Browsing by Author "Ajithkumar, B"

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Agroclimatic atlas of Kerala
(Kerala Agricultural University, Vellanikkara, 2015) Ajithkumar, B
Analysis of potential yield and yield gap of rice(Oryza sativa L.)using ceres rice model
(Department of Agricultural Meteorology, College of Horticulture, Vellanikkara, 2020) Harithalekshmi, V; Ajithkumar, B
Assessment of rice (oryza sativa L.) production under climate change scenarios
(Department of Agricultural Meteorology Vellanikkara, 2017) Jasti Venkata Satish; Ajithkumar, B
Agriculture is sensitive to short term changes in weather and to seasonal, annual and long term variations in climate. Climate change will have decisive impact on crop production and the prediction of this climate change emerged as a major research priority during the past decade. Numerous estimates for the impending decade projects that continuous rise of anthropogenic forcing leads to increase in greenhouse gas (GHG) atmospheric concentrations, is expected to alter regional temperature and precipitation patterns, also contributing to higher risk of extreme weather events and climate irregularity (IPCC, 2013), with obvious implications on crops (Porter and Semenov, 2005). Rice (Oryza sativa L.) is vulnerable to unfavourable weather events and climate conditions. Despite technological advances such as improved crop varieties and irrigation systems, weather and climate play significant roles in rice production. The present investigation “Assessment of rice (Oryza sativa L.) production under climate change scenarios” was carried out in the Department of Agricultural Meteorology, College of Horticulture, Vellanikkara during 2016-17, to determine the crop weather relationship, to validate the CERES (Crop Environment Resource Synthesis) -Rice model for the varieties Jyothi and Kanchana and to project the changes of rice yield and growth under climate change scenarios. The field experiment was conducted at Agricultural Research Station, Mannuthy during the kharif season of 2016. Split plot design was adopted with five dates of planting viz., 5th June, 20th June, 5th July, 20th July and 5th August as the main plot treatments and two varieties viz., Jyothi and Kanchana as the sub plot treatments. The number of replications for the experiment was four. Analysis of weather with crop duration and yield showed that maximum and minimum temperatures showed increasing trend towards late plantings, whereas the relative humidity, rainfall and rainy days were found to be low in late planting than during early plantings. To determine the critical weather elements affecting the crop duration, correlation analysis was performed. Number of days for panicle initiation to booting stage, decreased with increase in maximum and minimum temperature, whereas, the reverse was observed with afternoon relative humidity, afternoon vapour pressure deficit and rainfall in Jyothi. In case of Kanchana, days for transplanting to active tillering decreased with increase in maximum, minimum temperatures and bright sunshine hours, whereas relative humidity, afternoon vapour pressure deficit, rainfall and 159 number of rainy days showed a positive influence. The mean yield of Jyothi and Kanchana on June 5th planting found to be on par with June 20th planting. The correlation analysis showed that with increase in maximum and minimum temperature during transplanting to Active tillering will reduce the yield for both Jyothi and Kanchana The crop genetic coefficients that influence the occurrence of developmental stages in the CERES-Rice models were validated, to achieve the best possible agreement between the simulated and observed values. Predicted yield and phenology of both rice varieties, Jyothi and Kanchana under different planting dates were reasonably close to the observed values. Analysis of yield and growth phases of rice under different climate change scenarios ( Representative Concentration Pathways (RCP) 4.5 and 8.5) for the time periods 2050s and 2080s showed that, days taken to panicle initiation, anthesis and physiological maturity decreases for all the five different dates of planting. This may be due to increase in maximum and minimum temperatures during the future scenarios. The predicted values of rice yield for the climate change scenarios during first and second plantings for the time periods 2050s and 2080s showed a low yield whereas increase in yield was observed in third, fourth and fifth plantings compared with 2016. This increase in yield is may be due to combined effect of increase in CO2 (538 and 936ppm) and solar radiation during the panicle initiation, anthesis and physiological maturity for the delayed plantings. These findings suggests that, planting date need to be shifted to late July and early August in case of kharif crop in the central zone of Kerala in future.
