animal
Animal (2015), 9:6, pp 973–982 © The Animal Consortium 2015 doi:10.1017/S1751731114003279
A meta-analysis of nutrient intake, feed efficiency and performance in cattle grazing on tropical grasslands M. Boval1†, N. Edouard2,3 and D. Sauvant4,5 1
INRA, UR143, Unité de Recherches Zootechniques, 97170 Petit-Bourg, Guadeloupe, France (F.W.I.); 2INRA, UMR1348 Physiologie, Environnement et Génétique pour l’Animal et les Systèmes d’Elevage, F-35590 Saint Gilles, France; 3Agrocampus Ouest, UMR1348 Physiologie, Environnement et Génétique pour l’Animal et les Systèmes d’Elevage, F-35000 Rennes, France; 4INRA, UMR791 Modélisation Systémique Appliquée aux Ruminants, F-75005 Paris, France; 5AgroParisTech, UMR 791 Modélisation Systémique Appliquée aux Ruminants, F-75005 Paris, France
(Received 24 July 2013; Accepted 3 November 2014; First published online 20 January 2015)
It is essential to quantify the potential of tropical grasslands to allow significant feed efficiency for grazing livestock in controlled conditions such as at pasture. We conducted a quantitative analysis of published studies reporting the experimental results of average daily gains (ADG) and diet characteristics obtained specifically under grazing conditions (17 publications and 41 experiments), which have been less studied compared with controlled conditions in stalls. The database was analyzed to determine the average and range of values obtained for ADG (g/kg BW), dry matter digestibility, intake (DMI) and digestible DMI (DDMI, g/kg BW) and feed conversion efficiencies (FCE), as well as to predict the response of these parameters to the main strategies investigated in the literature – that is, mainly the stocking rate (SR) and the concentrate intake (CI). The ADG reached 1.2 kg BW per day and was directly linked to DDMI (ADG = − 1.63 + 0.42 DDMI −0.0084 DDMI2, n = 90, r.m.s.e = 0.584, R 2 = 0.93). The DDMI, which was representative of the nutrient input, was driven mainly by DMI rather than dry matter digestibility, whereas these two parameters did not correlate (r = 0.068, P = 0.56). The average global FCE (0.11 g ADG/g DDMI) showed a greater association with the metabolic FCE (0.17 g ADG/g DMI) than the digestive FCE (0.62). The CI (g DM/kg BW) increased ADG (ADG = 2376 + CI 56.1, n = 16, r.m.s.e. = 441, R 2 = 0.95). The SR expressed as kg BW/ha decreased the individual ADG by 1.19 g/kg BW per additional ton of BW/ha, whereas the global ADG calculated per ha increased by 0.57 per additional ton BW/ha. When the SR was expressed as kg BW/ton DM and per ha rather than as kg BW/ha, the impact on the individual ADG decreased by 0.18 or 0.86 g per additional ton BW/ha, depending on the initial BW of the cattle. These results provide a better view of the potential performance and feeding of cattle in tropical grasslands. The results provide an improved quantification of the relationships between diet and performance, as well as the overall quantitative impact of SR and supplementation. Keywords: grasslands, intake, digestibility, average daily gain, feed conversion efficiency
Implication The 41 studies compiled in this meta-analysis highlight that satisfactory average daily gain (ADG) is possible in tropical pastures. The feed conversion efficiencies calculated showed that natural grassland had great potential for animal feeding, which is primarily associated with dry matter intake rather than digestibility. The stocking rate managed as kg BW per ha, studied extensively in the literature, could efficiently increase ADG per ha. The main expected implications of this quantitative analysis of the literature are a better understanding of what governs grazing livestock performance and †
E-mail: [email protected]
appropriate management strategies, which prioritize consumption of the forage rather than its digestibility. Introduction Livestock production systems are facing challenges posed by increasing food demand and environmental issues (Godfray et al., 2010; Pretty et al., 2010; Reynolds et al., 2010). In the tropics, a main concern is the poor performance of growing animals on grasslands as compared with the high-production performance observed in stall-feeding systems. Indeed, most tropical ruminants are fed a diet based essentially on grasslands, which are considered to have a low nutritional value, with large seasonal variations in quantity and quality. 973
Boval, Edouard and Sauvant However, previous studies have reported satisfactory performances approaching 1 kg average daily gain (ADG) with only grass and sometimes with minimal amount of concentrate in tropical and Mediterranean settings (Esterhuizen et al., 2008; Braghieri et al., 2011). Current farming systems must balance agro-ecological services and enhancement of biodiversity while reducing inputs and controlling output of greenhouse gases to be truly multifunctional. Thus, grassland-based systems provide many benefits that must be considered in the balance of the cost equation for production efficiency (Boval and Dixon, 2012; O’Mara, 2012). Despite an array of studies conducted in various grazing conditions, it is not yet possible to easily determine the diet consumed by animals under various grazing conditions and the resulting performance in order to promote efficient new management strategies. Feeding is the main determinant of animal performance, because it interacts with the health and adaptive capacities of grazing animals. As a result, additional knowledge is needed to better quantify feed efficiency and its components in the pasture, as well as to promote efficient management strategies. Therefore, we performed a fine-grained quantitative analysis of the literature by considering only experiments that were conducted in the pasture and quantified results for digestible nutrient intake. This meta-analysis considered various tropical conditions with different cattle breeds and management strategies to quantify achievable nutrient intake levels in grasslands and the implications for animal performance. Material and methods
