Nigeria’s palm oil processing industry poses significant environmental pollution risks, jeopardizing the country’s ability to meet the UN’s 17 Sustainable Development Goals (SDGs) by 2030. Traditional processing methods generate palm oil mill effluent (POME), contaminating soil and shallow wells. This study investigated water samples from five locations (Edo, Akwa-Ibom, Cross River, Delta, and Imo states) with high effluent release. While some parameters met international and national standards (WHO guidelines, ASCE, NIS, and NSDWQ) others exceeded acceptable limits, detrimental to improved water quality. Results showed, pH values within acceptable ranges (6.5–8.5), high total conductivity and salinity (800–1150 µS/cm), acceptable hardness values (200–300 mg/L), nitrite concentrations (10–45 mg/L), excessive magnesium absorption (> 50 mg/L), biochemical oxygen demand (BOD) indicating significant pollution (75–290 mg/L), total dissolved solids (TDS) exceeding safe limits in four locations, total solids (TS) exceeding allowable limits for drinking water (310–845 mg/L), water quality index (WQI) values ranged from “poor” to “very poor”. POME contamination by metals like magnesium, nitrite, chloride, and sodium compromised shallow well water quality. Correlation analysis confirmed robust results, indicating strong positive correlations between conductivity and TDS (r = 0.85, p < 0.01) and pH and total hardness (r = 0.65, p < 0.05). The study emphasizes the need for environmentally friendly palm oil processing methods to mitigate pollution, ensure safe drinking water, and achieve Nigeria’s SDGs. Implementation of sustainable practices is crucial to protect public health and the environment.

This paper focuses on examining the relationship among organizational factor, work-related factor, psychological factor, personal factor and the commitment of oil palm smallholders toward Malaysian Sustainable Palm Oil (MSPO) certification. The study employed a descriptive research methodology and a structured survey instrument to gather data from oil palm smallholders (n = 441) through simple random sampling technique. Data analysis was conducted using SPSS and partial least square structural equation modeling (PLS-SEM) to test the proposed relationship. The findings reveal that organizational factors significantly impact the affective (β = 0.345, p < 0.05), normative (β = 0.424, p < 0.05), and continuance commitment (β = 0.339, p < 0.05) of oil palm smallholders. Additionally, work-related factors show a substantial effect on these same dimensions of commitment; affective (β = 0.277, p < 0.05), normative (β = 0.263, p < 0.05), and continuance (β = 0.413, p < 0.05). Psychological factors significantly impact the affective (β = 0.216, p < 0.05) and normative commitment (β = 0.146, p < 0.05), with no statistically significant influence on continuance commitment. Conversely, personal factors exhibit limited influence, affecting only continuance commitment (β = 0.104, p < 0.05) to a minor degree, with no statistically significant impact on affective and normative commitment. The present research is among the few empirical findings that have examined the oil palm smallholders' commitment towards MSPO certification. By emphasizing the role of organizational and work-related factors, the study offers valuable insights for stakeholders within the oil palm sector, highlighting areas to enhance smallholder commitment toward sustainability standards. Consequently, this study contributes a unique perspective to the existing body of literature on sustainable practices in the oil palm industry.

