Extreme learning machines for weather-based modelling of silk cocoon production

In spite of the immense popularity and sheer power of the neural network models, their application in sericulture is still very much limited. With this backdrop, this study evaluates the suitability of neural network models in comparison with the linear regression models in predicting silk cocoon production of the selected six districts (Kolar, Chikballapur, Ramanagara, Chamarajanagar, Mandya and Mysuru) of Karnataka by utilising weather variables for ten consecutive years (2009-2018). As the weather variables are found to be correlated, principal components are obtained and fed into the linear (principal component regression) and non-linear models (back propagation-artificial neural network and extreme learning machine) as inputs. Outcomes emanated from this experiment have revealed the clear advantages of employing extreme learning machines (ELMs) for weather-based modelling of silk cocoon production. Application of ELM would be particularly useful, when the relation between production and its attributing characters is complex and non-linear

Study on Relationship among the Various Physico-Chemical Soil Properties and Identification of Soil Acidifying Components

Soil acidity is one of the major obstacles to crop growth to a great extent. IN different districts of West Bengal, soil acidity has been reported as a considerable factor behind crop growth restriction. Hence, a comprehensive study has been conducted with a view to study the relationship among the various forms of soil acidity and other physico-chemical soil properties, covering Godkhali, Coochbehar and Purulia under investigation. Outcomes of the investigation clearly reveal that all the physico-chemical properties has been found to have significant influence on different forms of acidity. Along with this, it can be also inferred that among the forms of different soil acidity, for all the forms, significant positive linear association has been observed. It has been also obtained that hydrolytic acidity, extractable acidity and pH-dependent acidity can be considered as the most vital soil acidifying component

Supply response of major oilseeds in India: A mix of price and non-price factors

Oilseeds are one of the most important determinants of India’s agricultural economy, next only to cereals and pulses. The self-sufficiently in oilseed obtained during the early 1990s could not be sustained sufficiently. Despite, being the fourth largest oilseed crop-producing nation in the world, India is also one of the largest importers of vegetable oils. This study appraises the relationship between price and non-price factors to understand the behaviour of major oilseeds (mustard/rapeseed and groundnut) cultivated in India from 1997-98 to 2019-2020. Supply response is the responsiveness of supply, which can be identified using production response to different determining factors. Mustard/rapeseed and groundnut are the oilseeds that are mainly produced in India. This study specifically attempted to quantify the relationship between oilseed production and different factors, such as annual rainfall, annual temperature, yield, and revenue difference for both crops. The findings suggested that yield and revenue difference of mustard with wheat are the most determining factors for mustard production, whereas annual rainfall, the temperature during the growing season, and revenue difference between groundnut with rice and soyabean are the most significant determinants of groundnut’s production response. Crop equivalent productivity further validated that groundnut competed and outperformed the two promising crops (soybean and paddy). The trend analysis (1997-98 to 2019-2020) also indicated that wheat was the dominant crop over mustard from 1997-98 to 2013-14. Afterwards, i.e., from 2014-15 to 2019-20, mustard surpassed wheat productivity (on equivalent terms) and outperformed cereal.

Modelling the Relationship between Weather Variables and Rice Yellow Stem Borer Population: A Count Data Modelling Approach

Not AvailableAim: This study was conducted to model the relationship between discrete dependent variable (yellow stem borer population) and continuous weather variables. Data Description: The yellow stem borer (YSB) population and standard meteorological week (SMW) wise weather variables (temperature, relative humidity, rainfall and sunshine hours) data of Warangal centre (Telangana state) generated under All India Co-Ordinated Rice Improvement Project (AICRIP) from 2013-2021 were considered for the study. The YSB population were recorded daily using light trap with an incandescent bulb and are counted as weekly cumulative catches. Methodology: The weekly cumulative trapped YSB populations and weekly averages of climatological data were considered as inputs to the models under consideration. In this study the classical linear regression i.e. step-wise multiple linear regression and count regression models such as Poisson, negative binomial, zero inflated Poisson and zero inflated negative binomial regression models were employed. Result: The empirical results revealed that the zero inflated count regression models viz., zero inflated Poisson regression and zero inflated negative binomial regression models performed better compared to the classical linear regression, Poisson and negative binomial regression models, further the negative binomial regression model outperformed all models as it yielded lowest mean square error (MSE) and highest R2 values. The average percentage reduction in accuracy of zero-inflated negative binomial regression model over classical model was around 4 percent. Conclusion: Based on the results obtained in this study, it is concluded that the zero inflated models performs better compared to classical models as they are unable to handle the presence of excess zeroes, as a result provides more prediction error and lower R2 values. Further, the models developed in this study will be of great assistance in identifying the factors influencing occurrence of YSB population in rice.Not Availabl

Not Available

Not AvailableEducation is a Nation’s strength. Association analysis of academic performance and its infuential factors has remained research interest for all education researchers all over the world. India being an agriculture dominated country, for its development in agricultural front it requires ahuge numberof efcient technocrats having strong academic background. In this study an attempt has been made to examine the associationship of academic performance of the agriculture graduates, as measured through overall grade point average (OGPA) with the factors supposed to infuence the academic performance. Special emphasis has been given to visualize the performance in presence of the infuences of nominal factors. Students at masters level were surveyed for their social, economic, demographic and family and educational background through a designed questionnaire and tested accordingly. Statistical tools, starting from frequency, percentage, Chi-square test, test for normality, Cramer’s V test, multiple regression analysis with the inclusion of dummy variables were employed. Dependency of OGPA with gender, caste and expenditure on education is recorded. The dependency of educational expenditure on OGPA is quite obvious. But the dependency of OGPA with those of gender and caste is most probably not a good sign for a healthy higher education system. This study will help the education planners to take group oriented action plan for improving the education standard in higher education institutions.Not Availabl