Using agricultural and economic characteristics in African nations as test cases, new research by David Lobell and Marshall Burke demonstrates the use of satellite data to address the long-standing problem of accurate data collection in developing countries. An often cited challenge in achieving development goals aimed at poverty and hunger reduction is the lack of reliable on-the-ground data. Limited or insuffiient data makes it difficult to establish baseline conditions and to assess effectiveness of various aid programs. In the past, researchers and policymakers had to rely on ground surveys, which are expensive, time-consuming, and rarely conducted. This has led to large data gaps in mapping sustainable development goal progress, such as in agricultural and poverty statistics.
Women empowerment (WE) is increasingly viewed as an important strategy to reduce maternal and child undernutrition,1–3 which continues to be a major health burden in low- and middle-income countries causing 3.5 million preventable maternal and child deaths, 35% of the disease burden in children younger than 5 years, and 11% of total global disability-adjusted life years.4,5Global data show that one of the worst affected regions is sub-Saharan Africa (SSA), where about 20% of children are malnourished.6,7 Benin is no exception, as the prevalence of stunting, wasting, and underweight was 37%, 5%, and 17%, respectively, among children aged 6 to 59 months in the 2006 Benin Demographic and Health Survey (DHS),8 while 9% of women had chronic energy deficiency in the 2012 DHS.9 Greater rates were observed in rural areas where stunting was found in 40% of children, underweight in 19%, and wasting in 5%, while 10% of women had chronic energy deficiency.8,9 Additionally, Beninese women and children have a limited dietary diversity score (DDS), with diets predominately composed of starchy staples with little or no animal products and few fresh fruits and vegetables.10,11 Government, United Nation agencies, and nongovernmental organizations in Benin recognize that the state of maternal and child undernutrition requires multiple types of interventions.12
However, women’s low empowerment status in Benin can hinder the improvement in women’s and children’s undernutrition. Indeed, although females accounted for 47% of the economically active population in 2014,13 social and civil legislation is strongly influenced by tradition and customs, as women continue to be required to seek their husband’s authorization in certain areas such as family planning or health services.14 Rural women provided labor to the families’ commercial plots, were responsible for household food production and processing, and also had to work in the cooperative structures set up by the state in addition to their household tasks.14 In a more recent study of productivity differences by gender in central Benin, researchers noted that female rice farmers are particularly discriminated against with regard to access to land and equipment, resulting in significant negative impacts on their productivity and income.15 As in other areas of West Africa, women also have the responsibility of caring for children and preparing food for the household,16 but they may be vulnerable to food insecurity owing to unequal intrahousehold food distribution and their willingness to forego meals in favor of children during times of scarcity.17 Finally, no study to date has examined links between women’s empowerment and nutrition in Benin.
In addition, the evidence backing the effect of women’s empowerment on maternal and child undernutrition is inconsistent.18 Using the Women’s Empowerment in Agriculture Index (WEAI), Malapit et al19 reported positive and significant association between women’s group (WG) membership, control over income, overall empowerment, and women’s health (as measured by body mass index [BMI] and DDS) in Nepal. However, in Ghana, women’s aggregate empowerment and participation in credit decisions were positively correlated with women’s DDS, but not BMI.20 Mixed findings were also observed between women’s empowerment and child anthropometry. Moestue et al21 found a positive association between maternal involvement in social groups and length-for-age z score of 1-year-old children, but De Silva and Harpham22showed a negative association in 6- to 18-month-old children. Shroff et al23 found positive association between decision-making and child weight-for-age z score (WAZ), but Begum and Sen’s24 analysis of Bangladesh DHS data did not reveal any significant associations. Therefore, information about which domains of WE are associated with nutritional status is limited,20 and this lack of knowledge constrains the set of policy options that can be used to empower women and improve nutrition.
