Why The Green Revolution Was Short Run Phenomena In The Development Process Of Pakistan: A Lesson For Future

Agriculture is the most important sector of Pakistan’s economy. It provides food and fibre, source of scarce foreign exchange earning and a market for industrial goods. In 1960s various policy measures were taken for Agriculture development. The research tries to examine various issues related to this sector. Focus of the research, however, is to analyze the role of Green Revolution in the development process of Pakistan and its short and long term impact on the economy. The paper analyzes weaknesses due to which the Green Revolution remained a shortterm phenomena. The contributing factors of Green Revolution and other supporting institutions are also discussed. The findings of this study show that the Green Revolution increased agriculture production and employment level. It also had impact on distribution of income and the social and political environment in the country. However, there were certain policy gaps due to which the impact of Green Revolution remained a short-term phenomena.

Dynamics of Wheat Market Integration in Northern Punjab, Pakistan

The economy of Pakistan is largely dependent on the agriculture sector which contributes about 21 percent to the GDP and employs about 43.4 percent of the labour force. Agriculture and agro-based industrial products contribute about three fourth of the total foreign exchange earnings from export [Pakistan (2007)]. About 66 percent of the population lives in rural areas of Pakistan and directly or indirectly depends on agriculture for its livelihood. The welfare and participation of the rural population in the economy is therefore, central to the country’s progress. Despite the importance of agricultural sector in the national economy, there is a wide gap between food supply and demand due to low performance of agriculture [FAO (2000)]. The country is not producing enough commodities like wheat, rice and edible oil etc. to meet even the basic food needs of the population and as a consequence poverty is on the rise, particularly in the rural areas. In order to reduce poverty, agriculture has to grow faster and at a sustainable basis

Evaluating Causes of Delay in Construction Projects of Pakistan

A project takes extra time for completion is called delay. Schedule delay in construction industry is a global phenomenon. Schedule delay is the time overrun either beyond completion date specified in the contract, or beyond the date that the parties agreed upon for the delivery of the project. There are some hurdles causes project delay are called delay causes or factors. The study examines the fifty four delay causes in forty two first class construction firms of Pakistan. Study categorizes these fifty four delay causes in seven groups: (1) Owner related (2) Consultant related (3) Contractor related (4) Material related (5) Labor and equipment related (6) project related and (7) External related. Study use questionnaire for collection of feedback of different professionals. In total sixty questionnaires were sent to forty-two first class firms, out of which forty one responded with sixty nine percent of response rate. Severity index, frequency index, relative importance index and weighted median tools of descriptive statistics uses for analyzing data. From weighted median study conclude that owner related and external related delay groups have maximum impact over project schedule delay. Top five delay causes using severity index were (1) Poor site management and Supervision (2) improper project feasibility study (3) Delay in finance and payments by owner (4) Inadequate experience of consultant and Rework due to errors during construction (5) Difficulties in financing project by contractor, Unqualified workforce and effects of subsurface conditions. Using frequency index top five delay causes were 1) Delay in finance and payments by owner (2) Delay in getting work permit (3) Bureaucracy (4)  Slow decision making and Unrealistic inspection and testing methods proposed in contract (5) Slow permit by government. While according to RII top delay causes were (1) Delay in Finance and Payments by owner (2) poor site management and supervision (3) Delay in getting work permit from local govt authorities (4) Unqualified workforce (5) Slow decision making. Keywords: Construction delays, types of delays, Disputes, Statistical analysis

How innovative climate leads to project success: the moderating role of gender and work culture

Purpose – In modern times, innovation is considered as a vital component of sustainable competitiveadvantage. The purpose of this paper is to identify how innovation at the individual level [innovative workbehavior (IWB)] and at the organizational level [innovative organizational climate (IOC)] affects the chances ofsuccess of a particular project. Additionally, the moderating effect of gender and work culture on the relationbetween innovative climate and behavior is tested in the study.Design/methodology/approach – Survey technique was used to collect data from 425 employeesworking in project departments at the executive, middle level and senior level management in the paintmanufacturing industry of Pakistan. Multiple regression, as well as Preacher and Hayes (2004) tests, wereapplied to test the hypotheses.Findings – The result of the data analysis showed that IWB acts as a mediator between IOC and projectsuccess (PS), thereby supporting the hypothesized model of innovation and PS. Work culture was supportedas a moderator; however, no moderating effect of gender was validated by the results.Research limitations/implications – The management must make sure that to maximize the rate ofsuccess of projects, innovative work climate within the organizations and departments be given dueimportance. In addition to this, personnel’s individual innovation capabilities must also be enhanced bytaking steps toward improvement through training and development. Originality/value – Though attention has been given to research in innovation in light of other relatedvariables, its relation to PS remains yet to be studied. The effect of gender and work culture on innovation inPakistani paint industry was long over-due which has been addressed by this stud

