Soil Spatial Distribution Research Articles

Changes in soil chemical properties and their spatial distribution after logging and conversion to oil palm plantation in Sabah (Borneo)

Abstract Conversion of primary forest into oil palm plantations is common in tropical countries, affecting soil properties, ecosystem services and land-use management. However, little is known about the short-range spatial soil distribution that is important for soil scientists, ecologists, entomologists, mycologists or microbiologists. In this study, seven soil properties (pH, EC (µS/m), P (mg/kg), NO3- (mg/kg), N%, C% and C:N) were measured to quantify the spatial autocorrelation across primary forest, selectively logged forest and oil palm plantation in Sabah, Malaysian Borneo. Local variograms were calculated (range ∼5 m) to determine the short-range variation, and a decision tree as well as principal component analysis were implemented to determine if the overall (global) mean differed between land uses. As hypothesised, oil palm soils deviated the most from primary forest soils, which had more fluctuating variograms and in general, a shorter range. Oil palm plantations also showed a difference in the global mean except for electrical conductivity. Selectively logged forests also differed in their short-range spatial structure; however, the global mean and variance remained similar to primary forest soil with the exception of labile phosphorus and nitrate. These results were attributed to initial plantation development, removal of topsoil, fertiliser application and topography.

Application of geographic data for spatial modeling of lead in contaminated fluvial soils

The present study aims to determine the spatial distribution of soils with lead (Pb) content above the quality thresholds in a section of the Ogosta River valley (NW Bulgaria). The study area was contaminated with mine waste from the extraction and flotation of iron, lead-silver, and gold-bearing ores in the second half of the XX century. Predictive modeling was performed with the software Maximum Entropy Species Distribution Modeling (MaxEnt), Version 3.4.4, which uses machine learning algorithms and applies the maximum entropy method. The choice of predictors of contaminated soil distribution is consistent with the main factor for Pb dispersal within the valley floor - flooding from the Ogosta River. The following six parameters explained the environmental settings related to the accumulation of contaminated floodplain sediment: vertical distance to the river channel,  lateral distance to the Ogosta River, terrain slope, land cover (CORINE Land Cover, 2019), morphographic units of topography, and elevation. The results represent the average values of 10 replicates of the model. We evaluated the individual models by the value of the area under the relative operating characteristic curve (AUC) and the geographic logic of the obtained results. The AUC score for the test samples was 0.666 for the soil group 1 with Pb ≤120 mg/kg, 0.782 for group 2 with Pb (120-500] mg/kg, and 0.934 for group 3 with Pb>500 mg/kg. The most significant predictors for the models are the vertical and lateral distance to the river and the slope of the terrain. Lead concentrations tend to decrease with the distance from the main river and by increasing the elevation above the river channel due to lower inundation frequency and deposition rate of polluted river sediments. The soils with a Pb concentration below the permissible threshold of 120 mg/kg cover more than 58.42% of the valley floor of the studied section, and lands with Pb content above the intervention value of 500 mg/kg occupy nearly 10.82% of the investigated territory. The selected predictors describe the distribution of highly contaminated soils well and define the range of soils with lower Pb content worse. Combining clean and contaminated soil samples into one group is considered the main reason for the poor performance of MaxEnt for soils with Pb ≤120 mg/kg. However, the results prove the model's ability to predict the spatial distribution of not only biological species but also the dispersal of hazardous substances in soil.

Spatial distribution of lead concentration in peri-urban soil: Threshold and interaction effects of environmental variables

Understanding the spatial distribution of soil lead concentration in peri-urban soil and its underlying mechanism is critical to controlling lead contamination, ensuring food safety, and maintaining sustainable urban development. Although the linear and nonlinear relationships between Pb and environmental variables have been extensively studied, the threshold and interaction effects of environmental factors on Pb content are often ignored. This study aims to investigate the threshold and interaction effects and obtain the spatial distribution of Pb content via gradient boosting decision tree. The peri-urban area of Wuhan City, China, was selected as the study area, where soil health was threatened by agricultural production and urbanization and industrialization process. Results showed that the 14 covariates can explain 61.9 % variation of Pb content, in which anthropogenic factors played a major role. Specifically, the Pb content was mainly affected by distance from the industrial enterprises and gasoline stations, population density, elevation, and total nitrogen, which denoted that Pb contamination was mainly originated from industrial production, gasoline stations, urbanized population, and fertilization. All environmental variables had threshold effects on Pb, and the relationship between Pb and these variables mutated when the threshold was exceeded. The results of threshold effects exhibited that Pb was elevated in locations near industrial enterprises (<4 km) or gasoline stations (<1.5 km), with a dense population (>5 person/hectare), and with low elevation (<25 and 45–65 m). The two-way and three-way interaction effects of covariates on Pb content was determined, indicating that the influence of environmental variables on Pb concentration is a complex interaction rather than a simple superposition. These findings highlight the consideration of threshold and interaction effects to understand the underlying processes that lead to Pb contamination, and have important implications for sustainable urban development.

