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Food systems are at the center of a brewing storm consisting of a rapidly changing climate, rising hunger and malnutrition, and significant social inequities. At the same time, there are vast opportunities to ensure that food systems produce healthy and safe food in equitable ways that promote environmental sustainability, especially if the world can come together at the UN Food Systems Summit in late 2021 and make strong and binding commitments toward food system transformation. The NIH-funded Nutrition Obesity Research Center at Harvard and the Harvard Medical School Division of Nutrition held their 22nd annual Harvard Nutrition Obesity Symposium entitled "Global Food Systems and Sustainable Nutrition in the 21st Century" in June 2021. This article presents a synthesis of this symposium and highlights the importance of food systems to addressing the burden of malnutrition and noncommunicable diseases, climate change, and the related economic and social inequities. Transformation of food systems is possible, and the nutrition and health communities have a significant role to play in this transformative process. [ABSTRACT FROM AUTHOR]

• Women face unique risks associated with food insecurity and climate change. • Changing childbearing may be an explicit livelihood strategy. • Women respond to growing season conditions by adjusting fertility behaviors. • Ensuring availability of diverse family planning services is a priority. Agricultural production and household food security are hypothesized to play a critical role connecting climate change to downstream effects on women's health, especially in communities dependent on rainfed agriculture. Seasonal variability in agriculture strains food and income resources and makes it a challenging time for households to manage a pregnancy or afford a new child. Yet, there are few direct assessments of the role locally varying agricultural quality plays on women's health, especially reproductive health. In this paper we build on and integrate ideas from past studies focused on climate change and growing season quality in low-income countries with those on reproductive health to examine how variation in local seasonal agricultural quality relates to childbearing goals and family planning use in three countries in sub-Saharan Africa: Burkina Faso, Kenya, and Uganda. We use rich, spatially referenced data from the Performance Monitoring for Action (PMA) individual surveys with detailed information on childbearing preferences and family planning decisions. Building on recent advances in remote monitoring of seasonal agriculture, we construct multiple vegetation measures capturing different dimensions of growing season conditions across varying time frames. Results for the Kenya sample indicate that if the recent growing season is better a woman is more likely to want a child in the future. In Uganda, when the growing season conditions are better, women prefer to shorten the time until their next birth and are also more likely to discontinue using family planning. Additional analyses reveal the importance of education and birth spacing in moderating these findings. Overall, our findings suggest that, in some settings, women strategically respond to growing season conditions by adjusting fertility aspirations or family planning use. This study also highlights the importance of operationalizing agriculture in nuanced ways that align with women's lives to better understand how women are impacted by and respond to seasonal climate conditions. [ABSTRACT FROM AUTHOR]

Rapidly expanding global trade in the past three decades has lifted millions out of people out of poverty. Trade has also reduced manufacturing wages in high income countries and made entire industries uncompetitive in some communities, giving rise to nationalist politics that seek to stop or reverse further trade expansion in the United States and Europe. Given complex and uncertain political support for trade, how might changes in trade policy affect the global food system’s ability to adapt to climate change? Here we argue that we can best understand food security in a changing climate as a double exposure: the exposure of people and processes to both economic and climate-related shocks and stressors. Trade can help us adapt to climate change, or not. If trade restrictions proliferate, double exposure to both a rapidly changing climate and volatile markets will likely jeopardize the food security of millions. A changing climate will present both opportunities and challenges for the global food system, and adapting to its many impacts will affect food availability, food access, food utilization and food security stability for the poorest people across the world. Global trade can continue to play a central role in assuring that global food system adapts to a changing climate. This potential will only be realized, however, if trade is managed in ways that maximize the benefits of broadened access to new markets while minimizing the risks of increased exposure to international competition and market volatility. For regions like Africa, for example, enhanced transportation networks combined with greater national reserves of cash and enhanced social safety nets could reduce the impact of ‘double exposure’ on food security. [ABSTRACT FROM AUTHOR]

In the context of a changing climate, there is an urgent need to better understand the impact that weather disturbances have on food affordability in the developing world. While the influence of international markets on local food markets has received considerable attention, in contrast, the potential influence of weather disturbances on local food markets has received much less attention. In fact, local weather disturbances may have an adverse impact on the poorest households in developing countries. Here we quantify the short-run impact of both weather disturbances as well as international price changes on monthly food prices across 554 local commodity markets in 51 countries during the period between 2008 and 2012. We find that almost 20% of local market prices were affected by domestic weather disturbances in the short run, 9% by international price changes and 4% by both domestic weather disturbances and international price changes during the period. An improved understanding of the magnitude and relative importance of weather disturbances and international price changes on rural economies will inform public policies that are designed to mitigate the impact of adverse weather disturbances. [ABSTRACT FROM AUTHOR]

