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.

The future trajectory of crop yields in the United States will influence food supply and land use worldwide. We examine maize and soybean yields for 2000–2015 in the Midwestern U.S. using a new satellite-based dataset on crop yields at 30m resolution. We quantify heterogeneity both within and between fields, and find that the difference between average and top yielding fields is typically below 30% for both maize and soybean, as expected in advanced agricultural regions. In most counties, within-field heterogeneity is at least half as large as overall heterogeneity, illustrating the importance of non-management factors such as soil and landscape position. Surprisingly, we find that yield heterogeneity is rising in maize, both between and within fields, with average yield differences between the best and worst soils more than doubling since 2000. Heterogeneity trends were insignificant for soybean. The findings are consistent both with recent adoption of precision agriculture technologies and with recent trends toward denser sowing in maize, which disproportionately raise yields on better soils. The results imply that yield gains in the region are increasingly derived from the more productive land, and that sub-field precision management of nutrients and other inputs is increasingly warranted.

The potential impacts of climate change on crop productivity are of widespread interest to those concerned with addressing climate change and improving global food security. Two common approaches to assess these impacts are process-based simulation models, which attempt to represent key dynamic processes affecting crop yields, and statistical models, which estimate functional relationships between historical observations of weather and yields. Examples of both approaches are increasingly found in the scientific literature, although often published in different disciplinary journals. Here we compare published sensitivities to changes in temperature, precipitation, carbon dioxide (CO2), and ozone from each approach for the subset of crops, locations, and climate scenarios for which both have been applied. Despite a common perception that statistical models are more pessimistic, we find no systematic differences between the predicted sensitivities to warming from process-based and statistical models up to +2 °C, with limited evidence at higher levels of warming. For precipitation, there are many reasons why estimates could be expected to differ, but few estimates exist to develop robust comparisons, and precipitation changes are rarely the dominant factor for predicting impacts given the prominent role of temperature, CO2, and ozone changes. A common difference between process-based and statistical studies is that the former tend to include the effects of CO2 increases that accompany warming, whereas statistical models typically do not. Major needs moving forward include incorporating CO2 effects into statistical studies, improving both approaches' treatment of ozone, and increasing the use of both methods within the same study. At the same time, those who fund or use crop model projections should understand that in the short-term, both approaches when done well are likely to provide similar estimates of warming impacts, with statistical models generally requiring fewer resources to produce robust estimates, especially when applied to crops beyond the major grains.