Effect of Seasonal Variations and Weather Factors on Population Dynamics of Mango Mealybug (Drosicha mangiferae) in Bangladesh

An experiment was conducted in mango orchard of Sher-E Bangla Agricultural University (SAU) campus, Dhaka to determine the effect of seasonal variation round the year and weather on population dynamics of mango mealybug (Drosicha mangiferae) on mango tree. The mango mealybug was first appeared in early December and disappeared in June in a year. Its population started to increase gradually from December and continued up to February when the ambient average temperature was 20.95°C, relative humidity 63% and rainfall was absent. The highest population of the insect (33.33/30 cm branch) was recorded in February and the lowest (3.75/30cm branch) in May. Adult insects appeared in February on the ground under the selected mango tree showing the population of 6.67 males and 123.33 females per 81 m2. Population of males (32.5/81m2) and that of females (352.75/81m2) reached to the peak when the ambient temperature was 28.19°C, relative humidity 61% and the rainfall was 0.00 mm. When relative humidity was high and rainfall started adult females enter into the soil and disappeared. Correlation between population of mealybug and temperature was positive. Its population was negatively correlated with relative humidity and rainfall.

Investigating the effect of weather on punctuality of Norwegian railways： a case study of the Nordland Line

The overall punctuality of the railway system is of particular importance for customers in order to use trains as a means of public transport. Investigating the drivers of low punctuality of trains is an essential step for improving their punctuality. Severe weather conditions are assumed to be one of the drivers of delays and low punctuality of the transportation system. This paper addresses the extent to which the weather factors such as temperature, snow,precipitation and wind influence the punctuality of trains on the Norwegian railways. The data for the study are collected from the passenger trains on the Nordland Line.The study period is 10 years, from the beginning of 2007 to the end of 2016. Data are analysed on a daily and weekly basis, and correlation and regression analyses are used to perform the statistical analysis. The results show that extreme cold weather during the winters is a crucial influencing factor contributing to delays and low punctuality. We also found that the weather variable that best explains variations in punctuality of passenger trains on this line is snow depth.

Revealing the Circadian Rhythm of Taliang Crocodile Newt(Liangshantriton taliangensis) During Breeding Migration Using a Novel Monitoring Technique

Many animals migrate during the breeding season.It is important to study the patterns of breeding migration of wild animals,especially rare and endangered species.However,current data acquisition methods are too coarse and imprecise for investigating the circadian rhythms of migrations in amphibians.Based on the frustrated total reflection image(FTRI),we developed a new device and recorded the precise migration time of an endangered salamander,Liangshantriton taliangensis.During the breeding period,a total of 33 individuals were effectively recorded.Analysis of the data indicated that the circadian rhythm of breeding migration in L.taliangensis was bimodal,and migration mainly occurred from 05:00 to 13:00(81.82%of the total migration records).The average relative activity intensity index(RAI)of breeding migration peaked in the temperature range of 14.0–16.0°C.With increased average relative air humidity,the activity intensity first increased and then decreased,reaching a peak in the humidity range of 91.0%–97.0%.These results demonstrate that our new device is a viable and accurate method for recording the migration time of target species.It is important to reveal the breeding migration pattern of L.taliangensis,which is important for the conservation of this species.Meanwhile,this new device can be used for field monitoring and conservation studies of amphibians,reptiles and other animals.

Prediction model of moisture content of dead fine fuel in forest plantations on Maoer Mountain,Northeast China

Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study was carried out in forest plantations on Maoer Mountain in order to develop models for predicting the moisture content of dead fine fuel using meteorological and soil variables.Models by Nelson(Can J For Res 14:597-600,1984)and Van Wagner and Pickett(Can For Service 33,1985)describing the equilibrium moisture content as a function of relative humidity and temperature were evaluated.A random forest and generalized additive models were built to select the most important meteorological variables affecting fuel moisture content.Nelson’s(Can J For Res 14:597-600,1984)model was accurate for Pinus koraiensis,Pinus sylvestris,Larix gmelinii and mixed Larix gmelinii—Ulmus propinqua fuels.The random forest model showed that temperature and relative humidity were the most important factors affecting fuel moisture content.The generalized additive regression model showed that temperature,relative humidity and rain were the main drivers affecting fuel moisture content.In addition to the combined effects of temperature,rainfall and relative humidity,solar radiation or wind speed were also significant on some sites.In P.koraiensis and P.sylvestris plantations,where soil parameters were measured,rain,soil moisture and temperature were the main factors of fuel moisture content.The accuracies of the random forest model and generalized additive model were similar,however,the random forest model was more accurate but underestimated the effect of rain on fuel moisture.

Application of a new wind driving force model in soil wind erosion area of northern China

The shear stress generated by the wind on the land surface is the driving force that results in the wind erosion of the soil.It is an independent factor influencing soil wind erosion.The factors related to wind erosivity,known as submodels,mainly include the weather factor(WF)in revised wind erosion equation(RWEQ),the erosion submodel(ES)in wind erosion prediction system(WEPS),as well as the drift potential(DP)in wind energy environmental assessment.However,the essential factors of WF and ES contain wind,soil characteristics and surface coverings,which therefore results in the interdependence between WF or ES and other factors(e.g.,soil erodible factor)in soil erosion models.Considering that DP is a relative indicator of the wind energy environment and does not have the value of expressing wind to induce shear stress on the surface.Therefore,a new factor is needed to express accurately wind erosivity.Based on the theoretical basis that the soil loss by wind erosion(Q)is proportional to the shear stress of the wind on the soil surface,a new model of wind driving force(WDF)was established,which expresses the potential capacity of wind to drive soil mass in per unit area and a period of time.Through the calculations in the typical area,the WDF,WF and DP are compared and analyzed from the theoretical basis,construction goal,problem-solving ability and typical area application;the spatial distribution of soil wind erosion intensity was concurrently compared with the spatial distributions of the WDF,WF and DP values in the typical area.The results indicate that the WDF is better to reflect the potential capacity of wind erosivity than WF and DP,and that the WDF model is a good model with universal applicability and can be logically incorporated into the soil wind erosion models.