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Kerala Agricultural University, Thrissur

The history of agricultural education in Kerala can be traced back to the year 1896 when a scheme was evolved in the erstwhile Travancore State to train a few young men in scientific agriculture at the Demonstration Farm, Karamana, Thiruvananthapuram, presently, the Cropping Systems Research Centre under Kerala Agricultural University. Agriculture was introduced as an optional subject in the middle school classes in the State in 1922 when an Agricultural Middle School was started at Aluva, Ernakulam District. The popularity and usefulness of this school led to the starting of similar institutions at Kottarakkara and Konni in 1928 and 1931 respectively. Agriculture was later introduced as an optional subject for Intermediate Course in 1953. In 1955, the erstwhile Government of Travancore-Cochin started the Agricultural College and Research Institute at Vellayani, Thiruvananthapuram and the College of Veterinary and Animal Sciences at Mannuthy, Thrissur for imparting higher education in agricultural and veterinary sciences, respectively. These institutions were brought under the direct administrative control of the Department of Agriculture and the Department of Animal Husbandry, respectively. With the formation of Kerala State in 1956, these two colleges were affiliated to the University of Kerala. The post-graduate programmes leading to M.Sc. (Ag), M.V.Sc. and Ph.D. degrees were started in 1961, 1962 and 1965 respectively. On the recommendation of the Second National Education Commission (1964-66) headed by Dr. D.S. Kothari, the then Chairman of the University Grants Commission, one Agricultural University in each State was established. The State Agricultural Universities (SAUs) were established in India as an integral part of the National Agricultural Research System to give the much needed impetus to Agriculture Education and Research in the Country. As a result the Kerala Agricultural University (KAU) was established on 24th February 1971 by virtue of the Act 33 of 1971 and started functioning on 1st February 1972. The Kerala Agricultural University is the 15th in the series of the SAUs. In accordance with the provisions of KAU Act of 1971, the Agricultural College and Research Institute at Vellayani, and the College of Veterinary and Animal Sciences, Mannuthy, were brought under the Kerala Agricultural University. In addition, twenty one agricultural and animal husbandry research stations were also transferred to the KAU for taking up research and extension programmes on various crops, animals, birds, etc. During 2011, Kerala Agricultural University was trifurcated into Kerala Veterinary and Animal Sciences University (KVASU), Kerala University of Fisheries and Ocean Studies (KUFOS) and Kerala Agricultural University (KAU). Now the University has seven colleges (four Agriculture, one Agricultural Engineering, one Forestry, one Co-operation Banking & Management), six RARSs, seven KVKs, 15 Research Stations and 16 Research and Extension Units under the faculties of Agriculture, Agricultural Engineering and Forestry. In addition, one Academy on Climate Change Adaptation and one Institute of Agricultural Technology offering M.Sc. (Integrated) Climate Change Adaptation and Diploma in Agricultural Sciences respectively are also functioning in Kerala Agricultural University.

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  • ThesisItemOpen Access
    Multiphase analysis of cocoa production in Kerala
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2020) Shivakumar, M; KAU; Ajitha, T K
    Cocoa (Theobroma cacao L.) is a very important crop as it provides food, income, employment and resources for poverty reduction. It ensures livelihood for millions of small holder farmers and offers raw material for the multibillion global chocolate industries. Despite the fact that Kerala has enormous potential in terms of suitable agricultural land, cocoa has failed to become a significant crop. As its domestic production is not sufficient to meet the increased demand, the industry has to resort to substantial imports. So, a comprehensive study titled “Multiphase analysis of cocoa production in Kerala” has been made on different aspects of cocoa cultivation, management practices, production and the constraints faced by actual growers. The trend analysis and forecasting of yearly area, production and productivity of cocoa in Kerala using advanced time series models employed on the data for the period from 1980-2017 revealed a distinct quadratic trend for the area under cocoa, having an increasing trend now and more or less linear stochastic trends for production and productivity. The Holt’s exponential smoothing model was identified as the best to predict yearly area under cocoa with an adjusted R2 equal to 0.94. The yearly production of cocoa could be well modelled by ARIMA (0,1,1) with an adjusted R2 = 0.72. By incorporating area under cocoa as an independent variable, ARIMAX (0,1,0) model could improve the R2 to 0.84 to predict the yearly production of cocoa. The productivity of cocoa seemed to be constant for several years (0.45tonnes/ha) which was well predicted through the simple exponential smoothing model with an adjusted R2 = 0.84. Evaluation of the performance of 100 selected cocoa hybrids in the Cocoa Research Centre, College of Horticulture, KAU, Vellanikkara showed that the peak