Average Soil Organic Matter Research Articles

Remote Sensing Inversion of Soil Organic Matter Content in Straw-Returned Fields in China’s Black Soil Region

China’s black earth region is the country’s corn golden belt, and returning corn straw to the field not only helps improve the Soil Organic Matter (SOM) content and soil fertility, but also resolves environmental pollution caused by straw burning. To study the effects of different years and amounts of straw returned to the field on SOM content, this study used soil sampling data from a conservation tillage experimental base in Gaojia Village, Lishu County, combined with indoor measurements of imaging spectral data, to establish a prediction model of SOM content by applying partial least squares regression, and inverting the SOM content in the study area. The results showed that the PLSR model accuracy using indoor measured soil imaging spectral data as the independent variable was high. The accuracy coefficients of samples with different field return and different field return amounts, R2, were 0.9176 and 0.8901, respectively, which better predicted SOM content. In the 0–50 cm tillage layer, the highest average SOM content of 39.73 g/kg was found under the NT-1 treatment with different no-tillage straw return year treatments. The depth of the tillage layer in the typical black soil region of Northeast China is around 0–20 cm, and the most significant increase in SOM content was observed in the experimental samples under the NT-1 treatment. SOM content in NT-1 treatment increased by 31.83% compared with CK-1, 68.24% compared with CK-2, 72.18% compared with NT-0, 699.48% compared with NT-2, and 311.44% compared with NT-3, respectively. The highest SOM content of 31.9 g/kg was found in NT-100 under the different treatments of different years of field return. At the 0–20 cm soil layer, the SOM content increases most significantly under NT-100 treatment, which is the most suitable treatment method for straw return to the field. And NT-100 is 22.09% higher than CK-1, 55.36% higher than CK-2, 58.99% higher than NC-0, 115.95% higher than NT-33, and 48.72% higher than NT-67, respectively. This study provides data that can support the conservation of soil ecosystem diversity and sustainable soil use, and it also enriches the application of the PLSR model application.

Analyzing Spatial Distribution and Influencing Factors of Soil Organic Matter in Cultivated Land of Northeast China: Implications for Black Soil Protection

Soil organic matter (SOM) in cultivated land is vital for land quality and food security. This study examines SOM distribution and influencing factors in northeastern China, providing insights for sustainable agriculture. Utilizing 10 m resolution SOM data, the analysis covers regions including the Greater and Lesser Khingan Mountains, Liaohe Plain, Sanjiang Plain, Songnen Plain, the northwest semi-arid region, and the low hilly areas of Paektu Mountain. The Geodetector method is employed to assess various influencing factors. The key findings are as follows: (1) The average SOM content in Northeast China (37.70 g/kg) surpasses the national average, is highest in the Greater and Lesser Khingan Mountains (49.32 g/kg), and lowest in the northwest semi-arid region (26.15 g/kg). (2) SOM content is maximized in regions with high altitudes, steep slopes, low temperatures, and moderate precipitation. (3) The annual average temperature is the primary factor influencing SOM distribution, with a combination of temperature and administrative divisions providing better explanatory power. (4) SOM trends vary across protected areas, with slope being critical in semi-humid plains, elevation in arid regions, and no dominant factors identified in the Sanjiang Plain. These findings underscore the need for tailored black soil protection policies to effectively leverage local resources and preserve ecosystem integrity.

The assessment of soil organic matter in the KwaZulu-natal province of South Africa and its relationship to spectroscopy data

