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.
Mangrove ecosystem is a very potential area, generally located in ecoton areas (a combination of intertidal and supratidal areas), where there is interaction between waters (sea, brackish water, and rivers) with land areas. Indonesia, especially the Banten and West Java regions, have vast mangrove areas and are currently under threat of land conversion. Moreover, mapping the distribution of mangrove forests using the Google Earth Engine platform based on Cloud Computing is less published. Therefore, this research was conducted by introducing the distribution of mangrove forests which involved the Random Forest (RF) classification algorithm method, and looking for the best modification of the index. The combination test was carried out by involving the NDVI, EVI, ARVI, SLAVI, IRECI, RVI, DVI, SAVI, IBI, GNDVI, NDWI, MNDWI, and LSWI indexes. There is a distribution of mangroves in three provinces (West Java, Banten, and Jakarta) which are 933.54 ha (8.372%), 1,537.89 ha (18.231%), and 8,184.82 ha (73.397%). Of the 70 combination tests, the LSWI index (K13, Type-A) is the combination with the lowest accuracy rate of 58.45% (Overal Accuracy) and 39.59 (Kappa statistic), and the combination of K23 (SAVI-MNDWI-IBI) is a combination the best are 96.48% and 92.79. The results and recommendations in this study are expected to be used as a reference in determining policies for the protection of mangrove areas and a reference for further research
Agricultural waste has the potential of biomass as a raw material for producing renewable energy. The primary processing of coffee produces waste from pulping and hulling activities. Waste can be processed further through composting, anaerobic water waste treatment and burning to be converted into electrical energy. Therefore, the calculation is needed that estimates the amount of potential biomass that is converted. Then, the purpose of the paper is to analyze each stage in the life cycle of Gayo Arabica coffee and calculate the potential amount of electrical energy produced. The life cycle assessment method uses material and energy analysis intending to explain the flow of inputs and outputs within the system boundary and analyze the movement and transformation of materials, energy, waste, and emissions. In the context of the paper, the study uses material flow analysis to estimate the biomass potential from solid and water waste treatment. The study uses interviews, observations and a cooperative report located in Central Aceh district as an Arabica coffee producer area in Indonesia. Production of Arabica coffee is managed by cooperatives involving small farmers and collectors from cultivation, primary processing, packaging, and delivery. Cultivation uses the agroforestry system with a shade tree of the type of lamtoro (Leucaena leucocephala). Packing with a pack of burlap is done by the cooperative. Activities undertaken cooperatives include the acceptance of coffee beans from the collector. Since 2016 cooperatives implemented a policy of processing coffee beans at the collector level. The estimation of the study shows that waste treatment through anaerobic water waste treatment, composting and combustion from 1 ton of cherry coffee (primary processing) has an energy potential of 34 kwh.
The Ujung Kulon National Park (UKNT) is one of the national parks on the island of Java and has an essential role in saving endemic species in Indonesia. As a form of national park conservation effort, the completeness of LULC spatial data is a primary database that is indispensable in determining national park management policies. Therefore, this research was conducted to map the LULC (Land Use - Land Cover) in the forest landscape with high heterogeneity in UKNT. Sentinel-2 MSI (Multispectral Instrument) image data were classified using the Random Forest (RF) classification algorithm and tested using 11 index algorithms. The classification process takes place on a cloud computing-based geospatial platform, Google Earth Engine (GEE). This test resulted in 10 LULC classes; water had the broadest percentage of 45.44%. Meanwhile, the primary forest has an area of 21,868.41 or about 19.53% of the total area of the national park. However, there are some discrepancies in the spatial information generated by this classification process, so it is considered necessary to evaluate the combination of indexes, training data, and classification algorithms to limit the classification area. Therefore, this study is expected to be considered for further research related to LULC in high-heterogeneity landscapes.
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