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

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45 records · Page 3Linked to original sources
USGS2016

Quaternary geologic map of the Wolf Point 1° × 2° quadrangle, Montana and North Dakota

The Wolf Point quadrangle encompasses approximately 16,084 km 2 (6,210 mi 2 ). The northern boundary is the Montana/Saskatchewan (U.S.-Canada) boundary. The quadrangle is in the Northern Plains physiographic province and it includes the Peerless Plateau and Flaxville Plain. The primary river is the Missouri River. The map units are surficial deposits and materials, not landforms. Deposits that comprise some constructional landforms (for example, ground-moraine deposits, end-moraine deposits, and stagnation-moraine deposits, all composed of till) are distinguished for purposes of reconstruction of glacial history. Surficial deposits and materials are assigned to 23 map units on the basis of genesis, age, lithology or composition, texture or particle size, and other physical, chemical, and engineering characteristics. It is not a map of soils that are recognized in pedology or agronomy. Rather, it is a generalized map of soils recognized in engineering geology, or of substrata or parent materials in which pedologic or agronomic soils are formed. Glaciotectonic (ice-thrust) structures and deposits are mapped separately, represented by a symbol. The surficial deposits are glacial, ice-contact, glaciofluvial, alluvial, lacustrine, eolian, colluvial, and mass-movement deposits. Till of late Wisconsin age is represented by three map units. Till of Illinoian age also is mapped. Till deposited during pre-Illinoian glaciations is not mapped, but is widespread in the subsurface. Linear ice-molded landforms (primarily drumlins), shown by symbol, indicate directions of ice flow during late Wisconsin and Illinoian glaciations. The Quaternary geologic map of the Wolf Point quadrangle, northeastern Montana and North Dakota, was prepared to provide a database for compilation of a Quaternary geologic map of the Regina 4° × 6° quadrangle, United States and Canada, at scale 1:1,000,000, for the U.S. Geological Survey Quaternary Geologic Atlas of the United States map series. This map was compiled from data from many sources, at several different map scales. That information was generalized and simplified, and then transferred to a base map at 1:250,000 scale to serve as the base for final reduction to 1:1,000,000, the nominal reading scale of maps in the Quaternary Geologic Atlas of the United States map series. This map is the generalized and simplified 1:250,000 scale compilation. Letter symbols for the map units are those used for the same units in the Quaternary Geologic Atlas of the United States map series. The map summarizes new, and selected published and unpublished, geologic information for public use and for use by Federal, State, and local governmental agencies for land use planning, including assessment of natural resources, natural hazards, recreation potential, and land use management. It also is a base from which a variety of maps relating to earth surface processes and Quaternary geologic history can be derived.

Montana, North Dakota↗
USGS2023

Remote sensing evaluation of winter cover crop springtime performance and the impact of delayed termination

In 2019, the Maryland Department of Agriculture's Winter Cover Crop Program introduced a delayed termination incentive (after May 1) to promote springtime biomass accumulation. We used satellite imagery calibrated with springtime in situ measurements collected from 2006–2021 ( n = 722) to derive biomass estimates for Maryland fields planted to cereal cover crop species (286,200 ha total over two seasons). Cover crop C content remained steady throughout the cover crop growing season (42.6% of biomass), whereas N concentration had an inverse relationship with biomass and ranged from 1.7 to 2.9%. Throughout Maryland, delayed termination fields ( n = 19,120; average termination of May 18) were, on average, estimated to accumulate an additional 789 kg of biomass, 15 kg of N, and 336 kg of C per hectare when compared to fields associated with standard termination dates ( n = 28,811; average termination of April 16). Over two cover crop seasons (2019–2021), the delayed termination incentive yielded an extra 75,660,000 kg biomass, 1,526,000 kg N, and 32,230,000 kg C across 96,040 hectares. Regularly terminated field incentives cost an average of US$0.10 per kg of biomass and $4.09 per kg of N, with variability associated with agronomic management (species, planting method). Delayed termination fields cost of $0.08 per kg of biomass and $3.51 per kg of N. Late-planted cover crops that were terminated early had minimal environmental benefit, and wheat, which comprised 68% of cover crop area, performed poorly compared with other cereal species. Our findings demonstrate that substantial additional springtime biomass, C, and N accumulation were achieved through the delayed termination incentive.

