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Results for “Journal of Integrative Agriculture”

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63 records · Page 4Linked to original sources
USGS2007

Hydrologic connectivity and the contribution of stream headwaters to ecological integrity at regional scales

Cumulatively, headwater streams contribute to maintaining hydrologic connectivity and ecosystem integrity at regional scales. Hydrologic connectivity is the water-mediated transport of matter, energy and organisms within or between elements of the hydrologic cycle. Headwater streams compose over two-thirds of total stream length in a typical river drainage and directly connect the upland and riparian landscape to the rest of the stream ecosystem. Altering headwater streams, e.g., by channelization, diversion through pipes, impoundment and burial, modifies fluxes between uplands and downstream river segments and eliminates distinctive habitats. The large-scale ecological effects of altering headwaters are amplified by land uses that alter runoff and nutrient loads to streams, and by widespread dam construction on larger rivers (which frequently leaves free-flowing upstream portions of river systems essential to sustaining aquatic biodiversity). We discuss three examples of large-scale consequences of cumulative headwater alteration. Downstream eutrophication and coastal hypoxia result, in part, from agricultural practices that alter headwaters and wetlands while increasing nutrient runoff. Extensive headwater alteration is also expected to lower secondary productivity of river systems by reducing stream-system length and trophic subsidies to downstream river segments, affecting aquatic communities and terrestrial wildlife that utilize aquatic resources. Reduced viability of freshwater biota may occur with cumulative headwater alteration, including for species that occupy a range of stream sizes but for which headwater streams diversify the network of interconnected populations or enhance survival for particular life stages. Developing a more predictive understanding of ecological patterns that may emerge on regional scales as a result of headwater alterations will require studies focused on components and pathways that connect headwaters to river, coastal and terrestrial ecosystems. Linkages between headwaters and downstream ecosystems cannot be discounted when addressing large-scale issues such as hypoxia in the Gulf of Mexico and global losses of biodiversity.

Journal of the American Water Resources Associatio↗
USGS2014

Characterizing phosphorus dynamics in tile-drained agricultural fieldsof eastern Wisconsin

Artificial subsurface drainage provides an avenue for the rapid transfer of phosphorus (P) from agricultural fields to surface waters. This is of particular interest in eastern Wisconsin, where there is a concentrated population of dairy farms and high clay content soils prone to macropore development. Through collaboration with private landowners, surface and tile drainage was measured and analyzed for dissolved reactive P (DRP) and total P (TP) losses at four field sites in eastern Wisconsin between 2005 and 2009. These sites, which received frequent manure applications, represent a range of crop management practices which include: two chisel plowed corn fields (CP1, CP2), a no-till corn–soybean field (NT), and a grazed pasture (GP). Subsurface drainage was the dominant pathway of water loss at each site accounting for 66–96% of total water discharge. Average annual flow-weighted (FW) TP concentrations were 0.88, 0.57, 0.21, and 1.32 mg L −1 for sites CP1, CP2, NT, and GP, respectively. Low TP concentrations at the NT site were due to tile drain interception of groundwater flow where large volumes of tile drainage water diluted the FW-TP concentrations. Subsurface pathways contributed between 17% and 41% of the TP loss across sites. On a drainage event basis, total drainage explained between 36% and 72% of the event DRP loads across CP1, CP2, and GP; there was no relationship between event drainflow and event DRP load at the NT site. Manure applications did not consistently increase P concentrations in drainflow, but annual FW-P concentrations were greater in years receiving manure applications compared to years without manure application. Based on these field measures, P losses from tile drainage must be integrated into field level P budgets and P loss calculations on heavily manured soils, while also acknowledging the unique drainage patterns observed in eastern Wisconsin.

