Use of archived data to derive soil health and water quality indicators for monitoring shifts in natural resources.

Current gaps impeding researchers from developing a soil and watershed health nexus include design of long-term field-scale experiments and statistical methodologies that link soil health indicators (SHI) with water quality indicators (WQI). Land cover is often used to predict WQI but may not reflect the effects of previous management such as legacy fertilizer applications, disturbance, and shifts in plant populations) and soil texture. Our research objectives were to use nonparametric Spearman rank-order correlations to identify SHI and WQI that were related across the Fort Cobb Reservoir experimental watershed (FCREW); use the resulting rho (r) and p values (P) to explore potential drivers of SHI-WQI relationships, specifically land use, management, and inherent properties (soil texture, aspect, elevation, slope); and interpret findings to make recommendations regarding assessment of the sustainability of land use and management. The SHI values used in the correlation matrix were weighted by soil texture and land management. The SHI that were significantly correlated with one or more WQI were available water capacity (AWC), Mehlich III soil P, and the sand to clay ratio (S:C). Mehlich III soil P was highly correlated with three WQI: total dissolved solids (TDS) (0.80; P < 0.01), electrical conductivity of water (EC-H2 O) (0.79; P < 0.01), and water nitrates (NO3 -H2 O) (0.76; P < 0.01). The correlations verified that soil texture and management jointly influence water quality (WQ), but the size of the soils dataset prohibited determination of the specific processes. Adoption of conservation tillage and grasslands within the FCREW improved WQ such that water samples met the U.S. Environmental Protection Agency (EPA) drinking water standards. Future research should integrate current WQI sampling sites into an edge-of-field design representing all management by soil series combinations within the FCREW.

Factors important for bull purchasing decisions and management in extensive rangeland production systems of New Mexico: a producer survey.

There were 463,000 head of beef cows in New Mexico as of January 1, 2021 (NASS, 2020), down roughly 4% from 2020 (NASS, 2019). Frequent drought often results in herd reduction and loss of valuable genetic progress. Bull selection is critical due to their influence on herd development. A survey was conducted to identify traits important to bull selection in New Mexico. Surveys were collected digitally (n = 83) and via the mail (n = 74). Responses were largely by cow/calf producers averaging 57 ± 1 years old with 24 ± 1 years' experience. Survey respondents represented 4,384,296 acres of private owned, private leased, and leased public rangeland and irrigated pasture meadow in New Mexico and surrounding states. Average cow/calf operation size was 294 ± 39 head and average bull herd size was 21 ± 3 head. Average price paid for a bull in the past 2 years was $3,981 ± 213. Physical characteristics, individual bull performance information, and genetic information are all important traits to New Mexico bull buyers; however, most producers (56%) indicated that structural soundness was the most important factor influencing their selection decisions. Amongst expected progeny differences (EPDs), New Mexico producers consider the calving ease direct (CED) and birth weight (BW) EPDS to be most important (40% and 35%, respectively). Producers also indicated that multitrait selection indexes used by the American Angus Association were important to their selection decisions, with the beef value ($B) and weaned calf value ($W) indexes being cited most often (35% and 31%, respectively). Elements important to bull purchase include the bull's sale preview (87%), body condition score (86%), feed efficiency/average daily gain information (85%), and actual scrotal circumference (82%). Following purchase of a new bull, most (60%) keep the bull separate from the cow herd until the following breeding season, while the remaining 40% of producers turn newly purchased bulls out within 30 days of purchase. Sixty eight percent of producers evaluate semen quality annually or prior to the start of the breeding season. Interestingly, 39% of producers indicated they used reproductive technologies like artificial insemination and synchronization of estrus while most (80%) test for trichomoniasis. The primary factor influencing culling decisions is age, followed by soundness and fertility.

Temporal Dynamics of Bacterial Communities along a Gradient of Disturbance in a U.S. Southern Plains Agroecosystem.

