SEARCH · Search USGS

Results for “Agricultural Systems”

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 55 records · Page 3Linked to original sources

USGS2025

Multispectral red-edge indices accurately estimate nitrogen content in winter cereal cover crops

Winter cover crops reduce erosion and nutrient runoff from agricultural systems. Although cereal cover crops can decrease field nitrate leaching by 50%–95%, the magnitude of this reduction varies within and between fields, making it challenging to monitor the impact of cover crops on nitrate leaching at large spatial extents. Satellite remote sensing using red-edge bands has been shown to effectively estimate crop nitrogen (N) content (kg ha −1 ) in later growth-stage crops with a closed canopy. In this study, we evaluated 15 spectral indices derived from Sentinel-2 imagery to estimate N concentration (%) and content (kg ha −1 ) of cereal cover crops, using 1627 destructive samples collected from 2018 to 2023 in Maryland. Observed N content ranged from 0.1 to 214.7 kg ha −1 , while N concentration ranged from 0.6% to 5.5%. The 15 indices considered were poor predictors of N concentration (adj. R 2 = 0.089, root mean squared error [RMSE] = 0.802%), but were more successful at measuring N content (biomass × N concentration). Delta red-edge (ΔRE) was the best predictor of N content (adj. R 2 = 0.748, RMSE = 13.10 kg ha −1 from cross-validation with 80% train and 20% test splits iterated 100 times) using samples with imagery collected within ±4 days of destructive sampling ( n = 1110). Our findings indicate that longer red-edge wavelengths (783 and 740 nm) are more suited for estimating N content in cereal cover crops compared to shorter red-edge wavelengths, which have been shown to be more sensitive to biomass. Leave-one-year-out cross-validation demonstrated that the relationship between ΔRE and N content was robust across all four cover crop sampling years included in the study (adj. R 2 = 0.700–0.769, RMSE = 10.70–15.40 kg ha −1 ). Regression model performance improved with the addition of multiple predictors, including biomass (estimated from Normalized Difference Vegetation Index), weather variables (adj. R 2 = 0.765, RMSE = 12.37 kg ha −1 ), management variables (species, season, adj. R 2 = 0.772, and RMSE = 12.13 kg ha −1 ), and biophysical variables (height, fractional ground cover, adj. R 2 = 0.818, and RMSE = 10.29 kg ha −1 ). These findings demonstrate the feasibility of quantifying N content in cereal cover crops using a red-edge-based spectral index across large geographic extents and indicate the inclusion of additional predictors, such as weather and management data, improves model accuracy. This work has implications for quantifying reductions in N leaching associated with cover crops, aiding in policymaking and evaluation of conservation programs that impact water bodies such as Chesapeake Bay.

Maryland↗

USGS2014

Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States

BACKGROUND Complex environmental models are frequently extrapolated to overcome data limitations in space and time, but quantifying data worth to such models is rarely attempted. The authors determined which field observations most informed the parameters of agricultural system models applied to field sites in Nebraska (NE) and Maryland (MD), and identified parameters and observations that most influenced prediction uncertainty. RESULTS The standard error of regression of the calibrated models was about the same at both NE (0.59) and MD (0.58), and overall reductions in prediction uncertainties of metolachlor and metolachlor ethane sulfonic acid concentrations were 98.0 and 98.6% respectively. Observation data groups reduced the prediction uncertainty by 55–90% at NE and by 28–96% at MD. Soil hydraulic parameters were well informed by the observed data at both sites, but pesticide and macropore properties had comparatively larger contributions after model calibration. CONCLUSIONS Although the observed data were sparse, they substantially reduced prediction uncertainty in unsampled regions of pesticide breakthrough curves. Nitrate evidently functioned as a surrogate for soil hydraulic data in well-drained loam soils conducive to conservative transport of nitrogen. Pesticide properties and macropore parameters could most benefit from improved characterization further to reduce model misfit and prediction uncertainty. RESULTS: The standard error of regression of the calibrated models was about the same at both NE (0.59) and MD (0.58), and overall reductions in prediction uncertainties of metolachlor and metolachlor ethane sulfonic acid concentrations were 98.0 and 98.6% respectively. Observation data groups reduced the prediction uncertainty by 55–90% at NE and by 28–96% at MD. Soil hydraulic parameters were well informed by the observed data at both sites, but pesticide and macropore properties had comparatively larger contributions after model calibration. CONCLUSIONS: Although the observed data were sparse, they substantially reduced prediction uncertainty in unsampled regions of pesticide breakthrough curves. Nitrate evidently functioned as a surrogate for soil hydraulic data in well-drained loam soils conducive to conservative transport of nitrogen. Pesticide properties and macropore parameters could most benefit from improved characterization further to reduce model misfit and prediction uncertainty.

