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Start date: 2021Subject: search.filters.subject.productivity

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    Allelopathic Effects of Moringa oleifera Lam. on Cultivated and Non-Cultivated Plants: Implications for Crop Productivity and Sustainable Agriculture
    (MDPI (Basel, Switzerland), 2025-07-23) Kamanga BM; Cartmill DL; McGill C; Clavijo McCormick A; Mussury RM
    Moringa (Moringa oleifera Lam.) is widely recognised as a multipurpose crop suitable for human and animal consumption, medicinal, and industrial purposes, making it attractive for introduction into new ranges. Its extracts have been found to have beneficial impacts on various crop species and biological activity against multiple weeds, making their use in agriculture promising. However, concerns have also been raised about moringa’s potential to negatively impact the growth and development of other cultivated and non-cultivated plant species, especially in areas where it has been introduced outside its native range. To understand the positive and negative interactions between moringa and other plants, it is essential to investigate its allelopathic potential. Allelopathy is a biological activity by which one plant species produces and releases chemical compounds that influence the reproduction, growth, survival, or behaviour of other plants with either beneficial or detrimental effects on the receiver. Plants produce and release allelochemicals by leaching, volatilisation, or through root exudation. These biochemical compounds can affect critical biological processes such as seed germination, root and shoot elongation, photosynthesis, enzymatic activities, and hormonal balance in neighboring plants. Therefore, allelopathy is an important driver of plant composition and ecological interactions in an ecosystem. This review explores the positive and negative allelopathic effects of moringa extracts on other plant species, which may help to inform decisions regarding its introduction into new biogeographical regions and incorporation into existing farming systems, as well as the use of moringa plant extracts in agriculture.
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    Can the use of digital technology improve the cow milk productivity in large dairy herds? Evidence from China's Shandong Province
    (Frontiers Media S.A., 2022-12-02) Qi Y; Han J; Shadbolt NM; Zhang Q; Naseer MAUR
    Introduction: Improving milk productivity is essential for ensuring sustainable food production. However, the increasing difficulty of supervision and management, which is associated with farm size, is one of the major factors causing the inverse relationship between size and productivity. Digital technology, which has grown in popularity in recent years, can effectively substitute for manual labor and significantly improve farmers' monitoring and management capacities, potentially addressing the inverse relationship. Methods: Based on data from a survey of farms in Shandong Province in 2020, this paper employs a two-stage least squares regression model to estimate the impact of herd size on dairy cow productivity and investigate how the adoption of digital technology has altered the impact of herd size on dairy cow productivity. Results: According to the findings, there is a significant and negative impact of herd size on milk productivity for China's dairy farms. By accurately monitoring and identifying the time of estrus, coupled with timely insemination, digital technology can mitigate the negative impact of herd size on milk productivity per cow. Discussion: To increase dairy cow productivity in China, the government should promote both small-scale dairy farming and focus on enhancing management capacities of farm operators, as well as large-scale dairy farms and increase the adoption of digital technologies.
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    Using spectral indices derived from remote sensing imagery to represent arthropod biodiversity gradients in a European Sphagnum peat bog
    (MDPI (Basel, Switzerland), 2023-03) Minor MA; Ermilov SG; Joharchi O; Philippov DA; Oliveira Júnior JMB
    Monitoring of peatlands is an important conservation issue. We investigated communities of soil mites (Acari: Oribatida, Mesostigmata) inhabiting a relatively undisturbed European boreal mire characterized by a mosaic of oligotrophic and meso-eutrophic areas. We assess the potential of using remote sensing approach as a mapping and predictive tool for monitoring productivity and arthropod biodiversity in a peat bog. In georeferenced plots, Acari biodiversity, water table level, water pH and plot productivity class on the oligotrophic-eutrophic gradient were recorded. Data from the Landsat 8 OLI sensor were used to calculate several spectral indices known to represent productivity and surface moisture gradients in terrestrial ecosystems. We then explored the relationship between spectral indices, environmental gradients and biodiversity of mites. We found that several spectral indices were significantly and consistently correlated with local environmental variables and biodiversity of soil mites. The Excess Green Index performed best as a predictor of plot trophic class on the oligotrophic-eutrophic gradient and showed significant relationship with Oribatida diversity in 2016. However, following hot summer in 2019, there was no significant relationship between abundance and species richness of Oribatida and remotely sensed data; there was a weak correlation between abundance of Mesostigmata and spectral indices which represent surface moisture gradient (e.g., Normalised Difference Moisture Index). We discuss advantages and challenges of using spectral indices derived from remote sensing imagery to map biodiversity gradients in a peatland.
