1. KAUTIR (Kerala Agricultural University Theses Information and Retrieval)
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Item Yield prediction of kharif rice (Oryza sativa L.) in Kerala by various crop weather models(Department of Agricultural Meteorology, College of Agriculture,Vellanikkara, 2025-02-04) Chandana B Jyothi.Rice is a staple crop in Kerala, but its production faces challenges from adverse weather and climate changes, leading to yield fluctuations. Accurate yield forecasts are vital for farmers, policymakers, and exporters to ensure efficient resource allocation and strategic planning. Tools like DSSAT and Info-Crop simulate rice growth for yield prediction, while statistical models like Artificial Neural Networks (ANN) and Stepwise Multiple Linear Regression (SMLR) offer additional predictive capabilities. The present study “Yield prediction of kharif rice (Oryza sativa L.) in Kerala by various crop weather models” is aimed to predict kharif rice yield in different districts of Kerala using statistical and crop simulation models and compare above yield prediction models. Short duration variety, Jyothi and Manu Ratna were raised at Agricultural Research Station, Mannuthy, Kerala Agricultural University, Thrissur. 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, computed parameters, yield and yield attributes had been recorded to study the crop weather relationship. The data analysis has been done by using SPSS software and it was found that with increase in the maximum temperature (°C), minimum temperature (°C), temperature range (°C), bright sunshine hour (hrs) and rate of evaporation (mm) has reduced the crop duration, while amount of rainfall (mm), number of rainy days, forenoon and afternoon relative humidity (%) has positively influenced with the crop duration. A significant variation in the biometric and computed observations was also obtained. Plant height were found to be higher in Manu Ratna, when compared to Jyothi. Dry matter accumulation was higher in Manu Ratna during 75 DAP and there was no significant difference between varieties in the later stages. Both plant height and dry matter accumulation had significant variations among different planting dates. Leaf area index did not show any significant variation among varieties and date of planting. In Jyothi highest grain yield was found in July 5th planting, while in Manu Ratna July 20th planting was found to be higher. Maximum temperature in the P5 and P6 stage had a negative influence on the yield. Wind speed also showed a negative correlation with yield in the later stages. The genetic coefficients influencing the growth and yield of rice in the CERES- DSSAT model and Info-Crop model were calibrated to achieve the optimum agreement between observed and simulated values. Predicted yield of both rice varieties, Jyothi and Manu Ratna, under different planting dates were reasonably close to the observed values. These observations indicate that the DSSAT model generally performs better in districts like Thrissur, Pathanamthitta and Kollam, while the Info Crop model excels in Thrissur. However, both models require improvements in districts like Kottayam, Kasaragod and Alappuzha to enhance prediction accuracy. A dataset of 105 yield records (2013–2022) and weather indices was used for calibration. Stepwise regression identified the best statistical model having highest R2 for yield prediction. The ANN model, trained using the ‘caret’ package in R Studio, utilized 12 input variables. The dataset was split into 80% training and 20% testing. The developed model predicted 2023 yields for 12 districts. For comparing the accuracy of these models for districts of Kerala, MAPE and MAE were calculated. The data highlights the district-wise performance of both models, showing the ANN model generally outperforms the SMLR model in terms of accuracy, particularly in Kasaragod and Ernakulam. Yield prediction is crucial for ensuring food security, optimizing resource use and guiding agricultural planning and policy decisions. In conclusion, the comparison of crop-weather models for rice yield prediction reveals distinct strengths among the approaches. Machine learning models demonstrate superior accuracy with extensive datasets. A hybrid approach combining these models can optimize rice yield predictions, supporting sustainable and resilient rice farming systems.Item 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, BAgriculture 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 scenariosItem Micrometeorological modification with mulches to enhance the yield of Turmeric (Curcuma longa L.)