Effect of cutting management on the forage production and quality of tepary bean (Phaseolus acutifolius A. Gray).

Tepary bean (Phaseolus acutifolius A. Gray) is an underutilized drought tolerant annual legume, originating from the Sonoran Desert, that may be a beneficial forage/hay for beef cattle in the Southern Great Plains of the US (SGP). The SGP has erratic rainfall and periods of intermittent drought exacerbated by high summer temperatures. In 2020 and 2021, a split-plot design was used to evaluate 13 genotypes of tepary bean and a forage soybean (control) at El Reno, OK, USA to compare production of plant biomass and forage nutritive value parameters under seven harvest regimes. Genotypes were used as the main plot and cutting management as the sub-plot. Biomass production of all tepary bean genotypes equaled that of soybean (p > 0.05), while several genotypes had superior forage nutritive value traits (p ≤ 0.05). Overall, a 15-cm cutting height and 30-day harvest interval produced the best overall product (average dry biomass of 5.8 Mg ha-1 with average relative feed values (RFV) of 165). Although all harvest regimes reduced total seasonal biomass, forage nutritive value increased. However, the tradeoff between forage production and nutritive value may be unacceptable to most producers. Further agronomic and breeding research is needed to encourage producers to grow tepary bean as a forage/hay in the SGP.

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.

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.

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.

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.

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.