Numerical assessments of the influences of soil–boulder mixed flow impact on downstream facilities

Rapid soil–boulder mixed flow commonly occurs in steep mountainous regions and has the potential to damage critical infrastructures. Baffles are typically used as flow-impeding structures to mitigate the destructive power of this natural hazard. Although material heterogeneity plays an important role in these processes, the influence of particle size difference, particularly boulders, has often been overlooked due to its complex multi-scale nature. Therefore, we conduct various numerical examples by a Discrete Element Material Point Method, previously introduced by the authors, to provide insights into the effects of boulders. The results reveal that the collisions and interactions between different components (soils, boulders and baffles) can dissipate kinetic energy and reassemble the spatial distribution of soils and boulders. The simulations illustrate how the average impact force on the baffle becomes non-symmetric when there are boulders in the mixed flow and more significant when the rock volume fraction increases. Moreover, it is shown that increasing the baffle array is an effective way to reduce the velocity of mixed flow and dissipate the kinetic energy of whole system. Finally, the results obtained from this study are beneficial for designing optimal baffles and mitigating natural hazards.

V horizon development and factors associated in different geomorphological units of an arid-region alluvial fan. Pre-Andes of San Juan, Argentina

Alluvial fans are frequently associated with desert pavements and V horizons. In particular, the Talacasto River alluvial fan presents a spatial configuration that gives rise to different levels of deposition. The present work has the following objectives: 1: determine whether geomorphological and superficial characteristics of the landforms influence the presence, development and spatial distribution of soils and V horizons. Also, if vegetation patterns could be used as indicators of the V horizon, and 2: analyze the relationship between geomorphometric characteristics, soil properties and vegetation patterns in the different geomorphological units. Soil and vegetation studies were performed along the different geomorphological units (9) for the alluvial fan. Soil morphology and physical properties were analyzed, emphasizing the presence of the genetic V horizon. In addition, the surface properties and the attributes of plant communities were quantified. The relationship between soil, geomorphometric and vegetation variables was evaluated through Redundancy Analysis (RDA), Spearman's Correlation Analysis and Generalized Linear Models (GLM). The results show that the V horizon is heterogeneously distributed over the alluvial fan. In the older alluvial levels, the V horizon is thicker and finer-textured compared to modern levels. In addition, the V horizon of geomorphic surfaces is negatively correlated with slope and positively correlated with wind effect. V Horizon thickness is positively correlated with sediment coverage, however, it does not show correlation with the vegetation and rock coverage, while the vegetation and rock coverage present a strong negative correlation. The vegetation patterns show significant differences between geomorphological units analyzed. Species richness and diversity are positively correlated with sediment coverage, but are not correlated with V horizon thickness, therefore, species richness and diversity are not good predictors. Our main conclusion indicates that the presence of the V horizon depends on the geomorphometric properties. In contrast, the thickness of the V horizon is independent of vegetation and rock coverage. Two plant species could be used as predictors of the presence and thickness of the V horizon, L. cuneifolia and B. retama.

