Regional Soil Research Articles

Ecological risk assessment of microplastics in agricultural soils of Coimbatore region, India

Although plastics provide numerous conveniences for human life, concerns about ecosystems and human life are rising tremendously due to increased plastic production and consumption. The ubiquitous presence and undeniable distribution of microplastics (MPs) in agricultural usage created a major risk concern for soil ecology and human health. The ecological risk assessment of microplastics in agricultural sites in the Coimbatore region of South India has been evaluated using three ecological risk indices: Pollution Hazard Index (PHI), Pollution Load Index (PLI), and Potential Ecological Risk Index (PERI). Based on the concentration of hazardous MPs (Polyethylene, polystyrene, polyacrylonitrile, polyacrylamide, polyacrylamide, polypropylene, polyvinyl chloride) in sampling sites, risk assessment indicated the major risk level in inorganic fertilizers applied site. Meanwhile, PLI indicated that all the sites were in category I with low pollution load in the region. However, the hazard scores of polymers increased the PERI values and exhibited their values in the extremely dangerous category. Since the models for evaluating MPs risk in agricultural sites are not yet developed, this calculation is cautiously calculated and further research could gradually add base reference data for understanding the distribution, ecological risk, and trophic transfer of MPs.

Estimating the CSLE Biological Conservation Measures’ B-Factor Using Google Earth’s Engine

The biological conservation measures factor (B) in the Chinese Soil loss Equation (CSLE) model is one of the main components in evaluating soil erosion, and the accurate calculation of the B-factor at the regional scale is fundamental in predicting regional soil erosion and dynamic changes. In this study, we developed an optimal computational procedure for estimating and mapping the B-factor in the Google Earth Engine (GEE) cloud computing environment using multiple data sources through data suitability assessment and image fusion. Taking the Yanhe River Basin in the Loess Plateau of China as an example, we evaluated the availability of daily precipitation data (CHIRPS, ERA5, and PERSIANN-CDR data) against the data at national meteorological stations. We estimated the B-factor from Sentinel-2 data and proposed a new method, namely the trend migration method, to patch the missing values in Sentinel-2 data using three other remote sensing data (MOD09GA, Landsat 7, and Landsat 8). We then calculated and mapped the B-factor in the Yanhe River Basin based on rainfall erosivity, vegetation coverage, and land use types. The results show that the ERA5 precipitation dataset outperforms the CHIRPS and PERSIANN-CDR data in estimating rainfall and rainfall erosivity, and it can be utilized as an alternative data source for meteorological stations in soil erosion modeling. Compared to the harmonic analysis of time series (HANTS), the trend migration method proposed in this study is more suitable for patching the missing parts of Sentinel-2 data. The restored high-resolution Sentinel-2 data fit nicely with the 10 m resolution land use data, enhancing the B-factor calculation accuracy at local and region scales. The B-factor computation procedure developed in this study is applicable to various river basin and regional scales for soil erosion monitoring.

Long-term liming mitigates the positive responses of soil carbon mineralization to warming and labile carbon input

Liming, as a common amelioration practice worldwide, has the potential to alleviate soil acidification and ensure crop production. However, the impacts of long-term liming on the temperature sensitivity (Q10) of soil organic carbon (SOC) mineralization and its response to labile C input remain unclear. To fill the knowledge gap, soil samples were collected from a long-term (∼10 years) field trial with unlimed and limed (CaO) plots. These soil samples were incubated at 15 °C and 25 °C for 42 days, amended without and with 13C-labeled glucose. Results showed that compared to the unlimed soil (3.6–8.6 mg C g−1 SOC), liming increased SOC mineralization (6.1–11.2 mg C g−1 SOC). However, liming significantly mitigated the positive response of SOC mineralization to warming, resulting in a lower Q10. Long-term liming increased bacterial richness and Shannon diversity as well as their response to warming which were associated with the decreased Q10. Furthermore, the decreased Q10 due to liming was attributed to the decreased response of bacterial oligotrophs/copiotrophs ratio, β-glucosidase and xylosidase activities to warming. Labile C addition had a strong impact on Q10 in the unlimed soil, but only a marginal influence in the limed soil. Overall, our research highlights that acidification amelioration by long-term liming has the potential to alleviate the positive response of SOC mineralization to warming and labile C input, thereby facilitating SOC stability in agroecosystems, especially for acidic soils in subtropical regions.