This research explores the interactions within supply chains in the manufacturing sector, with a special emphasis on the distinctive obstacles encountered by the mosquito coil industry. The study is motivated by the need to comprehensively understand and address the multifaceted challenges encountered by manufacturers in their supply chain processes. The mosquito coil industry holds significant importance in Malaysia, primarily due to the country’s tropical climate, which is conducive to mosquito proliferation and the transmission of mosquito-borne diseases. Nowadays, there are growing complexities and disruptions experienced by the mosquito coil sector’s supply chain, prompting an in-depth investigation. The main objective is to identify the challenges and resilience strategies employed by manufacturers in this sector, providing an understanding that contributes to the broader discourse on supply chain dynamics. Employing a qualitative case study methodology, this research engages in extensive data collection through interviews, document analysis, and direct observations within the selected mosquito coil manufacturing entity. This methodology allows for an immersive exploration of the challenges faced, revealing insights into the factors influencing the supply chain dynamics. The study reveals a wide array of challenges, from obtaining raw materials to managing distribution logistics, underscoring the unique complexities specific to the sector. As a result, the research identifies and analyzes resilience strategies implemented by the mosquito coil manufacturer to mitigate challenges, such as procurement challenges faced in financial related issues, logistical complexities occurred from recent years’ worldwide pandemic, production disruptions from company’s human resource-related issues, global factors from the company’s competitors and market challenges, and technology integration from rapid technological advancements. Thus, implications of this study extend beyond the mosquito coil sector, contributing valuable knowledge to the academic community, practitioners, and policymakers involved in supply chain management. The research not only addresses the identified challenges but also serves as a foundation for enhancing the overall understanding of manufacturing supply chain dynamics, thereby fostering informed decision-making for improved industry resilience.

This study aims to investigate what influences local workers over the age of 40 to work and stay employed in oil palm plantations. 414 individuals participated in a face-to-face interview that provided the study’s primary source of data. Exploratory Factor Analysis was used to analyse the given data. The study revealed that factors influencing local workers over the age of 40 years to leave or continue working in oil palm plantations can be classified as income factors, internal factors and external factors. The income factor was the most significant factor as the percentage variance explained by the factor was 26.792% and Cronbach Alpha was high at 0.870. Therefore, the study suggested that the oil palm plantation managements pay more attention to income elements such as basic salary, wage rate paid to the workers and allowance given to the workers since these elements contribute to the monthly total income received by the workers and in turn be able to attract more local workers to work and remain in the plantations.

The coastal area of Bohai Bay of China has a wide distribution of salt-accumulated soils which could pose a problem to the sustainable development of the local ecology. As a result, the land remains largely degraded and unsuitable for biophysical and agricultural purposes. In this study, we characterized the soil and native plants in the area, to properly understand and identify species with satisfactory adaptation to saline soil and of high economic or ecological value that could be further developed or domesticated, using appropriate cultivation techniques. The goal was to determine the salinity parameters of the soil, identify the inhabiting plant species and contribute to the ecosystem data base for the Bay area. A field survey involving soil and plant sampling and analyses was conducted in Yanshan and Haixing Counties of Hebei Province, China, to estimate the level of salt ions as well as plant species population and type. The mean electrical conductivity (EC) of the soils ranged from 0.47 in more remote locations to 23.8 ds/m in locations closer to the coastline and the total salt ions from 0.05 to 8.8 g/kg, respectively. Each of the salinity parameters, except HCO₃⁻ showed wide variations as judged from the coefficient of variation (CV) values. The EC, as well as chloride, sulphate, Mg and Na ions increased significantly towards the coastline but the HCO₃⁻ ion showed a relatively even distribution across sampling points. Sodium was the most abundant cation and chloride and sulphate the most abundant anions. Therefore, the most dominant salinity-inducing salt that should be properly managed for sustainable ecosystem health was sodium chloride. Based on the EC readings, the most remote location from the coastline was non-saline but otherwise, the salinity ranged from slightly to strongly-very strongly saline towards the coast. There were considerably wide variations in the number and distribution of plant species across sampling locations, but most were dominated entirely Phragmites australis, Setaria viridis and Sueda salsa. Other species identified were Aeluropus littoralis, Chloris virgata, Heteropappus altaicus, Imperata cylindrica, Puccinellia distans, Puccinellia tenuiflora and Scorzonera austriaca. On average, the sampling points furthest from the coast produced the most biomass, and the point with the highest elevation had the most diverse species composition. Among species, Digitaria sanguinalis produced the highest dry mass, followed by Lolium perenne and H. altaicus, but there were considerable variations in biomass yield across sampling locations, with the location nearest the coastline having no vegetation. The observed variations in soil and vegetation should be strongly considered by planners to allow for the sustainable development of the Bahai bay area.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.