Much of the population in the developing world resides in rural areas, is dependent on local agriculture for survival, and thus directly subject to increasingly volatile and variable climatic patterns. Poverty limits options for adaptation to unpredictable weather and resultant food insecurity; these concerns are particularly salient in an era of climate change, which threatens to roll back years of development gains. Examining the association between the growing environment and child survival and nutrition is important in this context. Using NASA's satellite remote sensing data with Demographic and Health Surveys (DHS) data from four West African countries (Mali, Burkina Faso, Guinea and Benin), we assess the association between a climate-related environmental variable (vegetation index - NDVI) and child survival and nutrition. NDVI had a positive association with child survival and nutrition in countries with a wide distribution of NDVI values. NDVI was more likely to be positively associated with wasting rather than stunting. We find that environmental factors can be important for child survival and nutrition outcomes in specific contexts. Additional research is needed to further explore the ways NDVI can be used to inform our understanding of the environment's impact on child survival and nutrition. [ABSTRACT FROM AUTHOR]

Abstract: The World Bank has invested in infrastructure in developing countries for decades. This investment aims to reduce the isolation of markets, reducing both seasonality and variability in food availability and food prices. Here we combine city market price data, global distance to port, and country infrastructure data to create a new Isolation Index for countries and cities around the world. Our index quantifies the isolation of a city from the global market. We demonstrate that an index built at the country level can be applied at a sub-national level to quantify city isolation. In doing so, we offer policy makers with an alternative metric to assess food insecurity. We compare our isolation index with other indices and economic data found in the literature. We show that our Index measures economic isolation regardless of economic stability using correlation and analysis. [Copyright &y& Elsevier]

Abstract: Satellite remote sensing technology has contributed to the transformation of multiple earth science domains, putting space observations at the forefront of innovation in earth science. With new satellite missions being launched every year, new types of earth science data are being incorporated into science models and decision-making systems in a broad array of organizations. Policy guidance can influence the degree to which user needs influence mission design and when, and ensure that satellite missions serve both the scientific and user communities without becoming unfocused and overly expensive. By considering the needs of the user community early on in the mission-design process, agencies can ensure that satellites meet the needs of multiple constituencies. This paper describes the mission development process in NASA and ESA and compares and contrasts the successes and challenges faced by these agencies as they try to balance science and applications within their missions. [Copyright &y& Elsevier]

Abstract: Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the African continent. Analysis of changes in phenology can provide quantitative information on the effect of climate variability on growing seasons in agricultural regions. Using a robust statistical methodology, we describe the relationship between phenology metrics derived from the 26year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), and the Multivariate ENSO Index (MEI). We map the most significant positive and negative correlation for the four climate indices in Eastern, Western and Southern Africa between two phenological metrics and the climate indices. Our objective is to provide evidence of whether climate variability captured in the four indices has had a significant impact on the vegetative productivity of Africa during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by large scale variations in climate. The particular climate index and the timing showing highest correlation depended heavily on the region examined. In Western Africa the cumulative NDVI correlates with PDO in September–November. In Eastern Africa the start of the June–October season strongly correlates with PDO in March–May, while the PDO in December–February correlates with the start of the February–June season. The cumulative NDVI over this last season relates to the MEI of March–May. For Southern Africa, high correlations exist between SOS and NAO of September–November, and cumulative NDVI and MEI of March–May. The research shows that climate indices can be used to anticipate late start and variable vigor in the growing season of sensitive agricultural regions in Africa. [ABSTRACT FROM AUTHOR]

Abstract: Metrics that estimate the beginning of the growing season in semi-arid monsoonal ecosystems have become a central part of both crop models that are used to monitor crop production using satellite remote sensing and early warning indicators of possible future reductions in yield due to growing season length. This paper presents a new phenological model which is tuned to the semi-arid, monsoonal ecosystem of the West African Sahel and that uses humidity instead of calendar dates to identify the beginning of the season. We implement this model on vegetation index data from AVHRR, SPOT-Vegetation and MODIS data, and evaluate the results along with a rainfall-based start of season estimate for the region. Contributions of the paper include evaluating existing methods using ground observations of sowing date, comparing the same metric across multiple sensors and to rainfall-based SOS, and the development of a metric based on NDVI and relative humidity. [Copyright &y& Elsevier]

