Bright Sunshine Research Articles

Weather Parameter’s Impact on Population Fluctuation of Insect Pests in Sunflower

The population trend of insect pest of sunflower was studied at North bank plain zone of river Brahmaputra, Biswanath district in the state of Assam during 2021-2022. The simple correlation study showed bright sunshine hour had a positive and significant correlation with the whitefly population relative humidity had a positive and significant correlation with the Aphis gossypii and Amrasca biguttula biguttula Ishida population; A. gossypii had positive and significant relation with maximum temperature and Spilosoma obliqua had negative and significant relation with relative humidity.

Epidemiology and Management of MYMV Disease in Mung Bean (Vigna radiata (L.))

Mung bean is a short-duration ancient legume crop that can be grown as a sole or intercrop for grain and green manure in all three crop seasons, including winter, summer and rainy in various regions of the country. MYMV (Mungbean Yellow Mosaic Virus) disease is one of the most destructive diseases transmitted by whitefly (Bemisia tabaci Genn) persistently. Weather parameters have no direct effect on MYMV, but affect the whitefly population. Field experiments were conducted for three consecutive years from 2019-2021 as to know the effect of weather parameters on the whitefly population. Development of whitefly population and MYMV disease severity were recorded in mungbean crops at 7 days interval starting from 20 days after sowing (DAS). The present study showed that whitefly population builds up starting in July and reached to maximum at end of August to early September. There are various factors influencing the build-up of whitefly population viz., maximum temperature 36±2°C, minimum temperature 25±2°C, morning relative humidity more than 90 percent, more sunshine hours and no rainfall prevailing during that period. Whitefly population had positive significant correlation with maximum temperature and bright sunshine hours while evening relative humidity and rainfall were recorded as negatively significant. Three-year data is very helpful in guiding the farmers for timely and preventive sprays based on existing infections and the population of whitefly in a season.

Impact of Weather Parameters on Population Fluctuation of Chilli Thrips, Scirtothrips dorsalis Hood

Chilli var. Tejaswini was grown in three quadrates (5.0×6.0 m2) during pre-kharif season (January-June) of 2018 and 2019 at farmer’s field, Sriniketan, West Bengal, India to study the fluctuation of thrips population during the crop growing seasons under the influence of varying weather parameters. Population fluctuation of the thrips was recorded at weekly intervals starting from one week after transplanting till the last harvest. The insect first appeared on 6-8 leaves stages of the crop during both seasons and the population gradually reached to peak at 14 MSW (10.95 thrips leaf-1) and 13 MSW (9.83 thrips leaf-1) during 1st and 2nd seasons, respectively. Correlation of thrips population with both previous and same meteorological standard week data showed different results. Significant negative correlation with relative humidity (r=-0.561*) but positive with sunshine hours (r=0.476*) recorded only during 2018 when correlations were studied with previous week weather data. Studies on multiple regression revealed that combined effect of weather parameters on the population build up of thrips had 26.0% (R2= 0.260) and 55.4% (R2=0.554) during 1st season while it was 40.6% (R2=0.406) and 18.9% (R2=0.189) during the 2nd season. Trend analysis revealed that during 1st season maximum and minimum temperatures around 35.0°C and 20°C found favourable for pest population build-up, while relative humidity (65% or higher), rainfall (6.0 mm), and bright sunshine (8 h) negatively affected the thrips population. A similar trend in insect population build-up was also recorded during the 2nd season.

Influence of meteorological parameters on incidence of mungbean yellow mosaic virus disease in green gram in Assam

