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1. Introduction
In Uruguay, 89% of cattle fattening is carried out on grazing systems1, with cultivated pastures being of utmost importance in their feeding. An estimate of 30% of these animals are directly fattened with cultivated pastures2, and 50% had access to improved pastures at some stage of their life (rearing and/or fattening) so as to have the age and weight demanded by the meat processing industry at slaughter3. These rearing and/or fattening systems on cultivated pastures are identified as intensive livestock systems and are characterized by being productive systems where high animal stocking rates are managed (generally: 1.0-1.6 lu/ha4) with a rearing and fattening period of no more than two years5. In these systems, the type of grazing acquires particular relevance, especially rotational grazing, since it is more productive than continuous grazing only under high stocking rate conditions6)(7. The concept of forage allowance (fa), defined as the daily amount of dry matter (kg dm) of offered forage per 100 kg of animal body weight (bw), then acquires more relevance than animal stocking rates. Fractioning forage through the use of daily belts allows managing the forage resource better (greater control of the fa), to regulate the nutritional value of pastures more efficiently, and to balance the diet, as it has greater control of animal intake6. To estimate the belt size, the dm availability, the fa, and the number of animals8 should be considered. The number of days that animals will remain in each belt for longer occupations should also be taken into account.
On the other hand, in our country, pasture supplementation has increased as a result of the growth of the agricultural area with greater availability of grains and by-products. Supplementation with hay, silage or grain improves the nutritional condition of the animal9, which may result in better finishing and carcass and meat quality10. However, the most important role of supplementation in intensive systems is the increase in animal stocking rate and consequently the overall productivity of the farm. This is due to more rational use of pasture, as well as better use of crops and harvest residues within the system11.
There are reviews at international level on how the forage offered, and the type and level of supplementation affect animal performance, as well as programs that allow predicting the average daily gain (adg). Nevertheless, these present limitations, since they: 1) do not predict the animal response for a range of feed supply12)(13)(14; 2) evaluate fa in terms of kg dm of forage/kg of animal bw15)(16 without considering the time factor (commonly used in continuous grazing); 3) only evaluate the effect of the level and type of supplementation on incremental adg9)(17; 4) only predict animal performance for tropical pastures18, or 5) estimate adg, but the user must establish the dm intake19.
For these reasons, and as a way of generating national coefficients for Uruguayan technicians and farmers, this study aimed to analyze the effect of the quantity and quality of feed offered on the performance of male beef cattle over 6 months old, in intensive grazing systems in Uruguay. These arise from a bibliographic review and an analysis of experimental work carried out in Uruguay which studied how the quantity and quality of the offered feed affect animal performance.
2. Materials and methods
For this research, 68 experimental studies (nexp) on feeding with cultivated pastures (prairie or winter forage crops) as the only nutritional source were used and synthesized, as well as supplementation studies on the same type of pastures, carried out and published by the Agronomy College (Udelar) and inia in Uruguay between 1991 and 2020. Only the tests carried out with meat-breed castrated males over 6 months of age were included.
Trials that presented results of animal performance under different levels of forage allowance and in different seasons of the year were identified among the experimental studies. These (n=125, nexp=61) were analyzed by simple and quadratic linear regressions using the statistical package sas. The seasons of the year were defined as: summer (December, January and February), autumn (March, April and May), winter (June, July and August) and spring (September, October and November).
The correlation was estimated between fa and adg of animal weight in the different seasons of the year for each category (calves and steers). Subsequently, a multiple linear regression analysis was performed using the stepwise sas function where parameters were included to estimate the adg of beef cattle. The parameters were: fa (% bw), category (calves and steers), initial bw (kg), season of the year (summer, autumn, winter and spring), type of supplement (hay, silage and energy concentrates), and type of cultivated pasture (prairie and winter forage crops). A significance ≤0.15 was considered to include the parameters in the model. In total, 254 results were used (nexp=68).
Subsequently, a multivariate analysis was performed with the statistical package InfoStat using the recursive partition methodology, including fa and an index of the nutritional quality of pastures (1=lowest nutritional value, 5=highest nutritional value; table 1). It was estimated based on real laboratory values (organic matter digestibility (omd), crude protein content (cp) and percentage of neutral detergent fiber (ndf)), and when it was not reported it was estimated based on pasture characteristics (percentage of legumes and dry remains), used in the different trials (n=125, nexp=68).