Soil organic matter (SOM) is made up of decomposing biotic material in various stages, as well as compounds generated by plant roots and soil organisms—it helps the soil’s biophysical functions. Laboratory spectroscopy, for example, provides a novel technique to analyse SOM content because it is both cost and time-efficient. Because of its composition and biophysical features, SOM has a distinct spectral reflectance; this relationship has been effectively exploited to estimate and predict SOM. The purpose of this study is to investigate the link between SOM concentration and laboratory-based soil spectral reflectance in the Emakhosaneni subplace of uThukela District, KwaZulu-Natal, South Africa. We collected 13 random soil samples from each of the four major land use types (agricultural, residential, eroded land, and rangeland), totalling 52 samples. To assess the SOM content, we oven-dried the materials overnight (105 °C), crushed them, sieved them (1 mm), and analysed them using the Walkley-Black method. The spectral reflectance of the soil samples was then acquired using the Analytical Spectral Device (ASD) FieldSpec3 and pre-processed for noise reduction before analysis. We found that the area’s average SOM content was 2.19%, with agricultural land having the greatest average SOM content (2.98%), followed by rangeland (2.46%), residential (1.77%), and eroded land area (1.56%). However, the average reflectance of the spectra was higher on eroded areas and decreased to a minimum on agricultural areas. This is attributed to the relationship between soil colour and SOM. Correlation analysis demonstrated a moderately negative relationship between SOM concentration and spectral reflectance over the whole spectral range covered (400–2400 nm). Our partial least squares regression analysis revealed that pre-processed spectra data models (FDT; R2 = 0.46 and SG; R2 = 0.34) performed better than raw data models, in both calibration and validation data sets, respectively. Despite the influence of noise on raw data model performance, they modestly predicted SOM content, particularly on the validation data set. However, the developed model cannot be used due to the very low coefficient of determination. Our results highlight the importance of spectroscopy to assess SOM content, and further research can be carried out on a larger scale.

Estimating Organic Matter Content in Hyperspectral Wetland Soil Using Marine-Predators-Algorithm-Based Random Forest and Multiple Differential Transformations

To achieve a rapid and accurate estimation of the soil organic matter (SOM) content in wetland soil, we focused on surface soil samples from the Xianshan Lake wetland area in Zhejiang Province and proposed a novel method called Marine-Predators-Algorithm-Based Random Forest (MPARF) to establish a fast detection model for the SOM content. This study analyzed 85 soil samples from the study area with the aim of assessing the performance of various combinations of ten differential transformation methods and five regression algorithms in predicting the SOM content. Our research findings demonstrate that the combination of second-order differentiation (SD) and MPARF yields the best results, with the highest R2 value (0.92) and the lowest RMSE (1.32 g/kg). Furthermore, we determined that the average SOM content in the study area’s soil is 9.77 g/kg. Additionally, we confirmed that different differential transformation methods contribute to improving the correlation between spectral data and the SOM content, thereby enhancing the development of predictive models. This study provides a robust methodology and foundation for future soil organic matter monitoring in the region.

Monitoring regional soil organic matter content using a spatiotemporal model with time-series synthetic Landsat images

Accurately monitoring soil organic matter (SOM) content is crucial for food and soil security. Current methods of monitoring are expensive and existing sparse data cannot provide detailed spatial information about SOM content changes in an area. This study proposes using a spatiotemporal model with time-series synthetic Landsat images to monitor SOM content dynamics at the regional scale. The approach was implemented in Google Earth Engine (GEE) platform and tested in Jiangsu province, China, using the soil survey data from 2006 to 2007 and synthetic Landsat images from 1986 to 2007. The model generated SOM maps every three years between 1986 and 2007 and was evaluated using another soil survey from 2000 in southern Jiangsu. The model associated with 20 covariates derived from the synthetic Landsat image explained 70% of the variation in SOM content with root mean squared error (RMSE) and Lin's concordance correlation coefficient (CCC) of 5.17 g/kg and 0.57, respectively. The results showed that the model could reveal regional spatial and temporal differences in SOM content distribution. The SOM content in Jiangsu increased in the north, while decreasing in the central and southern areas. Temporally, the mean SOM contents increased from 1986 to 1992 by 0.17 g/kg, decreased in 1995, and increased again from 1998 to 2000 before decreasing from 2004 to 2007 by 0.14 g/kg. The validation based on the soil data in 2000 showed that the approach generated an RMSE of 5.97 g/kg, accounting for 22.77% of the average SOM content of the data. The study concluded that this approach could be used for monitoring SOM content and other soil properties. This approach had a relatively better accuracy than a previous study using the Integrated Nested Laplace Approximation with the Stochastic Partial Differential Equation approach with the same soil data but 4 times more samples.

