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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. [ABSTRACT FROM AUTHOR]

Conventional soil maps (CSM) have provided baseline soil information for land use planning for over 100 years. Although CSM have been widely used, they are not suitable to meet growing demands for high resolution soil information at field scales. We present a repeatable method to disaggregate 8SM data into ~30-meter resolution rasterized soil class maps that include continuous representation of probabilistic map uncertainty. Methods include training set creation for original CSM component soil classes from soil-landscape descriptions within the original survey database. Training sets are used to build a random forest predictive model utilizing environmental covariate maps derived from ASTER satellite imagery and the USGS National Elevation Dataset. Results showed agreement at 70 percent of independent field validation sites and equivalent accuracy between original 8SM map units and the disaggregated map. Uncertainty of predictions was mapped by relating prediction frequencies of the random forest model and success rates at validation sites. [ABSTRACT FROM AUTHOR]