Food security resilience to shocks in Niger: preliminary findings on potential measurement, drivers and challenges from LSMS-ISA data

The measurement of food security resilience (FSR) to shocks is yet hampered by inherent aspects of its complexity along with that of food security assessment itself. Yet, there is an urgent need for scientific evidence on which to base decision-making and policies to build resilience. Niger is one of the most underdeserved and underdevelopped coutries worldwide. We took advantage of the LSMS-ISA data to attempt defining as flexibly as possible the concept of FSR and move forward with its measurement and the investigation of policy-actionable drivers taking a multisectorial perspective. Food security was measured as reportedly self-assessed by household heads through Food Insecurity Experience Scale (FIES) collected by panel design in two waves from September to November 2014 (post-planting) and January to March 2015 (post-harvest) and representative of Niger and 26 additional strata representing settings and agroecological zones. According to changes in food security status (food secure vs food insecure) from one wave to the next, we identify four potential trajectories, two of which are compatible with resilient trajectories of recovery and resistance to shock impacts. Two exposures were considered, rain deficits at onset of rainy season (May-June) or being affected by drought in previous year to the time of interview. Weighted estimates of each trajectory were provided for the country and rural vs urban areas. Associations with socio-economic factors were explored using multinomial logistic regression models. Our preliminary findings point to a severe lack of food security in general and in particular lack of FSR to shocks in the country, and extremely low FSR in rural areas. A better road network, access to markets, improved rural-urban connectivity and increasing education level might be helpful in building up resilience. Farmers and female-headed households are particular vulnerable groups and need special and effective protection policies to improve their FSR.JRC.D.5-Food Securit

Quantitative Global Model for Armed Conflict Risk Assessment

Tools for automated quantitative analysis of information are more and more required in the framework of early warning systems, to support political decision makers in making timely evaluations of the risk of severe crises. This report describes a scientifically sound approach to build a statistical model to assess quantitatively the risk of intra-state armed conflict in any country in the world. Our models are based on structural indicators, and they therefore make a static assessment of country level performance, which can then be ranked for conflict risk. The temporal trend provide additional information on the evolution of the situation. Attention is paid to operationalise this approach in early situation assessment.JRC.G.2-Support to external securit

Building sustainable resilience for food security and livelihood dynamics: The case of rural farming households in Ethiopia

Building sustainable resilience for food security and livelihood dynamics is explored using the Ethiopia Rural Household Survey panel data. Household resilience scores are derived from measures taken to protect against shocks. The impact of several demographic and socio-economic factors on resilience dynamics is then tested. The result shows that the experience of resilience in the past leads to a subsequent higher chance of continuing to be resilient (‘true state-dependence’). It also demonstrates that measures that promote asset creation, diversified enterprises and access to improved technologies are positively and significantly correlated with dynamics of building resilience for food security.JRC.D.5-Food Securit

Exploring the new indicator Minimum Dietary Diversity-Women. Results from Burkina Faso.

Improving the quality of women's diet is the best way to stop the inter-generational cycle of malnutrition. The Minimum Dietary Diversity Women is a global indicator recently endorsed to monitor nutrition sensitive actions and programs aimed at improving the diet of women of reproductive age. This report explores the potential use of the indicator for programmatic action, and gauges how the indicator relates to other dimensions, and how sensitive it is to changes, in urban and rural Burkina Faso.JRC.H.4-Monitoring Agricultural Resource

Biomass estimation to support pasture management in Niger

Livestock plays a central economic role in Niger, but it is highly vulnerable due to the high inter-annual variability of rain and hence pasture production. This study aims to develop an approach for mapping pasture biomass production to support activities of the Niger Ministry of Livestock for effective pasture management. Our approach utilises the observed spatiotemporal variability of biomass production to build a predictive model based on ground and remote sensing data for the period 1998–2012. Measured biomass (63 sites) at the end of the growing season was used for the model parameterisation. The seasonal cumulative Fraction of Absorbed Photosynthetically Active Radiation (CFAPAR), calculated from 10-day image composites of SPOT-VEGETATION FAPAR, was computed as a phenology-tuned proxy of biomass production. A linear regression model was tested aggregating field data at different levels (global, department, agro-ecological zone, and intersection of agro-ecological and department units) and subjected to a cross validation (cv) by leaving one full year out. An increased complexity (i.e. spatial detail) of the model increased the estimation performances indicating the potential relevance of additional and spatially heterogeneous agro-ecological characteristics for the relationship between herbaceous biomass at the end of the season and CFAPAR. The model using the department aggregation yielded the best trade-off between model complexity and predictive power (R2 = 0.55, R2cv = 0.48). The proposed approach can be used to timely produce maps of estimated biomass at the end of the growing season before ground point measurements are made available.JRC.H.4-Monitoring Agricultural Resource

