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Abstract The harmful effects of mining waste on human and ecosystem health make reclamation of former mine sites an environmental management priority. However, field-based monitoring of reclamation requires significant investments of time, labor, and money. Remote sensing offers a less expensive alternative to field-based monitoring, but work is still needed to determine which metrics can be reliably estimated using remote sensing techniques. This study uses remote sensing to examine over 20 years of reclamation efforts at Tar Creek Superfund Site and to assess revegetation after site restoration. Using the Normalized Difference Vegetation Index (NDVI), we quantify key factors affecting vegetation recovery and stability after reclamation across 123 surface mining waste cleanup locations within Tar Creek. Leveraging long-term imagery from Landsat and high-resolution PlanetScope imagery, we combine time series analysis of vegetation regrowth and landcover change detection for a comprehensive picture of recovery at each site. Across all reclamation sites, the average recovery duration was 3.5 years, and average recovery rate was 0.1 NDVI year−1. After vegetative growth had plateaued, reclaimed sites had an average NDVI of 0.70. All reclamation areas were converted from majority barren landcover to vegetated landcover classes after reclamation, and vegetative stability at reclamation sites was high (70% of reclaimed area saw continuous vegetative cover from 2017 to 2023). These results demonstrate a strong potential for multi-method remote sensing techniques in tracking and explaining vegetation recovery after reclamation and represent a cost-effective approach for real-time monitoring of reclamation progress and outcomes.

Exponential population increases are threatening food security, particularly in mountainous areas. One potential solution is dual-use intercropped agroforestry systems such as olive (Olea europaea)–maize (Zea mays), which may mitigate risk by providing multiple market sources (oil and grain) for smallholder producers. Several studies have conducted integrated agroforestry land suitability analyses; however, few studies have used machine learning (ML) algorithms to evaluate multiple variables (i.e., soil physicochemical properties and climatic and topographic data) for the selection of suitable rainfed sites in mountainous terrain systems. The goal of this study is therefore to identify suitable land classes for an integrated olive–maize agroforestry system based on the Food and Agriculture Organization (FAO) land suitability assessment framework for 1757 km2 in Khyber Pakhtunkhwa province, Pakistan. Information on soil physical and chemical properties was obtained from 701 soil samples, along with climatic and topographic data. After determination of land suitability classes for an integrated olive–maize-crop agroforestry system, the region was then mapped through ML algorithms using random forest (RF) and support vector machine (SVM), as well as using traditional techniques of weighted overlay (WOL). Land suitability classes predicted by ML techniques varied greatly. For example, the S1 area (highly suitable) classified through RF was 9%↑ than that of SVM, and 8%↓ than that through WOL. The area of S2 (moderately suitable) classified through RF was 18%↑ than that of SWM and was 17%↓ than the area classified through WOL; similarly, the S3 (marginally suitable) class area via RF was 27%↓ than that of SVM, and 45%↓ than the area classified through WOL. Conversely, the area of N2 (permanently not suitable class) classified through RF and SVM was 6%↑ than the area classified through WOL. Model performance was assessed through overall accuracy and Kappa Index and indicated that RF performed better than SVM and WOL. Crop suitability limitations of the study area included high elevation, slope, pH, and large gravel content. Results can be used for sustainable intensification in mountainous rainfed regions by expanding intercrop agroforestry systems in developing nations to close yield gaps.

Study region: Mulberry River, Arkansas, USA Study focus: Low alkalinity streams within siliciclastic dominated catchments like the Mulberry River in the south-central United States experience episodic acidification. Since 2009, reaches of the Mulberry River have been classified as impaired due to low pH. We hypothesize that if pH time series patterns are similar among tributaries, external watershed factors like seasonal weather may be related to low stream pH. We investigated pH variability in the Mulberry River watershed including changepoint detection analysis. Patterns of pH and changepoints were compared with streamflow variability and drought conditions using Palmer Drought Severity Index (PDSI). New hydrological insights for the region: Changes in pH were near synchronous and similar magnitude across the study area, despite observed increases in median tributary pH from east to west. From the comparison of pH time series and PDSI, low pH values tended to occur during more negative PDSI, i.e. drier periods. The specific mechanisms driving low pH variability remain elusive, but we infer low stream pH is influenced, in part, by seasonal weather variability leading to drier watershed conditions and lower pH. The spatial variability of median pH values from east to west, though, are more related to spatial variability in bedrock lithology. We hypothesize other acid-sensitive streams in the region likely share similar pH behavior and with low pH occurring during drier periods.

Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed currently under pasture. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped by Soil Survey Geographic Database (SSURGO). If a connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrologic models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.