Impact of coconut-fiber biochar on lead translocation, accumulation, and detoxification mechanisms in a soil–rice system under elevated lead stress

Biochar, an environmentally friendly material, was found to passivate lead (Pb) in contaminated soil effectively. This study utilized spectroscopic investigations and partial least squares path modeling (PLS-PM) analysis to examine the impact of coconut-fiber biochar (CFB) on the translocation, accumulation, and detoxification mechanisms of Pb in soil-rice systems. The results demonstrated a significant decrease (p < 0.05) in bioavailable Pb concentration in paddy soils with CFB amendment, as well as reduced Pb concentrations in rice roots, shoots, and brown rice. Synchrotron-based micro X-ray fluorescence analyses revealed that CFB application inhibited the migration of Pb to the rhizospheric soil region, leading to reduced Pb uptake by rice roots. Additionally, the CFB treatment decreased Pb concentrations in the cellular protoplasm of both roots and shoots, and enhanced the activity of antioxidant enzymes in rice plants, improving their Pb stress tolerance. PLS-PM analyses quantified the effects of CFB on the accumulation and detoxification pathways of Pb in the soil–rice system. Understanding how biochar influences the immobilization and detoxification of Pb in soil–rice systems could provide valuable insights for strategically using biochar to address hazardous elements in complex agricultural settings.

Disentangling slope‐scale spatial variability of saturated hydraulic conductivity in the black soil region of northeast China using noise‐assisted multivariate empirical mode decomposition

AbstractReliable estimates of saturated hydraulic conductivity (Ks) are usually difficult to obtain, as Ks is regulated by a variety of soil processes acting at different spatial scales that may obscure each other's impacts. We hypothesized that these scale‐specific relationships could be well characterized with the aid of the noise‐assisted multivariate empirical mode decomposition (NA‐MEMD), thereby serving as a solid foundation for an accurate Ks prediction. The objective was to evaluate whether the incorporation of NA‐MEMD could improve the estimation of Ks based on the multiscale associations it unraveled. On a typical slope transect of 860 m in the black soil region of northeast China, Ks, mollic epipedon thickness, bulk density, soil organic carbon content, total (ϕ) and effective porosities (ϕe), and particle size distribution were investigated at every 20 m. Prior to NA‐MEMD, Ks was most strongly correlated with ϕe, and the linear regression models based on ϕe solely were satisfactory for Ks estimation at the scale of investigation. Adding other predictors significantly improved Ks prediction in calibration, but impaired it in validation. Upon decomposition by NA‐MEMD, Ks was found to be significantly associated with each attribute at two scales of oscillation at least. Summing up the estimates of each Ks component derived from the properties at the equivalent oscillation scales, the results outperformed the traditional multiple linear regressions made at the investigation scale, when the same sets of predictors were used. The application of NA‐MEMD, moreover, could save the tedious measurements of ϕe and ϕ. Excluding these two porosity‐related properties, Ks estimates obtained by incorporating NA‐MEMD were statistically similar or even better than those involving them before NA‐MEMD. These findings demonstrate the great potential of NA‐MEMD in untangling scale‐dependent relationships of Ks with various processes and hold important implications for future estimations of Ks and other hydraulic properties in the black soil region of northeast China and similar regions.

Heavy Metal Contamination of Gray Forest Soil in Oilseed Agrocenoses using Mineral Fertilizers

Background: The purpose of the research is to study the features of contamination of dark gray forest soils with heavy metals and to establish the yield of oil cabbage crops as a result of the use of various levels of mineral nutrition in the conditions of the Non-black earth zone of Russia. Methods: The experiments were carried out on the dark gray forest soils of Ryazan region in crops of spring rape, white mustard and gray mustard (Sarepta). Soil sampling to determine HMs was carried out before the experiment. Determination of heavy metals was carried out by atomic absorption spectrometry using a semi-quantitative spectral method on a spectrophotometer in the laboratory of the State Station of the Agrochemical Service “Ryazanskaya” and the laboratories of the university. Result: The studied types and combinations of mineral fertilizers had effect on the growth and development of plants. The effectiveness of the use of mineral nutrition doses proves its high efficiency in studies with oilseed crops in the conditions of the Non-Chernozem zone of Russia. The highest yields, for spring rapeseed (19.3 dt ha-1), for gray mustard (13.7 dt ha-1) and white mustard (12.2 dt ha-1), were observed in the variants with the action of N90-120P60K60.