Literature review and dataset construction This meta-analysis focused on growing cattle in grazing trials offering C4 grasses on all continents under various tropical conditions between −37 and 37° latitude. The literature research was performed using the INRA library’s ‘Web of Knowledge’, ‘Science Direct’, ‘EDP Sciences’ and ‘Cambridge Journals’ search engines. The resulting reference lists of publications were screened for further study. Only publications containing ADG and digestible intake characteristics (intake levels and/or diet digestibility) were selected for the subsequent construction of the database. For each retained publication, a specific study code was assigned and the following characteristics were recorded in the database: site (latitude, longitude), season (dry or rainy), livestock (breed, gender, age, BW) and management strategies (continuous or rotational grazing, herded or tethered, stocking rate (SR) or herbage allowance). For the climate data, the average annual temperature and rainfall were reported when provided or otherwise extrapolated from the UNFAO data (http://www.fao.org/nr/climpag/globgrids/KC_classification_en. asp) based on the Köppen–Geiger climate classification (Peel et al., 2007). This classification was also used to specify climatic conditions for every publication (tropical and humid, subtropical and humid, semi-arid, warm and arid conditions). The breeds involved in the studies were classified into the following three groups to facilitate the analysis, according to the 974
classification by Felius et al. (2011): Meat breed (i.e. Hereford), Mixed breeds (i.e. the Bos taurus Zebu and Brahman Shorthorn) and Zebu breeds (i.e. Creole breeds, Azawak, Borono and Zebu). In addition to a specific study code (identifying each publication), other codes were used to identify variation factors that were tested in the studies, which were mainly related to management strategies, to more precisely analyze their effects. Notably, we analyzed the impact of qualitative factors such as the season, continuous or rotational grazing and in a herd or with a tether, or quantitative indicators of management such as the SR or herbage mass. For each publication, we only integrated experiments and treatments for which there was at least one value for ADG, dry matter intake (DMI) and dry matter digestibility (DMD) or digestible DMI (DDMI). The final database comprised 17 publications (npub = 17), 41 experiments (nexp = 41) and 140 treatments (n = 140). The ADG was expressed in g per kg BW to account for between-breed differences in initial body mass. DDMI values were taken from the publications if available or calculated by multiplying DMI by diet DMD. Approximately half of the papers expressed intake, digestibility and/or forage and diet characteristics in terms of organic matter (OM). These data were expressed as % of dry matter or by using OM content when cited in the publication. Alternately, they were expressed using the average value for OM in the whole database (i.e. 90.4%, s.d. = 1.95, n = 41). In order to assess how well cattle utilized their ration at pasture, the global feed conversion efficiency (FCE) was calculated as ADG/g DMI, as well as its two components, simply calculated by the following equation: FCE ¼ ADG=DMI ¼ ðADG=DDMIÞ ´ ðDDMI=DMIÞ: In this equation, the metabolic FCE is calculated by dividing ADG by DDMI and the digestive FCE or digestibility is calculated by dividing DDMI by DMI. Forages were described as precisely as possible: species, height, biomass, NDF and CP, among others, were evaluated when available. Energy supplement data were also included in the database if available, as well as the amounts of DM offered and ingested. We also specified and classified the measurement methods for diet digestibility (in vitro DMD on extrusa samples or DMD from fecal analyses) and intake levels (based on markers, total collection of feces or other methods).
Statistical analyses Descriptive statistics were calculated for each variable (mean, r.m.s.e., range; Table 1) and treated in the meta-analysis according to the guidelines described by Sauvant et al. (2008), as described below. Relationships between dependent variables (ADG g/kg BW) and explanatory variables (Xj) were studied with variance–covariance analyses using the GLM procedure (Minitab 16). Thus, global variations – that is, across and intra-experimental variations – were split. Given the fairly low number of studies and that the experimental conditions were specific to each study (including the method used for DMD and DMI measurements) and not randomly distributed, the experimental effect was treated as a fixed effect.
