1. KAUTIR (Kerala Agricultural University Theses Information and Retrieval)

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    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).
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    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).
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    Spectral management for improving hotosynthetic efficiency in polyhouse cultivation of vegetables
    (Department of Plant Physiology, College of Agriculture, Vellayani, 2016) Anjana J Madhu; Roy Stephen
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    Varietal suitability and crop geometry of baby corn (Zea mays L.) in coconut garden
    (Department of Agronomy, College of Agriculture, vellayani, 2016) Dona Scaria; Rajasree, G
    The experiment entitled “Varietal suitability and crop geometry of baby corn (Zea mays L.) in coconut garden” was undertaken at the Coconut Research Station, Balaramapuram, Thiruvananthapuram, during the summer season (March to May) and the Kharif season (August to October) of 2015. The main objectives of the study were to understand the feasibility of introducing baby corn as intercrop in coconut garden, to assess the effect of varieties and spacings on its growth and productivity and to work out the economics of cultivation. The field experiment was laid out in Randomised Block Design with 9 treatments replicated thrice. The treatments comprised of combinations of three varieties and three spacings. The three varieties were Rasi 4212 (V1), G 5414 (V2) and CO-6 (V3) and the three spacings were 30 cm x 20 cm (S1), 45 cm x 20 cm (S2) and 60 cm x 20 cm (S3). The variety G 5414 recorded significantly higher baby cob weight with husk of 47.01 g cob-1 and 35.74 g cob-1, cob yield with husk of 10.97 t ha-1 and 9.98 t ha-1 and marketable baby cob yield of 3.67 t ha-1 and 3.36 t ha-1 in summer and Kharif respectively. This variety took less number of days from tasseling to harvest (2.11) and recorded the highest net income of RS. 133698 ha-1 and RS. 116629 ha-1 and B:C ratio of 2.70 and 2.49 in summer and Kharif seasons respectively. The variety G 5414 was followed by CO-6 in producing higher baby cob yield with husk and marketable baby cob yield. The growth attributes viz., plant height, number of leaves and leaf area index (LAI) at 15, 30 and 45 days after emergence (DAE), dry matter content and light interception were significantly higher for the variety CO-6. Green Stover yield was significantly higher for CO-6 (19.39 t ha-1 and 17.86 t ha-1 in summer and Kharif respectively) followed by G 5414 (16.08 t ha-1 and 14.35 t ha-1 in summer and Kharif respectively). The study revealed that spacing significantly influenced the growth attributes viz., plant height, number of leaves and LAI. The row spacing of 45 cm x 20 cm recorded the highest baby cob yield with husk of 10.90 t ha-1 and 9.63 t ha-1, marketable baby cob yield of 3.49 t ha-1 and 3.24 t ha-1 along with the highest net income of RS. 125839 ha-1 and RS. 114287 ha-1 and B:C ratio of 2.69 and 2.55 in summer and Kharif respectively. Baby cob weight with husk was significantly higher at 45 cm x 20 cm and baby cob-baby com ratio was the most desirable at 30 cm x 20 cm in summer. In summer, the interaction of CO-6 at 45 cm x 20 cm recorded the highest baby cob weight with husk (56.25 g cob-1), marketable baby cob yield (4.21 t ha-1) and B:C ratio (3.16). Net income was the highest with G 5414 at 45 cm x 20 cm and was on a par with CO-6 at 45 cm x 20 cm. In Kharif, CO-6 at 45 cm x 20 cm resulted in the highest baby cob yield with husk (11.16 t ha-1), marketable baby cob yield (3.68 t ha-1), net income (RS.145237 ha-1) and B:C ratio (3.03). The variety G 5414 at 45 cm x 20 cm was on a par with the variety CO-6 at 45 cm x 20 cm with respect to baby cob yield with husk (9.91 t ha-1) and marketable baby cob yield (3.49 t ha-1). The study revealed that, among the varieties tested, the variety G 5414 was superior and among the spacings, 45 cm x 20 cm was significantly superior in both summer and Kharif seasons. The interaction effects revealed that the variety G 5414 at 45 cm x 20 cm and the variety CO-6 at 45 cm x 20 cm were equally superior in terms of yield and economics. In baby com cultivation, detasseling is an important operation which is labour intensive. The variety G 5414 exhibited 50 per cent silking prior to tasseling and hence the detasseling before first harvesting could be avoided. This variety had a better appearance and uniformity compared to CO-6. In general, the baby com yield was higher in summer season compared to Kharif season. To conclude, the result of the study indicated that baby corn can be profitably intercropped in coconut gardens in summer and Kharif seasons. The baby corn hybrid G 5414 at 45 cm x 20 cm spacing resulted in higher baby cob yield with husk, marketable baby cob yield, net income and B:C ratio during both seasons in southern Kerala. The maize variety CO-6 also performed well in coconut garden during both summer and Kharif seasons.
