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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Open 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.
Open 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.
Open Access
Development of a suitable model for ascertaining the growth and egg production in quails
(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 1991) John Thomas, M; KAU; George, K C
An investigation was carried out into the growth and egg production aspect of Japanese quails at the Kerala Agricultural University Poultry Farm, Mannuthy on 1st February, 1989 with the following objectives. 1. to find a suitable relationship between age and body weight. 2. to investigate the" trend of egg production in quails through suitable mathematical models. ,3. to study the impact of climate parameters (temperature, ; , humidity) on egg production in quails. The birds were reared under uniform feed formula and ^identical management practices (recommended by Kerala Agricul tural University Package of Practices). The investigation mainly depended on' data consisting of weekly body weights of -ii^-dividual birds, daily egg production of birds (beginning from age at sexual maturity) and daily climatological para meters (temperature and humidity) from beginning till the end of experiment of 30th September, 1989. Mathematical models such as linear, quadratic, exponential, .Von-Bertalanffy, modified exponential, logistic and Gompertz were fitted for the purpose using body weights of ) individual birds as well as average body weights over twelve weeks and the fitted models were compared using coefficient of 2 determination (r ) and standard error of estimate(s). Mathematical models such as linear, exponentialf parabolic exponential, inverse polynomial. Gamma function. Gamma-type functic^n, quadratic function, quadratic function in logari'thmic scale, quadratic-cum-log, emperical and linear hyperbolic functions were fitted for the development of suitable models for ascertaining egg production using total weekly, fortnightly egg production, hen housed and hen day egg production and fitted models were compared using Furnival index, r^ and s. Multiple linear regression equation was fitted using average weekly egg production per bird as dependent variable and weekly temperature and humidity as explanatory variable to study the impact of climatological parameters on egg production in quails. The investigation has the following, salient features. (i) The hatching weight of Japanese quails were 7.1369 g. (ii) The females weighed more than the males during the entire period of experiment and the body weights have shown an increasing trend. At the end of 12th week the average body weights of males and females were 157.6552 g and 179.2500 g respectively. (iii) Rao's method justified that initial body weights • had no significant effect on growth rate. • (iv) Gompertz curve = a exp [-b exp(-kt)'] was most , suitable for , ascertaining growth in quails on individual basis as well as on the basis of • average body weights over twelve weeks. (v) Average age at sexual maturity (females) was found to be approximately 10 weeks and on an average the eggs weighed 12.20 g. (vi) Quadratic function in logarithmic scale ; = a f b(logJ^) + c(log^)^ was most suitable , for ascertaining egg production in quails (weekly, , fortnightly, hen housed and hen day production • basis). (vii) Climatic parameters had significant impact on egg production in quails.
Open Access
Study of genetic correlations under full -SIB mating system (Two loci case)
(Department of Statistics, College of veterinary and animal sciences Mannuthy, Thrissur, 1985) Khin Moe Moe; KAU; George, K C
A purely theoretical investigation entitled ,JA Study of Genetic Correlations under Fu ll-s ib Mating System (two lo c i case)*1 was carried out with the following objectives, i ) to derive the joint distribution (correlation table) and to find the correlation between fu ll -s ib pairs under fu l l -s ib mating system in the case of two lo c i when there i s no linkage as well as when there i s complete linkage. l i ) to derive the joint distribution (correlation table) and to find the correlation between parent-offspring pair© under fu l l -s ib mating system in the case of two loci when there is no linkage as well as when there is complete linkage, i i i ) to derive the joint distribution (correlation table) and to find the correlation between fu l l -s ib pairs under paront-offspring mating system in the case of two lo c i when there is no linkage as well as when there is complete linkage, iv) to derive the joint distribution (correlation table) and to find the correlation between parent-offspring pairs under parent-offspring mating system in the case of two lo c i when there is no linkage as well as when there is complete linkage. 2 Th© joint distributions of fu ll -s ib pairs and parent- ©Ffspring pairs undor fu ll-s ib gating system wore derived with the help of generation matrix technique and th© correlations wore worked out therefrom, assuming additive genie e ffec ts and using the product-momeni correlation coefficient formula. The correlations were worked out for tho f i r s t ten generations of fu ll -s ib mating in both cases of no linkage and complete linkage, & comparative study of fu ll -s ib correlations and parent-offspring correlationsf conducted both numerically and graphically, revealed that £i) evonthough fu ll -s ib correlation was greater than parent-offspring correlation in in i t ia l generation (random mating) when there was complete linkage, the la tte r increased more rapidly than the former from in it ia l generation to f ir s t generation and ( i i ) from the second generation onwards, the rate of increase in both o f correlations were nearly the same upto tenth generation. I t was interesting to note that the parent-offspring correlations wore of comparatively higher order than th© fu ll-s ib correlations in both cases of complete linkage and no linkage. Similarly, th© joint distributions (correlation tables) for fu ll-s ib pairs and parent-offspring pairs under parentoffspring mating system were derived employing generation matrix approach and the correlations for the f i r s t ten 3 generations of parent—offspring mating in both cases of no linkage and complete linkage were worked out therefrom. A comparative study of those correlations was carried out both numerically and graphically. It was found that the trend in both correlation curves remain the same, but the value of parent-offspring correlation was always greater than that of full-sib correlation in case of no linkage as well as in caso of complete linkage. In comparison of all these correlations, it was found that the correlations increased as the number of generation increased and ultimately reached the limit unity when the number of generations increased indefinitely large. It was also observed that the magnitude of correlation in case of complete linkage was more than that of correlation In case of no linkage even under the same system of mating*