Soil health reduction following the conversion of primary vegetation covers in a semi-arid environment

A degraded forest is the outcome of a degradation process that has adverse effects on ecosystem functions and services. This phenomenon results in alterations of soil physicochemical and biological properties, which serve as valuable indicators for assessing soil health that has been recognized as a crucial component of soil quality. For several decades, the conversion of forested areas into rangeland has been documented in specific regions of the world. There is a widespread lack of global understanding regarding the lasting consequences of land degradation on soil health indicators. The present study aims to investigate the impact of forest degradation on soil health indicators in a mountainous semi-arid region located in northern Iran. The study area was predominantly forested, but due to human activities over the past 30 years, it has been transformed into three distinct land uses: forest, forest-rangeland ecotones and rangeland. In each of these land covers, a total of 20 litter (O-horizon) and 20 soil (from two depths of 0–15 and 15–30 cm) samples were collected in the summer (August 2022) season. According to our results, the highest litter thickness, P and Mg were in forest ecosystem, the lowest in rangeland ecosystem. The findings indicated that following the conversion of forest to rangeland, there was a decrease in soil aggregate stability, porosity, soil organic matter, POC, PON, NH4+, NO3− and nutrient levels, while soil bulk density increased. The forest ecosystem showed notably higher C and N stocks (45 and 5.21 Mg ha−1) in comparison to the rangeland (38 and 3.32 Mg ha−1) ecosystem. In addition, P, K, Ca, and Mg exhibited elevated levels within the total root of the forest ecosystem (2.12, 1.23, 0.71, and 0.38 %, respectively), whereas the lower values (1.29, 1.01, 0.43, and 0.23 %, respectively) were found in the rangeland ecosystem. Following the shift of land cover from forest to rangeland, soil fauna, microflora populations, soil enzymes and microbial activities decreased (about 1–2 times higher in the forestland). This research emphasizes the urgent need to advance sustainable management practices to prevent further degradation and promote the implementation of restoration or rehabilitation techniques in degraded forests. Despite being conducted in a semi-arid region situated in northern Iran, the findings of this study have considerable value for the sustainable management of soil and land conservation in various other semi-arid regions around the world.

The impact of the military actions on the soil ecosystem of Mykolaiv region

A study has been conducted on the changes occurring in the soil cover of the Mykolaiv region due to military activities, aiming to develop strategies for protecting ecosystems and their revitalization. Military actions lead to significant soil pollution with inorganic chemicals and petroleum products and have a toxic impact on biota. Explosions and bombardments result in soil cover degradation and erosion. Additionally, the destruction of vegetation and disruption of the biological cycle threaten soil fertility. Ignoring such processes could have devastating consequences. In certain areas affected by military influence, doubts or concerns arise regarding land resource eco-safety and its appropriate consumer use. Radioecological monitoring, due to military actions, is particularly relevant. Environmental security issues are becoming increasingly significant, and preserving the ecological integrity of soil is becoming a crucial priority. One of the critical areas of interaction between humans and the environment is agriculture, which plays a vital role in forming the food base and utilizing natural resources. Developing agroecological systems is becoming increasingly important as they influence the biosphere and require systemic analysis to ensure ecological sustainability. Agroecosystems reflect all aspects of human-environment interaction, assessing its ecological consequences as a crucial element of sustainable development. The research aimed to identify signs of soil phytotoxicity in the context of ecocide caused by military actions. Preserving soils is a prerequisite for the region's sustainable development and food security. Thus, a study aimed at identifying various impacts on the soils of the Mykolaiv region is of great importance for developing strategies for conserving and restoring soils that meet modern needs.