(Department of Agricultural Meteorology, College of Agriculture,Vellanikkara, 2021) Abin Divakaran, A; Lincy Davis, PTurmeric (Curcuma longa L.) is one of the most important rhizomatous spices, belonging to Zingiberacea. It is an annual herbaceous plant native to tropical SouthEast Asia. Turmeric has high medicinal properties and it is wildly used in pharmaceutical, cosmetics and food industries. Due to the high value of the crop, it is getting good demand all over the world. India is one of the largest producer and consumer of turmeric around the world. In India turmeric is mainly planted in the hot summer months and grown as a rainfed crop, but due to the drastic changes in the agroclimatic conditions its production is influenced detrimentally. Mulching is an important cultural practice in turmeric, which helps to maintain an optimum microclimatic condition, reduce weed growth, add organic matter and conserve moisture throughout the high evaporative periods. Due to these changing climatic conditions assessment of an effective date of planting and finding a most suitable mulching practice are required for the effective production of turmeric. Hence, the goal of this study is to determine how planting dates and micrometeorological modifications with mulches affect turmeric yield. Turmeric variety Kanthi was raised in Plantation Crops and Spices farm, College of Agriculture, KAU, Vellanikkara with four different dates of planting (1st May, 15th May, 1 st June and 15th June) and four different mulching treatments (white polythene mulch, black polythene mulch, paddy straw mulch and green leaf mulch). The experiment was laid out in split plot design with four dates of planting as main plot treatments and four mulching practices as subplot treatments. Crop weather analysis was done by using SPSS software and crop yield prediction model was developed with the help of Principal Component Analysis (PCA) and regression analysis. The total crop period was divided into four phenophases (P1-planting to germination, P2-germination to initiation of active tillering, P3-initiation of active tillering to bulking, P4- bulking to physiological maturity). The days to reach each phenophases were different in every date of planting. May 1st planting took more days to reach 100 per cent germination and to reach physiological maturity both 1st and 2nd dates of plantings took more time. The plant biometric characters like plant height, number of leaves, leaf area, number of tillers and dry matter accumulation were found to be more in earlier dates of planting (May 1st and May 15th) in almost all the time. In mulching practices paddy straw mulch was superior and it was followed by green leaf mulch. The yield produced by May 1st and May 15th dates of planting were on par and in case of mulching treatments paddy straw mulch produced superior yield than any other mulching practice. In mulching treatments polythene mulches recorded more soil temperature and moisture content than organic mulches in almost all the time. The first phenophase of 1st date of planting recorded high maximum, minimum and soil temperature along with less rain fall and rainy days. This might have influenced the late emergence of turmeric. The increase in maximum temperature, wind speed, sunshine hours and evaporation reduced the plant height in third phenophase. Soil moisture content and relative humidity inside the plant canopy showed a positive correlation with yield, whereas soil temperature showed a negative correlation with yield during the bulking stage of turmeric. The decrease in maximum temperature, bright sunshine hours, wind speed and evaporation and the increase in the minimum temperature, forenoon and afternoon relative humidity and rainfall during bulking stage enhanced the yield in turmeric. The development of yield prediction model with principal component analysis of mulching treatments and dates of planting of four phenophases were done and the yields of turmeric crop with these equations were predicted. This showed that, the predicted yield was in accordance with the observed yield in all mulching treatments.Item 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, BRice 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.Item Ginger (Zingiber officinale) yield variability under different climate change scenarios(Department of Agricultural Meteorology, College of Agriculture, Vellanikkara, 2021) Fathima Sona, N; Shajeesh Jan, PGinger (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.Item Estimation of crop water requirement in rice using satellite data and GIS(Department of Agricultural Meteorology, College of Agriculture, Vellanikkara, 2021) Chinnu Raju; Ajith KWater shortage is one of the world's most critical issues, and climate change projections suggest that it will get worse in the future. Since, water availability and accessibility are the most significant constraints to agricultural production in waterscarce areas, resolving this issue is crucial. In order to overcome this, farmers must better estimate crop water requirements and use irrigation water more efficiently. Proper irrigation management and water