Open Access
Nonlinear models for major crops of Kerala
(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2007) Joshy, C G; KAU; Krishnan, S
Nonlinear modelling techniques are the most suited tools for describing any time series phenomenon. Among the various nonlinear models in vogue monomolecular, logistic, gompertz and mixed-influence models find a prominent place. With this idea the agricultural scenario of Kerala was measured through the three important descriptors namely area, production and productivity of the major crops viz; coconut, rubber, paddy, pepper, tapioca, cashew and banana for all the districts and the state as such. Monomolecular model was the most apt model in most of the cases. The data sets were further explored based on the carrying capacity achieved by 2002-03 coupled with intrinsic growth rate. When none of the nonlinear models were found satisfactory either simple linear regression model or quadratic model was tried to explore the nature of trend. Coconut production was found to have reached its near maximum in all the districts where it was a major crop but the productivity figures gave a warning note for increasing the productivity. Rubber was found to be one of the most gifted crops, which was not devoid of proper attention. Even with this stature, production of rubber can be improved through uniform management practices. Usually nonlinear and quadratic models aptly describe a time series data on crop production. It is astonishing that simple linear regression model aptly described the paddy production in the state. The regressive value of the regression coefficients indicated that paddy production in the state is facing extinction.Paddy production in the state has at least to be protected. The lack of fit of most of the nonlinear models and even quadratic models to the data of pepper production indicate the various devastating hazards that the crop faced with. These contrasting features bring out the fact that pepper cultivation be not allowed to be toyed with. The area specific crops like cashew, cardamom, coffee and banana be made nonspecific through innovative technologies. A concerted effort with valid stresses specific to each crop will make the agricultural scenario bright.
Open Access
Interaction effect under ammi model
(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2006) Eldho, Varghese; KAU; Krishnan, S
The study of interaction is one of the major objectives of most of agricultural experiments. Conceptually this is done based on regression technique. Among the interactions studied, two factor interaction derives its importance as it is the simplest of the interactions. The joint regression technique is employed to study the G x E interaction. The regression techniques are having the assumption of additivity of effects. When there is departure from these assumption the joint regression technique fails. Additive Main effects and Multiplicative Interaction studies have helped a lot at this juncture. Raju (2002) derived a more comprehensive measure of interaction based on AMMI model. This was achieved using the spectral decomposition of the relevant interaction matrix which enabled the study of interaction with the same precision as that of studying the main effects. Biplots formulations of interaction effects based on the PCA vector scores are the most simplest and explicit representation of interaction. The study of interaction based on spectral decomposition has been illustrated using the secondary data on the biometric, chemical and qualitative characters from the projects “Development of a bimodal phasic management system to improve both quantity and quality in Kacholam (Kaempferia galanga)” and “Development of a bimodal phasic management system to improve both quantity and quality in Njavara (Oriza Sativa)”. The DMRT tests for each level of the factors viz., calcium and source were carried out for the parameters viz., percentage content of phosphorus in rhizome, percentage content of potassium in rhizome and North – South foliage spread. In all these characters no specific interaction effect could be sorted out. These interactions when studied based on the factor analytical technique revealed that source II and second level of calcium had the highest positive interaction as regards the percentage content of phosphorus; source III and third level of calcium for percentage content of potassium and source II and third level of calcium for North – South foliage spread. When the order of the interaction matrix was high as in the case of the second experiment, DMRT tests failed to highlight the appropriate interactive effect in the characters viz., grain yield, percentage content of nitrogen in grain, percentage content of phosphorus in grain, percentage content of phosphorus in straw and percentage content of potassium in straw. The study based on the factor analytical technique revealed that the treatments T15, T8, T3, T1 and T4 respectively had the highest interactive effect with Payyanur for the above said characters where as for Badagara they were T3, T14, T4, T5 and T8 .
Open 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.
Open 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.
Open 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.

Kerala Agricultural University, Thrissur

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