Land conversion for intensive agriculture produces unfavorable changes to soil ecosystems, causing global concern. Soil bacterial communities mediate essential terrestrial ecosystem processes, making it imperative to understand their responses to agricultural perturbations. Here, we used high-throughput sequencing coupled with a functional gene array to study temporal dynamics of soil bacterial communities over 1 year under different disturbance intensities across a U.S. Southern Plains agroecosystem, including tallgrass prairie, Old World bluestem pasture, no-tillage (NT) canola, and conventional tillage (CT) wheat. Land use had the greatest impact on bacterial taxonomic diversity, whereas sampling time and its interaction with land use were central to functional diversity differences. The main drivers of taxonomic diversity were tillage > sampling time > temperature, while all measured factors explained similar amounts of variations in functional diversity. Temporal differences had the strongest correlation with total nitrogen > rainfall > nitrate. Within land uses, community variations for CT wheat were attributed to nitrogen levels, whereas soil organic matter and soil water content explained community variations for NT canola. In comparison, all measured factors contributed almost equally to variations in grassland bacterial communities. Finally, functional diversity had a stronger relationship with taxonomic diversity for CT wheat compared to phylogenetic diversity in the prairie. These findings reinforce that tillage management has the greatest impact on bacterial community diversity, with sampling time also critical. Hence, our study highlights the importance of the interaction between temporal dynamics and land use in influencing soil microbiomes, providing support for reducing agricultural disturbance to conserve soil biodiversity. IMPORTANCE Agricultural sustainability relies on healthy soils and microbial diversity. Agricultural management alters soil conditions and further influences the temporal dynamics of soil microbial communities essential to ecosystem functions, including organic matter dynamics, nutrient cycling, and plant nutrient availability. Yet, the responses to agricultural management are also dependent on soil type and climatic region, emphasizing the importance of assessing sustainability at local scales. To evaluate the impact of agricultural management practices, we examined bacterial communities across a management disturbance gradient over 1 year in a U.S. Southern Plains agroecosystem and determined that intensive management disturbance and sampling time critically impacted bacterial structural diversity, while their interactive effect influenced functional diversity and other soil health indicators. Overall, this study provides insights into how reducing soil disturbance can positively impact microbial community diversity and soil properties in the U.S. Southern Plains.

Runoff water quantity and quality data from native tallgrass prairie and crop-livestock systems in Oklahoma between 1977 and 1999.

Erosion and sedimentation pose serious threats to soil and water quality worldwide, including in the U.S. southern Great Plains. To better understand these processes in agricultural landscapes, eight 1.6-ha watersheds were established and instrumented in 1976 at the USDA-ARS Grazinglands Research Laboratory, ∼50 km west of Oklahoma City near El Reno, OK, to measure precipitation and surface runoff quantity and quality. Prior to construction, all watersheds were in native grass, primarily big bluestem (Andropogon gerardii Vitman.), little bluestem [Schizachyrium scoparium (Michx.) Nash], and Indiangrass [Sorghastrum nutans (L.) Nash]; afterwards, four of the eight watersheds were cropped initially into winter wheat (Triticum aestivum L.) (two conventionally tilled and two minimally or no-till). Although there have been many peer-reviewed papers from the Water Resources and Erosion (WRE) watersheds, none included all the datasets collected during the period 1977-1999. The objectives of this paper were (a) to present and discuss all archived historical data, including methods of collection and analysis, (b) to provide summary analyses of the variability in each dataset, and (c) to provide details about how to access these datasets. These datasets are valuable resources to improve modeling in relation to land use and management changes, climate variability, and other environmental factors and may be useful in developing strategies to mitigate environmental impacts of agricultural systems. They are available at https://doi.org/10.15482/USDA.ADC/1518421.

Field scale nitrogen load in surface runoff: Impacts of management practices and changing climate.

