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7. Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast. [version 1; peer review: awaiting peer review]

Author

Dheebakaran Ga, Kokilavani S, Santosh Ganapati Patil, Sankar T, Rathika K, Arul Prasath S, Balamurali B, and Sathyamoorthy N.K.

Subjects
Research Article, Articles, Astrometeorology, Weather Forecast, Ephemeris, Planet Activeness, Rain, Wind.
Abstract

Background Astrometeorology is an ancient science, that deals the relationship between planet position and weather events. Several Indian studies proved that Astrometeorology could be a complementary method to improve numerical weather forecast accuracy. Since 2011, Tamil Nadu Agricultural University is conducting astrometeorological research and devised a novel concept “Planet Activeness Chart”. The principle is that “planets’ influence on a location’s weather varies throughout the day and may be negative, inactive, active, highly active and rule depending on their angle to that location”. Most existing astromet studies used planetary position to predict the occurrence of weather events (yes/no) but failed to capture intensity of the event. The “Planet Activeness Concept” could address this limitation and enhance forecast usability. Methods A study was carried out from 2018 to 2021 with six years data (2011-16) to verify the “Planet activeness” on hourly rainfall and windspeed events in Tamil Nadu. The frequency of planet activeness for a weather event was calculated by dividing the number of times a planet was in the selected activeness during a specific event category by the total number of events. Results The results indicated that negative state of the Sun, active status of the Saturn, Uranus, Venus and Moon were positively associated with rainfall intensity. The windy planet Mercury and Neptune at active state, the Sun and Saturn at rule state, Venus and Uranus at negative state, Jupiter at highly active state had significant influence on the increased wind speed. Conclusion Applying the planet activeness concept with azimuth could enhance the accuracy and usability of Astrometeorological forecasts. This study establishes a mathematical relationship between planet activeness and weather as a first step to understand the science behind this relationship. It is suggested to study different combination of planet activeness during a weather event for more insights.

Published
2024
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9. Leveraging Extended Range Weather Forecast for Groundnut Bud Necrosis Disease Forewarning: A Data-driven Approach.

Author

Senbagavalli, G., Dheebakaran, Ga., Sathyamoorthy, N. K., Renukadevi, P., Kannan, Balaji, Ragunath, K. P., and Kokilavani, S.

Abstract

Background: Groundnut (Arachis hypogaea L.), a globally prominent oilseed crop, is experiencing significant losses due to the Groundnut Bud Necrosis Virus (GBNV). The extended range weather forecast (ERWF) provides timely weather information and protects crops from weather-induced biotic and abiotic risks. Methods: An ERWF-based GBNV disease forewarning study was conducted at Coimbatore, Tamil Nadu in 2023 and 2024. The ERWF output from Weather Research and Forecasting (WRFv4.4) with two microphysics (WSM3 and Kessler) were leveraged to forewarn the GBNV disease, using the thumb rule as adapted in Tamil Nadu Agricultural University-Agro Advisory Service (TNAU - AAS): Web cum Mobile App, which is maximum temperature (>33oC), relative humidity (40-70%), wind speed (>5 kmph) and rainfall (0 mm). Result: Among the two microphysics options, the WSM3 performed better and provided more usable ERWF than the Kessler scheme. The WSM3 based ERWF usability percentage was 50 to 100 for rainfall and 80-100 for all other weather variables. The higher performance of ERWF resulted in a more precise forewarning of thrips and GBNV. The first and peak activity of thrips and GBNV incidence was well correlated (70-80%) with the ERWF based forewarning. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Weather induced physiological responses on the flowering habits of neem trees (Azadirachta indica).

Author

BALASREE, R., DHEEBAKARAN, GA., SENTHIL, A., SATHYAMOORTHY, N. K., GANAPATI, PATIL SANTOSH, and PUGAZENTHI, K.

Subjects
INDOLEACETIC acid, NITRATE reductase, FLOWER shows, AGRICULTURAL colleges, FLOWERING trees, NEEM
Abstract

Adaptability and significant economic value of the neem tree are well-known, as it can flourish in a variety of environmental conditions. While the neem seed production is highly sensitive to prevailing weather conditions during the reproductive phase and flowering behaviour of the tree. A study was conducted at Tamil Nadu Agricultural University, Coimbatore in 2023 with the primary objective of validating the weather influence on neem seed production using the logics of physiological responses, as a continuation of research conducted the previous year (2022). During the pre-flowering and flowering stages, diverse weather conditions led to notable changes in the physiological traits of neem trees, which displayed varying patterns of flowering. Trees that flowered consistently showed elevated levels of indole acetic acid (IAA) oxidase, relative water content, and nitrate reductase compared to those that lacked flowers or produced intermittently. In the flowering stage, the neem trees responded positively in terms of physiological aspects like IAA oxidase, relative water content, nitrate reductase, and exhibited lower proline levels, which can be attributed to the optimal maximum temperature, rainfall, and soil moisture. Proline levels rose during the pre-flowering stage due to soil moisture deficits but fell during the flowering stage with the onset of rain. These physiological changes, driven by climatic factors, are likely to enhance the flowering, fruiting, and overall yield of neem trees. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Effect of Microbial Consortium for Nutrient Dynamics and Biological Activity of Paddy Field under insitu Decomposition

