Soil Invertebrates of \u3cem\u3eLasiurus sindicus\u3c/em\u3e Based Grazing Lands: Impact of Management and Grazing Intensity

Arid Western plains of India are dominated by pasture and grazing lands. Lasiurus sindicus (LS) is the dominant na-tive grass species growing on sandy plains and low dunes under the low rainfall extreme desert climate. Palatability and higher crude protein (8-14% in early vegetative growth, 4-6% in 80-120 days of growth) make this grass a highly preferred grazing species. Since drought is frequent (47%) in this part of the country the LS grasslands are under tremendous grazing pressures and classified under poor or very poor condition for livestock. It is imperative to re-store the natural resources on which this grassland depends. Soil invertebrates especially soil collembola and mites are an integral part of this grassland ecosystem. Their community structure changes in response to the changes in management and other factors, and may serve as a tool for rapid impact assessment of restoration measures. With this background, Lasiurus sindicus grazing lands in Jaisalmer District of Western Rajasthan of India were evaluated, to understand the impact of grazing intensity and management practices on the community structure of the soil invertebrates

Enhanced Room Temperature Coefficient of Resistance and Magneto-resistance of Ag-added La0.7Ca0.3-xBaxMnO3 Composites

In this paper we report an enhanced temperature coefficient of resistance (TCR) close to room temperature in La0.7Ca0.3-xBaxMnO3 + Agy (x = 0.10, 0.15 and y = 0.0 to 0.40) (LCBMO+Ag) composite manganites. The observed enhancement of TCR is attributed to the grain growth and opening of new conducting channels in the composites. Ag addition has also been found to enhance intra-granular magneto-resistance. Inter-granular MR, however, is seen to decrease with Ag addition. The enhanced TCR and MR at / near room temperature open up the possibility of the use of such materials as infrared bolometric and magnetic field sensors respectively.Comment: 22 pages of Text + Figs:comments/suggestions([email protected]

Wheat yield forecast using detrended yield over a sub-humid climatic environment in five districts of Uttar Pradesh, India

A study was carried out to forecast the yield of the wheat crop for five districts of Uttar Pradesh namely Lucknow, Kanpur, Banda, Jhansi and Faizabad. The daily weather data on variables such as maximum temperature, rainfall, minimum temperature, and relative humidity were arranged week wise from sowing to harvesting and the relations between the weather variables and yield was worked out using statistical tools like correlation and regression. The yield has been detrended by obtaining the parameter estimates of the model and subsequently the detrended yield was used to forecast the yield of the crop using ARIMA model. The proposed method of obtaining pre-harvest forecasting of yield of crops was compared with the traditional approaches of forecasting and the proposed method was evaluated in terms of criteria’s such as goodness of fit of the model. It was observed that in all the districts the proposed model performed better as compared to the traditional method both in terms of goodness of fit as well as forecasting performance. Thus it can be concluded that the proposed approach is better and more suitable as compared to the traditional approach for forecasting the wheat yield in the five districts of Uttar Pradesh

Forecasting of growth rates of wheat yield of Uttar Pradesh through non-linear growth models

Wheat production in India is about 70 million tonnes per year which counts for approximately 12 per cent of world’s production. Being the second largest in population, it is also the second largest in wheat consumption after China, with a huge and growing wheat demand. Major wheat growing states in India are Uttar Pradesh, Punjab, Haryana, Rajasthan, Madhya Pradesh, Gujarat and Bihar. All of north is replenished with wheat cultivation. Uttar Pradesh, the largest wheat growing region of the country, produces around 28 million tonnes of wheat and Bihar produces around 5 million tonnes. The usual parametric approach for growth rate analysis is to assume multiplicative error in the underlying nonlinear geometric model and then fit the linearized model by ‘method of least squares’. This paper deals with a critical study of wheat yield of Uttar Pradesh with a non-linear approach. The available data of rice during different years is taken into consideration and different statistical models are fitted for that. The time series data on annual yield of wheat in UP from 1970-2010 were collected from various sources. Growth rates are computed through non-linear models, viz. Logistic, Gompertz and Monomolecular models. Different nonlinear procedures such as Gauss-Newton Method, Steepest-Descent Method, Levenberg-Merquadt Technique and Do Not Use Derivative (DUD) Method were used in this study to estimate the nonlinear growth rates. The results showed that logistic model performed better followed by Gompertz and monomolecular