Effects of brackish water irrigation and biochar application on fertility, enzyme activity, and winter wheat yield in coastal saline‐alkali soils

AbstractSoil salinization is a common form of land degradation in coastal regions. The Yellow River Delta is a typical coastal saline‐alkali soils distribution region, with saline‐alkali soils accounting for nearly half of the total area of the region. In view of the lack of fresh water in the region, the use of brackish water and biochar made from agricultural waste to improve coastal saline‐alkali soils has a good application prospect, but there are fewer studies on this at present. To explore water conservation, fertilization, and yield enhancement solutions appropriate for saline‐alkali soils in the Yellow River Delta region, this study focused on investigating the impact of biochar addition on the fertility, enzyme activity, and winter wheat yield in saline‐alkali soils under brackish water irrigation conditions. The research was conducted through outdoor potting experiments, utilizing a local moderately saline‐alkali soil as the subject of study. The study design established three different irrigation water mineralization levels (0, 2, and 4 g L−1) during the research. Two types of biochar, wheat straw biochar (WB) and corn straw biochar (CB) were used with four different applications (0, 5, 10, and 20 t ha−1). There were 21 treatments; each replicated three times. The research findings indicated that brackish water irrigation adversely affected soil fertility and soil alkaline phosphatase activity and soil sucrase activity but increased soil urease activity and soil peroxidase activity. In contrast, biochar application improved soil fertility and enzyme activities. The soil fertility and enzyme activities of K2Y10 treatment was higher than that of CK (fresh water, no biochar application) except for soil total nitrogen, which increased by 5.5%–71.2%. While brackish water irrigation decreased winter wheat yield. Biochar application increased winter wheat yield, with the K2Y10 treatment showing the highest yield under brackish water irrigation conditions, and increased by 17.7% compared to CK. In conclusion, the study recommends a combination of water‐saving measures, fertilizer cultivation, and yield increase strategies to improve saline‐alkali soils in the Yellow River Delta region effectively. Specifically, irrigating with 2 g L−1 brackish water and application 10 t ha−1 of CB demonstrated greater efficacy in addressing the challenges of saline‐alkali soils in the region.

Evaluation of Okra Varieties under Sub-humid Plains of South-East Rajasthan for Morphological and Phenological Traits

Vegetable crop okra is well known for its Indian vegetable export and household consumption, contribution and thus should be explored for cultivation under various agro climatic zones. Enhanced production of this crop is only possible via improved and high yielding cultivars in comparison to other cultivars grown at same time or under same condition. Hadoti region of South-East Rajasthan, can serve a okra production in better way if the productive varieties can be screened for this region, which is lacking till date. Hence an experiment is made to recommend a variety, which performs better under South-East climatic conditions of Rajasthan. The present study was conducted in the summer season during 2022 and 2023 at the Instructional Farm, Department of Horticulture, School of Agricultural Sciences, Career Point University, Kota, Rajasthan. 12 varieties of okra viz. Parbhani Kranti, Parbhani Bhindi, Arka Anamika, Shahiba, Shan, No. 55, Harita, Jhilmil, No. 64, Sonal, Shakti, and Punjab Selection were evaluated in Randomized Block Design, with three replication. Analysis of variance revealed that all varieties differed significantly for all the traits. The results concluded that, variation for earliness, growth, yield and quality parameters as well. These results showed presence of significant varietal effect. However, the present study demonstrated the superiority of Arka Anamika over other varieties in terms of maximum mean plant height (89.07 cm), number of leaves per plant (25.64), number of branches per plant (13.88), leaf length (19.03 cm), leaf width (25.32 cm) and leaf area (250.04 cm2) while minimum mean days taken to flowering opening to fruit picking (5.51 DAS) and days taken to first picking (47.16 DAS) was recorded in variety Punjab Selection under soil and climatic condition of Hadoti region of Rajasthan.

UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF.

