Site-specific Management Strategies Research Articles

Short-term geomorphic adjustments of bars in the Elbe, a large regulated river in Czechia

Gravel and sandy bars constitute critical components of river channel morphology, yet their morphodynamics in large, heavily regulated rivers during periods without significant flows remain poorly understood. This study investigates changes in surface heterogeneity and sediment sizes through a two-year field monitoring program, focusing on the frontal, central, and distal sections of four bars along the Elbe River in Czechia. Despite the absence of high-flow events reaching at least a one-year recurrence interval, observable changes in surface heterogeneity and sediment sizes were noted across all bars. However, the changes did not follow a uniform pattern; individual bars and their sections exhibited varying degrees of surface sediment coarsening or fining, alongside increases or decreases in surface heterogeneity. These findings highlight the necessity for site-specific management strategies for individual bars within such human-impacted rivers, recognizing their value as ecological hotspots. Furthermore, the methodology presented in this study may serve as a blueprint for the cost-effective monitoring of bar dynamics in channelized river sections.

Survey on Optimal Crop Prediction using Soil and Weather Analysis

This survey paper provides a comprehensive overview of methodologies, technologies, and approaches for analyzing soil environments to optimize crop production. It covers traditional soil sampling, advanced techniques like remote sensing, and real-time monitoring using sensor networks. The integration of sensor data with IoT and machine learning for decision support is explored. Soil health assessment and site-specific management strategies enabled by precision agriculture are emphasized. Challenges such as data integration and sensor accuracy are addressed, along with future research directions including low-cost sensing solutions and improved data analytics. Overall, the paper serves as a valuable resource for optimizing crop production sustainably, contributing to efficient agricultural practices in a changing climate

Quantitative analysis of soil degradation in response to land use change in the Guinea savanna zone of Ghana

Soil degradation (SD) poses a significant challenge to tropical agricultural production systems due to its role in depleting soil nutrients. Recently, SD has threatened population growth by causing hunger and poverty among smallholder farmers in the Guinea savanna agro-ecological zone of Ghana. However, SD cannot be directly measured. Instead, assessment and modelling efforts rely on biological and physico-chemical indicators. This study sought to: (i) select a minimum dataset (MDS) using a multivariate statistical technique; (ii) identify SD processes, and evaluate the extent of degradation; and (iii) suggest strategies to monitor SD in the study area. Soil samples were collected using the Land Degradation Surveillance Framework (LDSF), specifically designed for sub-plot level (1000 m2) sampling. This scheme aligns well with the typical farm structure of smallholder farmers (<10 acres). Soil samples, taken from a depth of 0–20 cm, were analyzed to assess their physico-chemical properties. Principal component analysis (PCA) identified seven key SD processes: soil texture, pH, organic carbon (OC), total nitrogen (TN), available phosphorus (Av. P), base saturation (BS) and Effective Cation Exchange Capacity (ECEC). These identified processes lead to the loss of OC, deterioration of soil texture, reduction in soil nutrients, and increased soil acidity. There was a strong positive correlation (r = 0.70, p<0.05) between soil physical and chemical properties. PC1 explained 26.86% of the total variance, followed by PC2 at 15.31%, PC3 at 12.46%, and PC4 at 9.98%. Also, PC5, PC6, and PC7 accounted for 8.49%, 7.25% and 1.42% of the variance, respectively. The land-use systems we identified significantly affected the concentration of soil nutrients (p<0.001). In our computed Composite Soil Degradation Index (CSDI), CEC emerged as the most significant SD indicator with a weight of 0.328, followed by Si:CL ratio at 0.187. The results indicate that soil management activities should focus on increasing organic matter. Soil nutrient depletion primarily occurs through several processes. Land clearing and burning of organic materials during land preparation are primary causes. Furthermore, a total crop removal during harvest, inadequate manure application, and subsequent leaching and erosion aggravates SD, leading to nutrient-depleted soils. We conclude that the loss of nutrients, specifically CEC, OC, Ex. K and Av. P is the primary contributor to chemical SD. Therefore, implementing integrated soil fertility management (ISFM) strategies, such as applying organic (e.g., animal-based manures, green manure, crop residues, compost) and inorganic fertilizers (e.g., NPK + micronutrients) is crucial for increasing soil nutrient availability, enhancing SOM content, and promoting carbon sequestration in these farming systems. This study establishes a baseline for comparing and evaluating the effectiveness of land management interventions at the landscape level in northern Ghana. Consequently, soil nutrient management should be tailored to site-specific conditions, focusing on increasing the efficiency of N, P, K and micronutrient uptake by plants. Adopting such site-specific management strategies could significantly contribute to the sustainable intensification of agricultural production systems in the Guinea Savanna agro-ecological zone of Ghana.

