Browsing by Author "Laly John, C"

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Clustering genotypes based on genotype x environment interaction
(Kerala Agricultural University, 1993) Laly John, C; Unnithan, V K G
A procedure to form clusters of genotypes such that a genotype is stable relative to the cluster to which it belongs to and not so relative to any other is suggested.
Comparison of different techniques for the estimation of genotype-environment interaction
(Department of Statistics, College of Veterinary & Animal Sciences, Mannuthy, 1984) Laly John, C; Gopinathan Unnithan, V K
The genotypic stability analyses of Eberhart and Russell (1966), Perkins and Jinks (1968), Freeman and Perkins (1971), Wricke (1966) and Shukla (1972) were studied in detail. The mistakes in the analysis of variance of Perkins and Jinks (1968) were corrected. The first three analyses which used the theory of regression explains a large part of the genotypic environment interaction. On the otherhand, when the regression cannot explain a large part of the genotype - environment interaction, Wrioke's ecovalence ratio and Shukla's stability variance could satisfactorily be used.
Price forecast models for coconut and coconut oil
(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2016) Indraji, K N; Laly John, C
The study on “Price forecast models for coconut and coconut oil” was conducted to estimate seasonal variations in prices of coconut oil, copra and coconut, to evaluate different time series forecast models for prices of coconut oil, copra and coconut and to suggest suitable forecast models for Alappuzha, Kochi and Kozhikode markets. Time series data on monthly average prices of coconut oil and copra for Alappuzha, Kochi and Kozhikode markets from January 1990 to December 2015 and for coconut price at Alappuzha market from January 1998 to December 2015 were collected from Coconut Development Board (CDB), Kochi formed the database.Analysis of price pattern revealed that wide fluctuation exists in the prices of coconut oil and copra at Alappuzha, Kochi and Kozhikode markets and price of coconut at Alappuzha market. For coconut oil and copra price, the coefficient of variation was around 50 per cent indicating the instability in prices and a coefficient of variation of 37 per cent for coconut price showed that variability in price is lower than that of coconut oil and copra. Seasonal indices for the 12 months from January to December showed that December is the peak price month for coconut oil at Alappuzha and Kozhikode markets, whereas it is in January at Kochi. Lowest price is observed in May at Alappuzha and Kozhikode market, whereas, at Kochi it is in July. In all the three markets, September – February is the buoyant phase and price depression is during March - August. For copra, peak price is in December at Alappuzha and Kochi markets, whereas, it is in November at Kozhikode. Trough price for copra is in May in all the three markets. October to February is favourable for copra price in all the three markets, whereas, depressed phase is from March to September. For coconut, peak price at Alappuzha market is in December and the buoyant phase is from November to February. April is the low price month with depressed phase from March to October. During the summer months from March to May, harvest the coconuts as tender and increase the production of neera. Also, during March- September, where the price of coconut oil and copra is low, steps are to be taken to convert coconut into other value added products like desiccated coconut powder, virgin coconut oil, activated carbon etc. and to identify regular markets in major cities of India as also outside India. Different forecast models were fitted viz., Auto regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and exponential smoothing models (single, double, Holt-Winters’ additive and multiplicative) were fitted and compared for prices of coconut, coconut oil and copra in different market. Holt-Winters’ Multiplicative Seasonal (HWMS) model is the appropriate forecast model for price of coconut oil at Alappuzha and Kochi markets. At Kozhikode market, SARIMA(1,1,1)(1,0,1)12 and HWMS can be used. HWMS model is selected as the suitable forecast model for copra at all markets. ARIMA (0,1,1) model is suitable for forecasting price of coconut at Alappuzha market.
