In order to optimize the environmental factors for cucumber growth, a fertilizer and water control system was designed based on the Internet of Things (IoT) system. The IoT system monitors environmental factors such as temperature, light and soil Ec value, and uses image processing to obtain four growth indicators such as cucumber stem height, stem diameter size, number of leaves and number of fruit set to establish a single growth indicator model for temperature, light, soil Ec value and growth stage, and the four growth indicators were fused to obtain the comprehensive growth indicator I_c for cucumber, and calculates its deviation to determine the cucumber growth status. Based on the integrated growth index I_c of cucumber, a soil Ec control model was established to provide the optimal environment and fertilizer ration for cucumber at different growth stages to achieve stable and high yield of cucumber.

Excessive usage of chemicals in crops, especially in leafy vegetables, caused people exposed to health and environmental risks. In Iran, spinach used as a winter vegetable that believed has high Iron and is useful for anemia. The objective of the experiment was to determine the optimum use of each macronutrients to obtain safe maximum growth and yield for scaling up among farmers. Treatments were chemical fertilizers including ammonium sulfate, triple superphosphate and potassium sulfate at 50, 100, 150 and 200 kg/h against control in a randomized complete block design. Results showed that nitrogen caused elevation of fresh and dry weight in spinach as the maximum obtained in 200 kg/h ammonium sulfate. Results obtained from effect of phosphorus showed that super phosphate increased fresh and dry weight of spinach; but potassium sulfate had no effect on its growth and yield. Analysis of variance on cross effect of data showed significant differences in fresh and dry weight, number of leaves, chlorophyll content and nitrate, and non-significant differences in length and wide of leaves.

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.

When COVID-19 hit all the Asian countries, Indonesia issued various laws and regulations. This study investigates these laws that do not improve the country’s ability to increase its adaptive structuration and foresight-oriented investment. It analyzes all the new laws, which should be based on the requirements of both concepts. It considers that all the laws are intended to defend the Government of Indonesia’s economic performance (GoI). It means that all the established regulations were built on the premise that they only focused on national economic preservation, especially economic growth. In other words, this study stated that the absence of regulations containing adaptive restructuration and foresight-oriented investment would decrease the state’s agility. This absence potentially impacts Indonesia to zcategorize the future as the state’s political failure. It shows evidence that Indonesia could not enforce and empower its structural potential. This study indicates that Indonesia made no foresight-oriented investment to cover the disbursed costs due to the COVID-19 pandemic. Future policies should be improved by including growth opportunities to enhance Indonesia’s agility. This agility could finally be achieved when all the laws issued by the GoI do not contain the praxis.

This research aims to examine in more depth the changes resulting from the Job Creation Law, which impact the level of business friendliness in Indonesia, and how to analyze these changes to improve the business environment to be more conducive to carrying out business activities. This research uses normative legal research methods and is analytical descriptive research. There have been several changes since the emergence of the Job Creation Law, such as the establishment of a limited liability company. Changes to the Job Creation Law could improve the Indonesian economy. However, juridically, this regulation gives authority to the central government to manage micro and small businesses, contrary to the principle of decentralization, which prioritizes the provision of resources to local governments.

Objective: The influence of climate on forest stands cannot be ignored, but most of the previous forest stand growth models were constructed under the presumption of invariant climate and could not estimate the stand growth under climate change. The model was constructed to provide a theoretical basis for forest operators to take reasonable management measures for fir under the influence of climate. Methods: Based on the survey data of 638 cedar plantation plots in Hunan Province, the optimal base model was selected from four biologically significant alternative stand basal area models, and the significant climate factors without serious covariance were selected by multiple stepwise regression analysis. The optimal form of random effects was determined, and then a model with climatic effects was constructed for the cross-sectional growth of fir plantations. Results: Richards formula is the optimal form of the basic model of stand basal area growth. The coefficient of adjustment  was 0.8355; the average summer maximum temperature  and the water vapor loss  in Hargreaves climate affected the maximum and rate of fir stand stand growth respectively, and were negatively correlated with the stand growth. The adjusted coefficient of determination  of the fir stand area break model with climate effects was 0.8921, the root mean square error (RMSE) was 3.0792, and the mean relative error absolute value (MARE) was 9.9011; compared with the optimal base model,  improved by 6.77%, RMSE decreased by 19.04%, and MARE decreased by 15.95%. Conclusion: The construction of the stand cross-sectional area model with climate effects indicates that climate has a significant influence on stand growth, which supports the rationality of considering climate factors in the growth model, and it is important for the regional stand growth harvest and management of cedar while improving the accuracy and applicability of the model.