Livestock production in tropical grasslands Table 1 Descriptive statistics of various data on animals, resources and environment extracted from the various publications analyzed in this meta-analysis1 Item Climate Annual temperature (°C) Annual precipitation (mm) Animals Mean age (months) Mean initial BW (kg) Average daily gain (g/day) Average daily gain (g/kg BW/day) Indicators of management strategies Stocking rate (kg BW/ha) Sward re-growth (days) Sward height (cm) Herbage mass (t DM/ha) Forage DM (%) OM (%DM) Digestibility (%DM) CP (%DM) NDF (%DM) Diet composition Digestibility (%DM) CP (%DM) NDF (%DM) Intake Total DM intake (g DM/kg BW/day) Grass DM intake (g DM/kg BW/day) Legume DM intake (g DM/kg BW/day) Forage DM intake (g DM/kg BW/day)1 Supplement DM intake (g DM/kg BW/day)2 Digestible DM intake (g DM/kg BW/day)
n
Mean
s.e.
Minimum
Maximum
137 137
21.6 1145
0.4 71
14.5 400
31.7 3283
70 121 137 121
14.8 255.4 581 2.4
0.7 6.3 32 0.2
10.0 151.0 − 500 − 1.7
36.0 555.0 1210 6.6
71 13 32 69
1185 22 10.0 3.0
86 1 1.5 0.2
24 14 2.0 0.4
2300 28 32.0 6.2
28 41 54 78 46
34.6 90.4 54.7 12.3 67.5
0.9 0.3 0.6 0.6 2.0
25.5 86.5 43.5 4.4 43.7
40.5 93.2 63.0 20.0 81.5
137 88 11
59.5 12.8 65.6
0.6 0.3 2.4
43.4 5.0 54.6
75.3 20.8 74.9
91 91 42 31 31 91
26.0 19.6 10.8 23.4 4.2 16.1
0.7 1.1 0.8 1.4 0.6 0.5
10.0 2.0 2.0 12.4 0.0 3.2
44.0 42.0 22.0 42.0 11.0 27.5
DM = dry matter; OM = organic matter. 1 Chacon et al. (1978), Romero and Siebert (1980), Boval et al. (1996), Petty et al. (1998), Ramos et al. (1998), Cabrera et al. (2000), Sprinkle et al. (2000), Ackerman et al. (2001), Aranda et al. (2001), Ayantunde et al. (2001), Boval et al. (2002), Bodine and Purvis (2003), Gomez-Vazquez et al. (2003), Alonso et al. (2008), Ayantunde et al. (2008), Dixon and Coates (2008), Pereira et al. (2009). 2 Only papers offering supplemented diets, with or without a control level.
In addition, we wanted to interpret the inter-experimental heterogeneity, which precluded the use of a random effect. In some cases, factors were nested into the ‘publication code’ as in d’Alexis et al. (2013). The statistical model was as follows: Yijk ¼ μ + αi + β1j Xj + β2j Xj 2 + eijk ; where μ is the overall intercept, αi the effect of the experimental group (possibly nested in ‘publication code’) in the intercept, β1j and β2j the linear and quadratic terms (when significant) for the explanatory variables Xj. The term eijk is the random error term of the model. Interactions between αi and Xj were tested when relevant. R 2 and specific R 2 were calculated for each model. Specific R 2 was calculated as the ratio of the sum of squared deviations of the explanatory variable for the same sum plus the sum of the squared deviations of the error. The quality of fit for each relationship was examined by studying the normality of the residuals of the model. Outliers were identified based on the normalized residuals (less than −3 or greater than +3) and removed from the analyses (e.g. three outliers were removed for ADG).
The initial step of the analysis considered the contextual influences of the studies and the management strategies. As the calculation of reliable within-experiment responses requires a minimum variation for each explanatory variable within an experimental group (Sauvant et al., 2008), only those studies reporting ADG values were considered. If several ADG values were related to a particular level of Xj (i.e. one SR or one herbage allowance level), one mean ADG value was reported. The second step of the analysis considered the whole dataset to describe the relationship between ADG and DDMI (in g DM/kg BW/day), when stated, with the experimental group. Results
Description of the dataset and the meta-design The distribution of data varied throughout the publications, and the ADG values were more or less combined with the DMI measurements or with the various management strategies. Therefore, to maximize the precision of the factor 975
Boval, Edouard and Sauvant Table 2 Average daily gain values (ADG values g/kg BW) for different breeds (meat, mixed and zebu) and climatic zones taken into account in the publications considered under this meta-analysis
Semi-arid Sub-tropical Tropical humid Warm arid
Meat
Mixed
Zebu
1.36 ± 1.38 (npub = 1; n = 30) – – –
3.36 ± 1.60 (npub = 2; n = 41) 3.19 ± 1.84 (npub = 2; n = 14) 2.73 ± 1.39 (npub = 6; n = 36) –
– 1.33 ± 0.69 (npub = 3; n = 9) 0.55 ± 1.28 (npub = 2; n = 10)
Meat breed: Hereford. Mixed breeds: Zebu–Bos taurus or Brahman–Shorthorn. Zebu breeds: Creole, Azawak, Borono and Zebu.
