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 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 was impacted by 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.
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