About one-third of countries in Latin America express an intent to use agroforestry to meet national climate commitments. Despite this interest, technical and institutional barriers often prevent agroforestry from being represented and counted in United Nations Framework Convention on Climate Change (UNFCCC) MRV processes such as national greenhouse gas (GHG) inventories and REDD+. The fact that agroforestry often isn’t counted in MRV systems has serious implications. Only if agroforestry resources can be properly measured and reported will they gain access to finance and other support, and thereby assume a prominent role as a response to climate change.
Tag: measurement
Comparing the prediction performance, uncertainty quantification and extrapolation potential of regression kriging and random forest while accounting for soil measurement errors
Geostatistics and machine learning have been extensively applied for modelling and predicting the spatial distribution of continuous soil variables. In addition to providing predictions, both techniques quantify the uncertainty associated with the predictions, although geostatistics is more developed in this respect. Despite the increased use of these techniques, most algorithms ignore that the soil measurements are not error-free. Recently, concern has also arisen about the extrapolation risk of these techniques, be it in geographic space, feature space, or both. In this paper, regression kriging (RK) and random forest (RF) were compared with respect to their ability to deliver accurate predictions and quantify prediction uncertainties, while accounting for measurement errors in the soil data. The sensitivity of results of both models to soil measurement errors was also evaluated, as well as their spatial extrapolation potential. This was done for a case study in Cameroon where soil pH, clay and organic carbon were mapped from measurements obtained using both conventional and proximal soil sensing methods. The results showed that both models produced comparable ranges and maps of predicted values for the soil properties of interest. Compared to RF, RK outperformed RF by presenting generally a higher Model Efficiency Coefficient (MEC), lower Root Mean Squared Error (RMSE) values and better extrapolation performance. The improvement in RMSE was about 10, 12 and 2 % while the improvement in MEC was on average 5, 22 and 1 % for pH, clay and SOC, respectively Overestimation of the local uncertainty observed for RK was larger than that of RF as shown by accuracy plots, indicating that prediction uncertainties were better quantified by the RF model. Better extrapolation performance was obtained with RK that derived better predictions than RF at unsampled locations as shown by cross-validation metrics and scatter plots, particularly when RK and RF were used for spatial extrapolation. The effects of incorporating measurement errors were not significant both for the predictions and for the prediction uncertainties due to the fact that most calibration data had the same measurement error variance. Model comparison should go beyond common validation metrics that only evaluate prediction accuracy but must also account for the ability to quantify prediction uncertainty at unsampled locations.
The emergy-data envelopment analysis (EM-DEA) approach handbook: An illustrated guide on how to use the EM-DEA approach to assess resource- and energy-use efficiency and the sustainability of agricultural and forestry ecosystems
Emergy-Data Envelopment Analysis (EM-DEA) is a methodological approach for achieving complete environmental-economic accounting of different production systems. In an age when resources are scarcer than ever before, and the environmental impact of humanly designed systems of production is a major concern when deciding which system could better contribute to human and economic development without compromising the future of the global environment, using a reliable method for the comparative assessment of the efficiency and sustainability of different production systems is critical when making smart decisions. This handbook provides a step-by-step instruction to help users apply the EM-DEA approach to simultaneously assess the resource and energy use efficiencies, and sustainability of agricultural and forestry ecosystems as a whole. This approach was developed to address the lack of a singular method to assess complete environmental accounting and compare the sustainability performance of agro-ecosystems. The EM-DEA approach does so by combining emergy analysis (EMA) and data envelopment analysis (DEA) methods. By offering flexibility to account for various natural, human and economic resources such as land or input contributions from farm animals, it provides a means to do a comprehensive environmental accounting throughout the lifetime of agricultural and forestry systems. This approach was empirically tested with a comparative analysis of five maize production systems in Ghana, Africa. The results demonstrated that the application of the EM-DEA approach leads to complete environmental-economic accounting. Thus, EM-DEA is an innovative approach that could be used to support decision making when comparing different production systems as a whole.
Global Comparative Study on REDD+ story of change: CIFOR’s science on wetlands for Indonesian measurement, reporting and verification and forest reference emission level development
Key messages
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Tropical forested wetlands, including peatlands and mangroves, provide critical environmental services and store 3–5 times more carbon than other tropical forests.
- However, because these ecosystems are under significant pressure from unsustainable land-use practices, they must be credited in the national forest monitoring and measurement mechanism and integrated into the national policy agenda.
- In January 2016, the Government of Indonesia submitted a forest reference emission level (FREL) to the United Nations Framework Convention on Climate Change (UNFCCC) secretariat, and is currently in the process of finalizing its second FREL.
- To achieve the intended improvement in the FREL, CIFOR has played an active role by co-producing relevant data and knowledge products, building capacity among key personnel and stakeholders, and creating a platform for communication, engagement and outreach at national and international levels.