Alluvial plains are vexing landscapes for soil mapping and spatial soil property predictions. Alluvial sediments often exhibit unpredictable spatial variability from both fluvial and anthropogenic disturbance. The determination of optimal number of soil sampling points for capturing soil variability remains a persistent issue for mapping and monitoring soil conditions. Here, soil organic matter (SOM) and cation exchange capacity (CEC) were estimated in an alluvial plain from a dense array of 2145 soil samples over 250 ha. The primary goals were to i) use pixel-based statistics from time-series Sentinel-2 satellite reflectance data to estimate SOM and CEC; ii) evaluate the use of images with vegetation cover versus bare soil images; and, iii) investigate the optimal number of sampling points to map alluvial soils using different sampling strategies. The optimal sample density was 1 sample per 2.5 ha based on the overlapping of prediction distributions (OV > 0.9). Conditioned Latin Hypercube Sampling (cLHS) was the most efficient sampling strategy. Random grid sampling provided the least consistent results. The use of cLHS coupled with the mean and standard deviation bands was the optimal sampling strategy. Pixel-based statistics from readily available satellite imagery captured persistent soil-reflectance relationships that enabled the use of row crops as proxies to predict soil properties. The combined use of pixel-based statistics as inputs to cLHS can reduce the chances of oversampling and associated costs. Pixel-based statistics provided persistent spatial information that can enhance precision agriculture management, carbon mapping, and is likely applicable to other soil properties and remotely sensed products. Use of pixel-based statistics from Sentinel-2 and similar global-coverage imagery could provide readily-calculable inputs for improved soil mapping and more optimal site sample selections in other agricultural row crop areas, globally.

Soil nutrient content varies spatially across agricultural fields in hard-to-predict ways, particularly in floodplains with complex fluvial depositional history. Satellite reflectance data from the Sentinel-2 (S2) mission provides spatially continuous land reflectance data that can aid model development when used with point observations of nutrients. Reflectance from vegetation is assumed to obstruct land reflectance of bare soil, such that researchers have masked vegetation in models. We developed a routine for masking vegetation within Google Earth Engine (GEE) using Random Forest classification for iterative application to libraries of S2-images. Using gradient boosting, we then developed soil nutrient models for surface soils at a 250-ha agricultural site using S2 images. Soils were sampled at 2145 point locations to a 23-cm depth and analyzed for Ca, K, Mg, P, pH, S, and Zn. Results showed that masking vegetation improved model performance for models from subsets of the data (80% of samples used for model development, 20% validation), but full data sets did not require masking to achieve accuracy. Models of Ca, K, Mg, and S were successful (validation R2 > 0.60 to 0.96), but models for pH, P, and Zn failed. Bare soil composite images from S2 data are helpful in predicting soil fertility in low-relief floodplains.

Abstract Soil respiration is one of the main soil health indicators and is influenced by several factors in agricultural fields. Identifying key factors that control soil respiration is desirable for informed soil management decisions and for promoting and scaling up soil health. This study aimed to (i) quantify the relationships between potential soil respiration and selected soil properties, crops, and slope positions, and (ii) identify key factors controlling these relationships using a neural network model. Ninety soil samples from 0‐ to 5‐ and 5‐ to 20‐cm soil depth were collected from footslope, backslope, and summit in three fields planted with soybean (Glycine max L. Merr.), alfalfa (Medicago sativa L.), and corn (Zea mays L.). The model provided great accuracy (coefficient of determination: 0.96; root‐mean square error: 7.8; and mean absolute deviation: 3.8) and explained nearly 96% of variations in soil respiration across soil depth, crop, and slope positions. Soil depth, ammoniacal nitrogen (NH4‐N), crop types, slope position, and silt content were identified as the top five factors influencing potential soil respiration at the field level. Potential soil respiration was more sensitive to potassium, phosphorus, pH, cation exchange capacity, and mean weight diameter and less sensitive to NH4‐N, nitrate nitrogen, soil organic matter, and clay content. It increased with pH, electrical conductivity, mean weight diameter, potential nitrogen mineralization, and potassium, and it decreased with increasing silt content. Soil from 0 to 5 cm under soybean or at the summit slope position exhibited a higher respiration. Using a small dataset, this pilot study accurately predicted potential soil respiration in agricultural fields and identified key drivers controlling it. The results from this study highlight the complexity of using potential soil respiration as a standalone test for evaluating soil health. This does not diminish the usefulness of potential soil respiration as a soil health indicator to support agricultural management decisions and as a reference in future soil health studies. However, it emphasizes the importance of considering multiple factors when interpreting the significance of soil biological indicators for soil health assessments.

