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At least 91 records · Page 5Linked to original sources
USGS2017

Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008 to 2013. In this investigation, we sought to contribute to the availability of consistent CONUS crop cover maps by extending temporal coverage of the NASS CDL archive back eight additional years to 2000 by creating annual NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million records to train a classification tree algorithm and develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000–2013 at 250 m spatial resolution. The CCM and the maps for years 2008–2013 were assessed for accuracy relative to resampled NASS CDLs. The CCM performed well against a withheld test data set with a model prediction accuracy of over 90%. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains; however, the model did show a bias towards the ‘Other’ crop cover class, which caused frequent misclassifications of pixels around the periphery of large crop cover patch clusters and of pixels that form small, sparsely dispersed crop cover patches.

Remote Sensing Letters↗
USGS2010

Saltcedar (Tamarix spp.) and Russian Olive (Elaeagnus angustifolia) in the Western United States-A Report on the State of the Science

The Salt Cedar and Russian Olive Control Demonstration Act of 2006 (Public Law 109-320) directs the Department of the Interior to submit a report to Congress that includes an assessment of several issues surrounding these two nonnative trees, now dominant components of the vegetation along many rivers in the Western United States. This report was published in 2010 as a U.S. Geological Survey Scientific Investigations Report (available online at http://pubs.usgs.gov/sir/2009/5247). The report was produced through a collaborative effort led by the Bureau of Reclamation and U.S. Geological Survey, with critical contributions from the U.S. Department of Agriculture and from university researchers. The document synthesizes the state of the science and key research needs on the following topics related to management of saltcedar (Tamarix spp.) and Russian olive (Elaeagnus angustifolia) in the Western United States: their distribution and abundance (extent); the potential for water savings associated with controlling these species; considerations related to wildlife use of saltcedar and Russian olive habitat and restored habitats; methods of control and removal; possible utilization of dead biomass following control and removal; and approaches and challenges associated with site revegetation or restoration. A concluding chapter discusses possible long-term management strategies, potentially useful field-demonstration projects, and a planning process for on-the-ground projects involving removal of saltcedar and Russian olive.

Fact Sheet↗
USGS2026

Natomas basin giant gartersnake annual monitoring report 2024

The giant gartersnake ( Thamnophis gigas ) is a semi aquatic snake endemic to the Central Valley of California. After losing 95 percent of its historic wetland habitat (Frayer and others, 1989), giant gartersnakes became state and federally listed as a threatened species (California Fish and Game Commission, 1971; U.S. Fish and Wildlife Service 1993, 1999). Continued monitoring of current populations and implementation of suggested management actions is necessary to recover the species. The Natomas basin in Sacramento, California, supports a population of giant gartersnakes persisting in restored marshes and rice agriculture. This annual report summarizes the giant gartersnake monitoring project for 2024, focusing on the apparent survival, abundance, density, and distribution of the giant gartersnakes and the connectivity of habitat throughout the Natomas basin. In 2024, 131 giant gartersnakes were captured 216 times at 44 sites by hand or trap. The catch-per-unit effort decreased from 2023 to 2024 but was similar to other years of the study. Estimates of occupancy increased between 2023 and 2024, although the trend of occupancy from 2011 through 2024 is still decreasing overall at a mean annual rate of 3 percent per year. Apparent survival was much higher at Betts-Kismat-Silva from 2018 to 2019 and from 2021 to 2022 than in other years, but this may be partly attributed to different sampling efforts over the years. Trapping effort was more consistent in the Sills tract, and apparent survival was slightly higher in later years (2022–23 and 2023–24). Giant gartersnake populations appeared to remain stable in 2024, but abundance, density, survival, and distribution is highly variable across different sites and years of the study. Continued monitoring of the populations would allow for better trend estimates over time and assessment of the effects of management activities. Giant gartersnake populations throughout the basin and on reserve lands would likely benefit from the following: (1) creating more managed marsh; (2) increasing the amount of emergent tule vegetation in existing marshes (for example, Cummings, Natomas Farms, and Lucich South); (3) continuing to flood existing marshes in early spring; (4) maintaining rice agriculture; and (5) continuing research into conservation actions that target the giant gartersnake, such as habitat and water management and translocation.

