The global market’s sustainability demand for coffee as a result of environmental concerns has influenced coffee producers to practice green coffee production. The efforts to improve the environmental performance of coffee production should also consider the other sustainability aspects: energy and economics. Using a green fertilizer from agricultural biomass can lower carbon dioxide (CO₂) emissions since the cultivation process, which is directly impacted by fertilizer use, has been identified as an environmental damage hotspot for coffee production. This study aims to determine the impact of coffee pulp biomass utilization on coffee production in terms of energy savings, CO₂ emission reduction, and economic value added. The methodologies used were environmental Life Cycle Assessment, energy requirement analysis, life cycle costing, and eco-efficiency analysis. The study findings showed that using coffee pulp biomass in coffee cultivation impacted the energy savings, environmental damage reduction, and increased economic value added. Applying coffee pulp biomass can potentially reduce 39–87% of cumulative energy demand, 49.69–72% of CO₂ emissions, and 6–26% of the economic value-added increase. Moreover, coffee pulp utilization as a fertilizer is recommended to be applied broadly to promote sustainable coffee production according to its beneficial impact. This study provided that scientific information farmers need to apply green fertilizers in coffee production.

Indonesia has the most favorable climates for agriculture because of its location in the tropical climatic zones. The country has several commodities to support economics growth that are driven by key export commodities—e.g., oil palm, rubber, paddy, cacao, and coffee. Thus, identifying the main commodities in Indonesia using spatially-explicit tools is essential to understand the precise productivity derived from the agricultural sectors. Many previous studies have used predictions developed using binary maps of general crop cover. Here, we present national commodity maps for Indonesia based on remote sensing data using Google Earth Engine. We evaluated a machine learning algorithm—i.e., Random Forest to parameterize how the area in commodity varied in Indonesia. We used various predictors to estimate the productivity of various commodities based on multispectral satellite imageries (36 predictors) at 30-meters spatial resolution. The national commodity map has a relatively high accuracy, with an overall accuracy of about 95% and Kappa coefficient of about 0.90. The results suggest that the oil palm plantation was the highest commodity product that occupied the largest land of Indonesia. However, this study also showed that the land area in rubber, rice paddies, and cacao commodities was underestimated due to its lack of training samples. Improvement in training data collection for each commodity should be done to increase the accuracy of the commodity maps. The commodity data can be viewed online (website can be found in the end of conclusions). This data can further provide significant information related to the agricultural sectors to investigate food provisioning, particularly in Indonesia.

The weather anomaly phenomenon that occurs can have some negative impact such as flooding, floods will paralyze the economic activities of the community, transportation activities, damage public infrastructure. In this research forecasting weather parameters as a variable for predicting the amount of rainfall using the ANFIS method and Support Vector Regression (SVR) with the aim to provide information on future weather conditions quickly and accurately. The people can prepare themselves and prepare the equipment needed to deal with it. Rainfall predicted based on synop data such us relative humidity, wind, and temperature. Each parameters must forcasted by using ANFIS and the result used for predict rainfall. Accurate prediction calculated using MSE and RMSE. Predictions of parameters that affect rainfall using the ANFIS method shown that for wind speed predictions having RMSE of 1.975004, temperature predictions have RMSE of 0.742332, and predictions of relative humidity have RMSE of 3.871590. Predicted rainfall based on the data results of the nearest method pre-processing using the Support Vector Regression (SVR) method produces an MSE error value of 0.0928.

The demand for coffee in the local and global markets has encouraged massive production at upstream and downstream levels. The socioeconomic impact of coffee production still presents an issue, primarily related to the social benefit and economic value added for farmers. This study aims to identify the social impact of the coffee industry in rural areas in three different coffee industry management systems. Many coffee industries exist in rural areas, with various management systems: farmer group organizations, middlemen, and smallholder private coffee production. This study performed the social organization life cycle assessment to identify the social impact of the coffee industry in rural areas according to the management systems. The results indicated that the coffee industry managed by farmers is superior in providing a positive social impact to four stakeholders: workers, the local community, society, and suppliers, as indicated by the highest social impact scores of 0.46 for the workers, 0.8 for the local community, 0.54 for society, and 0.615 for the suppliers. The private coffee industry provides the highest social impact to consumers (0.43), and the middlemen were very loyal to the shareholders, with a total social impact score of 0.544. According to this social sustainability index analysis, the coffee industry managed by the farmer group has the highest endpoint of social impact at 0.64, which is categorized as the “sustainable” status. Meanwhile, the coffee industry managed by private companies and middlemen is categorized as “neutral or sufficient”. The coffee industry should implement improvement strategies to increase their social impact to all stakeholders in their business supply chain.