Secondary salinization is a crucial constraint on agricultural progress in arid regions. The specific mulching irrigation technique not only exacerbates secondary salinization but also complicates field-scale soil salinity monitoring. UAV hyperspectral remote sensing offers a monitoring method that is high-precision, high-efficiency, and short-cycle. In this study, UAV hyperspectral images were used to derive one-dimensional, textural, and three-dimensional feature variables using Competitive adaptive reweighted sampling (CARS), Gray-Level Co-occurrence Matrix (GLCM), Boruta Feature Selection (Boruta), and Brightness-Color-Index (BCI) with Fractional-order differentiation (FOD) processing. Additionally, three modeling strategies were developed (Strategy 1 involves constructing the model solely with the 20 single-band variable inputs screened by the CARS algorithm. In Strategy 2, 25 texture features augment Strategy 1, resulting in 45 feature variables for model construction. Strategy 3, building upon Strategy 2, incorporates six triple-band indices, totaling 51 variables used in the model's construction) and integrated with the Seagull Optimization Algorithm for Random Forest (SOA-RF) models to predict soil electrical conductivity (EC) and delineate spatial distribution. The results demonstrated that fractional order differentiation highlights spectral features in noisy spectra, and different orders of differentiation reveal different hidden information. The correlation between soil EC and spectra varies with the order. 1.9th order differentiation is proved to be the best order for constructing one-dimensional indices; although the addition of texture features slightly improves the accuracy of the model, the integration of the three-waveband indices significantly improves the accuracy of the estimation, with an R2 of 0.9476. In contrast to the conventional RF model, the SOA-RF algorithm optimizes its parameters thereby significantly improving the accuracy and model stability. The optimal soil salinity prediction model proposed in this study can accurately, non-invasively and rapidly identify excessive salt accumulation in drip irrigation under membrane. It is of great significance to improve the growing conditions of cotton, increase the cotton yield, and promote the sustainable development of Xinjiang's agricultural economy, and also provides a reference for the prevention and control of regional soil salinization.

Comparative effectiveness of the use of mineral and organic fertilizers in the cultivation of onions in an annual crop

We conducted a comparative study of the effect of mineral and organic fertilizers on the yield and quality of 5 medium-ripened domestic onion varieties in an annual crop on the alluvial meadow soil of the Moscow region. Laboratory and field experiments were carried out by the Department of Agriculture and Agrochemistry of the VNIIO branch of the FNCO in 2020–2022 on sufficiently cultivated medium-loamy alluvial meadow soils of the central part of the Moskvoretsky floodplain. In the experiment with the 5th varieties and the 3rd variants of the mineral fertilizer system, the commercial yield of onions was 53.0 t/ha, whereas with the use of an organic fertilizer system – 58.4 t/ha. Accordingly, the increase in yield relative to the control without fertilizers in the first case was equal to 106, in the second – 117%, marketability – 93 and 95%. It has been established that when determining the need of plants for nutrients during the growing season, it is preferable to use methods of leaf diagnostics. On average, for 5 varieties of onion, the yield increase, according to the results of plant diagnostics, turned out to be 13% more than in variants with NPK doses calculated according to soil diagnostics. Biochemical quality indicators for all onion varieties and all fertilizer variants did not differ statistically significantly from the control. The quality of onion products practically did not depend on the applied fertilizer systems. The organic fertilizer system provided the best supply of nutrients to onions in the phase of the beginning of bulb formation, which was crucial for obtaining the greatest yield in an annual crop. On average, for 3 variants of the organic system, the soil contained 2.34 mg/100 g of mineral nitrogen in the phase of the beginning of bulb formation, whereas with the use of the mineral system – 1.65 mg/100 g, or 42% less.

Inversion of large-scale citrus soil moisture using multi-temporal Sentinel-1 and Landsat-8 data

