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Accurate measurements of crop production in smallholder farming systems are critical to the understanding of yield constraints and, thus, setting the appropriate agronomic investments and policies for improving food security and reducing poverty. Nevertheless, mapping the yields of smallholder farms is challenging because of factors such as small field sizes and heterogeneous landscapes. Recent advances in fine-resolution satellite sensors offer promise for monitoring and characterizing the production of smallholder farms. In this study, we investigated the utility of different sensors, including the commercial Skysat and RapidEye satellites and the publicly accessible Sentinel-2, for tracking smallholder maize yield variation throughout a ~40,000 km2western Kenya region. We tested the potential of two types of multiple regression models for predicting yield: (i) a “calibrated model”, which required ground-measured yield and weather data for calibration, and (ii) an “uncalibrated model”, which used a process-based crop model to generate daily vegetation index and end-of-season biomass and/or yield as pseudo training samples. Model performance was evaluated at the field, division, and district scales using a combination of farmer surveys and crop cuts across thousands of smallholder plots in western Kenya. Results show that the “calibrated” approach captured a significant fraction (R2 between 0.3 and 0.6) of yield variations at aggregated administrative units (e.g., districts and divisions), while the “uncalibrated” approach performed only slightly worse. For both approaches, we found that predictions using the MERIS Terrestrial Chlorophyll Index (MTCI), which included the red edge band available in RapidEye and Sentinel-2, were superior to those made using other commonly used vegetation indices. We also found that multiple refinements to the crop simulation procedures led to improvements in the “uncalibrated” approach. We identified the prevalence of small field sizes, intercropping management, and cloudy satellite images as major challenges to improve the model performance. Overall, this study suggested that high-resolution satellite imagery can be used to map yields of smallholder farming systems, and the methodology presented in this study could serve as a good foundation for other smallholder farming systems in the world.

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David Lobell
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Walter Falcon, the Helen Farnsworth Professor of International Agricultural Policy in Economics (emeritus), writes from an unusual perspective. During the academic year he serves as a senior fellow with the Freeman Spogli Institute for International Studies and the Stanford Woods Institute for the Environment. He spends the summers on his family farm near Marion, Iowa. He returns to campus each year with reflections on the challenges and rewards of faming life in his "Almanac Report." Falcon is former deputy director of the Center on Food Security and the Environment. 

September means that it is time again for my annual Iowa farm report, the sixth edition in this series. As readers of prior postings will remember, my day job is Professor of International Agricultural Policy at Stanford University. However, my wife and I also own a 200-acre farm near Marion, Iowa, where we spend summers watching over corn, soybean, and alfalfa fields, and gazing out at a growing cow-calf herd.

After all these years, it is still difficult for me to describe the differences in pace, politics, and age structure in Iowa relative to California. I am now 81, and at Stanford I feel ancient; in Iowa, I am just one of the boys, since 41 percent of farm owners are 75 or older. 

This summer’s weather, especially rainfall, has been almost perfect for crops in our area. Although western Iowa and the northern Great Plains experienced drought, we are expecting record yields of both corn and soybeans, possibly reaching 225 and 55 bushels per acre, respectively. Unfortunately, December corn prices are only about $3.50 per bushel. This level is just half of what it was five years ago. The old adage that farmers should raise more hell and less corn has taken on new meaning. Average prices of Iowa farmland have slipped from about $9,000 to $7,000 per acre during the past five years (though still remarkably high relative to the $2,000 that prevailed in 2000). Renters of land are also feeling price pressures. Average cash rents have fallen about 10 percent over the past two years and now average about $230 per acre in our part of the state.

The difference between the “almost perfect” weather described above and an absolute disaster measured about three miles this year. During much of June, our area was hit with very unstable air. The worst episode was on June 28 when an EF-2 tornado came barreling right at our farm. The picture below was taken out of the west window before we scampered down to the safe room in our basement. At the last minute, the tornado veered slightly, going just between our farm and the bustling county fair (also shown) four miles to the north. The tornado then touched down a few miles to our east, crushed the historic Brown farm, and mostly destroyed the small town of Prairieburg. Amazingly, both our farm and the fair were completely spared except for a few broken tree limbs.

There is an interesting footnote on risk to this story. When I show the tornado picture to my California friends they cannot understand why I would live in such a risky place; however, my Iowa friends frequently remark that they cannot comprehend how I can live in the risky state of California with its earthquakes. Risk, like beauty, is sometimes in the eyes of the beholder.