Comparison of different weather based models for forecasting rice yield in central zone of Kerala
(Department of Agricultural Meteorology, College of Horticulture, Vellanikkara, 2018) Athira Ravindran; Ajithkumar, B
Comparison of info-crop and CERES-DSSAT models of rice under projected climatic conditions of Kerala
(Department of Agricultural Meteorology, College of Agriculture,Vellanikkara, 2023) Kindinti, Anusha; Ajithkumar, B
Agriculture is vulnerable to seasonal, annual, and long-term variations in climate as well as to short-term weather changes. Impact of climate change on crops has become a top focus for research during the past ten years. Unfavorable weather and climatic conditions might influence the growth and development of rice crop. Although irrigation systems and better crop varieties have been developed, climate continue to have high impact on rice production. Researchers can forecast future needs based on climate change by using an appropriate model to simulate the characteristics of a natural environmental system, that has been studied over a short time span. Crop growth models are formulated to overcome crop production variability issues in agricultural meteorology (Rauff and Bello, 2015). A general dynamic crop model called InfoCrop has been developed to provide an integrated assessment of how weather, crop variety, pests, soil, management strategies, and soil nitrogen and organic carbon dynamics in both aerobic and anaerobic environments affect crop growth and production (Aggarwal et al., 2006). A Decision Support System for Agrotechnology transfer (DSSAT) was created by an international team of scientists with integrated meteorological, soil, and crop data bases, strategy evaluation programmes, and cropsoil simulation models (Jones et al., 1998). It includes the Cropping System Model (CSM)-Crop Environment Resource Synthesis (CERES)-Rice model, which simulates the growth, development, and yield of rice crops based on interactions between soil, water, weather, atmosphere, plants, and crop management (Jones et al., 2003). The present investigation “Comparison of InfoCrop and CERES-DSSAT models of rice under projected climatic conditions of Kerala” was carried out to assess the impact of projected climate change on the performance of rice using InfoCrop and CERES-DSSAT simulation models and to quantify the uncertainty in climate change impact using GCM under various future climate scenarios (RCP 4.5 and 8.5). Short duration variety, Jyothi and medium duration variety, Jaya were raised at Agricultural Research Station, Mannuthy under KAU, Vellanikkara. The split plot design was used with five dates of planting (June 5th, June 20th, July 5th, July 20th and August 5th) as main plot treatments and two varieties as subplot treatments, with four replications. Various observations like weather, phenological, biometric, yield and yield attributes had been recorded to study the crop weather relationship. The crop weather analysis has been carried out with SPSS software. The results indicated that duration of phenophases had a negative correlation with the maximum temperature. A significant variation in the biometric observations was also obtained. Plant height and drymatter accumulation were found to be higher in Jyothi, when compared to Jaya. Highest yield was found in July 20th planting of Jyothi and June 5th planting of Jaya. The crop genetic coefficients that influence the growth and yield of rice in the InfoCrop and CERES-DSSAT model were calibrated and validated, to achieve the best possible agreement between the observed and simulated values. Predicted phenology and yield of both rice varieties, Jyothi and Jaya, under different planting dates were reasonably close to the observed values. To study the impact of climate change on rice growth and yield, climate change in Kerala had been estimated. The base climate (2021) has been compared with three different future conditions like 2030 (near century), 2050 (mid century) and 2080 (end century) simulations for Thrissur district of Kerala under Representative Concentration Pathway (RCP) 4.5 and 8.5. Weather data during the crop growth period has been compared the base with future conditions. Under RCP 4.5 and 8.5, the solar radiation expected to increase in all the future simulations. The maximum as well as minimum temperature projected to increase by +2°C under RCP 4.5 and +3°C under RCP 8.5 scenarios, by the end of the century. Analysis of growth phases and yield of rice under RCP 4.5 and 8.5 scenarios for the 2030 (near century), 2050 (mid century) and 2080 (end century) was done by using InfoCrop and CERES-DSSAT