Browsing by Author "Srinivasan, K"

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Comparative analysis of price forecasting models for black pepper
(Department of Agricultural Statistics, College of Agriculture,Vellanikkara, 2024-02-17) Akshaya Ajith.; Sajitha Vijayan, M; Srinivasan, K
Black pepper, the ‘king of spices’ is one of the most popular and widely consumed spices which shares a place on most dinner tables with salt. India is the third largest producer in the world (International Pepper Community, 2023), also a significant consumer and exporter of black pepper, with Kerala and Karnataka producing the majority of the nation's output. Kerala ranked second in terms of black pepper acreage (76,160 ha), and production (33290 metric tonnes) but seventh in terms of productivity (0.44 metric tonnes/ha) (GOI, 2023). Historically, the market value of pepper contributed to the development of the city of Kochi as a centre of international commerce. Kochi has the first exclusive pepper exchange in India which was established by the Indian Pepper and Spice Traders Association, IPSTA and the exchange was well regulated by the traditional players here, without any default on supply or delivery of the commodity and volatility. As per latest records of Spices Board, the price of black pepper surpassed Rs.500/Kg which was the maximum price in the past years since 2022 in Kochi market and this indicated that black pepper prices are highly volatile. Being a perennial crop, the large variation in prices of black pepper within a year is a major problem faced by farmers as well as consumers. Hence, analysis of time series data of prices of black pepper is of prime importance. In this context, the present study was undertaken to evaluate different time series models for prices of black pepper and to suggest suitable forecast models for Kochi market. Time series data on monthly and weekly average prices of garbled and ungarbled black pepper at Kochi market from January 2000 to December 2020, collected from Spices Board, Kochi formed the database for the study. Analysis of price pattern revealed that wide fluctuation exists in the prices of black pepper in Kochi market. In order to have a general idea about trend of prices of black pepper, models like linear, exponential and quadratic, were fitted. From among several models tried, exponential model was found to be best fit for the monthly and weekly prices of both garbled and ungarbled black pepper. To examine the time series data for the price of black pepper, a multiplicative model was employed for decomposition. Seasonal indices for the 12 months from January to December was calculated for both garbled and ungarbled black pepper prices as the seasonal variation were present in monthly and weekly data. Different traditional time series models such as exponential smoothing models (single, double, Holt-Winters’ multiplicative models (HWMS)), Auto Regressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Auto Regressive Conditional Heteroskedasticity (ARCH) and Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) were applied to the price data using R software. The Augmented Dickey-Fuller test and Heteroscedasticity Lagrange’s Multiplier test were used to test the stationarity and volatility of the time-series respectively. In addition to the traditional method of price forecasting, machine learning techniques like Artificial Neural Network (ANN), and Recurrent Neural Network (RNN) were applied to the data. An artificial neural network (ANN) is a mathematical model that aims to replicate the functionality and architecture of biological neural networks. Time- delay Neural Network (TDNN) which is a major type of ANN was employed for this temporal data as it uses time delays at the input layer of the network to build a short-term memory model for forecasting the prices of black pepper. An artificial neural network with a recurrent topology is called a recurrent neural network. Long Short-Term Memory (LSTM) neural network, a specialized type of RNN, as it possesses the capability to learn patterns with long dependencies and is adept at detecting complex patterns. The price data was split into training and testing data in the ratio of 80:20. The best forecasting model was determined based on the lower values of the Root Mean Square (RMSE) and Mean Absolute Percentage Error (MAPE). The predictability performance of the selected model was also evaluated using MAPE. The HWMS model was observed as the finest among the different exponential smoothing models for this time series data on prices of garbled and ungarbled black pepper. SARIMA(2,1,2)(3,0,2)12 , SARIMA(2,1,2)(2,0,2) 12, SARIMA(2,1,2) (1,0,0)52 and SARIMA(1,1,1) (1,0,1)52 were identified best among several ARIMA models tried for monthly and weekly garbled and ungarbled black pepper respectively. The GARCH (1,1) was considered best among the different ARCH family models for this price series data. TDNN (6:2s:1l), TDNN(6:3s:1l), TDNN (13:8s:1l) and TDNN (12:7s:1l) models were found to be the pinnacle artificial neural network model with lower MAPE values 4.29, 4.63, 1.99 and 2.22 