TY  - BOOK
AU  - Dhanya G
AU  - Brigit Joseph (Guide)
TI  - Statistical modelling for the impact of weather and soil parameters on the yield of paddy under  long term fertilizer experiment 
U1  - 630.31 
PY  - 2019///
CY  - Vellayani
PB  - Department of agricultural Statistics , College of Agriculture
KW  - Statistical modelling
KW  - agricultural statistics
N1  - MSc
N2  - The study entitled “Statistical modeling for the impact of weather and soil parameters on the yield of paddy under Long Term Fertilizer Experiment” was undertaken with the objective to develop suitable statistical models to analyse the change in weather variables over time. It also focused on changes in soil parameters across treatments in Long Term Fertilizer Experiment (LTFE) over the years and the impact of weather and soil parameters on the yield of paddy. The analysis was carried out based on secondary monthly data of weather parameters viz maximum temperature, minimum temperature and total rainfall, collected for a period 1985-2014 from the Department of Agricultural Meteorology, College of Agriculture, Vellayani. Data on soil organic carbon, phosphorus, potassium, grain yield and straw yield in kharif and rabi season were collected from the ‘Permanent plot experiment on integrated nutrient system for a cereal based crop sequence’ conducted at Integrated Farming System Research Station (IFSRS), Karamana on rice (variety Aiswarya) for a period 1985-2013. The experiment was conducted in Randomised Block Design with 12 treatments (named as T1, T2,…, T12) and 4 replications. 
Mean, Standard deviation and coefficient of variation of maximum temperature, minimum temperature and total rainfall was worked out for different years. Maximum temperature (2.69-5.36) and minimum temperature (2.78-7.26) have shown less coefficient of variation however, coefficient of variation was very high for total rainfall (74.11-127.17). Autoregressive Integrated Moving Average (ARIMA) models were used to model maximum and minimum temperature based on their own past lagged values. ARIMA (101) (111) was found to be the best model for maximum temperature as it has satisfied least AIC (Akaike Information Criteria) and BIC (Bayesian Information Criteria). ARIMA (011) (011) was found to be the best model for minimum temperature. Seasonal effect was removed to avoid cyclical fluctuations in rainfall and variation in monthly rainfall over time was estimated using Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) model. GARCH (2, 1) and E-GARCH (1, 1) with 1 lag were found to be the best model to explain the variability over the period (1985-2013). High fluctuation in total rainfall was noticed during the years 1999 and 2000 based on conditional standard deviation graph.
Multivariate Analysis of Variance (MANOVA) was performed on soil parameters to test the significant difference between treatments over the years in kharif and rabi. There was significant difference between soil organic carbon, phosphorus and potassium between 12 treatments during 6 years (1990, 1995, 2000, 2005, 2010, and 2013) in both seasons. Further ANOVA was done to test the significant difference between treatments based on each soil parameters. Results of Analysis of Variance (ANOVA) revealed that T8 had high soil organic carbon and potassium whereas T3, T8 and T9 showed high soil phosphorus in kharif. T8, T3 and T9 showed highest soil organic carbon, phosphorus and potassium respectively in rabi.  Split-split plot analysis was conducted with main plot as year, sub plot as seasons and sub-sub plot as treatments to test the interaction effect of treatments with season and year. Only the year×treatment interaction was found significant in case of organic carbon whereas year×treatment, season×treatment interactions were found significant for phosphorus and potassium. This result indicated that there was significant difference in potassium and phosphorous over the seasons with respect to treatments.   
On comparing the yield of different treatments T6 was found with highest grain yield and T1 was the least in both seasons. The graph for trend in grain yield and straw yield suggest same pattern for all the treatments over the entire period. Split-split plot analysis was carried out to find out the interaction effect of treatment×season, treatment×year and treatment×season×year on grain yield and straw yield. There was significant interaction between years and seasons for straw yield. 
To find out the impact of weather parameters and soil parameters on grain yield, regression was performed by taking soil and weather parameters as independent variables. The results of regression analysis suggest that there was significant and negative influence of maximum temperature and soil potassium on grain yield whereas the effect of total rainfall on grain yield was positive and significant in kharif season. However, minimum temperature and total rainfall were influencing positively and significantly the grain yield in rabi season. 
ARIMA models were found to be the best model for predicting maximum and minimum temperature and GARCH models were found to be good in estimating volatility of total rainfall. T6 (50 percent Recommended dose of fertilizers (RDF) - (90: 45: 45 kg NPK/ha) of NPK+ 50 percent FYM in kharif and 50 percent RDF of NPK in rabi) showed good result for grain yield by comparing treatment wise performance throughout the experiment during kahrif and rabi. The treatment absolute control (T1) has recorded with lowest yield.  The effect of weather and soil parameters on the yield of rice studied using regression analysis across treatments revealed that total rain fall had positive and significant effect on grain yield of twelve treatments except T2.  In case of treatments T6 and T7, minimum temperature also had positive effect on grain yield but in case of T1  soil phosphorus and maximum temperature  also showed positive significant result.
UR  - http://krishikosh.egranth.ac.in/handle/1/5810146784
ER  -