Soil Physical Variables Research Articles

Improving the Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing the Coupling Effect of Soil Physical Properties on the Spectrum: A Case Study in Northeast China

Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven to be a promising method for fast SOM content estimation. However, because of the neglect of the spectral response of soil physical properties, the accuracy and spatiotemporal transferability of the SOM prediction model are poor. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root-mean-square height, RMSH), and soil bulk weight (SBW), a soil spectral correction model was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPDs) exceeding 1.4 in almost all the bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after the model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost-corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, a root-mean-square error (RMSE) of 5.74 g/kg, and an RPD of 1.68. The prediction accuracy, R2 value, RMSE, and RPD of the model after the migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction model based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. This performance comparison highlighted the advantages for considering soil physical properties in regional-scale SOM predictions.

Production patterns of loblolly pine plantations along a geographic gradient in southern Brazil

Pinus taeda plantations depend directly on the supply of resources (light, water, and nutrients) available to the stand. However, studies quantifying edaphic and climatic factors in P. taeda stands are scarce. This study aimed to identify the main edaphic, climatic and stand factors that influence the productivity and growth pattern of P. taeda plantations in southern Brazil. A total of 24 stands, ranging between 8 and 10 years old were evaluated. Biometric variables such as height, diameter at breast height (DBH), stem analysis, growth rings, specific gravity, and soil chemical and physical variables were evaluated. Site index (SI), stem biomass, mean annual increment without bark (MAI) and correlations between variables were calculated. We observed that by nine years after planting, none of the plots had reached their maximum growth; and MAI, standing stem biomass and SI ranged from 15 to 35 m³ ha−1 year−1, 56–131 Mg ha−1, and 12–19 m, respectively, with higher values in Três Barras site. Stem biomass were positively related with SI (r = 0.89), and stem production was related with higher available water content (r = 0.41), warm temperatures (r = 0.61), and higher relative air humidity (r = 0.58). The study corroborates the role of nutrients, well-drained soils, mild climatic conditions, and low water deficit to explain the high yields of P. taeda in Brazil compared to other locations around the world. Based on these results, studies on fertilization, genetic material, and more frequent use of stem analysis, associated with X-ray densitometry, are recommended for P. taeda.

Estimation of soil organic carbon by combining hyperspectral and radar remote sensing to reduce coupling effects of soil surface moisture and roughness

Soil organic carbon (SOC) is important in the global carbon cycle. Accurate estimation of SOC content in cultivated land is a prerequisite for evaluating the carbon sequestration potential and quality of soils. However, existing SOC prediction studies based on hyperspectral remote sensing neglect the spectral response of the physical properties of surface soil, leading to inadequate model generalization. With the exponential growth of remote sensing data, the development of pixel-level soil spectral correction methods based on multi-source remote sensing data has become an interesting and challenging topic. This method aims to minimize the effect of soil physical properties on spectra, thus addressing the poor spatiotemporal transferability of SOC prediction models due to uncertain variations in surface soil physical properties. In this study, a soil spectral correction strategy is constructed using satellite hyperspectral image (HSI) and synthetic aperture radar (SAR) images through multi-order polynomial regression and convolutional neural networks. This strategy considers soil physical variables such as soil moisture (SM) content and root mean square height (RMSH) of soil surface roughness. The soil spectral correction model and SOC content prediction model were established using 80 soil samples collected from Site 1. Afterward, the performance and transferability of both models were verified using the remaining 25 samples from Site 1 and 50 samples from Site 2. The results showed that: 1) The effect of SM and RMSH on the soil pixel spectrum can be significantly reduced after correcting HSI using soil spectral correction strategy. The correlation coefficients between the corrected pixel spectrum and the ground-based spectrum increase by over 60 % compared with those between the original spectrum and the ground-based spectrum. 2) Soil spectral correction improves the prediction accuracy and mapping capability of HSI for SOC content, with the highest RP2 of 0.743 and RMSEP of 3.455 g/kg at Site 1. 3) Compared with the original HSI-based SOC prediction model, the soil spectral correction strategy based on multi-order polynomial and convolutional neural network reduced the RMSEP of SOC prediction results at Site 2 by 5.082 g/kg and 5.454 g/kg, and the RP2 increased by 0.390 and 0.409, respectively. 4) When predicting SOC content from raw HIS, SM and RMSH contribute to more than 60 % of the bias, with SM having a larger bias than RMSH. The findings of this study emphasize the influence of soil physical properties on SOC prediction and contribute to the existing research on SOC mapping using HSI and SAR data.