Green gram (Vigna radiata) is one of the most important pulse crops grown in India and abroad bean yellow mosaic virus disease caused by Mung bean yellow mosaic virus has become a major threat in greengram cultivation in Assam. An experiment was conducted in the Experimental field, Department of Plant Pathology, Biswanath College of Agriculture, Assam Agricultural University, Biswanath Chariali during 2021-22, where green gram was sown in five dates starting from 5th September 2021to 3rd October 2021 at 7 days interval to study the effect of meteorological parameters in the incidence of green gram yellow mosaic virus disease, effect on growth and yield parameters so as to find out the best sowing time of the crop as a strategy for managing the disease. During the study, symptoms like, appearance of chlorotic specks along the veins, puckering of leaves, typical mosaic pattern, complete yellowing and drying of the leaves were recorded. Highest whitefly population (12.42 no/plant) was recorded in the crop sown on 19.09.2021 and the lowest (6.24no/plant) was recorded in the 05.9.2021 sown crop. Lowest (40.32%) disease incidence with highest seed yield (4.50 q/ha) was recorded in the crop sown on 05.09.2021, whereas highest (88.25%) disease incidence with lowest yield (2.50 q/ha) was recorded in the crop sown in second fortnight of September (19.09.21). Maximum temperature ranging from 29-33 oC, minimum temperature 13-23 oC with 8-9 bright sunshine hours were found to be conducive for incidence and rapid spread of the disease.

Seasonal Incidence of Insect Pests and Predatory Fauna in Black Gram

The succession and seasonal incidence of insect pests and predatory fauna in black gram were studied in a field experiment at the Agronomy farm, Anand Agricultural University, Anand during kharif 2019 and 2020. The results revealed nine species of insects (7 pests and 2 predators) from seedling to pod maturity stage. Besides, the peak incidence of whitefly, jassid, flower thrips, tobacco caterpillar, Bihar hairy caterpillar, and spiders were observed during 37th standard meteorological week (SMW); with spotted pod borer and coccinellids, maximum incidence was observed during 38th SMW, and the green stink bug during 35th SMW. Correlation coefficients revealed that tobacco caterpillar had a significant positive correlation with minimum temperature, morning RH and evening vapour pressure; and the incidence of spotted pod borer showed a significant negative correlation with bright sunshine hours and rainfall.

INCIDENCE OF A SEVERE BUD BORER IN CARNATION (DIANTHUS CARYOPHYLLOUS L.) CV. CHABAUD MIX IN THE UPPER BRAHMAPUTRA VALLEY AGRO-CLIMATIC CONDITIONS OF ASSAM

The genus Helicoverpa is a polyphagous insect belonging to the order Lepidoptera that creates havoc in range of crops worldwide and is one of the most important and serious insect pests in carnation. During the field experiment of the Chabaud carnation (Dianthus caryophyllous L.) in the horticultural research farm of Assam Agricultural University, Jorhat, Assam, (India) during 2023, Helicoverpa armigera was found ravaging the carnation crop in the experimental field. The present preliminary study sought to examine the correlation between pest infestation and prevailing weather conditions. The sporadic occurrence (0.73%) of the pest started on 13th January and became widespread in the following months. The peak infestation was observed during March and April, and the maximum incidence (18.62%) was recorded from 1st April to 15th April. There was a positive and strong correlation between the incidence (%) of bud borer and minimum temperature (r= 0.90) and rainfall (r= 0.76). Likewise, the maximum RH had a negative and strong correlation (r= 0.74) with borer incidence (%). Meanwhile, the maximum temperature had a positive and moderate correlation (r= 0.67). Additionally, the minimum RH (r= 0.39) and bright sunshine hours (r= -0.36) had weak correlations with bud borer incidence (%). The present study found that the prevailing environmental conditions, the minimum temperature and average mean rainfall in particular had a significant influence on the incidence of insects on chabaud carnation. Bud borers cause serious havoc and damage to the developing flower bud in all stages, piercing, penetrating and feeding inside the bud, eventually leaving the flower bud hollow, empty, redundant and unproductive

Population Dynamics of Fruit Fly, B. dorsalis (Hendle) on Mango and Correlation with Weather Parameters

Investigation on the population dynamics of fruit fly Bactrocera dorsalis (Hendle) (Diptera: Tephritidae) was carried out by installing "Nauroji-Stonehouse Fruit Fly Trap" in the mango orchard of organic farm, Navsari Agricultural University, Navsari, Gujarat, India during 2021-22. The population of B. dorsalis prevailed throughout the year in mango orchard with its peak activity from 14th SMW to 31st SMW which coincided with the fruiting and harvesting period of fruits. The population of B. dorsalis decreased from August to February. Correlation analysis revealed a significant positive correlation of population of B. dorsalis with maximum temperature, minimum temperature, average temperature, evening relative humidity, average relative humidity, rainy days, rainfall and wind velocity but a significant negative correlation with duration of bright sunshine hours. However, there was a nonsignificant correlation of population of B. dorsalis with morning relative humidity.

