Prediction of agriculture yield is a job that requires unification of knowledge from several areas such as data mining, statistics and agriculture. Subject of crop Yield prediction has been very popular among various organizations working in agriculture, producers, etc. Prediction of crop yield helps in managing the storage of crops as well as it directs the transportation decisions, and risk management issues related to crops. Data mining focuses upon methodologies for extracting useful knowledge from data and there are several tools to extract the knowledge that is it is a proficiency of examining the dataset such that the end results can be deduced easily and rapidly from the dataset. Knowledge gathered can be used to forecast the paddy yield. We collected the data from different government organizations, after preprocessing of data applied k nearest neighbor algorithm using Data Mining using soil nutrients, fertilizers nutrients, rainfall and temperature.
Background: Animal phenotype performance depends on both genetic and non-genetic factors, but mostly the genetic part analysed leaving non genetic parameters unnoticed. The aim of the study is to understand impact of non-genetic factors governing milk yield performance in Deoni cows. Methods: A total of 821 lactation records from 340 lactating cows (2002-2017) along with their age at first calving, parity, season of calving were collected. These data were standardized and analysed to find significant differences using Duncan’s multiple range Test. Result: Deoni cows showed significant increase (P less than 0.05) in both lactation milk yield and lactation length with increase in parity. Season of calving had significant affects (P less than 0.05) on lactation milk yield but not in lactation length. Increase in age at first calving revealed there was a significant difference (P less than 0.05) in both lactation milk yield and lactation length.
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