54 research outputs found
Effect of Biochar Application on Soil Fertility and SOC Dynamics in Maize-based Vertisol Systems of Akola Region, Maharashtra, India
No full textIn many countries, after harvesting, farmers choose to burn crop residues in their fields as waste material. As a result, burning of crop residues like cotton stalks, maize stalks, rice husk, etc., releases a lot of carbon dioxide (CO2) into the atmosphere, contributing to climate change, which causes air pollution. The primary objective of this research was to practically demonstrate the conversion of biomass into biochar and to explore its application as a valuable substance with an aim to discuss the impact of biochar application on soil properties and SOC stock at harvest of maize grown on Vertisols. The field trial was carried out in 2020-21 (Kharif) at Research Farm, Department of Agronomy, Dr. PDKV, Akola, Maharashtra, India. The experiment was conducted by using a Randomised Block Design (RBD) with eight treatments comprised of control, different rates of nitrogen and their combinations with 2.5 and 5.0 t ha-1 biochar and three replications. The data were subjected to statistical analysis. Based on the results obtained from analysis, the physical and chemical properties of soil such as water holding capacity (55.35%), organic carbon (6.48 g kg-1), available nitrogen (249.33 kg ha-1), phosphorus (21.63 kg ha-1) and potassium (358 kg ha-1) after harvest of maize were found to be significantly higher by applying 125 % RDN + 5 t ha-1 of Biochar, while, the bulk density, electrical conductivity and soil pH were found to be non-significant by various treatments. Increasing the doses of biochar resulted in a slight increase in the availability of Zn, Cu, Fe & Mn, but the differences among the treatments were found to be statistically non-significant. The higher the quantity of biochar applied, the greater the soil organic carbon (SOC) stock was recorded. Based on the current study, it can be concluded that, application of 100% RDN + 5 t ha-1 Biochar improves the available nutrient status and physical-chemical properties of soil after the harvest of maize. Additionally, a favourable impact on the soil organic carbon (SOC) stock is also noted
Scalable data analytics for ensemble learning
No full textData Anlaytic techniques have enhanced human ability to solve a lot of data related problems. It has opened a window through which an analyst can devise techniques to solve a given problem by just looking at the related data alone. Such techniques may not be directly visible to the programmer. Machine learning in short is programming computers to generate such algorithm to solve a given problem using example data or past experience. Numerous models or functions can be developed which fits a distribution of data points. The magnitude and breadth of this data plays a major role in determining which model fits best. In the current world, data is growing at an alarming rate both in terms of size and the information it conveys. Analytics on large-scale data requires very high execution time with limited resources. Implementing such techniques to generate different models on a single machine becomes tedious and time consuming. Implementing the same on a distributed network is a possible solution which is highly interesting and challenging. Data Analytic Algorithms, to create these models, should be carefully chosen on the basis of reliability and data integrity and at the same time should be easily distributable. The range of algorithms should be wide enough to incorporate the same analytics on all the data with required performance. An analysis of various metrics such as measure of scalability, accuracy, execution time helps classify individual algorithm’s impact on the required performance. The above metrics are highly dependent on the data, type of algorithm, and type of platform under consideration. This work proposes a unique system for Scalable Data Analytics and gives an analysis of its scalability. It also shows how this system implements a unique way of ensemble learning in useful time without compromising on accuracy. Finally it demonstrates scalability on two levels 1. Scalability at an individual algorithm level 2. Scalability at implementing ensemble learning for different use cases.M.S.Includes bibliographical referencesIncludes vitaby Suhas R Aitha
Effect of Different RDF Regimes and Nano DAP on Growth, Yield and Nutrient Dynamics of Chickpea (Cicer arietinum)
No full textThis study investigated the effect of combining full and reduced recommended dose of fertilizer (RDF) levels with Nano Diammonium Phosphate (DAP) on growth, yield, and nutrient dynamics in chickpea (Cicer arietinum). Two-year field experiments was conducted using a randomized block design with 10 treatments and 3 replications. Treatments included the control, 100% RDF, 75% and 50% RDF, and combinations of these with Nano DAP seed treatments and foliar sprays. Key parameters measured included plant growth, yield components, seed and straw yields, nutrient content and uptake, The 100% RDF treatment produced the highest seed yield (15.98 q ha-1), followed by 75% RDF + Nano DAP (13.73 q ha-1). Nano DAP treatments increased yield compared to equivalent RDF levels alone. The 100% RDF treatment also resulted in maximum nutrient content and uptake in seed and straw. While Nano DAP improved nutrient use efficiency compared to conventional fertilizers, it did not match the performance of 100% RDF when combined with reduced fertilizer levels. The study demonstrated Nano DAP can partially substitute for conventional fertilizers in chickpea, but cannot fully replace RDF for achieving maximum productivity and nutrient uptake
Forces and interactions between nanoparticles for controlled structures
No full textIn recent years, structured nanomaterials have started to demonstrate their full potential in breakthrough technologies. However, in order to fulfill the expectations held for the field, it is necessary to carefully design these structures depending upon the targeted application. This tailoring process suggests that a feedback between theory and experiment could potentially allow us to obtain a structure as near optimum as possible. This thesis seeks to describe the theory and experiment needed to understand and control the interactions among nanoparticles to build a functional device for the efficient conversion of sunlight into energy. This thesis will discuss a simulation built from the existing theories explaining nanoparticle interactions and will present how its outcomes can be employed to describe real systems. The forces and dynamics of the nanoparticle system control the way their structure is formed. Thus, in order to understand and predict the formation of organized nanostructures, simulation of forces and dynamics and their corroboration with experimental results are necessary. These simulations will be extended to more complex systems, and the results will be used to provide a basis for the design of a specific nanoparticle structure, namely a linked linear chain. The envisioned application of the results achieved with the approach described is the design of a nanoparticle-based organic photovoltaic cell where linear chains of nanoparticles are tethered to the back of the device and then surrounded by a conducting polymer matrix to generate percolation pathways and improve light collection and scattering, and thus efficiency, of the device. To tether the chains in the cell, a foundation is needed to provide structure and control spacing. This foundation is designed and constructed by depositing gold nanoparticles on a substrate patterned using block-copolymer lithography to form a hexagonal array upon which the linear chains will be grown.Ph. D.Includes bibliographical referencesby Paul R. Mar
Soil Health and Nutrient Management
No full textSoil health and appropriate nutrient management are critical components of sustainable agriculture, influencing crop yield, environmental sustainability, and overall food security. The ability of soil to function as a living ecosystem is referred to as soil health. supports plant and animal life while protecting the environment. It refers to how physical, chemical, and biological activity interact in the soil. Mineral matter, organic matter, water, and air are all critical components of soil health, and each contributes to plant growth. Soil health indicators include physical, chemical, and biological factors that help assess the soil\u27s state. For optimal soil health, these indicators must be balanced.