in the case of monthly and weekly prices of garbled and ungarbled black pepper respectively. The results revealed that the TDNN model showed superiority in price forecasting of black pepper in all the cases when compared with all other models. Thus, the TDNN model was used to forecast the prices from January 2021 to December 2022. The MAPE value between the actual and forecasted prices for 2021 and 2022 was 4.19 and 4.86 respectively for monthly price of garbled black pepper, while for monthly price of ungarbled black pepper it was 4.09 and 5.05 respectively. The MAPE value between the actual and forecasted prices for 2021 and 2022 was 3.11 and 3.16 respectively for weekly price of garbled black pepper, while for weekly price of ungarbled black pepper it was 3.36 and 3.58 respectively. In conclusion, the analysis suggested that the TDNN model proves to be a reliable forecasting tool for predicting prices of black pepper in the Kochi market. The robustness of the TDNN model offers a plethora of opportunities for understanding the future price pattern of black pepper which enables, various stakeholders such as producers and traders to adapt with the price fluctuations and for policymakers to ensure market stability. The TDNN model's reliability in forecasting black pepper prices not only enhances market transparency fostering overall market efficiency in India.
Effect of different levels of fertilizers on the incidence of 'little leaf' disease of brinjal (Solanum melongena L.)
(Kerala Agricultural University, 1981) Srinivasan, K
Eftect of hormone application on root formation and yield in tapioca
(Kerala Agricultural University, Vellanikara, 1964) Srinivasan, K
Juvenility as a factor affecting air-layering in jackfruit
(Kerala Agricultural University, Vellanikara, 1961) Srinivasan, K
Landscape fragmentation analysis using geospatial tools in Periyar Tiger Reserve, Kerala
(Department of Natural Resource Management, College of Forestry, Vellanikkara, 2023-05-02) Merin, P Menachery; Srinivasan, K
Periyar Tiger Reserve (PTR) is one of the largest conservation units in Western Ghats and many conservational efforts are formulated and applied in this area. It is also important to monitor the outcome of these efforts to strategize further management techniques. Geoinformatics approach widely used to monitor land use change as well as forest fragmentation, as the satellite data are easily accessible and they are more consistent in assessing global forest change (Hansen et al., 2013, Carranza et al., 2014, Srinivasan et al., 2022). This study compared the fragmentation status of forest habitats of PTR in 1987, 2000 and 2020 by analysing the landscape level and class level conservation measures by employing remote sensing-based datasets on forest cover and fragmentation. The FRAGSTATS software analysis along with the LULC (Land Use Land Cover) classification and NDVI (Normalised difference vegetation Index) analysis of the forest/nonforest classifications for each of the three distinct years revealed the pattern of forest cover change over time. Image frames were compared for each year for PTR and a one km buffer around PTR boundary showed a considerable variation in the forest patch pattern. The results revealed that dense forest patches within PTR had a favourable increase in the area combined with a decrease in fragmentation. The increase in the largest patch index of the dense forest in 2020 showed the positive effect of the declarations, policies and other conservation measures implemented in this area. Comparative increment in NDVI values over the years, 2000 and 2020 indicated a steady increase of healthy vegetation. The prevalence of agriculture and habitation haven't increased noticeably in this study area, despite the fact that open forest, grassland, and rocky and barren terrain exhibit fragmentation during this particular study period. The meagre construction works inside the administrative buffer of PTR and the expansion of dense forest can vote for the decrease of barren land and grassland counted in this study. A slight increase of open patches and an increase of semi-evergreen and moist deciduous patches near the forest adjacent to the buffers showed minor negative effects of buffer on adjacent forest. At the same time a similar trend was observed while comparing PTR with buffer. In fragmentation analysis, buffer also showed decreased fragmentation in the dense vegetation patch after 2000 but here a proportional increase of agriculture and settlement was observed. The findings indicated that this tiger reserve area is very dynamic. After the declaration, efficient conservation measures and eco-friendly regulations showed positive results and has been successful in preventing forest fragmentation. The improvement of protected areas like PTR depends on conservation strategies for its ecological development. The baseline data from this study can be used to prioritise conservation efforts of Periyar Tiger Reserve in Western Ghats. The study also recommends PTR model of conservation to be further extended to other protected areas with local adaptations and modifications.