The use of Nitrogen (N) fertilizer boosted crop production to accommodate 7 billion people on Earth in the 20th century but with the consequence of exacerbating N losses from agricultural landscapes. Land management practices that can prevent high N load are constantly being sought for mitigation and conservation purposes. This study was aimed at evaluating the impacts of different land management practices under projected climate scenarios on surface runoff linked N load at the field scale level. A framework to analyze changes in N load at a high spatiotemporal resolution under high greenhouse emission climate projections was developed using the Pesticide Root Zone Model (PRZM) for the Willow Creek Watershed in the Fort Cobb Experimental Watershed in Oklahoma. Specifically, 12 combinations of land management and climate scenarios were evaluated based on their N load via surface runoff from 2020 to 2070. Results showed that crop rotation practices lowered both the N load and the probability of high N load events. Spring application reduced the negative effects in summer and fall from other land management practices but at the risk of increased probability of generating high N load in April and May. The fertilizer application rate was found to be the most critical factor that affected the amount and the probability of high N load events. By adopting a target application management approach, the monthly maximum N can be decreased by 13% while the annual mean N load by 6%. The model framework and analysis method developed in this research can be used to analyze tradeoffs between environmental welfare and economic benefits of N fertilizer at the field scale level.

Performance Assessment of Five Different Soil Moisture Sensors under Irrigated Field Conditions in Oklahoma.

Meeting the ever-increasing global food, feed, and fiber demands while conserving the quantity and quality of limited agricultural water resources and maintaining the sustainability of irrigated agriculture requires optimizing irrigation management using advanced technologies such as soil moisture sensors. In this study, the performance of five different soil moisture sensors was evaluated for their accuracy in two irrigated cropping systems, one each in central and southwest Oklahoma, with variable levels of soil salinity and clay content. With factory calibrations, three of the sensors had sufficient accuracies at the site with lower levels of salinity and clay, while none of them performed satisfactorily at the site with higher levels of salinity and clay. The study also investigated the performance of different approaches (laboratory, sensor-based, and the Rosetta model) to determine soil moisture thresholds required for irrigation scheduling, i.e., field capacity (FC) and wilting point (WP). The estimated FC and WP by the Rosetta model were closest to the laboratory-measured data using undisturbed soil cores, regardless of the type and number of input parameters used in the Rosetta model. The sensor-based method of ranking the readings resulted in overestimation of FC and WP. Finally, soil moisture depletion, a critical parameter in effective irrigation scheduling, was calculated by combining sensor readings and FC estimates. Ranking-based FC resulted in overestimation of soil moisture depletion, even for accurate sensors at the site with lower levels of salinity and clay.

Regional grassland productivity responses to precipitation during multiyear above- and below-average rainfall periods.

There is considerable uncertainty in the magnitude and direction of changes in precipitation associated with climate change, and ecosystem responses are also uncertain. Multiyear periods of above- and below-average rainfall may foretell consequences of changes in rainfall regime. We compiled long-term aboveground net primary productivity (ANPP) and precipitation (PPT) data for eight North American grasslands, and quantified relationships between ANPP and PPT at each site, and in 1-3 year periods of above- and below-average rainfall for mesic, semiarid cool, and semiarid warm grassland types. Our objective was to improve understanding of ANPP dynamics associated with changing climatic conditions by contrasting PPT-ANPP relationships in above- and below-average PPT years to those that occurred during sequences of multiple above- and below-average years. We found differences in PPT-ANPP relationships in above- and below-average years compared to long-term site averages, and variation in ANPP not explained by PPT totals that likely are attributed to legacy effects. The correlation between ANPP and current- and prior-year conditions changed from year to year throughout multiyear periods, with some legacy effects declining, and new responses emerging. Thus, ANPP in a given year was influenced by sequences of conditions that varied across grassland types and climates. Most importantly, the influence of prior-year ANPP often increased with the length of multiyear periods, whereas the influence of the amount of current-year PPT declined. Although the mechanisms by which a directional change in the frequency of above- and below-average years imposes a persistent change in grassland ANPP require further investigation, our results emphasize the importance of legacy effects on productivity for sequences of above- vs. below-average years, and illustrate the utility of long-term data to examine these patterns.

Knowledge and tools to enhance resilience of beef grazing systems for sustainable animal protein production.