Animal manure improves soil fertility and organic carbon, but long-term deposition may contribute to antibiotic resistance genes (ARGs) entering the soil-water environment. Additionally, long-term impacts of applying animal manure to soil on the soil-water microbiome, a crucial factor in soil health and fertility, are not well understood. The aim of this study is to assess: (1) impacts of long-term conservation practices on the distribution of ARGs and microbial dynamics in soil, and runoff; and (2) associations between bacterial taxa, heavy metals, soil health indicators, and ARGs in manures, soils, and surface runoff in a study following 15 years of continuous management. This management strategy consists of two conventional and three conservation systems, all receiving annual poultry litter. High throughput sequencing of the 16S ribosomal RNA was carried out on samples of cattle manure, poultry litter, soil, and runoff collected from each manureshed. In addition, four representative ARGs (intl1, sul1, ermB, and blactx-m-32) were quantified from manures, soil, and runoff using quantitative PCR. Results revealed that conventional practice increased soil ARGs, and microbial diversity compared to conservation systems. Further, ARGs were strongly correlated with each other in cattle manure and soil, but not in runoff. After 15-years of conservation practices, relationships existed between heavy metals and ARGs. In the soil, Cu, Fe and Mn were positively linked to intl1, sul1, and ermB, but trends varied in runoff. These findings were further supported by network analyses that indicated complex co-occurrence patterns between bacteria taxa, ARGs, and physicochemical parameters. Overall, this study provides system-level linkages of microbial communities, ARGs, and physicochemical conditions based on long-term conservation practices at the soil-water-animal nexus.

Abstract Minimal research has been conducted relating apparent electrical conductivity (ECa) surveys to plant and terrain properties in agroforestry systems. Objectives were to identify: (i) ECa–forage yield, –tree growth, and –terrain attribute relationships within ECa‐derived soil management zones (SMZs) and (ii) terrain attributes that drive ECa variability within a 20‐year‐old, 4.25‐ha, agroforestry system in the Ozark Highlands of northwest Arkansas. The average of 12 monthly perpendicular (PRP) and horizontal coplanar (HCP) ECa surveys (August 2020 to July 2021) and 14 terrain attributes were obtained. Tree diameter at breast height (DBH) and height (TH) measurements were made in December 2020 and March 2021, respectively, and forage yield samples were collected during Summer 2018 and 2019. Apparent EC‐tree property relationships were generally stronger within the whole site (averaged across tree property and ECa configuration, |r| = 0.38) than within the SMZs (averaged across tree property, ECa configuration, and SMZ, |r| = 0.27). The strength of the SMZs’ terrain‐attribute‐PRP‐ECa relationships were 9% to 205% greater than that for the whole site. In whole‐site, multi‐linear regressions, slope length and steepness factor (10.5%), mid‐slope (9.4%), and valley depth (7.2%) had the greatest influence (i.e., percent of total sum of squares) on PRP ECa variability, whereas valley depth (15.3%), wetness index (11.9%), and mid‐slope (11.2%) had the greatest influence on HCP ECa variability. Results show how ECa relates to plant (i.e., DBH, TH, and forage yield) and terrain data within SMZs in agroforestry systems with varying topography and could be used to precisely manage agroforestry systems.

Long-term continuous deposits and well-preserved thick loess sections are natural archives for the reconstruction of global climate change. However, the loess sections are often discontinuous owing to soil erosions and accumulations. This study aimed at reconstructing long-term continuous paleoclimatic records by integrating the target sections of two typical loess sequences in northeast (NE) China. One section, the Dajiugang (DJG) section, was investigated and sampled, and the Sanbahuo (SBH) section was used as the target for integrating and reconstructing the paleoclimate. Sedimentary, pedogenesis, and luminescence dating analyses were conducted to analyze the potentials for reconstructing the paleoclimate by integrating two typical loess sections. The results indicated that the paleoclimate records derived from the DJG section can be used to reconstruct the continuous paleoclimate of NE China by integrating them with the SBH section. The paleoclimate information inferred from the DJG section could impute paleoclimate data from 71 to 11.7 ka, which were missing in the SBH section. The paleoclimate of NE China generally consisted of three periods from 71 to 11.7 ka: cold and dry from 29 to 11.7 ka, alternately cold and warm from 57 to 29 ka, and warm and wet to cold and dry from 71 to 57 ka. Integrating the target loess sections can contribute to reconstructing regional long-term continuous paleoclimate records where discontinuous loess sequences occurred.

Abstract Systems-level studies aimed at determining how soil properties are linked to plant production and ultimately animal response spatially are lacking. This study aims to identify if grazing pressure is linked to soil properties, terrain attributes, and above-ground plant accumulation and nutritive value in a silvopastoral (or integrated tree-livestock) system. Overall, cattle prefer grazing native grasses (2.81 vs. 1.24 h ha−1 AU−1) and udic (dry) landscape positions compared to aquic (wet) areas (2.07 vs. 1.60 h ha−1 AU−1). Greater grazing frequency occurs in udic soils with greater phosphorus and potassium contents and with accumulated forage with less lignin (P ≤ 0.05), which correspond to reduced elevation and greater tree height and diameter (shade) during summer mob grazing. Combining spatial monitoring technologies (both soil and animal) with forage allowance can optimize grazing systems management and sustainability spatially and temporally.