models. The rice varieties (Jyothi and Jaya) showed decrease in duration in the near, mid and end century, compared to base period and the lowest duration was found in August 5th planting under RCP 4.5 and 8.5 scenarios for InfoCrop as well as CERESDSSAT models. InfoCrop model predicted higher duration for June 20th planting followed by July 5th planting and CERES-DSSAT model showed highest duration for June 5th. InfoCrop model predicted higher yield in the near, mid and end century compared to base period for both the varieties, in all the dates of planting under RCP 4.5 and 8.5 scenarios. Similarly, CERES-DSSAT model predicted higher yield for Jyothi in all the dates of planting and for Jaya variety, in July 5th and July 20th plantings, in both the scenarios
Crop weather relationship of rice varieties under different growing environments
(Department of Agricultural Meteorology, College of Horticulture, Vellanikkara, 2019) Haritharaj, S; Ajithkumar, B
Crop weather relationship of yard long bean (Vigna unguiculata subsp. sesquipedalis(L.) walp)
(Department of Agricultural Meteorology Vellanikkara, 2016) Aswini Haridasan; Ajithkumar, B
Crop weather relationship studies in finger millet (Eleusine coracana (L.) Gaertn) in central zone of Kerala
(Department of Agriculture Meteorology, College of Horticulture, Vellanikkara, 2019) Anunayana John, T; Ajithkumar, B
Crop weather simulation model in tomato (solanum lycopersicum L.)
(Department of Agricultural Meteorology, College of Horticulture, Vellanikkara, 2018) Navyasree, S; Ajithkumar, B
Crops weather relationship in tomato
(Department of Agricultural Meteorology, College of Horticulture, Vellanikkara, 1998) Ajithkumar, B; Lalitha Bai, E K
Implication of weather factors on mango (mangifera indica L.) phenology and prediction of yield using geospatial techniques
(Department of Agricultural Meteorology, College of Agriculture,Vellanikkara, 2025-02-03) Ankita Sinha.; Ajithkumar, B
The study evaluated mango phenology through three meteorological indices, Growing Degree Days (GDD), Photothermal Units (PTU), and the Standardized Precipitation Index (SPI). GDD and PTU illustrated cumulative heat requirements, highlighting varietal differences in growth rates and maturation. Banganpalli required higher GDD and PTU, indicating higher heat requirement for maturation, while Totapuri and Sindhooram matured under lower heat accumulation. SPI showed negative values, signifying dry conditions during the mango growth cycle. The study utilized remote sensing indices, like Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanched Vegetation Index (EVI), Land Surface Temperature (LST) and Soil Moisture Index (SMI), to monitor crop health and environmental conditions. NDVI and EVI effectively assessed vegetation health and canopy vigor, with NDVI peaking during crucial growth stages like pea-size fruit and maturity. LST highlighted temperature impacts during fruit maturity and harvest, higher LST delayed phenophase durations during all the phenophases. SMI was particularly useful in identifying moisture-sensitive stages, such as flowering. For yield prediction, correlation analysis and stepwise regression were initially performed to identify key predictors for each phenological stage. In order to achieve better model and prediction accuracy six machine learning models, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Partial Least Square Regression (PLSR) were tested with diverse input feature sets (weather variables, agrometeorological indices and remote sensing indices). Among these PLSR achieved the highest R² of 0.93 during leaf bud development (Phase I), while XGBoost performed best during days to marble stage (Phase V, R² = 0.75) and days to maturity stage (Phase VI, R² = 0.83). Ridge regression showed consistent performance across phases, with R² values 0.89 (using only remote sensing indices) and 0.81 (using all input features) during Phase I, in Phase VI, Ridge achieved an R² value of 0.71. The results demonstrated the feasibility of predicting yield using weather variables, agrometeorological indices and remote sensing indices as input data in machine learning models at key growth stages of mango. The findings of this study can empower farmers by providing actionable insights into optimizing mango cultivation based on weather and crop health data. By using remote sensing and machine learning-based yield prediction models, farmers can anticipate key growth stages, adjust irrigation and nutrient management and mitigate risks from erratic weather. This approach can enable timely decision-making, enhance productivity and maximize income of farmers.
