Influence of Weather Parameters and Thermal Time Approach on Green Gram at Coimbatore, Tamil Nadu

An investigation was carried out at the Agro Climate Research Centre, Tamil Nadu Agricultural University, on the effect of weather parameters on the green gram yield sown at various sowing dates during the rabi season of 2019. At various sowing dates, two green gram cultivars, VBN 4 and ADT 3, were sown. For both cultivars, the phonological crop length decreased with delays in sowing dates beyond October 23rd. The yield of green gram sown on 23rd October was significantly higher than the crops sown on 30th October and 6th November. The weather parameters Maximum Temperature (Tmax), Diurnal Range (Trange), Bright Sunshine Hours (BSS), Relative Humidity (RH I), Wind Speed (WS) were found to be negatively correlated with seed yield whereas Minimum Temperature (Tmin), Relative Humidity (RH II), Vapour Pressure (VP) were found to be positively correlated with the yield of green gram. The accurate prediction of green gram yield could be done with the maximum temperature, bright sunshine hours, wind speed and with thermal indices especially hygrothermal unit II with 82 percent, accuracy level.

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Long term monthly and inter-seasonal weather variability analysis for the lower Shivalik foothills of Punjab

MAUSAM ◽

10.54302/mausam.v73i1.5090 ◽

2022 ◽

Vol 73 (1) ◽

pp. 173-180

Author(s):

NAVNEET KAUR ◽

M.J. SINGH ◽

SUKHJEET KAUR

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Minimum Temperature ◽

Maximum Temperature ◽

Linear Regression Method ◽

Post Monsoon ◽

Sunshine Hours ◽

Annual Minimum Temperature ◽

Weather Parameters

This paper aims to study the long-term trends in different weather parameters, i.e., temperature, rainfall, rainy days, sunshine hours, evaporation, relative humidity and temperature over Lower Shivalik foothills of Punjab. The daily weather data of about 35 years from agrometeorological observatory of Regional Research Station Ballowal Saunkhri representing Lower Shivalik foothills had been used for trend analysis for kharif (May - October), rabi (November - April), winter (January - February), pre-monsoon (March - May), monsoon (June - September) and post monsoon (October - December) season. The linear regression method has been used to estimate the magnitude of change per year and its coefficient of determination, whose statistical significance was checked by the F test. The annual maximum temperature, morning and evening relative humidity has increased whereas rainfall, evaporation sunshine hours and wind speed has decreased significantly at this region. No significant change in annual minimum temperature and diurnal range has been observed. Monthly maximum temperature revealed significant increase except January, June and December, whereas, monthly minimum temperature increased significantly for February, March and October and decreased for June. Among different seasons, maximum temperature increased significantly for all seasons except winter season, whereas, minimum temperature increased significantly for kharif and post monsoon season only. The evaporation, sunshine hours and wind speed have also decreased and relative humidity decreased significantly at this region. Significant reduction in kharif, monsoon and post monsoon rainfall has been observed at Lower Shivalik foothills. As the region lacks assured irrigation facilities so decreasing rainfall and change in the other weather parameters will have profound effects on the agriculture in this region so there is need to develop climate resilient agricultural technologies.

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Comparison Statistical Rice Yield Prediction with Multiple Weather Parameters

International Journal of Plant & Soil Science ◽

10.9734/ijpss/2021/v33i2230680 ◽

2021 ◽

pp. 31-38

Author(s):

T. Thurkkaivel ◽

G. A. Dheebakaran ◽

V. Geethalakshmi ◽

S. G. Patil ◽

K. Bhuvaneshwari

Keyword(s):

Bayesian Model ◽

Tamil Nadu ◽

Rice Yield ◽

Model Performance ◽

Research Centre ◽

Yield Prediction ◽

Sunshine Hours ◽

Tamil Nadu State ◽

Weather Parameters ◽

Essential Food

Advance knowledge of harvestable products, especially essential food crops such as rice, wheat, maize, and pulses, would allow policymakers and traders to plan procurement, processing, pricing, marketing, and related infrastructure and procedures. There are many statistical models are being used for the yield prediction with different weather parameter combinations. The performance of these models are dependent on the location’s weather input and its accuracy. In this context, a study was conducted at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore during Kharif (2020) season to compare the performance of four multivariate weather-based models viz., SMLR, LASSO, ENET and Bayesian models for the rice yield prediction at Tanjore district of Tamil Nadu State with Tmax, Tmin, Mean RH, WS, SSH, EVP and RF. The results indicated that the R2, RMSE, and nRMSE values of the above models were ranged between 0.54 to 0.79 per cent, 149 to 398 kg/ha, 4.0 to 10.6 per cent, respectively. The study concluded that the Bayesian model was found to be more reliable followed by LASSO and ENET. In addition, it was found that the Bayesian model could perform better even with limited weather parameters and detention of wind speed, sunshine hours and evaporation data would not affect the model performance. It is concluded that Bayesian model may be a better option for rice yield forecasting in Thanjavur districts of Tamil Nadu.

