Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques.

Warm-season legumes have been receiving increased attention as forage resources in the southern United States and other countries. However, the near infrared spectroscopy (NIRS) technique has not been widely explored for predicting the forage quality of many of these legumes. The objective of this research was to assess the performance of NIRS in predicting the forage quality parameters of five warm-season legumes-guar (Cyamopsis tetragonoloba), tepary bean (Phaseolus acutifolius), pigeon pea (Cajanus cajan), soybean (Glycine max), and mothbean (Vigna aconitifolia)-using three machine learning techniques: partial least square (PLS), support vector machine (SVM), and Gaussian processes (GP). Additionally, the efficacy of global models in predicting forage quality was investigated. A set of 70 forage samples was used to develop species-based models for concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro true digestibility (IVTD) of guar and tepary bean forages, and CP and IVTD in pigeon pea and soybean. All species-based models were tested through 10-fold cross-validations, followed by external validations using 20 samples of each species. The global models for CP and IVTD of warm-season legumes were developed using a set of 150 random samples, including 30 samples for each of the five species. The global models were tested through 10-fold cross-validation, and external validation using five individual sets of 20 samples each for different legume species. Among techniques, PLS consistently performed best at calibrating (R2c = 0.94-0.98) all forage quality parameters in both species-based and global models. The SVM provided the most accurate predictions for guar and soybean crops, and global models, and both SVM and PLS performed better for tepary bean and pigeon pea forages. The global modeling approach that developed a single model for all five crops yielded sufficient accuracy (R2cv/R2v = 0.92-0.99) in predicting CP of the different legumes. However, the accuracy of predictions of in vitro true digestibility (IVTD) for the different legumes was variable (R2cv/R2v = 0.42-0.98). Machine learning algorithms like SVM could help develop robust NIRS-based models for predicting forage quality with a relatively small number of samples, and thus needs further attention in different NIRS based applications.

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.

Evaluation of Sensible Heat Flux and Evapotranspiration Estimates Using a Surface Layer Scintillometer and a Large Weighing Lysimeter.

Accurate estimates of actual crop evapotranspiration (ET) are important for optimal irrigation water management, especially in arid and semi-arid regions. Common ET sensing methods include Bowen Ratio, Eddy Covariance (EC), and scintillometers. Large weighing lysimeters are considered the ultimate standard for measurement of ET, however, they are expensive to install and maintain. Although EC and scintillometers are less costly and relatively portable, EC has known energy balance closure discrepancies. Previous scintillometer studies used EC for ground-truthing, but no studies considered weighing lysimeters. In this study, a Surface Layer Scintillometer (SLS) was evaluated for accuracy in determining ET as well as sensible and latent heat fluxes, as compared to a large weighing lysimeter in Bushland, TX. The SLS was installed over irrigated grain sorghum (Sorghum bicolor (L.) Moench) for the period 29 July-17 August 2015 and over grain corn (Zea mays L.) for the period 23 June-2 October 2016. Results showed poor correlation for sensible heat flux, but much better correlation with ET, with r² values of 0.83 and 0.87 for hourly and daily ET, respectively. The accuracy of the SLS was comparable to other ET sensing instruments with an RMSE of 0.13 mm·h-1 (31%) for hourly ET; however, summing hourly values to a daily time step reduced the ET error to 14% (0.75 mm·d-1). This level of accuracy indicates that potential exists for the SLS to be used in some water management applications. As few studies have been conducted to evaluate the SLS for ET estimation, or in combination with lysimetric data, further evaluations would be beneficial to investigate the applicability of the SLS in water resources management.

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.

U.S. winter wheat yield loss attributed to compound hot-dry-windy events.

Climate extremes cause significant winter wheat yield loss and can cause much greater impacts than single extremes in isolation when multiple extremes occur simultaneously. Here we show that compound hot-dry-windy events (HDW) significantly increased in the U.S. Great Plains from 1982 to 2020. These HDW events were the most impactful drivers for wheat yield loss, accounting for a 4% yield reduction per 10 h of HDW during heading to maturity. Current HDW trends are associated with yield reduction rates of up to 0.09 t ha-1 per decade and HDW variations are atmospheric-bridged with the Pacific Decadal Oscillation. We quantify the "yield shock", which is spatially distributed, with the losses in severely HDW-affected areas, presumably the same areas affected by the Dust Bowl of the 1930s. Our findings indicate that compound HDW, which traditional risk assessments overlooked, have significant implications for the U.S. winter wheat production and beyond.