In addition to a limited set of studies in SSA, examinations of the effects of WE on nutrition outcomes are constrained due to interstudy differences in population characteristics, settings, or methods/conceptualizations of WE.25–27 For example, despite recognition of the complex, multidimensional, and culturally defined nature and influence of empowerment on nutrition,20,26,28,29 only a few studies considered the multidimensional structure of empowerment domains in Africa or examined the varied relationships between each measure of WE and maternal and child nutrition status.30,31 Furthermore, in 2012, the International Food Policy Research Institute developed WEAI constructed from 5 prespecified domains of empowerment,32which may not be equally relevant in all areas. In contrast, in 2015, the United Nations adopted the Sustainable Development Goals (SDG), but the specific indicators for the SDG empowerment targets are largely equality metrics.33 To address the need for multidimensional and contextual examinations of WE and its influence on maternal and child health outcomes, we draw from the concepts put forward in the WEAI and the SDGs but took an approach more along the lines of the World Bank which gathers indicators, both equity and empowerment related, that can be used in contextually appropriate ways.34 The aims of this study were therefore to first explore the structure and domains of WE in Kalalé district of northern Benin and then to examine the effects of these constructs on nutritional status of women and their children in the region.
By using high-res images taken by the latest generation of compact satellites, Stanford scientists have developed a new capability for estimating crop yields from space. Measuring yields could improve productivity and eventually reduce hunger.
Stanford researchers have developed a new way to estimate crop yields from space, using high-resolution photos snapped by a new wave of compact satellites.
The approach, detailed in the Feb. 13 issue of Proceedings of the National Academy of Sciences, could help estimate agricultural productivity and test intervention strategies in poor regions of the world where data are currently extremely scarce.
“Improving agricultural productivity is going to be one of the main ways to reduce hunger and improve livelihoods in poor parts of the world,” said study-coauthor Marshall Burke, an assistant professor of Earth system science at Stanford’s School of Earth, Energy & Environmental Sciences. “But to improve agricultural productivity, we first have to measure it, and unfortunately this isn’t done on most farms around the world.”
Improved satellites
Earth-observing satellites have been around for over three decades, but most of the imagery they capture has not been of high enough resolution to visualize the very small agricultural fields typical in developing countries. Recently, however, satellites have shrunk in both size and cost while simultaneously improving in resolution, and today there are several companies competing to launch into space refrigerator- and shoebox-sized satellites that take high-resolution images of Earth.
“You can get lots of them up there, all capturing very small parts of the land surface at very high resolution,” said study-coauthor David Lobell, an associate professor of Earth system science. “Any one satellite doesn’t give you very much information, but the constellation of them actually means that you’re covering most of the world at very high resolution and at very low cost. That’s something we never really had even a few years ago.”
Accurate predictions
In the new study, Burke and Lobell set out to test whether the images from this new wave of satellites are good enough to reliably estimate crop yields. The pair focused on an area in western Kenya where there are a lot of smallholder farmers that grow maize, or corn, on small, half-acre or one-acre lots. “This was an area where there was already a lot of existing field work,” Lobell said. “It was an ideal site to test our approach.”
The scientists compared two different methods for estimating agricultural productivity yields using satellite imagery. The first approach involved “ground truthing,” or conducting ground surveys to check the accuracy of yield estimates calculated using the satellite data, which was donated by the company Terra Bella. For this part of the study, Burke and his field team spent weeks conducting house-to-house surveys with his staff, talking to farmers and gathering information about individual farms.
“We get a lot of great data, but it’s incredibly time consuming and fairly expensive, meaning we can only survey at most a thousand or so farmers during one campaign,” said Burke, who is also a Center Fellow at the Stanford Woods Institute for the Environment. “If you want to scale up our operation, you don’t want to have to recollect ground survey data everywhere in the world.”
For this reason, the team also tested an alternative “uncalibrated” approach that did not depend on ground survey data to make predictions. Instead, it uses a computer model of how crops grow, along with information on local weather conditions, to help interpret the satellite imagery and predict yields.
“Just combining the imagery with computer-based crop models allows us to make surprisingly accurate predictions, based on the imagery alone, of actual productivity on the field,” Burke said.
The researchers have plans to scale up their project and test their approach across more of Africa. “Our aspiration is to make accurate seasonal predictions of agricultural productivity for every corner of sub-Saharan Africa,” Burke said. “Our hope is that this approach we’ve developed using satellites could allow a huge leap in in our ability to understand and improve agricultural productivity in poor parts of the world.”