Table 1: Index of pasture nutritional value estimated based on the main parameters of its chemical composition or by visual appreciation of pasture characteristics
OM = organic matter; CP= crude protein; DM = dry matter; NDF = neutral detergent fiber; Leg = legumes; DR = dry remains
The methodology used allowed to group and characterize the animal response (adg) based on the aforementioned criteria (fa and pasture nutritional value). This methodology is used in human medicine20)(21)(22 and allows building decision trees that model the influence of a set of explanatory variables on the target variable. Minimum data size of 25% (n=31) was considered to continue partitioning each node. The main results are presented grouped into nodes sharing similar values for some of the evaluated characteristics.
The experimental results of supplementation with energy concentrates carried out in all seasons of the year (n=90, nexp=35) were also analyzed by a multivariate analysis using the recursive partition methodology to evaluate how the fa and the level of supplementation affect the “additional” adg (adga). The adga was calculated as the difference of adg between the supplemented group and the control group (without supplementation).
Additionally, the studies that evaluated the use of forage (percentage of disappeared forage in relation to offered forage) under different levels of fa (n=71, nexp=28) were identified and analyzed using simple and quadratic linear regressions, as well as the level of forage dry matter intake (fdmi) estimated as a percentage of bw (n = 50, nexp = 18). Also, the experimental studies that presented results of supplementation with energy concentrates on cultivated pastures were identified (n=90, nexp=35). For this case, adga was used as a result of supplementation with energy concentrates.
Finally, for the variables fdmi and adga, multiline linear regressions were performed using the stepwise function of sas, considering the following parameters: fa (% bw), animal category (calves or steers), initial bw (kg), season of the year (summer, autumn, winter or spring), and type of pasture (winter forage crop or prairie), and the level of supplementation (% bw) was considered for adga. A significance ≤0.15 was considered to include the parameters in the model.
3. Results and discussion
3.1 Estimation of adg during rearing and fattening on cultivated pastures without supplementation (Model 1 - Simple and quadratic linear regressions)
Table 2 shows the simple and quadratic linear regression parameters by season of the year to estimate the adg of calves and steers. It is observed that the estimated adg has a better fit (higher R2) in quadratic regressions than in simple linear regressions.
Table 2: Simple and quadratic linear regression parameters by season of the year and animal category to estimate adg of beef cattle
ADG: average daily gain (kg/day) // FA: forage allowance (kg DM/100kg body weight/day)
Figure 1 shows the synthesis of the 61 local studies (n=125) using the parameters described in Table 2, where it can be observed that as the forage allowance in cultivate pastures increases, the adg of animal increases but with decreasing rates. In turn, it can be observed that the adg potential for steers at the same fa level is greater for winter and spring than for summer and autumn. In this way, what Carámbula23 mentions regarding the influence of the advanced phenological state of the pastures in summer (reproductive state) on their quality becomes evident; as well as the reported by Rearte and Pieroni24 regarding the great proportion of water, low effective fiber level and high soluble proteins/soluble carbohydrates rate in pastures in autumn, determining that the achievable potential based only on cultivated pastures is lower in summer and autumn than in winter or spring. Additionally, during the summer, caloric stress can be another factor that determines the lower productive performance due to a lower dmi25)(26) and/or a greater maintenance effort to dissipate heat27)(28.
Figure 1: Average daily gain of calves and steers by season of the year according to level of forage allowance
On the other hand, in winter and spring, the adg potential is greater for steers than for calves. This has been published widely by international literature29)(30)(31)(32 and is due to a greater ruminal digestibility33, among other things, as a compensatory effect of steers due to some previous restriction34.
3.2 adg estimation during rearing and fattening on cultivated pastures with and without supplementation (Model 2 - Multiple linear regressions)
The parameters included in the model of multiple linear regression are shown in table 3. The parameters that affect the estimation of adg the most are supplementation with energy concentrates, fa, and summer (greater R2). Including other parameters such as spring and winter, initial body weight and supplementation with silages allow improving the fit in the estimation of adg. The inclusion of the type of pastures (winter forage crops or prairie) or supplementation with hay were not significant (P-value>0.15) for the developed model.
