols and Andisols were found to differ from the Mollisols and similar soils found in similar environments mainly under grasslands. We recommend excluding Vertisols and Andisols from the “black soils” cluster due to the peculiarity in their properties, potential use and management. While the Vertisols and Mollisols of the Soil Taxonomy were completely dissimilar in properties, the WRB Vertisol Reference group and Russian dark-humus compact soils fitted well the Mollisols cluster, presumably due to the different concept of the Vertisols in the studied system. The soil types of the “black soils” of the Russian soil classification had a short distance to the corresponding Reference Groups of the World Reference Base. Also, the similarity was high with the Great Groups of the US Soil Taxonomy for all soil types except of Kastanozems, but the relationship in places was not well interpreted from a pedogeographic point of view. We ascribed the phenomenon to the geographical bias of the databases. Though “black soils” form a distinct cluster that roughly corresponds to the Mollisols Order of the US Soil Taxonomy, the taxonomic distance within the group may be significant, contributing to the pedodiversity of soilscapes of “black soils”.

Rock fragments (RFs) play an important role in soils that can influence land use and management decisions. Rock fragment concentrations most often are visually estimated (VE) in the field, for which accuracy strongly depends on experience and practice of soil classifiers. The direct measurement (DM) method is more accurate, but is costly and labor-intensive. Due to its importance, a simple and inexpensive method for estimating RF concentrations without destructive soil sampling is necessary. The development of camera technology and digital image processing provide an opportunity for soil descriptions and quantitative analyses using an alternative soil profile imaging (PI) method. Therefore, the objective of this study was to explore the potential for RF concentration estimation using a PI method as compared to the VE and DM methods. Apart from VE and DM, different image processing procedures inside the PI method were compared in a natural, gravelly, alluvial profile (Entisol) with low contrast among RFs in order to develop an alternative and efficient RF quantification method. Results showed that the hue-saturation-value (HSV) color model performed better than the red-greenblue color model for identifying RFs. The proposed profile imaging method accurately segmented 84.9% of the RFs from the soil matrix. The PI had a greater correlation with DM (r = 0.81) compared to the VE method (r = 0.78). Results showed that the alternative PI method could be used to accurately and efficiently estimate RF concentrations in a natural, gravelly soil profile with low contrast among RFs.

Soil horizonation has traditionally relied upon morphological description, field sampling, and laboratory analysis as a means of establishing key diagnostic horizons and features within a soil profile. While this technique is useful for many soil properties, other quantifiable properties associated with elemental abundance may be visually imperceptible. Portable X-ray fluorescence (PXRF) spectrometry allows for rapid elemental data acquisition in-situ, providing more than 20 elements commonly detectable within a soil profile. In this study, a grid containing 130 cells was physically imposed upon two soil profiles in West Texas, USA. PXRF was used to sequentially scan each grid cell and acquire elemental data. The profiles were morphologically described by a panel of soil scientists, sampled, and subjected to traditional laboratory characterization. Data from the PXRF and field sampling were statistically compared, then visualized as depth functions. A data visualization tool was created to overly elemental data on top of a digital photograph of the soil profile in semitransparency. Using elemental data as a proxy, PXRF data was able to detect both argillic and calcic horizons within one soil profile and visually illustrate their extent and assist in delineating horizons in a second profile with nondescript horizons. The soil visualization tool has other features as well such as correlation analysis between elements, adjustable correlation threshold in real time, rasterized vs. smoothed data overlay, and box-plots of elemental concentrations. Summarily, this research developed a new tool for PXRF data visualization in support of soil pedon description, allowing for elemental data which may be visually imperceptible to be considered during soil profile characterization. Future advancements will seek to integrate this tool directly into the PXRF software for on-site visualization of any element specified by the analyst. Applications of such could include real-time mapping of contaminant metals in soil profiles.