Population Dynamics of a Declining White-Tailed Deer Population in the Southern Appalachian Region of the United States

Although generally abundant, white-tailed deer (Odocoileus virginianus) populations in the southeastern United States have recently experienced several localized declines attributed to reduced fawn recruitment following the establishment of coyotes (Canis latrans). The Southern Appalachians is a mountainous region suggested to be experiencing white-tailed deer declines, as harvest numbers and hunter success rates have substantially decreased in northern Georgia since 1979. Low fawn survival (16%) was also recently documented in the Chattahoochee National Forest (CNF) in northern Georgia, necessitating further examination. We radio-collared 14 yearling and 45 adult female white-tailed deer along with 71 fawns during 2018–2020 in the CNF to estimate field-based vital rates (i.e., survival and fecundity) and parameterize stage-structured population models. We projected population growth rates (λ) over 10 years to evaluate the current rate of decline and various other management scenarios. Our results indicated that the observed population would decline by an average of 4.0% annually (λ = 0.960) under current conditions. Only scenarios including antlerless harvest restrictions in addition to improved fawn survival resulted in positive growth (λ = 1.019, 1.085), suggesting these measures are likely necessary for population recovery in the region. This approach can be applied by wildlife managers to inform site-specific management strategies.

An analysis of long-term Everglades Stormwater Treatment Areas performance using structural equation models

Phosphorus (P) retention in Stormwater Treatment Areas (STAs) is influenced by various external and internal variables, making it necessary to employ multivariate techniques to understand the complex relationships among them. This study used 14 years of monthly data from four STAs (STA-1E, -1W, −2, and − 3/4) to explore the factors influencing long-term STA performance and function related to P retention. Structural Equation Model (SEM) with predictive variables of inflow total P (TP), total nitrogen (TN) and calcium (Ca) concentration, hydraulic loading rate (HLR), and water pH and temperature was applied to evaluate similarities and differences among individual STA performance. The SEM results suggested an improvement in operational performance of STAs over time. All models explained a substantial portion of the variation in retention rate of TP (R2 > 0.63), but only a small portion of the variation in outflow TP concentration (R2 < 0.24). Notable differences were observed among the four STAs. The relationship between inflow and outflow TP concentration differed among the STAs. Furthermore, inflow TN concentration was positively correlated with the TP retention rate in STA-1W, and outflow TP concentration in STA-2, while the co-precipitation of P with Ca likely played a critical role in STA-3/4. These findings highlight the complexity of P removal in STAs and emphasize the need for site-specific management strategies. Further studies should consider incorporating long-term observation of key vegetation and soil variables (e.g. vegetation coverage and soil TP content, etc.) to inform more effective approaches to optimize P removal and enhance the performance of STAs in water treatment and ecosystem restoration efforts.

Development of a Site-Specific Management Strategy for Managing Root-Knot Nematodes Affecting Tomatoes in Kenya

Development of a Site-Specific Management Strategy for Managing Root-Knot Nematodes Affecting Tomatoes in Kenya

Improving the mapping of coastal invasive species using UAV imagery and deep learning

ABSTRACT Spartina anglica C. E. Hubb. is an invasive species of saltmarsh and mudflats in Ireland. It spreads quickly and can form extensive meadows, which can lead to the extinction of native plant and animal species. Traditional ground-based field surveys would be expensive for the scale of monitoring needed to formulate site-specific management strategies. Advanced mapping techniques, such as Unoccupied Aerial Vehicle (UAV) remote sensing and deep learning (DL), offer an opportunity to automatically map invasive species’ occurrence. However, training a DL model requires lots of labelled data which can be prohibitively difficult and expensive to obtain. We implemented a DL semantic segmentation technique on UAV imagery of Spartina-invaded habitats and tested a range of hyperparameters—model architectures, encoder backbones and input image patch sizes—to determine an effective segmentation network structure. We also investigated applying data augmentation and pseudo-labelling techniques to increase the size of the labelled dataset. U-Net architecture with Inception-v3 as its backbone trained on 128 × 128-pixel image patches offered the best model performance: mean Intersection-Over-Union (mIOU) score = 0.832. The model trained on the combined augmented and pseudo-labelled data achieved an mIOU score of 0.712 on a test dataset, while there was a decrease of 0.158 in model performance when only the original labelled data were used. This result suggests the potential for using these techniques in creating more robust models. The proposed methodology demonstrates that the combination of UAV imagery and deep learning could be a promising tool for mapping the distribution of invasive plant species.