Abstract Tractor guidance (TG) improve environmental gains relative to nonprecision technologies; however, studies evaluating how tractor operator experience for nonguidance comparisons affect gains are nonexistent. This study explores spatial relationships of overlaps and gaps with operator experience level (0–1, 2–3, 6+ yr) during fertilizer and herbicide applications based on terrain attributes. Tractor paths recorded by global navigation satellite systems were used to create overlap polygons. Results illustrate operator experience level is critical for better efficiency gains estimation (for non‐TG comparisons). Operators with 6+ yr of experience reduced overlap by 7.7 and 20.6% compared with operators with 2–3 and 0–1 yr of experience, respectively. New operators had consistently higher overlap across all slope (1). A low interpersonal reliability value of 0.02–0.03 indicates operator experience is crucial to estimate TG efficiency gains and consistent drivers experience levels are needed when evaluating economic and environmental gains from TG.

Abstract Studies show a strong relationship between soil health and crop yield, but those relating soil health and grain quality are limited. We studied the relationship between soil health and grain protein and oil content from a corn (Zea mays L.) field in Texas. Protein and oil content data were collected in the field with a CropScan monitor. Soil health values were measured at 202 locations using the Haney Soil Health Tool. We first mapped protein and oil content using apparent electrical conductivity (ECa) and 14 terrain attributes as predictors, and we then quantified the relationship with data from sample locations. Soil health was positively correlated with protein and oil content, but the relationship was rather weak. Soil health accounted for up to 13% of the variability in protein (p

Broad-base agricultural terraces can be difficult to delineate in flat landscapes, particularly when covered by crops, due to subtle changes in elevation over relatively wide distances. In northeastern Oklahoma, these terraces are usually less than half a meter high and 15 to 20 m wide. The objective of this research was to develop and test a technique for identifying and classifying terraces using computer vision applied to terrain derivatives calculated from digital elevation models at five sites. We tested 38 terrain-derivative grid combinations or sets that represented 19 terrain characteristics, calculated from elevation models after two Gaussian smoothing strategies to provide some degree of generalization and a removal of excess noise. The best subsets achieved a 98% classification accuracy (kappa 0.96) and consisted of derivatives representing hydrology, morphometry, and visibility categories. Inaccuracies occurred primarily at the edges of some of the study sites, where agricultural fields bordered incised drainage areas where changes in elevation were similar to those for the terraces. Further study will elucidate the relationships between terrace “borrow” and “deposition” areas in the terrace areas and their relationships to yield and salinity issues. This work seeks to automate terrace identification for digital soil mapping on terraced fields for the improved delivery of soil information for resource conservation and land use.

Topographic wetness index (TWI) is used as a proxy for soil moisture, but how well it performs across varying timescales and methods of calculation is not well understood. To assess the effectiveness of TWI, we examined spatial correlations between in situ soil volumetric water content (VWC) and TWI values over 5 years in soils at 42 locations in an agroforestry catena in Fayetteville, Arkansas, USA. We calculated TWI 546 ways using different flow algorithms and digital elevation model (DEM) preparations. We found that most TWI algorithms performed poorly on DEMs that were not first filtered or resampled, but DEM filtration and resampling (collectively called generalization) greatly improved the TWI performance. Seasonal variation of soil moisture influenced TWI performance which was best when conditions were not saturated and not dry. Pearson correlation coefficients between TWI and grand mean VWC for the 5-year measurement period ranged from 0.18 to 0.64 on generalized DEMs and 0.15 to 0.59 for on DEMs that were not generalized. These results aid management of crop fields with variable moisture characteristics.

Greater adoption and better management of spatially complex, conservation systems such as agroforestry (AF) are dependent on determining methods suitable for delineating in-field variability. However, no work has been conducted using repeated electromagnetic induction (EMI) or apparent electrical conductivity (ECa) surveys in AF systems within the Ozark Highlands of northwest Arkansas. As a result, objectives were to (i) evaluate spatiotemporal ECa variability; (ii) identify ECa-derived soil management zones (SMZs); (iii) establish correlations among ECa survey data and in situ, soil-sensor volumetric water content, sentential site soil-sample EC, and gravimetric water content and pH; and (iv) determine the optimum frequency at which ECa surveys could be conducted to capture temporal changes in field variability. Monthly ECa surveys were conducted between August 2020 and July 2021 at a 4.25 ha AF site in Fayetteville, Arkansas. The overall mean perpendicular geometry (PRP) and horizontal coplanar geometry (HCP) ECa ranged from 1.8 to 18.0 and 3.1 to 25.8 mS m−1, respectively, and the overall mean HCP ECa was 67% greater than the mean PRP ECa. The largest measured ECa values occurred within the local drainage way or areas of potential groundwater movement, and the smallest measured ECa values occurred within areas with decreased effective soil depth and increased coarse fragments. The PRP and HCP mean ECa, standard deviation (SD), and coefficient of variation (CV) were unaffected (p > 0.05) by either the weather or growing/non-growing season. K-means clustering delineated three precision SMZs that were reflective of areas with similar ECa and ECa variability. Results from this study provided valuable information regarding the application of ECa surveys to quantify small-scale changes in soil properties and delineate SMZs in highly variable AF systems.