Soil moisture is a significant variable in agricultural study and precision irrigation decision-making. It determines the soil water availability for plants, directly influencing plant growth, yield and quality. Owing to the variations in regional microclimate, landform difference, soil type and vegetation coverage, the soil moisture has strong spatial-temporal heterogeneity on a large regional scale. Micro-wave remote sensing can be used to invert soil moisture based on the dielectric constant under different weather conditions, while optical remote sensing utilizes spectral characteristics to estimate the physiological and ecological information of vegetation. In this study, two new hybrid models (ACO-RF and SSA-RF) were structured by optimizing the standalone random forest (RF) based on the ant colony optimization algorithm (ACO) and sparrow search algorithm (SSA), and six input combinations based on the multi-temporal Sentinel-1 and Landsat-8 remote sensing data from different sensors (optical, thermal and radar sensors) were used. The standalone RF, ACO-RF, and SSA-RF models with different combinations of inputs were employed to predict the soil moisture at different depths (5 cm, 10 cm, 20 cm, 40 cm) in a large-scale drip-irrigated citrus orchard. The results showed that the ACO-RF and SSA-RF outperformed the standalone RF model in terms of prediction accuracy at a depth of 0–40 cm, with R2 of 0.800–0.921 and 0.504–0.798, RRMSE of 7.214–16.284% and 11.124–22.214%, respectively. In the hybrid model, the ACO-RF model had better prediction accuracy than the SSA-RF model, with R2 of 0.805–0.921 and 0.800–0.911, RRMSE of 7.214–13.244% and 8.274–16.284%, respectively. At depths of 5 cm, 10 cm and 20 cm, the inversion accuracy of the model with microwave inputs was higher than that with multispectral inputs, with R2 of 0.556–0.888 and 0.541–0.886, RRMSE of 9.015–19.544% and 9.124–22.214%, respectively. However, at a depth of 40 cm, the inversion accuracy of the model with multispectral inputs was higher than that with microwave inputs, with R2 of 0.532–0.841 and 0.508–0.831, RRMSE of 9.124–21.021% and 9.142–21.214%, respectively. The model with multispectral, thermal, and microwave inputs exhibited the highest accuracy in predicting soil moisture, with R2 of 0.635–0.921, RRMSE of 7.214−18.564%, respectively. Therefore, the ACO-RF with multisource remote sensing data is recommended to predict the soil moisture in the drip-irrigated citrus orchard. This approach can provide data support for making intelligent irrigation decisions on a large-scale grid land lots.

Responses of deep soil moisture to direct rainfall and groundwater in the red soil critical zone: A four-stage pattern

Deep soil moisture (DSM) plays a critical role in vadose zone hydrological processes. However, the recharge processes and their underlying mechanisms remain unclear due to the scarcity of comprehensive deep hydrological data, especially in regions characterized by active interactions between surface and subsurface hydrological processes. To address this knowledge gap, we performed an event-based study using a four-year hydrological monitoring dataset, encompassing rainfall, profile soil moisture, and groundwater along a hillslope at the Red Soil Critical Zone Observatory (CZO) in China. A total of 226 rainfall events were classified into four distinct types (I-IV) using a k-means clustering algorithm. The changes in DSM and groundwater table (GWT) were dependent on the rainfall type. The GWT (0.57–0.97) played a greater role in contributing to the DSM than the direct rainfall (0.48–0.64) based on a structural equation model, which was more pronounced from upper slope to foot slope. DSM recharge pattern can be described by a four-stage conceptual model, as indicated by the relationship between DSM and GWT: a slow and linear increase (stage 1), a small plateau (stage 2), a rapid linear rise (stage 3), and finally a great plateau (stage 4). DSM at the small and great plateau were closely related with the field capacity and saturation, respectively. Rainfall type, vadose zone features, and soil physical properties jointly shaped DSM recharge patterns. These findings can offer valuable insights into understanding the deep hydrological processes of the red soil region as well as regions with similar hydroclimatic conditions.

Regional Soil Patterns as Indicators of Late Cenozoic Change in the Critical Zone: A Baseline Synthesis for the Landscapes of Peninsular India

Regolith across the South Indian shield has not previously been mapped. Here we provide a diagnosis of directional and lasting climate change from humid to semi-arid since the late Cenozoic based on evidence provided by mosaics of 1) residual, 2) colluvial and 3) alluvial soils across 700,000 km2 of southern peninsular India. Results are inferred from a systematic geomorphological and palaeoenvironmental interpretation of 1:250,000 scale legacy soil maps at order to subgroup level, complemented by field surveys and controls of soil parent material—i.e., regolith. The inventory highlights two generations of residual soils: 1) deep Lixisols, hosting low-activity clays and large iron hydroxide concentrations indicative of humid conditions in the geological past; and 2) shallow Luvisols containing high-activity clays and large stocks of exchangeable bases, indicative of drier conditions compatible with the modern climate. Where still present, the relict Lixisol inliers straddle drainage divides and are in the final stages of being thinned or fully stripped by headward stream erosion. They are being replaced by the Luvisols over shallow weathering fronts. Colluvial and alluvial soils, including widespread Vertisols, are used as tools for detecting and mapping different generations of Quaternary flood deposits: fluvial terraces, coastal fan-deltas, and shallow upland palaeolakes. In a region mostly devoid of carbonate rock outcrops, the widely distributed pool of soils hosting abundant accumulations of pedogenic CaCO3 also reveals the magnitude of silicate bedrock weathering as a process for generating secondary calcium carbonate in the rock cycle, thereby highlighting an under-appreciated contribution to inorganic carbon sequestration in the global carbon cycle. The results and maps produced provide exploration tools for future, more systematic and coordinated investigations of the nature and chronology of Quaternary deposits in peninsular India. This includes assessing their potential for hosting different generations of prehistoric archaeological remains.

