measurement Archives - Resilient Landscapes (RL)

Madagascar country report on Measuring Agroecology and its Performance (MAP). TAPE application in the context of the Global Programme “Soil Protection and Rehabilitation for Food Security” (ProSoil)

The Measuring Agroecology and its Performance (MAP) project is a collaboration to generate evidence of how agroecology can contribute to societal goals. The project assessed the performance of agroecology in three of the six districts of the Boeny region in Madagascar (Mahajanga II, Ambatoboeny, Mitsinjo), which have been part of the GIZ global project, Soil Protection and Rehabilitation for Food Security (ProSoil), since 2018–2019. Analysts applied Tools for Agroecology Performance Evaluation (TAPE), as well as the Characterization of Agroecological Transition (CAET) on 102 farms that participated in the global project, and on 98 non-participating farms as a control group.

Overall, CAET scores indicated participating and non-participating groups had few significant differences. For some elements of agroecology, such as diversity, synergies, co-creation and sharing of knowledge, participating farms showed a positive trend. The number of farms below the poverty line, crop incomes and non-agricultural incomes were not significantly different between the two groups, while livestock income was higher in the participating group. Strong correlations were observed between crop incomes and the diversity score in both groups, and global scores for soil health were the same for both groups. On environmental and social performance, participating farms recorded higher livestock diversity and higher women’s empowerment, respectively. Dietary diversity, pesticide use and the percentage of children working in agriculture were the same, or nearly the same, in both groups.

In all, the ProSoil project had a slight impact on the agroecology level and performance of farms. Nevertheless, CAET scores were positively correlated with different economic, environmental and social indicators, providing evidence for decision makers to sustain agroecology scaling-out and scaling-up for food security.

Learning from agrarian dynamics to tailor community-led forest restoration in the Tshopo province, Democratic Republic of the Congo

The strategies and efficacy of forest restoration initiatives in Central Africa are poorly documented. To this end, we examined the usefulness of a holistic methodology (combining agricultural diagnosis with forestry measurement) to explain the results of a forest restoration project in Tshopo Province in the DRC. To do this, an initial analysis based on the agrarian diagnostic structure was carried out and linked to project monitoring data – interviews with all beneficiaries who had planted trees (n=89) and measurements in their fields (planting sites, species planted and mortality rates 12 months after planting). The study shows that the uptake and results of the forest restoration initiative can be largely explained by the diversity of farming systems. Finally, our diagnostic method offers interesting rationales for forest restoration interventions in Central Africa, in order to adapt project objectives to the local context and diversity of farming systems, and ultimately to improve project performance.

A general allometric equation for estimating biomass in Acacia mangium plantations

Acacia Mangium Willd. is one of the most important tree species grown in commercial plantation in Monsoon Asia. Recently, the need for accurate information in the biomass in plantations has become more urgent, especially since the amount of carbon sequestered in afforestation/reforestation Clean Development Mechanism (AR-CDM) projects in developing countries can be included under the Kyoto Protocol. We present here a general allometric equation for estimating aboveground biomass (AGB) of A. mangium plantations from the diameter of the trees recorded at the respectives sites. Destructive samplings were conducted in Papua New Guinea, Vietnam, and Indonesia. At each site, 4-12 trees were felled, their trunks, branches and leaves were separately weighed, and allometric models for estimating AGB was determined. A general allometric equation (A log-log model) was developed from an overall total of 26 sample trees from sampled sites. No significant differences were found between the biomass estimations derived from the site-specific and the general allometric equations. The general allometric equation may allow us to estimate AGB of A. mangium plantations in Monsoon Asia without destructive sampling.

Making Trees Count in Latin America and the Caribbean: Measurement, reporting and verification (MRV) of agroforestry in the UNFCCC

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.

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

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.

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