Table 3: Multiple linear regression parameters to estimate adg (kg/day) of beef cattle
ADG: average daily gain (kg/day) // Supplementation with energy concentrates: 0 (no), 1 (yes) // Forage allowance (kg DM/100kg body weight/day): 1 to 12% BW // Summer: 0 (no), 1 (yes) // Spring: 0 (no), 1 (yes) // Initial body weight (kg): 130 to 390 kg BW // Silage supplementation: 0 (no), 1 (yes) // Winter: 0 (no), 1 (yes)
To ratify the use of this model, the results obtained experimentally were compared to the prediction of the model from three studies chosen at random:
Based on these examples, the model will allow technicians and farmers to predict the expected adg considering the abovementioned parameters (fa, season of the year, initial body weight and supplementation with energy concentrates or silages). In this way, they can model feeding alternatives for animals and evaluate the most suitable for their livestock production system.
3.3 adg estimation during rearing and fattening on cultivated pastures with and without supplementation with energy concentrates (Model 3 - Classification and regression trees)
The multivariate analysis allowed to generate six nodes or terminal associated groups of adg based on the fa and the nutritional value of pasture (Figure 2). As can be observed, low levels of fa (≤1.75% bw) limit animal performance (0.2 kg/day; Node 1). To achieve adequate levels of adg of weight (0.6-0.7 kg/day), working with low to moderate fa (between 1.75 and 3.5% bw), it is necessary for the nutritional value of pasture to reach at least level 3 (>58% of omd, >13% of cp, <50% of ndf, >21% Leg, and <19% of dr; Node 3). With moderate to high levels of fa (>3.5% bw) weight gains in the range of 0.7 to 1.0 kg/day can be obtained (Nodes 4, 5 and 6). Under moderate to high fa conditions, the nutritional level of forage has a lower impact on adg, possibly associated with the increased selection capacity of animals in those grazing conditions38)(39.
Figure 2: Results of recursive partition analysis applied to adg of beef cattle on cultivated pastures
The adga of energy concentrate supplementation generated five terminal nodes, as shown in Figure 3. The highest response in adga (0.4-0.5 kg/day; Node 1) was at low fa levels (≤2.25% bw), emphasizing what was reported by Moore and others9, who found that when the fdmi is lower than 1.75% bw, the supplement has an additive effect. As fa increases (>2.25% bw), the adga decreases (Nodes 3, 4 and 5), highlighting that at higher fa levels the substitute effect of the supplement is greater9)(40. Under similar fa conditions, the adga due to supplementation increased (0.2 vs. 0.4 kg/day; Node 2 vs. Node 3), with higher levels (0.6 vs. 1.0% bw).
Figure 4 combined the two recursive partitions previously discussed (Figure 2 and 3). The analysis of adg based on different forage allowances and nutritional values of pastures is shown at the top. The analysis of adga due to supplementation based on different forage allowances and energy concentrates offers is presented at the bottom of the figure. The association of both partitions will allow technicians and farmers to predict the expected adg based on pasture characteristics (quantity and quality) and supplementation level. As an example, we can see that it is possible to achieve adg in animals of around 0.7 kg/day by working with low fa values (1.5-1.6% bw), average nutritional values of forage (2.6; scale 1 to 5), and supplementing with levels of 0.8% bw. However, adg of around 1.2 kg/day can be achieved if we give the steers greater fa (5.0-5.5% bw) with similar nutritional values (3.0; scale 1 to 5) and higher levels of supplementation (1.2% bw).
3.4 Estimation of the use and intake of forage dry matter (Model 4 - Simple, quadratic and multiple linear regressions)
Table 4 and Figure 5 summarize the local studies where the allowance, the forage utilization (n=71, nexp=28) and the fdmi estimate (n=50, nexp=18) were recorded. It is observed that forage use has a better fit (greater R2) in quadratic regression, while fdmi has a moderate fit (R2 = 0.39) and not different than the model used. As the fa increases, pasture utilization decreases, reaching values below 40% with fa levels greater than 6.5% of bw. On the other hand, when the fa is lower than 4% of bw, forage use exceeds 60%, being able to reach values of 85% with very low fa (1.5% bw). As for the fdmi estimate, it can be seen that to reach levels above 2% of bw it is necessary to work with fa above 3.5% bw, while it is necessary to work with fa over 6% bw to reach levels greater than 2.5% of bw.
