Texas High Plains (THP), one of the most important food and fiber producing regions in the Ogallala Aquifer Region, currently faces rapid decline of groundwater levels. Predicted climate extremes and high temporal variability in growing season precipitation may require growers to pump more groundwater from the Ogallala Aquifer to meet higher crop water demand. The Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM) is a widely used crop simulation tool for evaluating impacts of different water and crop management practices, including irrigation on crop yield and water use efficiency. In this study, CROPGRO‐Cotton module of the DSSAT was calibrated and validated using 2000, 2001, 2002, and 2010 irrigated lysimeter field data managed by the USDA‐ARS (United States Department of Agriculture ‐ Agricultural Research Service) Conservation and Production Research Laboratory at Bushland, TX. The lysimeter field consisted of four equal plots designated as NE, SE, NW, and SW. Crop growth characteristics including leaf area index (LAI), above ground biomass (AGB), evapotranspiration (ET), soil moisture, and lint yield of 2000‐NE, 2000‐SE, and 2001‐NE, were used for calibration and 2002‐NE, 2010‐NE, and 2010‐SE were used for validation. The calibrated and validated model was used to simulate the long term (1924–2012) crop yield and seasonal crop ET. During the calibration process, some of the cultivar and ecotype parameters that influence LAI, AGB, and lint yield were adjusted for better statistical results. Measured and simulated LAI, AGB, ET, soil moisture, and lint yield showed good agreement during calibration and validation as indicated by performance statistics such asr²from 0.70 to 0.82, and percent error (PE) = −0.85 to 17.3% for LAI;r²= 0.89 to 0.95, andPE= −7.36 to −13.66% for AGB; andr²= 0.90 to 0.94, and PE = 3.20 to 3.44% for ET during calibration and validation, respectively. The model underestimated ET during peak vegetative growth and development stage except in some circumstances. The calibrated and validated model was able to simulate lint yield and seasonal ET during a long term (1924–2012) historic period for Bushland, TX, under irrigated conditions. The calibrated model could be used to schedule ET based irrigation management practices in the THP and to estimate future ET for other modeling experiments.

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.

Low forage quality of available perennial warm-season grasses during mid-summer through late summer affects the production of stocker cattle in the U.S. Southern Great Plains (SGP). Finger millet (Eleusine coracana Gaertn L.), which is a drought tolerant annual grass, could be a promising forage for the SGP. This field study assessed the adaptability and forage characteristics of 11 finger millet accessions originally sourced (1964–1981) from different parts of the world. Results of this study suggested that finger millet can generate forage yields ranging from 5.0 to 12.3 Mg ha⁻¹ 165 days after planting. Finger millet forage contained 105 to 156 g kg⁻¹ crude protein, 598 to 734 g kg⁻¹ neutral detergent fiber, 268 to 382 g kg⁻¹ acid detergent fiber, 597 to 730 g kg⁻¹ in vitro true digestibility, and 387 to 552 g kg⁻¹ neutral detergent fiber digestibility. Ten of the 11 accessions flowered and produced grains with yields varying from 60 to 1636 kg ha⁻¹. Overall, finger millet has the potential to serve as an alternative crop for the production of forage and possibly grain in the SGP. Further research needs to be focused on developing strategies for agronomic management and evaluating the capacity of finger millet under different grazing and hay production settings in the SGP.

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.

Cultivation of winter wheat under reduced tillage systems is increasing in the U.S. Southern Great Plains. Likewise, there is revived interest for including summer legumes in monocultures of winter wheat as green sources of nitrogen (N). This study investigated the influence of tillage systems (no- and conventional tillage), and source and rates of N fertilizer (0, 45 and 90 kg N ha⁻¹ yr⁻¹ in inorganic N fertilizer, and cowpea as green manure) on emissions of carbon dioxide (CO₂) and nitrous oxide (N₂O) from winter wheat cultivation. The study was conducted within a long-term field experiment initiated in 2011, at upland and bottomland sites near El Reno, Oklahoma during the 2016–2017 growing season of winter wheat. The experiment was conducted site-wise as split-plots in a completely randomized design, with N treatment as main plots and tillage system as subplots. Thus, there were a total of eight treatment combinations with three replicated plots (4 m × 10 m) in each combination in both sites. Net ecosystem exchange (NEE) of CO₂ was measured by a closed chamber connected to an infra-red gas analyzer, and fluxes were partitioned to gross primary production (GPP) and ecosystem respiration (ER). Heterotrophic soil respiration (SR) was measured on bare soil spots. Fluxes of N₂O were measured with an opaque closed chamber system with a portable gas analyzer. Dynamics of canopy CO₂ fluxes (NEE, GPP and ER) were similar between tillage systems, while canopy CO₂ fluxes increased with rate of N fertilization. Canopy CO₂ fluxes from cowpea and an unfertilized control were similar, and the lowest, due to poor growth of winter wheat compared to the N fertilized treatments. Fluxes of N₂O approximated zero from all treatments throughout the study and no response of N fertilizer or tillage system was seen. In conclusion, the results from this study indicated that canopy fluxes of CO₂ from winter wheat are controlled by forms and rates of N fertilizers rather than tillage systems.