Modeling the Impact of Climate Change on Net Primary Productivity (NPP) of Selected Forest Ecosystems in Nilgiri Biosphere Reserve,India
(Department of natural resource management, College of forestry ,Vellanikkara, 2023-01-11) Srinivasan, K; KAU
As an inevitable component of global terrestrial ecosystem, vegetation plays a crucial role in energy transfer, carbon cycle, water balance and climate regulation. Its response to environment change has been considered as one of the key fields of ecological research. Net primary productivity (NPP) is defined as the net amount of carbon taken in by plants via photosynthesis, and is equal to the difference between the carbon assimilated during photosynthesis and that released during plant respiration. The present study tried to investigate mainly the following events: i) Land use and land cover changes over the NBR, India over 18 years (2000 to 2018) using MODIS data wherein, we, estimated the LULC of NBR, identified the changes in LULC in 18 years, and checked the accuracy of the LULC of 2018 with ground truth data using kappa coefficient. In the study, the importance of monitoring of LULC changes is revealed by the decrement in areas of closed shrub lands and grasslands during the period 2000-2018. Also, the human activities within the buffer regions of the NBR were understood by the increment of areas of classes like croplands and cropland/natural vegetation mosaics during the study period; Secondly, (ii) Assessed the impact of climate change on NPP over NBR during the period (1981 to 2019): wherein, The NPP that were expected to realize in location as well as in different forested ecosystems of the region were estimated through satellite derived data using CASA model. The data taken for modeling study was for a period of 38 years i.e., from 2018 to 2019. It was observed that the mean NPP of NBR was ranging from 47.2 to 183.73 (g C m-2 month-1). NPP trend ranged from (-0.154 to 0.176) for the period. Seasonal NPP of post monsoon season (ON) showed an increasing trend of NPP highlighting the positive influence of precipitation on NPP. During the study period, the trends observed per year were 0.14 W m-2 decrease in solar radiation, 0.02 0C increase in Air temperature, 0.10 mm increase in Precipitation and 0.06 g C m-2 month-1 increase in NPP respectively. Moreover, the estimated NPP showed spatial variation across the region of different LULC. Very large NPP (>103 g C m -2 month -1) for Evergreen Broadleaf Forest together with its smaller counterpart (57 g C m -2 month -1) for Persistent Wetlands were estimated over study region among different LULC. Highest overall mean NPP was in the order EBF>CNVM > DBF=MF>WS; and finally we, predicted the present and future NPP (2018 to 2028) using SARIMAX (0, 0, 2) (1, 1, 0) time series model using the output of CASA model. We divided the data sets in to two parts viz., 1981 to 2018 and 2010 to 2018; first set to build the model (R 2 = 0.552) and the second set for validation of the fitted model. We took data 1981-2010 and forecasted the NPP for the rest Eight years taking the air temperature, precipitation and solar radiation (2010 - 2018) and compared with the observed values of NPP (CASA derived NPP from 2010 to 2018). As there was good correlation between the observed and the predicted (8 years) (r = 0.63), hence validated the model and used for future prediction. As there were no explanatory variables for prediction (Ten years), hence assumed that the same trend for each explanatory variable for next Ten years to make the forecast of NPP and hence fitted individual ARIMA model for each of the explanatory variables (Seasonal ARIMA) viz., solar radiation, air temperature and precipitation. Later on, We used the SARIMAX(0, 0, 2) (1, 1, 0) model for prediction using the forecasted individual explanatory variables for Ten years and finally derived equation for predicted NPP using estimate for lag values as mentioned in the model fit statistic of NPP based on SARIMAX (0, 0, 2) (1, 1, 0).