conservation depend on accurate estimation of crop water demands. This study was done to estimate crop water requirement in rice crop during mundakan season 2020-21 in Palakkad district of Kerala using remote sensing and land based observations. Remote sensing technology relies on the spectral signatures of the vegetation and other land covers in an area. In order to proceed with the analysis of remote sensing products, the major rice growing areas were delineated using multi temporal cloud free Sentinel-2 imageries at a spatial resolution of 10 m following iso cluster unsupervised classification. The overall classification accuracy was 88.33 % with a Kappa coefficient of 0.77. Small fragmented heterogeneous rice areas and large homogeneous rice areas were classified equally well. A commonly used and recommended method for estimating crop water requirements is the use of reference evapotranspiration (ETo) and crop coefficient (Kc). Under field conditions, standard methods to estimate evapotranspiration (ET) over homogenous surfaces include conventional techniques such as weighing lysimeters that measure the water consumed through ET directly based on a mass balance, or flux measurements using Bowen Ratio or Eddy Covariance instrument systems that measure components of the surface energy balance to estimate evapotranspiration. However, a limitation of these systems is that they provide point measurements that may not adequately represent the ET from fields other than where the measurement is taken. To overcome this problem of estimating ET from multiple fields, satellite-based remote sensing is a useful method for estimating ET on a field-by-field basis at a regional scale The use of remotely sensed vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), has been tested by scientists to predict crop coefficient (Kc) at field and regional scale.In this study, analysis was done to establish a relationship between Normalized Difference Vegetation Index (NDVI) and crop coefficient (Kc) values for the 30 ground truth locations spread over 5 blocks viz, Alathur, Nenmara, Kollengode, Chittur and Kuzhalmannam, which represents the major rice growing tract of Palakkad district. A linear equation was set, between NDVI values obtained from MODIS NDVI (MOD13Q1) 16 day composite with a spatial resolution of 250 m and Kc table values collected from literature, and the equation showed a strong relation with an R2 value of 0.8156. The Normalized Difference Vegetation Index (NDVI) was calculated from reflectance of the red and near infrared bands. Kc values vary from season to season and field to field. Also, Kc depends on crop growth stage, plant density, and irrigation management. Hence, it becomes necessary to test the relationship between NDVI and Kc to confirm crop coefficient under local conditions. The Kc predicted values during early vegetative were in the range of 0.5-0.8, towards the late vegetative stage, it showed an increasing trend from 0.8 to 1.2, during the reproductive stage the value raised to 1.3, and when the rice crop reached maturity stage Kc values decreased to 0.58. The potential evapotranspiration during different crop growth stages ranged between 120-176 mm. The total crop evapotranspiration during the entire mundakan season 2020-21 in the training sites considered for the study was in the range of 500-626 mm. Water lost through crop evapotranspiration is compensated by effective rainfall and water supplied through irrigation. The rainfall received during early vegetative stage ie; during October and November months were sufficient to compensate evapotranspiration losses of the rice crop. But irrigation is necessary for sustaining crop growth during late vegetative, reproductive and maturity stages due to the lack of rainfall in the corresponding months so as to compensate crop evapotranspiration. In rice, total irrigation requirement includes water required to compensate crop evapotranspiration and additional water supplied to maintain standing water in the fields. The total irrigation requirement of rice during mundakan 2020-21 in Palakkad district was in the range of 611-975 mm. Crop coefficient (Kc) maps created at a regional scale provided Kc values during various stages of crop growth, allowing for more accurate estimation of crop evapotranspiration for the research area. Crop water demands maps were also created for the entire study area, demonstrating the spatial and temporal distribution of irrigation requirements. If the geographical coordinates of the place are known, these maps make estimates of crop water requirement of a rice field much easier. Global warming and climate change may lead to increased frequency of irrigation in the near future. This in turn causes increased the demand of water for irrigation purposes. Information regarding crop specific area under irrigated agriculture and crop growing season are