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The empirical Revised Universal Soil Loss Equation (RUSLE) has been adapted to geographical information system (GIS) frameworks to study the spatial variability of soil erosion across landscapes and has also been used to estimate reservoir sedimentation. The literature presents contradictory results about the efficacy of using RUSLE in a GIS context for quantifying reservoir sedimentation, requiring further evaluation and validation of its estimates relative to measured reservoir sedimentation. Our primary objective was to determine if these contradictory results may be a function of the RUSLE’s inability to account for sediments derived from gullies, stream channels, or stream banks; the temporal variability of some of RUSLE’s empirically based factors such as the land cover/land management (C-) factor; and in some model renditions, the choice of value for the sediment delivery ratio (SDR). The usefulness of adjusting these estimates using a regional representative value of gully/stream bank sediment contributions was also assessed. High-spatial horizontal resolution (2 m) digital elevation models (DEMs) for 12 watersheds were used together with C-factor data for five representative years in a GIS-based RUSLE model that incorporates SDR within a sediment routing routine to study the impacts of choice of C-factor and SDR on reservoir sedimentation estimates. Choice of image date for developing C-factors was found to impact reservoir estimates. We also found that the value of SDR for some of the study watersheds would have to be unrealistically small to produce sedimentation estimates comparable to measured values. Estimates of reservoir sedimentation were comparable to measured data for 5 of the 12 watersheds, when the regionally based adjustment for gully/stream bank contributions was applied. However, differences remained large for the remaining seven watersheds. Statistical analysis revealed that certain combinations of geomorphic, pedologic, or topographic variables could be used to predict the degree of sediment underestimation with a significant and high level of correlation (0.72 < R2 ≤ 0.99; p-value < 0.05). Our findings indicate that the level of agreement between GIS-based RUSLE estimates of reservoir sedimentation and measured values is a function of watershed characteristics; for example, the area-weighted soil erodibility (K-) factor of the soils within the watershed and stream channels, the stream entrenchment ratio and bank full depth, the percentage of the stream corridor having slopes ≥ 21°, and the width of the stream flood way as a percentage of the watershed area. Within the context of GIS, these metrics are easily obtained from digital elevation models and publicly available soils data and may be useful in prioritizing reservoirs’ assessments for function and safety.

A thermal hydraulic disaggregation of soil moisture (THySM) algorithm was implemented to downscale NASA's soil moisture active passive (SMAP) enhanced soil moisture (SM) product to 1 km over the continental United States (CONUS). This algorithm was developed by combining thermal inertia theory with a soil hydraulic-based approach that considers fine-scale SM spatial distribution driven by both heat fluxes and hydraulic conductivity in soils. Relative soil wetness values were estimated using land surface temperature and normalized difference vegetation index for the thermal inertia model and using soil properties for the hydraulic model. The relative soil wetness values at 1 km from both models were then combined by using weighting functions whereby the spatial distribution of SM was governed more by thermal fluxes during times of strong heat transport and infiltration during moisture abundant soil conditions. THySM values were evaluated using in situ SM measurements from SMAP Core Validation Sites (CVS), the US Department of Agriculture Soil Climate Analysis Network, and the National Oceanic and Atmospheric Administration Climate Reference Network over CONUS. THySM shows higher accuracy than the SMAP / Sentinel-1 (SPL2SMAP_S) 1 km SM product when compared to in situ measurements. The accuracy of THySM is 0.048 m3/m3 based on unbiased root mean square error (ubRMSE), outperforming SPL2SMAP_S by 0.01–0.02 m3/m3. The ubRMSE of THySM 1 km SM over the SMAP grassland/rangeland-dominated CVS sites is better than 0.04 m3/m3, which meets the SMAP mission SM accuracy requirement applied at 9 and 36 km.

In August 2020, soil moisture active passive (SMAP) released a new version of its soil moisture and vegetation optical depth (VOD) retrieval products. In this article, we review the methodology followed by the SMAP regularized dual-channel retrieval algorithm. We show that the new implementation generates SM retrievals that not only satisfy the SMAP accuracy requirements, but also show a performance comparable to the single-channel algorithm that uses the V polarized brightness temperature. Due to a lack of in situ measurements we cannot evaluate the accuracy of the VOD. In this article, we show analyses with the intention of providing an understanding of the VOD product. We compare the VOD results with those from SMOS. We also study the relation of the SMAP VOD with two vegetation parameters: tree height and biomass.