effects, we used various subsets of data: ADG and DDMI values, information on the distribution of concentrates, herbage allowance values or herbage mass values and ADG and SR values. The variables retrieved from all the publications selected according to our criteria are presented in Table 1.
Performance achieved in diverse climatic conditions and with different breeds of cattle The available data were first split into groups according to the breed and the climatic zone in each trial (Table 2). The lowest ADG estimates were associated with warm arid climate in Zebu breeds (0.55 g/kg BW, representing 132 g ADG/day) based on data sourced from two publications, whereas the highest ADG performance was found in semi-arid climate in mixed breeds (3.36 g/kg BW, representing 735 g ADG/day). However, these values were not significantly different when compared with the inter-publication variability (nested model). Beyond these global variations due to climate zones and breeds, the season was also identified as a determinant of the observed differences in ADG. Across the complete set of publications considered, ADG was higher during the rainy season by 1.09 g/kg BW (n = 137, nexp = 17, r.m.s.e. = 1.14, R 2 = 0.62, R 2specific = 0.03, 2 tons BW/ha), because the responses observed in this range of SR vary strongly according to the experimental conditions. In addition, in this range, it
Livestock production in tropical grasslands would be particularly useful to focus on indexes of overgrazing, durability and ecological impact (Vetter, 2005). In our database, estimates of grass mass enabled us to further express SR per kg BW and per ton DM, permitting the assessment of an actual SR that considered the true biomass available per ha. In this way (Figure 2b), the effect of SR on ADG was lower (with smaller slopes) compared with the expression of SR that considered only the land area. Beyond the importance of SR, its expression should be carefully considered in management implementation strategies. The SR expressed as kg BW per ton of DM is better suited for forecasting the instantaneous animal-to-forage relationship and appropriate grazing pressure, within a given context and without any deleterious use of the grassland (Allen et al., 2011). The inverse of this expression of SR represents, in fact, the herbage allowance per unit of animal (kg forage per kg BW). In addition, the evolution of ADG with the herbage allowance highlighted here provides a good explanation of the variations in forage intake by grazing ruminants, as calculated previously (Sollenberger and Vanzant, 2011; Boval and Dixon, 2012). With a greater focus on the herbage–animal relationships, the characteristics of the herbage mass may impact ADG in various ways. As demonstrated in the present study, the evolution of ADG with the herbage mass was lesser in some experiments when the ADG was already high (Figure 2) but increased substantially when the ADG was lower initially. This discrepancy was mainly due to the study by Chacon et al. (1978), who observed that for equivalent herbage masses (during summer and autumn in 1974) grazing cattle were unable to satisfy their requirements in the same way. For 3 consecutive years, the large seasonal variations in grazing conditions resulted in changes in the spatial distribution of the dry matter within the sward profile, and greatly influenced the growth of the steers. In fact, the lowADG animals, on some sward profile, took bites that were too small in size, and therefore they required more grazing time to compensate. Consequently, the change in herbage mass or SR (Figure 1b) had more effect on the evolution of their low ADG than for the high-ADG animals. Therefore, in addition to the herbage mass per ha, the inclusion of additional characteristics of the herbage – some physical others chemical – may modify how the SR is conceived and managed. Conclusions This meta-analysis of ~41 experiments increases knowledge of feed efficiency by shedding new light on many important parameters that affect feeding growing cattle and their performance in tropical grasslands. This meta-analysis (i) provides a better quantification in various real grazing situations of characteristics of diet and performance, while allowing an estimation of energy requirements for grazing animals, (ii) identifies relationships between digestibility, metabolizable energy consumption and performance, which are essential to effectively manage pastures and (iii) describes general data
about some management levers such as complementation and SR, in order to better consider their implementation. These results based on published data provide promising prospects for the rational improvement of cattle feeding and performance in tropical natural grasslands. Acknowledgements The authors wish to express their gratitude to Dr Michel Naves for his expertise for cattle production in the Tropics and his contribution to the coding of the studies.