Population Dynamics of Bactrocera zonata (Saunders) (Diptera: Tephretidae) on Mangifera indica (L.) (Anacardiaceae) and Correlation with Weather Parameters

The population dynamics of fruit fly Bactrocera zonata Saunders (Diptera: Tephritidae) was carried out by installing "Nauroji-Stonehouse Fruit Fly Trap" in the mango orchard of organic farm, Navsari Agricultural University, Navsari, Gujarat, India during the period 2021-22. The population of B. zonata prevailed throughout the year in mango orchard with its peak activity from 14th SMW to 31st SMW which coincided with the fruiting and harvesting period of fruits. The population was maximum in 27th SMW and minimum during second SMW. The population decreased from August to January. There was a significant positive correlation of population of B. zonata with maximum temperature (r=0.33), minimum temperature (r=0.83), average temperature (r=0.77), evening relative humidity (r=0.50), average relative humidity (r=0.41), rainy days (r=0.38), rainfall (r=0.37) and wind velocity (0.66). However, there was a nonsignificant correlation of population of B. zonata with morning relative humidity and duration of bright sunshine hours.

Population dynamics of two-spotted mite, Tetranychus urticae Koch (Acari: Tetranychidae) in relation to weather parameters on strawberry

Two-spotted mite, Tetranychus urticae (Koch) is the most common and often the most severe pest of strawberries always resulting in considerable yield losses. The present research work aimed to investigate the effect of temperature, humidity, rainfall and sunshine hours on the population dynamics and severity of mite attack. A field experiment was carried out during April 2020 to March 2022 at Regional Horticultural Research & Training Station, Mashobra, Shimla, India. Mite population was determined by counting the number of active stages and eggs on the dorsal as well as on the ventral surface of the leaf under a binocular microscope. Two leaves were selected randomly from each plant and thus a total of 50 leaves per 25 plants were observed. The results of the studies indicated that population of two-spotted mite on strawberry fluctuated during April to June. Due to heavy rainfall (RF=101.0mm) received during 2020 in the first week of June mite count dropped to 3.20 active stages and 6.78 eggs/leaf. Thereafter, the mite population started increasing gradually in the subsequent weeks and it reached 18.60 active stages and 37.15 eggs/leaf by the end of June. During July-August mite population declined drastically due to washing off mites by continuous rainfall and mite count fluctuated between 0.85 to14.85 active stages and 0.83 to 27.80 eggs/leaf (Temp=17.16 to 19.28°C; RH=70.57 to 90.79%; RF=10.80 to 85.10 mm; 1.31 to 6.07 hrs) during 2020 while it reduced to minimum level of 0.30 active stages and 1.20 eggs/leaf during 2021 due to heavy rainfall (RF=143.40 mm). It multiplied with fast rate during dry months and a hike in mite count was seen from the second week of October with a peak in the fourth week of November during 2020 with the highest mean population of 89.45 active stages and 131.80eggs/leaf with mean temperature 13.51°C, 37.50% relative humidity, 0.00 mm rainfall and 8.11 hrs of sunshine. During 2021 a hike in the mite population was recorded in the fourth week of November with the highest mean population of 108.85 active stages and 186.65 eggs/leaf with mean temperature 13.05°C, 46.64% RH, 0.00 mm rainfall and 7.74 hrs of sunshine. Due to rain or snowfall received during January to February a sharp decline in mite population was observed during both seasons and it fluctuated between 1.45 to 8.35 active stages and 8.58 to 24.15 eggs/leaf (T=2.21 to7.65oC; RH=29.71 to 83.21%; RF=0.00 to 58.70 mm; SS=0.66 to 8.34 hrs). A slight hike in mite population was observed during March and mite count reached to 49.28 active stages and 76.89 eggs/leaf by the end of March, 2022 due to rise in mean temperature. The correlation coefficients (r) between mite population and temperature showed positive and significant relationship, whereas a significant but negative correlation existed between mite population and relative humidity and rainfall. The coefficient of determination between active stages and egg with weather parameters during studies ranged between 71% to 74.0% and 58 to 68.0%, respectively. Thus, the present investigations showed that the severity of mite attack on strawberry was the highest in temperature range of 13–18oC, 35–50% RH, zero rainfall, and during bright sunshine hours. These studies will help in forecasting the mite damage in advance for timely application of control measures.