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Photo: Karla Hogan (just to the west of our house)

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Photo: David Roll (fair)

Not everything from the sky was bad this year, although one other episode also turned out to be a non-event. Our region was to have had 90 percent coverage during the eclipse. We were completely socked in by clouds, however, and could see absolutely nothing on this historic occasion. On the other hand, airplane applications of fungicides and pesticides were greater than I can ever remember. A combination of new weeds to the region (water hemp and Palmer amaranth) and growing weed resistance in Roundup-ready soybeans are causing increased problems for farmers. As for the applicators, I never cease to be impressed by the skill (craziness?) of those pilots who fly at 50 feet or less, dodging power lines, while managing controls of the spray equipment as well as the plane.

Describing another “sky” event at the farm requires that I first remove considerable amounts of egg from my face. Stanford sits in the middle of Silicon Valley, and over the past decade perhaps a dozen firms have visited my office regarding agricultural applications. Particularly in the earlier years, I assured them that precision agriculture was overrated and that drones would never have a place in agriculture. Those were not among my better forecasts!

My conjecture is that more than 90 percent of the fields in Iowa have now been laid out with GPS grid maps that permit automatic steering of tractors and harvesters. Famers rarely steer or look ahead; rather they mostly look backward at planters and other equipment. From gauge-filled cabs that resemble cockpits, farmers monitor yields, seed-planting rates, and fertilizer applications in ways that produce field maps for each 10x10 meter sub-plot. In some sense, producers already have more data than they can assimilate, so one could reasonably ask, can drones really help? It turns out that they can, and they can do so for only a small investment.

The high quality drone shown below, complete with two 30-minute batteries, costs about $2,000, with quality determined mostly by the precision of its camera. (That sum may not be petty cash, but it is not in the same league as a $600,000 combine-harvester either.) For mapping work, drones are connected to an off-site service center that costs about $100 per month. They produce video in real-time, snap images as well, and are proving useful in determining if the number of emergent plants (really the lack of plants) on areas that may require replanting; in checking fields for “wet spots” after rains for indicators of future tiling needs; and watching the cow herd from the back porch, as is also shown below. Applications are ever underway that can take the temperatures of animals via intricate heat-sensing devices.

Once corn grows to chest high, it is impossible to walk or drive through fields to isolate areas with particular weed problems or to view pest damage. These drones are also tied in with GPS systems, so that entire fields can be mapped “automatically” at very high resolution. A 100-acre field can be mapped within the 25 minutes of a single battery-powered flight. (The further good news is that the machines are smart enough to return to their takeoff point before losing power.) Drones seem to be here to stay because they save labor, generate useful data, and help improve farm-management practices

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Photo: Margaret Meythaler (drone demo)

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Photo: Mitch Meythaler (field map by drone—August 5th corn plant health (potential yield); red is low, green is high; dark red areas are waterways and fence rows; sandy soils show red to the north, and red streaks indicate water erosion.)

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Photo: Mitch Meythaler (part of cow herd by drone)

Drones, however, have not affected my image of the old limestone “restaurant” where neighborhood farmers gather about 8 a.m. Most of the “action” is around the big table where truly terrible coffee is self-served. Payment is on the honor system, since there is rarely a waitress around. Maybe it was just my imagination, but farmer discussions seemed more somber and narrower this year, despite the good weather. Perhaps it is the third successive year of low prices, or the uncertainty about corn exports to Mexico and China, or the general chaos in Washington, D.C. Perhaps it also reflects the ethnic and religious homogeneity of the local population. Stanford’s undergraduate student body, for example, is only 45 percent white. However, during the course of all of my personal interactions during four months in Iowa, I encountered only three minority persons – two medical doctors at the local hospital whose families came from India, and one African-American. Homogeneity and diversity make for different worldviews and different conversations – neither being necessarily better or worse, but certainly different.

The most animated discussion I participated in concerned technology gone astray. Large chemical companies, such as Monsanto and DuPont, have purchased many seed companies, thereby assuring markets for their particular brand of chemicals. In the case of corn, for example, a particular GMO variety has been bred such that, when sprayed by a particular brand, all plants are killed except for the corn. Spraying these herbicides requires training and specialized equipment, and herbicide applications are frequently hired – typically for about $8 per acre, plus the cost of chemicals. As part of the new technology, the specific corn variety and the particular brand of spray are entered into the software that then uses GPS maps to control the actual spraying. But what happens when the hired vendor, in this case a local co-operative, enters the wrong variety into the computer, as happened to two of our neighbors? The spray killed the weeds, but it also killed the corn. At that point, it was too late in the season to replant. These fields were sorry looking messes, and the debate still continues as to who is liable and for how much.