average monthly yield was in the month of November (18.14pods) followed by the yield in October (18.04) and December (14.56). A pattern of biennial tendency persisted for the yearly yields of the hybrids. The results of General linear model repeated measures ANOVA highlighted the existence of a significant Time x Factor interaction with a partial eta squared equal to 0.14 where factor denotes different subgroups of cocoa hybrids with homogeneous yield. After the first harvest, the peak average yield was noticed during the fifth year irrespective of different low and high yielding groups. The income from cocoa farming depends on healthy pods harvested. So, an attempt was also made to account for the frequency of number of infected pods from each tree and it could be well demonstrated by geometric distribution which is a special case of Negative binomial distribution. Owing to the fact that the infected pods might be the outcome of external factors like weather variables, the influence of those factors with cocoa yield was also investigated. A stepwise regression of yield on previous five month’s accumulated weather variables resulted in a parsimonious prediction equation with total number of rainy days as the single regressor which could explain 66% of the variation in yield. The adjusted R2 could be enhanced to 69% by incorporating maximum temperature as the second most important regressor. The vide variation realised in the average monthly yield of cocoa hybrids could be well captured through SARIMA (1,0,0) (1,1,0)12 model with an adjusted R2 = 0.92. An empirical analysis to identify the factors perceived by farmers to influence their cocoa production and ultimately their income was performed taking a total sample of 100 farmers from Veliyamattom Panchayat of Idukky district and Iritty Panchayat of Kannur district. From a path analysis through structural equation modelling several linear regression equations could be generated simultaneously leading to prediction equations for cocoa yield and income. The final model iterated resulted in goodness of fit measures viz; comparative fit index = 0.96 and Tucker Lewis index = 0.94. Price of cocoa turned out to be the most prominent factor which contributed to the income of a cocoa farmer highlighting the importance of fixing the marketing price of cocoa. Second factor was yield per tree which was the outcome of good quality seedlings, efficient cultivation practices, plant protection and disease management measures, protection from rodent attacks etc. Importance of access to credit which would help to overcome the problems of lack of capital was emphasised. Financial problems such as inability to get assistance from financial institutions, lack of proper marketing facilities including drying and fermentation facilities of cocoa beans also were noticed. Probit analysis identified the factors viz; age of the farmers, land holding size, experience in cocoa cultivation, membership in organisations like Krishibhavan, farmer’s club, Cooperative society, Banks, SHGs etc. and frequency of contact with extension personnel to be significant for decision making to implement plant protection measures which were inevitable for successful crop management and ultimately leading to the net income of farmers. The yield gap analysis revealed that as against the potential yield (dry bean weight) of 4kg/tree/year, the national average yield from cocoa farmers was only 2.5 kg/tree/year resulting in a yield gap of 37.5% which need adequate attention.
  • ThesisItemOpen Access
    Inter-regional disparity in soil fertility status of southern Kerala-a statistical analysis
    (Department of Agricultural Statistics, College of Agriculture, Vellayani, 2020) Nayana, Narayanan; KAU; Brigit, Joseph
    The research work entitled ‘Inter-regional disparity in soil fertility status of southern Kerala – a statistical analysis’ was carried out at College of Agriculture Vellayani during 2018-2020. The objective was to develop soil fertility status index to assess regional disparity among the panchayats of Thiruvananthapuram, Kollam and Pathanamthitta districts of southern Kerala and to classify the panchayats based on soil fertility index. Secondary data on 12 soil fertility parameters, collected as a part of Kerala State Planning Board Project conducted in 2013 were used for the analysis. Detection and elimination of outliers using box plot was the first step in the analysis followed by estimation of mean vector of 12 soil fertility parameters from 43 panchayats of Kollam, 50 panchayats of Pathanamthitta and 44 panchayats of Thiruvananthapuram. The descriptive statistics used include range, mean and coefficient of variation (CV) of all the parameters. The entire analysis was done with the help of SPSS software. Among the 12 soil fertility parameters the range of Cu (0.34 - 49.89 mg kg-1), S (0.19 - 91.78 mg kg-1) Fe (7.07 - 391.84 mg kg-1) and K (17.49 - 851.85 kg ha-1) was observed to be very high with CV 249.62, 188.98, 154.22 and 92.32 per cent respectively in Kollam district. This is an indication of deficiency to above adequate availability of these nutrients in the panchayats. Among all the parameters highest consistency was observed in pH with mean value of 6.01 and the values varied between 4.66 and 8.13. EC values varied between 0.03 and 0.73dS m-1 showing a mean valueof 0.17 dS m-1 with a CV of 72.03 per cent. Almost a similar CV was observed for both OC (40.45 %) and P (44.94 %) with their values in the range 0.46 to 2.06 per cent and 9.67 to 111.49 kg ha-1 respectively. There observed a wide spread deficiency of Ca and Mg in almost all the panchayats. Among the micronutrients considered highest consistency was shown by B with a CV of 39.78 per cent and the lowest observed value of B was 0.13 mg kg-1 and the highest was 1.17 mg kg-1. Mean value of Mn was 17.47 mg kg-1 with a CV of 63.59 per cent. Available content of Mn varied between 2.33 and 46.32mg kg-1. PCA (Principal Component Analysis) performed on the mean vector of 12 soil fertility parameters of 43 panchayats in Kollam, extracted four PCs which accounted for 72.73 per cent variation of the data and based on the extracted PCs weighted aggregate index known as Soil Fertility Index (SFI) was developed to quantify the soil fertility status. The SFI thus estimated varied from 16.34 to 435.12 in Kollam with a mean of 111.31 and CV of 82.45 per cent. Further, factor analysis was performed to reduce the dimension and the soil nutrients P, Ca, K, OC, Fe, S and Cu had loading above 0.5 with a communality of more than 50 percent were retained and PCA was repeated and SFI was re-estimated. The estimated SFI was normalized using min-max normalization and based on this, the panchayats were grouped into four categories as low (SFI from 0-25%), medium (25-50%), high (50-75%) and very high (75-100%). In Kollam about 70 per cent of the panchayats were included in low fertility class with respect to initial SFI and 79 per cent with respect to SFI- FA. The soils in the panchayats listed in the low fertile category reported to have deficiency in Ca, Mg, S and Cu. While considering Pathanamthitta, EC has shown highest CV with its values range from 0.06 to 2.75 dS m-1. All the soil nutrients except B and Mg were adequate in the soils of most of the Panchayats in Pathanamthitta. pH was said to have high consistency with a CV of 8.92 per cent with its values in the range 4.20 to 6.56. CV of 27.70 and 28.80 per cent were observed for OC and K respectively with their values in the range of 0.64 to 3.26 per cent and 148.32 to 475.67 kg ha-1. CV of P was 61.85 per cent with mean value 66.62 kg ha-1. Lowest observed value of P was 4.63kg ha-1 and highest was 195.87 kg ha-1. Ca content had shown variation from 82.10 to 1000.00 mg kg-1. Mean value of Ca was 634.92 mg kg-1 with a CV of 48.60 per cent. Values of Mg were in the range of 26.62 to 461.50 mg kg-1showing 94.30 per cent CV. CV of S was 36.89 per cent with mean value 20.60 mg kg-1. A CV of 102.39 per cent was observed for B with values in the range 0.05 to 3.06 mg kg-1. CV of Cu and Fe was 35.86 per cent (for values between 0.87 to 5.51 mg kg-1) and 41.56 per cent (values in the range 20.27 to 101.62mg kg-1) respectively with their mean values as 2.73 mg kg-1 and 35.20 mg kg-1. Values of available Mn varied between 3.73 to 69.88 mg kg-1 with a CV of 63.17 per cent. PCA extracted four PCs which accounted for 68.70 per cent variation in the data. The mean and CV of the estimated SFI of Panchayats were respectively 131.75 and 33.49 based on initial PCA. The parameters OC, Cu, K, B and Mn were exempted based on factor analysis and the mean and CV of SFI- FA was respectively 185.90 and 38.30 per cent. Classification based on SFI- FA has shown an improvement in fertility status and the results of classification revealed that about 56 percent Panchayats were in high to very high soil fertility category in Pathanamthitta.This was because the relatively good availability of most of the soil parameters like OC, P, K, S, Cu, Fe and Mn in the panchayats of Pathanamthitta. In Thiruvananthapuram also more consistency was shown by pH with a CV of 3.67 per cent with values in the range 5.01 to 6.32. Most of the soil fertility parameters except EC (152.15%), Cu (130.98%), K (80.66%), B (54.24%) and Fe (59.15%) were found to have CV below 50 percent indicating less variability among the Panchayats in Thiruvananthapuram. The range of values of OC and P were respectively 0.48 to 1.70 percent, 13.11 to 38.94 kg ha-1. CV of Ca was 34.94 per cent with majority of the panchayats having available Ca above 300 mg kg-1. For Ca, lowest value observed was 170.80 mg kg-1 and highest value was 882.50 mg kg-1. Mg availability varied between 29.60 to 200.79 mg kg-1 and that of S was 12.00 to 61.38 mg kg-1. There noticed an adequacy in Fe and Mn availability but B was deficient. PCA extracted five PCs which accounted for 75.06 per cent variation in Thiruvananthapuram. SFI constructed had a mean of 95.93 with a CV of 27.68 per cent. The result of factor analysis concluded that all the parameters were found to be relevant in explaining the variation in soil fertility. Based on SFI, it was observed that 51.35 percent of panchayats in Thiruvananthapuram were in the medium fertility status category with almost all the parameters were found to be sufficiently available in most of the panchayats except B and Mg in Thiruvananthapuram. The results of the study based on the developed SFI confirmed inter-regional disparity between panchayats within in each district as well as between districts. The soil fertility status of panchayats in Kollam was poor as compared to Panchayats in Pathanamthitta and Thiruvananthapuram, but soil fertility status of Panchayats in Thiruvananthapuram was low as compared to Pathanamthitta and this conclusion was statistically supported by Kruskal-Wallis test.