Analysis of soil organic matter influencing factors in the Huangshui River Basin by using the optimal parameter-based geographical detector model

To quantitatively analyze the spatial distribution of soil organic matter (SOM) content and its main influencing factors in the Huangshui River Basin, Qinghai Province, China, a total of 13 factors including topography, climate, soil properties, and vegetation cover were selected. The influence of these factors on the spatial distribution of SOM content was analyzed using the geographical detector model and the Pearson correlation coefficient method. The results showed that the average SOM content in the cultivated soil layer of the Huangshui River Basin was 28.38 g·kg−1. The results of factor detection and Pearson correlation analysis indicated that the three dominant factors causing differences in the spatial distribution of SOM content were mean annual temperature, altitude, and soil pH. Furthermore, SOM content was negatively correlated with temperature and positively correlated with elevation. When the factors interacted, they exhibited a type of bi-enhance or nonlinear-enhance. The interaction between two factors improved the explanatory power of SOM, indicating the complexity of factors causing spatial distribution differences in SOM. This study provides a favorable basis for precision fertilization, soil improvement, and of soil quality improvement in the Huangshui River Basin.

Determining soil health parameters controlling crop productivity in a Citrus Greening disease affected orange grove

Soil health is an important aspect for maintaining adequate crop production, but the specifics of what entails a healthy soil can vary from region to region and crop to crop. In highly managed agricultural systems, unhealthy soil can be masked by intensive management practices, yet there must be detrimental cutoff points in various characteristics, such as soil organic matter (SOM) concentrations, where even highly managed systems start to lose productivity. This negative impact was observed in a Florida citrus grove containing Valencia orange trees with observable differences in tree size yet were otherwise managed identically. A soil health index demonstrated that the areas with smaller trees had a significantly lower index score and those soils contained significantly less SOM (average SOM ​= ​0.57%) compared to areas with larger trees (average SOM ​= ​0.94%). The areas of lower crop productivity also had less enzymatic activity of common carbon-cycling enzymes and different microbial populations, which all together negatively affected soil health and corresponding plant productivity. This agricultural region is also known to have a Citrus Greening disease (HLB) infection rate of close to 100%, hence we developed a hypothesis that could explain how progression of this infection could be impacted by SOM concentrations and differences in microbial diversity. We posit that areas of this grove with healthier soil could have more resistance to the onset of fatal HLB symptoms. Consequently, soil organic matter distribution and concentration should be considered when establishing new groves in order to optimize soil and crop productivity.

Soil Organic Matter Inversion Based on Imaging Spectral Data in Straw‐Covered Noncultivated Land

The rapid inversion of soil organic matter is of great significance for agricultural soil testing and fertilization in order to protect and utilize land resources effectively. This study selected the developmental base in Lishu County with typical characteristics of China’s black soil as its research area. This study established a 600 nm “bow curvature difference” spectral index and the partial least squares regression model, and the accuracy of their results was compared. The correlation between the 600 nm “bow curvature” spectral index and the soil organic matter of straw cover no‐tillage is analyzed. The average soil organic matter content in the study area was 16.67 g·kg-1, and the organic matter increased significantly from NT‐0 to NT‐100 by 16.26 g·kg-1. The study provided a deep insight to improve the quantitative inversion methods to estimate soil organic matter.

Soil organic matter in temperate forest-grassland systems: A case study from the Southern Cis-Ural, Russia

Understanding the dynamics of SOM in different land use types is critical for effective land management and climate mitigation strategies. In this study, we investigated the differences in soil organic matter (SOM) content between forest and grassland landscapes in the mountainous zone of Republic of Bashkortostan, Russia. We collected soil samples from multiple locations under both land use types and analysed them for SOM content using standard laboratory techniques. The results showed that the SOM content varied from 2.2 to 15.3% under forest landscape with an average of 6.7% and was characterized by high variability (51.8%). The SOM concentrations under grassland ranged from 2.1 to 6.5% with an average of 3.2%, while the coefficient of variation was 31.8%. According to the classification, the average SOM value in forest soils was classified as “high”, while in grassland soils it was classified as “low”. Overall, the variability in SOM content within forested landscapes can be influenced by a range of factors, including topography, erosion, and redeposition of soil. These factors contributed to the complex patterns of SOM accumulation and decomposition observed in forest soils, and should be considered when evaluating the potential impacts of land use change on SOM. Our study highlights the importance of understanding the factors that influence SOM content in soils, and the need for careful management of land use systems to maintain or enhance soil fertility and productivity.

Sustainable strategies related to soil fertility, economic benefit, and environmental impact on pear orchards at the farmer scale in the Yangtze River Basin, China.