The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error 3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the European Space Agency Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio–temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

The U.S. Department of Agriculture’s Agricultural Research Service (USDA-ARS) maintains seven in situ soil moisture networks throughout the continental United States, some since 2002. These networks are crucial for understanding the spatial and temporal extent of droughts in their historical context, parameterization of hydrologic models, and local agricultural decision support. However, the estimates from these networks are dependent upon their ability to provide reliable soil moisture information at a large scale. It is also not known how many network stations are sufficient to monitor watershed scale dynamics. Therefore, the objectives of this research were to: (1) determine how temporally stable these networks are, including the relationships between various sensors on a year-to-year and seasonal basis, and (2) attempt to determine how many sensors are required, within a network, to approximate the full network average. Using data from seven in situ, it is concluded that approximately 12 soil moisture sensors are sufficient in most environments, presuming their locations are distributed to capture the hydrologic heterogeneity of the watershed. It is possible to install a temporary network containing a suitable number of sensors for an appropriate length of time, glean stable relationships between locations, and retain these insights moving forward with fewer sensor resources.

A soil moisture (SM) disaggregation algorithm based on thermal inertia (TI) theory was implemented to downscale the soil moisture active passive (SMAP) enhanced product (SPL2SMP$\_$E) from 9 to 1 km over the continental United States. The algorithm applies land surface temperature and normalized difference vegetation index from moderate resolution imaging spectroradiometer (MODIS) at higher spatial resolution to estimate relative soil wetness within a coarse SMAP grid-this MODIS-derived relative wetness is then used to produce the downscaled SMAP SM. Results from the algorithm were evaluated in terms of their spatio-temporal coverage and accuracy using in situ measurements from SMAP core validation sites (CVS), the U.S. Department of Agriculture Soil Climate Analysis Network (SCAN), and the National Oceanic and Atmospheric Administration Climate Reference Network (CRN). Results were also compared with the baseline SPL2SMP$\_$E and the SMAP/Sentinel-1 (SPL2SMAP$\_$S) 1 km product. Overall, the unbiased root-mean-square error (ubRMSE) of the disaggregated SM at the CVS using the TI approach is approximately 0.04 $\text{m}^3/\text{m}^3$, which is the SMAP mission requirement for the baseline products. The TI approach outperforms the SMAP/Sentinel SL2SMAP$\_$S 1 km product by approximately 0.02 $\text{m}^3/\text{m}^3$. Over the agriculture/crop areas from SCAN and CRN sparse network stations, the TI approach exhibits better ubRMSE compared to SPL2SMP$\_$E and SPL2SMAP$\_$S by about 0.01 and 0.02 $\text{m}^3/\text{m}^3$, indicating its advantage in these areas. However, a drawback of this approach is that there are data gaps due to cloud cover as optical sensors cannot have a clear view of the land surface.

Carbon dioxide (CO2) fluxes and evapotranspiration (ET) during the non-growing season can contribute significantly to the annual carbon and water budgets of agroecosystems. Comparative studies of vegetation phenology and the dynamics of CO2 fluxes and ET during the dormant season of native tallgrass prairies from different landscape positions under the same climatic regime are scarce. Thus, this study compared the dynamics of satellite-derived vegetation phenology (as captured by the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI)) and eddy covariance (EC)-measured CO2 fluxes and ET in six differently managed native tallgrass prairie pastures during dormant seasons (November through March). During December–February, vegetation phenology (EVI and NDVI) and the dynamics of eddy fluxes were comparable across all pastures in most years. Large discrepancies in fluxes were observed during March (the time of the initiation of growth of dominant warm-season grasses) across years and pastures due to the influence of weather conditions and management practices. The results illustrated the interactive effects between prescribed spring burns and rainfall on vegetation phenology (i.e., positive and negative impacts of prescribed spring burns under non-drought and drought conditions, respectively). The EVI better tracked the phenology of tallgrass prairie during the dormant season than did NDVI. Similar EVI and NDVI values for the periods when flux magnitudes were different among pastures and years, most likely due to the satellite sensors’ inability to fully observe the presence of some cool-season C3 species under residues, necessitated a multi-level validation approach of using ground-truth observations of species composition, EC measurements, PhenoCam (digital) images, and finer-resolution satellite data to further validate the vegetation phenology derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) during dormant seasons. This study provides novel insights into the dynamics of vegetation phenology, CO2 fluxes, and ET of tallgrass prairie during the dormant season in the U.S. Southern Great Plains.