Canopy gap impacts on soil organic carbon and nutrient dynamic: a meta-analysis

Key messageThe forest canopy gaps, formed by natural or anthropogenic factors, have been found to reduce soil carbon content and increase nutrient availability. The magnitudes of these effects have been observed to increase with gap age and size, and are largely influenced by changes in temperature, precipitation, and solar radiation.ContextLocal studies have illustrated the influence of canopy gaps on the spatial heterogeneity of soil carbon and nutrients, playing a pivotal role in driving forest regeneration and succession. Nevertheless, it remains largely unknown whether the response of soil carbon and nutrient content to gap formation is consistent across forest ecosystems at global scale.AimsThe aim of this paper is to assess the homogeneity of the observed responses of soil carbon and nutrients following gap formation among a wide array of forest ecosystems and climatic regions.MethodsWe performed a meta-analysis synthesizing 2127 pairwise observations from 52 published articles to quantify the changes in in soil physical, chemical, and microbial variables resulting from gap creation in natural forests and plantations spanning tropical to boreal regions.ResultsCanopy gaps resulted in significant decrease of soil organic carbon (Corg) and microbial carbon (Cmic). The concentrations of ammonium (NH4+), nitrate (NO3−), and available phosphorus (available P) increased following gap creation. These changes mainly occurred in the growing season and in the mineral soil layer, becoming more pronounced with increasing gap age and size. The change in Corg was negatively regulated by mean annual precipitation, and was associated with the changes in Nt and Nmic. The change in NH4+ was positively regulated by mean annual temperature, and was associated with the changes in available P and oxidoreductases (Ox-EEAs). The model explaining the change in soil carbon content exhibited a higher explanatory power than the one accounting for changes in soil nutrient availability.ConclusionThe results indicated that forest canopy gaps resulted in a reduction in soil carbon content and an increase in nutrient availability. These findings contribute to a better understanding of the role of small-scale disturbances as drivers of forest ecosystem succession.

Grassland management effect on ecosystem services in the livestock system in an oxisol from the Eastern high plains of Colombia

Colombia has a livestock population of approximately 28.2 million heads, of which 20.4% is found in the eastern high plains of the Orinoquia region. The extensive beef cattle system predominates, whose diet is based on native and introduced pastures of the genus Urochloa sp. which is deficiently managed, affecting productive and reproductive indexes due to the low adoption of technology. We evaluated potential sustainable intensive systems for cattle production, which contribute to maintaining the provision of environmental services. Between 2011 and 2015 in Agrosavia’s Research Center at Carimagua (4° 37'N and 71° 19' W) environmental and animal production variables were monitored in the following systems: a) degraded pastures recovered with tillage and fertilization, b) annual crop rotation with pastures, c) forest arrangements in strips, and d) forest remnants in perimeter areas. Productive and reproductive variables were determined in animals such as weight gain, calving interval, among others while the pasture/crop productive variables included yield and forage quality. Regarding soil ecosystem services (ES) the macrofauna biodiversity, biogeochemical cycles, soil physical and chemical variables were considered. Estimation of indicators was carried out through principal components analysis for soil physical, chemical and macrofauna variables to extract the two main components that explain the variance. For climate regulation of ES, measurement of soil organic carbon (SOC) storage at a depth of 20 cm and the annual accumulated greenhouse gas (GHG) emissions were included. The systems that involved lime amendments and fertilizers increased the value in the year of application. Values in the water regulation indicator did not show significant differences among the options implemented during the years. Edaphic macrofauna biodiversity indicator value was sensitive to changes in management practices, with termites being the group with highest abundance. The indicator related to SOC was higher in the forest area compared to the pasture-crop system. Systems under the agroforestry schemes which integrated various practices such as managed and recovered pastures, crop-pasture rotation, patched areas and tree strips contributed to maintain or improve the ES, although each one of the strategies proposed provided improvement in at leas one ES.