Probability models for rainfall in tropical monsoon climates
(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2004) Swapna, K; Laly John, C
The influence of soil moisture regimes and stage of host introduction on seedling growth of sandal provenances was investigated in a pot culture experiment at the College of Forestry, Kerala Agricultural University, Vellanikkara. Two provenances in the South India, Shimoga (Karnataka) and Marayoor (Kerala) were selected for this study. The results showed that the seedlings of Marayoor provenance were taller and having a higher collar diameter as compared to seedlings of Shimoga provenances. The stage of introduction of host did not have any effect on the growth of sandal seedlings. The seedlings where the host was introduced at the time of planting sandal had comparatively higher total chlorophyll in both the provenances as compared to seedlings where the host was introduced three and six months after planting sandal. Highest Nitrogen and Calcium content was observed in Marayoor provenance when the host was introduced at the time of planting sandal, whereas the P content was higher in both the provenances where the host was introduced at the time of planting sandal. The parameters like seedling height, collar diameter, number of leaves, leaf area, dry matter and chlorophyll content decreased due to water stress. The haustorial connections were found only at 300 days after planting sandal. The seedlings of Marayoor provenance recorded lower pre-dawn water potential as compared to seedlings of Shimoga provenance. Introducing host at the time of planting sandal or three months after planting sandal, in Marayoor provenance resulted in higher plant water potential. The leaf diffusive resistance was relatively high in Marayoor provenance when the host was introduced at the time of planting sandal. The leaf diffusive resistance was high in water stressed plants. As the haustorial connections were found only at 300 days after planting sandal, it can be concluded that the host need to be planted only six to nine months after planting sandal. This will avoid the early competition between sandal and host. Fast growing pot host during the early phase of its growth may suppress sandal by competition.
Rainfall probability analysis for Kerala
(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2020-08-11) Pooja, A; Laly John, C
The study entitled “Rainfall Probability Analysis for Kerala”, was conducted to estimate the probabilities and amounts of normal, excess and deficit rainfall during southwest and northeast monsoon periods, to determine the conditional probabilities for monsoon to be normal, excess and deficit when the monthly rainfall is normal, excess and deficit and to determine the assured monthly and weekly rainfall in the five agroclimatic zones of Kerala. Daily rainfall data from 1983 to 2019 collected from the five stations viz. Vellayani, Kumarakom, Vellanikkara, Pilicode and Ambalavayal representing southern zone, problem area zone, central zone, northern zone and high range zone respectively formed the database for the study. The daily rainfall data were converted to annual, seasonal, monthly and weekly rainfall data series for further analysis. Significant increasing trend is observed in annual rainfall at Vellayani. Non-significant increasing trend in annual rainfall is also observed at Vellanikkara, Pilicode and Ambalavayal. But, Kumarakom exhibited a non-significant negative trend for annual rainfall. Except the stations of Kumarakom and Vellanikkara, other stations have non-significant increasing trend in southwest monsoon rainfall. Only Vellayani has a non-significant increasing trend in northeast monsoon rainfall. All other stations exhibit declining trend for northeast monsoon rainfall over the period 1983-2019. The exploratory analysis showed that wide variation exists in rainfall over different agroclimatic zones. The rainfall variability was visually made possible by the use of box-and-whisker plots constructed for annual, seasonal, monthly and weekly rainfall over five stations. The boxplots explained the average and dispersion of rainfall data in terms of quartiles. In all the stations, annual, southwest monsoon and northeast monsoon rainfall did not follow normal distribution which was evident from the uneven boxes and dissimilar whisker lengths. A station-wise comparison showed that annual rainfall as well as southwest monsoon rainfall increased from south (Vellayani) to north (Pilicode) of Kerala and northeast monsoon rainfall declined from south to north. Outliers representing extreme rainfall events were also detected using boxplots which made the rainfall distribution skewed. At Ambalavayal, extreme annual rainfall (3093 mm) and extreme southwest monsoon rainfall (2380 mm) happened in 2018 and at Pilicode, extreme annual rainfall (4803 mm) occurred in 1994.The northeast monsoon witnessed extreme rainfall in 1992 (764 mm) and 2010 (950 mm), at Vellanikkara. The analysis of monthly rainfall using boxplots clearly depicted bimodal distribution at Vellayani and unimodal distribution at Pilicode. Thus, it was appropriate to use median and Inter Quartile Range (IQR) as the measures of central tendency and dispersion, rather than mean and standard deviation, as former measures were not affected by extreme observations. Conditional probabilities were estimated to determine whether the seasonal rainfall was excess, normal or deficient when monthly rainfall was excess, normal and deficient. Excess, normal and deficient rainfalls were defined in terms of quartiles for the purpose. At Vellayani, there was more than 50 per cent probability for the southwest monsoon to be in excess if the rainfall in the month of June was excess. At Kumarakom, there was a 60 per cent chance for receiving excess rainfall in southwest monsoon when July and September had excess rainfall. Rainfall in June and July determined excess rainfall in southwest monsoon at Vellanikkara with 70 per cent probability. There was 70 per cent chance to receive excess rainfall during southwest monsoon at Pilicode when August or September received excess rainfall. Ambalavayal had experienced excess rainfall with 80 per cent possibility when July had excess rainfall. Similarly, each station had varying effects of rainfall in October and November towards northeast monsoon season. This analysis can provide information about when to take precautionary measures to anticipate the threat of floods and droughts in near future. The assured amounts of rainfall varied during different months and weeks in each station. Selection of crops (short duration varieties for low rainfall regions), cropping pattern and other decision making strategies for harvesting rainfall or drainage management can be made on the basis of assured rainfall in different months. At Vellayani, 23rd (4th June to 10th June) and 24th (11th June to 17th June) standard meteorological weeks (SMWs) are the rainiest weeks. The rainiest week is 28th SMW (9th July to 15th July) at Kumarakom and Ambalavayal, whereas, it is 24th SMW (11th June to 17th June) at Vellanikkara and Pilicode. On farm operations and crop management plans like decision of times of sowing, irrigation, fertilizer application, harvesting, etc., could be executed on the basis of assured weekly rainfall during the different crop periods.