Integrating eddy fluxes and remote sensing products in a rotational grazing native tallgrass prairie pasture.

Eddy covariance (EC) systems provide integrated fluxes within their footprint areas. Spatial heterogeneity of up-scaled areas and spatio-temporal mismatches between EC footprint and remote sensing pixels jeopardize the performance of most satellite-based models. To examine the impact of spatial resolution of satellite products on up-scaling of fluxes, we compared the relationships between measured eddy fluxes and enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 and 250 m spatial resolutions, Visible Infrared Imaging Radiometer Suite (VIIRS) at 500 m spatial resolution, and Landsat at 30 m spatial resolution but integrated at the paddock-scale. The experiment was conducted over a grazed native tallgrass prairie pasture, which was divided into nine paddocks for rotational grazing. The EVI data from all satellites showed consistency in detecting vegetation phenology. Seasonality of EC-measured fluxes corresponded well with remotely-sensed vegetation phenology. Approximately 80% of contribution to eddy fluxes came from within 80 m upwind distance of the 2.7 m tall EC tower. As a result, the major contributing area for the measured fluxes was mostly limited to the paddock containing the EC tower. Different timings and duration of grazing caused some heterogeneity among paddocks within the pasture. The EVI of different spatial scales showed strong relationships with CO2 fluxes. However, Landsat-derived EVI integrated for the paddock containing the EC tower showed substantially stronger relationships with CO2 fluxes than did MODIS and VIIRS-derived EVI integrated for multiple paddocks, most likely due to similar spatial resolutions of remote sensing and EC observations. Results illustrate that satellite products of fine-scale spatial resolution that are comparable to EC footprints can help improve the performance of satellite-based models for modeling or up-scaling of eddy fluxes, especially in heterogeneous ecosystems.

Dynamics of CO2 and H2O fluxes in Johnson grass in the U.S. Southern Great Plains.

Johnson grass (Sorghum halepense (L.) Pers.) is rapidly spreading throughout the continental United States (U.S.). Thus, determining magnitudes and seasonal dynamics of carbon dioxide (CO2) and water vapor (H2O) fluxes in Johnson grass is crucial to understand regional changes in hydrology and carbon balance. Using eddy covariance (EC), CO2 and H2O fluxes were measured from June 2017 to October 2019 over a rainfed Johnson grass field in central Oklahoma. Hay was harvested from late May to early July each year, with biomass yield ~7.5 t ha-1. Weekly averaged daily integrated net ecosystem CO2 exchange (NEE), gross primary production (GPP), and evapotranspiration (ET) reached -8.28 ± 0.76 g C m-2, 20.02 ± 1.62 g C m-2, and 5.42 ± 0.26 mm, respectively. Ecosystem water use efficiency (EWUE) and ecosystem light use efficiency (ELUE) ranged from 3.22 to 3.93 g C mm-1 ET and 0.34 to 0.41 g C mol-1 PAR (photosynthetically active radiation), respectively, during peak growths. Based on aggregated fluxes for each month over the three years (2017-2019), cumulative annual NEE was -434 ± 112 g C m-2, indicating a carbon gain by the Johnson grass field. Cumulative annual ET (858 ± 72 mm) was ~86% of the average annual rainfall (996 ± 100 mm). Results showed Johnson grass could be a carbon sink from May to September in the U.S. Southern Great Plains. Both NEE and ET did not decline up to air temperature (Ta) of ~33 °C and vapor pressure deficit (VPD) of ~2 kPa, suggesting optimum Ta of ≥33 °C and VPD of ≥2 kPa for the fluxes. Results indicated that Johnson grass might be well suited for dryland production in the region. Additionally, these findings provide initial baseline information on CO2 fluxes and ET for Johnson grass relative to other forage species in the region.

Growth and physiological responses of three warm-season legumes to water stress.