Short-range predictions of crop yield provide valuable insights for agricultural resource management and likely economic impacts associated with low yield. Such predictions are difficult to achieve in regions that lack extensive observational records. Herein, we demonstrate how a number of basic or readily available input data can be used to train an Artificial Neural Network (ANN) model to provide months-ahead predictions of cotton yield for a case study in Menemen Plain, Turkey. We use limited reported yield (13 years) along cumulative precipitation, cumulative heat units, two meteorologically-based drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)), and three remotely-sensed vegetation indices (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI)) as ANN inputs. Results indicate that, when EVI is combined with the preceding 12-month SPEI, it has better sensitivity to cotton yield than other indicators. The ANN model predicted cotton yield four months before harvest with R2 > 0.80, showing potential as a yield prediction tool. We discuss the effects of different combinations of input data (explanatory variables), dataset size, and selection of training data to inform future applications of ANN for early prediction of cotton yield in data-scarce regions.

Abstract Water scarcity threatens the sustainability of irrigated cotton production in many regions around the world. Consequently, there is a critical need to identify and test strategies that optimize water use for cotton production. This 4‐yr study evaluated the effects of three irrigation treatments (full irrigation [FI], reduced irrigation [RI] at 75% of FI, and no irrigation [NI]) on cotton yield, fiber quality, and irrigation water use efficiency (IWUE) at a field that relies on groundwater for irrigation in west‐central Oklahoma. Compared with FI, lint and seed yields did not change significantly under RI. The reductions in lint and seed yields were significant at 64 and 65% under NI, respectively, compared with FI. No significant differences in fiber quality were observed among the irrigation treatments. In addition, reducing irrigation application improved the IWUE of lint and seed by 9 and 8%, respectively. Based on these findings, reducing groundwater extraction by 25% appears to be an effective strategy to achieve water conservation while limiting negative impacts on yield quantity and quality for cotton production in west‐central Oklahoma.

Study region: The Little Washita River Experimental Watershed (LWREW) is located between Chickasha and Lawton in southwestern Oklahoma, US. Study focus: In this study, frequency analyses were conducted on sub-hourly precipitation event descriptors to quantify the changes in precipitation patterns commonly masked in annual analysis. Precipitation events were derived from 5-min data collected by USDA-ARS from 1961 to 2015. New hydrological insights for the region: During the 1962–2015 period, this region experienced successive regional long-term wet (1962–1995) and dry (1996–2015) dominated periods of more than 20 years each. During the wet period, precipitation intensity ranging from 1 to 12 mm were 20% less likely to occur while event daily average precipitation and number of events increased by 10% and 11%, respectively. Also, the average daily precipitation occurred more often at lower intensities. In contrast, during the dry period, instantaneous intensity ranging from 1 to 12 mm became more likely to occur (64% increase; 1996–2015) while event daily average precipitation and number of events decreased by 16% and 18%, respectively. Overall, comparisons with the baseline data (1962–2015) indicated that in the last 20 years, sub-hourly precipitation intensities have increased while daily event duration and extreme 5-min precipitation intensities larger than 24 mm (precipitation intensification below extremes) have remained unchanged.

Currently, a gap exists in inventorying and monitoring the impact of land use and management on soil resources. Reducing the number of samples required to determine the impact of land management on soil carbon (C) and mineral constituents via proximal sensing techniques such as hyper-spectral radiometry can reduce the cost and personnel required to monitor changes in our natural resource base. Previously, we used an expensive, high signal-to-noise ratio (SNR) field spectrometer to correlate soil constituents to hyperspectral diffuse reflectance (HDR), over the 350–2500 nm (VIS-SWIR) wavelength range. This research is an extension of preceding research but focuses solely on the 400–1000 nm (VIS-NIR) region of the electromagnetic spectrum. This region can be measured using less expensive (albeit with lower SNR), miniaturized, field spectrometers that allow minimal sample preparation. Our objectives are to: (1) further evaluate the use of soil HDR in the visible and near-infrared (VIS-NIR) region acquired using an expensive field hyperspectral spectroradiometer for prediction of soil C and selected fractions and nitrogen (N) constituents, (2) repeat the above measurements using HDR data from samples examined in objective (1) using lower SNR hyperspectral radiometers, and (3) add to the limited literature that addresses determinations of selected soil properties using proximal sensing in the VIS-NIR region. Data analyzed in this study confirms that good to satisfactory prediction equations for soil constituents can be developed from spectral reflectance data within the 400–1000 nm wavelength region obtained using relatively inexpensive field radiometers. This application could reduce the time and resources required to monitor gains or losses in carbon constituents, information that can be used in programing such as Conservation Technical Assistance (CTA), the Conservation Reserve Program (CRP) and Climate-smart agriculture (CSA).