Optimization algorithm for determining working water head of subsurface irrigation with ceramic emitters based on soil wetting patterns

Appropriate working water head of subsurface irrigation systems with ceramic emitters (SICE) is crucial for providing a stable and suitable soil water environment to promote crop production and save water resources in intensive agriculture. However, working water head optimization of SICE is hindered by the uncertainties of the soil physic properties, increasing the risk of irrational irrigation. To effectively address this challenge, this study presented an optimization algorithm for optimizing the working water head of SICE based on soil wetting patterns, considering taking into account soil physical variables and management variables, and then the reliability of the algorithm was verified by three case studies. The results of three case studies showed that the relative error between the actual irrigation amount of SICE using the optimization algorithm, and the recommended irrigation amount was lower than 5%. In three case studies, the sand-based ceramic emitter and working water head of 40 cm, 30 cm, and 70 cm were recommended parameters for SICE. The crop yield under SICE with appropriate working water head for three case studies was 109.2 × 103 kg ha−1, 5643.3 kg ha−1, and 3182.6 kg ha−1, which were 8.01%, 30.56%, and 16.90% higher than that of subsurface drip irrigation, respectively. Our findings revealed that SICE with the appropriate working water head might be an efficient subsurface irrigation method for intensification agriculture with the goal of high crop production.

Confirmation of causality of climatic and soil physical factors affecting the corn (Zea mays L.) yield for silage in the climate-soil-yield network

Abstract This study aimed to confirm the causality of climatic and soil physical factors on silage corn yield based on the climatesoil-yield network in Korea. The climatic variables were growing degree days, high temperature, low temperature, surface temperature, rainfall, relative humidity, wind speed, and sunshine duration before and after silking. The soil physical variables were the effective depth, slope and drainage class of the soil. The yield variables were total digestible nutrients, dry matter and fresh matter yields. The network was constructed using structural equations and neural network models. In the result of the network, three causalities were remarkable. First, all longitudinal climatic causality before and after the silking stages were significant. It implies that the effect of climate in the vegetative stage reaches to yield through themselves in the reproductive stage. Second, there was the causality between climatic and soil physical factors based on indirect effects. Thus, it is likely to lead to an offset between the direct and indirect effects of soil physical factors. Finally, the effects of drought and heavy rainfall were clear before and after silking stages, respectively. It indicates that stress can damage the corn yield for silage. Here, the damage caused by the drought could be recovered due to various indirect effects, while the damage caused by heavy rainfall was fatal because there was a lack of an indirect path to recover from. This study contributed to identifying how various climatic and soil physical factors can affect production in the network. Furthermore, the climate-soil-yield network for silage corn in this study will help extend the structure with various factors in future studies.

Vegetation changes in coastal wetlands of the outer estuary of the Río de la Plata as a result of anthropic-induced hydrological modifications

Samborombón Bay in Argentina is one of the largest coastal wetlands of South America. The Ajó wetlands are located to the south of Samborombón Bay and despite being protected natural areas, the land is privately owned and used for extensive cattle ranching where producers build embankments to reclaim salt marshes land and expand cattle grazing areas. The aim of this study is to identify the occurrence of embankments in the landscape of the Ajó coastal wetlands, to describe their effect on-plant communities' structure (composition and species abundances), and environmental features (soil and water physical and chemical variables). Embankments were mapped using satellite images and aerial photographs. Based on the mapping, 7 sampling areas of marshes with embankments were selected for the study of vegetation, water, and soil. Each sampling area has an embankment that interrupts a tidal channel dividing the marsh into two sectors, one connected to the tidal flow and the other disconnected. Vegetation and soil sampling were performed along a transect covering 3 relative topography levels (High salt marshes, Low salt marshes, and Mudflat-tidal channel) on each side. The results show that embankments and livestock activity modify the plant community structure and environmental features. Disconnected marshes compared to connected ones have a higher abundance of grassland species, freshwater species, salt-tolerant species, and exotic species. Surface water has a greater range of variability in electrical conductivity and the majority ions and soil salinity is higher in disconnected high marshes than in connected ones. In a context of global change, there is uncertainty about the tendency of these observed changes. In this sense, the study carried out based on the monitoring of vegetation, soils, and water is a relevant tool for detecting environmental impacts in wetlands whose hydrology has been anthropically modified.