Chromobacterium violaceum Infection in a Domestic Shorthaired Cat with Dog Bite Wounds

Chromobacterium violaceum is a motile, facultative anaerobe and Gram-negative Bacillus, a common inhabitant of soil and water in tropical and subtropical regions. Chromobacterium violaceum is recognised as an opportunistic pathogen of humans and animals. Although rare, a few chromobacteriosis cases were reported in humans and several species of animals. However, there is no published report of Chromobacterium infection in cats. This report describes a C. violaceum infection in a cat with a history of non-healing dog bite wounds. The bacterial culture of the wounds revealed pure growth of C. violaceum on both aerobic and anaerobic cultures. The infection in this cat was successfully treated after changing the antibiotic to enrofloxacin based on an antibiotic sensitivity test (AST). This case demonstrates the importance of culture and AST tests in non-healing wounds. Chromobacteriosis can be included in the differentials, especially if wounds are contaminated by soil or stagnant water.

Enhancing Soybean and Maize Yields through Improved Nitrogen and Soil Water Use Efficiencies: A 40-Year Study on the Impact of Farmyard Manure Amendment in Northeast China.

The combined application of manure and chemical fertilizers has been recognized as a critical factor driving significant changes in crop yield and nutrient use efficiency, holding the potential to optimize agricultural management to achieve high yields. In this 40-year study, we investigated the effect of manure amendment on soybean and maize yields, water and nitrogen use efficiencies (WUE and NUE), and water and mineral N storage at 0-100 cm soil depths from 2017 to 2018 to explore the optimization of fertilization management strategies for soybean and maize production in Northeast China. To elucidate the impact of chemical fertilizers and manure, twelve treatments-control (CK); single N fertilizer at a low rate (N1) and that at a high rate (N2); N1, phosphorus (P), and potassium (K) fertilizer (N1PK); manure alone at 13.5 and 27 t ha-1 (M1 and M2); and those combined with N, P, or K fertilizer (M1N1, M1N2, and M1N1PK and M2N1, M2N2, and M2N1PK)-were selected and studied. The results showed that long-term amendment with manure significantly increased crop biomass and yield in the soybean-maize-maize rotation system. Combining with manure increased the WUE, the partial factor productivity of N fertilizer (PFPN), and N physiological efficiency (PEN) in both the soybean and maize seasons; conserved soil water (mainly at 40-60 cm); and increased soil N retention (in the upper 60 cm layer), which reduced the risk of N leaching, with a better effect being observed after the application of 13.5 t ha-1 manure. These results provide insight into the potential of using fertilization management strategies that include amendment with 13.5 t ha-1 manure in combination with N, P, and K fertilizer in the maize season and only chemical fertilizer in the soybean season, as these results indicate that such strategies can achieve high yields and be used to implement agricultural sustainable development in brown soil regions in Northeast China.

Pollution indices of selected metals in tea (Camellia sinensis L.) growing soils of the Upper Assam region divulge a non-trifling menace of National Highway

