Economical and Social Research Centre

of multiply-fortified salt.Child, mother, and household characteristics were assessed at baseline.Hemoglobin concentrations from a venous blood sample were measured using an automated hematology analyzer (Sysmex XP-300, Japan).Anemia was defined as a hemoglobin < 11 g/dL.The association between child age, sex, morbidity in past two weeks, anthropometry; maternal demographics, body mass index, anemia status; household food security, dietary diversity score, socioeconomic status and anemia were explored using chi-square tests of independence.Variables that were associated with anemia at p < 0.1 were included in a multivariate logistic regression model.Results: Mean AE SD child age was 39.4AE12.2months.The prevalences of child stunting, wasting, and underweight were 14%, 7%, and 10%, respectively.Mean AE SD hemoglobin concentration was 11.3AE1.4g/dL and 37% of children were anemic.In bivariate analysis, the prevalence of anemia was higher among children who were younger, stunted, and those with mothers who were underweight, overweight/obese, and/or anemic.In multivariate analysis, the following factors were associated with an increased prevalence of anemia: child age < 30 months (OR4.26;95% CI:2.35, 7.74), 30-49 months (OR1.75;95% CI: 1.04, 2.92); child stunting (OR 2.09; 95% CI: 1.20, 3.64); maternal underweight (OR: 2.59; 95%CI:1.25,5.39), maternal overweight/obese (OR1.96;95% CI:1.28, 3.0), and maternal anemia (OR 1.83: 95% CI: 1.23, 2.7).Conclusions: Younger age, child stunting, low or high maternal BMI, and maternal anemia were important factors associated with anemia among children 12-59 months of age in this setting.These factors should inform the targeting of interventions to reduce anemia.

The adoption of international standards is essential to producing internationally comparable official statistics. However, as the particular case of the adoption of SDG indicators demonstrates, this is by no means a linear process. According to FAO’s SDG data gap assessment conducted in 2019 and other statistical capacity assessment reports (e.g., World Bank, AfDB…), financial, professional and technological capacities of national statistical agencies vary greatly between countries and regions, as does the level of political support and commitment to statistics. These differences call for more targeted interventions by international institutions to support the uptake of statistical standards. To face this challenge, FAO is increasingly building the capacity of its regional and country offices to better mainstream food and agricultural statistics in regional and national cooperation strategies and activities, while also stepping up its resource mobilization efforts. Regional roadmaps are currently being rolled out to support countries in adopting food- and agriculture-related SDG indicators and other statistical standards, taking into consideration regional particularities, the impact of UN reform, action plans developed by UN Regional Commissions and other relevant regional partnership opportunities. This paper will highlight some of the key regional differences in countries’ capacity to adapt to statistical standards, describe FAO’s regional targeted interventions in the context of food and agricultural statistics, and discuss the remaining main challenges.

REDD+ and greenhouse gas reporting for the agriculture, forestry and other land use (AFOLU) sector requires land use changes to be characterized to estimate the associated greenhouse gas emissions or absorptions. It is becoming increasingly common for countries to track these changes using visually interpreted, sample-based approaches. Known as sample-based area estimation, the technique has been widely used in recent years in the generation of activity data for REDD+ Monitoring Reporting and Verification (MRV). However, implementing countries and agencies have repeatedly highlighted the lack of guidance on certain frequently-encountered issues with this approach. This paper responds to this need for guidance by trying to address the most urgent technical issues faced by countries relating to sample based area estimation. Among others, it tackles issue such as how to best monitor beyond deforestation or for multiple purposes, how to account for variability between interpreters looking at the same satellite image, what type of sample unit to use and how many measurements are needed per sample unit.

&amp;lt;p&amp;gt;Brazil is responsible for about one third of the global land use and land cover change (LULCC) carbon dioxide emissions. However, there is a disagreement among different methodologies on the magnitude and trends in emissions and their geographic distribution. One of the main uncertainties is associated with different LULCC datatasets used as input in the different approaches. In this work we perform an evaluation of LULCC datasets for Brazil, including the global dataset (HYDE 3.2) used in the annual Global Carbon Budget (GCB), and national Brazilian dataset (MapBiomas) over the period 2000-2018. We also analyze the latest global HYDE 3.3 dataset based on new FAO inventory estimates and multi-annual ESA CCI satellite-based land cover maps. Results show that the new HYDE 3.3 can represent well the observed spatial variation in cropland and pastures areas over the last decades compared to national data (MapBiomas) and shows an improvement compared to HYDE 3.2 used in GCB. However, the magnitude of LULCC assessed with HYDE 3.3 is lower than national estimates from MapBiomas. Finally, we used HYDE 3.3 as input to two different approaches included in GCB, a global bookkeeping model (BLUE) and a process-based Dynamic Global Vegetation Model (JULES-ES) to determine the impact of the new version of HYDE dataset on Brazil&amp;amp;#8217;s land-use emissions trends over the period 2000-2017. Both JULES-ES and BLUE now simulate a negative land-use emissions trend for the last two decades. This negative trend is in agreement with Brazilian INPE-EM, global H&amp;amp;N bookkeeping models, FAO and as reported in National GHG inventories (NGHGI), although magnitudes differ among approaches. Overall, the inclusion of the multi-annual ESA CCI Land Cover dataset to allocate spatially the FAO statistical data has improved spatial representation of agricultural area change in Brazil in the last two decades, contributing to improve global model capability to simulate Brazil&amp;amp;#8217;s LULCC emissions in agreement with national trends estimates and spatial distribution.&amp;lt;/p&amp;gt;