Seasonal variation modifies the spatial patterns of soil microbial community structure and enzyme activity in a meadow steppe

Understanding how spatial heterogeneity in soil microbial community structure and enzyme activities vary seasonally, and identifying the underlying mechanisms, are crucial for predicting how soil organic matter dynamics and nutrient cycling may respond to environmental change. We examined spatial variability in microbial community structure and enzyme activity and measured associated changes in plant biomass, soil microclimate, soil nutrients and other soil characteristics for 75 sampling points across a 15 × 15 m area of a Leymus chinensis meadow steppe in northeastern China over three seasons (summer, fall and spring). We observed seasonal variation in the spatial patterns of soil microbial community structure and enzyme activity, and the plant and soil variables associated with this variation also changed over time. Specifically, the microbial community structure exhibited strong spatial dependence, with high a relative spatial variance [C / (C + C0) ≥ 0.9] in summer, whereas it was more homogeneous in spring and fall. In summer, the spatial patterns of plants, soil dissolved inorganic N and dissolved organic C were the most significant predictors of spatial variation in microbial community structure. In fall, a mix of plant and soil physical explanatory variables explained 22–46 % of the spatial variation in microbial community structure. Meanwhile, only soil physical variables (soil bulk density and electrical conductivity) were strongly associated with spatial variation in microbial community structure in spring. The spatial links between microbial community structure and enzyme activity were reshaped throughout the growing season. We observed strong spatial links between soil microbial community structure and enzyme activity in spring and fall. In summer, the association between soil enzyme activities and microbial biomass was decoupled, indicating that microbial biomass showed higher turnover than soil enzymes at this time. Overall, our results reveal the drivers of spatial variation in soil microbial community structure and enzyme activity shift seasonally, highlighting the value of seasonal sampling to accurately estimate the heterogeneity and complexity of ecosystem-level processes in grasslands.

Mangrove distribution and afforestation potential in the Red Sea

Mangrove ecosystems represent one of the most effective natural environments for fixing and storing carbon (C). Mangroves also offer significant co-benefits, serving as nurseries for marine species, providing nutrients and food to support marine ecosystems, and stabilizing coastlines from erosion and extreme events. Given these considerations, mangrove afforestation and associated C sequestration has gained considerable attention as a nature-based solution to climate adaptation (e.g., protect against more frequent storm surges) and mitigation (e.g. offsetting other C-producing activities). To advance our understanding and description of these important ecosystems, we leverage Landsat-8 and Sentinel-2 satellite data to provide a current assessment of mangrove extent within the Red Sea region and also explore the effect of spatial resolution on mapping accuracy. We establish that Sentinel-2 provides a more precise spatial record of extent and subsequently use these data together with a maximum entropy (MaxEnt) modeling approach to: i) map the distribution of Red Sea mangrove systems, and ii) identify potential areas for future afforestation. From these current and potential mangrove distribution maps, we then estimate the carbon sequestration rate for the Red Sea (as well as for each bordering country) using a meta-analysis of sequestration values surveyed from the available literature. For the mangrove classification, we obtained mapping accuracies of 98 %, with a total Red Sea mangrove extent estimated at approximately 175 km2. Based on the MaxEnt approach, which used soil physical and environmental variables to identify the key factors limiting mangrove growth and distribution, an area of nearly 410 km2 was identified for potential mangrove afforestation expansion. The factors constraining the potential distribution of mangroves were related to soil physical properties, likely reflecting the low sediment load and limited nutrient input of the Red Sea. The current rate of carbon sequestration was calculated as 1034.09 ± 180.53 Mg C yr−1, and the potential sequestration rate as 2424.49 ± 423.26 Mg C yr−1. While our results confirm the maintenance of a positive trend in mangrove growth over the last few decades, they also provide the upper bounds on above ground carbon sequestration potential for the Red Sea mangroves.