Ruminant livestock provides meat and dairy products that sustain health and livelihood for much of the world's population. Grazing lands that support ruminant livestock provide numerous ecosystem services, including provision of food, water, and genetic resources; climate and water regulation; support of soil formation; nutrient cycling; and cultural services. In the U.S. southern Great Plains, beef production on pastures, rangelands, and hay is a major economic activity. The region's climate is characterized by extremes of heat and cold and extremes of drought and flooding. Grazing lands occupy a large portion of the region's land, significantly affecting carbon, nitrogen, and water budgets. To understand vulnerabilities and enhance resilience of beef production, a multi-institutional Coordinated Agricultural Project (CAP), the "grazing CAP," was established. Integrative research and extension spanning biophysical, socioeconomic, and agricultural disciplines address management effects on productivity and environmental footprints of production systems. Knowledge and tools being developed will allow farmers and ranchers to evaluate risks and increase resilience to dynamic conditions. The knowledge and tools developed will also have relevance to grazing lands in semiarid and subhumid regions of the world.

Production and supply of high-quality food protein for human consumption: sustainability, challenges, and innovations.

The Food and Agriculture Organization of the United Nations estimates that 843 million people worldwide are hungry and a greater number suffer from nutrient deficiencies. Approximately one billion people have inadequate protein intake. The challenge of preventing hunger and malnutrition will become even greater as the global population grows from the current 7.2 billion people to 9.6 billion by 2050. With increases in income, population, and demand for more nutrient-dense foods, global meat production is projected to increase by 206 million tons per year during the next 35 years. These changes in population and dietary practices have led to a tremendous rise in the demand for food protein, especially animal-source protein. Consuming the required amounts of protein is fundamental to human growth and health. Protein needs can be met through intakes of animal and plant-source foods. Increased consumption of food proteins is associated with increased greenhouse gas emissions and overutilization of water. Consequently, concerns exist regarding impacts of agricultural production, processing and distribution of food protein on the environment, ecosystem, and sustainability. To address these challenging issues, the New York Academy of Sciences organized the conference "Frontiers in Agricultural Sustainability: Studying the Protein Supply Chain to Improve Dietary Quality" to explore sustainable innovations in food science and programming aimed at producing the required quality and quantity of protein through improved supply chains worldwide. This report provides an extensive discussion of these issues and summaries of the presentations from the conference.

Long-term environmental research: the upper washita river experimental watersheds, oklahoma, USA.

Water is central to life and earth processes, connecting physical, biological, chemical, ecological, and economic forces across the landscape. The vast scope of hydrologic sciences requires research efforts worldwide and across a wide range of disciplines. While hydrologic processes and scientific investigations related to sustainable agricultural systems are based on universal principles, research to understand processes and evaluate management practices is often site-specific to achieve a critical mass of expertise and research infrastructure to address spatially, temporally, and ecologically complex systems. In the face of dynamic climate, market, and policy environments, long-term research is required to understand and predict risks and possible outcomes of alternative scenarios. This special section describes the USDA-ARS's long-term research (1961 to present) in the Upper Washita River basin of Oklahoma. Data papers document datasets in detail (weather, hydrology, physiography, land cover, and sediment and nutrient water quality), and associated research papers present analyses based on those data. This living history of research is presented to engage collaborative scientists across institutions and disciplines to further explore complex, interactive processes and systems. Application of scientific understanding to resolve pressing challenges to agriculture while enhancing resilience of linked land and human systems will require complex research approaches. Research areas that this watershed research program continues to address include: resilience to current and future climate pressures; sources, fate, and transport of contaminants at a watershed scale; linked atmospheric-surface-subsurface hydrologic processes; high spatiotemporal resolution analyses of linked hydrologic processes; and multiple-objective decision making across linked farm to watershed scales.

Upper washita river experimental watersheds: reservoir, groundwater, and stream flow data.