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Animal (2015), 9:6, pp 973–982 © The Animal Consortium 2015 doi:10.1017/S1751731114003279
A meta-analysis of nutrient intake, feed efficiency and performance in cattle grazing on tropical grasslands M. Boval1†, N. Edouard2,3 and D. Sauvant4,5 1
INRA, UR143, Unité de Recherches Zootechniques, 97170 Petit-Bourg, Guadeloupe, France (F.W.I.); 2INRA, UMR1348 Physiologie, Environnement et Génétique pour l’Animal et les Systèmes d’Elevage, F-35590 Saint Gilles, France; 3Agrocampus Ouest, UMR1348 Physiologie, Environnement et Génétique pour l’Animal et les Systèmes d’Elevage, F-35000 Rennes, France; 4INRA, UMR791 Modélisation Systémique Appliquée aux Ruminants, F-75005 Paris, France; 5AgroParisTech, UMR 791 Modélisation Systémique Appliquée aux Ruminants, F-75005 Paris, France
(Received 24 July 2013; Accepted 3 November 2014; First published online 20 January 2015)
It is essential to quantify the potential of tropical grasslands to allow significant feed efficiency for grazing livestock in controlled conditions such as at pasture. We conducted a quantitative analysis of published studies reporting the experimental results of average daily gains (ADG) and diet characteristics obtained specifically under grazing conditions (17 publications and 41 experiments), which have been less studied compared with controlled conditions in stalls. The database was analyzed to determine the average and range of values obtained for ADG (g/kg BW), dry matter digestibility, intake (DMI) and digestible DMI (DDMI, g/kg BW) and feed conversion efficiencies (FCE), as well as to predict the response of these parameters to the main strategies investigated in the literature – that is, mainly the stocking rate (SR) and the concentrate intake (CI). The ADG reached 1.2 kg BW per day and was directly linked to DDMI (ADG = − 1.63 + 0.42 DDMI −0.0084 DDMI2, n = 90, r.m.s.e = 0.584, R 2 = 0.93). The DDMI, which was representative of the nutrient input, was driven mainly by DMI rather than dry matter digestibility, whereas these two parameters did not correlate (r = 0.068, P = 0.56). The average global FCE (0.11 g ADG/g DDMI) showed a greater association with the metabolic FCE (0.17 g ADG/g DMI) than the digestive FCE (0.62). The CI (g DM/kg BW) increased ADG (ADG = 2376 + CI 56.1, n = 16, r.m.s.e. = 441, R 2 = 0.95). The SR expressed as kg BW/ha decreased the individual ADG by 1.19 g/kg BW per additional ton of BW/ha, whereas the global ADG calculated per ha increased by 0.57 per additional ton BW/ha. When the SR was expressed as kg BW/ton DM and per ha rather than as kg BW/ha, the impact on the individual ADG decreased by 0.18 or 0.86 g per additional ton BW/ha, depending on the initial BW of the cattle. These results provide a better view of the potential performance and feeding of cattle in tropical grasslands. The results provide an improved quantification of the relationships between diet and performance, as well as the overall quantitative impact of SR and supplementation. Keywords: grasslands, intake, digestibility, average daily gain, feed conversion efficiency
Implication The 41 studies compiled in this meta-analysis highlight that satisfactory average daily gain (ADG) is possible in tropical pastures. The feed conversion efficiencies calculated showed that natural grassland had great potential for animal feeding, which is primarily associated with dry matter intake rather than digestibility. The stocking rate managed as kg BW per ha, studied extensively in the literature, could efficiently increase ADG per ha. The main expected implications of this quantitative analysis of the literature are a better understanding of what governs grazing livestock performance and †
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appropriate management strategies, which prioritize consumption of the forage rather than its digestibility. Introduction Livestock production systems are facing challenges posed by increasing food demand and environmental issues (Godfray et al., 2010; Pretty et al., 2010; Reynolds et al., 2010). In the tropics, a main concern is the poor performance of growing animals on grasslands as compared with the high-production performance observed in stall-feeding systems. Indeed, most tropical ruminants are fed a diet based essentially on grasslands, which are considered to have a low nutritional value, with large seasonal variations in quantity and quality. 973
Boval, Edouard and Sauvant However, previous studies have reported satisfactory performances approaching 1 kg average daily gain (ADG) with only grass and sometimes with minimal amount of concentrate in tropical and Mediterranean settings (Esterhuizen et al., 2008; Braghieri et al., 2011). Current farming systems must balance agro-ecological services and enhancement of biodiversity while reducing inputs and controlling output of greenhouse gases to be truly multifunctional. Thus, grassland-based systems provide many benefits that must be considered in the balance of the cost equation for production efficiency (Boval and Dixon, 2012; O’Mara, 2012). Despite an array of studies conducted in various grazing conditions, it is not yet possible to easily determine the diet consumed by animals under various grazing conditions and the resulting performance in order to promote efficient new management strategies. Feeding is the main determinant of animal performance, because it interacts with the health and adaptive capacities of grazing animals. As a result, additional knowledge is needed to better quantify feed efficiency and its components in the pasture, as well as to promote efficient management strategies. Therefore, we performed a fine-grained quantitative analysis of the literature by considering only experiments that were conducted in the pasture and quantified results for digestible nutrient intake. This meta-analysis considered various tropical conditions with different cattle breeds and management strategies to quantify achievable nutrient intake levels in grasslands and the implications for animal performance. Material and methods