Another hot button item this year centered on the purchase of farmland for housing developments. Farmers almost universally regard such investments as unwarranted intrusions into their space. (The proposed relocation of the county landfill generated even more vehement responses.) The housing argument typically took two forms: more houses mean more children and therefore higher property taxes for schools; and theses houses take “all of the good Iowa farmland”, which is needed to feed the world. There is some correctness to the former argument, but as to the latter assertion – not so much. I argued that for the last five years, total acres of corn and soybeans in Iowa had trended upward rather than downward, and that furthermore, both current and future problems of hunger were driven primarily by poverty, not the lack of corn and soybean supplies. This comment was not regarded as being helpful to the coffee-crowd discussion!

Politics are rarely discussed in these conversations – at least in my presence. However, I sense several things. Although Iowans voted for Donald Trump, I think it was because they generally disliked him less than they disliked Hilary Clinton. Most of my neighbors now simply seem embarrassed by what is happening. My California friends continue to ask me about what Iowans think and what they believe in. There is not much open discussion about these matters either, which made a July poll of the Des Moines Register all the more interesting. When given a choice of 17 options of whom they believed, the top six in order were: the armed forces, God, the Iowa Department of Natural Resources, local schools, the Farm Bureau, and the FBI. The three options they believed in least, also in order from the bottom, were the U.S. Congress, the media, and the President. I do not know what a comparable survey in California would look like, but I believe that it would be considerably different.

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Photo: Daryl Hamey (2016 calves — the red heifer is now bred, and the black baldy steer is now in the freezer!)

At the end of last year’s report, I left readers hanging with the question of whether our seemingly disinterested yearling bull would produce a crop of calves. It turns out that my fears were misplaced, and that he was indeed working the night shift. Our problems were in fact on the female side—our best cow did not conceive, and another of our good cows produced a sickly calf that ended up being bottle-fed by my wife. To compete the story, we again rented a red Angus bull – the same one in fact that we had last year – and he is now a much larger two-year old. But he is still no competition for “Upward”, the strangely named Angus super-bull winner at the Iowa State Fair that weighed 2,798 pounds.

I leave in a week for yet another year of teaching and research at Stanford. I have only a limited number of lectures scheduled, and most of my time will be directed toward research on the growing importance of tropical vegetable oils, particularly from oil palm in Indonesia. Palm oil has recently replaced soybean oil as the most important in world commerce, so even when I am in California, there remain important and unusual Iowa connections.

My neighbor says that I must leave Iowa soon – because of the upcoming weather. In true Almanac fashion, he confidently predicts an early and harsh winter ahead. His evidence – the deer are weaning their young at an early date, and are busy consuming great quantities of corn from our fields, so as to layer on fat for the winter. We might even be able to see the extent of their gluttony on our autumn yield maps!

 

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Large-scale crop monitoring and yield estimation are important for both scientific research and practical applications. Satellite remote sensing provides an effective means for regional and global cropland monitoring, particularly in data-sparse regions that lack reliable ground observations and reporting. The conventional approach of using visible and near-infrared based vegetation index (VI) observations has prevailed for decades since the onset of the global satellite era. However, other satellite data encompass diverse spectral ranges that may contain complementary information on crop growth and yield, but have been largely understudied and underused. Here we conducted one of the first attempts at synergizing multiple satellite data spanning a diverse spectral range, including visible, near-infrared, thermal and microwave, into one framework to estimate crop yield for the U.S. Corn Belt, one of the world's most important food baskets. Overall, using satellite data from various spectral bands significantly improves regional crop yield predictions. The additional use of ancillary climate data (e.g. precipitation and temperature) further improves model skill, in part because the crop reproductive stage related to harvest index is highly sensitive to environmental stresses but they are not fully captured by the satellite data used in our study. We conclude that using satellite data across various spectral ranges can improve monitoring of large-scale crop growth and yield beyond what can be achieved from individual sensors. These results also inform the synergistic use and development of current and next generation satellite missions, including NASA ECOSTRESS, SMAP, and OCO-2, for agricultural applications.

