SEARCH · Search USGS

Results for “Food Security”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5Linked to original sources

USGS2009

Simulated dynamics of carbon stocks driven by changes in land use, management and climate in a tropical moist ecosystem of Ghana

Sub-Saharan Africa is large and diverse with regions of food insecurity and high vulnerability to climate change. This project quantifies carbon stocks and fluxes in the humid forest zone of Ghana, as a part of an assessment in West Africa. The General Ensemble biogeochemical Modeling System (GEMS) was used to simulate the responses of natural and managed systems to projected scenarios of changes in climate, land use and cover, and nitrogen fertilization in the Assin district of Ghana. Model inputs included historical land use and cover data, historical climate records and projected climate changes, and national management inventories. Our results show that deforestation for crop production led to a loss of soil organic carbon (SOC) by 33% from 1900 to 2000. The results also show that the trend of carbon emissions from cropland in the 20th century will continue through the 21st century and will be increased under the projected warming and drying scenarios. Nitrogen (N) fertilization in agricultural systems could offset SOC loss by 6% with 30 kg N ha −1 year −1 and by 11% with 60 kg N ha −1 year −1 . To increase N fertilizer input would be one of the vital adaptive measures to ensure food security and maintain agricultural sustainability through the 21st century.

Agriculture, Ecosystems and Environment↗

USGS2017

A guide to multi-objective optimization for ecological problems with an application to cackling goose management

Choices in ecological research and management are the result of balancing multiple, often competing, objectives. Multi-objective optimization (MOO) is a formal decision-theoretic framework for solving multiple objective problems. MOO is used extensively in other fields including engineering, economics, and operations research. However, its application for solving ecological problems has been sparse, perhaps due to a lack of widespread understanding. Thus, our objective was to provide an accessible primer on MOO, including a review of methods common in other fields, a review of their application in ecology, and a demonstration to an applied resource management problem. A large class of methods for solving MOO problems can be separated into two strategies: modelling preferences pre-optimization (the a priori strategy), or modelling preferences post-optimization (the a posteriori strategy). The a priori strategy requires describing preferences among objectives without knowledge of how preferences affect the resulting decision. In the a posteriori strategy, the decision maker simultaneously considers a set of solutions (the Pareto optimal set) and makes a choice based on the trade-offs observed in the set. We describe several methods for modelling preferences pre-optimization, including: the bounded objective function method, the lexicographic method, and the weighted-sum method. We discuss modelling preferences post-optimization through examination of the Pareto optimal set. We applied each MOO strategy to the natural resource management problem of selecting a population target for cackling goose ( Branta hutchinsii minima ) abundance. Cackling geese provide food security to Native Alaskan subsistence hunters in the goose's nesting area, but depredate crops on private agricultural fields in wintering areas. We developed objective functions to represent the competing objectives related to the cackling goose population target and identified an optimal solution first using the a priori strategy, and then by examining trade-offs in the Pareto set using the a posteriori strategy. We used four approaches for selecting a final solution within the a posteriori strategy; the most common optimal solution, the most robust optimal solution, and two solutions based on maximizing a restricted portion of the Pareto set. We discuss MOO with respect to natural resource management, but MOO is sufficiently general to cover any ecological problem that contains multiple competing objectives that can be quantified using objective functions.

Ecological Modelling↗

USGS2026

VegET evapotranspiration for Africa: Continental-scale simulation, multi-product evaluation, and drought assessment

