Coffee (Coffea arabica; Rubiaceae) is a potential and emerging commercial crop. Coffee is planted in the midhills of Nepal (800 to 1250 meter above mean sea level) in Gulmi and Lalitpur districts. To control the major insect pests of coffee plant, locally prepared ‘jaibik bishadi’ (botanical pesticides) are used as alternatives of the synthetic insecticides. This study was undertaken to see the contribution of ‘jaibik bishadi’ in the fertility of the coffee orchards soil, for which soil samples from botanicals used and not used coffee orchards were collected to see the level of soil characteristics and soil nutrients such as soil texture, organic matter, soil nutrients (phosphorous and potassium). There was some difference in the soil texture of topsoil, but no difference could be seen in sand, silt and clay content of the subsoils from botanical used and not used orchards. The pH was significantly different between botanical used and not used soils, but such difference could not be observed between the topsoil and subsoil from the same sites. Jaibik bishadi used to control the coffee pests significantly contributed in the soil fertility, which could be seen in high positive correlation (r=0.9886) between organic matter and nitrogen in the soil.Keywords: Coffee, jaibik bishadi, topsoil, subsoil, soil fertilityThe Journal of Agriculture and Environment Vol:9, Jun.2008 page: 16-22

This paper aims to understand the dynamics of the spread of COVID-19 for Nepal. It is carried out with the help of multivariate statistics techniques. Direct relationships among variables are obvious, as they are easily seen and measured. But, hidden variables and their interrelationships also have a significant effect on the spread of a pandemic. Multinomial logistic regression, odds ratio, linear mixed-effect models, and principal component analysis are used here to analyze these hidden variables and their interrelationships. Also, such studies are very important for countries with limited and scarce data. These countries do not have a backbone of good-quality official records. Understanding the spread of a disease in a developing country also helps in management and eradication of that disease. The multivariate daily data of new cases, deaths, recovered, total cases, total deaths, total recovered, and total infected (isolated) are used here. The daily incidence of new cases is also modeled here using nonlinear regression. Two best nonlinear models are discussed here. ARIMA models are used for analyzing and forecasting the progression of the variables for two months into the future. The impact of government restriction in the form of strict lockdown 1, partially relaxed lockdown 1, completely relaxed lockdown 1, and strict lockdown 2 is minutely analyzed. These controls were exercised to curtail the spread of the pandemic. The role of these controls in curbing the spread of the pandemic is also studied here. The results obtained from this study can be applied to other countries of South Asia and Africa.

Renewable energy helps in solving twin problems namely fast depletion of energy sources and climate change. In this paper the results of a statistically sound research with minimum possible white noise have been stated. The data collection scheme was designed by keeping biogas use in the core and getting all the possible information about a typical middleclass Nepalese family inhabiting in rural areas, its economic and social background and change after biogas was used in their household. All sources of error right from the data collection, its digitization and finally its analysis were identified and eliminated. In the stage of questionnaire design all the possible answers to a question were foreseen and given as one of the categories. Pretesting of the questionnaire was also conducted on 30 household. A consumer profile database of biogas consumers is constructed. Then the information collected is stored in an electronic database. An online questionnaire is constructed to store all the information in this database. The digitization of the data and then its statistical analysis can similarly be done for other sources of renewable energy. Statistical analysis is then done for the analysis of impact of renewable energy in extenuating climate change.

<p class="SAP-AffiliationLastline">Amount of night lights in an area is a proxy indicator of electricity consumption. This is interlinked to indicators of economic growth such as socio-economic activities, urban population size, physical capital, incidence of poverty. These night lights are generated by renewable and non renewable energy source. In this paper the behavior of night radiance RH data was minutely analyzed over a period of 28 hour; Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) satellite earth observation data were used. These 28 hours and 8936 observations time series data is from 2 September 2018 to 4 September 2018. The behavior of night radiance RH data over 122 time intervals was analyzed using box plots. It was seen that the arithmetic mean of RH data is more sensitive than the arithmetic mean of first order difference of RH data. The first order difference of night radiance RH was regressed on night radiance over 110 intervals of time. The box plot of slope and intercept of this linear regression showed the behavior of these regression parameters over 110 intervals of time. It is seen that the data are more scattered with respect to slope than with respect to intercept. </p>

This paper aims to understand and predict the dynamics of spread of COVID-19. It is based on government data on COVID-19 from February 1, 2021 to August 31, 2021. First, Vector Autoregression (VAR) model is used here to model the interrelationships between time series data of daily tested, infected, dead and discharged. The impact of under-reporting on interrelated variables is quantified. The behavior of the parameters of these VAR model is also analyzed. The entire time period of study is divided into three phases, according to the intensity of vaccination drive. The impact of vaccination in controlling the spread of the pandemic is measured by studying the behavior of the coefficients of VAR model for these three time periods. Then, Granger causality is also measured. At 10% level of significance, it is found that if the number of infected is under-reported today, this is due to the significant influence of number of infected until previous two days. The number of discharged one day ago and three days ago also significantly influence this number. Number of tests conducted two days ago also significantly contributes to this underreporting. The impact of latent variables on the spread of COVID-19 is measured here.