Funding for the study, titled “Satellite-based assessment of yield variation and its determinants in smallholder African systems,” was provided by AidData at the College of William and Mary, the USAID Global Development Lab and the Center for Effective Global Action.
Objective: To identify the magnitude of anaemia and deficiencies of Fe (ID) and vitamin A (VAD) and their associated factors among rural women and children.
Design: Cross-sectional, comprising a household, health and nutrition survey and determination of Hb, biochemical (serum concentrations of ferritin, retinol, C-reactive protein and α1-acid glycoprotein) and anthropometric parameters. Multivariate logistic regression examined associations of various factors with anaemia and micronutrient deficiencies.
Setting: Kalalé district, northern Benin. Subjects: Mother–child pairs (n 767): non-pregnant women of reproductive age (15–49 years) and children 6–59 months old.
Results: In women, the overall prevalence of anaemia, ID, Fe-deficiency anaemia (IDA) and VAD was 47·7, 18·3, 11·3 and 17·7%, respectively. A similar pattern for anaemia (82·4 %), ID (23·6%) and IDA (21·2%) was observed among children, while VAD was greater at 33·6%. Greater risk of anaemia, ID and VAD was found for low maternal education, maternal farming activity, maternal health status, low food diversity, lack of fruits and vegetables consumption, low protein foods consumption, high infection, anthropometric deficits, large family size, poor sanitary conditions and low socio-economic status. Strong differences were also observed by ethnicity, women’s group participation and source of information. Finally, age had a significant effect in children, with those aged 6–23 months having the highest risk for anaemia and those aged 12–23 months at risk for ID and IDA.
Conclusions: Anaemia, ID and VAD were high among rural women and their children in northern Benin, although ID accounted for a small proportion of anaemia. Multicentre studies in various parts of the country are needed to substantiate the present results, so that appropriate and beneficial strategies for micronutrient supplementation and interventions to improve food diversity and quality can be planned.
Recent reviews of dietary intake data from Benin showed that recommended daily intakes of key micronutrients, such as vitamin A and Fe, were not met( 1 – 4 ). At the sub-national level, in northern Benin, macronutrient intakes are also too low( 5 , 6 ). Lack of dietary diversity is a particularly severe problem in Benin where diets are based predominantly on starchy staples with little or no animal products and few fresh fruits and vegetables( 1 , 2 , 7 ). According to the last Demographic and Health Survey (DHS) carried out in 2012, only 28 % of rural children satisfied the minimum diversity criterion of eating at least four out of seven food groups and 14 % consumed the minimum acceptable diet. In addition, the prevalence of stunting, wasting and underweight was respectively 40, 5 and 19 % among children aged 6–59 months, while 9 % of rural women had chronic energy deficiency (BMI<18·5 kg/m2)( 7 ). To improve the nutrition situation of women and children in Benin, the Ministry of Health has undertaken several interventions through its Strategic Plan for Food and Nutrition Development, comprising the supplementation of three major nutrients (vitamin A, Fe and iodine) and other promotive activities, such as exclusive breast-feeding, appropriate complementary feeding, and improved maternal and child nutrition( 8 ).
Despite the efforts of the line ministry and its stakeholders, Beninese women aged 15–49 years (41 %) and children aged 6–59 months (58 %) are significantly affected by anaemia with greater prevalence in rural areas( 7 ). Other nutritional data, such as Fe and vitamin A status, however, were not documented in the Benin 2012 DHS. In the 2006 Benin DHS, vitamin A deficiency (VAD) as measured by serum retinol <20 μg/dl was estimated to affect 66·0 % of children aged 12–71 months while the prevalence of night blindness was 11·8 % among pregnant women( 9 ). The few studies of micronutrient deficiencies among rural populations were conducted in specific localized groups and revealed greater prevalence rates of VAD among 12–71 month-old children (82 %) and pregnant women (14 %) in northern Benin( 9 ), while 33–49 % of children under 5 years of age were Fe deficient( 10 ). Until now, to our knowledge, there have been no population-based studies permitting generalization about the epidemiology of anaemia and its principal determinants in non-pregnant women, despite the problem being among the top ten causes of morbidities in the country( 11 , 12 ). The only study that identified anaemia risk factors among Beninese children was carried out in 2007 and found that incomplete immunization, stunted growth, recent infection, absence of a bednet, low household living standard, low maternal education and low community development index increased the risk of anaemia( 13 ).