Grasslands in the Southern Great Plains of the United States have major ecological and economic importance, with strong climate and water cycle connections. The historic native prairie grassland has been managed differently for enhancing productivity, while consequently altering water vapor fluxes. However, little is known about the impacts of different management activities on evapotranspiration (ET) at different spatio-temporal scales. In this study, we quantified and compared ET between co-located introduced managed pasture (MP) and native prairie (NP) pasture. Additionally, we compared the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived ET at four different spatial scales: 30 m (ETMOD30), 200 m (ETMOD200), 500 m (ETMOD500), and 1000 m (ETMOD1000) with eddy covariance-measured ET (ETEC). Large differences in ETEC were observed between two pastures from half-hourly to seasonal scales, with variations mainly controlled by the amount of rainfall and management activities. The results demonstrated differential responses of MP and NP in a pluvial year. The ETMOD30 showed a better agreement with ETEC than did the ETMOD200, ETMOD500, and ETMOD1000. The ETMOD200, ETMOD500, and ETMOD1000 largely underestimated ETEC, most likely due to their inability to capture the spatial heterogeneity of vegetation growth impacted by various management activities. Our results facilitate understanding of the difference in ET of MP and NP due to differences in vegetation resulting from different management activities and their differential responses to precipitation.

The Great Plains of the USA is one of largest expanses of prairie ecosystems in the world. Prairies have been extensively converted to other land uses. The remaining prairie ecosystems are important for livestock grazing and provide benefits including habitat for avian, terrestrial, and aquatic species, carbon regulation, and hydrologic function. While producers, land management agencies, and some researchers have promoted livestock management using rotational stocking for increased production efficiency and enhanced ecosystem function, scientific literature has not provided a consensus on whether rotational stocking results in increased plant biomass or animal productivity. To address this research need, we established long-term grazing research using an adaptive management framework to encompass a wide range of production and ecological interactions on native grassland pastures. This paper describes objectives, design, and implementation of the long-term study to evaluate productivity and ecological effects of beef cow−calf management and production under continuous system (CS) or rotational system (RS) on native tallgrass prairie. Findings from 2009 to 2015 indicate that plant biomass and animal productivity were similar in the two grazing management systems. There were some indicators that forage nutritive value of standing biomass and soil nutrient content were enhanced in the RS system compared with the CS, yet individual calf body weight (BW) at weaning was greater in the CS. This prepares us to engage with producers to help determine the focus for the next phase of the research.

This research compared the accuracy of laboratory reference measurements of soil C and N fractions with soil reflectance spectra acquired using a portable field spectroradiometer with an illuminating contact probe. Soil samples were taken from eight, 1.6 ha watersheds, located in El Reno, Oklahoma on native warm season grasslands and agronomic managements with landform complexes serving as replicates within and among treatments. Soil samples were taken from 0−30-cm. Measurements included total soil organic carbon (TSOC), total soil nitrogen (TSN), residual C of acid hydrolysis (RCAH), and particulate organic matter C (POMC) and N (POMN). Soil reflectance in the 350 to 2500 nm region was correlated with individual laboratory measurements. Each reference dataset was divided into model development data (70%) and model validation data (30%). Calibrated models were applied to validation datasets. Statistical analysis revealed that prediction efficiencies of soil reflectance models were highly quantitative. Coefficients of determination (R2) were near 1 (≥0.90) and ratios of predicted values to the measured standard deviation (RPD) were >2, indicative of good predictive models. The field spectroradiometer enabled us to parameterize soil spatial variability and soil reflectance measurements, reducing the resources required to acquire edaphic measurements.