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    Development and performance evaluation of a solar dryer for copra
    (Department of Food and Agricultural Process Engineering, Kelappaji College of Agricultural Engineering and Technology, Tavanur, 2017) Sai Krishna, V; George Mathew
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    Carbon sequestration and crop weather relations in long term fertilizer experiments
    (Academy of Climete Change Education and Research, Vellanikkara, 2018) Sudhamani, P; Thulasi, V
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    Modeling the rice production under varied agro ecological situations of Palakkad district and its vulnerability to climate change
    (Academy of Climate Change Education and Research Vellanikkara, 2018) Anandu S Hari; Sunil, K M
    The research project entitled "Modeling the rice production under varied Agro-Ecological Situations of Palakkad district and its vulnerability to climate change". Was carried out at RARS Pattambi and the daily rainfall data for the period 1991-2014 was collected from the India Meteorological Department, Thiruvananthapuram. The weather data from General Circulation Models based on RCP 4.5 and 8.5 were used for the analysis and projections were made up to 2050. Weather cock v.1.5 was used for converting the daily weather data into standard week, month and seasonal formats. The rainfall parameters or indices like seasonal and monthly rainfall, rainy days and high rainfall events were calculated. It is also used to compute PET and Thornthwaite water balances. The crop simulation model DSSAT-developed by IBSNAT was used for studying the impact of climate change on these ecosystems. The monthly rainfall of various Agro ecological units of Palakkad district indicate an increased rainfall during the months June, July and August in Projected climate as per RCP 4.5 a weakening in rainfall can be noticed during the months January, February, September and December in projected climate, annually, the number of rainy days indicates a declining trend in projected climate. In a nut shell, the wet months will be watter and dry periods will be drier. The south west monsoon and summer season shows an increasing tendency in the number of rainy days and amount of rainfall in projected climate. Most of the agro-ecological units in Palakkad district showed a decreasing pattern in the length of growing period in projected climate as per RCP 4.5 In projected climate, the maximum amount of potential evapotranspiration can be observed during the months May, July and September whereas the minimum will be in January, November and December. The yearly potential evapotranspiration shows an increasing trend in projected climate as per RCP 4.5. The number of periods where deficit will happen indicate a decreasing trend whereas the annual amount of deficit shows an increasing pattern in projected climate. As per the projections maximum amount of water deficit will happen during the month March in most of the agro ecological units of central Kerala. Annually the amount of water surplus indicates an increasing trend in projected climate based on RCP 4.5. In RCP 4.5, which is the most likely scenario for India, the yield reduction will be 10 per cent by 2030s and 2050s respectively. It can be observed from the study that the impact of climate change on rice production varied widely under different agro ecological situations. The major rice growing tracts of Palakkad district except Palakkad eastern plains (AEU 23) showed decline in productivity.
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    Crop weather modelling of cocoa production in humid tropics under the purview of climate change
    (Academy of Climate Change Education and Research, Vellanikkara, 2018) Vishnu, R P; Sunil, K M
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    Influence of weather parameters on growth and yield of rice variety Jaya
    (Department of Agronomy, College of Horticulture, Vellanikkara, 1989) Sreelatha, P; Balakrishna Pillai, P
    An experiment was conducted at the Agricultural Research Station, Mannuthy, Kerala Agricultural University during May, 1988 to February, 1989 to study the influence of weather parameters on growth and yield of rice variety Jaya. The experiment was conducted in split plot design with twelve times of planting (June 18, July 2, July 16, July 30, August 13, August 27, September 10, September 24, October 8, October 22, November 5, and November 19) as main plot treatments and two spacings (20 x 10 cm and 20 x 15 cm) as subplot treatments and the treatments were replicated three times. Observations on all weather parameters were recorded daily. Crop growth characters like plant height and number of tillers at various stages of growth and time taken from transplanting to panicle initiation, panicle initiation to flowering and flowering to harvest were recorded. Yield components like number and percentage of productive tillers, length of panicle, number of spikelets per panicle, number and percentage of filled grains, grain yield, straw yield, drymatter production, thousand grain weight, harvest index and grain straw ratio were recorded. Observations on incidence of pests, diseases and nematodes were also recorded. However, no serious incidence was noticed. The time of planting greatly influenced all the growth and yield characters. Early plantings, generally recorded taller plants and more number of tillers and productive tillers. The time of planting had a significant influence on the duration taken from flowering to harvest. Number of spikelets and filled grains per panicle did not show any significant trend with delay in planting.
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    Climate change adaptation on rice production
    (Academy of Climate Change Education and Research Vellanikkara, 2016) Navya, M; Sunil, K M