Maryland↗
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↗
USGS2010

Climate warming-induced intensification of the hydrologic cycle: A review of the published record and assessment of the potential impacts on agriculture

Climate warming is expected to intensify and accelerate the global hydrologic cycle resulting in increases in evaporation, evapotranspiration (ET), atmospheric water-vapor content, and precipitation. The strength of the hydrologic response, or sensitivity of the response for a given degree of warming, is a critical outstanding question in climatology and hydrology. In this review chapter, I examine the published record of trends in various components of the hydrologic cycle and associated variables to assess observed hydrologic responses to warming during the period of observational records. Global and regional trends in evaporation, ET, and atmospheric water-vapor content and several large river basin water-balance studies support an ongoing intensification of the hydrologic cycle. Global trends in precipitation, runoff, and soil moisture are more uncertain than the trends in the variables noted above, in part because of high spatial and temporal variability. Trends in associated variables, such as systematic changes in ocean salinity, the length of the growing season, and the rate of precipitation recycling are generally consistent with intensification of the hydrologic cycle. The evidence for an increase in the frequency, intensity, or duration of extreme-weather events like hurricanes is mixed and remains uncertain. The largest potential impacts to agricultural systems depend greatly on the responses of hydrologic variables that are the most uncertain; for example, intensity and duration of heavy rainfall events; frequency, intensity, and duration of major storms and droughts; and rates of erosion. Impacts on agriculture will depend greatly on how insects, diseases, weeds, nutrient cycling, effectiveness of agrichemicals, and heat stress are affected by an intensification of the hydrologic cycle.

Advances in Agronomy↗
USGS2014

Productivity and carbon dioxide exchange of leguminous crops: estimates from flux tower measurements

Net CO 2 exchange data of legume crops at 17 flux tower sites in North America and three sites in Europe representing 29 site-years of measurements were partitioned into gross photosynthesis and ecosystem respiration by using the nonrectangular hyperbolic light-response function method. The analyses produced net CO 2 exchange data and new ecosystem-scale ecophysiological parameter estimates for legume crops determined at diurnal and weekly time steps. Dynamics and annual totals of gross photosynthesis, ecosystem respiration, and net ecosystem production were calculated by gap filling with multivariate nonlinear regression. Comparison with the data from grain crops obtained with the same method demonstrated that CO 2 exchange rates and ecophysiological parameters of legumes were lower than those of maize ( Zea mays L.) but higher than for wheat ( Triticum aestivum L.) crops. Year-round annual legume crops demonstrated a broad range of net ecosystem production, from sinks of 760 g CO 2 m –2 yr –1 to sources of –2100 g CO 2 m –2 yr –1 , with an average of –330 g CO 2 m –2 yr –1 , indicating overall moderate CO 2 –source activity related to a shorter period of photosynthetic uptake and metabolic costs of N 2 fixation. Perennial legumes (alfalfa, Medicago sativa L.) were strong sinks for atmospheric CO 2 , with an average net ecosystem production of 980 (range 550–1200) g CO 2 m –2 yr –1 .

Agronomy Journal↗
USGS2015

Simulating maize yield and bomass with spatial variability of soil field capacity

Spatial variability in field soil properties is a challenge for system modelers who use single representative values, such as means, for model inputs, rather than their distributions. In this study, the root zone water quality model (RZWQM2) was first calibrated for 4 yr of maize ( Zea mays L.) data at six irrigation levels in northern Colorado and then used to study spatial variability of soil field capacity (FC) estimated in 96 plots on maize yield and biomass. The best results were obtained when the crop parameters were fitted along with FCs, with a root mean squared error (RMSE) of 354 kg ha –1 for yield and 1202 kg ha –1 for biomass. When running the model using each of the 96 sets of field-estimated FC values, instead of calibrating FCs, the average simulated yield and biomass from the 96 runs were close to measured values with a RMSE of 376 kg ha –1 for yield and 1504 kg ha –1 for biomass. When an average of the 96 FC values for each soil layer was used, simulated yield and biomass were also acceptable with a RMSE of 438 kg ha –1 for yield and 1627 kg ha –1 for biomass. Therefore, when there are large numbers of FC measurements, an average value might be sufficient for model inputs. However, when the ranges of FC measurements were known for each soil layer, a sampled distribution of FCs using the Latin hypercube sampling (LHS) might be used for model inputs.

Agronomy Journal↗
USGS2023

Integration of remote sensing and field observations in evaluating DSSAT model for estimating maize and soybean growth and yield in Maryland, USA

Crop models are useful for evaluating crop growth and yield at the field and regional scales, but their applications and accuracies are restricted by input data availability and quality. To overcome difficulties inherent to crop modeling, input data can be enhanced by the incorporation of remotely sensed and field observations into crop growth models. This approach has been recognized to be an important way to monitor crop growth conditions and to predict yield at the field and regional scale. In recent years, satellite remote sensing has provided high-temporal and high-spatial-resolution data that allow for generating continuous time series of biophysical parameters such as vegetation indices, leaf area index, and phenology. The objectives of this study were to use remote sensing along with field observations as inputs to the Decision Support System for Agro-Technology (DSSAT) model to estimate soybean and maize growth and yield. The study used phenology and leaf area index (LAI) data derived from Planet Fusion (daily, 3 m) satellite imagery along with field observation data on crop growth stage, LAI and yield collected at the United State Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center (BARC), Beltsville, Maryland. For maize, a total of 17 treatments (site years) were used (ten treatments for model calibration and seven treatments for validation), while for soybean (maturity groups three and four), a total of 18 treatments were used (nine for calibration and nine for validation). The calibrated model was tested against an independent, multi-location and multi-year set of phenology and yield data (2017–2020) from BARC fields. The model accurately simulated maize and soybean days to flowering and maturity and produced reasonable yield estimates for most fields and years. Model run for independent locations and years produced good results for phenology and yields for both maize and soybean, as indicated by index of agreement (d) values ranging from 0.65 to 0.93 and normalized root-mean-squared error values ranging from 1 to 20%, except for soybean maturity group four. Overall, model performances with respect to phenology and grain yield for maize and soybean were good and consistent with other DSSAT evaluation studies. The inclusion of remote sensing along with field observations in crop-growth model inputs can provide an effective approach for assessing crop conditions, even in regions lacking ground data.