Modeling the Impact of Climate Change on Net Primary Productivity (NPP) of Selected Forest Ecosystems in Nilgiri Biosphere Reserve,India
(Department of natural resource management, College of forestry ,Vellanikkara, 2023-01-11) Srinivasan, K; KAU; Gopakumar, S
an inevitable component of global terrestrial ecosystem, vegetation plays a crucial role in energy transfer, carbon cycle, water balance and climate regulation. Its response to environment change has been considered as one of the key fields of ecological research. Net primary productivity (NPP) is defined as the net amount of carbon taken in by plants via photosynthesis, and is equal to the difference between the carbon assimilated during photosynthesis and that released during plant respiration. The present study tried to investigate mainly the following events: i) Land use and land cover changes over the NBR, India over 18 years (2000 to 2018) using MODIS data wherein, we, estimated the LULC of NBR, identified the changes in LULC in 18 years, and checked the accuracy of the LULC of 2018 with ground truth data using kappa coefficient. In the study, the importance of monitoring of LULC changes is revealed by the decrement in areas of closed shrub lands and grasslands during the period 2000-2018. Also, the human activities within the buffer regions of the NBR were understood by the increment of areas of classes like croplands and cropland/natural vegetation mosaics during the study period; Secondly, (ii) Assessed the impact of climate change on NPP over NBR during the period (1981 to 2019): wherein, The NPP that were expected to realize in location as well as in different forested ecosystems of the region were estimated through satellite derived data using CASA model. The data taken for modeling study was for a period of 38 years i.e., from 2018 to 2019. It was observed that the mean NPP of NBR was ranging from 47.2 to 183.73 (g C m-2 month-1). NPP trend ranged from (-0.154 to 0.176) for the period. Seasonal NPP of post monsoon season (ON) showed an increasing trend of NPP highlighting the positive influence of precipitation on NPP. During the study period, the trends observed per year were 0.14 W m-2 decrease in solar radiation, 0.02 0C increase in Air temperature, 0.10 mm increase in Precipitation and 0.06 g C m-2 month-1 increase in NPP respectively. Moreover, the estimated NPP showed spatial variation across the region of different LULC. Very large NPP (>103 g C m -2 month -1) for Evergreen Broadleaf Forest together with its smaller counterpart (57 g C m -2 month -1) for Persistent Wetlands were estimated over study region among different LULC. Highest overall mean NPP was in the order EBF>CNVM > DBF=MF>WS; and finally we, predicted the present and future NPP (2018 to 2028) using SARIMAX (0, 0, 2) (1, 1, 0) time series model using the output of CASA model. We divided the data sets in to two parts viz., 1981 to 2018 and 2010 to 2018; first set to build the model (R 2 = 0.552) and the second set for validation of the fitted model. We took data 1981-2010 and forecasted the NPP for the rest Eight years taking the air temperature, precipitation and solar radiation (2010 - 2018) and compared with the observed values of NPP (CASA derived NPP from 2010 to 2018). As there was good correlation between the observed and the predicted (8 years) (r = 0.63), hence validated the model and used for future prediction. As there were no explanatory variables for prediction (Ten years), hence assumed that the same trend for each explanatory variable for next Ten years to make the forecast of NPP and hence fitted individual ARIMA model for each of the explanatory variables (Seasonal ARIMA) viz., solar radiation, air temperature and precipitation. Later on, We used the SARIMAX(0, 0, 2) (1, 1, 0) model for prediction using the forecasted individual explanatory variables for Ten years and finally derived equation for predicted NPP using estimate for lag values as mentioned in the model fit statistic of NPP based on SARIMAX (0, 0, 2) (1, 1, 0).