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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.
 
This brief is based on findings from the papers “Satellite-based assessment of yield variation and its determinants in smallholder African systems,” published in Proceedings of the National Academy of Sciences in 2017 and “Combining satellite imagery and machine learning to predict poverty,” published in Science in 2016.
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Casey Maue, a PhD student in the Emmett Interdisciplinary Program in Environment and Resources alogn with Woods Institute Senior Fellow, Erica Plambeck, spent time this spring examining the oil palm supply chain in Ghana. Casey is a 3rd year PhD student and is advised by FSE Director Roz Naylor and FSE Senior Fellow Marshall Burke. Casey's research focuses on the economic dimensions of agricultural development in Sub-Saharan Africa, and the economic impacts of climate change on the agricultural sector. In his dissertation chapter focused on development in the oil palm industry in Ghana, Casey is examining how the provision of informal financial services (such as access to trade credit or informal savings) by different buyers (processors) in the oil palm supply chain determines farmers' output-market decisions, and how relational contracts between farmers and processors that provide access to these informal services can be leveraged to increase supply chain productivity, and the welfare of smallholder oil palm farmers.

Casey and Erica traveled to the municipality of Juaben, located in the Ashanti region of central Ghana, and conducted focus group discussions and in-person interviews with numerous stakeholders working in oil palm. They visited with oil palm farmers and buyers of their fruit – palm oil processors. There are two kinds of palm oil processors in the Ghanaian supply chain, small-scale artisanal mills and larger industrial mills, which compete with each other for the fruit produced by smallholder oil palm farmers. Through this research, Casey and Erica sought to better understand the economic forces that affect a farmer’s decision to sell their fruit to an industrial versus an artisanal mill. Doing so will provide insights that processors can use to design more effective incentives for their suppliers that will increase the quantity and reliability of fruit delivered to them. Fostering more consistent, and trusting relationships between farmers and processors is key to increasing the productivity and profitability of enterprises all along the supply chain, to increasing food security for poor oil palm farmers, and to promoting effective private governance of oil palm’s environmental impacts. 

 

 

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Satellite-derived land cover maps play an important role in many applications, including monitoring of smallholder-dominated agricultural landscapes. New cloud-based computing platforms and satellite sensors offer opportunities for generating land cover maps designed to meet the spatial and temporal requirements of specific applications. Such maps can be a significant improvement compared to existing products, which tend to be coarser than 300 m, are often not representative of areas with fast-paced land use change, and have a fixed set of cover classes. Here, we present two approaches for land cover classification using the Landsat archive within Google Earth Engine. Random forest classification was performed with (1) season-based composites, where median values of individual bands and vegetation indices were generated from four years for each of four seasons, and (2) metric-based composites, where different quantiles were computed for the entire four-year period. These approaches were tested for six land cover types spanning over 18,000 locations in Zambia, with ground “truth” determined by visual inspection of high-resolution imagery from Google Earth. The methods were trained on 30% of these points and tested on the remaining 70%, and results were also compared with existing land cover products. Overall accuracies of about 89% were achieved for the season- and metric-based approaches for individual classes, with 93%and 94% accuracy for distinguishing cropland from non-cropland. For the latter task, the existing Globeland30 dataset based on Landsat had much lower accuracies (around 77% on average), as did existing cover maps at coarser resolutions. Overall, the results support the use of either season or metric-based classification approaches. Both produce better results than those obtained from previous classifiers, which supports a general paradigm shift away from dependence on standard static products and towards custom generation of on-demand cover maps designed to fulfill the needs of each specific application.