  • ThesisItemOpen Access
    Comparative analysis of different stability models on superior cultures of paddy (Oryza sativa)
    (Department of Agricultural Statistics, College of Agriculture, Vellayani, 2020) Adarsh, V S; KAU; Brigit, Joseph
    The research work entitled ‘Comparative analysis of different stability models on superior cultures of paddy (Oryza sativa)’ was carried out at College of Agriculture, Vellayani during 2018-2020. The objective was to compare various linear and nonlinear stability models to identify stable superior cultures of paddy and to study the clustering pattern of cultures over the years. Secondary data from various performance evaluation trials conducted on superior cultures of paddy (13 cultures) over the years (2015-2018) at RARS, Pattambi was used for the analysis. The five characters included in the study were plant height (cm), number of panicles per plant, straw yield (t ha-1), grain yield (t ha-1) and 100 grain weight (g). Bartlett’s Chi square test was used for testing the homogeneity of the error variance over the years for all the characters as an initial step. Among the five characters only straw yield has shown heterogeneity in error variance. Further, pooled analysis was done to test G X E interactions based on all the characters. The results of pooled analysis confirmed a significant G X E interaction for grain yield, straw yield and plant height revealing that genotypes responded differentially to environment. The stability of 13 paddy cultures over the years was done using Eberhart and Russell’s model, Perkins and Jinks model, Freeman and Perkins model and AMMI model. ANOVA and stability parameters viz regression coefficient and deviation from regression for each model was estimated. The stable genotypes identified in Eberhart and Russell’s model based on regression coefficient (b^Ei) and deviation from regression (S_di^2 (E)) were Cul2 followed by Cul5 and Cul15. In case of Perkins and Jinks model the stable genotypes determined on the basis of the regression coefficient (B_i) were Cul2 and Cul5. However, Cul10 followed by Cul2 and Cul5 were found to be the stable genotype under Freeman and Perkins model with respect to the stability parameter- regression coefficient (b_Fi). AMMI model incorporates Principal Components Analysis (PCA) for GEI and based on the results of this model cultures Cul2, Cul5, Cul15 and Cul9 were identified as the stable genotypes across different environments particularly in first and third environments. Env 1 and Env 2 showed opposite characteristics while Env 2 was comparatively more stable than other two but was less yielding. One among the AMMI Model selection indices named as AMMI based Selection Index (ASTABi) was used to rank the genotypes to obtain the stable genotypes. This also resulted in obtaining the most stable genotypes as Cul2. Comparison of the four stability models was carried out using Kendall’s coefficient of concordance revealed no similarity among the ranking of parameters of these four models. Spearman’s rank correlation coefficient was determined for the pair wise comparison of fur models. The correlation matrix and correlogram was also obtained. A perfect positive rank correlation was observed between the parameters of Eberhart and Russell model and Perkins and Jinks model suggesting the similarity of the parameters under these two models. Whereas the rank correlation between Eberhart and Russell and Freeman and Perkins with AMMI model were non-significant indicated the deviation of the results in AMMI model from other two models. However, AMMI model was found to be the best since complete enumeration, summarization, and pattern of GEI interaction was made possible only in this model. The most stable genotype based on the entire four models was Cul2 followed by Cul5, Cul9 and Cul15. The hierarchical cluster analysis using euclidean distance as similarity measure and average linkage as clustering method was performed using the grain yield over the three years (2015-2018). This result of the study also emphasised that the highly stable genotypes identified under different stability models were clustered together in a single cluster (Cluster II). The intra cluster and inter cluster distance measure revealed that there was high genetic divergence between the clusters in which the stable genotypes are included. Cluster analysis was also performed for different years based on all the characters under study. The clustering pattern in the year 2015-16 and 2017-18 was found to be almost similar in nature since the genotypes enclosed in the clusters were nearly same. In the year 2016-17 the clustering pattern was found to be different from other two years. This shows the influence of environment in the performance of the genotype. The clusters in which high genetic divergence was found in the year 2015-16 which was similar to that of the clusters in 2017-18. The cluster mean for the five characters under study showed extensive difference. Cluster II had recorded highest mean for most of the character (straw yield (t ha-1), grain yield (t ha-1) and 100 grain wt (g)). Therefore, hybridization between the selected genotypes from the divergent clusters is essential to judicously combine all the targeted traits. Among the different stability models studied Eberhart and Russells and Perkins and Jinks models provided almost similar stable cultures which was highly related to the cultures selected on the basis of AMMI model. Moreover, the different stability cultures identified were put together in one cluster in cluster analysis further confirmed the superiority of the stable genotypes over the others.
  • ThesisItemOpen Access
    Statistical models for climate change in northern and central Kerala
    (Department of Agricultural Statistics, College of Agriculture, Vellayani, 2020) Gokul Krishna, K B; KAU; Brigit, Joseph