Literature review and dataset construction This meta-analysis focused on growing cattle in grazing trials offering C4 grasses on all continents under various tropical conditions between −37 and 37° latitude. The literature research was performed using the INRA library’s ‘Web of Knowledge’, ‘Science Direct’, ‘EDP Sciences’ and ‘Cambridge Journals’ search engines. The resulting reference lists of publications were screened for further study. Only publications containing ADG and digestible intake characteristics (intake levels and/or diet digestibility) were selected for the subsequent construction of the database. For each retained publication, a specific study code was assigned and the following characteristics were recorded in the database: site (latitude, longitude), season (dry or rainy), livestock (breed, gender, age, BW) and management strategies (continuous or rotational grazing, herded or tethered, stocking rate (SR) or herbage allowance). For the climate data, the average annual temperature and rainfall were reported when provided or otherwise extrapolated from the UNFAO data (http://www.fao.org/nr/climpag/globgrids/KC_classification_en. asp) based on the Köppen–Geiger climate classification (Peel et al., 2007). This classification was also used to specify climatic conditions for every publication (tropical and humid, subtropical and humid, semi-arid, warm and arid conditions). The breeds involved in the studies were classified into the following three groups to facilitate the analysis, according to the 974
classification by Felius et al. (2011): Meat breed (i.e. Hereford), Mixed breeds (i.e. the Bos taurus Zebu and Brahman Shorthorn) and Zebu breeds (i.e. Creole breeds, Azawak, Borono and Zebu). In addition to a specific study code (identifying each publication), other codes were used to identify variation factors that were tested in the studies, which were mainly related to management strategies, to more precisely analyze their effects. Notably, we analyzed the impact of qualitative factors such as the season, continuous or rotational grazing and in a herd or with a tether, or quantitative indicators of management such as the SR or herbage mass. For each publication, we only integrated experiments and treatments for which there was at least one value for ADG, dry matter intake (DMI) and dry matter digestibility (DMD) or digestible DMI (DDMI). The final database comprised 17 publications (npub = 17), 41 experiments (nexp = 41) and 140 treatments (n = 140). The ADG was expressed in g per kg BW to account for between-breed differences in initial body mass. DDMI values were taken from the publications if available or calculated by multiplying DMI by diet DMD. Approximately half of the papers expressed intake, digestibility and/or forage and diet characteristics in terms of organic matter (OM). These data were expressed as % of dry matter or by using OM content when cited in the publication. Alternately, they were expressed using the average value for OM in the whole database (i.e. 90.4%, s.d. = 1.95, n = 41). In order to assess how well cattle utilized their ration at pasture, the global feed conversion efficiency (FCE) was calculated as ADG/g DMI, as well as its two components, simply calculated by the following equation: FCE ¼ ADG=DMI ¼ ðADG=DDMIÞ ´ ðDDMI=DMIÞ: In this equation, the metabolic FCE is calculated by dividing ADG by DDMI and the digestive FCE or digestibility is calculated by dividing DDMI by DMI. Forages were described as precisely as possible: species, height, biomass, NDF and CP, among others, were evaluated when available. Energy supplement data were also included in the database if available, as well as the amounts of DM offered and ingested. We also specified and classified the measurement methods for diet digestibility (in vitro DMD on extrusa samples or DMD from fecal analyses) and intake levels (based on markers, total collection of feces or other methods).
Statistical analyses Descriptive statistics were calculated for each variable (mean, r.m.s.e., range; Table 1) and treated in the meta-analysis according to the guidelines described by Sauvant et al. (2008), as described below. Relationships between dependent variables (ADG g/kg BW) and explanatory variables (Xj) were studied with variance–covariance analyses using the GLM procedure (Minitab 16). Thus, global variations – that is, across and intra-experimental variations – were split. Given the fairly low number of studies and that the experimental conditions were specific to each study (including the method used for DMD and DMI measurements) and not randomly distributed, the experimental effect was treated as a fixed effect.