Pears are an important income source in China, and unreasonable management practices have had a negative impact on the sustainability of pear orchards. However, multi-objective synergistic strategies are unclear on a farmer scale. In this study, we quantified indicators of soil fertility (soil organic matter (SOM)), environmental impact (global warming potentials (GWP)), and economic benefit (ratio of benefit and cost (BCR)) and analysed the synergetic strategies based on survey data from 230 smallholders in the Yangtze River Basin (Shanghai City, Chongqing City, Zhejiang province, and Jiangxi province). The average SOM, GWP, and BCR were 28.9 g kg-1, 17.3 t CO2-eq ha-1, and 3.63, respectively. Furthermore, optimised solutions using the Pareto multiple-objective optimisation model can reduce the GWP by 44.6% and improve the SOM and BCR by 34.4% and 43.9%, respectively, when fertiliser N rate and density are both decreased and the ratio of organic fertiliser application is increased compared to farmer management practices. The structural equation model indicated that planting density and fertiliser N rate can directly influence GWP and indirectly increase SOM and BCR; organic fertiliser application directly affects the GWP, SOM, and BCR. Our research provides a bottom-up approach based on the farmer scale, which can improve the sustainability of pear systems, and these findings can be used as guidelines for policymakers and pear orchard managers.

Comparison of natural and technogenic soils developed on volcanic ash by Vis-NIR spectroscopy

Soil biological properties and carbon storage of three young Technosols deriving from volcanic soil of Campi Flegrei, Italy, were compared with two natural volcanic soils, sampled from two forest ecosystem sites (chestnut and Mediterranean maquis). The soil properties were examined with Vis-NIR spectroscopy and portable XRF, along the soil profile. The results showed that the average soil organic matter (SOM) amount in Technosols profiles were lower than in natural volcanic soils. The differences in SOM content between the two investigated natural volcanic soils probably resulted from their land-use histories and different plant cover. Indeed, the chestnut soil profile, which has a higher SOM content, was covered by an old chestnut grove, while the second soil profile, after a severe burning, showed a change in natural Oak forest vegetation towards the typical Mediterranean maquis. The microbial biomass can be predicted with Vis-NIR spectroscopy coupled with PLS Regression. Our results showed that 6-year natural incorporation of organic residue in Technosols significantly increased the C storage on the top soils due to the increase in C input with rhizodeposition. The forest soils show higher TOC content also with the depth that indicate a greater capacity of C storage in response to litter incorporation, whereas the Technosols accumulated about half the amount of TOC, compared to the chestnut grove. Indeed, after six years of natural vegetation, the Technosols showed a large potential to sequester C and to sustain biological activity. Overall, the emerging knowledge on Technosols make them highly promising for the regeneration of urban area.

Influence of Alfalfa (Medicago sativa L.) at Various Growing Years on the Physico-chemical Properties and Microbiology of Irrigated Desert Soils

Background: The arid area of northwest China is the main producing area of alfalfa (Medicago sativa L.) characterized by low rainfall, low vegetation coverage and vulnerable ecosystem. However, there are still very few reports about the influence of alfalfa at various growing years on the physico-chemical properties and microbial community structure in typical northwestern irrigated desert soil. This study has important theoretical and practical guiding significance for providing reference and scientific evidence for local alfalfa industry development and protection of soil ecosystem to maintain efficient alfalfa production. Methods: The rhizosphere soils of “Sanditi” alfalfa and Longdong” alfalfa were collected to assess the influence of alfalfa varieties, growing years and growth stages on the physico-chemical properties, microbial biomass and microbial community structure. There were four treatments in this experiment: 1. “Sanditi” alfalfa planted in 2015 (S1); 2. “Sanditi” alfalfa planted in 2016 (S2); 3. “Sanditi” alfalfa planted in 2017 (S3); 4. “Longdong” alfalfa planted in 2017 (LD). Result: Soil of all treatments had consistent pH at 8.11-8.45. The average soil organic matter content was 11.02 g. kg-1, 11.43 g. kg-1, 11.26 g. kg-1 and 11.20 g. kg-1 respectively for LD, S1, S2 and S3 (P greater than 0.05). The total nitrogen content was 0.451 g. kg-1, 0.468 g. kg-1, 0.437 g. kg-1 and 0.428 g. kg-1, respectively for growing seasons of LD, S1, S2 and S3 (P greater than 0.05). The results suggested that alfalfa cropping did not significantly influence the soil organic matter content in 3 years. S1 significantly changed the ratio of fungal to bacterial typical fatty acids, which was significantly higher than S3 and LD, but insignificantly higher than S2. Continuous cropping of alfalfa in arid area affected the diversity of the fungi community and reduced soil bacterial community richness and diversity.