    STATISTICAL MODELS FOR CLIMATE CHANGE IN NORTHERN KERALA The research work entitled “Statistical models for climate change in northern Kerala” was carried out at college of agriculture, Vellayani during 2018-2020. The objective was to develop statistical models to evaluate climate change over time across different regions of northern Kerala and to determine the effect of climate change on paddy production. The secondary data of rainfall, maximum temperature and minimum temperature was collected from RARS Pattambi for a period of 37 years (1982-2018) and from RARS Pilicode for a period of 36 years (1983-2018). The secondary data on paddy production from Palakkad district of Kerala was collected from the report of agricultural statistics, GOK for a period of 23 years (1995-2017). The descriptive statistics for the monthly data of weather parameters include mean, range, coefficient of variation, skewness and kurtosis. The analysis of weather data was done with the help of R programming and open software Gretl. The results of descriptive statistics and box plot of weather parameters showed that heavy rains was received in some months during several years at Pilicode as compared to Pattambi. Moreover, the Skewness for all the weather parameters of both Pilicode and Pattambi showed positive and negative skewness which indicated the absence of normal distribution among the weather data. The climate change over the years of different weather parameters was analysed in terms of trends and its presence and direction was determined. Shapiro-wilks test was initially conducted for the weather parameters which showed that all the parameters didn’t follow normal distribution for both Pilicode and Pattambi. The trend was detected using non parametric Mann-Kendall (MK) test and trend was estimated using Sen's slope estimator. The results of MK test and Sen's slope estimator revealed positive nonsignificant trend for annual, summer and monsoon rainfall and maximum temperature in all the seasons at Pilicode suggests a nonsignificant increase in both parameters overtime. However a significant increase in summer rainfall and significant decrease in annual maximum temperature was recorded in Pattambi. Moreover the annual rainfall of Pilicode was more as compared to Pattambi with low maximum temperature in Pilicode. The deseasonalized rainfall (Z value is 2.00, P value is 0.04) of Pilicode also showed a significant positive trend. The classification of weather data in to different seasons also helped to identify season wise significant trend in different weather parameters Modeling of weather parameters was done in order to develop best model which is suitable for determining the climate change. Seasonal ARIMA model was selected for modelling the weather data since the weather data consist of seasonality. The best model was estimated with the help of X12 ARIMA in Gretl open source software. The best model was selected on the basis of least AIC value, BIC value and Hannan-Quinn criterion value. The X12 ARIMA automatically detect the best model and then the best model was confirmed by trial and error method that no other models have the least value for the criterion. The best estimated models were respectively SARIMA ((0,1,1)(0,1,1)12) for rainfall, SARIMA ((1,0,1)(0,1,1)12) for Maximum Temperature and SARIMA ((1,0,1)(0,1,1)12) for minimum temperature for Pilicode. Similarly the best models identified for rainfall, maximum temperature and minimum temperature were respectively SARIMA ((0,0,0)(0,1,1)12), SARIMA ((1,0,1)(0,1,1)12) and SARIMA ((0,1,1)(0,1,1)12) for Pattambi. The validation of the model was done with the help of forecasting and comparing for 2018 and mean absolute error for forecasted was calculated. The mean absolute error was low for forecasted maximum temperature and minimum temperature in both Pilicode and Pattambi but the rainfall had high standard error and mean absolute error which was due to the fluctuations in rainfall at both Pilicode and Pattambi respectively. The multiple linear regression was done to analyse the impact of weather parameters on the production of Paddy uder unirrigated area in virippu and mundakan season from 1995 to 2017 at Palakkad district. The results of the analysis reported no significant influence of weather parameters on virippu season but maximum and minimum temperature in October and maximum temperature in November had significant negative influence on mundakan Paddy production. While an increase in maximum temperature during December was favourable for mundakan Paddy production. The results of the analysis on climate change indicated an increase in rainfall and decrease in maximum temperature over time at Pilicode as compared to Pattambi. SARIMA models were found to be the best model for weather parameters for prediction or forecasting. An increase in maximum temperature during December was favourable while increase in maximum and minimum temperature during October was unfavourable to Paddy production under unirrigated area in mundakan season.
  • ThesisItemOpen Access
    Statistical modelling of climate change in southern Kerala
    (Department of Agricultural Statistics, College of Agriculture, Vellayani, 2020) Neethu, R S; KAU; Brigit, Joseph
    The research work entitled ‘Statistical modelling of climate change in Southern Kerala’ was carried out at College of Agriculture Vellayani during 2018-2020. The objective was to develop statistical models to analyze climate change overtime across different regions in Southern Kerala and to determine its impact on the yield of paddy and cardamom. The weather data corresponds to maximum temperature, minimum temperature and rainfall was collected from four agrometeorological stations under KAU in Southern Kerala viz., College of agriculture, Vellayani, RARS, Kumarakom, CRS, Pampadumpara for a period of 29 years (1991-2019) and from RRS, Moncompu for a period of 22 years (1997-2018). Secondary data on production of Paddy and cardamom was collected from the Report of Agricultural Statistics, GOK for a period of 24 years (1995-2018) and from the Spices Board for a period of 22 years (1997-2018) respectively. The entire analysis was done with the help of Microsoft excel, SPSS, Gretl and R programming software. Descriptive statistics includes mean, range, standard deviation and coefficient of variation and box plot was used to describe the features of monthly weather parameters over the years. The results from the box plot and descriptive statistics indicated that heavy rainfall was received in July, August 2018 at Kumarakom and Moncompu followed by Pampadmpara and Vellayani. Rainfall patterns in Kumarakom, Moncompu and Idukki showed more deviations from normal rainfall as compared to Vellayani over the study period. Among the four regions the highest annual maximum temperature was observed at Kumarakom and the lowest at Pampadumpara and highest precipitation was observed in Kumarakom followed by Moncompu, Pampadumpara and lowest in Vellayani. Trend analysis was performed to understand the climate change on the basis of rainfall, maximum and minimum temperature with the help of Mann-Kendall test and Sen’s slope estimator for annual and different seasons in four regions. No significant trend was noticed in annual and different seasons rainfall while a decline in rainfall was noticed for south west and north east monsoon at Vellayani. At the same time a significant increasing trend was observed for maximum temperature