Livestock production in tropical grasslands Table 1 Descriptive statistics of various data on animals, resources and environment extracted from the various publications analyzed in this meta-analysis1 Item Climate Annual temperature (°C) Annual precipitation (mm) Animals Mean age (months) Mean initial BW (kg) Average daily gain (g/day) Average daily gain (g/kg BW/day) Indicators of management strategies Stocking rate (kg BW/ha) Sward re-growth (days) Sward height (cm) Herbage mass (t DM/ha) Forage DM (%) OM (%DM) Digestibility (%DM) CP (%DM) NDF (%DM) Diet composition Digestibility (%DM) CP (%DM) NDF (%DM) Intake Total DM intake (g DM/kg BW/day) Grass DM intake (g DM/kg BW/day) Legume DM intake (g DM/kg BW/day) Forage DM intake (g DM/kg BW/day)1 Supplement DM intake (g DM/kg BW/day)2 Digestible DM intake (g DM/kg BW/day)
n
Mean
s.e.
Minimum
Maximum
137 137
21.6 1145
0.4 71
14.5 400
31.7 3283
70 121 137 121
14.8 255.4 581 2.4
0.7 6.3 32 0.2
10.0 151.0 − 500 − 1.7
36.0 555.0 1210 6.6
71 13 32 69
1185 22 10.0 3.0
86 1 1.5 0.2
24 14 2.0 0.4
2300 28 32.0 6.2
28 41 54 78 46
34.6 90.4 54.7 12.3 67.5
0.9 0.3 0.6 0.6 2.0
25.5 86.5 43.5 4.4 43.7
40.5 93.2 63.0 20.0 81.5
137 88 11
59.5 12.8 65.6
0.6 0.3 2.4
43.4 5.0 54.6
75.3 20.8 74.9
91 91 42 31 31 91
26.0 19.6 10.8 23.4 4.2 16.1
0.7 1.1 0.8 1.4 0.6 0.5
10.0 2.0 2.0 12.4 0.0 3.2
44.0 42.0 22.0 42.0 11.0 27.5
DM = dry matter; OM = organic matter. 1 Chacon et al. (1978), Romero and Siebert (1980), Boval et al. (1996), Petty et al. (1998), Ramos et al. (1998), Cabrera et al. (2000), Sprinkle et al. (2000), Ackerman et al. (2001), Aranda et al. (2001), Ayantunde et al. (2001), Boval et al. (2002), Bodine and Purvis (2003), Gomez-Vazquez et al. (2003), Alonso et al. (2008), Ayantunde et al. (2008), Dixon and Coates (2008), Pereira et al. (2009). 2 Only papers offering supplemented diets, with or without a control level.
In addition, we wanted to interpret the inter-experimental heterogeneity, which precluded the use of a random effect. In some cases, factors were nested into the ‘publication code’ as in d’Alexis et al. (2013). The statistical model was as follows: Yijk ¼ μ + αi + β1j Xj + β2j Xj 2 + eijk ; where μ is the overall intercept, αi the effect of the experimental group (possibly nested in ‘publication code’) in the intercept, β1j and β2j the linear and quadratic terms (when significant) for the explanatory variables Xj. The term eijk is the random error term of the model. Interactions between αi and Xj were tested when relevant. R 2 and specific R 2 were calculated for each model. Specific R 2 was calculated as the ratio of the sum of squared deviations of the explanatory variable for the same sum plus the sum of the squared deviations of the error. The quality of fit for each relationship was examined by studying the normality of the residuals of the model. Outliers were identified based on the normalized residuals (less than −3 or greater than +3) and removed from the analyses (e.g. three outliers were removed for ADG).
The initial step of the analysis considered the contextual influences of the studies and the management strategies. As the calculation of reliable within-experiment responses requires a minimum variation for each explanatory variable within an experimental group (Sauvant et al., 2008), only those studies reporting ADG values were considered. If several ADG values were related to a particular level of Xj (i.e. one SR or one herbage allowance level), one mean ADG value was reported. The second step of the analysis considered the whole dataset to describe the relationship between ADG and DDMI (in g DM/kg BW/day), when stated, with the experimental group. Results
Description of the dataset and the meta-design The distribution of data varied throughout the publications, and the ADG values were more or less combined with the DMI measurements or with the various management strategies. Therefore, to maximize the precision of the factor 975
Boval, Edouard and Sauvant Table 2 Average daily gain values (ADG values g/kg BW) for different breeds (meat, mixed and zebu) and climatic zones taken into account in the publications considered under this meta-analysis
Semi-arid Sub-tropical Tropical humid Warm arid
Meat
Mixed
Zebu
1.36 ± 1.38 (npub = 1; n = 30) – – –
3.36 ± 1.60 (npub = 2; n = 41) 3.19 ± 1.84 (npub = 2; n = 14) 2.73 ± 1.39 (npub = 6; n = 36) –
– 1.33 ± 0.69 (npub = 3; n = 9) 0.55 ± 1.28 (npub = 2; n = 10)
Meat breed: Hereford. Mixed breeds: Zebu–Bos taurus or Brahman–Shorthorn. Zebu breeds: Creole, Azawak, Borono and Zebu.