Analysis of mechanisms of soil potassium‐holding capacity using different soils of China

AbstractSoil is the most important source of K for plants; many studies have focused on soil K status and phytoavailability. However, there is still a lack of quantitative explanation for how soils hold plant‐available K. Based on the previous study, the ability to hold plant‐available K for soil was defined as soil potassium‐holding capacity (SKHC). Therefore, this study aimed to investigate the mechanisms underlying SKHC. Fourteen soils from different sites in China were collected and the contribution of soil organic matter (SOM) and clay minerals to SKHC were analyzed based on waterlogging simulation experiments, chemical determination, and data‐fitting analysis. Results showed that SKHC mainly depended on the absorption and holding effects of exchangeable sites deriving from both SOM and clay minerals (presented as cation exchange capacity [CEC]) and nonexchangeable sites deriving from clay minerals, which contributed 14.7 and 82.5%, respectively, to SKHC, at similar soil water‐soluble K levels. After removing the SOM from the soil, the average SOM and CEC decreased from 26.0 to 7.73 g kg–1 and from 13.7 to 11.8 cmol kg–1, respectively. The SOM contributed an average of 24.5% to CEC, with the remaining 75.5% provided by clay minerals. Further analysis showed when SOM increased by 1%, CEC increased by 1.14 cmol kg–1, a relative proportion of 8.32% of the initial soil. Therefore, SKHC is mainly determined by the nonexchangeable sites of soil clay minerals, and increasing CEC by improving SOM content to enhance SKHC should be an effective but slow approach.

Soil Erodibility Analysis and Mapping in Gilgel Gibe-I Catchment, Omo-Gibe River Basin, Ethiopia

The soil erosion factor, erodibility, measures the susceptibility of soil particles to transport and detachment by erosive agents. Soil erosion and sedimentation models use soil properties and erodibility as the main input. However, in developing countries such as Ethiopia, data on soil erosion and soil-related properties are limited. For this reason, different researchers use different data sources that are adopted from a large scale and come with very different results. For this reason, the study was proposed to analyze and map the soil erodibility of the catchment area using primary data. 80 mixed soil samples were taken from the catchment with GPS coordinates and analyzed in the laboratory for soil texture class and soil organic matter. Accordingly, sandy clay loam is a dominant soil texture class covering 65% of the catchment area with 2.46% average soil organic matter, which is high in the mountainous part and lower in the lower valley of the catchment area. Most of the catchment area, which accounts for more than 78% of the area, was dominated by medium- or coarse-grained soil structure, and in the upper parts of the catchment area, 21% of the catchment area was covered with fine-grained soil structure. Similarly, 66% of the catchment area was covered with slow to moderate soil permeability, followed by slow soil permeability covering 21% of the area. Finally, the soil erodibility value of the Gilgel Gibe-I catchment was determined to be 0.046 ton h·MJ−1·mm−1 with a range of 0.032 to 0.063 ton·h·MJ−1·mm−1. In general, soils with slow permeability, high silt content, and medium- to fine-grained soil structures are the most erodible. They are conveniently separate; they tend to crust and form high drainage. Knowing this, the catchment has a moderate soil erodibility value. Thus, the study recommends evidence of land cover and the protection of arable land through suitable soil and water protection measures to improve soil permeability and soil structure.

A multi-scale eco-evolutionary model of cooperation reveals how microbial adaptation influences soil decomposition

The decomposition of soil organic matter (SOM) is a critical process in global terrestrial ecosystems. SOM decomposition is driven by micro-organisms that cooperate by secreting costly extracellular (exo-)enzymes. This raises a fundamental puzzle: the stability of microbial decomposition in spite of its evolutionary vulnerability to “cheaters”—mutant strains that reap the benefits of cooperation while paying a lower cost. Resolving this puzzle requires a multi-scale eco-evolutionary model that captures the spatio-temporal dynamics of molecule-molecule, molecule-cell, and cell-cell interactions. The analysis of such a model reveals local extinctions, microbial dispersal, and limited soil diffusivity as key factors of the evolutionary stability of microbial decomposition. At the scale of whole-ecosystem function, soil diffusivity influences the evolution of exo-enzyme production, which feeds back to the average SOM decomposition rate and stock. Microbial adaptive evolution may thus be an important factor in the response of soil carbon fluxes to global environmental change.