in all the seasons at Vellayani. Whereas a significant and decreasing trend in annual and north east monsoon rainfall with a significant increase in north east and south west monsoon maximum temperature and a decrease in winter maximum temperature at Kumarakom is an indication of the adverse change in climate change overtime. Even though no significant trend was noticed in rainfall except for annual and north east monsoon, the slope estimator was negative indicating a decline in rainfall with a non-significant increase in maximum temperature at Moncompu. Pampadumpara in the high range zone also recorded with decrease in rainfall during all the seasons. The magnitude of trends in rainfall and maximum and minimum temperature of different stations provides a more precise picture about the change in weather variables. The magnitude of the negative trend for the annual rainfall was highest at Moncompu (-43.5 mmyear-1) and lowest at Vellayani (-0.871 mm year-1). But for maximum temperature a positive trend was obtained in majority of the stations and a significant negative trend was seen at Pampadumpara. The magnitude of the positive slopes of the annual maximum temperature was highest at Vellayani (0.038 0 Cyear-1) and lowest at Moncompu (0.004 0 C year-1). Pampadumpara had recorded with a highest positive slope of 0.121 0 C year-1 for minimum temperature followed by 0.024 0 Cyear-1 at Moncompu. In general a decrease in annual rainfall and an increase in temperature were observed in Southern Kerala over the years. Seasonal ARIMA models were used to model rainfall data of the three stations. Rainfall data in the level form was stationary and a prominent seasonality was found out which indicated that the order of integrating factor was 0 for non-seasonal component and 1 for seasonal component. Using trial and error method the best model among the randomly chosen models was selected on the basis of least AIC, BIC and Hannan- Quinn criteria. The best identified SARIMA models for rainfall were respectively ARIMA (1, 0, 0) (0, 1, 1)12 for Vellayani, ARIMA (0, 0, 0) (0, 1, 1)12 for Kumarakom and ARIMA (0, 0, 0) (0, 1, 1)12 for Pampadumpara to forecast monthly rainfall in these regions. Multiple regression analysis was performed to analyse the impact of weather parameters on the production of paddy in Kottayam indicated that rainfall during July had a negative significant effect, maximum temperature during August and minimum temperature during June had a positive significant influence on the production of paddy in Viruppu season without considering the influence of other factors of production. While maximum and minimum temperature during October had a positive significant effect and minimum temperature during December and excess rainfall during October and November had a negative significant effect on Puncha paddy yield. The estimated regression coefficients of rainfall during July to September, maximum temperature during April to June and minimum temperature during January to March and July to September were found to be significant and the negative sign of the parameters indicating the adverse influence weather parameters on the production of cardamom. However, maximum temperature during July to September, minimum temperature during April to June had a positive impact on yield once again suggesting the negative impact of excess rain during this period. Moreover, the coefficient of rainfall in all the quarters had a negative sign indicating the negative effect of excess rainfall on cardamom productivity particularly during the harvesting period. The results of the study based on trend analysis indicated clear evidence about climate change in terms of weather parameters occurred across different regions in Southern Kerala during the study period. SARIMA model was found to be the best model for prediction of rainfall in the selected regions. The impact analysis of weather parameters on the production concluded that the weather parameters during different months had either a positive or a negative influence on the production of paddy and cardamom.
  • ThesisItemOpen Access
    Comparison of methods for optimum plot size and shape for field experiments on paddy (Oryza sativa)
    (Department of Agricultural Statistics, College of Agriculture, Vellayani, 2019) Athulya, C K; KAU; Brigit Joseph
    The research work entitled “Comparison of methods for optimum plot size and shape for field experiments on paddy (Oryza sativa)” was conducted with the objective of estimation and comparison of methods for optimum plot size and shape for field experiments on high yielding variety of paddy. The study was based on primary data collected from a uniformity trial conducted in an area of 800m2 with Uma variety of paddy in virippu season 2018 at Integrated Farming System Research Station (IFSRS), Karamana. The crop was transplanted at a spacing of 20 cm × 15 cm. The field was divided in to 1.2 m × 1.2 m (1.44 m2) plots, after leaving a border of one meter from all the sides of the plot to eliminate the border effects, thus give rise to 400 basic units. Observations on plant height and number of tillers were recorded separately from each basic unit at monthly intervals and number of productive tillers, thousand grain weight, grain yield and straw yield were recorded separately from each basic unit at the time of harvest. The average height of the plant increased from 40.55 cm at one month after planting (MAP) to 121.37 cm at four MAP. The number of tillers per plant varied from 4 at two MAP to 14 at four MAP. The grain yield per basic unit varied from a minimum of 200 g to a maximum of 650 g with an average yield of 391.13 g per plot. The average straw yield was 0.501 kg. The first quartile (Q1) was observed at 0.410 kg and third quartile (Q3) was at 0.572 kg. The estimated average harvest index was 0.438 with a coefficient of variation (CV) of 20.78 per cent. The mean productive tillers estimated was 9 per plant. The correlation between productive tillers and grain yield was significant (0.128). Harvest index showed a very high significant correlation of 0.744 with grain yield. Soil fertility contour map was constructed based on yield data of all original basic units and by taking 3 × 3 and 5 × 5 moving average and the results of the analysis have shown that 3 × 3 moving average provided a more prominent picture of fertility status of the field and thus concluded that fertility gradient was more in horizontal direction. Serial correlation of horizontal and vertical strip and mean squares between vertical and horizontal strips also revealed that fertility gradient was more pronounced in horizontal direction. The optimum plot