California↗
USGS2001

Stream-water chemistry, nutrients, and pesticides in Town Brook, a headwater stream of the Cannonsville Reservoir Watershed, Delaware County, New York, 1999

Stream-water chemistry was monitored from January 1 through December 31, 1999, in the Town Brook watershed (TBW) in Delaware County, N.Y. to provide a basis for future evaluation of the effectiveness of Best Management Practices (BMPs) in decreasing agricultural nutrient and pesticide leaching to receiving waters. Total runoff from the watershed during 1999 was 664 millimeters (mm). Annual nutrient export (in kilograms per hectare) values were: ammonia (NH 3 ), 0.25; nitrate (NO 3 - ), 4.3; total nitrogen (TN), 10.6; orthophosphate (OP), 0.26; total dissolved phosphorus (TDP), 0.30; and total phosphorus (TP), 1.2 during 1999. Streamwater samples were collected during baseflow, elevated baseflow, and stormflow conditions. Stormflow, which produced the greatest flowweighted mean nutrient concentrations, represented only 41 percent of the annual runoff but accounted from 49 to 68 percent of the annual nutrient export. The highest seasonal flow-weighted mean concentrations were measured during the summer; the highest concentrations occurred during a large storm on July 4, 1999 with a recurrence interval greater than 100 years. The greatest seasonal export of dissolved nutrients (NH 3 , NO 3 - , OP, and TDP) occurred during the winter, whereas the greatest export of TN and TP was during the summer. Most of the TN and TP export during the summer occurred during the July 4 storm. That storm, together with a second large storm on September 16, 1999, accounted for the following percentages of annual export: ammonia, 17 percent; NO 3 - , 21 percent; TN, 45 percent; OP, 21 percent; TDP, 21 percent; and TP, 56 percent. Although these results provide information on the quantity and timing of nutrient export, they do not indicate the nutrient source nor the transport mechanisms by which nutrients are delivered to the stream. Baseflow and stormflow samples were collected for pesticide analyses at the Town Brook watershed outlet from January through July 1999. Eight pesticides and pesticide metabolites (degradation products) were detected in the samples. Four compounds (metolachlor, atrazine, metolachlor ESA, and metolachlor OA) were detected in concentrations greater than 1 micrograms per liter (μg/L) in one or more samples. Two of these compounds.the herbicide metabolites metalochlor ESA and metalochlor OA.were detected in concentrations higher than those of the parent compound metolachlor. Only one sample, collected during the July 4 storm, exceeded New York State surface-water-quality standards for any pesticide (simazine); its concentration of 0.53 μg/L was 0.03 μg/L higher than the New York State standard (0.50 μg/L). No concentrations exceeded Federal water-quality standards. Pesticide and metabolite concentrations were as much as 25 times greater during stormflow than during baseflow. Stormflow pesticide concentrations were indicative of a spring 'flushing', in which stream pesticide concentrations are elevated from concentrations typical during the rest of the year during the first few storms after pesticide application. Pesticides and pesticide metabolites were detected in all stormflow samples. These results illustrate the need to include baseflow and stormflow in pesticide sampling routines. The results of this study emphasize the need for (1) baseflow and stormflow sampling to capture the range of nutrient and pesticide concentrations from agricultural watersheds, and (2) research to define the mechanisms of nutrient and pesticide export in agriculutral watersheds.

