Influence of meteorological parameters on wheat yield under different sowing conditions

Field experiments were conducted on the research farm of IARI, New Delhi during Rabi 2016-17 and 2017-18. Three varieties of wheat (PBW-723, HD-2967 and HD-3086) were sown on three different dates for generating different weather condition during various phenological stages of crop. Results showed that during early crop growth stages soil moisture had higher value and soil temperature had lower value and with progress of crop growth stage, the moisture in the upper layer decreased and soil temperature increased significantly as compared to the bottom layers. During tillering and jointing stage, air temperature within canopy was more and relative humidity was less while during flowering and grain filling stage, air temperature within canopy was less and relative humidity was more in timely sown crop as compared to late and very late sown crop. Radiation use efficiency and relative leaf water content had significantly higher value while leaf water potential had lower value in timely sown crop followed by late and very late sown crop. Yield had higher value in HD-3086 followed by HD-2967 and PBW-723 in all weather conditions. Canopy air temperature difference had positive value in very late sown crop particularly during flowering and grain-filling stages. This reflects in the yield. Yield was more in timely sown crop as compared to late and very late sown crop.

Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

Highway crashes, along with the property damage, personal injuries, and fatalities that they cause, continue to present one of the most significant and critical transportation problems. At the same time, provision of safe travel is one of the main goals of any transportation system. For this reason, both in transportation research and practice much attention has been given to the analysis and modeling of traffic crashes, including the development of models that can be applied to predict crash occurrence and crash severity. In general, such models assess short-term crash risks at a given highway facility, thus providing intelligence that can be used to identify and implement traffic operations strategies for crash mitigation and prevention. This paper presents several crash risk and injury severity assessment models applied at a highway segment level, considering the input data that is typically collected or readily available to most transportation agencies in real-time and at a regional network scale, which would render them readily applicable in practice. The input data included roadway geometry characteristics, traffic flow characteristics, and weather condition data. The paper develops, tests, and compares the performance of models that employ Random effects Bayesian Logistics Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine methods. The paper applies random oversampling examples (ROSE) method to deal with the problem of data imbalance associated with the injury severity analysis. The models were trained and tested using a dataset of 10,155 crashes that occurred on two interstate highways in New Jersey over a two-year period. The paper also analyzes the potential improvement in the prediction abilities of the tested models by adding reactive data to the analysis. To that end, traffic crashes were classified in multiple classes based on the driver age and the vehicle age to assess the impact of these attributes on driver injury severity outcomes. The results of this analysis are promising, showing that the simultaneous use of reactive and proactive data can improve the prediction performance of the presented models.

Understanding the Impact of Monsoon Season in Malaysia Water Based on Data Analytics for Planning and Executing Drilling Operation

Drilling operation in Malaysia are typically from offshore, thus offshore weather condition does contributed to the success or delay of a drilling operation. Wait on Weather (WOW) especially during monsoon season in Malaysia has impacted Operator's drilling operation, thus incurring additional cost to Operator. Monsoon season in Malaysia is typically from November to February every year. This paper will discuss and share the statistics of actual WOW happening from 2008 to 2019 in Malaysia water especially for jack-up rig (JUR) and tender assisted drilling rig (TADR) which are two common rigs in Malaysia water. The data was collected from one of the drilling operator in Malaysia. These data will be of assistance to Operator in better planning and executing drilling operation with the actual statistics as the risk factor. WOW is considered as non-productive time (NPT), thus NPT data gathering from Operators in Malaysia water were conducted. Data was then filtered to achieve the WOW data. WOW data was segregated between region in Malaysia which are Peninsular Malaysia (PM), Sabah (SB) and Sarawak (SK) as well as rig type, which are JUR and TADR. Distribution analysis were made to calculate the average and observe the maximum numbers of actual WOW occurrence. Further analysis was made to zoom into monsoon season in Malaysia which typically in November to February. 11 years data is generally good coverage for the analysis since it covers the up and down of oil and gas industry. Analysis was also done for both mob/demob and operation stage where it can be observed that WOW for mob/demob stage during monsoon season is significantly higher compared to operation stage. At the end of the analysis, the average or maximum numbers of WOW will be shared, and it will be used as recommendation for future projects to consider these figures as WOW risk factor and embed in the planning stage. This paper will help not only Operators in Malaysia water but the host authority on understanding the WOW risk factor during monsoon season. As WOW is not something that can be predicted, utilizing the standard results from actual statistic data for the past 11 years will assist engineers to incorporate the WOW risk factor during planning and execution stage. Rig and project sequencing can be optimized with understanding of WOW impact thus reducing the value leakage during operation due to WOW.