Identification and delineation of potential castor growing areas in different Agro-eco sub regions of India

Castor (Ricinus communis L.) is one of the oldest cultivated crops in the world. Castor oil is having globalimportance specially in chemical industry because it is the only commercial source of a hydroxylated fatty acid.Castor also hastremendousfuture potential as an industrial oilseed crop because of its high seed oil content, unique fatty acid composition, potentially high oil yields, and ability to be grown under drought and saline conditions. Soils and prevailing climatic conditions across different Agro-Eco Sub Regions (AESR) of India were assessed and matched with crop requirement criteria to identify the potential castor growing areas. Potential castor growing areas comprise favourable soil depth (deep soils) with longer Length of Growing Period (LGP). Among the traditional castor growing areas, AESR 5.2, 7.3, 8.2 and 8.3 have potential to grow castor whereas in non-traditional castor growing areas, AESR6.3, 6.2, 13.1, 15.4 and 17.1 are most potential for expanding castor cultivation. Relative yield and spread index of castor indicated that most of the potential areasidentified in different AESRsfall in zone II and III, need special attention to increase productivity and area expansion programme. Site specific management strategies like selection of suitable variety matching with LGP and proper adoption of nutrient management will overcome the limitations observed in these zones.

Fuzzy K-Means and Principal Component Analysis for Classifying Soil Properties for Efficient Farm Management and Maintaining Soil Health

Soil health indicators can guide soil management-related decisions for sustainable agriculture. Principle component (PC) analysis and the fuzzy k-means technique, also known as continuous classification, are useful for designing site-specific management strategies for varying soil properties within a contiguous area. The objective of this study was to identify appropriate soil health indicators as well as to create contiguous areas for precision management of a large diverse farm from measured soil properties. From the farm, which is sited on Armijo–Harkey soil, 286 loose and intact samples were obtained, representing a depth of 15 cm from the soil surface. Statistical analysis showed that several data were log-normally distributed. PCA analysis showed that the first three PCs explained 73% of the variation with PC1, consisting of factors related to the soil’s physical condition; PC2, containing factors related to chemical properties; and PC3, including factors related to macro- and micro-porosities. Minimizing the fuzziness performance index (FPI) and modified partition entropy (MPE) delineated four management classes. The membership class maps showed that the contrasting management strategies could be developed for the four management zones to achieve yield goals while conserving scarce surface water for irrigation, increasing water use efficiency, and decreasing nitrate leaching in arid and semi-arid irrigated farmlands.

Potential of Precision Farming Technologies for Eco-Friendly Agriculture

Precision farming technologies have the potential to revolutionize agriculture by promoting eco-friendly practices. The review explores the potential advantages of precision farming technologies in achieving sustainable and environmentally conscious agriculture. The application of precision farming techniques allows for the targeted use of resources such as water, fertilizers, and pesticides, minimizing waste and reducing environmental impact. By incorporating advanced technologies like GPS, GIS, remote sensing, and data analytics, precision farming enables farmers to make informed decisions based on real-time data, leading to improved crop management and increased productivity. Additionally, precision farming promotes the adoption of site-specific management strategies, taking into account the unique characteristics of each field, optimizing resource allocation, and reducing the risk of environmental pollution. This review highlights the importance of precision farming in achieving eco-friendly agriculture and calls for further research and adoption of these technologies to promote sustainable farming practices.

Coral taxonomy and local stressors drive bleaching prevalence across the Hawaiian Archipelago in 2019.