Summary: This study demonstrates that there is a relationship between socioeconomic problems in parts of West Africa and remote-sensing-derived environmental information about the region (normalized difference vegetation index (NDVI), net primary production (NPP), and gridded rainfall data). Further, it finds that using both remotely sensed data and site-specific information from participatory rural appraisal (PRA) reports enables an improved understanding of natural resource management problems in the region. The study uses 100 PRA reports as sources of data on socioeconomic and natural resource management problems in Senegal and The Gambia. Utilizing a binary variable to extract semi-quantitative information from the reports, the study examines 10 PRA tools for their usefulness. [Copyright &y& Elsevier]

Abstract: Early warning systems (EWS) tend to focus on the identification of slow onset disasters such famine and epidemic disease. Since hazardous environmental conditions often precede disastrous outcomes by many months, effective monitoring via satellite and in situ observations can successfully guide mitigation activities. Accurate short term forecasts of NDVI could increase lead times, making early warning earlier. This paper presents a simple empirical model for making 1 to 4 month NDVI projections. These statistical projections are based on parameterized satellite rainfall estimates (RFE) and relative humidity demand (RHD). A quasi-global, 1 month ahead, 1° study demonstrates reasonable accuracies in many semi-arid regions. In Africa, a 0.1° cross-validated skill assessment quantifies the technique''s applicability at 1 to 4 month forecast intervals. These results suggest that useful projections can be made over many semi-arid, food insecure regions of Africa, with plausible extensions to drought prone areas of Asia, Australia and South America. [Copyright &y& Elsevier]

Since the advent of NASA's Earth Observing System, knowledge of the practical benefits of Earth science data has grown considerably. The community using NASA Earth science observations in applications has grown significantly, with increasing sophistication to serve national interests. Data latency, or how quickly communities receive science observations after acquisition, can have a direct impact on the applications and usability of the information. This study was conducted to determine how users are incorporating NASA data into applications and operational processes to benefit society beyond scientific research, as well as to determine the need for data latency of less than 12 h. The results of the analysis clearly show the significant benefit to society of serving the needs of the agricultural, emergency response, environmental monitoring and weather communities who use rapidly delivered, accurate Earth science data. The study also showed the potential of expanding the communities who use low latency NASA science data products to provide new ways of transforming data into information. These benefits can be achieved with a clear and consistent NASA policy on product latency. [ABSTRACT FROM AUTHOR]

NASA provides daily satellite remote sensing observations on a wide variety of environmental parameters at the global scale, including rainfall, temperature, vegetation health, ocean productivity and soil moisture conditions. Many organizations and individuals use this data to better understand the Earth system using models and diagnose the impact of climate on agricultural productivity. Weather related extreme events trigger food insecurity by reducing the supply of food and incomes of households working in the agriculture sector. Models and other ecosystem-based assessments can be used to inform decisions regarding food security and human health outcomes of changes in access to food. In this talk, I provide a description of the various monitoring products that have been developed by the US Agency for International Development and World Food Program's systems to monitor food production in food insecure regions, along with graphics and explanations as to how the products are used. Information on local food market prices and food affordability, nutrition outcomes, school attendance, the use of safety nets and other ancillary data are brought together with remote sensing information to inform food security. Satellite data is increasingly used to assess the amount of food grown globally, particularly in a new system entitled ‘Global Agriculture Geo-monitoring Initiative (GEOGLAM). The system seeks to harmonize production assessments across nations and regions using satellite data to ensure a comprehensive and transparent assessment of how much food will be available for sale in international markets every month. As the climate changes, improved integration of social and economic food security indicators with climate observations from satellites will become increasingly important to ensure food security for all. [ABSTRACT FROM AUTHOR]

In this article we specify a model of millet prices in the three West African countries of Burkina Faso, Mali, and Niger. Using data obtained from USAID’s Famine Early Warning Systems Network (FEWS NET) we present a unique regional millet price forecasting model that takes advantage of the panel nature of our data and accounts for the distance of rural markets from capital cities. Another novel aspect of our analysis is our use of the Normalized Difference Vegetation Index (NDVI) to detect and control for variation in conditions for productivity. We find that including NDVI information significantly improves price forecasts. [ABSTRACT FROM AUTHOR]