Study region Continental Africa, encompassing diverse climatic zones—tropical, arid, and temperate—and spanning major transboundary river basins such as the Nile, Niger, Congo, Volta, and Zambezi River Basins. The region exhibits pronounced hydroclimatic gradients and heterogeneous land use systems ranging from rainfed croplands and rangelands to dense tropical forests and irrigated schemes. Study focus Actual evapotranspiration (ETa) is a central component of the terrestrial water balance, governing the redistribution of water and energy between the land surface and the atmosphere. Accurate estimation of ETa at continental scale is critical for hydrological monitoring, water resource management, and climate adaptation, as well as for quantifying water, energy, and carbon fluxes that underpin sustainable development. In this study, we applied the agro-hydrologic VegET v2 model to simulate a new, high-resolution, continental-scale ETa dataset for Africa (2000–2021). The model results were benchmarked against four widely used remote sensing-based products—MODIS16 v6.1, SSEBop v6.1, WaPOR v3, and GLEAM v4.1a—across major climate zones, land use types, and River Basins, providing a comprehensive multi-product evaluation of evapotranspiration dynamics across the continent. New hydrological insights for the region Validation against eddy covariance flux tower observations at eight representative sites confirmed that VegET v2 accurately reproduces the seasonal dynamics of observed ETa, achieving a correlation (r) of 0.8 and an RMSE of 25 mm month⁻¹ —accuracy that is comparable to or higher than accuracies of satellite-based products MODIS16, SSEBop, and GLEAM. This study represents one of the first Africa-wide hydrological simulations of ETa, extending the VegET model beyond basin-scale applications. Intercomparisons reveal that VegET aligns closely with MODIS16, SSEBop, and GLEAM in humid and tropical regions (r = 0.80–0.90; RMSE < 20 mm month⁻¹), while greater discrepancies appear in arid and semi-arid zones, where WaPOR tends to overestimate ETa (RMSE ≥ 28 mm month⁻¹). Despite these differences, VegET effectively captures spatial and temporal ETa variability across rainfed croplands, forests, and savannas, supporting its utility in regional water balance assessments, water accounting, and drought monitoring. A key application of VegET v2 is the Evapotranspiration Deficit Index (ETDI), derived by integrating VegET-based ETa with potential evapotranspiration (PET) to quantify water stress. ETDI successfully captured major drought episodes across Africa, including persistent Sahelian and southern African dry spells, the 2020–2021 winter drought in the Maghreb, and the 2018–2019 austral summer drought in southern Africa, while identifying positive anomalies over central Africa indicative of recurrent wetness. These results underscore VegET’s capability as a hydrologically consistent, operational tool for continental ETa monitoring and drought assessment, offering support for basin-scale water balance studies, food security planning, and climate resilience across Africa’s diverse hydrological environments.

Journal of Hydrology: Regional Studies↗

USGS2021

Coffee plantations, hurricanes and avian resiliency: Insights from occupancy, and local colonization and extinction rates in Puerto Rico

Insights on impacts and resiliency of avian species with respect to hurricanes in the Caribbean have largely focused on responses measured in protected habitats. We assessed avian responses in non-protected habitat, specifically shade-restored coffee plantations, because their structural complexity retains many attributes of secondary forests, and may contribute to landscape scale species resiliency. We tallied species richness, and estimated occupancy probability of 12 resident avian species, after adjusting for imperfect detection, to assess the impact of hurricane Maria (20 September 2017) in the context of two stages of shade-restored coffee plantations. For 5 of those species, we also estimated local colonization and extinction probabilities to assess their prospect of rebounding (resiliency). We used survey data collected March-June 2015-2017 (pre-hurricane) and 2018 (post-hurricane) in 58 coffee farms and satellite imagery to assess vegetation structure. Restored farms were grouped into two categories based on time-since-restoration: newly-restored and fully-restored. We predicted that mean percent forest cover in fully-restored farms (~30-40%) would revert to levels in newly-restored farms (<15%), with concomitant changes in occupancy by avian species. As predicted, mean percent forest cover (16.17 4.27%) in fully-restored farms post-hurricane reverted to pre-hurricane levels in newly-restored farms (15.00 5.61%). The loss represented 30-38% relative to the pre-hurricane cover levels. Detections of focal species dropped an average of 41% post-hurricane, with associated reductions in occupancy for 9/11 species. Occupancy of the Puerto Rican Bullfinch and Puerto Rican Spindalis reverted to levels detected in newly-restored plantations prior to the hurricane as predicted. Prospects of rebounding were more likely for species with invariant or increases in colonization probability (e.g., Yellow-faced Grassquit, Northern Mockingbird, Puerto Rican Spindalis). Rebounding for frugivores like the Puerto Rican Bullfinch would be protracted given that colonization rates dropped from 0.56 0.12 (pre-hurricane) to 0.04 0.2 (post-hurricane), regardless of restoration stage. Our work showed that the avian community associated with restored coffee farms exhibited as high degree of ecological resistance as the similarity in species composition before and after the hurricane was 81%, and all 12 focal species continued to occupy farms under both restoration stages. The prospect of the focal species to rebound (resiliency) was specific-specific, and in some cases, mediated by their affinity to a particular farm restoration stage. The strength of hurricanes is projected to intensify with global warming. Pockets of undamaged or partially damaged shade-grown or fully-restored coffee plantations may contribute to species resiliency by increasing landscape level habitat redundancy, and facilitate habitat shifts to secure food resources or harbor source populations to colonize recovering, hurricane-damaged habitat tracts.