New York↗
USGS2003

Preliminary assessment of DOC and THM precursor loads from a freshwater restored wetland, an agricultural field, and a tidal wetland in the Sacramento-San Joaquin River Delta

Water exported from the Sacramento-San Joaquin River Delta supplies drinking water to more than 22 million people in California. At certain times of the year, Delta waters contain relatively high concentrations of dissolved organic carbon (DOC) and bromide. During these times, chlorination of Delta water for drinking water disinfection will form disinfection byproducts, such as trihalomethanes (THMs), that can exceed the U.S. Environmental Protection Agency's maximum contaminant level for THMs of 80 mg/L. Important sources of DOC and THM precursors (types of DOC that form THMs when chlorinated) to the Delta include rivers, drainage water from peat islands, water from wetlands and areas with extensive riparian vegetation, and in-channel growth of algae and macrophytes. Due to proposed ecosystem restoration and creation of wetlands in the Delta, there is an urgent need for information on the relative loads of DOC and THM precursors produced from three different land uses: restored wetlands constructed for subsidence mitigation, tidal wetlands, and agricultural operations. We have been conducting research in the Delta to provide this information. A restored wetland and agricultural field located on Twitchell Island, and a tidal wetland on Browns Island have been monitored for flow, DOC, and THM precursors. Initial results indicate that the loads of DOC and THM precursors are similar for the restored wetland (surface water only) and the agricultural field. These land uses produce DOC loads of about 14 and 11 g C/m2/yr, respectively, and THM precursor loads of about 1.7 and 1.0 g THM/m2/yr, respectively. Estimates of DOC and THM precursor loads for the tidal wetland site on Browns Island and seepage associated with the restored wetland are being developed.

Conference Paper↗
USGS2007

Investigating the Environmental Effects of Agriculture Practices on Natural Resources: Scientific Contributions of the U.S. Geological Survey to Enhance the Management of Agricultural Landscapes

The U.S. Geological Survey (USGS) enhances and protects the quality of life in the United States by advancing scientific knowledge to facilitate effective management of hydrologic, biologic, and geologic resources. Results of selected USGS research and monitoring projects in agricultural landscapes are presented in this Fact Sheet. Significant environmental and social issues associated with agricultural production include changes in the hydrologic cycle; introduction of toxic chemicals, nutrients, and pathogens; reduction and alteration of wildlife habitats; and invasive species. Understanding environmental consequences of agricultural production is critical to minimize unintended environmental consequences. The preservation and enhancement of our natural resources can be achieved by measuring the success of improved management practices and by adjusting conservation policies as needed to ensure long-term protection.

Fact Sheet↗
USGS2011

Hyperspectral remote sensing of vegetation and agricultural crops: Knowledge gain and knowledge gap after 40 years of research

The focus of this chapter was to summarize the advances made over last 40+ years, as reported in various chapters of this book, in understanding, modeling, and mapping terrestrial vegetation using hyperspectral remote sensing (or imaging spectroscopy) using sensors that are ground-based, truck-mounted, airborne, and spaceborne. As we have seen in various chapters of this book and synthesized in this chapter, the advances made include: (a) significantly improved characterization and modeling of a wide array of biophysical and biochemical properties of vegetation, (b) ability to discriminate plant species and vegetation types with high degree of accuracies (c) reducing uncertainties in determining net primary productivity or carbon assessments from terrestrial vegetation, (d) improved crop productivity and water productivity models, (b), (e) ability to access stress resulting from causes such as management practices, pests and disease, water deficit or excess; , and (f) establishing more sensitive wavebands and indices to detect plant water\moisture content. The advent of spaceborne hyperspectral sensors (e.g., NASA’s Hyperion, ESA’s PROBA, and upcoming NASA’s HyspIRI) and numerous methods and techniques espoused in this book to overcome Hughes phenomenon or data redundancy when handling large volumes of hyperspectral data have generated tremendous interest in advancing our hyperspectral applications knowledge base over larger spatial extent such as region, nation, continent, and globe.