The study investigated the influence of a National Highway (NH) traversing tea estates (TEs) on heavy metal (HM) contamination in the top soils of Upper Assam, India. The dispersion and accumulation of six HMs, viz. cadmium (Cd), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), and zinc (Zn), within tea-growing soils were assessed using diverse indices: contamination factor (CF), degree of contamination (DC), enrichment factor (EF), geo-accumulation index (Igeo), modified degree of contamination (MDC), Nemerow pollution index (PINemerow), pollution load index (PLI), potential ecological risk factor (Eri), and potential ecological risk index (RI). The order of HM prevalence was Fe > Mn > Zn > Ni > Cu > Cd. Elevated Cd levels near the NH prompted immediate attention, while Cd and Zn showed moderate pollution in CF, EF, and RI. The remaining metals posed minimal individual risk (Eri< 40), resulting in an overall contamination range of “nil to shallow,” signifying slight contamination from the studied metals. From MDC values for investigated metals, it was found to be “zero to very low degree of contamination” at all locations except the vicinity of NH. Soil pollution, as determined by PLI, indicated unpolluted soils in both districts, yet PINemerow values indicated slight pollution. The statistical analysis revealed that there is a significant decrease in most of the indices of HM as the distance from NH increases. The application of multivariate statistical techniques namely Principal Component Analysis and Cluster Analysis showed the presence of three distinct homogenous groups of distances based on different indices. This investigation underscores NH-associated anthropogenic effects on TE soil quality due to HM deposition, warranting proactive mitigation measures.

High Spatiotemporal Remote Sensing Images Reveal Spatial Heterogeneity Details of Soil Organic Matter

Soil is the foundation of sustainable agricultural development. Soil organic matter (SOM) is a key indicator for characterizing soil degradation, and remote sensing has been applied in SOM prediction. However, the differences in SOM prediction from different remote sensing data and the ability to combine multi-source and multi-phase remote sensing data for SOM prediction urgently need to be explored. The following research employed Landsat-8, Sentinel-2, and Gaofen-6 satellite data, utilizing a random forest algorithm to establish a SOM prediction model. It aimed to explore the variations in SOM prediction capabilities among these satellites in typical black soil regions. Additionally, the study involved creating multi-phase synthetic images for SOM prediction using Landsat-8 and Sentinel-2 images captured during three years of bare soil periods. Finally, the research examined the ability to combine three satellites to construct high spatiotemporal remote sensing images for SOM prediction. The results showed that (1) using Landsat-8 and Sentinel-2 to extract the principal components of the three-year bare soil period to construct the multi-phase synthetic image for SOM prediction, higher prediction accuracies can be obtained compared with the single-phase images. (2) The highest accuracy can be obtained using multi-phase synthetic images and high spatial resolution images to construct high spatiotemporal remote sensing images and perform SOM prediction (R2 is 0.65, RMSE is 0.67%, MAE is 0.42%). (3) Simultaneously, high spatiotemporal remote sensing images can reach 2 m spatial resolution to reveal the spatial heterogeneity of SOM. The causes of SOM spatial anomalies can be determined after analysis combined with soil degradation information. In subsequent research, SOM prediction should focus more on multi-sensor collaborative prediction.

Arsenic in a Karstic Paddy Soil with a High Geochemical Background in Guangxi, China: Its Bioavailability and Controlling Factors

Arsenic (As) is of widespread concern, as its elevated contents in soil and water have a serious impact on the ecological environment and human health. Soils in karst regions are characterized by a high geochemical background of As. However, the bioavailability of As in paddy soils and the potential risk of As transfer from the soil to rice remain unclear. In this study, 305 paired soil–rice samples were collected from karst regions in Guangxi, China, in order to examine the controlling variables and As bioavailability in the soil–rice system. According to this study, the karst region’s paddy soil had higher As concentrations than the non-karst region’s paddy soil. The As concentration in the rice grains was low, with only 0.62% of the rice samples exceeding the permissible value of inorganic As (0.2 mg/kg). Arsenic in the karstic paddy soils existed mainly in the residual fraction, while the water-soluble and exchangeable fractions, which are readily absorbed by rice, accounted for a relatively small proportion. The high content but low bioavailability of As in the karstic paddy soil was mostly attributed to the abundant Fe–Mn nodules, which contributed 64.45% of the As content in the soil. Within the Fe–Mn nodules, As was primarily bound to Fe-(oxyhydr)oxides, which could be released into the paddy soil under certain reduction conditions via the reductive dissolution of Fe-(oxyhydr)oxides. Under the natural pH conditions of the karstic paddy soil (pH 4.9–8.38), the leaching of As was almost negligible, and As could be steadily retained within the Fe–Mn nodules. However, extremely acidic or alkaline conditions promoted the release of As from the Fe–Mn nodules.