Do Spatially Structured Soil Variables Influence the Plant Diversity in Tabuk Arid Region, Saudi Arabia?

Plant diversity is affected by spatial variables as well as soil physical and chemical variables. In this study, plant species and soil variables were investigated in five sites of Tabuk Province (Saudi Arabia), namely Aldesah, Alzetah, Alawz, Harra and Sharma, to understand if the spatially structured soil variables (pH, electric conductivity (EC), soil texture, calcium, potassium, phosphorus, phosphate, total organic matter (OM), bicarbonate and sodium) influence the plant diversity. A total of 163 plant species belong to 41 families and 124 genera were reported from the 5 sites. Diversity indices including the species richness (alpha), evenness, Brillouin, Menhinick, Margalef, equitability and estimated Chao-1 were significantly different among the studied sites with pronounced high values in Sharma and Aldesah. The highest value of beta diversity was reported in Aldesah (0.253) followed by Sharma (0.171). According to the principal coordinates of neighbourhood matrix (PCNM) analysis, 11 positive spatial vectors (variables) were found. However, after running the forward selection procedures (using 2 stopping criteria), only 3 spatial vectors were retained (PCNM 1 (adj–R2 = 0.043, F = 5.201, p = 0.004), PCNM 2 (adj–R2 = 0.027, F = 3.97, p = 0.006) and PCNM 3 (adj–R2 = 0.019, F = 3.36, p = 0.007)). The linear models between the selected spatial variables (PCNM vectors) and soil variables were produced to investigate their spatial structure. In the first model, the first PCNM 1 axis showed significant relationship with pH and potassium (adj–R2 = 0.175, p = 0.046). In the second model, the second PCNM 2 axis had a significant relationship with OM and sodium (adj–R2 = 0.561, p < 0.001). Lastly, sodium was the only factor significantly correlated with the third PCNM 3 axis (adj–R2 = 0.365, p = 0.002). In conclusion, the spatially structured variables of soil did not show strong influence on plant diversity except pH and potassium, which were correlated with PCNM 1, OM and sodium, which were correlated with PCNM 2, and sodium, which was correlated with PCNM 3.

Predicting Soil Water Content on Rainfed Maize through Aerial Thermal Imaging

Restrictions on soil water supply can dramatically reduce crop yields by affecting the growth and development of plants. For this reason, screening tools that can detect crop water stress early have been long investigated, with canopy temperature (CT) being widely used for this purpose. In this study, we investigated the relationship between canopy temperature retrieved from unmanned aerial vehicles (UAV) based thermal imagery with soil and plant attributes, using a rainfed maize field as the area of study. The flight mission was conducted during the late vegetative stage and at solar noon, when a considerable soil water deficit was detected according to the soil water balance model used. While the images were being taken, soil sampling was conducted to determine the soil water content across the field. The sampling results demonstrated the spatial variability of soil water status, with soil volumetric water content (SVWC) presenting 10.4% of variation and values close to the permanent wilting point (PWP), reflecting CT readings that ranged from 32.8 to 40.6 °C among the sampling locations. Although CT correlated well with many of the physical attributes of soil that are related to water dynamics, the simple linear regression between CT and soil water content variables yielded coefficients of determination (R2) = 0.42, indicating that CT alone might not be sufficient to predict soil water status. Nonetheless, when CT was combined with some soil physical attributes in a multiple linear regression, the prediction capacity was significantly increased, achieving an R2 value = 0.88. This result indicates the potential use of CT along with certain soil physical variables to predict crop water status, making it a useful tool for studies exploring the spatial variability of in-season drought stress.

Modifications induced in soil physicochemical properties by repeated fire for different fuel load treatment in a West African savanna-woodland

Fire is known to stimulate growth of savanna vegetation, promote species’ diversity, and regulate tree and grass balance. However, the timing and frequency at which savannas are burned and the fuel properties can affect residual soil nutrient content and, ultimately, productivity. The objective of this study is to characterize changes in soil physical and chemical properties in a savanna-woodland subject to different fuel load treatments of burning. To characterize the soil physical and chemical variables, particle size distribution, total carbon, pH, phosphorous content and nitrogen were analyzed for three different soil layers. The results indicated no difference in soil texture between the different topsoil profiles before and after the burning event. Further, there was greater nutrient enrichment of the upper soil layer (p=0.014) due to ash deposition. The spatial patterns of soil temperature during the studied experimental fires could affect soil properties, resulting in new spatial pattern of soil nutrients. The findings of the present study have practical implications for savanna management. The current implementation of prescribed early fires should be continued with due consideration of the burning and fuel properties to avoid detrimental effects of intense fire on soil layers. Key words: Early fire, fire behavior, fire temperature, soil properties, Savanna ecosystem.