Optimizing the Time of Grafting in Jackfruit in the Subtropical Environment of Assam

An experiment was carried out at the instructional cum research farm, Department of Horticulture, BNCA, AAU, Biswanath Chariali during 2019-2020. The objective of the experiment was to check the best time of wedge grafting in Jackfruit in the subtropical conditions of Assam. A total of seven treatments were taken, viz. Grafting in March (T1), April (T2), May (T3), June (T4), July (T5), August (T6), and September (T7). Six to seven months old, vigorously growing uniform seedlings of Jackfruit grown from the seeds were selected as rootstocks. Mother plants of desirable traits were selected for the experiment. Scions of about 10-15 cm in length with a thickness of 5-7 mm and free pests and diseases were collected from one season (4-5 months) old terminal shoots. The selected scion shoots were defoliated (pre-cured) 7-10 days before grafting. Wedge grafting was adopted in the present study. Further, wedge grafting was performed with compatible rootstocks in the different months stated above and conclusions were made that the period from March to June could be considered as the suitable time for producing jackfruit grafts by wedge grafting method in Assam. March and April are the best months for grafting jackfruit, as weather conditions in terms of temperatures with daily maximum (21.1 -35.6°C) and minimum temperature (8.3 to 23.0°C), rainfall more than 100 mm, comparatively higher bright sun-shine hours are congenial from the success of grafting in terms of success percentage of stock and scion union, the time required for spouting, several buds developed and length of the scion on 90 days after grafting. While the temperature gradually rose from June onwards up to September and there was also quite a reduction in the amount of rainfall, due to which the moisture content present in the leaves of the scions dried up because of evapotranspiration and decreased in the percentage of graft success.

Development and comparison of artificial intelligence models for estimating daily reference evapotranspiration from limited input variables

The accurate and efficient computational modelling framework for computation of reference evapotranspiration (ET0) is the need of time and is highly essential for several agricultural, hydrological and hydro-climatological studies and applications. ET0 has its own role especially for the water resource system management including irrigation water allocation and management, utilization and demand assessments of water resources. Considering the potential and wide applicability of Artificial Intelligence (AI) models in input-output mapping, these were examined in this research for modelling ET0 utilising a variety of meteorological data, including maximum temperature (Tmax) and minimum temperature (Tmin), relative humidity (RH), wind speed (WS), and bright sunshine hours (BSS). The different AI models investigated in this study include Artificial Neural Network (ANN), Extreme Learning Machine (ELM), M5 Tree and Multiple Linear Regression (MLR). The implemented daily dataset was collected from the Sardarkrushinagar station which is located at the North Gujarat region of India. For performance verification of the developed AI models, the FAO-56 Penman-Monteith equation was employed as a benchmark. The developed model's accuracy was assessed using multiple quantitative metrics, including R2, NSE, RMSE, Pdv and MAE. The performance of ANN models was found better compared to the ELM, M5 Tree, and MLR models in terms of different performance indicators. The model's outcomes were highly practical and reliable for the investigated case studies. Another important finding in this research was that the selection of the appropriate input variables in models allows not only error minimization but also enhances the co-relationship amongst the dependant as well as independent variables.