The combined application of organic and inorganic fertilizers increases soil organic matter and improves soil microenvironment in wheat-maize field

This long-term study used a lysimeter platform to monitor the changes of organic matter and total nitrogen content in deep soils of various fertilization treatments of wheat-maize in the Huanghuaihai area, with the goal to improve the soil environment and increase the quantity and diversity of soil microbes, and then provide a theoretical basis for synergetic improvement of production and resource utilization efficiency. The experiment included a no-N-fertilizer treatment as a control (CK) to study the following three fertilizer treatments: the exclusive application of urea (U), the exclusive application of cattle manure (M), and the combined application of organic and inorganic fertilizers (UM). These results showed that fertilization significantly increased the soil organic matter content. Compared with CK, the average soil organic matter content of each soil layer treated with M, UM, and U was increased by 25.00%, 22.81%, and 8.96% in wheat growing season, respectively. The average soil organic matter content of each soil layer in the 2-year average M, UM, and U treatments increased by 148.70%, 16.81%, and − 7.18% maize growing season, respectively. In the 2-year wheat field, in the 0–20 cm soil layer, the effects of each treatment on the total nitrogen content of the soil were significant, and compared with the CK treatment, the UM, M, and U treatments increased by 105.72%, 75.07%, and 47.13%. For maize field, compared with the CK treatment, the total nitrogen content of M, UM, and U increased by 83.34%, 50.27%, and 7.10%. The addition of organic fertilizer can increase the number and diversity of bacteria and fungi in the soil, and the application of urea significantly reduced the quantities and diversities of bacteria. The combined application of organic and inorganic fertilizers can increase the number and diversity of soil actinomycetes. The combined application of organic and inorganic fertilizers could significantly increase the number of maize roots. Compared with CK treatment, the number of roots increased by 66.67%. After long-term experimental treatment, the yields of wheat and maize were all expressed as UM > U > M > CK. The combination of organic and inorganic N fertilizers increased the soil organic matter content, improved the soil environment, increased the quantity and diversity of soil microbes, promoted the growth and development of crop roots, and increased the yield of wheat and maize. It was a reasonable fertilization method to synergistically improve the production and resource utilization efficiency.

Determining the effects of land use on soil erodibility in the Mediterranean highland regions of Turkey: a case study of the Korsulu stream watershed

Sustainable soil management can be concisely defined as using soil without impairing soil function. It has become crucial due to soil degradation, especially that caused by soil erosion, throughout the world. In this context, this study aimed to determine the erodibility and some soil properties to evaluate the actual state of soil resources in a watershed located in the Mediterranean highland of Turkey. A total of 180, 90 disturbed and 90 undisturbed, soil samples were collected from different land-use types, namely, forest, pasture, and agriculture. Erodibility and soil properties such as texture, soil organic matter, permeability, particle density, bulk density, porosity, pH, electrical conductivity, field capacity, permanent wilting point, and water holding capacity were determined. A soil erodibility map was also produced using ArcGIS software. According to the results, the average soil organic matter was 6.27%, 4.56%, and 2.05% in forest, pasture, and agriculture, respectively, and the differences among land-use types were significantly different. The average erodibility (USLE-K) value was 0.09 for forest, while it was 0.12 and 0.22 for pasture and agriculture, respectively. The difference between agriculture and forest and pasture was statistically significant, while no statistically significant difference was found between forest and pasture in the study area. Forest was included in the slightly erodible class, while pasture and agriculture were included in the moderately and highly erodible classes, respectively. The erodibility map also revealed that a major part of the study area is susceptible to erosion. The study clearly showed that sustainable soil management is a necessity, especially for agricultural lands.

Compensatory effects stabilize the functioning of Baltic brackish and salt marsh plant communities

The variation in plant species abundances and in community composition appears to be a basic ecological trigger that determines changes in community functionality and resilience. Particularly, fluctuations in abundance of dominant species might indicate major changes in resource use and ecosystem functioning. Here we use quantitative surveys of Baltic brackish and salt marsh plant communities to infer how variability in species abundances and environmental conditions influence plant assimilation rates and whether compensatory effects in abundance mask variability in functionality. The variability in species richness and assimilation rates was negatively correlated with average soil organic matter content and moisture and positively correlated with soil pH. It was independent of the variability in soil conditions. The abundances of rare species and species of intermediate abundances increased with increasing community wide assimilation rates. Our findings indicate that the strength of compensatory mechanisms that stabilise assimilation rates in salt marsh environments heavily depend on soil conditions. In the present study sites benign soil conditions stabilized assimilation rates while more stressful conditions caused synchronous fluctuations in total abundance and assimilation.