Influence of weather parameters on growth and yield of black pepper (Piper nigrum L.)
(Department of Agricultural Meteorology, College of Horticulture, Vellanikkara, 2016) Sushna, K; Ajithkumar, B
Black pepper (Piper nigrum L.) (Family: Piperaceae) is a perennial vine grown for its berries extensively used as spice and in medicine. India is one of the major producer, consumer and exporter of black pepper in the world. It is cultivated to a large extent in Kerala, Karnataka and Tamil Nadu and to a limited extent in Maharashtra, North eastern states and Andaman & Nicobar Islands. Black pepper is a plant of humid tropics requiring high rainfall and humidity.
Microclimate modification using Carbon dioxide Enrichment Technologies (CET) and crop responses
(Department of Agricultural Meteorology, College of Horticulture, Kerala Agricultural University, 2020) Harithalekshmi, V; Ajithkumar, B
Phasic development model using thermal indices for rice (Oryza sativa L.) in the central zone of Kerala
(Department of Agricultural Meteorology, College of Horticulture, Vellanikkara, 2016) Aswany, K S; Ajithkumar, B
The present study, “Phasic development model using thermal indices for rice (Oryza sativa L.) in the central zone of Kerala” was carried out at the Department of Agricultural Meteorology, College of Horticulture, Vellanikkara, Thrissur during 2015-2016. The experiment was laid out in split plot design with five dates of planting viz., 5th June, 20th June, 5th July, 20th July and 5th August as the main plot treatments and two varieties viz., Jyothi and Kanchana as subplot treatments and there were four replications. Location of the experiment was Agricultural Research Station, Mannuthy. Growth and yield characters like plant height, leaf area index, dry matter accumulation, 1000 grain weight, grain yield, straw yield, number of panicles per unit area, spikelets per panicle, filled grains per panicle and duration of different growth phases were recorded along with monitoring of the incidence of various pests and diseases. The daily weather parameters like maximum and minimum temperatures, forenoon and afternoon relative humidity, forenoon and afternoon vapour pressure deficits, bright sunshine hours, evaporation, wind speed, rainfall and rainy days were recorded during the experimental period. Heat units viz., Growing Degree Days (GDD), Heliothermal Units (HTU), and Photothermal Units (PTU) were found to affect the yield of both Jyothi and Kanchana varieties of rice. In both varieties, early dates of planting accumulated more heat units to attain physiological maturity compared to later plantings. Reduction in yield in the later plantings was noticed due to the increase in GDD, HTU and PTU.The weather parameters such as minimum temperature (23.8°C), forenoon (23.0mmHg) and afternoon vapour pressure deficit (23.6mmHg), forenoon relative humidity (94.7%) and afternoon relative humidity (77.1%), rainfall (1581.5 mm) and rainy days (71days) were found to be higher in early dates of planting, while maximum temperature (31.8°C), bright sunshine hours (5.2h) , evaporation (2.9mm). Number of days taken to complete different phenological stages of both varieties was low for late planted crops. Plant height, dry matter accumulation, yield and yield parameters such as number of panicles per unit area , spikelets per panicle, filled grains per panicle and 1000 grain weight were highly variable among the different planting dates. The total chlorophyll content (soluble protein and growth indices such as LAI, CGR, LAD and NAR were found to be highest on June 5th planting. Grain yield was highest for June 5th planting for both varieties. The recorded grain yield for Jyothi and Kanchana was The crop genetic coefficients that influence the occurrence of developmental stages in the CERES-rice models were derived, to achieve the best possible agreement between the simulated and observed values. The performance of the CERES-rice simulation model was tested and evaluated using the calibrated genetic coefficients for both the varieties with their respective planting dates. The results of simulation studies in respect of phenophases and yield of rice were compared with the observed values from the field experiment. Root Mean Square Error (RMSE) and D- stat (index of agreement) were used to evaluate the model performance and found that predicted yield of both rice varieties Jyothi and Kanchana under different planting dates were reasonably close to the observed values.