Seasonal activity of spotted pod borer, Maruca vitrata (Fabricius) in relation to weather factors infesting cowpea

A field experiment was conducted to study the seasonal incidence of spotted pod borer, Maruca vitrata in cowpea variety AVCP-1 at Entomology farm, B. A. College of Agriculture, AAU, Anand during Kharif, 2021. The incidence of M. vitrata in cowpea was commenced from 6th week after sowing i.e. 4th week of August (34th SMW) with 0.60 larva/plant and gradually increased to reached its first peak (3.51 larvae/plant) and the highest peak (6.97 larvae/plant) during the 1st week of September (36th SMW) and 2nd week of October (41st SMW) when crop age was 8th and 13th weeks old, respectively. Thereafter, the population gradually declined towards the crop maturity. Concerned to the damage of M. vitrata on pods of cowpea, it was recorded between 8.63 to 59.93% per plant. The maximum pod damage i.e. 59.93% per plant was observed in the second week of October (41st SMW) when M. vitrata incidence was at its highest peak. The population of M. vitrata had highly significant negative correlation with wind speed (-0.882**) whereas, bright sunshine hours (0.586*) and maximum temperature (0.625*) showed significant positive association on cowpea. Furthermore, it was worked out that the bright sunshine hours, maximum temperature and wind speed each contributed 11, 10.9 and 68.2% of the variation in the M. vitrata larval population, respectively.

Influence of Different Weather Parameters and Dates of Sowings on Growth and Yield of Pre-released Rice (Oryza sativa L.) Cultures

The field experiment was conducted during kharif season of 2019 at Agricultural Research Station, Kunaram, Telangana state, India, to study the influence of different sowing dates on growth and yield of pre released rice cultures. The experiment was laid out in split plot design with consisting of four sowing dates (June 2nd week, July 1st week, July 3rd week and August 1st week) in main plots and 4 pre released rice cultures (RNR 21278, JGL 28545, KNM 1638 and WGL 962 and 2 check varieties (KNM 118 and RNR 15048). The experimental findings revealed that the higher values of dry matter accumulation and grain yield (6839 kg ha-1) were recorded with the crop sown on July 3rd week. The crop sown on August 1st week gave lowest values on number of tillers m-2, number of panicles m-2 and grain yield (5029 kg ha-1). Among all pre-released rice cultures, the culture JGL 28545 showed the higher grain yield (6734 kg ha-1) which was on par with the cultures KNM 1638 (6526 kg ha-1) and WGL 962 (6514 kg ha-1). Among the treatment combinations, data results concluded that the rice cultures JGL 28545 and KNM 1638 recorded highest grain yield (7850.0 and 7849.0 kg ha-1), when sowing was taken up on June 2nd week and July 3rd week, respectively. The linear correlation analysis with SPSS-model has been found that grain yield showed negative correlation with evaporation (-0.076) and bright sunshine hours (BSSH) (-0.372) at tillering to maturity stage (P6).

Seasonal Incidence of Rice Gundhi Bug, Leptocorisaoratorius F. (Hemiptera: Alydidae) in Three Different rice Growing Seasons of Assam

Study on seasonal incidence of rice gundhi bugwas carriedout to know their incidence in threedifferent rice growing seasons of Assam, viz., sali (var. Ranjit), boro (var. Joymoti) and ahu (var. Disang) during 2020-21 at Regional Agricultural Research Station, AAU, Titabor.In sali rice, the lowest population (0.05 gundhi bug hill-1) was observed on 40th Standard Meteorological Week (SMW) and the highest (0.9 gundhi bug hill-1) was recorded on 46th SMW during 2020. In boro rice, the lowest population (0.3 gundhi bug hill-1) was recorded on 14th SMW and the highest of 3.75 gundhi bug hill-1 was recorded on 20th SMW during 2021. During ahu season, the lowest population (0.25 gundhi bug hill-1) was also recorded on 14th SMW and the highest (1.2 gundhi bug hill-1) was on 19th SMW during 2021. On an average, the highest population of gundhi bug was observed in bororice followed by ahu and sali. During saliseason, population build-up of gundhi bug showed significant positive correlation with morning relative humidity (r=0.734) and bright sunshine hour (r= 0.661). While, during boro and ahu rice the gundhi bug showed significant positive correlation with minimum temperature (boro, r = 0.600; ahu, r =0.649), evening relative humidity (boro, r= 0.643; ahu, r= 0.614) and number of rainy days (boro, r = 0.742; ahu, r= 710).