Yields, Fruit Quality, and Water Use in a Jalapeno Pepper and Tomatoes under Open Field and High-tunnel Production Systems in the Texas High Plains

The Texas High Plains has a semi-arid, hot, windy climate that features high evapotranspiration (ET) demands for crop production. Irrigation is essential for vegetable production in the region, but it is constrained by depleting groundwater from the Ogallala Aquifer. High-tunnel (HT) production systems may reduce irrigation water demand and protect crops from severe weather events (e.g., hail, high wind, freezing) common to the region. The objective of this study was to compare yields, fruit quality, crop water use, and crop water use efficiency (WUE) of jalapeno pepper (Capsicum annuum L.) and tomatoes (Solanum lycopersicum L.) in HT and open field (OF) production systems. We hypothesized that the protection from dry and high winds by HT would improve yields and quality of fruits and reduce water use of peppers and tomatoes. During the 2018 and 2019 growing seasons, peppers and tomatoes were transplanted on two HT plots and two identical OF plots. Plastic mulch was used in combination with a surface drip irrigation system. Micrometeorological variables (incoming solar irradiance, air temperature, relative humidity, and wind speed) and soil physical variables (soil temperature and volumetric soil water) were measured. Air temperatures were significantly higher during the daytime, and wind speed and light intensity were significantly lower in HT compared with OF. Despite the lower light intensity, yields were greater in HT compared with OF. The fruits grown in HT did not show significant differences in chemical quality attributes, such as ascorbic acid and lycopene contents, compared with those grown in OF. Because of protection from dry, high winds, plants in HT required less total water over the growing seasons compared with OF, resulting in increased WUE. The 2018 and 2019 data showed that HT production is advantageous as compared to conventional OF production in terms of increased WUE and severe weather risk mitigation for high-value vegetable production in the Texas High Plains.

A new dataset of soil carbon and nitrogen stocks and profiles from an instrumented Greenlandic fen designed to evaluate land-surface models

Abstract. Arctic and boreal peatlands play a major role in the global carbon (C) cycle. They are particularly efficient at sequestering carbon because their high water content limits decomposition rates to levels below their net primary productivity. Their future in a climate-change context is quite uncertain in terms of carbon emissions and carbon sequestration. Nuuk fen is a well-instrumented Greenlandic fen with monitoring of soil physical variables and greenhouse gas fluxes (CH4 and CO2) and is of particular interest for testing and validating land-surface models. But knowledge of soil carbon stocks and profiles is missing. This is a crucial shortcoming for a complete evaluation of models, as soil carbon is one of the primary drivers of CH4 and CO2 soil emissions. To address this issue, we measured, for the first time, soil carbon and nitrogen density, profiles and stocks in the Nuuk peatland (64∘07′51′′ N, 51∘23′10′′ W), colocated with the greenhouse gas measurements. Measurements were made along two transects, 60 and 90 m long and with a horizontal resolution of 5 m and a vertical resolution of 5 to 10 cm, using a 4 cm diameter gouge auger. A total of 135 soil samples were analyzed. Soil carbon density varied between 6.2 and 160.2 kg C m−3 with a mean value of 50.2 kg C m−3. Mean soil nitrogen density was 2.37 kg N m−3. Mean soil carbon and nitrogen stocks are 36.3 kg C m−2 and 1.7 kg N m−2. These new data are in the range of those encountered in other arctic peatlands. This new dataset, one of very few in Greenland, can contribute to further development of joint modeling of greenhouse gas emissions and soil carbon and nitrogen in land-surface models. The dataset is open-access and available at https://doi.org/10.1594/PANGAEA.909899 (Morel et al., 2019b).