Surface and groundwater quantity and quality data are essential in many hydrologic applications and to the development of hydrologic and water quality simulation models. We describe the hydrologic data available in the Little Washita River Experimental Watershed (LWREW) of the Southern Great Plains Research Watershed (SGPRW) and Fort Cobb Reservoir Experimental Watershed (FCREW), both located in southwest Oklahoma. Specifically, we describe the flood retarding structures and corresponding stage, discharge, seepage, and consumptive use data (), stream gauges, and groundwater wells and their corresponding stream flow (; LWREW ARS 522-526 stream gauges) and groundwater level data (SGPRW groundwater levels data; LWREW groundwater data; ; ), respectively. Data collection is a collaborative effort between federal and state agencies. Stage, discharge, seepage, and consumptive use data for the Fort Cobb Reservoir are available from the Bureau of Reclamation and cover a period of 1959 to present. There are 15 stream gauges in the LWREW and six in the FCREW with varying data records. There were 479 observation wells with data in the SGPRW and 80 in the LWREW, with the latest records collected in 1992. In addition, groundwater level data are available from five real-time monitoring wells and 34 historical wells within the FCREW. These data sets have been used for several research applications. Plans for detailed groundwater data collection are underway to calibrate and validate the linked Soil and Water Assessment Tool (SWAT)-MODFLOW model. Also, plans are underway to conduct reservoir bathymetric surveys to determine the current reservoir capacity as affected by land use/land cover and overland and stream channel soil erosion.

Upper washita river experimental watersheds: data screening procedure for data quality assurance.

The presence of non-stationary conditions in long-term hydrologic observation networks is associated with natural and anthropogenic stressors or network operation problems. Detection and identification of network operation drivers is fundamental in hydrologic investigation due to changes in systematic errors that can exacerbate modeling results or bias research conclusions. We applied a data screening procedure to the USDA-ARS experimental watersheds data sets () in Oklahoma. Detection of statistically significant monotonic trends and changes in mean and variance were used to investigate non-stationary conditions with network operation drivers to assess the impact of changes in the amount of systematic error. Detection of spurious data, filling in missing data, and data screening procedures were applied to >1000 time series, and processed data were made publicly available. The SPELLmap application was used for data processing and statistical tests on watershed segregated data sets and temporally aggregated data. A test for independency (Anderson test), normality, monotonic trend (Spearman test), detection of change point (Pettitt test), and split record test ( and -tests) were used to assess non-stationary conditions. Statistically significant (95% confidence limit) monotonic trends and changes in mean and variance were detected for annual maximum air temperature, rainfall, relative humidity, and solar radiation and in maximum and minimum soil temperature time series. Network operation procedures such as change in calibration protocols and sensor upgrades as well as natural regional weather trends were suspected as driving the detection of statistically significant trends and changes in mean and variance. We concluded that a data screening procedure that identifies changes in systematic errors and detection of false non-stationary conditions in hydrologic problems is fundamental before any modeling applications.

Upper washita river experimental watersheds: physiography data.

Physiographic data such as digital elevation models (DEMs), soils, geology, stream channel network characteristics, channel stability, and land use data are essential for understanding the complex hydrologic cycle and chemical transport processes of any given study area. We describe the physiographic data available in the Little Washita River Experimental Watershed (LWREW) and Fort Cobb Reservoir Experimental Watershed (FCREW) in Oklahoma. Specifically, we describe (i) available raw and post-processed DEM products (), (ii) available soils data ( and ) and associated error analysis based on limited measured data, (iii) geologic formations in the LWREW and FCREW ( and ), and (iv) available rapid geomorphic assessment measurements () and their uses. Data collection is a collaborative effort among USGS, NRCS, and ARS. These data sets have been used for several research applications by USDA-ARS scientists and researchers from other institutions and agencies. Plans for detailed geomorphic assessment of stream channel networks in the FCREW are underway in collaboration with Oklahoma State University in Stillwater. The collected data will enable updating of the channel stability stage condition since there have been several major rainfall events in the watershed since the last geomorphic assessment.

Upper washita river experimental watersheds: land cover data sets (1974-2007) for two southwestern oklahoma agricultural watersheds.

