Introduction: The presence of ecotourism is more oriented toward environmental conservation, utilization of natural resources, cultural preservation, and economic benefits. However, post-COVID-19 pandemic tourist visits to the Kampung Blekok mangrove ecotourism have not recovered, and the condition of the ecotourism is not maintained and is damaged. This research aims to analyze the influence of digital marketing, electronic word of mouth, and interest in visiting on the decision to visit the Kampung Blekok mangrove ecotourism. Methods: Data collection on ecotourism behavior was obtained from 172 respondents using a questionnaire. The data analysis technique used was Structural Equation Modeling Partial Least Squares (SEM-PLS) with WarpPLS version 8.0. Results: The results of the research analysis show that digital marketing and eWOM produce a positive and significant influence on visit intention and decision. The mediating role of visiting interest in the relationship between digital marketing and eWOM on visiting decisions is partial. Mangrove ecotourism managers in Kampung Blekok can optimize digital marketing and eWOM strategies, and adapt to current tourism situations and trends. Conclusion: Digital marketing and eWOM are important in shaping visit interest and determining tourist decisions. Changes in tourist behavior using digital media can be a tool for progressing the ecotourism business by increasing tourists' visiting decisions.

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Introduction: Meeting rice needs is largely determined by the level of domestic rice production. However, the various dynamics that occur provide encouragement to provide an overview of the conditions for meeting food needs, namely in the form of rice, based on trends in rice production and how independent variables correlate with rice production. The aim of this research is to determine the projected rice production for 2023-2032 and determine the strength of the relationship between the dependent variable (rice production) and the independent variables in the form of harvested area, price of harvested dry grain, price of urea fertilizer and price. Methods: namely a quantitative descriptive method with secondary time series data from 1993-2022. Data analysis techniques for forecasting studies are by using Autoregressive Integrated Moving Average (ARIMA) analysis. Meanwhile, in the correlation analysis of rice production using the Pearson correlation. Results: Projections for rice commodity production in Indonesia from 2023 to 2032 using the MA1 model show a positive trend. Analysis of the relationship between the independent variable and the dependent variable, namely harvest area (X1), rice price (X2), urea fertilizer price (X3), and price of other food commodities, namely corn (X4) shows a significant positive relationship with rice production in Indonesia. Conclusion: Projections for rice commodity production in Indonesia from 2023 to 2032, which were analyzed using the MA 1 model, obtained data with a positive trend which states that based on past data, Indonesian rice production will continue to increase every year in line with domestic food needs.

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Introduction: Efforts to enhance the agribusiness sector, particularly in dairy farming, require integrated development alongside other local potentials. This study aims to establish a model for dairy cattle agribusiness development integrated with the village's superior potential, utilizing the Business Model Canvas (BMC). Methods: The research involved mapping the dairy cattle agribusiness in Kalipucang Tutur Pasuruan Village using the BMC framework. The study analyzed the fulfillment of the nine BMC element blocks and identified strategies for enhancing productivity through SWOT analysis. Results: The mapping revealed that the dairy agribusiness in the village offers animal husbandry educational tours and satisfies the BMC element blocks. However, there is a need for strategic enhancements to boost productivity. The strategic analysis also identified the potential for developing agroedutourism due to the advantageous location near tourist spots. Conclusion: The integration of dairy cattle agribusiness with local potentials, as outlined in the BMC mapping, presents opportunities for further development. Strategic recommendations derived from the SWOT analysis provide insights for optimizing productivity and leveraging the area's potential for agroedutourism.

Introduction: Nutrient-poor soil can lead to reduced crop yields. Biochar-organic fertilizer has demonstrated positive effects over several growing seasons. However, the efficacy of biochar-organic fertilizers varies based on the raw materials used and the duration of their application in the soil. This study aims to assess the residues of biochar, manure, and compost one year after their application on maize plants in Alfisol. Methods: A 300 kg/ha dosage was employed for a single treatment of biochar, manure, and compost. Additionally, biochar was combined with manure or compost at a dose of 150 kg/ha each. The experiment utilized a randomized block design with nine treatments and three replications, including a control group. The treatments comprised biochar made from coconut shell and husk, compost, manure, shell+compost biochar mixture, shell+manure biochar mixture, husk+compost biochar mixture, and husk+manure biochar mixture. Corn variety Pertiwi 3 was harvested upon reaching physiological maturity. Results: Combining husk biochar with chicken manure exhibited the most favorable response regarding plant height, leaf area, and weight. Furthermore, combining cob biochar and chicken manure yielded optimal cob length, weight without seeds, and seed weight. Conclusion: After one year, applying shell biochar mixed with manure demonstrated the most significant residual effect on corn plants.