Table 4: Simple and quadratic linear regression parameters for forage utilization and fdmi
FDMI: forage dry matter intake (kg DM/100 kg body weight/day) // BW: body weight (kg) // FA: forage allowance (kg of DM/100kg body weight/day)
Figure 5: Utilization and estimation of forage intake of beef cattle according to forage allowance level
The multiple linear regression using the function stepwise of sas allowed to improve (R2 = 0.66) the estimation of fdmi in relation to the simple linear and quadratic regression (Table 5). It is observed that fa is the most related parameter to fdmi (greater R2), but the inclusion of the season, initial body weight and type of cultivated pasture allows to improve this estimation. The lower fdmi at the same level of fa during autumn and winter could be due to the low content of dm in forage41. Concentrations of dm below 24% have been reported to increase the number of bites42. Concentrations below 22% of dm decrease the intake rate (grams of dm/min)43, while concentrations below 18-20% of dm limit animal intake (kg dm/day)44)(45.
Table 5: Parameters of multiple linear regression to estimate dry matter intake (% bw) of forage
Forage allowance (kg of DM/100kg body weight/day): 2 to 9 % BW// Summer: 0 (no), 1 (yes) // Autumn: 0 (no), 1 (yes) // Winter: 0 (no), 1 (yes) // Initial body weight (kg): 150 to 390 kg BW // Type of pasture: 0 (winter forage crop), 1 (prairie)
The lower fdmi during the summer may be due to the lower nutritional value of forage. During the summer, the cultivated pastures present the highest values of ndf23)(46, which is inversely related to the capacity of fdmi47. Similarly, the lower fdmi when using prairie relative to winter forage crops can be attributed to the higher values of ndf of the first46. In fact, Mertens47 reports that values greater than 40% ndf in the diet begin to lower the fdmi. Finally, the lowest fdmi in relative terms (% bw) to greater animal bw has been reported in the literature32)(48, where it obeys a smaller relative size of the gastrointestinal tract compared to the rest of the body49.
3.5 Estimation of the additional adg by the effect of supplementation with energy concentrates (Model 5 - Simple, quadratic and multiple linear regressions)
The fittings for simple linear and quadratic regressions to determine the adga were low (R2=0.10 and R2=0.11, respectively). Using the multiple linear regression allowed improving this fit (R2=0.34) when animals were supplemented with energy concentrates. Table 6 shows that for each increase in the level of supplementation (% bw) the generated adga is 0.224 kg/day. In turn, for each increase in fa (% bw) the adga is lower (0.060 kg/day). This is due to a higher replacement rate of forage with concentrates50. Finally, at the same level of supplementation and fa (in relative terms -%bw-), 0.13 kg/day of additional adg are obtained for every 100 kg of bw.
Table 6: Parameters of multiple linear regression to estimate additional adg (kg/day) by supplementation with energy concentrates
Additional ADG: additional average daily gain (kg/day) // Supplementation level: 0.5 to 2% BW// Forage allowance (DM/100kg body weight.day): 1.5 to 9% BW// Initial body weight (kg): 130 to 390 kg BW
The multiple linear regression model allows us to predict the adga considering the abovementioned parameters. It is possible to evaluate whether or not supplementation is appropriate with this estimate and knowing the price of the concentrate and the produced animal bw. As an example, calves of 160 kg of bw, in winter, managed with a fa of 2.5% bw and supplemented with energy concentrates at 1% of bw would achieve an agda of 0.26 kg/day. This value is similar (0.25 kg/day) to that reported by Simeone and Beretta51) under these management and feeding conditions. With this value, assuming a price of usd 1.8 per kg of bw produced, a daily intake of 2 kg of supplement (fresh base) at a price of usd 190 a ton, supplementation would be convenient since the result is positive (usd 0.092/calf.day = 0.26 kg /day * usd 1.8/kg of bw produced - 2.0 kg supplement * usd 0.190/kg supplement). In 100 days of supplementation, a feeding margin of usd 9.2/calf would be achieved.
4. Considerations
In the studies evaluated in this research, the relationship that exists between fa and adg for the different categories and seasons of the year is, in general, quadratic, whereas the fa increases in cultivated pastures, the adg of beef cattle increases, but with decreasing rates. The inclusion of additional parameters such as initial body weight and supplementation with energy concentrates or silages at fa and season of the year allows predicting the expected adg of the animals under a wide range of feeding alternatives, evaluating the most convenient for the production system.
Considering parameters such as fa, season of the year, initial body weight and type of cultivated pasture allows estimating the fdmi with greater precision, a key variable to determine the production efficiency (fdmi/adg) in grazing systems. Finally, the prediction of the adga using the parameters of the multiple linear regression allows evaluating the convenience of supplementation, considering the price of the concentrate and the produced bw.