Monitoring and evaluation of forest canopy density model in Nilambur forests, Kerala using geospatial techniques
(Department of Forest Resource Management, College of Forestry,Vellanikkara, 2025) Ajay Antony.; Srinivasan, K
The study entitled ‘Integrated management of Fusarium wilt of yard long bean in homesteads’ was undertaken at College of Agriculture, Vellayani and Integrated Farming System Research Station (IFSRS), Karamana during 2023-25 with an objective to develop an integrated management package for the vascular wilt of yard long bean incited by Fusarium oxysporum using bioagents and biofumigants in homesteads. The culture of F. oxysporum (accession no: MZ706472.1) maintained at IFSRS, Karamana was used for the study. The mycelia of the pathogen appeared as white and fluffy with a characteristic pale pink to purplish pigmentation. Microconidia were ovoid or elliptical with 0 - 1 septa, while macroconidia were fusiform with 3 – 4 septa. Chlamydospores were globose with a diameter of 6 -10 μm. The pathogen was mass multiplied in sorghum-sand medium (SSM) (2:1:1 ratio) and complete colonization of the fungus was recorded at 5 days after inoculation (DAI). Koch’s postulates were proved in 15-days-old yard long bean seedlings (var. Githika). The disease symptoms initiated as yellowing of older leaves at 7 days after transplanting, which further progressed as defoliation, withering, wilting and seedling death. In vitro studies on the evaluation of antifungal potential of bioagents, botanicals and biofumigants revealed that garlic bulbs (2 g plate-1) completely (100%) inhibited the pathogen. Rhizobium sp. (KAU) inhibited the mycelial growth by 67.77 per cent and tested positive for siderophore production, indicated by a colour change of Chrome Azurol S (CAS) agar from blue to orange. In vivo seed treatment studies revealed that sowing of seeds in soil applied with AMF @ 5 g seed-1 followed by transplanting was the promising treatment (no incidence of disease) for the management of Fusarium wilt, along with enhanced seed germination (100%), leaf number (11.00), leaf area (24.17 cm2), shoot length (16.17 cm), root length (19.83 cm) and root-shoot ratio (5.33) of the seedling. Seed treatment with Rhizobium sp. followed by transplanting was the next promising treatment with the highest leaf area (27 cm2). Scanning electron microscopy revealed intact cell structure and absence of clogging 128 in vascular tissues of the above plants where as the pathogen inoculated control recorded its hypha emerging out of xylem vessels, which were damaged and extensively clogged. A pot culture study was undertaken in yard long bean var. Githika to develop an integrated package for management of vascular wilt disease. The treatment viz., soil testbased lime application at 2 weeks before planting + soil application of Trichoderma sp. enriched in cow dung – neem cake mixture (9:1) @ 1 kg pot-1 at one week before sowing (WBS) + soil application of AMF @ 5 g seed⁻¹ at sowing followed by transplanting + soil application of PGPR mix II @ 20 g L-1 at 20, 40 and 60 DAS (T6), recorded the least disease incidence (55.50%) and disease severity (19.44%), with the highest yield (945 g plant-1with 54 pods), among the treatments. The highest AMF root colonization (49%) and number of nodules (47.00) were also recorded in this treatment. Significant reduction in the population of the pathogen in soil/ pot was also recorded in this treatment at 30 and 60 DAS. The next promising treatment was same as the best one, with soil application of PGPR mix II @ 20 g L-1 replaced by Trichoderma sp. enriched in cow dung – neem cake mixture (9:1) @ 1 kg pot-1 at 20, 40, and 60 DAS (T3) which recorded reduced disease incidence (66.66%) and severity (31.76%) with higher yield (908.33 g plant-1from 50.00 pods), AMF root colonization (45.00%) and nodules (40.67). Peak activity of peroxidase (12.07 μg g-1 min-1), polyphenol oxidase (1.587 μg g-1 min-1), and phenylalanine ammonia lyase (11.703 μg g-1 min-1) at 72 hours after inoculation was recorded in the promising treatment. Thus, the present study revealed the integrated disease management package viz., soil test-based lime application at 2 weeks before planting + soil application of Trichoderma sp. enriched in cow dung – neem cake mixture (9:1) @ 1 kg pot-1 at one WBS + soil application of AMF @ 5 g seed⁻¹ at sowing followed by transplanting + soil application of PGPR mix II @ 20 g L-1 at 20, 40 and 60 DAS can effectively manage vascular wilt of yard long bean incited by F. oxysporum in homesteads. Furthermore, the study confirms a beneficial quadripartite association among AMF, Trichoderma sp. and the plant growth promoting rhizobacteria in yard long bean plants, as observed from the effective management of the disease and enhanced plant growth and yield attributes even in the presence of the soil borne pathogen.