Introduction: This research aims to identify types of risks and sources of risks, analyze risk priority levels, and formulate mitigation strategies for the tobacco products industry supply chain in Pasuruan Regency. Methods: This research was conducted at the tobacco products industry PT. XZ in Pasuruan Regency. Respondents focused on company managers (focal firms), namely product development, raw material management, research and development pilot plant and tobacco clue management who knew about supply chain risks. Supply chain network integration uses a snowball method approach. Data collection methods use primary and secondary data. At the interview stage, the Delphi method was used to identify risk events and risk sources. Furthermore, the results of the interview were identified using the Supply Chain Operation Reverence (SCOR) and House of Risk (HoR) models. Results: There are 39 risk events and risk agents in the IHT in Pasuruan Regency. There are eight risk priority levels for tobacco supply chain risk agents using the SCOR approach and the Pareto diagram, namely workers not paying attention, problems with machines, high rainfall/high rainfall, production machines need to be repaired, workers' skills are not good, production machines are old, handling bad goods, and errors in the machine. Conclusion: The results of the IHT supply chain risk mitigation strategy formulation are tightening the use of work SOPs, checking production machines regularly, strengthening information between agents and factories, checking machines every 4 hours, conducting worker training, using Google Maps technology in delivering goods, cleaning machines, maintaining product quality, drying/air-drying tobacco leaves, improving quality control during transactions with farmers, and increasing coordination with the Government.

Introduction: This research aims to describe the factors that influence livestock farmers in accessing Kredit Usaha Rakyat (KUR) in Burno Village, Senduro Subdistrict, Lumajang Regency. Methods: The research method uses a descriptive quantitative approach, determining the sample of respondents using simple random sampling and the Slovin formula for as many as 51 people. Data analysis uses multiple linear regression analysis to measure the influence of internal factors (age, education level, income, length of farming, number of livestock, and cosmopolitan level) and external factors (socio-cultural values, family involvement, role of community leaders, role of breeder organizations, and access to experts) on the capacity of dairy farmers in accessing KUR. Results: The research results show that the variables income, length of farming, cosmopolitan level, and the role of community leaders have a significant effect on the capacity of breeders. Conclusion: The capacity of dairy farmers to access KUR is categorized as moderate and the capacity of dairy farmers is influenced by income, length of farming, cosmopolitan level, and the role of community leaders.

Introduction: Banana plants are easy to find and grow in Indonesia, including in Jember. Banana production in Jember in 2020 increased by 50% and has the potential to be inventoried and developed. Information regarding the characteristics of various banana cultivars is needed for future banana breeding. This research was conducted to identify and classify the genome of bananas growing in Jember and determine the level of banana diversity. Methods: The banana exploration using a purposive sampling method. The observation of 15 morphological characters followed the method of Simmonds and Shepherd. Determination of the genome followed the method of Silayoi & Chamchalow and Singh & Uma. The kinship analysis uses cluster analysis using the average linkage method and Gower distance, by R Studio software. Results: There were 23 banana cultivars obtained in this research, consisting of 35% dessert bananas and 65% plantain types. Genome identification results in four genome groups, namely AA/AAA, AAB, ABB, and BB. Cluster analysis produces three main groups, where its branch is generally consistent with the genome grouping. The closest relationship is between Rayap and Seribu bananas (0.96), while the furthest is between Gaje and Agung bananas (0.38). The dissimilarity value for 23 banana cultivars is 0.36, or the similarity is 0.64. Conclusion: The 23 banana cultivars are closely related or have low diversity based on the 15 morphological characters observed. Further identification can be carried out with a greater number of morphological characters, accompanied by molecular identification.

Introduction: rhizobacteria Bacillus spp. are microorganisms that reside in plant roots that function as biocontrol agents of plant diseases and increase plant growth and yield. Bacillus spp. consortium is a combination of several Bacillus spp. that synergize and do not inhibit each other. The study aimed to obtain the best Bacillus spp. consortium to control Meloidogyne spp. and increase the growth and yield of tomato plants. Methods: This research is experimental using a completely randomized design consisting of 2 stages, namely 1.) Compatibility test of rhizobakteria consortium Bacillus spp. consists of 6 treatments 4 replicates and 2.) Test consortium rhizobakteria Bacillus spp. selected to control Meloidogyne spp. and increase the yield of tomato plants. The observed variables were the development of root swelling by Meloidogyne spp., and the growth of seedling, vegetative, and generative phases. Results: The results showed that testing all isolates of rhizobacteria Bacillus spp. showed compatibility (compatible). Bacillus spp. rhizobacteria consortium can suppress the development of Meloidogyne spp. and showed different results compared to the control. Bacillus spp. rhizobacteria consortium can increase the growth of tomato seedlings and showed different results compared to the control. Bacillus spp. rhizobacteria consortium can increase the growth of tomato plants in the vegetative phase and showed different results compared to the control. rhizobacteria consortium Bacillus spp. can increase the growth of tomato plants in the vegetative phase and shows different results compared to the control. Conclusion: The best rhizobakteria Bacillus spp. consortium in suppressing the development of Meloidogyne spp. and increasing the growth and yield of tomato plants is Bacillus cereus strain RBI2AB2.2 + Bacillus cereus strain RBIKDA2.2.