Simulation of environmental and varietal effects in rice using ceres model
(Department of Agricultural Meteorology, College of Horticulture, Vellanikkara, 2014) Naziya; Ajithkumar, B
The present investigation on “Simulation of environmental and varietal effects in rice using CERES model” were carried out in Department of Agricultural Meteorology, College of Horticulture, Vellanikkara during 2012-13 to determine the crop weather relationship, to calibrate the genetic coefficient and simulation of phenology, growth and yield of Jyothi and Kanchana varieties of rice. The experiment was laid out in split plot design with four replications at Agricultural Research Station, Mannuthy during the Kharif season of 2013. Five dates of planting was assigned as a main plot treatment viz., 5th June, 20th June, 5th July, 20th July and 5th August with two varieties (Jyothi and Kanchana) as sub plot treatment. The different growth and yield characters like plant height, leaf area index, dry matter accumulation,1000 grain weight, grain yield, straw yield, number of panicles, spikelets, filled grains and duration of different growth phases were recorded along with monitoring the incidence of various pest and diseases. The daily weather parameters like maximum and minimum temperatures, forenoon and afternoon relative humidity, forenoon and afternoon vapour pressure deficits, bright sunshine hours, evaporation, wind speed, rainfall and rainy days were determined. The minimum temperature, afternoon and forenoon relative humidity, rainfall, rainy days, bright sunshine hours and evaporation were found to be higher in early planting dates compared to late plantings. Plant height, leaf area index, dry matter accumulation, yield and yield attributes were highly variable among the different planting dates. Yield and yield attributes were influenced by various weather parameters experienced by the crop during different dates of planting. Days taken to complete maturity were reduced with each successive delay in planting dates in both the varieties. Genotypic variations are found between the varieties but days taken for each phenophases were found to be similar. June 5th and July 20th planting recorded the highest yield in Jyothi whereas June 20th and July 5th planting gave highest yield in Kanchana. Jyothi was found to be superior to Kanchana during the crop season. To determine the critical weather elements affecting the crop growth, correlation analysis was done and it was observed that crop duration would decrease with increase in temperature and bright sunshine hours whereas, the forenoon and afternoon relative humidity, rainfall and rainy days showed a positive influence on crop duration. Multiple linear regression models were fitted, to predict the grain yield based on weather variables. The crop genetic coefficients that influence the occurrence of developmental stages in the CERES-rice models were derived, to achieve the best possible agreement between the simulated and observed values. Calibration was done with the independent data sets of two rice varieties viz. Jyothi and Kanchana for different genetic coefficients, which characterize the performance of the crop. The performance of the CERES-rice simulation model was tested and evaluated using the calibrated genetic coefficients for both the varieties with their respective planting dates. The results of simulation studies in respect of phenophases and yield of rice were compared with the observed values from the field experiment. Root Mean Square Error (RMSE) and D- stat (index of agreement) were used to evaluate the model performance and found that predicted yield of both rice varieties Jyothi and Kanchana under different planting dates were reasonably close to the observed values.