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Predicting the Outbreak of Tikka and Rust in Grountnut (Arachis Hypogaea)

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e6431.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 1862-1867

Keyword(s):

Wind Speed ◽

Arachis Hypogaea ◽

Heavy Rainfall ◽

Climatic Conditions ◽

Maximum Temperature ◽

Rust Disease ◽

Sunshine Hours ◽

Weather Parameters ◽

The Relationship ◽

Recorded Observation

Groundnut (Arachis hypogaea) is one among the most important oil seed crop cultivated in India. Tikka leaf spot and Rust are the major disease of groundnut that effects on production and productivity. The prediction was made based on factors such as minimum and maximum temperature, morning and evening humidity, wind speed, sunshine hours that quantifies the disease infestation in groundnut. The relationship between the weather, pest and disease infestation are identified which supports the model to predict the occurrence of the disease. The observations were recorded at an interval of one week from the occurrence of tikka and rust. The percent disease intensity is calculated based on the scale explained by Mayee and Data. The favourable climatic conditions for tikka and rust disease development ranges between 26OC – 31OC and 25OC – 30OC respectively, relative humidity greater than 85%, prolonged heavy rainfall, wind speed and rain. The rules are generated based on the recorded observation and the weather parameters. The main objective is to diagnose the existence of tikka and rust disease coupled with weather parameters.

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Forewarning of incidence of Spodoptera litura (Tobacco caterpillar) in soybean and cotton using statistical and synoptic approach

Journal of Agrometeorology ◽

10.54386/jam.v21i1.208 ◽

2021 ◽

Vol 21 (1) ◽

pp. 68-75

Author(s):

N. CHATTOPADHYAY ◽

R. BALASUBRAMANIAM ◽

S.D. ATTRI ◽

KAMALJEET RAY ◽

GRACY JOHN ◽

...

Keyword(s):

Relative Humidity ◽

Spodoptera Litura ◽

Weather Forecast ◽

Pheromone Trap ◽

Maximum Temperature ◽

Cotton Crop ◽

Sunshine Hours ◽

Weather Parameters ◽

Relationship Of ◽

Trap Catches

A study on the effect of weather parameters on the the population dynamics of Spodoptera litura (S.litura) in soybean and cotton during kharif season using six years pest data (pheromone trap catches) at Niphad and Rahuri in Maharashtra showed that rainfall two weeks prior, Tmax and Tmin during the week of incidence signifiantly contributed towards the occurrence of S.litura in soybean. Maximum temperature and morning humidity during the week and one week prior were found to be favourable for the incidence of S. litura in cotton. Temperature (maximum: 26-27°C & minimum: 21-22°C), morning relative humidity (above 90%) and rainfall during one week prior were found to be congenial weather parameters for the outbreak of the pest in soybean. Similarly, maximum temperature around 32-33°C, minimum temperature around 22-23°C, morning relative humidity around 90 per cent, sunshine hours about 4 hrs day-1 and rainfall during the previous 2 weeks favoured heavy incidence of S.litura in cotton crop during flowering to boll formation stages. It is also shown how the incidence of S.litura in soybean and cotton can be predicted well in advance using the observed relationship of the pest with weather parameters as well as weather forecast.