Wisconsin↗
USGS2026

What is the (real) rate of soil health practice adoption? Making sense of three data sources

Conservation stakeholders looking to quantify the impact of their investments to increase soil health practice adoption over time often face challenges in interpreting practice adoption data due to discrepancies in language and results among data sources. Similarly, efforts to estimate environmental outcomes of practice adoption, such as water quality and greenhouse gas emissions, can vary depending on different practice adoption input data. To help make sense of different adoption data sources, we compared county-level adoption data for winter cover crops (WCC), no-till (NT), and reduced tillage (RT) in three areas of the United States with contrasting climates and production systems: central Illinois (CIL), southern Illinois (SIL), and western New York (WNY). We analyzed data available during 2015 through 2022 from the Operational Tillage Information System (OpTIS, remote sensing), US Census of Agriculture (AgCensus, a farmer survey), and, specifically in Illinois, the Illinois Soil Conservation Transect Survey (Transect, a roadside survey). The magnitude of differences between the datasets depended on the practice and geographic location. For example, OpTIS and AgCensus tillage data were much more similar in Illinois (average difference of less than 4 percentage points) compared to New York (average differences of 20 percentage points). Similarly, there was less variability and smaller differences between OpTIS and AgCensus WCC data in Illinois compared to WNY. AgCensus tended to report lower WCC adoption for Illinois and greater adoption in WNY compared to OpTIS. All data sources agreed that the rate of change in tillage practices is slow (mainly –1% to 1%) and that adoption of WCC is low (assuming linear growth, it could take nearly a century to reach 50% WCC adoption in CIL). Differences among the datasets were attributed to definitional inconsistencies for RT and NT and how WCC data were acquired. For example, the AgCensus asks if a WCC was planted, whereas OpTIS and Transect evaluate the presence of a standing WCC. Data sources also reflect different time periods (calendar years or crop years) and types of cropland assessed (corn [ Zea mays L.], soybean [ Glycine max {L.} Merr.], or all cropland). We propose two recommendations to improve interpretation and consistency: (1) a working group to harmonize definitions and protocols and develop educational materials for data users, and (2) a research effort that integrates different adoption data types and produces publicly available adoption data at HUC-10 and county scales. Such activities could help improve data access and utility for evidence-based conservation decision-making and enhance the accuracy of environmental models that rely on adoption data as input.

Journal of Soil and Water Conservation↗
USGS2024

Causal inference approaches reveal both positive and negative unintended effects of agricultural and urban management practices on instream biological condition

Agricultural and urban management practices (MPs) are primarily designed and implemented to reduce nutrient and sediment concentrations in streams. However, there is growing interest in determining if MPs produce any unintended positive effects, or co-benefits, to instream biological and habitat conditions. Identifying co-benefits is challenging though because of confounding variables (i.e., those that affect both where MPs are applied and stream biota), which can be accounted for in novel causal inference approaches. Here, we used two causal inference approaches, propensity score matching (PSM) and Bayesian network learning (BNL), to identify potential MP co-benefits in the Chesapeake Bay watershed portion of Maryland, USA. Specifically, we examined how MPs may modify instream conditions that impact fish and macroinvertebrate indices of biotic integrity (IBI) and functional and taxonomic endpoints. We found evidence of positive unintended effects of MPs for both benthic macroinvertebrates and fish indicated by higher IBI scores and specific endpoints like the number of scraper macroinvertebrate taxa and lithophilic spawning fish taxa in a subset of regions. However, our results also suggest MPs have negative unintended effects, especially on sensitive benthic macroinvertebrate taxa and key instream habitat and water quality metrics like specific conductivity. Overall, our results suggest MPs offer co-benefits in some regions and catchments with largely degraded conditions but can have negative unintended effects in some regions, especially in catchments with good biological conditions. We suggest the number and types of MPs drove these mixed results and highlight carefully designed MP implementation that incorporates instream biological data at the catchment scale could facilitate co-benefits to instream biological conditions. Our study underscores the need for more research on identifying effects of individual MP types on instream biological and habitat conditions.