The Hawaiian Archipelago experienced a moderate bleaching event in 2019-the third major bleaching event over a 6-year period to impact the islands. In response, the Hawai'i Coral Bleaching Collaborative (HCBC) conducted 2,177 coral bleaching surveys across the Hawaiian Archipelago. The HCBC was established to coordinate bleaching monitoring efforts across the state between academic institutions, non-governmental organizations, and governmental agencies to facilitate data sharing and provide management recommendations. In 2019, the goals of this unique partnership were to: 1) assess the spatial and temporal patterns of thermal stress; 2) examine taxa-level patterns in bleaching susceptibility; 3) quantify spatial variation in bleaching extent; 4) compare 2019 patterns to those of prior bleaching events; 5) identify predictors of bleaching in 2019; and 6) explore site-specific management strategies to mitigate future bleaching events. Both acute thermal stress and bleaching in 2019 were less severe overall compared to the last major marine heatwave events in 2014 and 2015. Bleaching observed was highly site- and taxon-specific, driven by the susceptibility of remaining coral assemblages whose structure was likely shaped by previous bleaching and subsequent mortality. A suite of environmental and anthropogenic predictors was significantly correlated with observed bleaching in 2019. Acute environmental stressors, such as temperature and surface light, were equally important as previous conditions (e.g. historical thermal stress and historical bleaching) in accounting for variation in bleaching during the 2019 event. We found little evidence for acclimation by reefs to thermal stress in the main Hawaiian Islands. Moreover, our findings illustrate how detrimental effects of local anthropogenic stressors, such as tourism and urban run-off, may be exacerbated under high thermal stress. In light of the forecasted increase in severity and frequency of bleaching events, future mitigation of both local and global stressors is a high priority for the future of corals in Hawai'i.

Soil Organic Matter Storage in Irrigated Tsitsikamma Dairy Farms with Minimum Tilled Pasture Mixtures: Case Studies

In recent years, pasture production changed from conventional tilled single pastures to minimum tilled mixed pastures in the Tsitsikamma region, South Africa. However, storage of soil organic matter (SOM) under minimum tilled mixed pastures is not yet quantified. This study evaluated SOM indices in the upper 60 cm soil of six-year-old mixed pastures in the Upper (UT) and Lower (LT) Tsitsikamma regions. Soil samples were collected at 0–15, 15–30, 30–45, and 45–60 cm soil layers of five farms (F1, F2, F3, F4, and F5) treated with different rates of fertilizer (NPK) alone and in combination with dairy effluent (DE) and/or poultry manure (PM). Results of this study indicated that there were no significant differences in bulk density, total nitrogen (N), and rate of potentially mineralizable N (PMN) between farms in the UT region. In the LT region, NPK, DE, and PM combinations improved soil C accumulation relative to the soil application of NPK. Higher C/N ratios in the LT region suggested adequate C for microbial energy and maintenance. Integrating manure into minimum tilled pasture mixtures as a replacement for synthetic fertilizers seems to be a feasible option to promote SOM storage, but remains only feasible by applying site-specific management strategies.

Geochemical evaluation of groundwater and suitability of groundwater quality for irrigation purpose in an agricultural region of South India

The objective of the present study was to evaluate the geochemical processes controlling the groundwater chemistry and also to assess the groundwater quality suitability criteria for irrigation purposes. An agricultural region of Telangana, South India, was selected for the present study. A total of 100 groundwater samples were collected and estimated for pH, electrical conductivity (EC), total dissolved solids (TDS), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), bicarbonate ({text{HCO}}_{3}^{ - }), chloride (Cl−), sulfate ({text{SO}}_{4}^{2 - }), nitrate ({text{NO}}_{3}^{ - }), and fluoride (F−). The groundwater was characterized by mostly alkaline conditions with a dominance of Na+ and {text{HCO}}_{3}^{ - } ions, indicating the prevailing conditions of weathering and dissolution of silicate minerals. The various geochemical signatures such as Na+ vs Cl−, Ca2+ + Mg2+ vs {text{HCO}}_{3}^{ - }, Ca2+ + Mg2+ vs {text{HCO}}_{3}^{ - } + {text{SO}}_{4}^{2 - }, {text{HCO}}_{3}^{ - } vs Cl− + {text{SO}}_{4}^{2 - }, Ca2+ + Mg2+ vs total cations, and Ca2+ + Mg2+ vs Na+ + K+ and the saturation indices with respect to calcite, halite, and gypsum suggest obviously the dominant conditions of carbonate weathering associated with the reverse ion exchange and evaporation processes as the geogenic factors. The linear trend of TDS vs {text{NO}}_{3}^{ - } + Cl−/{text{HCO}}_{3}^{ - } clearly specifies the influence of non-geogenic sources on the aquifer system. These are the important contributors to the variation in the groundwater chemistry. However, the impact of the geogenic source is masking the influence of the anthropogenic source in some areas of the present study region. According to the salinity vs sodium adsorption ratio, residual sodium carbonate, magnesium ratio, and Kelly ratio, 99.9%, 7.06%, 63.07%, and 51.27% of the total study region come under the unsuitable categories for irrigation purposes, respectively. Therefore, the findings of this study recommended some site-specific appropriate management strategies for the safe supply of groundwater for proper crop growth and consequently for sustainable development of the rural environment.