Shifting from the Hidden Shadow to the Bright Sunshine under the COVID-19 Pandemic

The sudden and unprecedented outbreak of the global pandemic of coronavirus disease 2019 (COVID-19) has brought along a series of abrupt and sweeping disruptions on almost all learning systems around the world. The pandemic has simultaneously revealed many weaknesses and problems associated with the existing educational model, which serves as a timely reminder that educators should start thinking and doing education in a very different manner. Nonetheless, this does not simply means restoring the long-standing norms, or reorganising and perpetuating existing practices as “back to normal”, but discarding and transforming many obsolete assumptions and conventional operations as “new normal”. As one of the high-performing learning systems around the world throughout the decades, Hong Kong has successfully transitioned from disruptive schooling to “new normal” throughout the pandemic. With the collection of the series of rich experiences and concrete examples emerging and evolving across different layers of Hong Kong’s learning system, this conceptual article aims to shed light on ten key principles in terms of shaping a more responsive, resilient, and sustainable curriculum system for all students to survive or even thrive in the uncertain and unpredictable environment ahead of them.

Advances in rice yield estimation using Neural Networks

Timely and reliable estimation of crop yield is an important dimension in agriculture as it aids in effective and timely policy decisions. Weather is a the most important factor, affecting crop yield in the agricultural domain and rice crop is no exception. The present study has been taken up to identify the effect of weekly weather parameters namely bright sunshine hours, maximum temperature, minimum temperature, morning relative humidity, evening relative humidity, and weekly total rainfall on rice crop yield being an important staple food of India. In order to suggest a suitable neural network model for rice yield estimation, Ranga Reddy District of Telangana state was chosen and weekly averages of weather variables from the 30th to 47th meteorological standard weeks (MSWs) of 31 years and rice yield data from 1988-89 to 2018-19 were considered in the study. Back A back propagation neural network and two activation functions namely logistic sigmoid and linear were employed in the neural network model. The proposed neural network model “F” (Input Neurons =11, Hidden Neurons=12, Output Neuron=1, Train Data Size = 80 % and Test data Size=20%) exhibited better results with the low MAE and AEER% while estimating rice yields. All the estimated yields of respective years were close to the actual yields as the multiple correlation coefficients (R) values for train and test data were also close to 1. The errors of simulated estimation of rice yield ranged between -8.1 to -3.8 % for the proposed neural networks model. Thus, better rice yield was estimated by using the proposed neural network model “F”.

Groundwater Level Forecasting Using Multiple Linear Regression and Artificial Neural Network Approaches

Accurate and reliable groundwater level prediction is a critical component in water resources management. This paper developed two methods to predict forty-six months of groundwater level fluctuation. The approaches of Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) were compared for predicting groundwater levels. MLR and ANN approaches were performed at two monitoring wells, Ubung and Ngurah Rai, in the Denpasar region of Bali, Indonesia, considering all significant inputs of hydrometeorological time series data: barometric pressure, evaporation, temperature, wind, bright sunshine, rainfall, and groundwater level. The model's performance was assessed statistically and graphically. The ANN-predicted groundwater levels agreed better with the observed groundwater levels than the MLR-predicted groundwater levels at all sites. The results show the ANN performs better than MLR in terms of statistical errors, notably mean square error (MSE) value of 0.6325; root mean square error (RMSE) value of 0.7953; mean absolute error (MAE) value of 0.6122 based on the MLR in the Ubung monitoring well, while ANN models got an MSE value of 0.143; RMSE value of 0.379, and MAE value of 0.311. For the Ngurah Rai monitoring well, the MSE value is of 1.3406, RMSE value of 1.1579, and MAE value of 0.9152 for MLR, while ANN models obtained MSE value of 0.0483, RMSE value of 0.2198, and MAE value of 0.1266.