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    Office Distractions and the Productivity of Building Users: The Effect of Workgroup Sizes and Demographic Characteristics
    (MDPI (Basel Switzerland), 2021-02-06) Khoshbakht M; Rasheed EO; Baird G; Arditi D
    Knowledge workers are experiencing ever-increasing distractions or unwanted interruptions at workplaces. We explored the effect of unwanted interruptions on an individual’s perceived productivity in various building types, user groups and workgroups. A case study of 68 buildings and their 5149 occupants using the Building Use Studies methodology was employed in this study. The database contains information on the occupants’ perceptions of physical and environmental parameters, including unmined data on the frequency of unwanted interruptions. Pearson’s correlation was used to test the correlation between the variables. In order to determine whether there are any statistically significant differences between the means of two or more independent (unrelated) groups, one-way ANOVA was employed to examine the significance of differences in mean scores between various user groups and workgroups. The evidence of clear correlations between the frequency of unwanted interruptions and perceived productivity is detailed in various user groups and in multiple building types. The Pearson correlation coefficients were−0.361 and−0.348 for sustainable and conventional buildings, respectively, demonstrating a lower sensitivity to unwanted interruptions in sustainable buildings. Females and older participants were more sensitive to unwanted interruptions and their productivity levels were reduced much more by unwanted interruptions. Comparing different sized workgroups, the highest sensitivity to unwanted interruptions for occupants in offices shared with more than 8 people was found. The findings of this study contribute to the understanding of different user needs and preferences in the design of workplaces
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    Organizational Compliance During COVID-19: Investigating the Effects of Anxiety, Productivity, and Individual Risk Factors Among Iranian Healthcare Employees
    (Frontiers Media S.A., 2021-02-08) Rahmani D; Zeng C; Goodarzi AM; Vahid F; Ahmed R
    This study investigates the impact of anxiety, productivity, and individual characteristics on employee compliance in an Iranian medical science university during the COVID-19 outbreak. The data of 160 healthcare employees of various professions were collected with reliability and validity on the measurements performed. Two regression tests revealed that higher anxiety reduces and higher productivity increased compliance. Participants with higher education and non-medical professions were found to have higher compliance. Productivity was also found to be positively associated with tenure and having a medical position. Implication and limitation are discussed.
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    Productivity in road pavement maintenance & rehabilitation projects : perspectives of New Zealand roading contractors on the constraints and improvement measures : a thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy (PhD) in Construction, School of Built Environment, Massey University, Albany, New Zealand
    (Massey University, 2023) Haji Karimian, Saeed
    Road Maintenance and Rehabilitation contractors (RMRCs) – and indeed all contractors handling public sector projects – face the challenge of performance-based rewards, which seek to maximise tax dollars by ensuring whole-of-life best value in the project delivery process. To be successful, a contractor’s productivity and performance should remain high and resilient to internal and external constraints in the project environment. There is a lack of research on a practical approach to modelling and prioritising performance constraints in the roading sector. This study aimed to investigate the priority constraints RMRCs face in New Zealand (NZ) as well as strategies for improvement. The thesis presents the final findings of interview-based qualitative surveys of medium- to large-sized roading contractors in NZ, followed by a questionnaire survey (quantitative) to prioritise the constraints found during the first stage of the research. Empirical data then were analysed using descriptive statistics and SPSS-based principal component analysis. The results showed - in diminishing order of influence - the following four principal items extracted from the initial 68 constraints identified from the interviews: process, operating environment; input; and output constraint groups. The most influential constraints in the four groups are inclement weather, frequency of design changes, inadequate supply or high cost of required resources, and post-construction defective or non-compliant work. The findings contribute to the relevant body of knowledge by revealing critical factors constraining the productivity performance of NZ RMRCs and associated improvement measures. The Input-Process-Environment-Output (IPEO) constraint model is seen as being more practical and easy to follow by industry stakeholders than the internal-external risk approach reported in the literature; it presents new and more enriching perspectives into how contractors could leverage their limited resources to address key constraints.
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    Development of a decision support tool for automation adoption and optimisation in precast concrete plants : a New Zealand case study : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Construction Project Management at Massey University, Albany, New Zealand
    (Massey University, 2022) Ansari, Reza
    In response to the growing demand in the New Zealand construction market, this study aims to develop a decision-support framework for adopting and optimising automation in precast concrete plants, which are increasingly recognised for their numerous benefits. The primary resources required by these plants include labour, equipment, and materials, and their efficient use is essential for maintaining competitiveness. Automation has been identified as a potential solution for improving productivity and profitability in precast concrete manufacturing; however, an appropriate decision-support tool is currently lacking. The current study commences with a comprehensive literature review, followed by historical data collection, face-to-face interviews, and site observations of precast concrete plants to address this research gap. These methods help identify attributes that affect profitability, leading to developing and validating of a theoretical framework named the Precast Plant Automation System Tool (PPAST) through a case study. The PPAST framework comprises two sequential phases: the strategic phase, which uses the direct rating method for preliminary feasibility evaluation of automation adoption, and the tactical phase, where the AHP method assesses the appropriate automation sequence for the plant. The study’s main findings indicate that the developed decision support system enables decision-makers to articulate their objectives and attitudes towards risk as they explore the feasibility of automation and formulate an optimal automation strategy. Specifically, the system aids in evaluating the impact of automation on cost and quality and identifying necessary process changes before implementing new technologies. The primary contribution of this research is its novel approach to systematically evaluating alternative automation scenarios in precast concrete production plants. The results demonstrate that the proposed model is a valuable and effective decision-making tool for adopting and optimising automation in precast concrete plants. This research fills a critical knowledge gap concerning the crucial measurements of precast concrete plant profitability and the absence of an automation adoption tool. The developed framework can be extended to investigate automation adoption and optimisation in other precast concrete plants across New Zealand. This study's practical implications include empowering precast plants to meet their organisation's profitability measures, thus satisfying stakeholder value propositions. A thriving precast concrete industry will lead to more satisfied clients, attract additional investment, and improve the overall construction industry's quality, productivity, and profitability at the national level. Theoretically, this research contributes a reliable benchmark for future studies by developing decision support tools that facilitate selecting optimised automation methods for precast concrete plants and contributing to theoretical knowledge by establishing an optimised automation decision support method that guides researchers in exploring other avenues for maximising profitability.