Understanding the consequences of different management practices on vegetation phenology, forage production and quality, plant and microbial species composition, greenhouse gas emissions, and water budgets in tallgrass prairie systems is vital to identify best management practices. As part of the Southern Plains Long-Term Agroecosystem Research (SP-LTAR) grassland study, a long-term integrated Grassland-LivestOck Burning Experiment (iGLOBE) has been established with a cluster of six eddy covariance (EC) systems on differently managed (i.e., different burning and grazing regimes) native tallgrass prairie systems located in different landscape positions. The purpose of this paper is to describe this long-term experiment, report preliminary results on the responses of differently managed tallgrass prairies under variable climates using satellite remote sensing and EC data, and present future research directions. In general, vegetation greened-up and peaked early, and produced greater forage yields in burned years. However, drought impacts were greater in burned sites due to reductions in soil water availability by burning. The impact of grazing on vegetation phenology was confounded by several factors (e.g., cattle size, stocking rate, precipitation). Moreover, prairie systems located in different landscapes responded differently, especially in dry years due to differences in water availability. The strong correspondence between vegetation phenology and eddy fluxes was evidenced by strong linear relationships of a greenness index (i.e., enhanced vegetation index) with evapotranspiration and gross primary production. Results indicate that impacts of climate and management practices on vegetation phenology may profoundly impact carbon and water budgets of tallgrass prairie. Interacting effects of multiple management practices and inter-annual climatic variability on the responses of tallgrass prairie highlight the necessity of establishing an innovative and comprehensive long-term experiment to address inconsistent responses of tallgrass prairie to different intensities, frequencies, timing, and duration of management practices, and to identify best management practices.

Macronutrient (N, P, S, K, Ca, and Mg) availability and distribution in soils of grassland ecosystems are affected by diverse factors, including landscape position, climate, and forms of management. This study examined flux in plant-available macronutrients in production-scale (60 to 80 ha) paddocks of southern tallgrass prairie of central Oklahoma, United States, managed (2009−15) under two contrasting stocking methods (continuous yearlong; rotational stocking among 10 sub-paddocks). Macronutrient availability within the 0−7.5 cm and 7.5−15 cm soil depths were determined with sets of anion-cation exchange membrane probes at 16 locations within paddocks, oriented along transects from water sources to far corners. No clear overall effect related to stocking method was recorded for all macronutrient distributions. The only significant stocking method × location interaction occurred for K (p = 0.01). All other macronutrients displayed significant (p < 0.08) location effects that were common across stocking methods. Effects relatable to stocking method occurred in interactions with soil depth or time of year (p < 0.10), but responses of macronutrient flux to stocking method in these interactions varied. Higher flux occurred in available S, Ca, and Mg in proximity (

This study evaluated the impacts of different grazing treatments (continuous (C) and rotational (R) grazing) on tallgrass prairie landscape, using high-spatial-resolution aerial imagery (1-m at RGB and near-infrared bands) of experimental C and R pastures within two replicates (Rep A and Rep B) in the southern Great Plains (SGP) of the United States. The imagery was acquired by the National Agriculture Imagery Program (NAIP) during the agricultural growing season of selected years (2010, 2013, 2015, and 2017) in the continental United States. Land cover maps were generated by combining visual interpolation, a support vector machine, and a decision tree classifier. Landscape metrics (class area, patch number, percentage of landscape, and fragmentation indices) were calculated from the FRAGSTATS (a computer software program designed to compute a wide variety of landscape metrics for categorical map patterns) based on land cover results. Both the metrics and land cover results were used to analyze landscape dynamics in the experiment pastures. Results showed that both grass and shrubs of different pastures differed largely in the same year and had significant annual dynamics controlled by climate. High stocking intensity delayed grass growth. A large proportion of bare soil occurred in sub-paddocks of rotational grazing that were just grazed or under grazing. Rep A experienced rapid shrub encroachment, with a large proportion of shrub at the beginning of the experiment. Shrub may occupy 41% of C and 15% of R in Rep A by 2030, as revealed by the linear regression analysis of shrub encroachment. In contrast, shrub encroachment was not significant in Rep B, which only had a small number of shrub patches at the beginning of the experiment. This result indicates that the shrub encroachment is mainly controlled by the initial status of the pastures instead of grazing management. However, the low temporal resolution of the NAIP imagery (one snapshot in two or three years) limits our comparison of the continuous and rotational grazing at the annual scale. Future studies need to combine NAIP imagery with other higher temporal resolution imagery (e.g., WorldView), in order to better evaluate the interannual variabilities of grass productivity and shrub encroachment.