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Comparative Study of Evapotranspiration Variation and its Relationship with other Climatic Parameters in Asaba and Uyo

FUOYE Journal of Engineering and Technology ◽

10.46792/fuoyejet.v3i2.221 ◽

2018 ◽

Vol 3 (2) ◽

Author(s):

Okwunna M Umego ◽

Temitayo A Ewemoje ◽

Oluwaseun A Ilesanmi

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Reference Evapotranspiration ◽

Meteorological Data ◽

Small Variation ◽

Climatic Variables ◽

Maximum Temperature ◽

Climatic Parameters ◽

Sunshine Hours

This study was carried out to assess the variations of Reference Evapotranspiration (ETO also denoted with RET) calculated using FAO-56 Penman Monteith model of two locations Asaba and Uyo and evaluate its relationships with the variations of other climatic parameters. Meteorological data of forty one years (1975-2015) and thirty five years (1981-2015) period for Asaba and Uyo, respectively gotten from Nigeria Meteorological Agency, Abuja were used. It was observed that the variations of Evapotranspiration (ET) in both locations were in line with two seasons (rainy and dry) normally experienced in Nigeria having its highest value in March (4.8 mm/day) for Asaba and for Uyo in February (4.5 mm/day); and its lowest value in August (3.1 mm/day) for Asaba and in July (2.9 mm/day) for Uyo. ET variation when compared with other climatic variables in both locations was observed to have the same trend with maximum temperature, solar radiation and sunshine hours. It also has the same variation with minimum temperature though with slight deviation. It was observed that ET variation is inversely proportional to the variation relative humidity. Wind speed displayed relatively small variation in its trend over the study period and is not in line with the variations of ET.Keywords— Evapotranspiration, Climatic Variables, FAO Penman-Monteith Model, Variations

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Surveillance of pea leaf miner (P. horticola) infesting pea (Pisum sativum) in relation with abiotic factors

FLORA AND FAUNA ◽

10.33451/florafauna.v25i2pp209-216 ◽

2019 ◽

Vol 25 (2) ◽

Author(s):

Ram Keval ◽

H.S. Vanajakshi ◽

Sunil Verma ◽

Babli Bagri

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Minimum Temperature ◽

Abiotic Factors ◽

Negative Relationship ◽

Positive Association ◽

Leaf Miner ◽

Maximum Temperature ◽

Seasonal Incidence ◽

Sunshine Hours

To study the seasonal incidence of insect pests of pea (P. sativum) the investigation was carried out during Rabi session of 2016-17 and 2017-18, at Agricultural Research Farm, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi. The incidence of pests infesting pea was recorded from 50th SMW to 11th SMW. During the observation it was found that P. horticola showed its appearance in the field from 1st to 11th SMW with peak population (71% leaf infestation) in 7th SMW. When population was correlated with abiotic factors it was found that there was positive association with maximum temperature (r = 0.759**), minimum temperature (r = 0.672**), wind speed (r = 0.449).and sunshine hours (r =0.583*) whereas a negative relationship was maintained with morning relative humidity (r =-0.496) and evening relative humidity (r=-0.515), during 2016- 17. Similarly, during 2017-18 there was a positive association with maximum temperature (r = 0.360), minimum temperature (r =0.431), wind speed (r = 0.544*) and sunshine hours(r=0.493) whereas a negative relationship was maintained with morning relative humidity (r =-0.277) and evening relative humidity (r=-0.365).

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Use of Statistical Models in Predicting Groundnut Yield in Relation to Weather Parameters

Madras Agricultural Journal ◽

10.29321/maj.10.000546 ◽

2021 ◽

Vol 108 (special) ◽

Author(s):

Abhinaya D ◽

◽

Patil SG ◽

Dheebakaran Ga ◽

Djanaguiraman M ◽

...

Keyword(s):

Relative Humidity ◽

Regression Models ◽

Tamil Nadu ◽

Goodness Of Fit ◽

Penalized Regression ◽

Maximum Temperature ◽

Percentage Error ◽

Oilseed Crop ◽

Weather Parameters ◽

Yield Forecasting

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.

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Forewarning incidence of American boll worm (Heliothis armigera H.) of cotton at Akola in Vidarbha region of Maharashtra

MAUSAM ◽

10.54302/mausam.v65i1.888 ◽

2021 ◽

Vol 65 (1) ◽

pp. 73-82

Author(s):

KGHOSH GHOSH ◽

MRAJAVEL RAJAVEL ◽

R.P. SAMUI ◽

G.P. SINGH ◽

C. KARMAKAR

Keyword(s):