size estimated by combining the basic units of 1.44 m2 into plots of different sizes along with CV for each plot size. The different methods used for the estimation of optimum plot size are maximum curvature method, Fairfield Smith’s variance law method, modified maximum curvature method, comparable variance method, cost ratio method, covariate method, based on shape of the plot method and Hatheway’s method. Generally these methods need not provide a unique estimate. The optimum plot size estimated under maximum curvature method and comparable variance method was 34.56 m2 (24 basic units) with rectangular shape and it was same for both methods. The optimum plot size estimated under covariate method by taking harvest index as covariate was also 34.56 m2. The optimum plot size estimated by considering length (X1) and breadth (X2) also provided same plot size (34.56 m2) with X1 =3 units and X2 =8. Optimum plot size under Hatheway’s method was estimated by choosing varying number of replications and difference between treatment means. A plot size of 37.44 m2 (26 basic units) for four replications and 10 per cent difference between the treatment means was found to be optimum under this method. The optimum plot size estimated under Fairfield Smith’s variance law method and modified maximum curvature method was 8.64 m2 and it was not considered as optimum because it was smaller in size. Optimum plot size under cost ratio method was obtained by considering different cost ratios of fixed cost K1 and variable cost K2. The estimated plot size under cost ratio method was 5.95 units with K1 = 10 and K2 = 1. The comparison of methods for optimum plot size was done based on CV. The maximum percentage reduction in CV was found to be with a plot size of 24 basic units and percentage reduction was very low thereafter. Hence maximum curvature method, comparable variance method, covariate method and shape of the plot methods can be recommended for estimating optimum plot size for Uma variety of paddy for field experiments and the estimated optimum plot size was 34.56 m2 and the recommended shape was rectangular
  • ThesisItemOpen Access
    Pre-harvest forecasting models and trends in production of banana (Musa spp.) in Kerala
    (Department of Agricultural Statistics, College of Agriculture, vellayani, 2016) Sharath Kumar, M P; KAU; Vijayaraghava Kumar
    The study entitled “Pre-harvest forecasting models and trends in production of banana (Musa spp.) in Kerala” was conducted at Instructional farm, College of Agriculture, Vellayani. The objectives of the study were to develop models for early forecasting of yield in four major banana cultivars grown in Kerala viz., Nendran, Robusta, Redbanana and Njalipoovan and also to carry out the time series analysis of the trend in area and production of banana in Kerala. The study was based on both primary and secondary data. Initial and monthly observations on growth habits and yield of commonly grown banana cultivars were used for forecasting. Secondary data on area, production and productivity over a period of twenty five years (1991-2015) were collected from published sources of Directorate of Economics and Statistics, Govt. of Kerala and State Department of Agriculture. Additional information on price change and climatic factors were also incorporated in state level time series analysis. . Pre-harvest forecasting models developed for the first three months, using sucker characters and numbers of leaves were not found to be sufficient in forecasting yield and best models were identified from the fourth month onwards in all cultivars. Correlation analysis of yield (bunch weight) with biometrical characters in all four cultivars showed that correlation is positive and significant in 4th, 5th and 6th months of growing. Among biometrical characters, plant height and plant girth showed significant relationship with yield in all cultivars. In Njalipoovan, in addition to plant height and plant girth, number of leaves and leaf area (D-leaf) had some positive relationship with the ultimate yield. Meanwhile fruit characters like number of fruits, weight of second hand, fruit weight had significant correlations with yield in all cultivars. Stepwise multiple linear regressions were attempted to primary data at every month. The statistically most suited forecasting models were selected on the basis of coefficient of determination (R2), adjusted R2 and mallow‟s Cp criteria. It resulted that, in nendran variety, plant height and plant girth were contributing to yield with highest R2 of 0.80 in the 5th month (model Y= -1.37+0.025 H4+0.10 G5). Fruit characters were statistically significant to making of a 55 per cent of variation in total yield. In Njalipoovan, models from 4th month onwards were found good for early forecasting of yield. Number of leaves, plant height, and leaf area and plant girth could predict yield with R2 of 81.7%, while fruit characters, viz., number of fruits, fruit length, fruit girth and fruit weight could predict yield with an R2 of 71.88 %. In Red banana, it was found that plant height and plant girth at fourth gave suitable prediction with an R2 of 0.762, meanwhile fruit characters could predicted yield with an R2 of 71 .28%. In Robusta variety, prediction can be made from 4th month onwards as best predictor as plant height and girth (with an R2 of 75.24 %). At harvesting stage, fruit characters could predict the maximum yield up to 96.76 %. Principal component analysis resulted that first three principal components are sufficient for getting maximum information from explanatory variables in all four cultivars with 75 % explained variation. Linear and nonlinear growth models were developed for the purpose of estimating the growth rate and fitting the best model. The use of R2, criteria of randomness and normality of time series data were used as a measure of goodness of fit. Cubic model was found as best fit for estimated trends in area, productivity, whole sale price and cost of cultivation under banana production. Quadratic function was selected as best suited for production trend. However, rainfall and rainy days were found to have less effect on changing in area, production and productivity of banana. Area, production, wholesale price and cost of cultivation showed a positive trend during past twenty- five years. Hence, reliable estimate of a crop yield, well before harvest can be made of from 4th month onwards in all cultivars studied. Policy decisions regarding planning of crop procurement, storage, distribution, price fixation, movement of agricultural processing commodity, import-export plans, marketing can be formulated based on these forecasts.