Abstract. Drainage of large areas with organic soils was conducted over the past century to free land for agriculture. A significant acceleration of such trends was observed in recent decades in South-East Asia, largely driven by drainage of tropical peatlands, an important category of organic soils, for cultivation of oil palm. This work presents methods and main results of a new methodology developed for FAOSTAT, whereby the overlay of dynamic maps of land cover and the use of information on histosols allows the production of a global annual dataset of drained area and emissions over a time series, covering the period 1990–2019. This is an improvement over the existing FAO approach, which had produced only a static map of drained organic soils for the year 2000. Results indicate that drained area and emissions increased by 13 percent globally since 1990, reaching in 2019 24 million ha of drained organic soils, with world total emissions of 830 million tonnes of carbon dioxide (CO2) equivalent. Of these totals, the largest contribution was from the drainage of tropical peatlands in South-East Asia, generating nearly half of global emissions. Results were validated against national data reported by countries to the UN Climate Convention and to well established literature. Overall, the validation yielded a good agreement with these sources. FAOSTAT estimates explained about 60 percent of the variability in official country reported data. The predicted emissions were virtually identical – with over 90 percent of explained variability – to official data from Indonesia, currently the top emitting country by drained organic soils. Also, calculated emissions factors for oil palm plantations in Indonesia and Malaysia were in the same range and very close to emissions factors derived from detailed field measurements. This validation suggests that the FAO estimates may be a useful and sound reference in support of countries reporting needs. Data are made available as open access via the Zenodo portal (Tubiello and Conchedda, 2020) with DOI https://doi.org/10.5281/zenodo.3942370.

As part of a national action plan to manage animal genetic resources in Burundi, we characterized smallholders’ goat production systems and assessed the effect of Boer crossbreeding on animal body measures. To that end, 319 farmers were surveyed in the five agro-ecological zones of Burundi and the zootechnical measures of 939 adult goats were taken. Cluster analysis of the goat production systems resulted into two opposed groups and one intermediate. On the one hand, there are the dry lowlands systems characterized by large herds composed mainly of indigenous animals grazing freely, and having high market characteristics. On the other hand, there are the humid highlands systems characterized by small herds composed of indigenous and crossbred animals in stall feeding or herding/tethering systems, and by low market characteristics. Fixed effect linear models were used to evaluate the effect of Boer crossbreeding on body weights and linear body measures, after adjustment for age, sex, grazing systems and agro-ecological zones. Results showed that least square means for body weights of adult Boer crossbreds were 4.74 kg higher than those of indigenous goats, which was lower than expected. Least square means for linear body measures of Boer crossbreds were significantly higher than those of indigenous breeds: differences in chest girth, body length and height at withers were 4.88 cm, 5.59 cm and 4.82 cm, respectively. Goats were heavier and greater in linear measures in lowlands than in highlands. The advantages and issues of a selection programme within indigenous breeds are discussed as an alternative to the crossbreeding programme.

Analysis of incentives and disincentives for coffee in Uganda

Off-farm employment in rural areas can be a major contributor to rural poverty reduction and decent rural employment. While women are highly active in the agricultural sector, they are less active than men in off-farm employment. This study analyzes the determinants of participation in off-farm employment of women in rural Uganda. The study is based on a field survey conducted in nine districts with the sample size of 1200 individual females. A two-stage Hechman’s sample selection model was applied to capture women’s decision to participate and the level of participation in non-farm economic activities. Summary statistics of the survey data from rural Uganda shows that: i) poverty and non-farm employment has a strong correlation, implying the importance of non-farm employment as a means for poverty reduction; and ii) there is a large gender gap to access non-farm employment, but the gender gap has been significantly reduced from group of older age to younger generation. The econometric results finds that the following factors have a significant influence on women’s participation in off-farm employment: education level of both the individual and household head (positive in both stages); women’s age (negative in both stages); female-headed household (negative in first stage); household head of polygamous marriage (negative in both stages); distance from major town (negative in the first stage); household size (positive in the second stage); dependency ratio (negative in the second stage); access to and use of government extension services (positive in the first stage); access to and use of an agricultural loan (negative in the second stage); and various district dummies variables. The implications of these findings suggest that those policies aimed at enhancing the identified determinants of women off-farm employment can promote income-generating opportunities for women groups in comparable contexts. In order to capitalize on these positive linkages, policies should be designed to improve skills and knowledge by providing education opportunities and increasing access to employment training, assistance services and loans for non-farm activities and by targeting women in female-headed, large and distant households. The government should increase investments in public infrastructure and services, such as roads, telecommunications and emergency support.

Analysis of incentives and disincentives for meat in Mali