Author

E. Parameswari, Dheebakaran Ga, M. Maheswari, P. Kalaiselvi, V. Davamani, and Nivedha Babu

Subjects
Materials Chemistry
Abstract

The production of rice and rice straw are directly proportional to each other and becomes a major problem in disposal of rice straw. Though there are many suitable methods in the reduction of rice straw few are bane to environment. Thus this study focuses only on the sustainable and ecofriendly manner of straw disposal. Insitu decomposition of rice stubbles using TNAU biomineralizer is the experimental study which was carried out in randomised block design with six Treatment and four replications. To determine the most effective methods of managing rice stubble, nutrient dynamics, and growth parameters of the rice crop, CO 53 variety of short duration was selected and laid down at Tamil Nadu Agricultural University, Coimbatore from 2021 to 2022. The six treatments includes T1: Stubble (Natural degradation), T2: Stubble+ balancing C:N ratio with urea, T3: Stubble applied with TNAU biomineralizer @ 2kg /ton of residue, T4: Stubbles applied with TNAU biomineralizer @ 2kg/ton of residue +balancing C:N ratio with urea, T5: Stubbles incorporated in soil using rotavator and applied with TNAU biomineralizer @ 2kg/ton of residue, T6: Stubbles incorporated in soil using rotavator and applied with TNAU biomineralizer @ 2kg/ton of residue + balancing C:N ratio with urea. The study findings showed that incorporation of straw with addition of biomineralizer for decomposition of straw @ 2kg/ton of residue along with balancing C:N ratio urea recorded the highest rice crop growth at harvest stage (115.70cm) and nutrient dynamics (N, P K) of 20.9 %, 4.6 % and 19.2 % higher at tillering stage and micro nutrients Cu-22.0 %, Zn- 20.9 %, Fe- 2.8 %, Mn- 9.7 % in panicle initiation stage of rice crop.

Published
2022
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28. Studies on the relationship of weather on Fall armyworm damage in maize (Zea mays L.) under different growing environments

Author

Haroli, Mahesh, Ramanathan, SP., Dheebakaran, GA., Geethalakshmi, V., Kennedy, J.S., Duraisamy, M.R., Haroli, Mahesh, Ramanathan, SP., Dheebakaran, GA., Geethalakshmi, V., Kennedy, J.S., and Duraisamy, M.R.

Abstract

Fall armyworm is a recently occurring invasive pest in India, the most important defoliator causing drastic damage to maize production. Hence, the present study aimed to understand the temporal infestation level of Fall armyworms on maize (Zea mays L.) with weather patterns. Field experiments were conducted during Summer (February-May) and Rainy seasons, 2022 (August-December) at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore. Three different growing environments (GE1, GE2 and GE3) were created by providing staggered sowing. Regression models were developed for per cent leaf damage against three-days lagged (LT3) and seven-day lagged (LT7) weather variables. Results showed that irrespective of growing environments, weather variables showed negative correlation (Tmax: r = -0.57, -0.81*, -0.31; SSH: -0.30, -0.48, -0.39; Tmean: -0.49, -0.23, -0.30; and SR: -0.48, -0.94*, -0.40) during summer season whereas same variables (i.e Tmax =0.62*, 0.41, 0.33; SSH = 0.09, 0.68*, 0.24; Tmean = 0.29, 0.32, 0.44; and SR=0.13, 0 .67*, 0.26 ) showed a positive correlation with PLD. Rainfall exhibits positive relation (0.06, 0.54, 0.53) and negative correlation (-0.64*, -0.10, -0.02) during summer and rainy season, respectively. Among the regression models, LT7 model had higher R2 (0.65 and 0.76) than LT3 (0.57 and 0.68) during summer and rainy seasons, respectively. These models had good regression values of 0.56 and 0.70 during Rainy and Summer, respectively. It was concluded that Tmax (32.9 °C), Tmin (23.7 °C), Tmean (28.3 °C), RH-I (85.6%), RH-II (56.4%), SSH (4.1), SR (274.6 cal cm-2 m-2), afternoon cloud cover (4.8 okta) and weekly total rainfall (10.2 mm) were very conducive for the greater leaf damage.

Published
2023

30. Influence of weather variables on the incidence and development of rice leaf blast (Magnaporthe oryzae)​

Author

AMITH G., GEETHALAKSHMI. V., DHEEBAKARAN Ga., KARTHIKEYAN G., and BALAJIKANNAN .

