The prediction of DMIpasture is complex and dependent on various factors. Coleman et al. (1999) found that the DMIpasture varies as a function of the quality and physical characteristics of the forage and is inherent to the physiological state of the animal, as well as to environmental conditions. In this manner, the interactions to be considered as estimates for prediction are more complex than for animals fed in a feedlot system (Azevêdo et al., 2016).
When analyzing the coefficient of determination provided by the models proposed for the DMIpasture, it was found that they did not present high values. However, it would be wrong to say that a non-elevated R2 indicates low correlation between the predicted and observed values, given that this relation can be curvilinear (Tedeschi, 2006). In these cases, submitting the data to further analysis, to verify the accuracy and precision between the predicted and observed values becomes necessary.
In this context, it was found that Models I and III presented close results without difference for RMSPE, indicating that both models are adequate to predict the DMIpasture. Despite Model II had lower average bias than Models I and III, it presented an intercept different to zero and the slope was different from the unity, and indicates that the relationship between observed and predicted DMIpasture was not adequate. Therefore, its use is not recommended.
In addition to the RMSPE, other measures have been employed to evaluate the models, since the evaluation should be based on diverse measures that evaluate precision, accuracy and adequation. The CCC, which is an index of reproducibility, simultaneously considers the exactitude and precision of the model. In this context, Model III presents better adjustment to estimate the DMIpasture given that it showed higher CCC than the other models.
Generally, Model I provided acceptable prediction for DMIpasture. However, given that this model was similar to Model III for average bias and RMSPE, but presented lower CCC, its utilization was limited. Therefore, the hypothesis that the use of more complete models, which include variables associated with the animal, supplements and pasture, better predicts the DMIpasture, was confirmed. By presenting the proximity of the RMSPE with Model I, higher CCC and lower average bias, Model III can be considered the most adequate for prediction of DMIpasture.
Although the equation proposed by Azevedo et al., (2016) had been developed using observation of beef cattle raised on pasture, the quality of its prediction for the DMIpasture was inferior to that of the new models proposed and to the equation of Minson and McDonald (1987). Additionally, this model presented a significant result for the line’s intercept and slope (P < 0.05), being found to be inadequate for the prediction of DMIpasture.
However, it is worth noting that the equation proposed by Azevedo et al. (2016), was developed to estimate the total CDM and not just DMIpasture, which may have led to a lower predictive capacity and a significant result for the line’s intercept and slope (P < 0.05), indicating a lack of adequacy between the predicted and observed DMIpasture.
The equation proposed by Minson and McDonald (1987) did not adequately estimate DMIpasture. Despite the intercept between the predicted and observed DMIpasture values not being different from zero and the slope not being different from the unity, this equation explained 41% of the variation of the DMIpasture, and overestimated intake by 16.4%. Additionally, it presented a lower CCC and little participation by the random error during the partition of the MSPE of all the adjusted models.
This lower predictive capacity can be associated with the non-inclusion of the variables related to the supplements and pasture consumed by the animals. This fact may have diminished the quality of the prediction, which is similar to what was observed for the proposed Model I, which only included variables related to the animal, and which also presented a lower predictive capacity than the more complete model containing variables related to the animal, supplement and pasture.
Heat stress can cause a reduction in the intake of the animals leading to a negative impact on performance (West, 2003). However, due to the majority of the studies published not reporting variables related to climatic conditions, its inclusion in the adjustment of the models, which could improve the prediction, was not possible. However, when the environmental conditions are outside the zone of thermal comfort due to an increase in air temperature or the temperature-humidity index, the DMIpasture and efficiency of feeding drop significantly (West, 2003).
Through the results obtained, it is possible to observe the greater complexity of the factors that affect the DMI in beef cattle systems based on pasture, in addition to the greater challenge in predicting this variable under these conditions. This demonstrates that there are other factors beyond those evaluated in this study that should be identified and added to future predictive models.
Two main factors that help to explain the greater difficulty in predicting DMIpasture can be highlighted: 1) the greater complexity of the factors that affect ingestion, such as availability of forage, and the structural and morphogenic characteristics of the pasture, (Stobes, 1973; Carvalho et al., 2007); 2) Difficulties in measuring the DMIpasture under grazing conditions due to the grazing tests frequently not taking the selection of the diet into account, as well as the representativity of the pasture sample effectively consumed by the animals (Lopes, 2008).
The selectivity of the animal can negatively influence the DMIpasture since the animal uses a greater part of its time searching for better quality food (Cosgrove, 1997). Further, when the animal encounters some type of limitation in grazing on the pasture and/or low availability of pasture, there is an increase in the chew rate and grazing time, which ends up interfering in the DMIpasture, a variable which is also complex when included in the predictive models.