Deep learning in land-use classification and geostatistics in soil pH mapping: a case study at Lakehead University Agricultural Research Station, Thunder Bay, Ontario, Canada

Grain agriculture is one of the main economic activities in Thunder Bay. However, farming faces many challenges: shorter growing seasons, severe weather conditions, climate change, and limited services and economic support. Lakehead University Agricultural Research Station (LUARS) is continuously working toward improving farming in Northern Ontario and testing different agricultural initiatives. Precision agriculture (PA) is one of the strategic means to improve Northern Ontario farming and sustain agricultural operations. Hence, PA requires accurate and up-to-date baseline information; the aim of this study is to develop baseline spatial information (land cover and soil pH variability maps) for the LUARS using remote sensing technology. A set of aerial images were acquired from the remotely piloted aircraft system (RPAS) and created an orthophoto (7-cm spatial resolution). A detailed land cover map was produced using a deep learning algorithm. We collected soil samples from three different sections (plots) of the area to analyze soil pH variations. Different geostatistical methods [ordinary kriging, Simple kriging, and empirical Bayesian kriging (EBK)] and deterministic interpolation techniques: local polynomial interpolation (LPI) and inverse distance weighted (IDW) were tested to map the spatial distribution of soil pH. A land cover classification achieved an overall accuracy of 73% and a Kappa coefficient of 0.70. Most of the homogeneous agricultural plots indicated higher producer’s and user’s classification accuracies. There was no linear correlation between soil sample data and vegetation indices derived from RPAS images to test the linear regression for soil pH mapping. EBK, LPI, and IDW produced soil pH maps with higher accuracies for Secs. 1–3, respectively.

Physical and chemical properties of soil on selected sugarcane farms in Mt. Nebo, Valencia City, Bukidnon, Philippines

Abstract. Labajo JRN, Pabiona MG. 2022. Physical and chemical properties of soil on selected sugarcane farms in Mt. Nebo, Valencia City, Bukidnon, Philippines. Asian J Agric 6: 79-86. The study was conducted to determine soil's physical and chemical properties on selected sugarcane farms in Mt. Nebo, Valencia City, Bukidnon, Philippines. Soil samples were collected from 24 different sugarcane farms planted with sugarcane following Sloven's Formula for farm selection. Soil samples were collected from August 2018 to April 2019, and the physical and chemical properties of the soil were determined at SPAL, CMU, Musuan, Maramag, Bukidnon, Philippines. Soil texture and percent Water Holding Capacity (%WHC) soil of the sugarcane farms were clayey textured, with %WHC ranging from 54.24-76.30% and bulk Density (BD) and Particle Density (PD) values ranging from 1.05-1.31 Mg m-3 (BD) and 2.28-2.41 Mg m-3, which is ideal for growing sugarcane. Soil samples collected were categorized as medium acidic, marginal in soil organic matter content (SOMC), extractable phosphorus (P) was very deficient, and low on exchangeable potassium (K). That implies these sugarcane farms need soil amendments to meet sugarcane's desired nutrient and pH requirements. Therefore, the sugarcane farms in Mt. Nebo, Valencia City, Bukidnon are required to apply 1.64-7.04 Mg ha-1 of lime and a recommendation rate of 80-200 kg ha-1 Nitrogen (N), 200-500 kg ha-1 of Phosphorus (P), and 170-200 kg ha-1 potassium (K). Moreover, a soil fertility map is necessary to classify and group the delineated soils into map units and capture soil property information to interpret and depict soil spatial distribution on a map.

Artificial neural network and spatial distribution of soil attributes for farming management in Western Desert, Egypt

Artificial neural network and spatial distribution of soil attributes for farming management in Western Desert, Egypt

Spatial variability and uncertainty of soil nitrogen across the conterminous United States at different depths

AbstractSoil nitrogen (N) is an important driver of plant productivity and ecosystem functioning; consequently, it is critical to understand its spatial variability from local‐to‐global scales. Here, we provide a quantitative assessment of the three‐dimensional spatial distribution of soil N across the United States (CONUS) using a digital soil mapping approach. We used a random forest‐regression kriging algorithm to predict soil N concentrations and associated uncertainty across six soil depths (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) at 5‐km spatial grids. Across CONUS, there is a strong spatial dependence of soil N, where soil N concentrations decrease but uncertainty increases with soil depth. Soil N was higher in Pacific Northwest, Northeast, and Great Lakes National Ecological Observatory Network (NEON) ecoclimatic domains. Model uncertainty was higher in Atlantic Neotropical, Southern Rockies/Colorado Plateau, and Southeast NEON domains. We also compared our soil N predictions with satellite‐derived gross primary production and forest biomass from the National Biomass and Carbon Dataset. Finally, we used uncertainty information to propose optimized locations for designing future soil surveys and found that the Atlantic Neotropical, Pacific Northwest, Pacific Southwest, and Appalachian/Cumberland Plateau NEON domains may require larger survey efforts. We highlight the need to increase knowledge of biophysical factors regulating soil processes at deeper depths to better characterize the three‐dimensional space of soils. Our results provide a national benchmark regarding the spatial variability and uncertainty of soil N and reveal areas in need of a better representation.