Book chapter↗
USGS2004

Geospatial data resampling and resolution effects on watershed modeling: A case study using the agricultural non-point source pollution model

Researchers have been coupling geographic information systems (GIS) data handling and processing capability to watershed and waterquality models for many years. This capability is suited for the development of databases appropriate for water modeling. However, it is rare for GIS to provide direct inputs to the models. To demonstrate the logical procedure of coupling GIS for model parameter extraction, we selected the Agricultural Non-Point Source (AGNPS) pollution model. Investigators can generate data layers at various resolutions and resample to pixel sizes to support models at particular scales. We developed databases of elevation, land cover, and soils at various resolutions in four watersheds. The ability to use multiresolution databases for the generation of model parameters is problematic for grid-based models. We used database development procedures and observed the effects of resolution and resampling on GIS input datasets and parameters generated from those inputs for AGNPS. Results indicate that elevation values at specific points compare favorably between 3- and 30-m raster datasets. Categorical data analysis indicates that land cover classes vary significantly. Derived parameters parallel the results of the base GIS datasets. Analysis of data resampled from 30-m to 60-, 120-, 210-, 240-, 480-, 960-, and 1920-m pixels indicates a general degradation of both elevation and land cover correlations as resolution decreases. Initial evaluation of model output values for soluble nitrogen and phosphorous indicates similar degradation with resolution. ?? Springer-Verlag 2004.

Journal of Geographical Systems↗
USGS2000

Importance of the Mississippi River Basin for investigating agricultural–chemical contamination of the hydrologic cycle

This special issue is devoted to recent and ongoing research relating to the fate and transport of agricultural chemicals in the Mississippi River Basin by the US Geological Survey Toxic Substances Hydrology (Toxics) Program. The Mississippi River Basin drains approximately 3 200 000 km 2 representing 41% of the United States. This is the largest river in the United States and the third largest in the world. The Mississippi River discharges an average of 19 920 m 3 /s of water into the Gulf of Mexico. The river is an extensively used resource, supplying drinking water to 70 cities in the United States. The Mississippi River Basin has undergone dramatic land use and cultural changes over the last 150 years. Approximately 70 million people now live within the basin, representing approximately 27% of the nation's population. This basin has also become one of the most productive agricultural regions in the world in terms of both crops and livestock grown. Approximately 65% of the nation's harvested cropland is grown in this basin, with more than 100 000 metric tons (t) of pesticides and approximately 6 500 000 t of commercial nitrogen fertilizers applied to cropland within the basin annually. The drainage of more than 20 000 000 ha within the basin has been enhanced by means of tile lines and ditches to lower the water table to make the cropland more productive. While removing the water from the soil as intended, this practice also leads to more rapid transport of contaminants to the river, and ultimately the Gulf of Mexico. Furthermore, the extensive chemical use in the Mississippi River Basin has led to the transport of pesticides and nitrate into the region’s streams, aquifers, and atmosphere. An estimated 1 000 000 t of nitrate-N is transported from the Mississippi River Basin into the Gulf of Mexico annually. The peak annual load of herbicides to the Gulf of Mexico has been documented at 1920 t. The fundamental goal of the papers presented in this volume is to provide a scientific basis for decisions necessary to promote sound and efficient agricultural practices and protect the quality of the nation's water resources.

Science of the Total Environment↗
USGS2019

Reducing wet ammonium deposition in Rocky Mountain National Park: The development and evaluation of a pilot early warning system for agricultural operations in eastern Colorado