Muttom varikka—a promising jackfruit variety
(Kerala Agricultural University, 1970) Srinivasan, K
Nutritional status of soils and the incidence of the 'bunchy top' disease of bananas (Musa sp.): part IV- anatomical variations in virus infected and healthy plants as a function of calcium/magnesium ratio in soils
(Kerala Agricultural University, Vellanikara, 1966) Nair, C K N; Srinivasan, K; Sreekumari Amma
Organic matter addition and its nutrient contribution in cardamom plantations
(Kerala Agricultural University, 1993) Srinivasan, K; Rama Rao, K V V; Naidu, R
Performance of multipurpose trees in coconut based agroforestry systems and their influence on soil physico-chemical and biological properties
(Department of Tree Physiology and Breeding,College of Forestry, Vellanikkara, 2006) Srinivasan, K; Ashokan, P K
An experiment was conducted to study the effects of intercropping of three fast growing MPTs viz. Casuarina equisetifolia, Ailanthus triphysa and Leucaena leucocephala in coconut plantations, on soil physico-chemical and biological properties; the field experiment was laid out at the Instructional farm, Kerala Agricultural University, Vellanikkara. The influence of three water harvesting structures viz. simple pits, contour trench and ring trench, which were established in the seedling phase were also compared. The experiment was laid out during 1993 in one year old coconut plantation spaced at 7 x 7m .The MPTs were planted between rows of coconut at a spacing of 2.33 m. The experiment was laid out in Randomised Block Design (RBD), with three replications. The result showed that casuarina recorded maximum height (28.02 m) and girth (GBH of 71.76 cm) and biomass accumulation among different MPTs studied. Ailanthus intercropped systems intercepted about 93 percent available sunlight and control plots (coconut alone) intercepted the least with around 57 percent due to their stand leaf area index of 3.21 and 1.52 respectively. It was observed that MPTs had an adverse effect on the productivity of coconut during later stages of the cropping system. MPTs had tremendously increased the water holding capacity and infiltration rate of the soil. Ailanthus interplanted plots showed better water holding capacity and improved the infiltration capacity of the soil. Resorting to agroforestry practices considerably increased the organic carbon content of the soil. Casuarina interplanted plots showed higher available N, P and K and the surface layer had more concentration of available nutrients. As the soil depth increases the available nutrient concentration was found to decrease. Microbial population viz., bacteria, fungi and actinomycetes were also found to increase due to the influence of MPTs intercropped in coconut plantations. The microbial population viz. bacteria, fungi and actinomycetes was found more in coconut intercropped with casuarina plots. Fungi and bacterial population were found more in the 30-45 cm soil layer but actinomycetes was found more in the surface layers. The percentage of VAM infection on the roots was also seen more in casuarina intercropped in coconut garden.
Resistance of a wild brinjal variety to bacterial wilt-Research Note
(Kerala Agricultural University, Vellanikara, 1969) Srinivasan, K; Gopimony, R; Swaminathan, M; Kumara Pillai, P
Studies on brinjal hybridisation-II
(Kerala Agricultural University, 1971) Swaminathan, M; Srinivasan, K
'Vellayani-I’, A new short duration paddy strain
(Kerala Agricultural University, Vellanikara, 1966) V Gopinathan Nair, P; Kumara Plllai, P; Srinivasan, K