Spatial distribution of soil nutrients and evaluation of cultivated land in Xuwen county.

It is of great significance to promote the quantitative research of soil science and the implementation of precision agriculture. On this basis, taking Xuwen County as the research object, this paper comprehensively analyzed the soil characteristics of cultivated land in Xuwen County and clarify the soil nutrient content and spatial distribution characteristics of cultivated land in Xuwen County, this paper comprehensively applied the methods of geostatistics, geographic information system (GIS) and fuzzy mathematics, and referred to the cultivated land quality grade standard (GB/T 33469-2016), to analyze the soil characteristics and evaluate the soil fertility of this region. The results show that the optimal interpolation model of soil pH and available phosphorus (AP) is a Gaussian model, and the optimal interpolation model of soil organic matter (SOM), available nitrogen (AN) and available potassium (AK) is a J-Bessel model. In addition, the spatial correlation of AK is weak, whereas pH, SOM, AN and AP show moderate spatial correlation. The proportion of excellent, good, average, medium and poor comprehensive fertility index are 26.00%, 32.67%, 19.33%, 19.00% and 3.00%, respectively. The overall level of soil fertility in Xuwen County is above the average, and the fertility quality presents an obvious trend of high in the South and low in the North. Areas that above average fertility are mainly distributed in Maichen Town, Qujie Town, Nanshan Town and Chengbei Town. The results can provide theoretical basis for improving the utilization rate of chemical fertilizer, fine management of cultivated land and ecological environment in this region, which can help in decision-making of precision fertilization.

Digital Soil Mapping of Soil Organic Matter with Deep Learning Algorithms

Digital soil mapping has emerged as a new method to describe the spatial distribution of soils economically and efficiently. In this study, a lightweight soil organic matter (SOM) mapping method based on a deep residual network, which we call LSM-ResNet, is proposed to make accurate predictions with background covariates. ResNet not only integrates spatial background information around the observed environmental covariates, but also reduces problems such as information loss, which undermines the integrity of information and reduces prediction uncertainty. To train the model, rectified linear units, mean squared error, and adaptive momentum estimation were used as the activation function, loss/cost function, and optimizer, respectively. The method was tested with Landsat5, the meteorological data from WorldClim, and the 1602 sampling points set from Xinxiang, China. The performance of the proposed LSM-ResNet was compared to a traditional machine learning algorithm, the random forest (RF) algorithm, and a training set (80%) and a test set (20%) were created to test both models. The results showed that the LSM-ResNet (RMSE = 6.40, R2 = 0.51) model outperformed the RF model in both the roots mean square error (RMSE) and coefficient of determination (R2), and the training accuracy was significantly improved compared to RF (RMSE = 6.81, R2 = 0.46). The trained LSM-ResNet model was used for SOM prediction in Xinxiang, a district of plain terrain in China. The prediction maps can be deemed an accurate reflection of the spatial variability of the SOM distribution.

Ride the dust: linking dust dispersal and spatial distribution of microorganisms across an arid landscape.

In arid ecosystems, where the soil is directly exposed to the action of the wind due to sparse vegetation, dust aerosolization is a consequence of soil degradation and concomitantly, a major vector of microbial dispersal. Disturbances such as livestock grazing or fire can exacerbate wind erosion and dust production. Here, we sampled surface soils in 29 locations across an arid landscape in southwestern USA and characterized their prokaryotic and fungal communities. At four of these locations, we also sampled potential fugitive dust. By comparing the composition of soil and dust samples, we determined the role of dust dispersal in structuring the biogeography of soil microorganisms across the landscape. For Bacteria/Archaea, we found dust associated taxa to have on average, higher regional occupancies compared to soil associated taxa. Complementarily, we found dust samples to harbour a higher amount of widely distributed taxa compared to soil samples. Overall, our study shows how dust dispersal plays a role in the spatial distribution of soil Bacteria/Archaea, but not soil Fungi, and might inform indicators of soil health and stability in arid ecosystems.