Soil abiotic factors associated with Meloidogyne spp. and Pratylenchus spp. populations in sugarcane

Summary Meloidogyne spp. and Pratylenchus spp. are the most damaging plant-parasitic nematodes to sugarcane and the knowledge of soil characteristics that influence the occurrence of these pathogens can be useful in their management. With the objective to investigate the relationships between soil variables and populations of Meloidogyne spp. and Pratylenchus spp. in sugarcane fields, root-zone soil and root samples were collected from 20 farms located in 16 municipalities in Alagoas state, Brazil. The multivariate regression tree technique was used to model the population density’s response variables for Meloidogyne spp. and Pratylenchus spp. in the root-zone soil and the roots according to sugarcane cultivars, soil organic matter, and soil chemical and physical variables. The results showed the existence of associations between soil factors, sugarcane cultivars and populations of nematodes. Root samples from soils with K > 37.79 mmolc dm−3, Al < 5.93 mmolc dm−3 and sugarcane ‘RB92579’, ‘SP753046’, ‘SP813250’ and ‘SP921631’ showed the lowest Pratylenchus spp. population densities. However, the lowest densities in the root-zone were found in soils with sum of bases > 1.91 mmolc dm−3, Ca:Mg ratio < 1:2 and soil organic matter < 10.7 g dm−3. The lowest population densities of Meloidogyne spp. in roots were associated with sugarcane ‘RB92579’, ‘RB93509’, ‘RB98710’, ‘SP791011’ and ‘SP921631’ and soil aggregate mean diam. > 0.48 mm, while root-zone densities were minimum in soils with P > 28.59 mg dm−3. This information should support further studies to investigate the effect of each variable on populations of Pratylenchus and Meloidogyne.

Grinding and spectra replication often improves mid‐DRIFTS predictions of soil properties

AbstractThere is an increased interest in using diffuse reflectance infrared Fourier transform spectroscopy in the mid‐infrared region (mid‐DRIFTS) for high‐throughput prediction of soil properties, but basic methodological factors toward this end have yet to be thoroughly vetted. This study aimed to determine how the combined effects of soil grinding (sieved to <2.0 mm and finely ground to <0.5 mm) and sample replication (single or multiple soil subsamples, using one‐to‐four replicates) affect the spectral quality and predictive performance (accuracy) of automated plate‐based mid‐DRIFTS for soil analysis. We evaluated chemometric prediction performance of soil physical, chemical, and biological variables (clay, sand, pH, total organic C, and permanganate‐oxidizable C [POXC]) in 397 soils from the U.S. Midwest. Sieved soils (<2.0 mm) increased the overall spectral variability compared to finely ground soils (<0.5 mm) and led to a distinct wavenumber importance allocation in support vector machine models. These spectral changes degraded prediction performance of <2.0 mm samples when compared to <0.5 mm samples. The number of spectral replicates had a smaller effect on spectral properties, but impacted prediction accuracies of soil properties. In general, prediction outcomes improved with four spectral replicates either within a single soil subsample or across different soil subsamples. Our data collectively suggest that soil particle‐size reduction to <0.5 mm and collecting multiple spectra improve mid‐DRIFTS predictions. Recommendations to optimize high‐throughput mid‐DRIFTS should consider the tradeoffs between prediction accuracy and the effort needed to prepare soil samples and acquire spectra.

Harvester ants as ecological engineers for Mediterranean grassland restoration: Impacts on soil and vegetation

Although not widely used, ecosystem engineers represent a promising and sustainable tool in nature-based ecosystem management and restoration. In grassland ecosystems, a few invertebrates that engineer soils have been identified as key species regulating soil nutrients and plant communities' diversity and dynamics. Here, we assessed the role of the harvester ant Messor barbarus, an ecological engineer, in a Mediterranean dry grassland under restoration by characterising its nest environment, particularly the soil and vegetation. We found profound differences in soil physical and chemical variables and plant community structure between nests and ant-free patches in the restored grassland. Messor barbarus has improved soil fertility, driven the seed bank towards the reference grassland and significantly increased plant biomass, species richness and micro-local-heterogeneity. As biological filters, M. barbarus has driven plant communities towards a new trajectory in the restored site. Ant patches are characterised by mesotrophic species, whereas ant-free patches are dominated by species characteristic of compacted soils. They have accelerated the ecological recovery of Mediterranean dry grassland plants by directly and indirectly facilitating their re-establishment. These results illustrate the potential key role of ants as ecological engineers for the conservation and restoration of Mediterranean grasslands.