Why The Green Revolution Was Short Run Phenomena In The Development Process Of Pakistan: A Lesson For Future

Agriculture is the most important sector of Pakistan’s economy. It provides food and fibre, source of scarce foreign exchange earning and a market for industrial goods. In 1960s various policy measures were taken for Agriculture development. The research tries to examine various issues related to this sector. Focus of the research, however, is to analyze the role of Green Revolution in the development process of Pakistan and its short and long term impact on the economy. The paper analyzes weaknesses due to which the Green Revolution remained a shortterm phenomena. The contributing factors of Green Revolution and other supporting institutions are also discussed. The findings of this study show that the Green Revolution increased agriculture production and employment level. It also had impact on distribution of income and the social and political environment in the country. However, there were certain policy gaps due to which the impact of Green Revolution remained a short-term phenomena

Why The Green Revolution Was Short Run Phenomena In The Development Process Of Pakistan: A Lesson For Future

Agriculture is the most important sector of Pakistan’s economy. It provides food and fibre, source of scarce foreign exchange earning and a market for industrial goods. In 1960s various policy measures were taken for Agriculture development. The research tries to examine various issues related to this sector. Focus of the research, however, is to analyze the role of Green Revolution in the development process of Pakistan and its short and long term impact on the economy. The paper analyzes weaknesses due to which the Green Revolution remained a shortterm phenomena. The contributing factors of Green Revolution and other supporting institutions are also discussed. The findings of this study show that the Green Revolution increased agriculture production and employment level. It also had impact on distribution of income and the social and political environment in the country. However, there were certain policy gaps due to which the impact of Green Revolution remained a short-term phenomena

Application of Geospatial Techniques in Agricultural Resource Management

Although technological advancements have sparked the beginning of the fourth agricultural revolution, human beings are still facing severe problems such as shrinking croplands, dwindling water supplies, negative consequences of climate change, and so on in achieving agricultural resilience to meet the demands of the growing population over the globe. Geospatial techniques involving the integrated use of geographic information system (GIS), remote sensing (RS), and artificial intelligence (AI) provide a strong basis for sustainable management of agricultural resources aimed at increased agricultural production. In recent times, these advanced tools have been increasingly used in agricultural production at local, regional, and global levels. This chapter focuses on the widespread application of geospatial techniques for agricultural resource management by monitoring crop growth and yield forecasting, crop disease and pest infestation, land use and land cover mapping, flood monitoring, and water resource management. Moreover, we also discuss various methodologies involved in monitoring and mapping abovementioned agricultural resources. This chapter will provide deep insight into the available literature on the use of geospatial techniques in the monitoring and management of agricultural resources. Moreover, it will be helpful for scientists to develop integrated methodologies focused on exploring satellite data for sustainable management of agricultural resources

Toward explainable AI-empowered cognitive health assessment

Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR, a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves 0.96% of F-score for health and dementia classification and 0.95 and 0.97% for activity recognition of dementia and healthy individuals, respectively.This research was supported by Qatar National Library and Qatar University's Internal Grant IRCC-2021-010

Multi-Response Optimization of Resin Finishing by Using a Taguchi-Based Grey Relational Analysis

In this study, the influence and optimization of the factors of a non-formaldehyde resin finishing process on cotton fabric using a Taguchi-based grey relational analysis were experimentally investigated. An L27 orthogonal array was selected for five parameters and three levels by applying Taguchi’s design of experiments. The Taguchi technique was coupled with a grey relational analysis to obtain a grey relational grade for evaluating multiple responses, i.e., crease recovery angle (CRA), tearing strength (TE), and whiteness index (WI). The optimum parameters (values) for resin finishing were the resin concentration (80 g·L−1), the polyethylene softener (40 g·L−1), the catalyst (25 g·L−1), the curing temperature (140 °C), and the curing time (2 min). The goodness-of-fit of the data was validated by an analysis of variance (ANOVA). The optimized sample was characterized by Fourier-transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and scanning electron microscope (SEM) to better understand the structural details of the resin finishing process. The results showed an improved thermal stability and confirmed the presence of well deposited of resin on the optimized fabric surface