Assessment of Nutrients and Load of Organic Carbon in Soil and Sediments Due To Flood and Heavy Irrigation of River Krishna in the Bagalkot District

Kalshetty, Basavaraj M.; Sheth, R. C.; Shobha, N; Kalshetty, Mallikarjun B.

doi:10.9790/2402-081033744

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…Substantial carbonation is reported for coarse kimberlite tailings (Jones et al., 2023 ), and the milling thereof during the extraction process to form fine tailings, may have enhanced the fertility of the latter. Organic carbon is important in soil fertility and has multiple roles such as binding of soil particles, development of soil structure, adsorption of nutrients and water retention (Kalshetty et al., 2014 ; Musinguzi et al., 2013 ). Fine tailings also have more magnesium and calcium than coarse tailings and they are considered important components of the carbonation process reported by Jones et al.…”

Section: Discussionmentioning

confidence: 99%

Success in restoring native plant communities on kimberlite mining dumps in the Afro‐alpine Drakensberg region of Lesotho

Ntloko,

Mokotjomela,

Mphafi

et al. 2024

Ecology and Evolution

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Rehabilitation strategies for degraded mine dumps have generally seen limited success due to different complications associated with mining biophysical disturbance. In this study, we tested a combination of two methods to expedite revegetation of kimberlite tailings at Letseng Diamond Mine (i.e., in the Afro‐alpine areas of Lesotho). We ran trials on different growth media located on fine and coarse kimberlite tailings (i.e. sites) mixed with different substrate combinations and topsoil and sowing a seed mix comprised of native plant species. Overall, as predicted, fine kimberlite tailings displayed significantly higher plant abundance than coarse kimberlite tailings, and sown seeds performed significantly better than spontaneous colonisation by emerging species. Kimberlite tailings mixed with topsoil (100 mm) showed significantly greater plant abundance, and similarly, when coarse kimberlite tailings were introduced to fine tailings. Physicochemical analyses of growth media components suggested that topsoil provided additional nutrients and that plants could readily access available nutrients in the fine kimberlite tailings. We noted a gradual significant increase in plant abundance over 5 years, enhanced by new plant species emerging from the topsoil seed bank or by natural seed dispersal. Although plant abundance differed significantly, both fine and coarse kimberlite tailings displayed high plant species diversity (H = 3.4 and D = 0.95 and H = 3.5 and D = 0.95, respectively). Out of 36 emerging plant species, 15 species spontaneously colonised both growth media. The significant variation in abundance among plant species between treatments was mostly attributed to dominant forb species, namely Chrysocoma ciliata, Glumicalyx montanus, Oxalis obliquifolia, Senecio inaequidens and Trifolium burchellianum. We have identified suitable growth media for plant community restoration on kimberlite tailings in the Drakensberg alpine area using a seed mix of native plant species in combination with natural seed dispersal from the surrounding pristine environment. We provide evidence for using two complementary approaches to optimise native plant community development during restoration in the Drakensberg alpine area.

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Section: Discussionmentioning

confidence: 99%

Success in restoring native plant communities on kimberlite mining dumps in the Afro‐alpine Drakensberg region of Lesotho

Ntloko,

Mokotjomela,

Mphafi

et al. 2024

Ecology and Evolution

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“…This strategy employs cutting-edge technologies for production via autonomous system [6]. The data collected is transferred to cloud through reliable communication technology and cloud platform analyzes the data and process the data using ML technologies and makes the intelligent decision to improve the productivity [7]. The proposed model use hybrid approach ELSTM for yield prediction and ARIMA for agro-products price prediction.…”

Section: Using the Modern Technologies Like Iot Artificialmentioning

confidence: 99%

“…Soil already consists of some number of macronutrients in terms of animal and plant waste. The analysis is done for uptake of NPK and farm yard manure (FYM) which is required for increased crop production [6,7]. The market price prediction is done for Taiwan market.…”

Section: Literature Surveymentioning

confidence: 99%

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Optimized Farming: Crop Recommendation System Using Predictive Analytics

2023

IJIES

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Agriculture is been majorly contributing to the country's GDP. So, it is foremost important to improve the production quality and quantity to improve the agricultural economy. Using modern technologies like IoT, big data, blockchain, robotics, 5G technologies, etc., farming can be improved by replacing humans in agricultural operations. The proposed work focuses on forecasting of yield and prices of agro-products which will be useful for farmers to increase their productivity and which in-turn increase their economy. Data science techniques can be used to perform this by predicting which crop can be grown for given environmental features and what is the outcome before harvesting period. The proposed model uses computational data-driven approach for crop yield prediction and price forecasting of agro-products. The study uses hybrid approach for crop yield prediction which integrates LSTM and genetic algorithm based evolutionary algorithm known as enhanced long short-term memory (ELSTM). The weight assignment plays a major role in learning the behaviour of the network. The gradient value with respect to weight is calculated for each epoch, and weight is updated to compute the new weight of the network to minimize error. The model accurately predicts the crop yield for the dataset which comprises of features like soil data, rainfall, history of production, fertilizers etc., the accuracy of the model is measured using metric root mean square error (RMSE) and mean absolute error (MAE). In the proposed study, the computation model is used to enhance the knowledge about agricultural yield before the crop sowing period. The proposed model will help farmers and government agencies to improve production. The proposed ELSTM model is compared with other machine learning models such as naïve bayes, decision tree, which shows accuracy between 75% to 80% and the experimental result show the proposed ELSTM model is performing better with 85% accuracy. The price forecasting model is necessary to identify the commodity prices well in advance. The agro-product price forecasting is made using auto-regressive integrated moving average (ARIMA) which uses dataset collected from various agricultural produce market committee (APMC). Auto correlation function (ACF) and partial auto correlation function (PACF) time series plots have been used to assess the proposed model's performance in predicting prices of crops.

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Pattern of spatial variability in granulometric and mineralogical characteristics across an ecologically sensitive sub-tropical wetland (Bihar, India)

Aslam

Ranjan

Dass

2023

International Journal of Sediment Research

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Assessment of Nutrients and Load of Organic Carbon in Soil and Sediments Due To Flood and Heavy Irrigation of River Krishna in the Bagalkot District

Abstract: Levels of chemical parameters (pH, EC, Na & Ca), Major nutrients (Organic Carbon, Available Nitrogen, Phosphorous and Potassium) and Micro nutrients ( Fe, Mn, Zn, Cu, Boron and Molybdenum)

Cited by 3 publications

References 4 publications

Success in restoring native plant communities on kimberlite mining dumps in the Afro‐alpine Drakensberg region of Lesotho

Success in restoring native plant communities on kimberlite mining dumps in the Afro‐alpine Drakensberg region of Lesotho

Optimized Farming: Crop Recommendation System Using Predictive Analytics

Pattern of spatial variability in granulometric and mineralogical characteristics across an ecologically sensitive sub-tropical wetland (Bihar, India)

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