However, there are significant issues to address, such as decreased soil organic matter, declining soil fertility due to nutrient deficits, physical soil degradation, and chemical soil degradation caused by excessive chemical use. Practices for sustainable soil management are critical for addressing these concerns. Balanced fertilization, organic matter incorporation, crop rotation, cover cropping, reduced tillage, and precision nutrient delivery are examples of these. Mitigating issues such as nutrient pollution, soil erosion, and soil deterioration is critical to maintaining agriculture\u27s long-term viability
Development of a Rapid Phenotyping Method to Screen for Didymella arachidicola Resistance in Peanuts
No full textNet or web blotch (NB), caused by Didymella arachidicola, is an emerging threat causing a significant reduction in yield of peanuts in cooler production regions globally, including Australia, China, and Southern Africa. Host resistance against NB is poorly understood, with field screenings reliant on favorable climatic conditions. A rapid and effective phenotyping method is essential to differentiate between resistant and susceptible breeding lines and improve selection accuracy. In this study, a greenhouse screening method using mycelium as the inoculum to determine host response to D. arachidicola was developed. A liquid-culture mycelia suspension of the pathogen was sprayed onto 9-week-old peanut plants, and one disease assessment was made between 14 and 17 days after inoculation. Lesion growth in the greenhouse was found to correlate with disease response assessed under field conditions (r = 89%). The phenotyping method developed in this study can facilitate the identification of NB resistance lines and assist in identifying regions of genetic resistance within the peanut host. [Figure: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license
Cyclical nursing patterns in wild orangutans
No full textabstract: Nursing behavior is notoriously difficult to study in arboreal primates, particularly when offspring suckle inconspicuously in nests. Orangutans have the most prolonged nursing period of any mammal, with the cessation of suckling (weaning) estimated to occur at 6 to 8 years of age in the wild. Milk consumption is hypothesized to be relatively constant over this period, but direct evidence is limited. We previously demonstrated that trace element analysis of bioavailable elements from milk, such as barium, provides accurate estimates of early-life diet transitions and developmental stress when coupled with growth lines in the teeth of humans and nonhuman primates. We provide the first detailed nursing histories of wild, unprovisioned orangutans (Pongo abelii and Pongo pygmaeus) using chemical and histological analyses. Laser ablation inductively coupled plasma mass spectrometry was used to determine barium distributions across the teeth of four wild-shot individuals aged from postnatal biological rhythms. Barium levels rose during the first year of life in all individuals and began to decline shortly after, consistent with behavioral observations of intensive nursing followed by solid food supplementation. Subsequent barium levels show large sustained fluctuations on an approximately annual basis. These patterns appear to be due to cycles of varying milk consumption, continuing until death in an 8.8-year-old Sumatran individual. A female Bornean orangutan ceased suckling at 8.1 years of age. These individuals exceed the maximum weaning age reported for any nonhuman primate. Orangutan nursing may reflect cycles of infant demand that relate to fluctuating resource availability
Impact of Long-term Manuring and Fertilization on Micronutrient Status under Sorghum-wheat Cropping Sequence in Vertisols
No full textA long-term fertilizer experiment has been under progress since 1988-89 at LTFE field department of soil science PGI, Dr. PDKV, Akola with sorghum wheat cropping sequence consisting of twelve treatments and replicated four times in a randomized block design. The present study was conducted to assess the influence of continuous fertilizer and manure application on micronutrient availability in long term fertilizer experiment during 2023-24. Long term application of organic and inorganic fertilizers in combination (100 % NPK+ FYM @ 5 t ha -1, FYM @ 10 t ha -1 and 75% NPK + 25 % N through FYM) or balanced fertilization (100% NPK, 150% NPK and 100 % NPK + Zn@ 2.5 kg ha -1) for about 36th cropping cycles lead to marked increase in soil micronutrient availability. All four micronutrient cations were significantly higher under INM treatments. On an average, the INM treatments (T8) had 53.29, 22.86, 69.56 and 34.44 % higher content of available Fe, Mn, Zn and Cu respectively, over the balance fertilizer (T2). However, continuous cultivation of crops without or imbalanced nutrient supply (50% NPK, 100 %NPK (-S), 100 % NP, 100 % N and Control) lead to decline in soil micronutrient status. Balanced fertilization and organic matter addition has played an important role in improving micronutrient status in soil after 36 years of cropping cycles
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