Subjects
Soil Science, Agronomy and Crop Science
Abstract

Rice is the second largest grown cereal crop in the world and the most consumed main staple meal. The devastating nature of rice blast disease has resulted in large annual losses. Therefore, a pot experiment was conducted at Paddy Breeding Station, Tamil Nadu Agricultural University, Coimbatore, during Rabi seasons of 2018-19, 2019-20, 2020-21, and 2021-22, with the objective to investigate the possible role of weather variables on rice leaf blast. Research analysis performed through correlation and decision tree analysis was carried under R studio to study the relation between weather parameters and per cent disease index (PDI) of leaf blast. The results revealed that, morning dew point temperature (X4, r = 0.69** and 0.67**) and minimum temperature (X2, r = 0.68** and 0.65**) had higher negative correlation both in real time and in one week lead time (LT1W) respectively. Further, the decision tree analysis showed the combination of rainfall of 5.9 mm and minimum temperature less than 22.6 ºC helped achieve disease incidence of greater than 80 per cent. Thus, the occurrence and development of leaf blast are significantly influenced by the minimum temperature and dew point temperature with drizzling rainfall, and these weather variables can be employed for weather-based disease forewarning against rice leaf blast.

Published
2022
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42. Usability of monthly ERFS (Extended Range Forecast System) to predict maize yield using DSSAT (Decision Support System for Agro-technology Transfer) model over Erode District of Tamil Nadu

Author

M.N, Harinarayanan, V , Manivannan., Dheebakaran , Ga, M , Guna., M.N, Harinarayanan, V , Manivannan., Dheebakaran , Ga, and M , Guna.

Abstract

Extended Range of Forecast Service (ERFS) is highly useful for planning of cropping season and midterm correction at the farm level. The medium-range and long-range forecast validation have many studies, whereas ERF has less that needs to be studied. Maize is an important field crop in India after rice and wheat. Therefore, the prediction of maize yield has significant importance. In the present study, ERFS data were validated by correlation analysis using monthly observed rainfall frequency and intensity. This data was imported to DSSAT (Decision Support System for Agro-technology Transfer) to simulate maize yield of Erode district of Tamil Nadu. The model output and actual yield data from Erode were compared. Forecasted monthly total rainfall was correlated at a rate of 0.97r value with that observed. Yield simulation of maize was done using DSSAT by integrating ERFS data and the observed monthly data. Mean per cent deviation among the yields of observed weather and the disaggregated one tended to be -15.7 %. The average deviation between the yields of ERF forecasted weather data and actual yield was very high ( -29.7 % ) for Erode. Mean % deviation between the yields of observed weather and the actual yield was -14.7 %. Downscaled and accurate weather forecasts could be facilitated for yield prediction of crops by DSSAT model. Yield prediction by the model under observed weather was convenient and usable. Model under-predicted the yields when using ERF data. Both model and ERF forecast need to be improved further for higher resolution.

Published
2022

44. Decadal Study of Changing Frequency and Intensity of Rainfall for Selected Locations of Tamil Nadu

Author

S. KOKILAVANI, S. KOKILAVANI, primary, SP. Ramanathan, SP. Ramanathan, additional, Dheebakaran, GA., additional, Sathyamoorthy, N.K., additional, B. Arthirani, B. Arthirani, additional, T. Ramesh, T. Ramesh, additional, K. Sathyabama, K. Sathyabama, additional, M. Joseph, M. Joseph, additional, P. Balasubramanian, P. Balasubramanian, additional, and Arunkumar, P., additional

Published
2021
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47. Use of Statistical Models in Predicting Groundnut Yield in Relation to Weather Parameters

Author

Abhinaya D, Dheebakaran Ga, Arockia Stephen Raj, Patil Sg, and Djanaguiraman M

Subjects
Yield (engineering), Relation (database), Statistics, Statistical model, Mathematics
Abstract

In Tamil Nadu, groundnut is an essentialand major oilseed crop, mainly grown under rainfed conditions. The changes in weather parameters might affect the productivity of groundnut. Hence, crop yield forecasting based on weather parameters is essential for proper planning, decision-making, and buffer stocking policy formulation. As for the data with multicollinearity, penalized regression models i.e.Ridge, Least Absolute Selection and Shrinkage Operator (LASSO) and Elastic Net (ENet), are better alternatives to classical linear regression. The data on weather parameters such as maximum temperature(Tmax), minimum temperature (Tmin), morning relative humidity (RH I), evening relative humidity (RH II), and rainfall were collected for 29 years from1991-2019. The weather indices approach was used in this study. The collected data were partitioned into training, and testing datasets and the hyperparameters of penalized regression models were tuned using cross-validation. The performance of the models wasevaluated using an adjusted coefficient of determination (R2adj), Root Mean Squared Error (RMSE), normalized RMSE (nRMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) as the goodness of fit criteria. The results revealed that all the Penalized regression models provide a better fit to data. The SMLR and ENet were found to predict with better accuracy. Hence, these methods can be used for groundnut yield forecasting during Kharif season for the Coimbatore district of Tamil Nadu.

Published
2021
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