Description
Calculate Weather Indices.
Description
Weather indices represent the overall weekly effect of a weather variable on crop yield throughout the cropping season. This package contains functions that can convert the weekly weather data into yearly weighted Weather indices with weights being the correlation coefficient between weekly weather data over the years and crop yield over the years. This can be done for an individual weather variable and for two weather variables at a time as the interaction effect. This method was first devised by Jain, RC, Agrawal R, and Jha, MP (1980), "Effect of climatic variables on rice yield and its forecast",MAUSAM, 31(4), 591–596, <doi:10.54302/mausam.v31i4.3477>. Later, the method have been used by various researchers and the latest can found in Gupta, AK, Sarkar, KA, Dhakre, DS, & Bhattacharya, D (2022), "Weather Based Potato Yield Modelling using Statistical and Machine Learning Technique",Environment and Ecology, 40(3B), 1444–1449,<https://www.environmentandecology.com/volume-40-2022>.
README.md
weatherindices
The goal of weatherindices is to facilitate the calculation of Weather Indices for crop yield modeling.
Installation
You can install the development version of weatherindices from GitHub
# install.packages("devtools")
devtools::install_github("akstat21/weatherindices")
Example
To calculate weather indices you require weekly weather data and yearly yield data. Both the data should be separate variables. The number of data points in weather variable should be multiple of numbers of years of yield data and number of weeks considered in each year.
library(weatherindices)
data(Burdwanweather) #Weekly weather data for the rice growing season in Burdwan
data(Burdwanriceyield) #Yearly Yield data of rice in Burdwan
wwi.maxtem<-wwi(Burdwanriceyield$burdwan,Burdwanweather$Max.Temperature)
wwi.maxtem
## [1] 33.14249 32.51634 35.96486 31.53676 34.88116 35.34955 33.88576 33.68535
## [9] 34.35228 33.81517 33.76889 34.35719 33.42879 32.97895 33.25367 33.55808
## [17] 33.69856 34.04214 32.90105 30.63825 31.80091 32.37349 34.51790 32.84096
## [25] 35.32921 31.43451 32.36830 31.46721 34.06736 33.93954 32.20486 34.58240
## [33] 31.96571 33.42984 32.72208 33.19853 32.53645 32.91940 35.10816