Spatial heterogeneity of water quality in a small watershed of an ionic rare earth mining area

Abstract The tailwater of ion-type rare earth mines in southern Jiangxi has caused serious water pollution problems in small watersheds. In this study, seven physical and chemical indicators, namely, pH, TOC, dissolved oxygen, ammonia nitrogen, nitrate-nitrogen, salinity and redox potential were selected and spatial interpolation, principal component analysis, and geographic detector methods, were used to quantitatively analyze and evaluate the spatial heterogeneity of water quality in the small watersheds in this region. Tailwater pollution in the whole basin of the study area is serious. The spatial difference is manifested as east tributary &amp;gt; mainstream and reservoir area &amp;gt; southern tributary. Source pollution is the smallest, and water quality pollution is mainly manifested by excessive ammonia nitrogen, salinity, and TOC. The spatial differences in the physical and chemical properties of water bodies are significant. The distribution of ammonia nitrogen content is consistent with the spatial distribution of soil and water pollution, while nitrate-nitrogen is the opposite. Other indicators also show a certain spatial regularity, and the spatial regularity of dissolved oxygen content is not obvious. Water pollution in mining areas is mainly due to a large amount of ammonia nitrogen ions remaining in the mountains. As a result, the southern tributary ecological pool has an obvious purification effect on water quality. Salinity is extremely affected by ammonia nitrogen and TOC, oxidation-reduction potential is affected by pH and TOC, and TOC is influenced by ammonia nitrogen and pH. Salinity, ammonia nitrogen, and TOC are the potential risk factors of major pollution. The changes in the physical and chemical properties of the pollution index are not independent of each other. From the perspective of macro and micro, the comprehensive water pollution assessment system is adopted to provide a new idea for the prevention and control of rare earth industry tail water pollution, and further make exploratory efforts for the study of industrial wastewater pollution.

Role of Environment Variables in Spatial Distribution of Soil C, N, P Ecological Stoichiometry in the Typical Black Soil Region of Northeast China

The effects of environmental factors on topsoil nutrient distribution have been extensively discussed, but it remains unclear how they affect spatial characteristics of soil carbon (C), nitrogen (N), and phosphorus (P) stoichiometry at different depths. We collected 184 soil samples in the typical black soil region of northeast China. Ordinary kriging was performed to describe the spatial distribution of soil C, N, and P eco-stoichiometry. Redundancy analysis was used to explore relationships between C:N:P ratios and physicochemical characteristics. The soil classification was studied by hierarchical cluster analysis. The mean C, N, and P contents ranged from 15.67 to 20.08 g·kg−1, 1.15 to 1.51 g·kg−1, and 0.80 to 0.90 g·kg−1 within measured depths. C, N, and P concentrations and stoichiometry increased from southwest to northeast, and the Songhua River was identified as an important transition zone. At 0–20 cm, soil water content explained most of the C, N, and P content levels and ratios in cluster 1, while latitude had the highest explanatory ability in cluster 2. For 20–40 cm, soil bulk density was the main influencing factor in both clusters. Our findings contribute to an improved knowledge of the balance and ecological interactions of C, N, and P in northeast China for its sustainability.

Different pipe burial depths associated with subsurface drip irrigation significantly affected soil gas emissions

AbstractEfforts are needed to reduce soil greenhouse gas emissions from irrigated croplands. Thus, in this study, patterns of soil carbon dioxide (CO2) and nitrous oxide (N2O) emissions in the root zone of tomato plants grown under subsurface drip irrigation (SDI) involving different pipe burial depths were studied, with surface drip irrigation (DI) used as a control (CK). The soil CO2 and N2O emission curves under DI and SDI with pipe burial depths of 10 and 20 cm tended to be similar. In contrast, SDI with a pipe depth of 30 cm resulted in peak CO2 and N2O emissions occurring at different times. The different pipe burial depths led to significantly different spatial distributions of soil water‐filled pore space, which indirectly affected root–soil interactions and soil gas emissions through direct effects on total tomato root fork numbers and microbial community composition. The SDI treatments involving 10‐ and 20‐cm pipe burial depths increased the number of root forks to 1.85 and 2.77 times that in the DI treatment, respectively, and increased the Shannon index of the fungal community by 11.9 and 7.29% compared with that in the DI treatment, respectively. The SDI treatment involving the 30‐cm pipe burial depth resulted in 2.22 times the number of root forks compared with that in the DI treatment; however, the Shannon index of the fungal community decreased by 13.6% compared with that in the DI treatment. As a result, the total soil CO2 emissions and total soil N2O emissions in the SDI treatment involving the 10‐cm pipe burial depth were 1.37 and 2.03 times those in the DI treatment, and those in the SDI treatment involving the 20‐cm pipe burial depth were 1.33 and 2.17 times those in the DI treatment, respectively. In the SDI treatment involving the 30‐cm pipe burial depth, the total amount of soil CO2 emissions was similar to that in the DI treatment; however, the total amount of soil N2O emissions was 28.98% lower than that in the DI treatment. Therefore, soil gas emissions can be reduced by increasing the burial depth of irrigation pipes. These results provide insights into the control of soil gas emissions in agroecosystems.