effects, we used various subsets of data: ADG and DDMI values, information on the distribution of concentrates, herbage allowance values or herbage mass values and ADG and SR values. The variables retrieved from all the publications selected according to our criteria are presented in Table 1.
Performance achieved in diverse climatic conditions and with different breeds of cattle The available data were first split into groups according to the breed and the climatic zone in each trial (Table 2). The lowest ADG estimates were associated with warm arid climate in Zebu breeds (0.55 g/kg BW, representing 132 g ADG/day) based on data sourced from two publications, whereas the highest ADG performance was found in semi-arid climate in mixed breeds (3.36 g/kg BW, representing 735 g ADG/day). However, these values were not significantly different when compared with the inter-publication variability (nested model). Beyond these global variations due to climate zones and breeds, the season was also identified as a determinant of the observed differences in ADG. Across the complete set of publications considered, ADG was higher during the rainy season by 1.09 g/kg BW (n = 137, nexp = 17, r.m.s.e. = 1.14, R 2 = 0.62, R 2specific = 0.03, 2 tons BW/ha), because the responses observed in this range of SR vary strongly according to the experimental conditions. In addition, in this range, it
Livestock production in tropical grasslands would be particularly useful to focus on indexes of overgrazing, durability and ecological impact (Vetter, 2005). In our database, estimates of grass mass enabled us to further express SR per kg BW and per ton DM, permitting the assessment of an actual SR that considered the true biomass available per ha. In this way (Figure 2b), the effect of SR on ADG was lower (with smaller slopes) compared with the expression of SR that considered only the land area. Beyond the importance of SR, its expression should be carefully considered in management implementation strategies. The SR expressed as kg BW per ton of DM is better suited for forecasting the instantaneous animal-to-forage relationship and appropriate grazing pressure, within a given context and without any deleterious use of the grassland (Allen et al., 2011). The inverse of this expression of SR represents, in fact, the herbage allowance per unit of animal (kg forage per kg BW). In addition, the evolution of ADG with the herbage allowance highlighted here provides a good explanation of the variations in forage intake by grazing ruminants, as calculated previously (Sollenberger and Vanzant, 2011; Boval and Dixon, 2012). With a greater focus on the herbage–animal relationships, the characteristics of the herbage mass may impact ADG in various ways. As demonstrated in the present study, the evolution of ADG with the herbage mass was lesser in some experiments when the ADG was already high (Figure 2) but increased substantially when the ADG was lower initially. This discrepancy was mainly due to the study by Chacon et al. (1978), who observed that for equivalent herbage masses (during summer and autumn in 1974) grazing cattle were unable to satisfy their requirements in the same way. For 3 consecutive years, the large seasonal variations in grazing conditions resulted in changes in the spatial distribution of the dry matter within the sward profile, and greatly influenced the growth of the steers. In fact, the lowADG animals, on some sward profile, took bites that were too small in size, and therefore they required more grazing time to compensate. Consequently, the change in herbage mass or SR (Figure 1b) had more effect on the evolution of their low ADG than for the high-ADG animals. Therefore, in addition to the herbage mass per ha, the inclusion of additional characteristics of the herbage – some physical others chemical – may modify how the SR is conceived and managed. Conclusions This meta-analysis of ~41 experiments increases knowledge of feed efficiency by shedding new light on many important parameters that affect feeding growing cattle and their performance in tropical grasslands. This meta-analysis (i) provides a better quantification in various real grazing situations of characteristics of diet and performance, while allowing an estimation of energy requirements for grazing animals, (ii) identifies relationships between digestibility, metabolizable energy consumption and performance, which are essential to effectively manage pastures and (iii) describes general data
about some management levers such as complementation and SR, in order to better consider their implementation. These results based on published data provide promising prospects for the rational improvement of cattle feeding and performance in tropical natural grasslands. Acknowledgements The authors wish to express their gratitude to Dr Michel Naves for his expertise for cattle production in the Tropics and his contribution to the coding of the studies.
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