First results of evaporation residue cross-section measurements of 32^{32}S+208^{208}Pb system

The dynamics of heavy ion-induced reactions play a critical role in forming super heavy elements (SHE), and one clear signature of the SHE formation is the evaporation residue (ER). In our pursuit of SHE, we present the heaviest element populated in India for ER cross-section measurements. These are the first-ever measurements of the Evaporation Residue (ER) cross-sections for the nuclear reactions between $^{32}$S and $^{208}$Pb. These measurements were conducted above the Coulomb barrier at four distinct beam energies in the laboratory frame, ranging from 176 to 191 MeV at the pelletron Linac facility at the Inter-University Accelerator Centre (IUAC), New Delhi. The Hybrid Recoil Mass Analyzer (HYRA) in a gas-filled mode was employed for these experiments. The obtained range of ER cross-sections enriches our knowledge and helps advance the field of heavy ion-induced reactions, especially in the context of super heavy element formation.Comment: 12 pages, 10 figures. arXiv admin note: text overlap with arXiv:2311.0904

Forecasting of crop yield using weather parameters–two step nonlinear regression model approach

Concept of the paper is firstly to remove the trend of crop yield and then to develop the forecasting models using detrended yield. Not much work is available or development of forecast models or modelling due to their non-linear behaviour. For that, in this paper, methodology developed for forecasting using nonlinear growth models, which will help in forecasting yield, pest and disease incidences etc with high accuracy. Crop yield forecast models for wheat crop have been developed (using non-linear growth models, linear models and weather indices approach with weekly weather data) for different districts of Uttar Pradesh (UP). Weather Indices (WI) were obtained using above two approaches. Weather indices based regression models were developed using weather indices as independent variables while character under study such as crop yield was used as dependent variable for wheat crop, i.e. two step non-linear forecast model. Technique of forecasting using non-linear approach and using weather indices will enrich the knowledge in developing customized models on forecasting for different types of crops and for different locations. The approach provided reliable yield forecast about two months before harvest

Measurements of evaporation residue cross-sections and evaporation residue-gated γ\gamma-ray fold distributions for 32^{32}S+154^{154}Sm system

Evaporation Residue (ER) cross-sections and ER-gated $\gamma$-ray fold distributions are measured for the $^{32}$S + $^{154}$Sm nuclear reaction above the Coulomb barrier at six different beam energies from 148 to 191 MeV. $\gamma$-ray multiplicities and spin distributions are extracted from the ER-gated fold distributions. The ER cross-sections measured in the present work are found to be much higher than what was reported in a previous work using a very different target-projectile ($^{48}$Ti + $^{138}$Ba) combination, leading to the same compound nucleus $^{186}$Pt, with much less mass asymmetry in the entrance channel than the present reaction. This clearly demonstrates the effect of the entrance channel on ER production cross-section. The ER cross-sections measured in the present work are compared with the results of both the statistical model calculations and the dynamical model calculations. Statistical model calculations have been performed to generate a range of parameter space for both the barrier height and Kramers' viscosity parameter over which the ER cross-section data can be reproduced. The calculations performed using the dinuclear system (DNS) model reproduce the data considering both complete and incomplete fusion processes. DNS calculations indicate the need for the inclusion of incomplete fusion channel at higher energies to reproduce the ER cross-sections.Comment: 13 pages, 18 figure

Performance evaluation of yield crop forecasting models using weather index regression analysis

A crop forecast is a statement of the most likely magnitude of yield or production of a crop. It is made on the basis of known facts on a given date and it assumes that the weather conditions and damages during the remainder of the growing season will be about the same as the average of previous year. The present paper deals with use of non-linear regression analysis for developing wheat yield forecast model for Allahabad district (India). A novel statistical approach attempted in this study to use nonlinear models with different weather variables and their indices and compare them to identify a suitable forecasting model. Time series yield data of 40 years (1970-2010) and weather data for the year 1970-71 to 2009-10 have been utilized. The models have been used to forecast yield in the subsequent three years 2008-09 to 2009-10 (which were not included in model development). The approach provided reliable yield forecast about two months before harvest