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.

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).

Dynamics of CO and HO 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.

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 (), tepary bean (), pigeon pea (), soybean (), and mothbean ()-using three machine learning techniques: partial least square (PLS), support vector machine (SVM), and Gaussian processes (GP).

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.

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.