Greater Antilles↗

USGS2015

Global land cover mapping using Earth observation satellite data: Recent progresses and challenges

Land cover is an important variable for many studies involving the Earth surface, such as climate, food security, hydrology, soil erosion, atmospheric quality, conservation biology, and plant functioning. Land cover not only changes with human caused land use changes, but also changes with nature. Therefore, the state of land cover is highly dynamic. In winter snow shields underneath various other land cover types in higher latitudes. Floods may persist for a long period in a year over low land areas in the tropical and subtropical regions. Forest maybe burnt or clear cut in a few days and changes to bare land. Within several months, the coverage of crops may vary from bare land to nearly 100% crops and then back to bare land following harvest. The highly dynamic nature of land cover creates a challenge in mapping and monitoring which remains to be adequately addressed. As economic globalization continues to intensify, there is an increasing trend of land cover/land use change, environmental pollution, land degradation, biodiversity loss at the global scale, timely and reliable information on global land cover and its changes is urgently needed to mitigate the negative impact of global environment change.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2024

A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale

Agriculture consumes the largest share of freshwater globally; therefore, distinguishing between rainfed and irrigated croplands is essential for agricultural water management and food security. In this study, a framework incorporating the Budyko model was used to differentiate between rainfed and irrigated cropland areas in Africa for eight remote sensing landcover products and a high-confidence cropland map (HCCM). The HCCM was generated for calibration and validation of the crop partitioning framework as an alternative to individual cropland masks which exhibit high disagreement. The accuracy of the framework in partitioning the HCCM was evaluated using an independent validation dataset, yielding an overall accuracy rate of 73 %. The findings of this study indicate that out of the total area covered by the HCCM (2.36 million km 2 ), about 461,000 km 2 (19 %) is irrigated cropland. The partitioning framework was applied on eight landcover products, and the extent of irrigated areas varied between 19 % and 30 % of the total cropland area. The framework demonstrated high precision and specificity scores, indicating its effectiveness in correctly identifying irrigated areas while minimizing the misclassification of rainfed areas as irrigated. This study provides an enhanced understanding of rainfed and irrigation patterns across Africa, supporting efforts towards achieving sustainable and resilient agricultural systems. Consequently, the approach outlined expands on the suite of remote sensing landcover products that can be used for agricultural water studies in Africa by enabling the extraction of irrigated and rainfed cropland data from landcover products that do not have disaggregated cropland classes.

International Journal of Applied Earth Observation↗

USGS2004

Impacts of land use and climate change on carbon dynamics in south-central Senegal

Total carbon stock in vegetation and soils was reduced 37% in south-central Senegal from 1900 to 2000. The decreasing trend will continue during the 21st century unless forest clearing is stopped, selective logging dramatically reduced, and climate change, if any, relatively small. Developing a sustainable fuelwood and charcoal production system could be the most feasible and significant carbon sequestration project in the region. If future climate changes dramatically as some models have predicted, cropland productivity will drop more than 65% around 2100, posing a serious threat to food security and the efficiency of carbon sequestration projects.