Novel drought-tolerant grain legumes like mothbean (Vigna acontifolia), tepary bean (Phaseolus acutifolius), and guar (Cyamopsis tetragonoloba) may also serve as summer forages, and add resilience to agricultural systems in the Southern Great Plains (SGP). However, limited information on the comparative response of these species to different water regimes prevents identification of the most reliable option. This study was conducted to compare mothbean, tepary bean and guar for their vegetative growth and physiological responses to four different water regimes: 100% (control), and 75%, 50% and 25% of control, applied from 27 to 77 days after planting (DAP). Tepary bean showed the lowest stomatal conductance (gs) and photosynthetic rate (A), but also maintained the highest instantaneous water use efficiency (WUEi) among species at 0.06 and 0.042 m3 m-3 soil moisture levels. Despite maintaining higher A, rates of vegetative growth by guar and mothbean were lower than tepary bean due to their limited leaf sink activity. At final harvest (77 DAP), biomass yield of tepary bean was 38-60% and 41-56% greater than guar and mothbean, respectively, across water deficits. Tepary bean was the most drought-tolerant legume under greenhouse conditions, and hence future research should focus on evaluating this species in extensive production settings.

Surface Energy Balance Based Evapotranspiration Mapping in the Texas High Plains.

Agriculture on the Texas High Plains (THP) uses approximately 89% of groundwater withdrawals from the Ogallala Aquifer. Consequently, groundwater levels are declining faster than the recharge rate. Therefore, efficient agricultural water use is essential for economic viability and sustainability of the THP. Accurate regional evapotranspiration (ET) maps would provide valuable information on actual crop water use. In this study, METRIC (Mapping Evapotranspiration at High Resolution using Internalized Calibration), a remote sensing based ET algorithm, was evaluated for mapping ET in the THP. Two Landsat 5 Thematic Mapper images acquired on 27 June (DOY 178) and 29 July (DOY 210) 2005 were used for this purpose. The performance of the ET model was evaluated by comparing the predicted daily ET with values derived from soil moisture budget at four commercial agricultural fields. Daily ET estimates resulted with a prediction error of 12.7±8.1% (mean bias error ± root mean square error) on DOY 178 and -4.7±9.4% on DOY 210 when compared with ET derived from measured soil moisture through the soil water balance. These results are good considering the prevailing advective conditions in the THP. METRIC have the potential to be used for mapping regional ET in the THP region. However, more evaluation is needed under different agroclimatological conditions.

Net ecosystem exchange of CO2 and H2O fluxes from irrigated grain sorghum and maize in the Texas High Plains.

Net ecosystem exchange (NEE) of carbon dioxide (CO2) and water vapor (H2O) fluxes from irrigated grain sorghum (Sorghum bicolor L. Moench) and maize (Zea mays L.) fields in the Texas High Plains were quantified using the eddy covariance (EC) technique during 2014-2016 growing seasons and examined in terms of relevant controlling climatic variables. Eddy covariance measured evapotranspiration (ETEC) was also compared against lysimeter measured ET (ETLys). Daily peak (7-day averages) NEE reached approximately -12 g C m-2 for sorghum and -14.78 g C m-2 for maize. Daily peak (7-day averages) ETEC reached approximately 6.5 mm for sorghum and 7.3 mm for maize. Higher leaf area index (5.7 vs 4-4.5 m2 m-2) and grain yield (14 vs 8-9 t ha-1) of maize compared to sorghum caused larger magnitudes of NEE and ETEC in maize. Comparisons of ETEC and ETLys showed a strong agreement (R2 = 0.93-0.96), while the EC system underestimated ET by 15-24% as compared to lysimeter without any corrections or energy balance adjustments. Both NEE and ETEC were not inhibited by climatic variables during peak photosynthetic period even though diurnal peak values (~2-weeks average) of photosynthetic photon flux density (PPFD), air temperature (Ta), and vapor pressure deficit (VPD) had reached over 2000 µmol m-2 s-1, 30 °C, and 2.5 kPa, respectively, indicating well adaptation of both C4 crops in the Texas High Plains under irrigation. However, more sensitivity of NEE and H2O fluxes beyond threshold Ta and VPD for maize than for sorghum indicated higher adaptability of sorghum for the region. These findings provide baseline information on CO2 fluxes and ET for a minimally studied grain sorghum and offer a robust geographic comparison for maize outside the United States Corn Belt. However, longer-term measurements are required for assessing carbon and water dynamics of these globally important agro-ecosystems.