Maryl;Nebraska↗

USGS2014

Avian response to conservation buffers in agricultural landscapes during winter

Native herbaceous vegetation cover along row‐crop field edges (i.e., field buffers) increases breeding densities of many bird species. However, the effect of field buffers on bird species during the non‐breeding season is less understood. We compared density, avian richness, and avian conservation value on row‐crop fields containing buffers strategically designed for wildlife versus fields without buffers in 3 southeastern U.S. states during winter 2007 and 2008. Fields with buffers were enrolled in U.S. Department of Agriculture, Conservation Reserve Program practice Habitat Buffers for Upland Birds (CP33), which targets restoration of northern bobwhite ( Colinus virginianus ) and other upland bird species. Overall species richness did not differ on fields with buffers versus fields without buffers in 2007, but was 29% greater on fields with buffers in 2008. Swamp sparrows ( Melospiza georgiana ), song sparrows ( M. melodia ), field sparrows ( Spizella pusilla ), and red‐bellied woodpeckers ( Melanerpes carolinus ) had greater densities on fields with buffers compared with fields without buffers. Increasing field‐buffer width did not result in greater bird densities. Our results suggest a small change in primary land use (≈7%) produced a disproportionate population response by some grassland‐dependent and woodland bird species during winter. Because field buffers provide a direct source of winter food and cover resources, they may be a pragmatic means to provide critical non‐breeding habitat with little alteration of existing agricultural systems.

Arkansas, Kentucky, Mississippi↗

USGS2019

Typha (cattail) invasion in North American wetlands: Biology, regional problems, impacts, ecosystem services, and management

Typha is an iconic wetland plant found worldwide. Hybridization and anthropogenic disturbances have resulted in large increases in Typha abundance in wetland ecosystems throughout North America at a cost to native floral and faunal biodiversity. As demonstrated by three regional case studies, Typha is capable of rapidly colonizing habitats and forming monodominant vegetation stands due to traits such as robust size, rapid growth rate, and rhizomatic expansion. Increased nutrient inputs into wetlands and altered hydrologic regimes are among the principal anthropogenic drivers of Typha invasion. Typha is associated with a wide range of negative ecological impacts to wetland and agricultural systems, but also is linked with a variety of ecosystem services such as bioremediation and provisioning of biomass, as well as an assortment of traditional cultural uses. Numerous physical, chemical, and hydrologic control methods are used to manage invasive Typha, but results are inconsistent and multiple methods and repeated treatments often are required. While this review focuses on invasive Typha in North America, the literature cited comes from research on Typha and other invasive species from around the world. As such, many of the underlying concepts in this review are relevant to invasive species in other wetland ecosystems worldwide.

Wetlands↗

USGS2022

Estimating bee abundance: Can mark-recapture methods validate common sampling protocols?

Wild bees are essential pollinators in natural and agricultural systems, but populations of some species have declined. Efforts to assess the status of wild bees are hindered by uncertainty in common sampling methods, such as pan traps and aerial netting, which may or may not provide a valid index of abundance across species and habitats. Mark-recapture methods are a common and effective means of estimating population size, widely used in vertebrates but rarely applied to bees. Here we review existing mark-recapture studies of wild bees and present a new case study comparing mark-recapture population estimates to pan trap and net capture for four taxa in a wild bee community. Net, but not trap, capture was correlated with abundance estimates across sites and taxa. Logistical limitations ensure that mark-recapture studies will not fully replace other bee sampling methods, but they do provide a feasible way to monitor selected species and measure the performance of other sampling methods.