Spatially explicit estimation of soil organic carbon stock of an estuarine mangrove wetland of eastern India using elemental analysis and very-fine resolution satellite data

BackgroundThis study estimated the total soil organic C (SOC) stock of the wetland influence zone of Bichitrapur mangroves in eastern India in a spatially explicit manner. Both spatial and vertical distribution of SOC densities with respect to land use/land cover (LULC) pattern were assessed. Subsequently, some site-specific management strategies were forwarded towards enhancement of C sequestration potential.MethodsThe changing patterns of LULC within the wetland influence zone of the site were analyzed using Landsat TM (30 m) and Pleiades-1A (2 m) imageries from 1988 to 2018. Point-specific SOC measurement was done using samples taken from four core-depth intervals (viz. D1: 0–20 cm, D2: 20–40 cm, D3: 40–70 cm, D4: 70–100 cm) at 89 locations belonging to different LULC categories. Spatial interpolation was applied on this point-based data to produce SOC density and stock models as a whole and at all core-depths. Relationships between SOC density, core-depth and present LULC were evaluated through multivariate statistical analyses.ResultsThe LULC transformations during last three decades suggested the gradual growth of mangrove plantations as well as agricultural and aquacultural activities. Most amount of SOC was concentrated in D1 (37.17%) followed by D3 (26.51%), while D4 had the lowest (10.87%). The highest mean SOC density was observed in the dense mangrove patches (248.92 Mg ha−1) and the lowest mean was in the Casuarina plantations (2.78 Mg ha−1). Here, Spline method emerged as the best-fit interpolation technique to model SOC data (R2 = 0.74) and estimated total SOC stock of the entire wetland influence zone as 169,569.40 Mg and the grand mean as 125.56 Mg ha−1. Overall, LULC was inferred as a major determinant of SOC dynamics with a statistically significant effect (p < 0.001), whereas no such inference could be drawn for soil core-depth.ConclusionsThe C sequestration potential of sites such as the present one could be increased with appropriate zone-wise plantation strategies, restriction on the land conversion to aquaculture and promotion of ecotourism. Periodic monitoring through integration of geospatial techniques and elemental analyses would be immensely beneficial in this regard.Graphic

Management Zones in Pastures Based on Soil Apparent Electrical Conductivity and Altitude: NDVI, Soil and Biomass Sampling Validation

The intensification of the Montado mixed ecosystem (agro–silvo–pastoral) is a current endeavor in the context of promoting the sustainability of extensive livestock production in the Mediterranean region. Increased pasture productivity and extensive animal production involves the use of technologies to monitor spatial variability and to implement differentiated management of pasture grazing, fertilization or soil amendment. An intermediate step should lead to the identification and demarcation of areas with similar characteristics (soil and/or crop development), known as homogeneous management zones (HMZ) to implement site-specific management strategies. In this study, soil apparent electrical conductivity (ECa) and altimetry surveys were carried out in six experimental pasture fields with a non-contact electromagnetic induction sensor (EM38) associated with a Global Navigation Satellite System (GNSS) receiver. These ECa and topographic maps were used in geostatistical analyses for designing and establishing final classification maps with three HMZ (less, intermediate and more potential). The normalized difference vegetation index (NDVI), obtained from a proximal optical sensor, and soil and biomass sampling were used to validate these HMZ. From a practical perspective, these HMZ are the basis for preparation of fertilizer prescription maps and use of variable rate technology (VRT) in a Precision Agriculture project.

Estimating Forest Soil Properties for Humus Assessment—Is Vis-NIR the Way to Go?