Journal of Arid Environments↗

USGS2019

MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types

Innovative, open, and rapid methods to map crop types over large areas are needed for long-term cropland monitoring. We developed two novel and automated decision tree classification approaches to map crop types across the conterminous United States (U.S.) using MODIS 250 m resolution data: 1) generalized, and 2) year-specific classification. The classification approaches use similarities and dissimilarities in crop type phenologyderived from NDVI time-series data for the two approaches. The year-specific approach uses the training samples from one year and classifies crop types for that year only, whereas the generalized classification approach uses above-average, average, and below-average precipitation years for training to produce crop type maps for one or multiple years more robustly. We produced annual crop type maps using the generalized classification approach for 2001–2014 and the year-specific approach for 2008, 2010, 2011 and 2012. The year-specific classification had overall accuracies > 78%, while the generalized classifier had accuracies > 75% for the conterminous U.S. for 2008, 2010, 2011, and 2012. The generalized classifier enables automated and routine crop type mapping without repeated and expensive ground sample collection year after year. The resulting crop type maps for years prior to 2007 are new and especially important for long-term cropland monitoring and food security analysis because no other map products are currently available for 2001–2007.

Remote Sensing of Environment↗

USGS2025

Climate-driven deoxygenation of lakes alters the nutrient-toxin profile of a food fish

Climate change is rapidly altering fisheries supporting aquatic ecosystems. The implications for food security depend not only on harvest biomass but also concentrations of nutrients and toxins in fish. Using brook trout from Adirondack lakes (New York, USA), we tested whether ongoing lake deoxygenation trends will affect fish muscle omega-3 fatty acids, selenium, and mercury concentrations. Across space (16 lakes: 1 year) and time (6 years: 1 lake), anoxia decreased selenium and was associated with elevated fish mercury, with no effect on omega-3 content. Because selenium may mitigate some end points of mercury toxicity, highly variable Se:Hg molar ratios (0.70–35.79) in neighboring lakes may have health risk implications. For fish consumers, ongoing lake deoxygenation under climate change could potentially reduce selenium intake while enhancing mercury exposure. Simultaneous alteration of beneficial compounds and toxins by environmental change complicates the development of fish consumption advisories to safeguard public health in a warming world.

New York↗

USGS2019

Fungicides: An overlooked pesticide class?

Fungicides are indispensable to global food security and their use is forecasted to intensify. Fungicides can reach aquatic ecosystems and occur in surface water bodies in agricultural catchments throughout the whole growing season due to their frequent, prophylactic application. However, in comparison to herbicides and insecticides, the exposure to and effects of fungicides have received less attention. We provide an overview of the risk of fungicides to aquatic ecosystems covering fungicide exposure (i.e., environmental fate, exposure modelling, and mitigation measures) as well as direct and indirect effects of fungicides on microorganisms, macrophytes, invertebrates, and vertebrates. We show that fungicides occur widely in aquatic systems, that the accuracy of predicted environmental concentrations is debatable, and that fungicide exposure can be effectively mitigated. We additionally demonstrate that fungicides can be highly toxic to a broad range of organisms and can pose a risk to aquatic biota. Finally, we outline central research gaps that currently challenge our ability to predict fungicide exposure and effects, promising research avenues, and shortcomings of the current environmental risk assessment for fungicides.