Agricultural emissions are the primary source of ammonia (NH3) deposition in Rocky Mountain National Park (RMNP), a Class I area, that is granted special air quality protections under the Clean Air Act. Between 2014 and 2016, the pilot phase of the Colorado agricultural nitrogen early warning system (CANEWS) was developed for agricultural producers to voluntarily and temporarily minimize emissions of NH3 during periods of upslope winds. The CANEWS was created using trajectory analyses driven by outputs from an ensemble of numerical weather forecasts together with the climatological expertise of human forecasters. Here, we discuss the methods for the CANEWS and offer preliminary analyses of 33 months of the CANEWS based on atmospheric deposition data from two sites in RMNP as well as responses from agricultural producers after warnings were issued. Results showed that the CANEWS accurately predicted 6 of 9 high N deposition weeks at a lower-elevation observation site, but only 4 of 11 high N deposition weeks at a higher-elevation site. Sixty agricultural producers from 39 of Colorado’s agricultural operations volunteered for the CANEWS, and a two-way line of communication between agricultural producers and scientists was formed. For each warning issued, an average of 23 producers responded to a post-warning survey. Over 75% of responding CANEWS participants altered their practices after an alert. While the current effort was insufficient to reduce atmospheric deposition, we were encouraged by the collaborative spirit between agricultural, scientific, and resource management communities. Solving a broad and complex social-ecological problem requires both a technological approach, such as the CANEWS, and collaboration and trust from all participants, including agricultural producers, land managers, university researchers, and environmental agencies.

Colorado↗
USGS2022

Mapping actual evapotranspiration using Landsat for the conterminous United States: Google Earth Engine implementation and assessment of the SSEBop model

The estimation and mapping of actual evapotranspiration (ETa) is an active area of applied research in the fields of agriculture and water resources. Thermal remote sensing-based methods, using coarse resolution satellites, have been successful at estimating ETa over the conterminous United States (CONUS) and other regions of the world. In this study, we present CONUS-wide ETa from Landsat thermal imagery-using the Operational Simplified Surface Energy Balance (SSEBop) model in the Google Earth Engine (GEE) cloud computing platform. Over 150,000 Landsat satellite images were used to produce 10 years of annual ETa (2010–2019) at unprecedented scale. The accuracy assessment of the SSEBop results included point-based evaluation using monthly Eddy Covariance (EC) data from 25 AmeriFlux stations as well as basin-scale comparison with annual Water Balance ETa (WBET) for more than 1000 sub-basins. Evaluations using EC data showed generally mixed performance with weaker (R 2 < 0.6) correlation on sparsely vegetated surfaces such as grasslands or woody savanna and stronger correlation (R 2 > 0.7) over well-vegetated surfaces such as croplands and forests, but location-specific conditions rather than cover type were attributed to the variability in accuracy. Croplands performed best with R 2 of 0.82, root mean square error of 29 mm/month, and average bias of 12%. The WBET evaluation indicated that the SSEBop model is strong in explaining the spatial variability (up to R 2 > 0.90) of ETa across large basins, but it also identified broad hydro-climatic regions where the SSEBop ETa showed directional biases, requiring region-specific model parameter improvement and/or bias correction with an overall 7% bias nationwide. Annual ETa anomalies over the 10-year period captured widely reported drought-affected regions, for the most part, in different parts of the CONUS, indicating their potential applications for mapping regional- and field-scale drought and fire effects. Due to the coverage of the Landsat Path/Row system, the availability of cloud-free image pixels ranged from less than 12 (mountainous cloud-prone regions and U.S. Northeast) to more than 60 (U.S. Southwest) per year. However, this study reinforces a promising application of Landsat satellite data with cloud-computing for quick and efficient mapping of ETa for agricultural and water resources assessments at the field scale.

Remote Sensing of Environment↗
USGS2000

Panola Mountain, Georgia: A Water, Energy, and Biogeochemical Budgets Program Site

The Panola Mountain Research Watershed (PMRW) is a 41-hectare forested watershed in the southern Piedmont physiographic province near Atlanta, Georgia. The watershed contains a naturally regenerated second-growth forest on abandoned agricultural land, typical of the Piedmont. Research at PMRW has focused on how streamflow is generated, and in particular, on how water and solutes move from hillslopes to the stream. The research shows that streamflow and solute concentrations of precipitation, throughfall, soil water and streamwater change rapidly during rainstorms. Furthermore, these variations are strongly associated with the soil-moisture conditions, the soil type and thickness, and the rainfall intensity, all of which effect the chemical interactions and mixing of water traveling along hydrological pathways over and through the bedrock and soils.