Relative Humidity ◽

Minimum Temperature ◽

Heliothis Armigera ◽

Statistical Correlation ◽

Maximum Activity ◽

Maximum Temperature ◽

Cotton Crop ◽

Pest Infestation ◽

Sunshine Hours ◽

Weather Parameters

A study on pest population of American boll worm (Heliothis armigera H.) in cotton crop as influenced by weather parameters like rainfall (RF), maximum temperature (Tmax), minimum temperature (Tmin), morning relative humidity (RH I), evening relative humidity (RH II) and bright sunshine hours (BSS) and its statistical correlation was undertaken with data recorded at Dr. Punjabrao Deshmukh Krishi Vidhyapeeth, Akola. The maximum activity and damage due to high population of Heliothis was observed during 35th to 50th standard weeks. Maximum temperature (40th week) and minimum temperature (37th week), morning and evening relative humidity during 38th week play an important role in pest infestation during 40th standard week. Flowering to boll formation stages of the crop suffered heavy incidence of Heliothis. Critical weather parameters causing the outbreak of Heliothis in Akola was maximum temperature around 32 °C and minimum temperature around 23 °C, morning relative humidity below 88%, evening relative humidity below 60% and hours of bright sunshine above 6.5 hrs / day.

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Clustering analysis of regional reference evapotranspiration and its components based on climatic variables across northeast China, 1961–2010

Journal of Water and Climate Change ◽

10.2166/wcc.2015.217 ◽

2015 ◽

Vol 7 (1) ◽

pp. 128-141 ◽

Cited By ~ 2

Author(s):

Yuan Liu ◽

Buchun Liu ◽

Xiaojuan Yang ◽

Wei Bai

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Northeast China ◽

Reference Evapotranspiration ◽

Potential Evapotranspiration ◽

Sunshine Duration ◽

Regional Distribution ◽

Climatic Variables ◽

Maximum Temperature ◽

Sunshine Hours

Evapotranspiration integrates atmospheric demand and surface conditions. The Penman-Monteith equation was used to calculate annual and seasonal reference evapotranspiration (ET0) and thermodynamic and aerodynamic components (ETrad and ETaero) at 77 stations across northeast China, 1961–2010. The results were: (1) annual ETrad and ETaero had different regional distribution, annual ETrad values decreased from south to north, whereas the highest ETaero values were recorded in the eastern and western regions, the lowest in the central region; (2) seasonal ETaero distributions were similar to seasonal ET0, with a south–north longitudinal pattern, while seasonal ETrad distributions had a latitudinal east-west pattern; and (3) in the group for ET0 containing 69 sampling stations, effects of climatic variables on ET0 followed sunshine hours > relative humidity > maximum temperature > wind speed. Changes in sunshine hours had the greatest effect on ETrad, but wind speed and relative humidity were the most important variables to ETaero. The decline in sunshine duration, wind speed, or both over the study period appeared to be the major cause of reduced potential evapotranspiration in most of NEC. Wind speed had opposite effects on ETrad and ETaero, and therefore the effect of wind speed on ET0 was not significant.

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Potential Evapotranspiration Reduction and Its Influence on Crop Yield in the North China Plain in 1961–2014

Advances in Meteorology ◽

10.1155/2020/3691421 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Wanlin Dong ◽

Chao Li ◽

Qi Hu ◽

Feifei Pan ◽

Jyoti Bhandari ◽

...

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Crop Yield ◽

North China Plain ◽

North China ◽

Potential Evapotranspiration ◽

The North ◽

The North China Plain ◽

Sunshine Hours ◽

Humidity Index

Climate change has caused uneven changes in hydrological processes (precipitation and evapotranspiration) on a space-temporal scale, which would influence climate types, eventually impact agricultural production. Based on data from 61 meteorological stations from 1961 to 2014 in the North China Plain (NCP), the spatiotemporal characteristics of climate variables, such as humidity index, precipitation, and potential evapotranspiration (ET0), were analyzed. The sensitivity coefficients and contribution rates were applied to ET0. The NCP has experienced a semiarid to humid climate from north to south due to the significant decline of ET0 (−13.8 mm decade−1). In the study region, 71.0% of the sites showed a “pan evaporation paradox” phenomenon. Relative humidity had the most negative influence on ET0, while wind speed, sunshine hours, and air temperature had a positive effect on ET0. Wind speed and sunshine hours contributed the most to the spatiotemporal variation of ET0, followed by relative humidity and air temperature. Overall, the key climate factor impacting ET0 was wind speed decline in the NCP, particularly in Beijing and Tianjin. The crop yield in Shandong and Henan provinces was higher than that in the other regions with a higher humidity index. The lower the humidity index in Hebei province, the lower the crop yield. Therefore, potential water shortages and water conflict should be considered in the future because of spatiotemporal humidity variations in the NCP.

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