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The research introduces a revolutionary Internet of Things (IoT)-based system for fish farming, designed to significantly enhance efficiency and cost-effectiveness. By integrating the NodeMcu12E ESP8266 microcontroller, this system automates the management of critical water quality parameters such as pH, temperature, and oxygen levels, essential for fostering optimal fish growth conditions and minimizing mortality rates. The core of this innovation lies in its intelligent monitoring and control mechanism, which not only supports accelerated fish development but also ensures the robustness of the farming process through automated adjustments whenever the monitored parameters deviate from desired thresholds. This smart fish farming solution features an Arduino IoT cloud-based framework, offering a user-friendly web interface that enables fish farmers to remotely monitor and manage their operations from any global location. This aspect of the system emphasizes the importance of efficient information management and the transformation of sensor data into actionable insights, thereby reducing the need for constant human oversight and significantly increasing operational reliability. The autonomous functionality of the system is a key highlight, designed to persist in adjusting the environmental conditions within the fish farm until the optimal parameters are restored. This capability greatly diminishes the risks associated with manual monitoring and adjustments, allowing even those with limited expertise in aquaculture to achieve high levels of production efficiency and sustainability. By leveraging data-driven technologies and IoT innovations, this study not only addresses the immediate needs of the fish farming industry but also contributes to solving the broader global challenge of protein production. It presents a scalable and accessible approach to modern aquaculture, empowering stakeholders to maximize output and minimize risks associated with fish farming, thereby paving the way for a more sustainable and efficient future in the global food supply.

Species diversity and soil quality are deteriorating due to continuous disturbances in ecosystems caused by human interference. However, agroforestry is considered a good approach to minimizing environmental problems. Therefore, the objective of this study was to determine the impacts of participatory agroforestry on restoring soil fertility, farm productivity and biodiversity in the degraded Madhupur Sal forest of Bangladesh. The study purposefully selected 40 common agroforestry programs in Madhupur Sal forest for the collection of soil and plant data from 2020 to 2023. Agroforestry programs have improved soil organic matter, soil carbon, pH, and available N, P and K content to a substantial degree and protected soil degradation, enhancing yield. The soil improvement index represents the potentiality of agroforestry in restoring soil nutrients and carbon in the form of organic matter, which is an important indicator for carbon sequestration and mitigating the impacts of climate change. The resultant cost–benefit and land equivalent ratios were steadily higher, which corroborates the greater productivity and profitability of agroforestry compared to monoculture systems. In contrast, agroforestry restored 31 plant species, opening up opportunities for restoring plant species in the threatened forest ecosystem. Therefore, this study recommended selecting appropriate site-specific species for managing agroforestry and restoring ecosystems.

Policymakers, practitioners, and researchers around the globe have been acting in a coordinated manner, yet remaining independent, to achieve the seventeen Sustainable Development Goals (SDGs) defined by the United Nations. Remarkably, SDG-centric activities have manifested a huge information silo known as big data. In most cases, a relevant subset of big data is visualized using several two-dimensional plots. These plots are then used to decide a course of action for achieving the relevant SDGs, and the whole process remains rather informal. Consequently, the question of how to make a formal decision using big data-generated two-dimensional plots is a critical one. This article fills this gap by presenting a novel decision-making approach (method and tool). The approach formally makes decisions where the decision-relevant information is two-dimensional plots rather than numerical data. The efficacy of the proposed approach is demonstrated by conducting two case studies relevant to SDG 12 (responsible consumption and production). The first case study confirms whether or not the proposed decision-making approach produces reliable results. In this case study, datasets of wooden and polymeric materials regarding two eco-indicators (CO2 footprint and water usage) are represented using two two-dimensional plots. The plots show that wooden and polymeric materials are indifferent in water usage, whereas wooden materials are better than polymeric materials in terms of CO2 footprint. The proposed decision-making approach correctly captures this fact and correctly ranks the materials. For the other case study, three materials (mild steel, aluminum alloys, and magnesium alloys) are ranked using six criteria (strength, modulus of elasticity, cost, density, CO2 footprint, and water usage) and their relative weights. The datasets relevant to the six criteria are made available using three two-dimensional plots. The plots show the relative positions of mild steel, aluminum alloys, and magnesium alloys. The proposed decision-making approach correctly captures the decision-relevant information of these three plots and correctly ranks the materials. Thus, the outcomes of this article can help those who wish to develop pragmatic decision support systems leveraging the capacity of big data in fulfilling SDGs.

Landscape scale assessment of temporal variations in soil organic carbon (SOC) contents and soil pH and the implications for long-term agricultural sustainability was determined using legacy datasets collected over two periods separated by 20 years: the 1990s and 2010s. Soil data on SOC and pH were categorized according to the prevailing land types (based on inundation as highland (HL), medium highland (MHL), and medium lowland (MLL)), and physiographic types (i.e., Himalayan Piedmont plain, Tista Floodplain and Barind tract/Terrace) to determine which variable or combination of variables was more influential in spatial and temporal changes of these properties. SOC contents in the physiographic types were generally found to be low, varying between 8 to 12 g/kg. While, SOC contents were significantly higher in MHL and MLL compared with HL that experienced less inundation. The change in SOC contents over 20 years was significant with a 14.5% increase of SOC. There was a greater influence of land type compared with physiography on SOC contents over time. Inundation land types and associated cropping intensity were considered likely to influence SOC of soils under rice-based cropping systems. Furthermore, the levels of soil pH decreased by 0.5 units over 20 years with an approximately 50% increase in soils within a pH category of 4.6–5.5. The majority of soil pH results shift from slightly acidic to strongly acidic in the intervening 20-year period between samplings. Soil acidification is potentially a combination of inefficient and excess use of ammonium-based fertilizers with higher application rates and low input from residues. We conclude that acidification may continue with more intensive land use. However, trends in SOC contents over time under certain combinations of physiography and land type either increased slightly or showed a significant loss and in the latter, specifically, the role of land management is not clear. The legacy datasets would be useful for monitoring spatial and temporal soil quality trends at a regional scale, but has limited capacity to capture field level variations in soil properties as data on smallholder cropping practice and management were not collected. Therefore, future research examining the role of management in SOC and pH dynamics at the field-scale would guide the use of fertilizers, crop residue management, and amelioration of acidic soil, to improve the sustainability of rice-based cropping systems in Bangladesh.