Georgia↗
USGS2023

Improving the operational simplified surface energy balance evapotranspiration model using the forcing and normalizing operation

Actual evapotranspiration modeling is providing useful information for researchers and resource managers in agriculture and water resources around the world. The performance of models depends on the accuracy of forcing inputs and model parameters. We developed an improved approach to the parameterization of the Operational Simplified Surface Energy Balance (SSEBop) model using the Forcing and Normalizing Operation (FANO). SSEBop has two key model parameters that define the model boundary conditions. The FANO algorithm computes the wet-bulb boundary condition using a linear FANO Equation relating surface temperature, surface psychrometric constant, and the Normalized Difference Vegetation Index (NDVI). The FANO parameterization was implemented on two computing platforms using Landsat and gridded meteorological datasets: (1) Google Earth Engine (GEE) and (2) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA). Evaluation was conducted by comparing modeled actual evapotranspiration ( ETa ) estimates with AmeriFlux eddy covariance (EC) and water balance ETa from level-8 Hydrologic Unit Code sub-basins in the conterminous United States. FANO brought substantial improvements in model accuracy and operational implementation. Compared to the earlier version (v0.1.7), SSEBop FANO (v0.2.6) reduced grassland bias from 47% to −2% while maintaining comparable bias for croplands (11% versus −7%) against EC data. A water balance-based ETa bias evaluation showed an overall improvement from 7% to −1%. Climatology versus annual gridded reference evapotranspiration ( ETr ) produced comparable ETa results, justifying the use of climatology ETr for the global SSEBop Landsat ETa that is accessible through the ESPA website. Besides improvements in model accuracy, SSEBop FANO increases the spatiotemporal coverage of ET modeling due to the elimination of high NDVI requirements for model parameterization. Because of the existence of potential biases from forcing inputs and model parameters, continued evaluation and bias corrections are necessary to improve the absolute magnitude of ETa for localized water budget applications.

Remote Sensing↗
USGS2014

Predicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices

In southern Ethiopia, Eastern Kenya, and southern Somalia poor boreal spring rains in 1999, 2000, 2004, 2007, 2008, 2009 and 2011 contributed to severe food insecurity and high levels of malnutrition. Predicting rainfall deficits in this region on seasonal and decadal time frames can help decision makers support disaster risk reduction while guiding climate-smart adaptation and agricultural development. Building on recent research that links more frequent droughts to a stronger Walker Circulation, warming in the Indo-Pacific warm pool, and an increased western Pacific sea surface temperature (SST) gradient, we explore the dominant modes of East African rainfall variability, links between these modes and sea surface temperatures, and a simple index-based monitoring-prediction system suitable for drought early warning.

Hydrology and Earth System Sciences↗
USGS2018

Current research in land, water, and agroecosystems: ASABE journals 2017 year in review

This article highlights current research into land and water resources, agroecosystems, and agricultural production systems published by the Natural Resources and Environmental Systems (NRES) community of ASABE journals (Transactions of the ASABE and Applied Engineering in Agriculture) in 2017. This article reviews the context, scope, and key results of the published articles and perhaps more importantly recommends areas for increased research attention. Experimental and modeling advances were described in hydrology, agroecosystems, climate-change effects, soil erosion, irrigation, drainage, forest resources, livestock systems, natural treatment systems, international water issues, and water quality topic areas. Three special collections were published (International Watershed Technology, Crop Modeling to Optimize Water Use, and Advances in Drainage). Other focal areas included 14 articles relating to livestock waste management, 13 concerning irrigated agricultural systems, 8 addressing climate change effects on land and water resources, and 16 on various aspects of soil erosion measurement and modeling. Building on the articles reviewed from 2017 and toward a vision of future agroecosystems research, the NRES community of ASABE journals strives to grow its role in making new knowledge accessible to sustain agricultural and natural systems in a changing world. In this vane, recommendations for future research direction are discussed with an emphasis on increased application of remote sensing data to agroecosystems research, improved assessment of agroecosystem resiliency and vulnerability to land and climate change, development of integrated models of agroecosystem services, meeting stubborn water management challenges in agricultural production systems, and focusing on publishing fully reproducible model results.