  • ThesisItemOpen Access
    Time series modelling for comparitive performance and influencing factors of production on paddy and coconut in South India
    (Department of Agricultural Statistics, College of Agriculture, Vellayani, 2019) Suresh, A; KAU; Brigit Joseph
    The research entitled “Time Series modelling for comparative performance and influencing factors of production on paddy and coconut in South India” was conducted with the objective of developing statistical models on trend in area, production and productivity of paddy and coconut across Kerala, Karnataka and Tamil Nadu and to develop different statistical models for analysing the price movement of these crops across the states overtime and to develop models for analysing the influencing factors of production. Secondary data regarding area, production, productivity and rainfall were collected for a period of past 25 years from Directorate of Economics and Statistics (Govt. of Karnataka), Department of Economics and Statistics (Govt. of Kerala and Tamil Nadu) and Coconut Development Board. Secondary data on price was collected for major markets of paddy (Thanjavur and Raichur) and copra (Kochi, Kangayam and Tumkur) from indiastat and Agmarknet. Trend analysis was used to understand the trends in area, production and productivity using different linear and nonlinear growth models. Compound Annual Growth Rate (CAGR) was estimated using exponential model to compare the performance in area, production and productivity of paddy and coconut in South India. Johansen’s co-integration technique was used to understand the price movement in the markets across the states for price of paddy and copra. Panel data regression analysis was done to identify the climatic variables that influence the production of paddy and coconut. From trend analysis, the best model was selected based on adj. R2, criteria of randomness, normality and Root Mean Square Error (RMSE). In paddy, quadratic model was found to be the best fitted model for area and production in Karnataka, production and productivity in Kerala and area in Tamil Nadu. Cubic model was found to be the best model for area in Kerala, productivity in Tamil Nadu and power model for productivity in Karnataka and compound model for production in Tamil Nadu. In case of coconut, quadratic model was found to be the best fitted model for area, production and productivity in Karnataka and area and productivity in Tamil Nadu. Cubic model was found to be the best model for area, production and productivity in Kerala and production in Tamil Nadu. Comparative performance of paddy and coconut in Southern states was compared based on CAGR for a period from 1987-2017. CAGR revealed that production (1.1%) and productivity (1.0%) of paddy in Karnataka and productivity (1.5%) in Kerala was found to be positive and significant. Area (-4.5%) and production (-3.0%) of paddy in Kerala and area (-0.7%) in Tamil Nadu was found to be negative and significant. In case of coconut, positive and significant CAGR was noticed for area, production and productivity in Karnataka and Tamil Nadu and production (1.4%) and productivity (2.0%) in Kerala where as a declining trend in area (-0.6%) was noticed in Kerala. Stationarity is the prime requirement for co-integration analysis of price of paddy and coconut in various markets and it was tested using Augmented Dickey Fuller test (ADF). The results of ADF test indicated that price of paddy in Thanjavur (TN) and Raichur (Karnataka) markets and price of copra in Kochi (Kerala), Kangayam (TN) and Tumkur (Karnataka) markets were stationary after taking the first difference which suggested that all the price series were integrated of order one I(1). The result of Johansen’s co-integration test revealed that monthly wholesale price of paddy in Thanjavur and Raichur markets were co-integrated. Similarly price of copra in Kochi (Kerala), Kangayam (TN) and Tumkur (Karnataka) markets was also co-integrated which means that price in different markets are moving together. Granger Causality test was applied to find the direction of causality from one market to another and it revealed that there was a bidirectional influence in Thanjavur and Raichur market price of paddy. In case of copra, there was a bidirectional influence between Kochi and Kangayam market price and unidirectional influence on prices of Kochi and Tumkur. The effect of climatic factors on production was analysed using panel data regression with fixed effect model suggests that average rainfall during Q3 (July - September) and Q4 (October - December) had a positive and significant effect on production of paddy. In case of coconut, previous year average rainfall during Q1t-1 (January - March) and Q4t-1 (October - December) had a positive and significant influence on production of coconut. Trend in area, production and productivity was well explained by cubic and quadratic model for paddy and coconut with high adj R2 and least RMSE. CAGR of productivity of paddy in three South Indian states has shown a positive trend but there was a declining trend in area under paddy in Kerala and Tamil Nadu. There was a significant positive growth rate in area, production and productivity of coconut in Karnataka and Tamil Nadu and production and productivity in Kerala. However, the productivity in Tamil Nadu (14251 nuts ha-1) and Karnataka (13181 nuts ha-1) was far ahead as compared to that of Kerala (9664 nuts ha-1). The monthly wholesale price of paddy in Thanjavur and Raichur markets and price of copra in Kochi, Kangayam and Tumkur markets were co-integrated which indicates that any price change in one market influence the price in other markets. Production of paddy was influenced by Q3 (July - September) and Q4 (October - December) rainfall, in case of coconut, production was influenced by previous year average rainfall during Q1t-1 (January - March) and Q4t-1 (October - December).
  • ThesisItemOpen Access
    Statistical modelling for the impact of weather and soil parameters on the yield of paddy under long term fertilizer experiment
    (Department of Agricultural Statistics, College of Agriculture, Vellayani, 2019) Dhanya, G; KAU; Brigit Joseph
    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.