Spatial variability of climate change impacts on Rice (Oryza sativa L.) yield in Kerala
(Department of Agricultural Meteorology, College of Agriculture, Vellanikkara, 2021) Riya, K R; Ajithkumar, B
Rice is one of the essential food crops of the world. Almost 40% of the world’s population consumes rice as their staple food. Nearly 12% of the total cultivated area in Kerala accounts for rice cultivation. It is cultivated in both plains and high altitudes, therefore long-term climatic changes within a region and their impact on productivity is very important. Crop weather models have a vital role in climate change studies. Rice studies are mainly carried out in the CERES- Rice (Crop Environment Resource Synthesis- Rice) model. The CERES - Rice has been calibrated and validated and found suitable for simulation of rice growth and development in the tropical humid climate. The present experiment was aimed to study the impact of climate change on phenophase and yield aspects of rice varieties under climate change scenarios of RCP 4.5 and 8.5 in 14 districts of Kerala. Short-duration rice variety, Jyothi and medium-duration variety, Jaya have been selected for the experiment. The experiment was carried out with the split-plot design. The main plot treatments were five dates of plantings (June 5 th , June 20th , July 5 th , July 20th and August 5 th) and subplot treatments were two varieties (Jyothi and Jaya) with four replications. Various observations like weather, phenological, biometric, physiological, yield and yield attributes had been recorded to studythe crop weather relationship. The crop weather analysis has been carried out with SPSS software. The results indicated that duration of phenophases had a negative correlation with the maximum temperature. A significant variation in the biometric observations was also obtained. Plant height and dry matter accumulation were found to be higher in Jyothi when compared to Jaya. Both varieties recorded the maximum leaf area index (LAI) and leaf area duration (LAD) at 75 days after planting. The crop growth rate was obtained maximum at an interval of 45 to 60 days after planting irrespective of the variety. The highest grain yield in Jyothi was obtained during June 5th and August 5th plantings which were found to be on par. In Jaya, July 20th planting and August 5 th planting were found to be on par. Using the observations from the field, validation of genetic coefficient of DSSAT- CERES model. To study the impact of climate change on rice production, climate change in Kerala had been estimated. The current climate (1980 – 2020) has been compared with three different future 2010-2030 (near-century), 2021-2050 (mid-century) and 2051-2080 (latecentury) simulations for the 14 districts of Kerala under Representative Concentration Pathway (RCP) 4.5 and 8.5. The annual and seasonal (southwest monsoon, northeast monsoon, winter and summer season) comparison of weather data has been carried out. Under RCP 4.5 the amount of solar radiation is expected increase by greater than 1 MJ/m2 districts like Wayanad, Malappuram, Thrissur, Ernakulam, Kottaym and Pathanamthitta by the end of century. Under RCP 8.5 a normal departure (-1 to 1 MJ/m2 ) is expected by the end of century in most parts except in Wayanad and Thrissur, where an above normal increase is predicted. At the same time in Palakkad a below normal decrease is expected in solar radiation. During the southwest monsoon an increase in solar radiation is expected in mid-century and by the end of century a normal departure is expected except in Malappuram, Palakkad and problematic zone where the solar radiation is expected to increase. Under RCP 8.5 a normal departure is expected by the end of century except in Kasargod, Kozhikode, Wayanad and Thiruvananthapuram where a below normal departure is expected. In northeast monsoon season solar radiation is expected to increase in central and problematic zone and a normal departure is expected in other parts under RCP 4.5. In RCP 8.5 by the end of century solar radiation is expected to decrease in Kozhikode, Idukki and Thiruvananthapuram during northeast monsoon. An increase in solar radiation is expected in central, problematic and southern zone during RCP 4.5 in winter season while in northern zone solar radiation is expected to be normal in RCP 4.5 and a below normal departure is expected in RCP 8.5. In Thiruvananthapuram a solar radiation is expected to decrease in both scenarios. A normal departure of solar radiation is expected to increase in most parts of Kerala during the summer season. In Kannur, Kozhikode, Thiruvananthapuram and Wayanad solar radiation are expected to decrease by -1.5 MJ m2 or less than that by the end of century under RCP 8.5 except in Malappuram where an above-normal increase is expected. An increasing trend in maximum and minimum temperature is expected in future simulations. The annual temperature is expected to increase in all parts of Kerala except in Idukki where a below normal departure (-1.5 to 1.5°C) is expected in near and mid-century. By the end of century a normal departure of annual maximum temperature is expected in high range zone and Thiruvananthapuram under RCP 4.5 and while under RCP 8.5 only Idukki and Thiruvananthapuram is showing a normal departure. In southwest monsoon season temperature is