Environmental Science & Technology↗

USGS2008

Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

Journal of Geophysical Research D: Atmospheres↗

USGS2012

Ground water and climate change

As the world's largest distributed store of fresh water, ground water plays a central part in sustaining ecosystems and enabling human adaptation to climate variability and change. The strategic importance of ground water for global water and food security will probably intensify under climate change as more frequent and intense climate extremes (droughts and floods) increase variability in precipitation, soil moisture and surface water. Here we critically review recent research assessing the impacts of climate on ground water through natural and human-induced processes as well as through groundwater-driven feedbacks on the climate system. Furthermore, we examine the possible opportunities and challenges of using and sustaining groundwater resources in climate adaptation strategies, and highlight the lack of groundwater observations, which, at present, limits our understanding of the dynamic relationship between ground water and climate.

Nature Climate Change↗

USGS2010

Groundwater sustainability strategies

Groundwater extraction has facilitated significant social development and economic growth, enhanced food security and alleviated drought in many farming regions. But groundwater development has also depressed water tables, degraded ecosystems and led to the deterioration of groundwater quality, as well as to conflict among water users. The effects are not evenly spread. In some areas of India, for example, groundwater depletion has preferentially affected the poor. Importantly, groundwater in some aquifers is renewed slowly, over decades to millennia, and coupled climate–aquifer models predict that the flux and/or timing of recharge to many aquifers will change under future climate scenarios. Here we argue that communities need to set multigenerational goals if groundwater is to be managed sustainably.

Nature Geoscience↗

USGS2026

Environment, taxonomy, and socioeconomics predict non-imperilment in freshwater fishes

Freshwater fishes are among the most threatened taxa, yet conservation assessments remain incomplete for many species. Freshwater fishes provide essential ecosystem services such as food security, recreational opportunities, and cultural significance. Despite heavy alterations to freshwater ecosystems, the reasons for species’ sensitivity and resistance to imperilment are unclear. To address this need, we develop a machine learning framework to predict global imperilment status for 10,631 freshwater fish species using a comprehensive set of environmental, socioeconomic, and intrinsic species-level predictors. Using updated IUCN Red List data, we train and validate Random Forest classifiers to distinguish imperiled (Vulnerable, Endangered, Critically Endangered) from non-imperiled species. We examine the relative influence of 52 variables derived from 12 global sources describing extrinsic environmental and socioeconomic factors and intrinsic species-specific characteristics. Our models achieve higher accuracy for non-imperiled species (90.1%) compared to imperiled species (81.8%), reflecting the greater heterogeneity of threats and conditions driving imperilment. Across models, key predictors include habitat variables, taxonomic order, hydrological characteristics, and disturbance indicators, underscoring the interplay between ecology, geography, and human pressures. This integrative, reproducible approach demonstrates the utility of machine learning for guiding proactive conservation and provides a scalable framework for global biodiversity risk assessment.

Nature Communications↗

USGS2022

Global dataset of species-specific inland recreational fisheries harvest for consumption

Inland recreational fisheries, found in lakes, rivers, and other landlocked waters, are important to livelihoods, nutrition, leisure, and other societal ecosystem services worldwide. Although recreationally-caught fish are frequently harvested and consumed by fishers, their contribution to food and nutrition has not been adequately quantified due to lack of data, poor monitoring, and under-reporting, especially in developing countries. Beyond limited global harvest estimates, few have explored species-specific harvest patterns, although this variability has implications for fisheries management and food security. Given the continued growth of the recreational fishery sector, understanding inland recreational fish harvest and consumption rates represents a critical knowledge gap. Based on a comprehensive literature search and expert knowledge review, we quantified multiple aspects of global inland recreational fisheries for 81 countries spanning ~192 species. For each country, we assembled recreational fishing participation rate and estimated species-specific harvest and consumption rate. This dataset provides a foundation for future assessments, including understanding nutritional and economic contributions of inland recreational fisheries.