Global NDVI data are routinely derived from the AVHRR, SPOT-VGT, and MODIS/Terra earth observation records for a range of applications from terrestrial vegetation monitoring to climate change modeling. This has led to a substantial interest in the harmonization of multisensor records. Most evaluations of the internal consistency and continuity of global multisensor NDVI products have focused on time-series harmonization in the spectral domain, often neglecting the spatial domain. We fill this void by applying variogram modeling (a) to evaluate the differences in spatial variability between 8-km AVHRR, 1-km SPOT-VGT, and 1-km, 500-m, and 250-m MODIS NDVI products over eight EOS (Earth Observing System) validation sites, and (b) to characterize the decay of spatial variability as a function of pixel size (i.e. data regularization) for spatially aggregated Landsat ETM+ NDVI products and a real multisensor dataset. First, we demonstrate that the conjunctive analysis of two variogram properties – the sill and the mean length scale metric – provides a robust assessment of the differences in spatial variability between multiscale NDVI products that are due to spatial (nominal pixel size, point spread function, and view angle) and non-spatial (sensor calibration, cloud clearing, atmospheric corrections, and length of multi-day compositing period) factors. Next, we show that as the nominal pixel size increases, the decay of spatial information content follows a logarithmic relationship with stronger fit value for the spatially aggregated NDVI products (R 2 =0.9321) than for the native-resolution AVHRR, SPOT-VGT, and MODIS NDVI products (R 2 =0.5064). This relationship serves as a reference for evaluation of the differences in spatial variability and length scales in multiscale datasets at native or aggregated spatial resolutions. The outcomes of this study suggest that multisensor NDVI records cannot be integrated into a long-term data record without proper consideration of all factors affecting their spatial consistency. Hence, we propose an approach for selecting the spatial resolution, at which differences in spatial variability between NDVI products from multiple sensors are minimized. This approach provides practical guidance for the harmonization of long-term multisensor datasets. [Copyright &y& Elsevier]

North Africa, the world's largest dust source, is non-uniform, consisting of a permanently arid region (Sahara), a semi-arid region (Sahel), and a relatively moist vegetated region (Savanna), each with very different rainfall patterns and surface conditions. This study aims to better understand the controlling factors that determine the variation of dust emission in North Africa over a 27-year period from 1982 to 2008, using observational data and model simulations. The results show that the model-derived Saharan dust emission is only correlated with the 10-m winds (W10m) obtained from reanalysis data, but the model-derived Sahel dust emission is correlated with both W10m and the Normalized Difference Vegetation Index (NDVI) that is obtained from satellite. While the Saharan dust accounts for 82% of the continental North Africa dust emission (1340–1570 Tg year −1 ) in the 27-year average, the Sahel accounts for 17% with a larger seasonal and inter-annual variation (230–380 Tg year −1 ), contributing about a quarter of the transatlantic dust transported to the northern part of South America. The decreasing dust emission trend over the 27-year period is highly correlated with W10m over the Sahara (R = 0.92). Over the Sahel, the dust emission is correlated with W10m (R = 0.69) but is also anti-correlated with the trend of NDVI (R = -0.65). W10m is decreasing over both the Sahara and the Sahel between 1982 and 2008, and the trends are correlated (R = 0.53), suggesting that Saharan/Sahelian surface winds are a coupled system, driving the inter-annual variation of dust emission. [ABSTRACT FROM AUTHOR]

The annual cycle of vegetation growth may be altered in response to climate changes affecting ecosystem dynamics. However, our understanding of vegetation seasonality is mostly limited to the mechanisms and attributes of phenological events, such as spring emergence and fall senescence. Here we have investigated the seasonal evolution of vegetation growth from winter dormancy to summer maturity of four forest types in the Northern Hemisphere (NH) temperate and boreal forests for 1982–2011. The present study assesses large-scale variations in the vegetation green-up rate ( VG rate ) and its connection to temperature variability using remotely sensed normalized difference vegetation index (NDVI) and surface air temperature. The average of the VG rate of the analysis period increases with latitude, which indicates that the canopy develops more rapidly from dormancy to maturity for vegetation in higher-latitude or colder climate zones. VG rate and precedent temperature also show a positive correlation (r) over temperate and boreal forests (67% of the forest area in the NH), indicating that increased temperatures lead to faster canopy development within the same climate zone or latitude band. Responsiveness of VG rate to temperature variability shows that despite the same magnitude of local temperature variability during extremely cold and warm years, the magnitude of VG rate acceleration in warm years (0.07 (15-day) − 1 ) is larger than the VG rate deceleration in cold years (− 0.03 (15-day) − 1 ), suggesting that the response of VG rate to temperature variability is nonlinear. Among the four forest types examined in this study, the nonlinear responses are most clearly observed in deciduous broadleaf forests indicating that forest composition may regulate the large-scale response of canopy development to temperature variability. Overall, our results suggest that anomalous seasonal warming will significantly affect canopy developments over wide deciduous forest areas. [ABSTRACT FROM AUTHOR]