Clean Water Production Enhancement through the Integration of Small-Scale Solar Stills with Solar Dish Concentrators (SDCs)—A Review

The conventional solar still, as a water treatment technique, has been reported to produce water at a low working temperature where various thermal resistance pathogens could survive in their distillate. In this work, the reviews of previous research on the quality of water produced by passive solar stills and their productivities in initial basin water temperatures were first presented and discussed. The next review discussed some recent studies on the performances of small-scale solar stills integrated with SDCs (with and without sun-tracking systems (STSs)) to observe the operating temperatures from early hours until the end of operations, daily water yield, and cost per liter. Based on these findings, it was revealed that SDCs with STSs indicated an instant increase in the absorber water temperature up to 70 °C at the starting point of the experiments in which this temperature range marked the unbearable survival of the pathogenic organisms and viruses, particularly the recent SARS-CoV-2. Furthermore, disinfection was also observed when the absorbers’ water temperature reached beyond the boiling point until the end of operations. This indicates the effectiveness of SDCs with STS in reflecting a large amount of sun’s rays and heat to the small-scale absorbers and providing higher operating absorbers temperatures compared to immobile SDCs. Daily productivities and costs per liter of the SDCs with STSs were found to be higher and lower than those of the other previous passive and active solar stills. Therefore, it is recommended that small-scale absorbers integrated with SDCs and STS can be used as a cost-effective and reliable method to produce hygienic pathogen-free water for the communities in remote and rural areas which encounter water scarcity and abundant annual bright sunshine hours.

Seasonal Incidence of Jasmine Leaf Webworm, Palpita unionalis (Pyralidae: U. T. of Puducherry)

The seasonal incidence of P. unionalis was studied under in situ and in light trap collections. Under in situ, the seasonal incidence of jasmine leaf webworm, P. unionalis was recorded at weekly intervals based on per cent leaf and bud damage respectively from February 2012 to January 2013. The damage by P. unionalis was higher during January 2013 (18.08%) and lower per cent damage was recorded during May 2012 (1.13%). The multiple regression coefficient of weather parameters under in situ against the incidence of leaf webworm, P. unionalis in terms of per cent leaf damage showed a positive correlation with morning relative humidity, bright sunshine hours and a negative correlation with the maximum and minimum temperature, evening relative humidity and rainfall. In the light trap collections of leaf webworm, P. unionalis moths, a positive correlation was observed with the evening relative humidity, bright sunshine hours and a negative correlation with the weather parameters viz., maximum and minimum temperature, morning relative humidity and rainfall.

Effect of weather variability on soil temperature, enzyme activities and nutrient availability under pear tree orchard in semi arid condition

The present study was conducted to quantify the effect of weather variability on soil temperature, enzyme activities and nutrient availability in arid soil under semi arid condition of Bathinda district of Punjab. In this context, soil samples were collected on weekly basis for three consecutive years (2016-2018) randomly from pear tree (Pyrus communis L.) orchard at three depths: 0-5 cm (top), 5-15 cm (middle) and 15-30 cm (lower), while, soil temperature observations for the respective depths were taken. The value of organic carbon content in soil decreased with increase in soil depth and reported 24 and 40 per cent lower organic carbon in middle and lower depths, respectively. Similarly, K content was also found more at 0-5 cm soil depths and decreased with depth. While, the value of dehydrogenase, acid and alkaline phosphatase and P was found more at 5-15 cm soil depth. Furthermore, nutrients (P and K) availability was found slightly higher in 2017 followed by 2018 and 2016. Alternatively, due to more moderate to heavy rainfall activities during 2016, soil nutrients were leached down from upper layers than rest years. Additionally, among the soil depths and years of study, soil temperature was watched higher than air temperature. Lesser deviations in soil enzyme activities and nutrient availability were found during the investigation year of 2016 followed by 2017 and 2018, likewise, lesser deviations were additionally found in 2016 than rest of the years in regard of weather parameters. Among soil depths and years of study, correlation coefficient (r) value between air and soil temperature was found positive and significant at 1% probability level. However, In respect of bright sunshine and rainfall vs. soil temperature, non-significant but positive correlation was found except 2018 for rainfall which was significant.