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DC Field | Value | Language |
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dc.contributor.advisor | Shajeesh Jan, P | - |
dc.contributor.author | Fathima Sona, N | - |
dc.date.accessioned | 2022-08-19T09:52:58Z | - |
dc.date.available | 2022-08-19T09:52:58Z | - |
dc.date.issued | 2021 | - |
dc.identifier.sici | 175191 | en_US |
dc.identifier.uri | http://hdl.handle.net/123456789/10945 | - |
dc.description.abstract | Ginger (Zingiber Officinale) is an important commercial spice crop grown from very ancient times in India. Because of its valued aroma and lemon flavour, it has spread to tropical and subtropical regions from the Indo-China region and became one of the earliest oriental spices known to Europe (Nybe and Miniraj, 2005). Species diversity and yield of spices are threatened due to the ever increasing temperature and changes in precipitation pattern. Since spices are grownboth in plains and high altitudes, it is important to assess the long term climatic changes within a region and its influence on productivity (Sing, 2008). The present experiment was aimed to study the impact of climate change on growth and yield aspects of ginger crop under climate change scenarios of RCP 4.5 and 8.5. Ginger varieties, Maran and Varada were raised at Instructional Farm (IF) Vellanikkara, by adopting split plot design. The experiment was laid out with four dates of planting (15th May, 1st June, 15th June and1 st July) as main plot treatment and two varieties (Maran and Varada) as sub plot treatments. Fourreplications were given for the experiment. The crop weather relationship was analysed with correlation with the help of SPSS software. Principal Component Analysis (PCA), a multivariate statistical technique was done in order to reduce the multicollinarity of large data sets of weather variables to substantially smaller sets of new variables. The developed principal components were utilized for model development using stepwise regression analysis. The future climate was estimated by climate change projections generated using CCSM4 models for 2030, 2050 and 2080based on scenarios RCP 4.5 and 8.5. The life cycle of ginger was characterized by four distinct stages, ie., sowing to 50% germination, 50% germination to active tillering, active tillering to bulking and bulking to physiological maturity. Duration taken for each phenophases found to vary for all the four date of planting in both Maran and Varada. The May 15th date of planting took more days to germinate compared to other date of plantings. The number of days taken for sowing to germination was positively correlated with maximum temperature, minimum temperature, rainfall and soil temperature in both varieties. The number of days taken to attain physiological maturity also foundto be more in May 15th planted crop. The weather experienced during various phenophases have significant influence on yield and other yield attributes of ginger crop. It was found that the yield of both varieties of ginger havepositive correlation with minimum temperature at all the four phenophases except bulking t o physiological maturity. Maximum temperature observed at sowing to germination was positively correlated with yield of both Maran and Varada. At active tillering to bulking stage, rainfall, rainy days and minimum temperature showed a significant positive correlation with yield, but maximum temperature, wind speed and solar radiation showed a significant negative correlation with yield. The Principal Component Analysis was carried out for ginger varieties Maran and Varadaby using the weather parameters experienced in four stages ie., sowing to 50% germination, 50% germination to active tillering, active tillering to bulking and bulking to physiological maturity. Weather parameters considered include maximum temperature (Tmax), minimum temperature (Tmin), rainfall (RF), rainy days (RD), relative humidity (RH), wind speed (WS) and solar radiation (SRAD). The statistical model was developed with principal components as independentand yield as dependent variable. Projected climatic conditions of Vellanikkara, Thrissur under climate change scenarios of RCP 4.5 and 8.5 were downscaled from CCSM4 model. The projected climate of near century (2010-2039), midcentury (2040-2069) and end of century (2070-2099) were downscaled for the study. The projected yield of the ginger variety Maran was found to decrease at all planting datesexcept July 1 st under both the RCP 4.5 and 8.5 scenarios. Under RCP 4.5 scenario, more reductionwas reported at the end of the century on May 15th (38%), June 1st (29%) and June 15th (54.5%) dates of planting. July 1 st planted crop reported increase in projected yield under near, mid and end of centuries. More increase in yield was observed in midcentury (17.7%). Under RCP 8.5 scenario,May 15th (50.2%) and June 1st (20.3%) dates of planting reported the highest percentage of yield reduction during midcentury. June 15th date of planting, recorded the highest yield reduction of 52.6% at the end of the century. July 1st planting date recorded increase in yield and it was more in end of century (15.5%). In case of Varada, under RCP 4.5 scenario, more yield reduction was reported at the end of century during May 15th (24%) and June 1 st (7.2%) dates of planting. DuringJune 15th planting dates, near and end of century reported the same yield reduction of 24.9%. TheJuly 1 st planted crop reported an increase in yield, which was more (15.5%) during end of century. Under RCP 8.5 scenario, more yield reduction was observed in midcentury on May 15th (28.8%) date of planting. June 1st (6.6%) and June 15th (19.9%) dates of planting reported more reduction in end of century. July 1 st reported more increase of 24% in end of century. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Department of Agricultural Meteorology, College of Agriculture, Vellanikkara | en_US |
dc.subject | Agricultural Meteorology | en_US |
dc.subject | Ginger | en_US |
dc.subject | Zingiber officinale | en_US |
dc.subject | Representative concentration pathway (RCP) | en_US |
dc.subject | Weather variables | en_US |
dc.title | Ginger (Zingiber officinale) yield variability under different climate change scenarios | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | PG Thesis |
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175191.pdf | 5.46 MB | Adobe PDF | View/Open |
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