Phenology-Based Biomass Estimation to Support Rangeland Management in Semi-Arid Environments

Livestock plays an important economic role in Niger, especially in the semi-arid regions, while being highly vulnerable as a result of the large inter-annual variability of precipitation and, hence, rangeland production. This study aims to support effective rangeland management by developing an approach for mapping rangeland biomass production. The observed spatiotemporal variability of biomass production is utilised to build a model based on ground and remote sensing data for the period 2001 to 2015. Once established, the model can also be used to estimate herbaceous biomass for the current year at the end of the season without the need for new ground data. The phenology-based seasonal cumulative Normalised Difference Vegetation Index (cNDVI), computed from 10-day image composites of the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data, was used as proxy for biomass production. A linear regression model was fitted with multi-annual field measurements of herbaceous biomass at the end of the growing season. In addition to a general model utilising all available sites for calibration, different aggregation schemes (i.e., grouping of sites into calibration units) of the study area with a varying number of calibration units and different biophysical meaning were tested. The sampling sites belonging to a specific calibration unit of a selected scheme were aggregated to compute the regression. The different aggregation schemes were evaluated with respect to their predictive power. The results gathered at the different aggregation levels were subjected to cross-validation (cv), applying a jackknife technique (leaving out one year at a time). In general, the model performance increased with increasing model parameterization, indicating the importance of additional unobserved and spatially heterogeneous agro-ecological effects (which might relate to grazing, species composition, optical soil properties, etc.) in modifying the relationship between cNDVI and herbaceous biomass at the end of the season. The biophysical aggregation scheme, the calibration units for which were derived from an unsupervised ISODATA classification utilising 10-day NDVI images taken between January 2001 and December 2015, showed the best performance in respect to the predictive power (R2cv = 0.47) and the cross-validated root-mean-square error (398 kg·ha−1) values, although it was not the model with the highest number of calibration units. The proposed approach can be applied for the timely production of maps of estimated biomass at the end of the growing season before field measurements are made available. These maps can be used for the improved management of rangeland resources, for decisions on fire prevention and aid allocation, and for the planning of more in-depth field missions

The use of the Global Food Security Index to inform the situation in food insecure countries

This report is meant to document the use of the Global Food Security Index (GFSI) to inform the food security situation in countries of interest for DEVCO. The review of conceptual framework indicates that the GFSIhas to be interpreted as a food security environment rating. It focuses on the food security determinants rather than on the food security outcomes. It includes some of the usual food security determinants such as food supply, food share in total expenditure, poverty or nutritional policies and enlarge to less direct determinants like access to financial services, corruption, political stability. It thus only partially overlap with existing food security indicators. The statistical assessement of the index finds that GFSI exhibits good statistical properties. The index is statistically coherent and robust to changes in the weight and aggregation methods. The data coverage is good and the effect of outliers on the final score is not important. The conclusion recommends to use the GFSI in conjunction with other indicator of food insecurity namely those measuring the outcomes of food security in terms of food consumption and the nutritional status of the population to have a good assessment of the actual food security and nutrition situation in food insecure countries.JRC.D.5-Food Securit

Harmonizing and combining existing land cover and land use datasets for cropland area monitoring at the African continental scale

Mapping cropland areas is of great interest in diverse fields, from crop monitoring to climate change and food security. Recognizing the value of a reliable and harmonized crop mask that entirely covers the African continent, the objectives of this study were to (i) consolidate the best existing land cover/land use datasets, (ii) adopt the Land Cover Classification System (LCCS) for harmonization and (iii) assess the final product. Ten datasets were compared and combined through an expert-based approach to create the derived map of cropland areas at 250m covering the whole of Africa. The resulting cropland mask was compared with two recent cropland extent maps at 1km: one derived from MODIS and one derived from five existing products. The accuracy of the three products was assessed against a validation sample of 3591 pixels of 1km² regularly distributed over Africa and interpreted using high resolution images, which were collected using the agriculture.geo.wiki.org tool. The comparison of the resulting crop mask with existing products shows that it has a greater agreement with the expert validation dataset, in particular for cropland above 30%.JRC.H.4-Monitoring Agricultural Resource