Apidologie↗

USGS2016

Effect of variable annual precipitation and nutrient input on nitrogen and phosphorus transport from two Midwestern agricultural watersheds

Precipitation patterns and nutrient inputs affect transport of nitrate (NO 3 -N) and phosphorus (TP) from Midwest watersheds. Nutrient concentrations and yields from two subsurface-drained watersheds, the Little Cobb River (LCR) in southern Minnesota and the South Fork Iowa River (SFIR) in northern Iowa, were evaluated during 1996–2007 to document relative differences in timings and amounts of nutrients transported. Both watersheds are located in the prairie pothole region, but the SFIR exhibits a longer growing season and more livestock production. The SFIR yielded significantly more NO 3 -N than the LCR watershed (31.2 versus 21.3 kg NO 3 -N ha − 1 y − 1 ). The SFIR watershed also yielded more TP than the LCR watershed (1.13 versus 0.51 kg TP ha − 1 yr − 1 ), despite greater TP concentrations in the LCR. About 65% of NO 3 -N and 50% of TP loads were transported during April–June, and < 20% of the annual loads were transported later in the growing season from July–September. Monthly NO 3 -N and TP loads peaked in April from the LCR but peaked in June from the SFIR; this difference was attributed to greater snowmelt runoff in the LCR. The annual NO 3 -N yield increased with increasing annual runoff at a similar rate in both watersheds, but the LCR watershed yielded less annual NO 3 -N than the SFIR for a similar annual runoff. These two watersheds are within 150 km of one another and have similar dominant agricultural systems, but differences in climate and cropping inputs affected amounts and timing of nutrient transport.

Science of the Total Environment↗

USGS2025

Multiyear crop residue cover mapping using narrow-band vs. broad-band shortwave infrared satellite imagery

Crop residue serves an important role in agricultural systems as high levels of fractional crop residue cover ( f R ) can reduce erosion, preserve soil moisture, and build soil organic carbon. However, the ability to accurately quantify f R at scale has been limited. In this study we produced annual maps of f R for farmland in Maryland, USA using WorldView-3 (WV3) imagery paired with on-farm photographs ( n = 895) classified to f R using SamplePoint software. Univariate linear regressions were used to compare photograph f R to WV3 crop residue indices including: 1) Shortwave Infrared Normalized Difference Residue Index (SINDRI), 2) Shortwave Infrared Difference Residue Index (SIDRI), 3) Normalized Difference Tillage Index (NDTI), and 4) Shortwave Infrared Angle Index (SWIRA). SINDRI and SIDRI are based on narrow bands capable of measuring lignocellulose absorption features. NDTI and SWIRA are based on Landsat-comparable broad bands. Our findings demonstrated that SINDRI outperformed other indices in f R estimation in terms of coefficient of determination ( R 2 = 0.869) and root mean square error (RMSE = 0.111), when R 2 and RMSE were averaged across six individual years. For a univariate analysis combining five years of high-quality WV3 imagery, SINDRI again exhibited the highest f R estimation performance ( R 2 = 0.795; RMSE = 0.141), suggesting that SINDRI can map f R accurately with a singular relationship, potentially reducing the need for labor-intensive ground data collection. For broad-band indices, a multiple linear regression analysis that included a Water Index (WI) and Normalized Difference Vegetation Index (NDVI) as additional predictors increased the accuracy of f R estimation significantly, particularly for SWIRA ( R 2 = 0.767; RMSE = 0.144), but also NDTI ( R 2 = 0.654; RMSE = 0.174). Our findings suggest that while indices computed from narrow-band imagery are most accurate for f R estimation, SWIRA has the potential to improve f R estimation compared to NDTI, especially when used in conjunction with WI and NDVI. An index suite of SWIRA, WI, and NDVI can be computed with Landsat 4–9 imagery, providing a more accurate record of global f R dating back to 1982.

