Organomineral fertilizer is used to improve and ameliorate the supply of nutrients in soils. Right and adequate application of fertilizers are determinants of its nutrient supply efficiency, which in turn enhances the vegetative growth and yield of cucumber. Field experiments were conducted at the Research Farm of the Federal University of Agriculture, Abeokuta, Nigeria, to assess the effects of variety and rate of organomineral fertilizer on cucumber growth and yield. Trials were conducted from June to August 2019 and repeated from September to November 2019. The cultivars were Poinsett, Greengo, and Monalisa. The rates of organomineral fertilizer were 0, 2.5, or 5.0 tons. ha⁻¹. The treatments were replicated three times. Cucumber vegetative characters, yield, and yield components were studied. ‘Greengo’ produced the most leaves, followed by ‘Monalisa’; ‘Poinsett’ produced the least. Application of 5.0 tons. ha⁻¹ organomineral fertilizer produced the longest vines and fruits. ‘Greengo’ had the earliest days to 50% flowering, followed by ‘Monalisa’; ‘Poinsett’ had the most days to 50% flowering. Plants treated with an application of 5.0 tons. ha⁻¹ organomineral fertilizer attained 50% flowering in 29 days, but in 30 days with an application of 2.5 tons. ha⁻¹ organomineral fertilizer; the control treatment attained 50% flowering in 33 days. Application of 5.0 tons. ha⁻¹ organomineral fertilizer produced the longest fruits, thicker fruit diameter, and highest fruit yield compared with 2.5 and 0 tons. ha⁻¹ of organomineral fertilizer treatments. The Greengo variety with application of 5.0 tons. ha⁻¹ of organomineral fertilizer is recommended for optimum growth and yield in south western Nigeria.

Payment for forest ecosystem services (PFES) policy is a prevalent strategy designed to establish a marketplace where users compensate providers for forest ecosystem services. This research endeavours to scrutinise the impact of PFES on households’ perceptions of forest values and their behaviour towards forest conservation, in conjunction with their socio-economic circumstances and their communal involvement in forest management. By incorporating the social-ecological system framework and the theory of human behaviours in environmental conservation, this study employs a structural equations model to analyse the factors influencing individuals’ perceptions and behaviours towards forest conservation. The findings indicate that the payment of PFES significantly increases forest protection behaviour at the household level and has achieved partial success in activating community mechanisms to guide human behaviour towards forest conservation. Furthermore, it has effectively leveraged the role of state-led social organisations to alter local individuals’ perceptions and behaviours towards forest protection.

A reservoir of vegetation, wildlife, and medicinal plant abundance is represented by the Haridwar forest divisions. This study deals with the results of ethnobotanical survey of medicinal plants conducted in the Haridwar forest division during the period of December 2016 and March 2019. The information on folk medicinal use of plants were gathered by interviewing with local healers and Vaidya’s who have long been advising the folk medicines for medication of various disorders. The important folk medicinal data of 33 medicinal plants species belonging to 22 families and 33 genera practiced by tribal and local people of the study area has been recorded by the survey team of the Institute. Fabaceae followed by the Lamiacea and Asteraceae were the dominant families. The species diversity showed maximum exploration of Trees, Herbs followed by Shrubs and Climbers. Leaves, seed and root were the most prevalently used part in study followed by the stem bark, fruit, flower, stem and fruit pulp. During the study it was observed that the traditional practices of Gujjars of Uttarakhand have close relation with forests and have strong dependency on the same for food, medicine, timber and fodder etc. The information recorded for the treatment in different ailments has been presented in the paper in the pie charts and tabular form. In the recorded information most of the plants along with Plant name, Family name, Voucher Specimen No., Local Name/Unani name, Part Used, Diseases/Condition and Habitat/ICBN status so as to enrich the existing knowledge on ethnopharmacology. Many of the medications used today have their roots in traditional knowledge of medicinal plants and indigenous uses of plant material, and there are still a plethora of potentially useful pharmaceutical chemicals to be found. In this regard, more in-depth field research could aid in the discovery of novel plant species utilized in indigenous medical systems to improve patient needs. With this aim this study was conducted to explore and trace the ethnobotanical potential of flora of the Haridwar forest division so that it could prove to be immensely advantageous for both the development of new medications to treat dreadful and catastrophic illnesses as well as for the study and preservation of cultural and social variety.

Information transparency is a basic principle of good governance that few studies in the literature have thoroughly examined. Riau Province in particular has a high record of land and forest conflicts that needs urgent response, yet environmental policies have mostly been scrutinized for its resource extraction and regulation aspects, not their aspect of information transparency. Low proactive disclosure of information from local governments is a recurring issue in Riau Province, so FITRA Riau initiated the Public Information Openness Index (IKIP) to cover the Riau Province and 12 regencies/cities. To address this research gap of governmental public bodies’ information transparency, this study conducted the novel substantive approach critical review to see the extent of local government’s transparency regarding their budgeting for one of Riau’s most prevalent issues, namely land and forest governance (TKHL). From March to September 2019, this study used a triangulation of data collected from information access tests, IKIP evaluation, and focus group discussion involving the Riau Information Commission, the Information Management and Documentation Officers (PPID) of the 12 regencies, and the Governor of Riau Province. After analyzing the four aspects of regulation, institution, budget, and TKHL information, results determined that the most open region in Riau Province is Indragiri Hulu, and the least open region is Kuantan Singingi. Information transparency is still limited in procedural terms, in which all regions have more or less fulfilled the administrative regulation demands but the substance of the public information across all aspects is too generic to truly inform the public of the regions’ TKHL.

Due to the gradual growth of urbanization in cities, urban forests can play an essential role in sequestering atmospheric carbon, trapping pollution, and providing recreational spaces and ecosystem services. However, in many developing countries, the areas of urban forests have sharply been declining due to the lack of conservation incentives. While many green city spaces have been on the decline in Thailand, most university campuses are primarily covered by trees and have been serving as urban forests. In this study, the carbon sequestration of the university campuses in the Bangkok Metropolitan Region was analyzed using geoinformatics technology, Sentinal-2 satellite data, and aerial drone photos. Seventeen campuses were selected as study areas, and the dendrometric parameters in the tree databases of two areas at Chulalongkorn University and Thammasat University were used for validation. The results showed that the weight average carbon stock density of the selected university campuses is 46.77 tons per hectare and that the total carbon stock and sequestration of the study area are 22,546.97 tons and 1402.78 tons per year, respectively. Many universities in Thailand have joined the Green University Initiative (UI) and UI GreenMetric ranking and have implemented several campus improvements while focusing on environmental concerns. Overall, the used methods in this study can be useful for university leaders and policymakers to obtain empirical evidence for developing carbon storage solutions and campus development strategies to realize green universities and urban sustainability.

This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.