Broad ranges of factors (parent materials, climate, plant community, landscape position, management) can influence macronutrient availability in rangeland soils. Two important factors in production-scale paddocks are the influences of location in space and land management. This study examined plant-available macronutrients (total mineral and nitrate-N, P, S, K, Ca, and Mg) in soils, with paired sets of probes (anion and cation exchange membranes) that simulate uptake by plant roots. Data were collected from sets of paddocks of southern tallgrass prairie in central Oklahoma, managed by four stocking methods during the 2015 growing season (mid-March, growth initiation by native grasses, and early-August, time of peak living plant biomass). Macronutrient availability in the 0−7.5 cm and 7.5−15 cm depths were determined at locations in close proximity to water (water tanks and 25% of the distance between tanks and paddock mid-points (PMP)), and distances near the mid-points of paddocks (70% of the distance between water and mid-points (0.7 PMP), and PMP). All of the tested stocking methods affected levels of availability of macronutrients at different times of the growing season, and among different locations within paddocks. Such responses indicated stocking methods may not result in uniform distributions of flux in plant-available macronutrients. The overall exposure of landscapes and arrangement of features within paddocks also appeared to influence macronutrient distributions.

Early warning of detrimental weather and climate (particularly drought) on forage production would allow for tactical decision-making for the management of pastures, supplemental feed/forage resources, and livestock. The standardized precipitation and evaporation index (SPEI) has been shown to be correlated with production of various cereal and vegetable crops, and with above-ground tree mass. Its correlation with above-ground grassland or forage mass (AGFM) is less clear. To investigate the utility of SPEI for assessing future biomass status, we used biomass data from a site on the Konza Prairie (KP; for years 1984−1991) and from a site at the United States Department of Agriculture-Agricultural Research Service’s (USDA-ARS) Grazinglands Research Laboratory (GRL; for years 2009−2015), and a publicly-available SPEI product. Using discriminant analysis and artificial neural networks (ANN), we analyzed the monthly timescale SPEI to categorize AGFM into above average, average, and below average conditions for selected months in the grazing season. Assessment of the confusion matrices from the analyses suggested that the ANN better predicted class membership from the SPEI than did the discriminant analysis. Within-site cross validation of the ANNs revealed classification errors ranging from 0 to 50%, depending upon month of class prediction and study site. Across-site ANN validation indicated that the GRL ANN algorithm better predicted KP AGFM class membership than did the KP ANN prediction of GRL AGFM class membership; however, misclassification rates were ≥25% in all months. The ANN developed from the combined datasets exhibited cross-validation misclassification rates of ≤20% for three of the five months being predicted, with the remaining two months having misclassification rates of 33%. Redefinition of the AGFM classes to identify truly adequate AGFM (i.e., average to above average forage availability) improved prediction accuracy. In this regard, results suggest that the SPEI has potential for use as a predictive tool for classifying AGFM, and, thus, for grassland and livestock management. However, a more comprehensive investigation that includes a larger dataset, or combinations of datasets representing other areas, and inclusion of a bi-weekly SPEI may provide additional insights into the usefulness of the SPEI as an indicator for biomass production.

Native grasslands in the Great Plains of North America have mostly disappeared in the past century due to agricultural expansion. A grazing study was established on Paleustolls and Argiustolls supporting a remnant, but historically grazed tallgrass prairie in central Oklahoma. Stocking method of beef cattle was differentiated into continuous and rotational treatments (10 sub-paddocks) in 2009 and these treatments continued until present. Soil was sampled in 2009 and 2012 at depths of 0–6, 6–12, 12–20, and 20–30 cm and in 2017 at depths of 0–15 and 15–30 cm. Total, particulate, microbial biomass, and mineralizable C and N fractions were highly stratified with depth, having 2–10 times greater concentration at a depth of 0–6 cm as that at 20–30 cm. Strong associations existed among most of these soil organic C and N fractions, given the large range that resulted from sampling at multiple depths. No discernable differences in soil organic C and N fractions occurred due to stocking method at any sampling time or depth. Evidence for biological nitrification inhibition suggested a mechanism for conservation of available N with less opportunity for loss. In addition, strong association of available N with biologically active C indicated slow, but sustained release of N that was strongly coupled to C cycling. We conclude that stocking method had a neutral effect on conservation of already high antecedent conditions of soil organic C and N fractions during the first 8 years of differentially imposed management.

This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill, and reduced the surface and root zone unbiased root-mean-square error (ubRMSE) by 0.005 m 3 m − 3 and 0.001 m 3 m − 3 , respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m 3 m − 3 , but increased the root zone bias by 0.014 m 3 m − 3 . Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to skill degradation in other land surface variables.