Soil and Tillage Research↗

USGS2001

Processes controlling the episodic streamwater transport of atrazine and other agrichemicals in an agricultural watershed

Episodic streamwater transport of atrazine (a common agricultural herbicide) and nutrients has been observed throughout agricultural watersheds in the United States and poses a serious threat to the quality of its water resources. Catchment-scale atrazine and nutrient transport processes after agricultural application are still poorly understood, and predicting episodic streamwater composition remains an elusive goal. We instrumented a 1.2-km 2 agricultural catchment near Harrisonburg, Virginia, and examined streamwater, overland flow, soil water, groundwater, and rainfall during the summer of 1998. Storm chemographs demonstrated different patterns for constituents derived primarily from weathering (silica and calcium), compared to constituents derived primarily from early spring land applications (nitrate, atrazine, DOC, potassium, chloride, and sulfate). During storms, the concentrations of silica and calcium decreased, the atrazine response was variable, and the concentrations of nitrate, DOC, potassium, chloride, and sulfate increased; the elevated nitrate signal lagged several hours behind the other elevated constituents. Graphical and statistical analyses indicated a relatively stable spring-fed baseflow was modified by a mixture of overland flow and soil water. A rapid, short-duration overland-flow pulse dominated the streamflow early in the event and contributed most of the potassium, DOC, chloride, suspended sediment, and atrazine. A longer-duration soil–water pulse dominated the streamflow later in the event and contributed the nitrate as well as additional potassium, DOC, sulfate, and atrazine. The contributions to the episodic streamflow were quantified using a flushing model in which overland-flow and soil–water concentrations decreased exponentially with time during an episode. Flushing time constants for the overland-flow and soil–water reservoirs were calculated on a storm-by-storm basis using separate tracers for each time-variable reservoir. Initial component concentrations were estimated through regression analyses. Mass-balance calculations were used for flow separations and to predict the observed streamwater composition. Model forecasts indicated that reduced fertilizer and pesticide application (rather than elimination of overland-flow or soil–water contributions) was necessary to improve the episodic streamwater composition. This study provides important additional understanding of the catchment-scale processes by which land-applied pesticides and nutrients can move through agricultural systems.

Virginia↗

USGS2021

A comprehensive statewide spatiotemporal stream assessment of per- and polyfluoroalkyl substances (PFAS) in an agricultural region of the United States

Public concern regarding per- and polyfluoroalkyl substances (PFAS) has grown substantially in recent years. In addition, research has documented multiple potential agriculture-related release pathways for PFAS (e.g., biosolids and livestock manure). Nevertheless, little research on the environmental prevalence of PFAS has been conducted in agricultural regions of the United States. To fill this gap, we conducted the first statewide spatiotemporal assessment of PFAS in Iowa streams across a region of intense agricultural activity. At least one PFAS was detected at 19 of the 60 stream sites sampled (32%) with 10 different PFAS detected statewide. The number of PFAS detected in the stream samples ranged from one to nine. While PFAS were detected in agricultural streams, sites with the most PFAS detected and in the highest concentration were small, effluent-affected streams where wastewater treatment plant discharge is driving stream PFAS concentrations. No individual PFAS had an exposure:activity ratio (EAR) of >1.0 (exposure concentration shown to trigger observed adverse biological activity). Five stream locations, however, had at least one EAR of >0.001, a precautionary effect screening threshold. Additional targeted temporal sampling would be beneficial to specifically capture potential agricultural source applications and corresponding runoff conditions to fully characterize the prevalence of PFAS in such agricultural systems.

Iowa↗

USGS2021

Integrated hydrology and operations modeling to evaluate climate change impacts in an agricultural valley irrigated with snowmelt runoff