Carbon dioxide and water vapor fluxes in winter wheat and tallgrass prairie in central Oklahoma.

Winter wheat (Triticum aestivum L.) and tallgrass prairie are common land cover types in the Southern Plains of the United States. During the last century, agricultural expansion into native grasslands was extensive, particularly managed pasture or winter wheat. In this study, we measured carbon dioxide (CO2) and water vapor (H2O) fluxes from winter wheat and tallgrass prairie sites in Central Oklahoma using the eddy covariance in 2015 and 2016. The objective of this study was to contrast CO2 and H2O fluxes between these two ecosystems to provide insights on the impacts of conversion of tallgrass prairie to winter wheat on carbon and water budgets. Daily net ecosystem CO2 exchange (NEE) reached seasonal peaks of -9.4 and -8.8 g C m-2 in 2015 and -6.2 and -7.5 g C m-2 in 2016 at winter wheat and tall grass prairie sites, respectively. Both sites were net sink of carbon during their growing seasons. At the annual scale, the winter wheat site was a net source of carbon (56 ±â€¯13 and 33 ±â€¯9 g C m-2 year-1 in 2015 and 2016, respectively). In contrast, the tallgrass prairie site was a net sink of carbon (-128 ±â€¯69 and -119 ±â€¯53 g C m-2 year-1 in 2015 and 2016, respectively). Daily ET reached seasonal maximums of 6.0 and 5.3 mm day-1 in 2015, and 7.2 and 8.2 mm day-1 in 2016 at the winter wheat and tallgrass prairie sites, respectively. Although ecosystem water use efficiency (EWUE) was higher in winter wheat than in tallgrass prairie at the seasonal scale, summer fallow contributed higher water loss from the wheat site per unit of carbon fixed, resulting into lower EWUE at the annual scale. Results indicate that the differences in magnitudes and patterns of fluxes between the two ecosystems can influence carbon and water budgets.

Growing season variability in carbon dioxide exchange of irrigated and rainfed soybean in the southern United States.

Measurement of carbon dynamics of soybean (Glycine max L.) ecosystems outside Corn Belt of the United States (U.S.) is lacking. This study examines the seasonal variability of net ecosystem CO2 exchange (NEE) and its components (gross primary production, GPP and ecosystem respiration, ER), and relevant controlling environmental factors between rainfed (El Reno, Oklahoma) and irrigated (Stoneville, Mississippi) soybean fields in the southern U.S. during the 2016 growing season. Grain yield was about 1.6tha-1 for rainfed soybean and 4.9tha-1 for irrigated soybean. The magnitudes of diurnal NEE (~2-weeks average) reached seasonal peak values of -23.18 and -34.78µmolm-2s-1 in rainfed and irrigated soybean, respectively, approximately two months after planting (i.e., during peak growth). Similar thresholds of air temperature (Ta, slightly over 30°C) and vapor pressure deficit (VPD, ~2.5kPa) for NEE were observed at both sites. Daily (7-day average) NEE, GPP, and ER reached seasonal peak values of -4.55, 13.54, and 9.95gCm-2d-1 in rainfed soybean and -7.48, 18.13, and 14.93gCm-2d-1 in irrigated soybean, respectively. The growing season (DOY 132-243) NEE, GPP, and ER totals were -54, 783, and 729gCm-2, respectively, in rainfed soybean. Similarly, cumulative NEE, GPP, and ER totals for DOY 163-256 (flux measurement was initiated on DOY 163, missing first 45days after planting) were -291, 1239, and 948gCm-2, respectively, in irrigated soybean. Rainfed soybean was a net carbon sink for only two months, while irrigated soybean appeared to be a net carbon sink for about three months. However, grain yield and the magnitudes and seasonal sums of CO2 fluxes for irrigated soybean in this study were comparable to those for soybean in the U.S. Corn Belt, but they were lower for rainfed soybean.

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.