Optimizing soil—crop—landscape occurrence is essential for sustainable intensification and food security, but little work has been done to evaluate these parameters on Tribal lands. The objective of this study was to develop first ever high-resolution crop suitability maps and compare two established crop suitability models for their ability to optimize soil resource management of the Quapaw Tribal lands. We built on previously developed continuous soil properties maps for 22,880 ha of Quapaw Tribal lands that used a digital elevation model and a fuzzy-logic based data mining approach to calculate and evaluate the Dideriksen and Storie crop suitability indices. Suitability index results were evaluated against observed yield (n ≥ 130,000) within the study area. Results showed that the observed yield was positively correlated with the Storie suitability index (Spearman rho = 0.16, p < 0.01), but not the Dideriksen index, suggesting the Storie index is more appropriate than the Dideriksen for modeling crop suitability in this area. Additionally, very little (

Runoff from land-applied manure and poultry litter is one mechanism by which manure-borne bacteria are transported over large distances in the environment. There is a global concern that antimicrobial resistant (AMR) genes may be transmitted through the food chain from animal manures to soil to surface water. However, details are lacking on the ecology of AMR genes in water runoff as well as how conservation management practices may affect the runoff microbiome or minimize the movement of AMR genes. The aim of this study was to identify microbial community structure and diversity in water runoff following 14-years of poultry litter and cattle manure deposition and to evaluate the amount of AMR genes under five conventional and conservation pasture management strategies. Since 2004, all watersheds received annual poultry litter at a rate of 5.6 Mg ha−1 and were consistently managed. Surface runoff samples were collected from each watershed from 2018 to 2019, characterized using Illumina 16S rRNA gene amplicon sequencing and enumerated for four AMR-associated genes (ermB, sulI, intlI, and blactx-m-32) using quantitative PCR. Overall, long-term pasture management influenced water microbial community structure, with effects differing by year (p 0.05) from pasture management on the abundance of these genes, indicating both conventional and conservation practices have similar ecologies for these targets; however, there was a greater detection of sulI genes from runoff in continuously grazed systems in 2019, with hay being lowest in 2019. Results illustrate that the edge of field buffer strips may increase bacterial richness in water runoff, but these changes in richness do not greatly impact target AMR genes in the United States largest land-use category.

Producers in Northwest Arkansas and globally need alternative management practices to ensure long-term sustainable and economical use of poultry litter, which is an abundant source of valuable carbon (C), nitrogen (N) and phosphorus (P). Project objectives were to measure the efficacy of conservation management practices (i.e., pasture aeration and subsurface litter incorporation) to reduce nutrient runoff compared to poultry litter surface applications from small watersheds under rainfed and grazed conditions. Watersheds (0.23 ha each) were assigned a treatment [pasture aeration, subsurface litter incorporation, or surface application of litter (positive control)] on a Leadvale (fine-silty, siliceous, thermic Typic Fragiudult) silt loam. Poultry litter was applied annually to each watershed from 2007-2012. Over the 4-yr study period, runoff loads of NO3–N, total nitrogen (TN), soluble reactive phosphorus (SRP), and total phosphorus (TP) varied per conservation practice (P ≤ 0.05). Specifically, average annual loads of NO3–N, TN, SRP, and TP loads were reduced 49, 42, 28, and 35% following pasture aeration and by 78, 72, 55, and 59% from subsurface applying poultry litter, relative to surface applications, respectively. Greatest annual N loads and runoff corresponded with surface poultry litter applications, followed by pasture aeration, with subsurface incorporation of poultry litter resulting in lowest (P ≤ 0.05) TN and NO3–N loads. Overall, subsurface incorporation of poultry litter and pasture aeration are two promising conservation practices for reducing non-point source pollution in watersheds with nutrient imbalances. Further work needs to be done on factors influencing the efficacy of these conservation practices under rainfed conditions, as well as the economic feasibility of these conservation agricultural practices.