Applying models to developed agricultural regions remains a difficult problem because there are no existing modeling codes that represent both the complex physics of the hydrology and anthropogenic manipulations to water distribution and consumption. We apply an integrated groundwater – surface water and hydrologic river operations model to an irrigated river valley in northwestern Nevada/northern California, United States to evaluate the impacts of climate change on snow-fed agricultural systems that use surface water and groundwater conjunctively. We explicitly represent individual surface water rights within the hydrologic model and allow the integrated code to change river diversions in response to earlier snowmelt runoff and water availability. Historically under-used supplemental groundwater rights are dynamically activated within the model to offset diminished surface water deliveries. The model accounts for feedbacks between the natural hydrology and anthropogenic stresses, which is a first-of-its-kind assessment of the impacts of climate change on individual water rights, and more broadly on river basin operations. Earlier snowmelt decreases annual surface water deliveries to all water rights, not just the junior water rights, owing to a lack of surface water storage in the upper river basin capable of capturing earlier runoff. Conversely, downstream irrigators with access to reservoir storage benefit from earlier runoff flowing past upstream points of diversion prior to the start of the irrigation season. Despite regional shifts toward greater reliance on groundwater for irrigation, crop consumption (a common surrogate for crop yield) decreases due to spatiotemporal changes in water supply that preferentially impact a subset of growers in the region.

California↗

USGS2009

Seasonal movements and home ranges of white-tailed deer in north-central South Dakota

Knowledge of movement patterns of white-tailed deer ( Odocoileus virginianus (Zimmermann, 1780)) inhabiting landscapes intensively modified by agricultural systems is important to the present and future understanding of deer ecology. Little information exists regarding daily and seasonal movements of white-tailed deer in north-central South Dakota. Therefore, our goal was to determine movement patterns and home-range use of female white-tailed deer in north-central South Dakota. From January 2005 to January 2007, 29 adult (>18 months) and 13 yearling (8–18 months) white-tailed deer were monitored for movement using radiotelemetry. We collected 2822 locations, calculated 76 home ranges, and documented 50 seasonal movements. Mean migration distance between summer and winter home ranges was 19.4 km (SE = 2.0 km). Mean 95% home-range size was 10.2 km 2 (SE = 1.2 km 2 , n = 27) during winter and 9.2 km 2 (SE = 1.0 km 2 , n = 49) during summer. Ambient temperature appeared to be a primary cause of seasonal migration. Additionally, movements exhibited by white-tailed deer in north-central South Dakota were influenced by a highly fragmented landscape dominated by row crops and pasture or grassland.

South Dakota↗

USGS2014

Assessing landscape constraints on species abundance: Does the neighborhood limit species response to local habitat conservation programs?

Landscapes in agricultural systems continue to undergo significant change, and the loss of biodiversity is an ever-increasing threat. Although habitat restoration is beneficial, management actions do not always result in the desired outcome. Managers must understand why management actions fail; yet, past studies have focused on assessing habitat attributes at a single spatial scale, and often fail to consider the importance of ecological mechanisms that act across spatial scales. We located survey sites across southern Nebraska, USA and conducted point counts to estimate Ring-necked Pheasant abundance, an economically important species to the region, while simultaneously quantifying landscape effects using a geographic information system. To identify suitable areas for allocating limited management resources, we assessed land cover relationships to our counts using a Bayesian binomial-Poisson hierarchical model to construct predictive Species Distribution Models of relative abundance. Our results indicated that landscape scale land cover variables severely constrained or, alternatively, facilitated the positive effects of local land management for Ring-necked Pheasants.

Nebraska↗

USGS2000

An assessment of irrigation needs and crop yield for the United States under potential climate changes

Past assessments of climate change on U.S. agriculture have mostly focused on changes in crop yield. Few studies have included the entire conterminous U.S., and few studies have assessed changing irrigation requirements. None have included the effects of changing soil moisture characteristics as determined by changing climatic forcing. This study assesses changes in irrigation requirements and crop yields for five crops in the areas of the U.S. where they have traditionally been grown. Physiologically-based crop models are used to incorporate inputs of climate, soils, agricultural management, and drought stress tolerance. Soil moisture values from a macroscale hydrologic model run under a future climate scenario are used to initialize soil moisture content at the beginning of each growing season. Historical crop yield data is used to calibrate model parameters and determine locally acceptable drought stress as a management parameter. Changes in irrigation demand and crop yield are assessed for both means and extremes by comparing results for atmospheric forcing close to the present climate with those for a future climate scenario. Assessments using the Canadian Center for Climate Modeling and Analysis General Circulation Model (CGCM1) indicate greater irrigation demands in the southern U.S. and decreased irrigation demands in the northern and western U.S. Crop yields typically increase except for winter wheat in the southern U.S. and corn. Variability in both irrigation demands and crop yields increases in most cases. Assessment results for the CGCM1 climate scenario are compared to those for the Hadley Centre for Climate Prediction and Research GCM (HadCM2) scenario for southwestern Georgia. The comparison shows significant differences in irrigation and yield trends, both in magnitude and direction. The differences reflect the high forecast uncertainty of current GCMs. Nonetheless, both GCMs indicate higher variability in future climatic forcing and, consequently, in the response of agricultural systems.