A dryland cover state mapping using catastrophe model in a spectral endmember space of OLI: a case study in Minqin, China

ABSTRACT Alternative state theory with catastrophe conceptual model is very attractive for dryland zero net land degradation (ZNLD) management, but practically challenging for mapping cover state and pathway with conventional approaches. This paper developed a methodological framework in the generic spectral endmember space (SVD, soils and substrates, vegetation, and dark surface endmembers ) of Landsat-8 OLI (operational land imager) imagery to concretize the catastrophe model of dryland cover state based on the co-development of plant and soil variables in a landscape. A cover function index (LCI) was proposed together with cover type growth form as cover variables in multi-seasonal SVD space. Furthermore, ecological site (ES) and topographic wetness index (TWI) were used as the landscape variables. The four variables as the explanatory variates, soil organic matter (SOM) as a slow response variable of dryland cover state, were explored by classification and regression trees (CART) for the state space. Using the Minqin dryland system in China as a case study, CART explored 65.9% amounts of SOM variance of 85 training samples with the four explanatory variables, and the measured SOM had liner relationship with CART predicted SOM at 0.05 significant level with the explained 39% SOM variance for 10 test samples. In the learned transparent tree, the two alternative stable states were identified correctly and validated by another soil intrinsic clay content variable independently, one was the current highest average SOM (26.757 g kg−1) of perennial forest/shrub covers, the other was the bared or sparse vegetation cover with low average SOM (≤8.518 g kg−1) as a critical SOM limit in a given ecological site type. The mean clay content of the undesirable alternative states was all below 60 g kg−1 with severe soil physical degradation, and had a clear breaking gap with other states. Moreover, the SOM-LCI plane projected the unstable states being folded in the basins of the attraction of two alternative stable states (i.e. 0.39–0.76 LCI) with relative resilience analysis of pathways. In the 10 test sites, only 1 site of the state was not predicted correctly. The results suggest the model has a level of predictability for dryland cover state and its pathway. According to the estimated states, there has 12.74% study area improved and continued to recovering the desirable states, and the study area has already net improvement at the landscape level. Therefore, without the comparable series of historical data for adaptive management, the developed framework in SVD of Landsat imagery is a cost-effective and time-saving tool, which helps managers to assess and develop appropriate management with a more spatially diversified strategy for zero net land degradation using a space-for-time substitution in an era of unprecedented change.

Mapping soil organic matter in the Baranja region (Croatia): Geological and anthropic forcing parameters

Spatial mapping of soil organic matter (SOM) and evaluation of the related natural and anthropic influencing factors are crucial to monitor the extent of degraded land and the evolution of soil functions. The objective of this work is to study the spatial distribution of SOM in a highly exploited agricultural area in the Baranja Region (Croatia). The spatially dense dataset available (4825 top-soil samples from 0 to 30 cm) allowed to produce reliable SOM maps using geostatistical interpolation kriging algorithms and to study the relationships with possible influencing factors. The interpolation has been conducted by means of two approaches. In one approach, the overall data set is considered for computing a global variogram and performing a direct interpolation of SOM values. In the second approach, the data are stratified according to two different geological and morphogenetic domains, Holocene Domain (HD) and Pleistocene Domain (PD), and a distinct geostatistical analysis is performed in each domain. The results showed that average SOM in the studied region was 2.29%, indicating a future need for adopting sustainable soil management practices in this region. SOM was significantly higher in HD (2.64%) than PD (1.97%) domain. SOM in PD generally had a much lower global variability. Global dataset analysis reveals that regional intrinsic factors prevail over local intrinsic and extrinsic factors in determining SOM spatial patterns. In contrast, the stratified approach can filter the effect of regional variability related to the main geological and geomorphological setting. The structural spatial correlation in PD is weaker than in HD, as manifested by spatial patches of low and high SOM content with smaller extension in PD with respect to HD. The strong relationships between SOM spatial patterns and geological/geomorphological factors suggest the possibility of adopting finer subdivision criteria in future research.