Mineral nitrogen accumulation and its spatial distribution in soils in dense planting dwarf rootstock apple orchard on the Weibei dry plateau, Northwest China

With the rapid development of dense apple tree plantings with the dwarf rootstock cultivation method, determining accumulation and distribution characteristics of soil mineral nitrogen in densely planted orchards with dwarf rootstock is important to enable scientific fertilization of apple orchards. We investigated densely planted apple orchards with dwarf rootstocks and different plant ages (6 a, 9 a, and 12 a). We collected soil samples under trees, between trees, between rows, and at the midpoints between the trees and rows, and examined the accumulation and distribution characteristics of nitrate, ammonium, and mineral nitrogen. The cumulative amount of nitrate in the 0-300 cm soil layer increased with plant age. The difference between orchards with different plant ages was significant and showed the trend 6 a<9 a<12 a. The cumulative amount of nitrate increased from 1729 kg·hm-2 to 3771 kg·hm-2 with increasing plant age. The ammonium content was low for orchards of all plant ages and had little effect on the accumulation and spatial distribution of mineral nitrogen. There were two accumulation peaks of nitrate nitrogen in the vertical direction. The depth of soil layer where the second accumulation peak was located decreased from 180 cm to 220 cm with increasing plant age. In the horizontal direction, soil nitrate nitrogen content between rows increased from 27 mg·kg-1 to 138 mg·kg-1 with increasing plant age, representing a more than 400% increase. The difference between orchards with different plant ages was significant. In summary, excessive usage of nitrogen fertilizer and serious leaching of nitrate were problematic in all orchards with different ages. Less nitrogen fertilizer should be applied, and anti-seepage measures should be used at the fertilization location to prevent the leaching of nitrate to deep layers.

A novel method for measuring and evaluating spatial distribution of straw incorporated by rotary tillage

AbstractStraw incorporation after tillage plays an essential role in soil organic matter and fertility. Despite the importance of understanding the effect of tillage on straw distribution in the soil, an accurate method to measure and evaluate the spatial distribution of straw has not been developed. In this work, we proposed a method for quantifying straw distribution in soil following tillage. The approach uses a straw spatial coordinate digitizer, Pro/Engineer drawing software, and MATLAB visualization software. Straw of four different lengths was mixed into soil by a rotary tillage. Results indicated that the uniformity of straw spatial distribution decreased with the increase of straw length. T50 (50‐mm straw length) had the greatest uniformity. As a result, it is recommended that straw be cut to 50 mm or shorter. This work offers a new insight into the quantitative analysis of straw spatial distribution in soil.

Accuracy analysis of remote sensing index enhancement for SVM salt inversion model

The accurate and rapid acquisition of the soil salt content and spatial distribution is of great significance for effectively monitoring cropland soil salinization. Using OLI/Landsat-8 images as data sources, shortwave infrared bands (SWIR1, SWIR2) are introduced to the traditional vegetation and salinity indices, and the enhanced indices are put forward to obtain an improved inversion effect. After enhancement, the correlation coefficient between the indices and soil salt content increased by 0.01–0.44, and the multicollinearity between indices significantly decreased. The prediction ability of the support vector machine salt inversion model improved from general to fairly good, and the predicted and measured values were close to a linear distribution. The predicted and measured values ranged from 0.39–36.38 g·kg−1 and 0.35–38.09 g·kg−1, respectively, and 63.25% of the regional salt status could be accurately predicted. The results showed that the accuracy of soil salinity inversion can be effectively enhanced by introducing shortwave infrared bands.