expected to rise by 1.5°C by end of century under RCP 8.5 while under RCP 4.5 a normal departure is expected in Wayanad, Idukki and Kollam. During the northeast monsoon season and summer season the maximum temperature is expected to increase in all parts expect in Idukki in near and mid-century under both RCPs. In RCP 4.5, a normal departure of annual maximum temperature is projected in the high range zone and Thiruvananthapuram by the end of the century, but only Idukki and Thiruvananthapuram will show a normal departure under RCP 8.5. During the winter season a decrease in temperature is expected in Idukki while in other districts the temperature is expected to increase. The minimum temperature is expected to increase in Kerala except in Idukki in all the future simulations under both RCPs in all seasons. In Idukki the minimum temperature is expected to decrease in near century and then increase in mid and end of century with a normal depature. A spatial variation in rainfall is expected in Kerala in future simulations, with an excess or normal rainfall in some parts at the same time deficiency in other parts of Kerala. The annual rainfall is expected to increase in most parts of Kerala. In districts like Kasargod, Idukki and Alappuzha a normal departure (+19 to -19%) in annual rainfall is expected in all the future simulations under RCP 4.5 ad 8.5. During the southwest monsoon season, rainfall is expected to show a large excess and excess in most parts of Kerala except in Kasargod where a normal departure is expected. Under RCP 4.5 the rainfall is expected to decrease in Wayanad and Alappuzha by the end of century. While under RCP 8.5 the rainfall is expected to increase in Wayanad and Thiruvanathapuram by the end of century. Northeast monsoon is expected to be show a normal departure in most places. Under RCP 4.5 it is expected to decrease in Idukki, Pathanamthitta, Kollam and Kasargod. While under RCP 8.5 it is expected to increase in Ernakulam, Alappuzha, Pathanamthitta and Kollam at the same time a deficit rainfall is expected in Kannur and Thrissur. Winter rainfall was predicted to decrease from normal in almost all parts of Kerala in near and mid-century. By the end of century under RCP 4.5 and by mid and end of the century under RCP 8.5 and excess rainfall is predicted in parts of the northern zone and problematic zone. Summer rainfall is expected to be large excess and excess in most parts during near and mid century under RCP 4.5 and in the near century of RCP 8.5. In the end of century under RCP 4.5 and in mid and end of the century under RCP 8.5 a normal rainfall is expected in most places. In Idukki and Thiruvananthapuram the rainfall is expected to be deficient. The potential yield had been predicted with the DSSAT- CERES model using the genetic coefficient validated using field experiment. the predicted weather for 13 districts of Kerala. The duration of crop is expected to decrease as a result of increase in temperature in both varieties. Yield reduction is expected in future simulations under both the RCPs in most places of Kerala. Under RCP 4.5 in Jyothi, June 5th planting showed maximum deviation from base period (2020). The maximum deviation was observed in Kozhikode i.e. in near (-58%), mid (-63%) and end of century (-60%) under RCP 4.5 and in near (- 62%), mid (-60%) and end of century (-64%) under RCP 8.5. The least deviation was found in July 20th planting in all the future simulations. In Idukki, an increase in yield had been observed in July 5 th , July 20th and August 5 th plantings. An increase by 34%, 28% and 23% in near, mid and end of century respectively is expected. Under RCP 8.5 in July 20th planting higher yield has been observed and shows a positive deviation of 38%, 28% in near and mid century respectively in Idukki. By the end of century yield is expected to decrease except in August 5th planting which showed a positive deviation of 12%. In southern zone the highest potential yield had been observed in August 5 th planting. In Jaya also the maximum deviation had been observed during June 5th palnting in Kozhikode i.e. in near (-58%), mid (- 63%) and end of century (-60%) under RCP 4.5 and in near (-62%), mid (-60%) and end of century (-64%) under RCP 8.5. In Idukki under RCP 4.5 an increase in yield was observed during July 20th planting with an increase by 27% in near and mid century and by the end of century a deviation of 20% was observed. Under RCP 8.5 in July 20th planting higher yield has been observed and shows a positive deviation of 27%, 24% and 10% in near, mid and end of century. In southern zone of Kerala, highest potential yield of Jaya has been observed in July 20th planting in all the future simulations under both RCPs. The duration of crop showed a negative correlation with the temperature. As a result decrease in duration of phenophase had been observed in future simulations. An increased temperature and precipitation patterns during panicle initiation to anthesis may be the reason for the yield variability. During the base period higher yield was obtained during June 5th and June 20th planting i.e. early plantings while in future simulations the higher yield is expected in July 5th, July 20th and August 5th plantings i.e. late plantings. Hence there is a chance of shift in date of planting in Kerala in future.