Maryland↗
USGS2011

The human dimension of fire regimes on Earth

Humans and their ancestors are unique in being a fire‐making species, but ‘natural’ (i.e. independent of humans) fires have an ancient, geological history on Earth. Natural fires have influenced biological evolution and global biogeochemical cycles, making fire integral to the functioning of some biomes. Globally, debate rages about the impact on ecosystems of prehistoric human‐set fires, with views ranging from catastrophic to negligible. Understanding of the diversity of human fire regimes on Earth in the past, present and future remains rudimentary. It remains uncertain how humans have caused a departure from ‘natural’ background levels that vary with climate change. Available evidence shows that modern humans can increase or decrease background levels of natural fire activity by clearing forests, promoting grazing, dispersing plants, altering ignition patterns and actively suppressing fires, thereby causing substantial ecosystem changes and loss of biodiversity. Some of these contemporary fire regimes cause substantial economic disruptions owing to the destruction of infrastructure, degradation of ecosystem services, loss of life, and smoke‐related health effects. These episodic disasters help frame negative public attitudes towards landscape fires, despite the need for burning to sustain some ecosystems. Greenhouse gas‐induced warming and changes in the hydrological cycle may increase the occurrence of large, severe fires, with potentially significant feedbacks to the Earth system. Improved understanding of human fire regimes demands: (1) better data on past and current human influences on fire regimes to enable global comparative analyses, (2) a greater understanding of different cultural traditions of landscape burning and their positive and negative social, economic and ecological effects, and (3) more realistic representations of anthropogenic fire in global vegetation and climate change models. We provide an historical framework to promote understanding of the development and diversification of fire regimes, covering the pre‐human period, human domestication of fire, and the subsequent transition from subsistence agriculture to industrial economies. All of these phases still occur on Earth, providing opportunities for comparative research.

Journal of Biogeography↗
USGS2023

Machine-learning model to delineate sub-surface agricultural drainage from satellite imagery

Knowing subsurface drainage (tile-drain) extent is integral to understanding how landscapes respond to precipitation events and subsequent days of drying, as well as how soil characteristics and land management influence stream response. Consequently, a time series of tile-drain extent would inform one aspect of land management that complicates our ability to explain streamflow and water-quality as a function of climate variability or conservation management. We trained a UNet machine-learning model, a convolutional neural network designed to highlight objects of interest within an image, to delineate tile-drain networks in panchromatic satellite imagery without additional data on soils, topography, or historical tile-drain extent. This was done by training the model to match the accuracy of human experts manually tracing the surface representation of tile drains in satellite imagery. Our approach began with a library of images that were used to train and quantify the accuracy of the model, with model performance tested on imagery from two areas that were not used to train the model. Satellite imagery included acquisition dates from 2008 to 2020. Training imagery was from agricultural areas within the US Great Lakes basin. Validation imagery was from the upper Maumee River, tributary to western Lake Erie, and an Indiana, Ohio-River headwater tributary. Our analysis of the satellite imagery paired with meteorological and soil data found that during spring, a combination of relatively high solar radiation, intermediate soil-water content and bare fields enabled the best model performance. Each area of interest was heavily tile-drained, where better understanding the movement of water, nutrients, and sediment from fields to downstream water bodies is key to managing harmful algal blooms and hypoxia. The trained UNet model successfully identified tile drains visible in the validation imagery with an accuracy of 93%–96% and balanced accuracy of 52%–54%, similar to performance for training data (95% and 63%, respectively). Model performance will benefit from ongoing contributions to the training library.

Indiana, Michigan, Ohio, Wisconsin↗
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↗
USGS2022

Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis

The Upper Rio Grande Basin (URGB) is a critical international water resource under pressure from a myriad of climatic, ecological, infrastructural, water-use, and legal constraints. The objective of this study is to provide a comprehensive assessment of the spatial distribution and temporal trends of selected water-budget components (snow processes, evapotranspiration (ET), streamflow processes, and groundwater storage) using integrated analyses, such as watershed modeling and water availability and use data in the URGB over the past three decades. A spatially distributed snow evolution modeling system simulated snowpack processes over 34 years (1984–2017). It highlighted snow water equivalent declines from -35 to -77 mm/decade with widespread variability across elevation zones and land cover types. Gridded actual ET data from the SSEBop model were developed and tested for the URGB and demonstrated that all land-cover types had significant decreasing trends (1986-2015) ranging from -14 to -80 mm/decade. Conductivity-mass-balance (CMB) hydrograph separation results found that baseflow forms a large component of total streamflow, ranging from 29 to 69% (49% average) of total streamflow at 17 URGB sites upstream of Albuquerque, NM. Three of 4 graphical hydrograph separation methods in the U.S. Geological Survey Groundwater Toolbox were found to be inappropriate for estimating baseflow in the URGB; the most promising method, baseflow index (BFI) Standard, was optimized using CMB data and tested at three URGB sites, with resulting overestimation of 0 to 47%. Simulated changes in groundwater storage were extracted from historical and recent groundwater-flow models of select alluvial basins (San Luis, Española, Middle Rio Grande, and Tularosa-Hueco). In general, decreases in groundwater storage were observed from 1903 to 2013 except for the San Luis alluvial basin (Colorado), where periods of recovery are observed. The PRMS hydrologic model was successfully calibrated for 9 near-native subbasins (Nash-Sutcliffe efficiency 0.47 to 0.85) and parameters translated to the remaining subbasins; compared to simulated near-native flows (with minimal influence of reservoirs or diversions), observed Rio Grande streamgage flows demonstrated reductions of 40% or more for New Mexico and Texas areas of the basin. Significant decreasing trends (1980-2015) in precipitation, snowmelt rate, streamflow, and baseflow were observed at many of the 12 streamgage basins studied, which suggests that the decreasing trends for actual ET may be related to overall decreasing water availability in the basin, with negative implications for agricultural production and groundwater abstraction. Water security concerns arise from our findings of higher fraction precipitation as rain, slower snowmelt rates leading to decreasing streamflow production, and an increasing fraction of baseflow, all of which will affect the timing and magnitude of water available for human needs in the basin.