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David Lobell
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One of the greatest challenges in monitoring food security is to provide reliable crop yield information that is temporally consistent and spatially scalable. An ideal yield dataset would not only extend globally and across multiple years, but would also have enough spatial granularity to characterize productivity at the field and subfield level. Rapid increases in satellite data acquisition and platforms such as Google Earth Engine that can efficiently access and process vast archives of new and historical data offer an opportunity to map yields globally, but require efficient and robust algorithms to combine various data streams into yield estimates. We recently introduced a Scalable satellite-based Crop Yield Mapper (SCYM) that combines crop models simulations with imagery and weather data to generate 30 m resolution yield estimates without the need for ground calibration. In this study, we tested new large-scale implementations of SCYM, focusing on three regions with varying crops, field sizes and landscape heterogeneity: maize in the U.S. corn belt (390,000 km2), maize in Southern Zambia (86,000 km2), and wheat in northern India (450,000 km2). As a benchmark, we also tested a simpler empirical approach (PEAKVI) that relates yield to the peak value of a time series of spatially aggregated vegetation indices, similar to methods used in current operational monitoring. Both SCYM and PEAKVI were applied to data from all Landsat's sensors and MODIS for more than a decade in each region, and evaluated against ground-based estimates at the finest available administrative level (e.g., counties in the U.S.). We found consistently high correlations (R2 ≥ 0.5) between the spatial pattern of ground- and satellite-based estimates in both U.S. maize and India wheat, with small differences between methods and source of satellite data. In the U.S., SCYM outperformed PEAKVI in tracking temporal yield variations, likely owing to its explicit consideration of weather. In India, both methods failed to track temporal yield changes, with various possible explanations discussed. In Zambia, the PEAKVI approach applied to MODIS tracked yield variations much better (R2 > 0.5) than any other yield estimate, likely because the frequent cloud cover in this region confounds the other approaches. Overall, this study demonstrates successful approaches to yield estimation in each region, and illustrates the importance of distinguishing between accuracy for spatial and temporal variation. The 30 m resolution of Landsat-based SCYM does not appear to offer large benefits for tracking aggregate yields, but enables finer scale analyses than possible with the other approaches.

 

 

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Global biodiesel production grew by 23% per annum between 2005 and 2015, leading to a seven-fold expansion of the sector in a single decade. Rapid development in the biodiesel sector corresponded to high crude oil prices, but since mid-2014, oil prices have fallen dramatically. This paper assesses the economic and policy factors that underpinned the expansion of biodiesel, and examines the near-term prospects for biodiesel growth under conditions of low fossil fuel prices. We show that the dramatic increase in biodiesel output would not have occurred without strong policy directives, subsidies, and trade policies designed to support agricultural interests, rural economic development, energy security, and climate targets. Given the important role of policy—and the political context within each country that shapes policy objectives, instruments, and priorities—case studies of major biodiesel producing countries are presented as a key element of our analysis. Although the narrative of biodiesel policies in most countries conveys win-win outcomes across multiple objectives, the case studies show that support of particular constituents, such as farm lobbies or energy interests, often dominates policy action and generates large social costs. Looking out to 2020, the paper highlights risks to the biodiesel industry associated with ongoing regulatory and market uncertainties.

 

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Renewable and Sustainable Energy Reviews
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Rosamond L. Naylor
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Women empowerment (WE) is increasingly viewed as an important strategy to reduce maternal and child undernutrition,13 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.2527 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.

 

 

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Food and Nutrition Bulletin
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Rosamond L. Naylor

Although sheep are only one of the important domesticates exploited in many parts of the world, it has played a near-paradigmatic role throughout the emergence and spread of European civilization. Domestic sheep and goat unambiguously originate from Southwest Asia where their wild ancestors live. Therefore sheep distributions across Europe represent an element of evident diffusion in the otherwise complex neolithization process. The numerical increase in sheep remains can be spectacular at Early Neolithic sites in Central Europe, even in habitats less than favorable for sheep. In various instances mutton outcompeted locally available pork in the diet as shown by animal remains from archaeological sites across Eurasia. Reasons for this trend seem to be diverse, ranging from greater pastoral mobility through secondary products (wool and dairy) to side effects of religious regulations such as the Iron Age taboo imposed on pork first documented in Judaism. Concomitant strict regulations concerning the “proper” way of slaughtering livestock link the increased dietary importance of sheep to the emergence of metallurgy, i.e. availability of quality blades.

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László Bartosiewicz has worked as an archaeozoologist since 1979. He has studied animal-human relationships during various time periods in several countries of Europe and some in the Near East as well as South America. His research often has a cultural anthropological focus viewing animals as material culture. Recently he has specialized in animal palaeopathology. He published three books and over 350 academic papers. Following teaching positions at the Universities of Budapest (Hungary) and Edinburgh (UK), he currently heads the Osteoarchaeological Research Laboratory at Stockholm University (Sweden). He was twice elected president of the International Council for Archaeozoology (2006–2014).

 

 

This event is part of the Origins of Europe Series and is sponsored by the Stanford Archaeology Center and co-sponsored by The Europe Center.

Archaeology Center, Building 500

László Bartosiewicz Speaker Stockholm University
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