Open-File Report↗

USGS2023

Climate change and maple syrup: Producer observations, perceptions, knowledge, and adaptation strategies

Introduction: Climate change is impacting forest-based agricultural systems with implications for producer decision-making and livelihoods. This article presents a case study on the observations, perceptions, knowledge, and adaptation strategies of maple syrup producers in the United States to climate change. Methods: We carried out two semi-structured surveys with maple producers on: (1) climate change and its impacts on the maple system ( n = 106 participants); and (2) responses to climate adaptation scenarios ( n = 98 participants). Additionally, we carried out two focus groups and key informant interviews ( n = 70+) to understand barriers and opportunities for climate adaptation. One of these focus groups and follow up key informant interviews was with tribally affiliated community members with the intention to acknowledge Indigenous Peoples’ voices, history, and relationships to the land. Results: Findings highlight that most of the surveyed producers (89%) have experienced the negative impacts of climate on maple syrup production. While 40% of participants feel concerned regarding the future of the maple system, 39% feel hopeful, with significant differences based on the age of the surveyed producers. The majority of producers have adapted their harvesting practices to climate effects. Producers shared knowledge of multiple adaptation strategies in response to climate scenarios comprised of: (1) stand management practices such as diversification of sap species tapped; (2) harvesting practices such as changing the type and number of taps; (3) sap processing practices focused on the integration of technology such as the use of an evaporator and reverse osmosis; and (4) marketing practices such as innovation of products and marketing different maple syrup characteristics. Responses shared by tribally affiliated producers highlight knowledge of multiple adaptation strategies that focus on long-term ecological management of forests rather than technological solutions. Discussion: Overall, findings emphasize the importance of cooperation and diversification at every level and dimension of the maple system for its long-term resilience.

Frontiers in Forests and Global Change↗

USGS2017

Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016.

Remote Sensing↗

USGS1983

Impacts of forest herbicides on wildlife: Toxicity and habitat alteration

This paper begins with a review of both laboratory and field studies on tbe possible direct toxic effects of herbicides on terrestrial vertebrates, primarily birds and mammals. Alteration of the palatability of forage and changes in reproductive success are also discussed. Emphasis is placed on the use of herbicides in forestry; studies dealing with agricultural systems are referenced where appropriate. The indirect effects of herbicides on wildlife-habitat are then conceptualized and quantified using data from a 3-year study on effects of phenoxy and glyphosate herbicides on bird and small mammal communities in western Oregon. Data on density and habitat use are presented and compared with data available from other geographic regions.

Transactions of the North American Wildlife and Na↗

USGS2014

A seasonal agricultural drought forecast system for food-insecure regions of East Africa

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall is critical for end-of-season outcomes. Finally we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years. This means that this system might be particularity useful for identifying the events that present the greatest risk to the region.

Hydrology and Earth System Sciences↗

USGS1995

Spatial data in geographic information system format on agricultural chemical use, land use, and cropping practices in the United States

The spatial data in geographic information system format described in this report consist of estimates for all counties in the conterminous United States of the annual use of 96 herbicides in 1989; annual sales of nitrogen fertilizer, in tons, for 1985-91; and agricultural expenses, land use, chemical use, livestock holdings, and cropping practices in 1987. The source information, originally in tabular form, is summarized as digital polygon attribute data in the 18 geographic information system spatial data layers (coverages) provided. The information in these coverages can be used in estimating regional agricultural-chemical use or agricultural practices and in producing visual displays and mapping relative rates of agricultural-chemical use or agricultural practices across broad regions of the United States.

Water-Resources Investigations Report↗