Validation of ceres model to calibrate the genetic coefficients of rice (Oryza sativa L.)
(Department of Agricultural Meteorology, College of Horticulture, Vellanikkara, 2015) Arjun Vysakh; Ajithkumar, B
Rice (Oryza saiva L.) is vulnerable to unfavourable weather events and climate conditions. Despite technological advances such as improved crop varieties and irrigation systems, weather and climate are important factors which play a significant role in rice production. The present investigation “Validation of CERES model to calibrate the genetic coefficients of rice (Oryza sativa L.)” was carried out in the Department of Agricultural Meteorology, College of Horticulture, Vellanikkara during 2013-15, to determine the crop weather relationship, to validate the CERES (Crop Environment Resource Synthesis) -Rice model and to calibrate the genetic coefficients for rice. The field experiment was conducted at Agricultural Research Station, Mannuthy during the kharif season of 2014. Split plot design was adopted with five dates of planting viz., 5th June, 20th June, 5th July, 20th July and 5th August as the main plot treatments and two varieties viz., Jyothi and Kanchana as the sub plot treatments. The number of replications for the experiment was four. Different growth and yield characters viz., plant height, dry matter accumulation, number of panicles, spikelets, filled grains, 1000 grain weight, grain yield, straw yield and duration of different crop growth phases were recorded. The daily weather parameters like maximum and minimum temperatures, forenoon and afternoon relative humidity, forenoon and afternoon vapour pressure deficits, bright sunshine hours, pan evaporation, wind speed, rainfall and rainy days were recorded during the entire crop growing period, to determine the crop weather relationship. The maximum temperature showed an increasing trend towards the late plantings. The minimum temperature, afternoon and forenoon relative humidity, rainfall and rainy days were found to be higher in early planting dates compared to late plantings. Plant height, dry matter accumulation, yield and yield attributes were highly variable among the different planting dates. Yield and yield attributes were influenced by various weather parameters experienced by the crop during different dates of planting. Days taken to complete maturity got reduced with delay in planting dates in both the varieties. Jyothi variety took more days to complete different phenophases, compared to Kanchana. The highest yield in Jyothi was recorded for June 5th planting, whereas June 20th planted crop recorded highest yield in Kanchana. The various growth indices such as leaf area index, leaf area ratio, leaf area duration, absolute growth rate, crop growth rate, net assimilation rate and relative growth rate were worked out to study the crop growth and development. During the early growth stages, these growth indices showed an increasing trend and decreasing trend was noticed in the later stages. To determine the critical weather elements affecting the crop growth, correlation analysis was performed. It was observed that crop duration decreased with increase in temperature and bright sunshine hours, whereas, the forenoon and afternoon relative humidity, rainfall and rainy days showed positive influence on crop duration. Multiple linear regression equations were fitted, to predict the grain yield based on weather variables. A crop model can simulate the actual system of field in the lab. CERES-Rice model has been widely used to understand the relationship between rice and its environment. Crop performance in terms of genetic coefficients used in the model can be used as a tool for strategic decision making. The crop genetic coefficients that influence the occurrence of developmental stages in the CERES-Rice models were derived and validated, to achieve the best possible agreement between the simulated and observed values. Calibration was done with independent data sets of two rice varieties viz., Jyothi and Kanchana for different genetic coefficients, which characterize the performance of the crop. The results of simulation studies in respect of phenophases and yield of rice were compared with the observed values. Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and D-stat (index of agreement) were used as model accuracy measures. Predicted yield and phenology of both rice varieties, Jyothi and Kanchana under different planting dates were reasonably close to the observed values, as indicated by the RMSE, MAPE and D-stat values.