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.

The Agriculture Trading Platform (ATP) represents a significant innovation in the realm of agricultural trade in Malaysia. This web-based platform is designed to address the prevalent inefficiencies and lack of transparency in the current agricultural trading environment. By centralizing real-time data on agricultural production, consumption, and pricing, ATP provides a comprehensive dashboard that facilitates data-driven decision-making for all stakeholders in the agricultural supply chain. The platform employs advanced deep learning algorithms, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to forecast market trends and consumption patterns. These predictive capabilities enable producers to optimize their market strategies, negotiate better prices, and access broader markets, thereby enhancing the overall efficiency and transparency of agricultural trading in Malaysia. The ATP’s user-friendly interface and robust analytical tools have the potential to revolutionize the agricultural sector by empowering farmers, reducing reliance on intermediaries, and fostering a more equitable trading environment.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

With the rapid development of China’s economy and society, the reform of talent training mode for business administration has become the most concerned and valued issue in the current teaching work in colleges and universities. From the current situation of undergraduate education curriculum system construction in vocational colleges, the traditional teaching methods of higher English still occupy the majority. The all English bilingual course for the undergraduate major of business administration takes the basic knowledge of language and the theory of natural science as the core content. Therefore, this paper will focus on how to build a perfect talent training mode for business administration majors that meets the actual needs and employment direction of students, and put forward specific teaching strategies in order to provide more application-oriented and professional development platforms for business administration students.

English writing is an important output skill cultivated in college English courses. And in the age of informationization, college English writing teaching relies on the support of information technology. In order to cope with the solution of this dilemma, Production-Oriented Approach(POA) proposes Teacher-Student Collaborative Assessment (TSCA), which creates a new form of assessment. Through TSCA, teachers can not only alleviate the heavy teaching load, but also fully tap the network resources to efficiently use students' time before, during and after class to maximize and optimize the teaching effect. In this paper, the teaching concept of TSCA is used to conduct writing teaching experiments, to study and analyze the writing ability of students who write effectively, so as to provide insights for the practice of English writing teaching.

The epidemic has had a great impact on people and improved students' awareness of paying attention to their own health. Through the investigation of higher vocational students, the author and the research team collected 4741 questionnaires for research and analysis, and analyzed the data in four aspects: the impact of the epidemic on sports concept and psychology, the impact of intelligent equipment on physical exercise, the purpose of sports activities and the selection of online teaching content, and the impact of gender differences. This paper puts forward some suggestions on the development of online teaching of physical education courses in higher vocational colleges.