Transactions of the ASABE↗
USGS2019

Identifying the molecular signatures of agricultural expansion in Amazonian headwater streams

Agricultural impacts on aquatic ecosystems are well-studied, however, most research has focused on temperate regions, whereas the forefront of agricultural expansion is currently in the tropics. At the vanguard of this growth is the boundary between the Amazon and Cerrado biomes in Brazil, driven primarily by expansion of soybean and corn croplands. Here we examine the impacts of cropland expansion on receiving lowland Amazon Basin headwater streams in terms of dissolved organic carbon (DOC) concentration and dissolved organic matter (DOM) composition via ultra-high resolution mass spectrometry. Streams draining croplands had lower DOC concentrations and DOM molecular signatures enriched in N- and S-containing formula in comparison to forested streams. Cropland streams were also enriched in aliphatic, peptide-like and highly unsaturated and phenolic (low O/C) compound categories in comparison to forest streams (enriched in polyphenolics, condensed aromatics and highly unsaturated and phenolic (high O/C) compound categories) indicative of the shifting of sources from organic-rich surface soils and litter layers to autochthonous and more microbial biomass. Distinct molecular assemblages were strongly correlated with cropland and forest catchments, highlighting headwater streams as sentinels for detecting change. On investigation of unique molecular formulae present in only cropland sites, four cropland markers provided the ability to track agricultural impacts in the region. Overall, these patterns indicate reduced organic matter inputs in croplands and greater microbial degradation at these sites leading to declining DOC concentrations, and DOM of more microbial character in receiving streams that is more biolabile, with clear ramifications for downstream ecology and biogeochemical cycles.

Amazon River basin, Tanguro Ranch, Xinga River↗
USGS2011

Derived crop management data for the LandCarbon Project

The LandCarbon project is assessing potential carbon pools and greenhouse gas fluxes under various scenarios and land management regimes to provide information to support the formulation of policies governing climate change mitigation, adaptation and land management strategies. The project is unique in that spatially explicit maps of annual land cover and land-use change are created at the 250-meter pixel resolution. The project uses vast amounts of data as input to the models, including satellite, climate, land cover, soil, and land management data. Management data have been obtained from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) and USDA Economic Research Service (ERS) that provides information regarding crop type, crop harvesting, manure, fertilizer, tillage, and cover crop (U.S. Department of Agriculture, 2011a, b, c). The LandCarbon team queried the USDA databases to pull historic crop-related management data relative to the needs of the project. The data obtained was in table form with the County or State Federal Information Processing Standard (FIPS) and the year as the primary and secondary keys. Future projections were generated for the A1B, A2, B1, and B2 Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) scenarios using the historic data values along with coefficients generated by the project. The PBL Netherlands Environmental Assessment Agency (PBL) Integrated Model to Assess the Global Environment (IMAGE) modeling framework (Integrated Model to Assess the Global Environment, 2006) was used to develop coefficients for each IPCC SRES scenario, which were applied to the historic management data to produce future land management practice projections. The LandCarbon project developed algorithms for deriving gridded data, using these tabular management data products as input. The derived gridded crop type, crop harvesting, manure, fertilizer, tillage, and cover crop products are used as input to the LandCarbon models to represent the historic and the future scenario management data. The overall algorithm to generate each of the gridded management products is based on the land cover and the derived crop type. For each year in the land cover dataset, the algorithm loops through each 250-meter pixel in the ecoregion. If the current pixel in the land cover dataset is an agriculture pixel, then the crop type is determined. Once the crop type is derived, then the crop harvest, manure, fertilizer, tillage, and cover crop values are derived independently for that crop type. The following is the overall algorithm used for the set of derived grids. The specific algorithm to generate each management dataset is discussed in the respective section for that dataset, along with special data handling and a description of the output product.

Open-File Report↗