Statistical forecast models in agriculture
(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2020) Pooja, A; Laly John, C
Time series modeling and forecasting of tea prices in India
(Department of Agricultural Statistics, College of Agriculture, Vellanikkara, 2021) Deenamol Joy; Laly John, C
The study entitled “Time series modeling and forecasting of tea price in India” was conducted to study the components of time series data on prices of tea in India, to develop time series forecast models for the prices, to develop statistical models for price volatility and to study the integration between international and Indian tea prices. Monthly auction prices of tea for North India, South India and All India for the period from January 1980 to December 2020 collected from the Tea Board formed the main database for the present study. International price of tea for Colombo (Sri Lanka) and Mombasa (Kenya) for the period from January 1980 to December 2020 were collected. To have an idea about the trend in A- Pr- Pd of tea in India, annual data on A- Pr- Pd of tea from 1970 to 2019 in North India, South India and All India were also collected. To have a general idea about trend in A- Pr- Pd of tea in North India, South India and All India, models like exponential, quadratic, cubic etc were fitted. From among several models tried, quadratic model was found to be the best fit for area under tea in North India, while, cubic model was found to be the appropriate fit for production and productivity of tea in North India and, A- Pr- Pd of tea in South India as well as All India. North India and South India tea price data was decomposed to time series components like trend, seasonal variation, cyclic variation and irregular variation. North India and South India showed an overall increasing trend and a prominent seasonal variation. Cyclic variations showed that South India exhibited more cycle of price volatility compared to North India. All India tea price was found to be the simple average of North India and South India tea prices. Compound Annual Growth Rate (CAGR) was estimated for A- Pr- Pd of tea in North India and South India for the period from 1970 to 2019. For North India, growth rate in production was more during 1996-2019 compared to period 1970-1995. For South India, a decline in production was observed during 1970 to 1995. Price forecast models like exponential Smoothing models and ARIMA models were fitted to forecast the tea prices in North India and South India from January 2021 to April 2021. For North India tea price, SARIMA (0,1,3)(0,1,1)12 was identified as the best forecast model whereas for tea price of South India SARIMA (0,1,1)(1,0,1)12 was selected to forecast tea prices. For tea prices in North India and South India, volatility in prices were estimated using intra and inter annual volatility and its significance was tested by fitting suitable ARCH model. Intra annual volatility indices of monthly tea prices in both regions were varying irregularly. In most of the years, North India showed large variation in tea price compared to South India. ARCH (1) model was fitted to check the significance of tea prices and the estimate of ARCH parameter showed high volatility for tea prices for North India and South India. Cointegration analysis was carried out for tea prices to study the integration between international and domestic Indian tea markets. One cointegrating relationship exists between the market pairs, North India - South India, North India – Mombasa and South India – Colombo. No cointegration exist between the market pairs, All India -Mombasa and All India - Colombo. Unidirectional causality was observed between South India and Colombo whereas, bidirectional causality was observed between market pairs, North India - Mombasa and North India - South India.
Yield prediction in cocoa (Theobrama cacao L.)
(Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2009) Jayasree, K; Laly John, C