Recently, forest management faces new challenges resulting from increasing temperatures and drought occurrences. For sustainable, site-specific management strategies, the availability of up to date soil information is crucial. Proximal soil sensing techniques are a promising approach for rapid and inexpensive collection of data, and could facilitate the provision of the necessary information. This study evaluates the potential of visual and near-infrared spectroscopy (vis-NIRS) for estimating soil parameters relevant for humus mapping in Saxon forests. Therefore, soil samples from the organic layer are included. So far there is little knowledge about the applicability of vis-NIRS in the humus layer of forests. We investigate the spectral behaviour of samples from organic (Oh) and mineral (0–5 cm, Ah) horizons, pointing out differences in the occurring absorption features. Further, we identify and assess the accuracy of selected soil properties based on vis-NIRS for forest sites, compare the outcome of different regression methods, investigate the implications for forest soils due to the presence and different composition of the humus layer and organic horizons and interpret the results regarding their usefulness for soil mapping and monitoring purposes. For this, we used retained humus soil samples of forests from Saxony. Regression models were built with Partial Least Squares Regression, Support Vector Machine and Cubist. Investigated properties were carbon (C) and nitrogen (N) content, C/N ratio, pH value, cation exchange capacity (CEC) and base saturation (BS) due to their importance for assessing humus conditions in forests. In organic Oh horizons, prediction results for C and N content achieved R2 values between 0.44 and 0.58, with corresponding RPIQ ranging from 1.58 to 2.06 depending on the used algorithm. Estimations of C/N ratio were more precise with R2 = 0.65 and RMSE = 2.16. Best results were reported for pH value, with R2 = 0.90 and RMSE = 0.20. Regarding BS, the best model accuracy was R2 = 0.71, with RMSE = 13.97. In mineral topsoil, C and N content models achieved higher values of R2 = 0.59 to 0.72, with RPIQ values between 2.22 and 2.54. However, prediction accuracy was lower for C/N ratio (R2 = 0.50, RMSE = 3.52) and pH values (R2 = 0.62, RMSE = 0.29). Models for CEC achieved R2 = 0.65, with RPIQ = 2.81. In general, prediction precision varied dependent on the used algorithm, without showing clear tendencies. Classification into pH classes was exemplified since this offers a new perspective for humus mapping on forest soils. Balanced accuracy for the defined classes ranged from 0.50 to 0.87. We show that vis-NIR spectroscopy is suitable for assessing humus conditions in Saxon forests (Germany), in particular not only for mineral horizons but also for organic Oh horizons.

Phenotypic plasticity in Barilius vagra (Hamilton, 1822) (Teleostei: Danionidae) from two geographically distinct river basins of Indian Himalaya

Truss-based morphometric analysis was used to examine phenotypic plasticity of Barilius vagra (Hamilton, 1822) inhabiting the tributaries of the Alaknanda (Ganga River basin) and Chenab (Indus River basin), two geographically distinct river basins in the Indian Himalaya. Fourteen landmarks were connected to generate a truss network of 90 parameters on the body of fish. Eighty morphometric traits out of ninety morphometric measurements explained statistically significant difference among six sampling locations of Barilius vagra from streams in the Alaknanda and Chenab basins. Discriminant function analysis revealed 82% of Barilius vagra specimens originally classified into their own groups. 95% of the variance was explained by 13 principal components. Morphometric characters (1–6, 1–13, 2–5, 2–6, 2–14, 3–6, 4–6, 4–14, 6–12, 7–8, 7–9, 10–11, and 13–14) contributed greatly in differentiation of B. vagra populations from different river basins. The Alaknanda basin reflected some mixing within populations, which may be due to common environmental conditions and fish migration in these streams. This study will be helpful in framing site-specific conservation and management strategies, such as net mesh size selection, avoiding overexploitation, stock augmentation and food availability for different fish populations.

Estimating temporal and spatial changes in soil organic carbon stocks and its controlling factors in moraine landscapes in Denmark