ECONOMIC ANALYSIS OF YAM PROCESSING INTO YAM FLOUR IN SAKI AGRO ECOLOGICAL ZONE OF OYO STATE, NIGERIA

The study examined the economic analysis of yam processing into yam flour in Saki Agro ecological Zone of Oyo State. It specifically described the profitability of yam processing; examined the technical efficiency of yam processors and determined the effect of the socio-economic characteristics on technical efficiency. Data were collected with the aid of structured questionnaire and analyzed using descriptive statistics, profitability and budgetary analysis as well as stochastic frontier model and Garrett scale. The study reveals that majority of the respondents were female (88%) with a mean age of 47. The result shows that the rate of return on investment was 12 percent. The mean technical efficiency of the processors was 85% indicating that the yam processors were relatively efficient in allocating their limited resources. Some observable variables relating to socioeconomic characteristics such as processing experience and sex of the respondents significantly explains the variation in technical efficiency. Factors such as high cost of yam tubers, poor weather condition and inadequate processing facilities are the major factors that hinder the processing activities in the study area. The study therefore recommends that government policies should be made to improve the provision of inputs such as yam tubers and capital equipment at affordable price.

ISSUES AND CHALLENGES FACED BY VILLAGE GUIDES IN KUNDASANG AND KAMPUNG SAYAP

The study aims to identify the issues and challenges faced by village guides in the highlands along the west coast of Sabah. The destinations chosen in this study were the famous Kundasang located in the district of Ranau and Kampung Sayap a blooming highland destination in the district of Kota Belud. A qualitative approach was used to identify the issues and challenges faced by the village guides. The methodology used was divided into three stages. At stage 1, the researchers hiked along with the village guides to personally encounter the experience the hiking journey with the village guide. At stage 2, the village guides were placed in groups and a list of questions were brought forward for them to express the grievances they face while performing their guide duties and the desire to obtain certain assistance from the stakeholders to improve their socioeconomic status. Finally at stage 3, the village chief was included in the discussion in providing recommendation on how to improve the services as well as the standard of living of the village guides. The study revealed that the ratio of tourist to the village guide is too high, inability to communicate in English, do not have the knowledge on the intrinsic as well as the extrinsic value of the flora and fauna, lack of facilities, the village guides were mainly man, changes in the weather condition, schedule is too tight and the demand is seasonal. Many of the guides did not have a village guide licence because they did not have school certificates. Thus recommendations were given to focus on capacity building, working condition, status of the tour guide, safety, security and well being of guides.

Internet of Things as an element of the frost protection system in orchards

Frosts cause serious damage to fruit and vegetable crops. In Poland, temperature decreases, and the occurrence of inflow (advective) frosts most often fall during the flowering period, i.e., in a phase extremely important for the development of the plant. For orchards, this causes the inflorescences of early flowering trees (cherries, plums and certain varieties of apple and pear trees) to freeze. A modern idea for reducing frost losses in orchards is to heat the air with mobile heaters. Protection by these machines consists of passing using an agricultural tractor in rows of trees or shrubs and heating and mixing the air. The problem that farmers may encounter during frosts is the awareness of when exactly such a weather condition occurs in their orchard. Weather forecasts are not detailed and usually apply to the entire region. Dangerous temperature declines below the critical minimum can occur locally and are also conditioned by geographical location and terrain diversity. The aim of the article is to present a measuring system that allows the construction of an individualised temperature model taking into account the unique shape of the orchard surface. The system is made in Internet of Things technology using long-range radio communication protocol LoRaWAN. Data from distributed measurement sensors are processed on a network server and displayed as a final application. The task of the system is to monitor the current situation in the orchard and to notify the farmer of the need to initiate a protective procedure. The operation of the system also supports the efficient use of mobile heating machines. The system facilitates the location of the areas in the orchard with the lowest temperature, as well as provides feedback on temperature changes inside the treetops caused by the passage of the mobile heater.

Investigating the severity of expressway crash based on the random parameter logit model accounting for unobserved heterogeneity

The present study utilized a random parameter logit (RPL) model to explore the nonlinear relationship between explanatory variables and the likelihood of expressway crash severity. The potential unobserved heterogeneity of data brought by China’s road traffic characteristics was fully considered. A total of 1154 crashes happened on Hang-Jin-Qu Expressway from 2013 to 2018 were analyzed. In addition to the conventional impact factors considered in the past, variables related to road geometry were also introduced, which contributed to expressway accidents significantly. The overall stability of the model estimation was examined by likelihood ratio test. Then, the average elastic coefficient of the significant factors at each severity level was also calculated. Several factors that significantly increase the fatal crash probability were highlighted: rainy/snowy/cloudy weather condition, low visibility (100– m), night without light, wet-skid road surface, being female, aged 41+ years, collision with a rigid barrier and some other obstacles, radius and length of horizontal curve, and longitudinal gradient. The parameters of four factors were random and obeyed normal distribution: night without light, being female, driving experience with 10 + years and with large vehicle responsible. These findings provide insights for better understanding of expressway crash severity. Some countermeasures were proposed about driver education, traffic law enforcement, vehicle and road design, environmental improvement, and so on.