Abstract The Soil Management Assessment Framework (SMAF) is a quantitative soil quality (SQ) evaluation tool that is widely applied to assess soil response to specific agricultural management practices over time. Considering the reported SQ benefits of agroforestry (AF) systems and the potential usefulness of SMAF, the objective of this study was to evaluate the effects of tree species (pecan [Carya illinoinensis (Wangenh.) K. Koch] and northern red oak [Quercus rubra L.]), soil fertility source (poultry litter [PL] and inorganic N fertilizer [control]), and soil depth (0–15 and 15–30 cm) on SMAF‐derived SQ indices after 17 yr of management at an AF site in northwest Arkansas. Averaged across soil depth, soil organic C scores under red oak with PL application had a lower score (0.48) than red oak fertilized with inorganic N (0.60) and pecan receiving long‐term PL applications (0.60), which did not differ from pecan with inorganic N fertilizer application (0.51). Averaged across soil depth, the soil quality index (SQI) for pecan receiving PL applications was 1.1 times greater than that under red oak receiving PL and soils under pecan receiving inorganic N fertilizer. Soil quality assessments use in AF are novel, as SMAF has not been used to identify soil health in these systems, although specific tree crop codes need to be developed in SMAF. Results of this study demonstrate that soils planted under various tree species respond dissimilarly to fertilizer sources and that management may improve overall SQ.

The Chaoyang profile represents a rare multi-period, continuous and complete sequence of aeolian paleo-deposits with a stable sedimentary origin and multi-stage paleoclimatic cycles. Benchmark profiles including soil types at different pedogenic stages can be used for the recognition and classification of paleosols and paleoclimate reconstruction. The loess–paleosol sequence benchmark profile (LBP) is also helpful in comparing the results of paleoenvironment reconstruction from different ecological regions. In this study, a loess–paleosol profile derived from thick loess in Chaoyang city of Liaoning province, Northeast China, was investigated as a well-preserved LBP that included various paleosol types. To determine the nature and origin of the Chaoyang profile, the geographic, stratigraphic and morphological characteristics were described in the field. Bulk samples from 42 horizons were collected for chemical and physical analysis, and sub-sampling of 946 samples at 2 cm intervals from the surface to the bottom were taken to measure grain size distributions and magnetic susceptibility. Results showed that the 19.85 m thick loess–paleosol profile had been continuously deposited since 423 ka BP. The upper part (0–195 cm), or UPP, was predominantly of aeolian loess deposition origin but was mixed with water-reworked materials from a nearby secondary loess source. The middle part (195–228 cm), or MIP, was also indirectly affected by the water-reworking process through the leaching of materials from the overlying UPP. The lower part (228–1985 cm), or LOP, was characterized by four reddish stratigraphic layers interbedded with five yellowish ones, indicating several types of paleosols developed under different ecological environments. The multi-stage paleoclimatic cycles as evidenced by morphological and physical characteristics as well as age dating and magnetic susceptibility correlated well with the Lingtai section and LR04 benthic δ18O. Because of these attributes, the Chaoyang profile can be deemed as a benchmark loess–paleosol profile for the recognition and classification of paleosols and paleoclimate reconstruction in Northeast China. The differences in morphological and physical properties between paleosols and loess suggest different soil fertility and agronomic properties and need further studies to assess their functionality with climate fluctuation.

High-resolution maps of soil health measurements could help farmers finetune input resources and management practices for profit maximization. Within-field soil heath variations can be mapped using local topography and apparent electrical conductivity (ECa) as predictors. To address these issues, a study was conducted in Texas Blackland Prairie soils with the following objectives: (i) to assess and map within-field soil health variations using machine learning; (ii) to evaluate the usefulness of topography and ECa as soil health predictors; and (iii) to quantify the relationship between ECa and soil health index and use ECa to estimate soil health spatial distribution. We collected 218 topsoil (0–15 cm) samples following a 35 m × 35 m grid design and analyzed for one-day CO2, organic C, organic N, and soil health index (SHI) based on the Haney Soil Health Tool. A random forest model was applied to predict and map those properties on a 5 m × 5 m grid where ECa, and terrain attributes were used as predictors. Furthermore, the empirical relationship between SHI and ECa was established and mapped across the field. Results showed that the study area was variable in terms of one-day CO2, organic C, organic N, SHI, and ECa distribution. The ECa, wetness index, multiresolution valley bottom flatness, and topographic position index were among the top predictors of soil health measurements. The model was sufficiently robust to predict one day CO2, organic C, organic N (R2 between 0.24–0.90), and SHI (R2 between 0.47–0.90). Overall, we observed a moderate to strong spatial dependency of soil health measurements which could impact within-field yield variability. The study confirmed the applicability of easy to obtain ECa as a good predictor of SHI, and the predicted maps at high resolution which could be useful in site-specific management decisions within these types of soils.