IMPACT OF USED MOTOR OIL ON THE SOIL QUALITIES OF ORJI MECHANIC VILLAGE OWERRI, NIGERIA

The study investigated the effects of used motor oil on soil physical and chemical variables and their particle size distribution. Two (2) replicate soil samples were collected from three radial coordinates 50 31.114N 70 02.703E, 50 31.165 N 70 2.670E and 5023.35N 6059.176E respectively from the auto-mechanic village and agricultural land which serves as control; at depths of 0-15cm and 15-30cm. Standard methods were adopted both for field and laboratory analysis, utilizing such instruments as Atomic Absorption Spectrometer (model FS 240 Varian), Jewniary, digital pH meter, Refractometer (model E- Line R.ATC- ), lovibond instrument model Cm-21 and mercury-in-built thermometer. Twenty three (23) different soils parameters were analyzed comprising total organic carbon, heavy metals, soil texture and particle size (clay, sand and silt) organic carbon, exchangeable cations (Na, Ca and Mg), conductivity, salinity, nutrients (N, P, K), bulk density and organic matter. The result indicated a variation at the mechanic site and control point. The pH of the auto-mechanic soils were acidic with the range of 4.3- 5.0 in comparison with the control point which had a mean value of 5.6. Heavy metals had higher values in auto mechanic soils than in the control and their concentration decreased in this trend, Cu 0.940> Hg 0.211> Ni 0.093> Zn 0.051> Pb 0.021> Cd 0.015 > Ar 0.003; Hg 1.385> Cu 0.853> Ni 0.079> Zn 0.041> Cd 0.04> Pb 0.034> Ar 0.004. This showed that disposal of used motor oil on the soil affected the soil quality negatively. It is therefore; recommended that there should be a legislation to stop the indiscriminate dumping of spent oil and lubricants on the surface of the soil at Orji Mechanic village without treatment prior to disposal.

PENGARUH DOSIS PUPUK KANDANG SAPI TERHADAP SIFAT FISIK DAN KIMIA TANAH PADA TANAMAN KACANG TANAH (Arachis Hypogaea L.)

The Effect of Dosage Organik Fertilizer on Physical Properties and Land Chemistry in Soan Beans (Arachis Hypogaea L.). This Research Aims To: Determine The Effect Of Cow Manure Does On Soil Physical Properties Of Peanut Plants. Knowing The Effect Of Cow Manure Does On Soil Chemical Properties In Peanut Plants. To Know Optimum Dose Of Cow Manure Can Affect Physical And Chemical Properties Of Soil Peanut Plant.
 The treatments used in this study were Pk0 (0 Ton Ha-1 or without Cow Manure), Pk1 (10 Ton Ha-1 Cow Manure), Pk2 (20 Ton Ha-1 Cow Manure), Pk3 (30 Ton Ha- 1 Cow Manure, Pk4 (40 Ton Ha-1 Cow Manure). The Observing Variables in This Study are Soil Physical Properties Variables, which include Bulk Density (G Cm-3) and Total Soil Pore Space (%), And Soil Chemical Properties Variables That Cover N-Total Soil, P-Soil Available, K-Available Soil, C-Organic Soil, And Ph Soil.
 Research Results Show That Giving Cow Cage Fertilizer in Peanut Plants In General Has an Effect of Improving Soil Physical Properties in the Form of Increasing Soil Volume Weight and Total Soil Pore Space, Giving Cow Cage Fertilizer in Peanut Plants Generally Affecting Improving Soil Chemical Properties in the Form of Increasing N-Total , P-Available, K-Available, C-Organic And Soil Ph As Well As The Optimum Dose Of Cow Manure That Can Improve The Physical And Chemical Properties Of Soil In The Best Peanut Plants Are 40 Tons / Ha.