Modifying the National Research Council weight gain model to estimate daily gain for stockers grazing bermudagrass in the southern United States.

The energy requirements, feed intake, and performance of grazing animals vary daily due to changes in weather conditions, forage nutritive values, and plant and animal maturity throughout the grazing season. Hence, realistic simulations of daily animal performance can be made only by the models that can address these changes. Given the dearth of simple, user-friendly models of this kind, especially for pastures, we developed a daily gain model for large-frame stockers grazing bermudagrass sCynodon dactylon (L.) Pers.], a widely used warm-season perennial grass in the southern United States. For model development, we first assembled some of the classic works in forage-beef modeling in the last 50 yr into the National Research Council (NRC) weight gain model. Then, we tested it using the average daily gain (ADG) data obtained from several locations in the southern United States. The evaluation results showed that the performance of the NRC model was poor as it consistently underpredicted ADG throughout the grazing season. To improve the predictive accuracy of the NRC model to make it perform under bermudagrass grazing conditions, we made an adjustment to the model by adding the daily departures of the modeled values from the data trendline. Subsequently, we tested the revised model against an independent set of ADG data obtained from eight research locations in the region involving about 4,800 animals, using 30 yr (1991-2020) of daily weather data. The values of the various measures of fit used, namely the Willmott index of 0.92, the modeling efficiency of 0.75, the R2 of 0.76, the root mean square error of 0.13 kg d-1, and the prediction error relative to the mean observed data of 24%, demonstrated that the revised model mimicked the pattern of observed ADG data satisfactorily. Unlike the original model, the revised model predicted more closely the ADG value throughout the grazing season. The revised model may be useful to accurately reflect the impacts of daily weather conditions, forage nutritive values, seasonality, and plant and animal maturity on animal performance.

Modification of the summative equation to estimate daily total digestible nutrients for bermudagrass pasture.

In forage-animal nutrition modeling, diet energy is estimated mainly from the forage total digestible nutrients (TDN). As digestibility trials are expensive, TDN is usually estimated using summative equations. Early summative equations assumed a fixed coefficient to compute digestible fiber using the lignin-to-neutral detergent fiber (NDF) ratio. Subsequently, a structural coefficient (φ) was added to the summative equations to reflect an association between lignin and cell wall components. Additional modifications to the summative equations assumed a constant φ value, and they have been used as a standard method by many commercial laboratories and scientists. For feeds with nutritive values that do not change much over time, a constant φ value may suffice. However, for forages with nutritive values that keep changing during the grazing season owing to changes in weather and plant maturity, a constant φ value may add a systematic bias to prediction because it is associated with the variable lignin-to-NDF ratio. In this study, we developed a model to estimate φ as a function of the day of the year by using the daily TDN values of bermudagrass [Cynodon dactylon (L.) Pers.], a popular warm-season perennial grass in the southern United States. The variable φ model was evaluated by using it in the TDN equation and comparing the estimated values with the observed ones obtained from several locations. Values of the various measures of fit used-the Willmott index (WI), the modeling efficiency (ME), R2, root mean square error (RMSE), and percent error (PE)-showed that using the variable φ vis-à-vis the constant φ improved the TDN equation significantly. The WI, ME, R2, RMSE, and PE values of 0.94, 0.80, 0.80, 2.5, and 4.7, respectively, indicated that the TDN equation with the variable φ model was able to mimic the observed values of TDN satisfactorily. Unlike the constant φ, the variable φ predicted more closely the forage nutritive value throughout the grazing season. The variable φ model may be useful to forage-beef modeling in accurately reflecting the impacts of plant maturity and weather on daily forage nutritive value and animal performance.

A potato model intercomparison across varying climates and productivity levels.

A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low-input (Chinoli, Bolivia and Gisozi, Burundi)- and high-input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low- vs. high-input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.