The persistence of antimicrobial resistant (AMR) genes in the soil-environment is a concern, yet practices that mitigate AMR are poorly understood, especially in grasslands. Animal manures are widely deposited on grasslands, which are the largest agricultural land-use in the United States. These nutrient-rich manures may contain AMR genes. The aim of this study was to enumerate AMR genes in grassland soils following 14-years of poultry litter and cattle manure deposition and evaluate if best management practices (rotationally grazed with a riparian (RBR) area and a fenced riparian buffer strip (RBS), which excluded cattle grazing and poultry litter applications) relative to standard pasture management (continuously grazed (CG) and hayed (H)) minimize the presence and amount of AMR genes. Quantitative PCR (Q-PCR) was performed to enumerate four AMR genes (ermB, sulI, intlI, and blactx-m-32) in soil, cattle manure, and poultry litter environments. Six soil samples were additionally subjected to metagenomic sequencing and resistance genes were identified from assembled sequences. Following 14-years of continuous management, ermB, sulI, and intlI genes in soil were greatest (P < 0.05) in samples collected under long-term continuous grazing (relative to conservation best management practices), under suggesting overgrazing and continuous cattle manure deposition may increase AMR gene presence. In general, AMR gene prevalence increased downslope, suggesting potential lateral movement and accumulation based on landscape position. Poultry litter had lower abundance of AMR genes (ermB, sulI, and intlI) relative to cattle manure. Long-term applications of poultry litter increased the abundance of sulI and intlI genes in soil (P

Abstract Tractor guidance (TG) systems presumably improve agricultural production efficiencies by reducing overlaps and gaps during field operations. However, replicated field studies that calculate TG gains are limited but necessary to estimate efficiencies on small farms (82% of U.S. producers). We applied fertilizer with a wheel spreader and herbicides with a boom sprayer on six and four fields, respectively, with and without TG. This study developed novel postprocessing methodology to calculate spatial coverage efficiency from TG systems. Overlap and gap estimation was implemented using the individual pass polygon method (IPPM). We propose a correction factor to adjust biased estimation of overlap area by IPPM. Results from IPPM suggest TG systems reduced overlaps (up to 6% of field area) and gaps (up to 16%) during field operations and improved overall efficiency up to 20%. Consequently, TG systems lead to reduced input use and in‐field operation time, as well as improved equipment efficiency, leading to potential economic and environmental savings for small‐scale producers.

Abstract Tractor overlaps and gaps during field operations reduce production efficiencies; however, studies evaluating linkages between overlaps and field terrain attributes are limited. Our objective is to explore relationships between tractor guidance (TG) systems and field shape and terrain attributes on tractor path overlap. Fields with varying attributes were used for fertilizer (six fields) and herbicide (four fields) applications (with and without TG). Overlap polygon and terrain attributes were overlaid and sampled per 50‐ × 50‐m grid cell. Overlap increased with increasing field irregularities (6 to 11% field area), shorter pass lengths (2 to 19 m2), greater slope (17 to 53 m2), and roughness index (13 to 38 m2) per grid cell. Averaged across slope classes, overlap area decreased 41 to 17 m2 (58% reduction) per grid cell with TG. These results are important for future design of machinery, sensors, and improved TG efficiencies for automated field operations.

Abstract The Soil Management Assessment Framework (SMAF) may provide insight into how conservation practices affect soil quality (SQ) regionally. Therefore, we aimed to quantify SQ in a long‐term (15‐yr) crop rotation and bio‐covers experiment under no‐tillage using SMAF. Main effects were cropping rotations of soybean [Glycine max (L.) Merr.], corn (Zea mays L.), and cotton (Gossypium hirsutum L.). Split‐block bio‐cover treatments consisted of winter wheat (Triticum aestivum L.), Austrian winter pea (Pisum sativum L. sativum var. arvense), hairy vetch (Vicia villosa Roth), poultry litter, and fallow (control). Seven SQ indicators—soil pH, total organic carbon (TOC), bulk density (BD), soil extractable P and K, electrical conductivity (EC), and sodium adsorption ration (SAR)—were scored using SMAF algorithms, and investigated individually and as an overall soil quality index (SQI). Simple linear regressions were performed between SQI and crop yields. Differences (p

Combining data into a centralized, searchable, and linked platform will provide a data exploration platform to agricultural stakeholders and researchers for better agricultural decision making, thus fully utilizing existing data and preventing redundant research. Such a data repository requires readiness to share data, knowledge, and skillsets and working with Big Data infrastructures. With the adoption of new technologies and increased data collection, agricultural workforces need to update their knowledge, skills, and abilities. The partnerships for data innovation (PDI) effort integrates agricultural data by efficiently capturing them from field, lab, and greenhouse studies using a variety of sensors, tools, and apps and provides a quick visualization and summary of statistics for real-time decision making. This paper aims to evaluate and provide examples of case studies currently using PDI and use its long-term continental US database (18 locations and 24 years) to test the cover crop and grazing effects on soil organic carbon (SOC) storage. The results show that legume and rye (Secale cereale L.) cover crops increased SOC storage by 36% and 50%, respectively, compared with oat (Avena sativa L.) and rye mixtures and low and high grazing intensities improving the upper SOC by 69–72% compared with a medium grazing intensity. This was likely due to legumes providing a more favorable substrate for SOC formation and high grazing intensity systems having continuous manure deposition. Overall, PDI can be used to democratize data regionally and nationally and therefore can address large-scale research questions aimed at addressing agricultural grand challenges.