Land cover data sets were developed for 1997, 1999, 2003, 2005, 2006, and 2007 for the Little Washita River and Fort Cobb Reservoir experimental watersheds (LWREW and FCREW, respectively), located in southwestern Oklahoma, to support remote sensing based studies of soil water content. A previously unpublished retrospective land cover analysis covering the years 1974, 1981, 1985, 1989, and 1994 was conducted to complement these data sets to gain a sense of the dynamics of land cover in both the LWREW and FCREW over the 33 yr. Each of these studies used satellite-based sensors of various spatial, radiometric, and spectral resolutions, but the number of images used, image date, and methods used to analyze the images varied from study to study. Our purpose was to document the details of the retrospective land cover study, to compare land cover between watersheds with time, and to compare findings from the various studies to elucidate changes or trends in land cover in each watershed during the 33 yr the data sets represent. Information on how to access to the data sets is also given. The LWREW was a grassland watershed that changed little during the study period. The FCREW was divided between grassland and cropland, but the cropland portion exhibited dynamic behavior that appeared correlated with peanut ( L.) price supports and Conservation Reserve programs. Dynamic land use information coupled with information concerning conservation practices will enhance assessment of conservation practice effectiveness as well as improve modeling of the fate and transport of chemicals and nutrients in watersheds.

Evaluation of the hooghoudt and kirkham tile drain equations in the soil and water assessment tool to simulate tile flow and nitrate-nitrogen.

Subsurface tile drains in agricultural systems of the midwestern United States are a major contributor of nitrate-N (NO-N) loadings to hypoxic conditions in the Gulf of Mexico. Hydrologic and water quality models, such as the Soil and Water Assessment Tool, are widely used to simulate tile drainage systems. The Hooghoudt and Kirkham tile drain equations in the Soil and Water Assessment Tool have not been rigorously tested for predicting tile flow and the corresponding NO-N losses. In this study, long-term (1983-1996) monitoring plot data from southern Minnesota were used to evaluate the SWAT version 2009 revision 531 (hereafter referred to as SWAT) model for accurately estimating subsurface tile drain flows and associated NO-N losses. A retention parameter adjustment factor was incorporated to account for the effects of tile drainage and slope changes on the computation of surface runoff using the curve number method (hereafter referred to as Revised SWAT). The SWAT and Revised SWAT models were calibrated and validated for tile flow and associated NO-N losses. Results indicated that, on average, Revised SWAT predicted monthly tile flow and associated NO-N losses better than SWAT by 48 and 28%, respectively. For the calibration period, the Revised SWAT model simulated tile flow and NO-N losses within 4 and 1% of the observed data, respectively. For the validation period, it simulated tile flow and NO-N losses within 8 and 2%, respectively, of the observed values. Therefore, the Revised SWAT model is expected to provide more accurate simulation of the effectiveness of tile drainage and NO-N management practices.

Sediment measurement and transport modeling: impact of riparian and filter strip buffers.

Well-calibrated models are cost-effective tools to quantify environmental benefits of conservation practices, but lack of data for parameterization and evaluation remains a weakness to modeling. Research was conducted in southwestern Oklahoma within the Cobb Creek subwatershed (CCSW) to develop cost-effective methods to collect stream channel parameterization and evaluation data for modeling in watersheds with sparse data. Specifically, (i) simple stream channel observations obtained by rapid geomorphic assessment (RGA) were used to parameterize the Soil and Water Assessment Tool (SWAT) model stream channel variables before calibrating SWAT for streamflow and sediment, and (ii) average annual reservoir sedimentation rate, measured at the Crowder Lake using the acoustic profiling system (APS), was used to cross-check Crowder Lake sediment accumulation rate simulated by SWAT. Additionally, the calibrated and cross-checked SWAT model was used to simulate impacts of riparian forest buffer (RF) and bermudagrass [ (L.) Pers.] filter strip buffer (BFS) on sediment yield and concentration in the CCSW. The measured average annual sedimentation rate was between 1.7 and 3.5 t ha yr compared with simulated sediment rate of 2.4 t ha yr Application of BFS across cropped fields resulted in a 72% reduction of sediment delivery to the stream, while the RF and the combined RF and BFS reduced the suspended sediment concentration at the CCSW outlet by 68 and 73%, respectively. Effective riparian practices have potential to increase reservoir life. These results indicate promise for using the RGA and APS methods to obtain data to improve water quality simulations in ungauged watersheds.