Species richness, diversity, density and spatial distribution of soil seed banks in the riparian woodland along the Thamalakane River of the Okavango Delta, northern Botswana

Soil seed banks serve as reservoirs of seeds for subsequent regeneration of plants. Soil seed banks were investigated along the Thamalakane Riparian Woodlands (hereafter referred to as TRWs) of the Okavango Delta, northern Botswana, from January-July 2015 and January-July 2016. The study aimed at examining species richness and diversity, determining densities, assessing the spatial distribution of seeds in the soil and comparing the similarity in species composition between the standing vegetation and soil seed bank flora. The vegetation was sampled in 71 plots (20 × 50 m) and soil samples were collected from 568 subplots. Agglomerative hierarchical cluster analysis was used to determine soil seed bank communities. Indicator species analysis was used to calculate indicator values for species in each community of germinated seeds and across different soil layers. Multi-response permutation procedures (MRPP) were used to compare similarity in soil seed bank composition. Bray-Curtis ordination was used to infer spatial relationships of soil layers in terms of soil seed bank composition. A total of 109 plant species were identified in the litter and top 9 cm soil layers with a mean density of 2101 seeds m−2. Herbs, grasses, sedges and woody plants were represented by 68, 19, 9 and 13 species, respectively, in 30 families and 87 genera. The overall total diversity and evenness of the soil seed bank in the TRWs was 3.25 and 0.69, respectively. Four plant communities were identified from the soil seed bank, namely Setaria verticilata-Amaranthus hybridus, Acanthospermum hispidum-Setaria sagittifolia, Digitaria eriantha-Eclipta prostrata and Cyperus longus-Fimbristylis dichotoma. Bray-Curtis ordination showed that there was overlap between these communities in terms of seed bank composition. However, MRPP analysis showed that there was significant (P < 0.05) separation between germinated soil seed bank communities. The overall horizontal distribution of seeds varied among sampling quadrats while the vertical distribution of seeds exhibited the highest densities occurring in the upper 3 cm of the soil and gradually decreasing densities with increasing depth. Relatively high densities also occurred in the litter layer. There were large differences in depth distribution between species, suggesting differences in seed longevity. The similarity in species composition between the soil seed flora and standing vegetation was low (27%). The results demonstrated that many species in TRWs store quantities of seeds in the soil. The fact that most of the woody species do not accumulate more seeds in the soil suggests that their regeneration from seeds would be unlikely if mature individuals disappeared (die or are harvested). Because of its diverse seed banks, the herbaceous flora would have a better chance of re-establishing in the event of anthropogenic or natural disturbances. Therefore, the future of the TRW woody flora seems to depend on the successful conservation of the standing vegetation.

Prostorna analiza tala i utjecaj na produktivnost u gospodarskoj jedinici Mužljanski rit

The paper presents analysis of soil spatial distribution and productivity of Populus x euramericana I-214 forest cultures in forest management unit (hereinafter: FMU) Muzljanski rit. Soil cover in the FMU is heterogeneous in relation to position and altitude in floodplain. Analysis were conducted on eugley and humogley soil types. Eugley, soil type, was delineated according to physiologically active soil depth as &amp;#945;, &amp;#946; or &amp;#946;/&amp;#947; gley soils and humogley was delineated as one soil unit. Cultures of the poplar clones Populus x euramericana I-214 are found on at least two but usually more different site types within the same forest management section (18.43 %). Single forest management section is a by definition a single forest spatial unit having similar ecologic factors. Aim of our research was to enable consistent forest section delineation, based on the interaction of soil productivity properties and distribution as well as Populus x euramericana I-214 productivity dataset. Based on our anaysis (spatial analysis of raster layers of soil systematic unit distribution-soil subunit, digital elevation model and productivity according to inner delineation of FMU Muzljanski rit), the resuts show eugley share in lower systematic soil unit. The results show contribution of lower systematic units of eugley in forest managemet section/culture. We found a raising trend of &amp;#945; and &amp;#946;-gley, as well as humogley. On one side the higher contribution of this sistematic units indicates reduction in the forest culture yield. On the other side higher contribution of &amp;#946;/&amp;#947; gley indicates an increase of culture yeald. The Populus x euramericana I-214 cultures are spatialy concentrated, extending over soil units with different characteristics, pointing in alternative economicaly justified section delineation, based on the soil types.