important for efficient use of available water resources. The delineated rice field will provide a clear view of the geographical coverage of irrigation requirements, and the crop water demand maps will show the stage wise irrigation water requirements. The irrigation requirement map prepared for the study area covering 5 blocks of Palakkad district can be used for water resource planning and management. This is particularly useful for understanding inter seasonal variations in irrigation water demand at different geographical and temporal dimensions.Item 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, BItem Crop weather relationship in okra(Department of Agricultural Meteorology, College of Horticulture, Vellanikkara, 1999) Kavitha, S; Kesava Rao, A V RItem Crop weather relationship in cauliflower (Brassica oleracea var.botrytis L.)(Department of Agricultural Meteorology, College of Horticulture, Vellanikkara, 2012) Karthika, V P; Prasada Rao, G S L H VA field experiment was conducted during 2010-11 and 2011-12 at the Department of Agricultural Meteorology, College of Horticulture, Vellanikkara with the objectives to study the effect of weather on growth and yield of cauliflower and to assess the suitability of cauliflower under various crop growing environments. The study included five planting times at an interval of 15 days (1st November, 15th November, 1st December, 15th December and1st January) and two tropical hybrid varieties (Basant and Pusa Kartik Sankar). The different growth and yield characters like plant height, number of leaves, plant biomass, duration of different growth stages and curd weight were recorded along with monitoring of the incidence of various pests, diseases and physiological disorders. The daily weather parameters like maximum and minimum temperatures, forenoon and afternoon relative humidity, forenoon and afternoon vapour pressure, bright sunshine hours, wind speed, rainfall and rainy days were collected and used in this study. Based on these weather parameters, other important weather variables like mean temperature, diurnal temperature range, forenoon and afternoon vapour pressure deficits and solar radiation were determined. Various heat units like growing degree days, heliothermal units and photothermal units were also worked out. The maximum and mean temperature, diurnal temperature range, forenoon and afternoon relative humidity, forenoon and afternoon vapour pressure deficits, bright sunshine hours and solar radiation were found to be higher in 2010-11 as compared to 2011-12. Plant height, number of leaves and the duration of different growth stages were found to be highly variable among the different planting times in both the years, but when pooled over years, these characters became non-significant (except the duration from curd initiation to harvest) with respect to the planting time as a result of the higher variability between the two years for the different weather parameters. The curd weight and the plant fresh and weights exhibited high significant difference for the different planting times. Duration from transplanting to curd initiation was found to be more critical for the curd yield. To determine the critical weather elements affecting the crop growth, correlation analysis was done and it was observed that the crop duration would increase with increase in the maximum temperature, bright sunshine hours, solar radiation and afternoon vapour pressure deficit whereas, the afternoon relative humidity showed a negative influence on crop duration. The curd yield and plant weight were found to be decreasing with increase in the maximum temperature and sunshine hours. The various heat units exhibited positive correlation with the duration of different growth stages. Based on the weather parameters experienced by the crop during the transplanting to curd initiation period, a regression equation with an R2 value of 0.95 was developed to predict the curd weight. The present study revealed that first fortnight of November is the optimum planting time for tropical cauliflower in Thrissur District, since the maximum curd size was obtained when planted on 1st November in 2011-12. The optimum weather for the planting of tropical cauliflower was observed to be less than 31.2°C of maximum temperature, less than 26.8°C of mean temperature, less than 8.8°C of diurnal temperature range, less than 6.0 hrs of bright sunshine hours and less than 22.3 MJ m-2 of solar radiation, with 22.5°C of minimum temperature. Intermittent rainfall and higher relative humidity observed during the earlier planting times were found to be conducive for the incidence of pests and diseases and the bacterial disease black rot was observed as a serious threat to cauliflower cultivation in this region.Item 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, BThe 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.