Maryland↗
USGS2025

Agronomic practices vs. natural soil factors: Influences on nitrous oxide emissions from corn and soybean fields.

Nitrous oxide (N 2 O), a potent greenhouse gas (GHG) and major contributor to climate change, is primarily released through agricultural activities. To better understand and quantify how land management practices, local climate conditions, and soil physicochemical properties affect these agricultural N 2 O emissions, we conducted a review of the peer-reviewed literature on N 2 O emission from corn [ Zea mays L.] and soybean [ Glycine max (L.) Merr.] fields. We evaluated the seasonal, cumulative effects of three nitrogen fertilizer rates—no fertilizer (0), low (<188 kg N ha −1 ), and high (188–400 kg N ha −1 )—tillage practices, local climate (precipitation and temperature), soil texture, and soil pH on soil N 2 O emissions. This meta-analysis included 77 articles for corn and 22 articles for soybean fields. Average N 2 O emissions during the corn rotation were 2.34 and 2.45 kg N 2 O-N ha −1 season −1 under low and high N fertilizer rates, respectively, and were both substantially ( p < 0.0001) greater than those of non-fertilized corn fields (0.91 kg N 2 O-N ha −1 season −1 ). Non-fertilized soybean fields showed seasonal N 2 O emissions of 0.74 kg N 2 O-N ha −1 , while low fertilizer application triggered a sharp increase (1.87 kg N 2 O-N ha −1 ) in N 2 O emissions by roughly 2.5 times ( p < 0.028). Increased temperature did not significantly ( p > 0.05) affect the emission of N 2 O from fertilized or non-fertilized corn fields. Regardless of fertilization and tillage practices, our analysis, including Principal Component Analysis, revealed that in corn fields, precipitation and soil pH are the dominant factors influencing soil N 2 O emissions. This study uniquely quantifies the influence of climate–soil factors, such as precipitation and soil pH, alongside agronomic practices, on N 2 O emissions, offering new insights beyond previous reviews focused primarily on fertilizer rates or tillage effects.

Agronomy↗
USGS2013

Biochar effect on maize yield and soil characteristics in five conservation farming sites in Zambia

Biochar addition to agricultural soils can improve soil fertility, with the added bonus of climate change mitigation through carbon sequestration. Conservation farming (CF) is precision farming, often combining minimum tillage, crop rotation and residue retention. In the present farmer-led field trials carried out in Zambia, the use of a low dosage biochar combined with CF minimum tillage was tested as a way to increase crop yields. Using CF minimum tillage allows the biochar to be applied to the area where most of the plant roots are present and mirrors the fertilizer application in CF practices. The CF practice used comprised manually hoe-dug planting 10-L sized basins, where 10%–12% of the land was tilled. Pilot trials were performed with maize cob biochar and wood biochar on five soils with variable physical/chemical characteristics. At a dosage as low as 4 tons/ha, both biochars had a strong positive effect on maize yields in the coarse white aeolian sand of Kaoma, West-Zambia, with yields of 444% ± 114% ( p = 0.06) and 352% ± 139% ( p = 0.1) of the fertilized reference plots for maize and wood biochar, respectively. Thus for sandy acidic soils, CF and biochar amendment can be a promising combination for increasing harvest yield. Moderate but non-significant effects on yields were observed for maize and wood biochar in a red sandy clay loam ultisol east of Lusaka, central Zambia (University of Zambia, UNZA, site) with growth of 142% ± 42% ( p > 0.2) and 131% ± 62% ( p > 0.2) of fertilized reference plots, respectively. For three other soils (acidic and neutral clay loams and silty clay with variable cation exchange capacity, CEC), no significant effects on maize yields were observed ( p > 0.2). In laboratory trials, 5% of the two biochars were added to the soil samples in order to study the effect of the biochar on physical and chemical soil characteristics. The large increase in crop yield in Kaoma soil was tentatively explained by a combination of an increased base saturation (from <50% to 60%–100%) and cation exchange capacity (CEC; from 2–3 to 5–9 cmol/kg) and increased plant-available water (from 17% to 21%) as well as water vapor uptake (70 mg/g on maize cob biochar at 50% relative humidity).

Agronomy Journal↗