Assessing temporal changes in soil organic carbon (SOC) stocks is important when evaluating the SOC sequestration potential of soil management strategies. The monitoring of SOC stocks is challenging, particularly in eroding landscapes where erosion-induced changes in SOC stocks are superimposed on changes related to management. In this study we focused on the spatio-temporal variations of SOC in eroding cropland soils of the northeast of thepeninsulaofJutland (Denmark) at field scale. We examined soil redistribution processes as control factors of SOC changes (ΔSOC) during a 16-year period by using soil data (both spatial and with depth) collected over two distinct periods in 1998 and 2014. Topographic changes between 1998 and 2014, derived from high resolution digital elevation models, were used to identify eroded, stable and depositional areas within the field. There was a predominance of soil loss, with 44% of the sampling points corresponding to eroded sites and 28% to depositional sites, while the remainder was considered as stable soils. Substantial erosion was detected and at the field scale, soil deposition equalled (29.7 ± 17.5 Mg ha−1 y−1) while soil erosion rates were lower with a mean of 25.8 ± 12.9 Mg ha−1 y−1. Comparing SOC stocks over the 16 year-period showed that on average, and when considering the whole soil profile, SOC contents and stocks were stable. However, this masked substantial dynamics that were related to erosion processes. Despite the occurrence of net soil erosion at the field scale, there was an overall increase in SOC storage of 1.88 Mg ha (SOC2014:SOC1998 ratio ≥ 1). Losses of SOC in topsoils (6%) and SOC gain (34%) in soil layers below the plough depth (25–45 cm) could be related to soil disturbances caused by tillage and the significant downward transport of topsoil SOC within the soil profile. Soil disturbances caused by tillage practices (i.e. soil management) and SOC transportation from topsoil to subsoil may be responsible for the downward movement of organic carbon and consequent SOC accumulation in subsoils. In addition the successive deposition and deep burial of SOC rich topsoils at depositional sites due to the effect of topography and slope position favours the enrichment of SOC in subsoils. It was estimated that as much as 11% of the sampling points changed from eroded to depositional sites and 15% of them varied from erosion to stable sites, and this might have favoured SOC accumulation. These results clearly show that understanding and quantifying soil redistribution processes is key to assess SOC temporal changes in agricultural soils. This is important to develop site-specific management strategies that improve SOC sequestration at local scale.

High residual sodium carbonate water in the Indian subcontinent: concerns, challenges and remediation

Water scarcity has become one of the major limiting factors in global agriculture, particularly in the arid and semi-arid regions of the world. This necessitates irrigation with marginal-quality waters including those with high levels of residual sodium carbonate (RSC) and an important water quality parameter. Extended use of high RSC water leads to accumulate sodium in the soil which alters physical and chemical properties of soils. The electrical conductivity is a good index of salinity while sodium adsorption ratio (SAR) that of sodicity hazard of irrigation water. It has been found that waters with high RSC values are more hazardous compared to no RSC water of equal SAR. Sustainable agriculture on these soils demands to adopt certain site-specific management strategies prior to use such low-quality waters for the economic production of crops. Strategies like dilution with low salt water and addition of calcium-based amendments, along with organic manures have been traditionally practiced and giving some promising results. It is well documented that for improving plant growth, irrigation to achieve low leaching fraction is necessary for the purpose of reducing the transport of solutes that are responsible for the degradation of water quality and provides an efficient use of limited water supplies. In addition, implementations of government policies and public–private sector collaborations are necessary to find new horizons for the management of high RSC irrigation waters in arid and semi-arid regions of the world. This article provides an overview of soil permeability problems associated with the irrigation using high RSC waters.

Prediction of Spatial Distribution of Organic Carbon in Lower Brahmaputra Active Floodplain Soils of Bangladesh

Soil acts as a large reservoir of Organic Carbon (OC) but the amount varies significantly with space and time. Thus, soil analysis and interpretation of spatial variability of Soil Organic Carbon (SOC) are keys to site-specific management. The study aimed to characterize the spatial variability of SOC in an active floodplain. Soil samples were collected in three major landform categories (natural levee, back slope, marsh land) from the lower Brahmaputra River floodplain and then analyzed for SOC measurement in the laboratory. The measured data were then analyzed for spatial variability interpretation using descriptive statistics and geo-statistical analysis. The study found that the amount of SOC varies with landform variation, soil texture and distance between sample points. The topsoil of marsh land has the highest (1.41%), back slope holds a moderate amount (1.15%) and the natural levee has the lowest (0.75%) amount of SOC. The amount of clay particles at the top layer was found to be positively correlated to the SOC whereas in the same layer of sand and silt showed a negative correlation. The geo-statistical analysis illustrated the nugget effect. Low (&lt;1%) SOC is commonly found in the agricultural soils of Bangladesh which was corroborated in this study; moderate (1.1%) SOC was found in the floodplain. This study aimed to provide an insight into spatial variability to assist in predicting SOC in the active floodplain; consequently, the interpretation of spatial variability analysis can be implemented for site specific management strategies and to calculate carbon stock in floodplain soils.&#x0D; The Dhaka University Journal of Earth and Environmental Sciences, Vol. 8(2), 2019, P 33-40