Silvopasture systems combine tree and livestock production to minimize market risk and enhance ecological services. Our objective was to explore and develop a method for identifying driving factors linked to productivity in a silvopastoral system using machine learning. A multi-variable approach was used to detect factors that affect system-level output (i.e., plant production (tree and forage), soil factors, and animal response based on grazing preference). Variables from a three-year (2017–2019) grazing study, including forage, tree, soil, and terrain attribute parameters, were analyzed. Hierarchical variable clustering and random forest model selected 10 important variables for each of four major clusters. A stepwise multiple linear regression and regression tree approach was used to predict cattle grazing hours per animal unit (h ha−1 AU−1) using 40 variables (10 per cluster) selected from 130 total variables. Overall, the variable ranking method selected more weighted variables for systems-level analysis. The regression tree performed better than stepwise linear regression for interpreting factor-level effects on animal grazing preference. Cattle were more likely to graze forage on soils with Cd levels −1 (126% greater grazing hours per AU), soil Cr −1 (108%), and a SAGA wetness index of Dactylis glomerata L.). The result shows water flow within the landscape position (wetness index), and associated metals distribution may be used as an indicator of animal grazing preference. Overall, soil nutrient distribution patterns drove grazing response, although animal grazing preference was also influenced by aboveground (forage and tree), soil, and landscape attributes. Machine learning approaches helped explain pasture use and overall drivers of grazing preference in a multifunctional system.

Since the onset of land application of poultry litter, transportation of microorganisms, antibiotics, and disinfectants to new locations has occurred. While some studies provide evidence that antimicrobial resistance (AMR), an evolutionary phenomenon, could be influenced by animal production systems, other research suggests AMR originates in the environment from non-anthropogenic sources. In addition, AMR impacts the effective prevention and treatment of poultry illnesses and is increasingly a threat to global public health. Therefore, there is a need to understand the dissemination of AMR genes to the environment, particularly those directly relevant to animal health using the One Health Approach. This review focuses on the potential movement of resistance genes to the soil via land application of poultry litter. Additionally, we highlight impacts of AMR on microbial ecology and explore hypotheses explaining gene movement pathways from U.S. broiler operations to the environment. Current approaches for decreasing antibiotic use in U.S. poultry operations are also described in this review.

Soil microorganisms are important for maintaining soil health, decomposing organic matter, and recycling nutrients in pasture systems. However, the impact of long-term conservation pasture management on soil microbial communities remains unclear. Therefore, soil microbiome responses to conservation pasture management is an important component of soil health, especially in the largest agricultural land-use in the US. The aim of this study was to identify soil microbiome community differences following 13-years of pasture management (hayed (no cattle), continuously grazed, rotationally grazed with a fenced, un-grazed and unfertilized buffer strip, and a control (no poultry litter or cattle manure inputs)). Since 2004, all pastures (excluding the control) received annual poultry litter at a rate of 5.6 Mg ha−1. Soil samples were collected at a 0–15 cm depth from 2016–2017 either pre or post poultry litter applications, and bacterial communities were characterized using Illumina 16S rRNA gene amplicon sequencing. Overall, pasture management influenced soil microbial community structure, and effects were different by year (P

Successful hydrological model predictions depend on appropriate framing of scale and the spatial-temporal accuracy of input parameters describing soil hydraulic properties. Saturated soil hydraulic conductivity () is one of the most important properties influencing water movement through soil under saturated conditions. It is also one of the most expensive to measure and is highly variable. The objectives of this research were (i) to assess the ability of Amoozemeters, wells, piezometers, and flumes to accurately represent at a small catchment scale and (ii) to extrapolate to a larger watershed based on available soil data and soil landscape models for simulating streamflow using the Distributed Hydrological Soil Vegetation Model. The mean between Amoozemeters, wells, and flumes varied from 2.4 to 4.9 × 10 m s, and differences were not significant. Mixed trends in mean for slope positions and soil series were observed. The strongest significant and consistent trend in mean was observed for soil depth. The mean decreased exponentially with depth, from 6.51 × 10 m s for upper horizons to 2.37 × 10 m s for bottom horizons. Recognizing the significantly decreasing trend of with soil depth and the lack of consistent trends between soils and slope positions for small catchments, values were extrapolated from the small catchments occurring in Dillon Creek to another large watershed (Hall Creek) based on soil similarity and distribution. The Nash–Sutcliffe model overall efficiency of 0.52 indicated a good performance in simulating streamflows without model calibration. Combining measurement methods in small catchments with an understanding of soil landscapes and soil distribution relationships allowed successful upscaling of localized soil hydraulic properties for streamflow predictions to larger watersheds.