Upper Rio Grande Basin↗
USGS2014

Analysis of projected water availability with current basin management plan, Pajaro Valley, California

The projection and analysis of the Pajaro Valley Hydrologic Model (PVHM) 34 years into the future using MODFLOW with the Farm Process (MF-FMP) facilitates assessment of potential future water availability. The projection is facilitated by the integrated hydrologic model, MF-FMP that fully couples the simulation of the use and movement of water from precipitation, streamflow, runoff, groundwater flow, and consumption by natural and agricultural vegetation throughout the hydrologic system at all times. MF-FMP allows for more complete analysis of conjunctive-use water-resource systems than previously possible with MODFLOW by combining relevant aspects of the landscape with the groundwater and surface-water components. This analysis is accomplished using distributed cell-by-cell supply-constrained and demand-driven components across the landscape within “water-balance subregions” (WBS) comprised of one or more model cells that can represent a single farm, a group of farms, watersheds, or other hydrologic or geopolitical entities. Analysis of conjunctive use would be difficult without embedding the fully coupled supply-and-demand into a fully coupled simulation, and are difficult to estimate a priori. The analysis of projected supply and demand for the Pajaro Valley indicate that the current water supply facilities constructed to provide alternative local sources of supplemental water to replace coastal groundwater pumpage, but may not completely eliminate additional overdraft. The simulation of the coastal distribution system (CDS) replicates: 20 miles of conveyance pipeline, managed aquifer recharge and recovery (MARR) system that captures local runoff, and recycled-water treatment facility (RWF) from urban wastewater, along with the use of other blend water supplies, provide partial relief and substitution for coastal pumpage (aka in-lieu recharge). The effects of these Basin Management Plan (BMP) projects were analyzed subject to historical climate variations and assumptions of 2009 urban water demand and land use. Water supplied directly from precipitation, and indirectly from reuse, captured local runoff, and groundwater is necessary but inadequate to satisfy agricultural demand without coastal and regional storage depletion that facilitates seawater intrusion. These facilities reduce potential seawater intrusion by about 45% with groundwater levels in the four regions served by the CDS projected to recover to levels a few feet above sea level. The projected recoveries are not high enough to prevent additional seawater intrusion during dry-year periods or in the deeper aquifers where pumpage is greater. While these facilities could reduce coastal pumpage by about 55% of the historical 2000–2009 pumpage for these regions, and some of the water is delivered in excess of demand, other coastal regions continue to create demands on coastal pumpage that will need to be replaced to reduce seawater intrusion. In addition, inland urban and agricultural demands continue to sustain water levels below sea level causing regional landward gradients that also drive seawater intrusion. Seawater intrusion is reduced by about 45% but it supplies about 55% of the recovery of groundwater levels in the coastal regions served by the CDS. If economically feasible, water from summer agricultural runoff and tile-drain returnflows could be another potential local source of water that, if captured and reused, could offset the imbalance between supply and demand as well as reducing discharge of agricultural runoff into the National Marine Sanctuary of Monterey Bay. A BMP update (2012) identifies projects and programs that will fund a conservation program and will provide additional, alternative water sources to reduce or replace coastal and inland pumpage, and to replenish the aquifers with managed aquifer recharge in an inland portion of the Pajaro Valley.

California↗