Scientific Data↗

USGS2020

Waterfowl occurrence and residence time as indicators of H5 and H7 avian influenza in North American Poultry

Avian inﬂuenza (AI) affects wild aquatic birds and poses hazards to human health, food security, and wildlife conservation globally. Accordingly, there is a recognized need for new methods and tools to help quantify the dynamic interaction between wild bird hosts and commercial poultry. Using satellite-marked waterfowl, we applied Bayesian joint hierarchical modeling to concurrently model species distributions, residency times, migration timing, and disease occurrence probability under an integrated animal movement and disease distribution modeling framework. Our results indicate that migratory waterfowl are positively related to AI occurrence over North America such that as waterfowl occurrence probability or residence time increase at a given location, so too does the chance of a commercial poultry AI outbreak. Analyses also suggest that AI occurrence probability is greatest during our observed waterfowl northward migration, and less during the southward migration. Methodologically, we found that when modeling disparate facets of disease systems at the wildlife-agriculture interface, it is essential that multiscale spatial patterns be addressed to avoid mistakenly inferring a disease process or disease-environment relationship from a pattern evaluated at the improper spatial scale. The study offers important insights into migratory waterfowl ecology and AI disease dynamics that aid in better preparing for future outbreaks.

Scientific Reports↗

USGS2025

Quantitative subsurface characterization illuminates the origin of the Quaternary Mississippi River Valley alluvial aquifer

The Mississippi River Valley alluvial aquifer (MRVA) is vital to U.S. food security and global agricultural supply. However, quantitative understanding of its Quaternary origin, architecture, and hydrologic function remains incomplete. Here we develop a three-dimensional hydrostratigraphic model to characterize the deposition of clay and silt, fine-medium sands, and graveliferous sands using lithologic data from 75,000 boreholes compiled across the Lower Mississippi Valley and a geostatistical method—interval kriging. We find that cyclic glacial entrenchments, evidenced by remnants of pre-Wisconsinan postglacial sediments, alongside geodynamic activities shaped the MRVA basal configuration. Stratal weakening from faulting and salt diapirism enhanced glacial incision and thereby produced abrupt aquifer thickening. We demarcate the top of graveliferous sands as the regional marker of the Pleistocene-Holocene transition. The MRVA hydrostratigraphy reveals hydrologic function and geologic controls on groundwater storage and quality, advancing the assessment of aquifer sustainability under a changing climate, with implications for alluvial aquifers globally.

Arkansas, Illinois, Kentucky, Louisiana, Mississip↗

USGS2025

Behavioral plasticity in detection height of an invasive, arboreal snake based on size, condition, and prey

Context Animals may adjust their behavior in predictable ways to balance tradeoffs between resource acquisition and survival or fecundity. Microhabitat selection based on individual traits or environmental conditions is one measure of risk–reward tradeoffs by individuals. Aims We used data from observational and manipulative studies to investigate whether an arboreal snake (brown treesnake, Boiga irregularis ) had context-dependent behavior based on the relationship between estimated prey availability, body condition, size, and detection height (microhabitat use) in two Mariana Islands. Methods We used observational data collected in four study sites and data from a manipulative study that we collected over a 5-year period. The observational data focused on four sites with different counts of three prey types, including lizards, birds, and small mammals. During the manipulative study we removed snakes, which resulted in increased prey counts over time. Using these two approaches, we tested whether prey counts predicted body condition and then evaluated how prey counts, snake size, and snake condition interactively predicted the detection height of captured individuals. Key results We found that body condition was greater at sites or in years with greater prey counts across both the observational and manipulative studies. We also found that snakes displayed differential microhabitat use based on both their condition and size. Larger snakes tended to be detected lower than smaller snakes, but only at sites or during years with few bird or small mammal counts. Snakes at sites with greater mammal and bird counts had a positive relationship between size and detection height. Snakes with greater condition scores tended to be detected higher irrespective of size, but this was also dependent on prey counts. At sites with low bird counts, snakes that were in better condition tended to be closer to the ground. Conclusions Brown treesnakes modified microhabitat use based on their condition, size, and the number or type of prey available. Our findings were consistent with a hypothesis that they optimized habitat use to secure food resources and maximize survival. Implications Context-dependent behavioral plasticity may be an important consideration for management of reptiles for population control or growth.

Wildlife Research↗