As such, identifying the magnitude of anaemia and deficiencies of Fe and vitamin A and their determinants in high-risk groups, such as women of childbearing age and children, is essential for evidence-based intervention modalities, particularly in rural areas, where women and children may suffer not only from micronutrient deficiencies but also a shortage of food( 14 ). The present study is a very important step forward to avail of evidence-based information on the distribution of anaemia and micronutrient deficits and their predisposing diet and health factors among rural women and children in northern Benin. It will help understand the contemporary health profile of the rural populations of the study area in terms of dietary, socio-economic and environmental factors.
Temperature data are commonly used to estimate the sensitivity of many societally relevant outcomes, including crop yields, mortality, and economic output, to ongoing climate changes. In many tropical regions, however, temperature measures are often very sparse and unreliable, limiting our ability to understand climate change impacts. Here we evaluate satellite measures of near-surface temperature (Ts) as an alternative to traditional air temperatures (Ta) from weather stations, and in particular their ability to replace Ta in econometric estimation of climate response functions. We show that for maize yields in Africa and the United States, and for economic output in the United States, regressions that use Ts produce very similar results to those using Ta, despite the fact that daily correlation between the two temperature measures is often low. Moreover, for regions such as Africa with poor station coverage, we find that models with Ts outperform models with Ta, as measured by both R2 values and out-of-sample prediction error. The results indicate that Ts can be used to study climate impacts in areas with limited station data, and should enable faster progress in assessing risks and adaptation needs in these regions.
It is widely recognized that an “African green revolution” will require greater use of inorganic fertilizers. Often-made comparisons note that fertilizer use rates in Africa are just 10–20% of those in Asia, Europe and the Americas. Most attempts to explain relatively low-adoption of fertilizer assume yield responses to inorganic fertilization warrant higher application rates and hypothesize that observed use rates are limited by market-based factors. Another explanation may be that application rates are low because African yields are less responsive to inorganic fertilizer than yields in other regions, and less responsive than analysts perceive. Examining the case of Zambia, we evaluate whether yield response to fertilizers could explain adoption and application rates. A model of yield response is constructed and a combination of estimators is employed to mitigate potential biases related to correlation between fertilizer use and unobserved heterogeneity as well as stochastic shocks. Results indicate higher fertilization rates would be marginally profitable or unprofitable in many cases given commercial fertilizer and maize prices. Phosphoric fertilizer is particularly unprofitable on acidic soils, which are common in Zambia and other areas of sub-Saharan Africa. We propose feasible recommendations for diversifying the current government strategy to enhance crop productivity.
This Global Food Security Strategy presents an integrated whole-of-government strategy and agency-specific implementation plans as required by the Global Food Security Act of 2016 (GFSA).
"Right now, the world is closer than ever before to ending global hunger, undernutrition, and extreme poverty, but significant challenges and opportunities remain, including urbanization, gender inequality, instability and conflict, the effects of a changing climate, and environmental degradation. Despite our collective progress in global food security and nutrition over recent years, a projected 702 million people still live in extreme poverty, nearly 800 million people around the world are chronically undernourished, and 159 million children under five are stunted. Food security is not just an economic and humanitarian issue; it is also a matter of security, as growing concentrations of poverty and hunger leave countries and communities vulnerable to increased instability, conflict, and violence." From the USAID Oct. 3 release.
You can read more and download the pdf of the Global Food Security Strategy at the USAID website.
Policy-makers in the world's poorest countries are often forced to make decisions based on limited data. Consider Angola, which recently conducted its first postcolonial census. In the 44 years that elapsed between the prior census and the recent one, the country's population grew from 5.6 million to 24.3 million, and the country experienced a protracted civil war that displaced millions of citizens. In situations where reliable survey data are missing or out of date, a novel line of research offers promising alternatives. On page 790 of this issue, Jean et al.(1) apply recent advances in machine learning to high-resolution satellite imagery to accurately measure regional poverty in Africa.
