Lead Factors for Total Mixed Ration Formulation

Stallings, Charles C.; McGilliard, M.L.

doi:10.3168/jds.s0022-0302(84)81386-7

Cited by 24 publications

(28 citation statements)

References 2 publications

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“…Flock and milk production level have a significant effect on lead factors, with higher producing flocks requiring slightly lower lead factors (Table 4) because of lower within-flock variation. This is in accordance with the findings of Stallings and McGilliard (1984). Moreover, the 90th percentile strategy can be adopted for the higher producing groups, thereby allowing a more rigorous program of breeding management and a more accurate estimation of genetic values, while the 83rd percentile seems sufficient for the low producing groups.…”

Section: Discussionsupporting

confidence: 85%

“…However, feeding for the average production of each group would still compromise the performance of the best ewes. An alternative option could be the proposal by Stallings and McGilliard (1984), who introduced the use of lead factors in feeding strategies for dairy cows. Lead factors, adjusting energy and protein densities of diets by adjusting upwards the average production level, can be used to minimize both the proportion of underfed animals and the extent of underfeeding.…”

Section: Introductionmentioning

confidence: 98%

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Grouping strategies and lead factors for ration formulation in milking ewes of the Chios breed

Valergakis

Arsenos

Basdagianni³

et al. 2008

Livestock Science

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The aim of this study was to develop an efficient strategy for ration formulation for milking ewes of the Chios breed in Greece. The strategy involved two and three groupings according to production level and challenge feeding using lead factors. Lead factors, that adjust upwards the average production of a flock or a group of ewes, were calculated for the 83rd and the 90th percentile, using 49,237 milk test-day records from 549 flock-test-days, referring to 64 flocks and 97 complete lactations. Lead factors were 1.25 for the single-group, 1.14 and 1.17 for the two-group, and 1.11, 1.07 and 1.15 for the three-group strategy for the 83rd percentile. Regarding the 90th percentile, these were: 1.33 for the single-group, 1.18 and 1.19 for the two-group, and 1.15, 1.09 and 1.16 for the three-group strategy. Analysis of variance was used to assess the influence of several effects on lead factors. Flock-year and mean and standard deviation milk yield were significant (P b 0.05) in nearly all cases, leading to calculation of different lead factors for high (N 250 kg of milk/ewe/year) and low (b250 kg of milk/ewe/year) producing flocks. Higher producing flocks were associated with somewhat lower than average lead factors, while the opposite was true for lower producing flocks. In order to allow the sufficient expression of the genetic potential of the best ewes and accurately estimate their genetic value under Greek conditions, the 90th percentile strategy can be adopted for the higher producing groups and the 83rd for the lower ones.

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Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 98%

Grouping strategies and lead factors for ration formulation in milking ewes of the Chios breed

Valergakis

Arsenos

Basdagianni³

et al. 2008

Livestock Science

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“…However, the actual variation is probably higher, since the production data used in our study were obtained from dairy farms connected to the advisory service and with higher average milk yield and better feed efficiency than the average Swedish milk produced (Table 2). In addition, the set of animal production data did not include production systems using total mixed rations, a feeding system in which overconsumption of feed is common (Stallings and McGilliard, 1984). Neither did we consider other parameters in feed crop cultivation at farm level than the use of N-fertiliser (e.g.…”

Section: Discussionmentioning

confidence: 99%

Variation in carbon footprint of milk due to management differences between Swedish dairy farms

Henriksson

Flysjö

Cederberg

et al. 2011

Animal

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To identify mitigation options to reduce greenhouse gas (GHG) emissions from milk production (i.e. the carbon footprint (CF) of milk), this study examined the variation in GHG emissions among dairy farms using data from previous CF studies on Swedish milk. Variations between farms in these production data, which were found to have a strong influence on milk CF, were obtained from existing databases of 1051 dairy farms in Sweden in 2005. Monte Carlo (MC) analysis was used to analyse the impact of variations in seven important parameters on milk CF concerning milk yield (energy-corrected milk (ECM) produced and delivered), feed dry matter intake (DMI), enteric CH 4 emissions, N content in feed DMI, N-fertiliser rate and diesel used on farm. The largest between-farm variations among the analysed production data were N-fertiliser rate (kg/ha) and diesel used (l/ha) on farm (CV 5 31% to 38%). For the parameters concerning milk yield and feed DMI, the CV was approximately 11% and 8%, respectively. The smallest variation in production data was found for N content in feed DMI. According to the MC analysis, these variations in production data led to a variation in milk CF of between 0.94 and 1.33 kg CO 2 equivalents (CO 2 e)/kg ECM, with an average value of 1.13 kg CO 2 e/kg ECM. We consider that this variation of 617%, which was found to be based on the used farm data, would be even greater if all Swedish dairy farms were included, as the sample of farms in this study was not totally unbiased. The variation identified in milk CF indicates that a potential exists to reduce GHG emissions from milk production on both the national and farm levels through changes in management. As milk yield and feed DMI are two of the most influential parameters for milk CF, feed conversion efficiency (i.e. units ECM produced/unit DMI) can be used as a rough key performance indicator for predicting CF reductions. However, it must be borne in mind that feeds have different CF due to where and how they are produced.

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“…Pierre and Harvey, 1986). Another potential method is lead feeding, which accounts for variability in the requirements of a group of animals, such as milking cow groups-for which production varies throughout the group due to different stages of lactation and body weight-by calculating a factor that is based on the variability of production (Stallings and McGilliard, 1984). The nutrient requirements of the group are then multiplied by this factor; hence, lead feeding is similar to a combination of the SM and RS formulations.…”

Section: Introductionmentioning

confidence: 99%

Application Of Fuzzy Optimization In Diet Formulation

Salookolayi¹,

Yansari²,

Nasseri³

2011

J. Math. Computer Sci.

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Feeding cost comprised about 65 to 75 percentage of dairy cattle production systems. Reduction feed cost and consideration seasonal or regional limitation of feed sources especially some forages increased necessity of the optimization of feed formulation in dairy caws. However, without a positive answer and accrue methods based on linear models those used on ration formulation, application of new mathematical models as fuzzy models seems to be very useful to taken account and meeting nutrient requirements and formulation based on ration least cost and composition in different levels. Fuzzy models promise to be a valuable tool as they link measurable information to linguistic interpretation using membership functions. The objective of this paper was using linear fuzzy model in formulation of dairy cow ration in early lactation and compare to linear programming models. Using linear programming models, the final cost of one kilogram of total mixed ration was 1333.5 Rails, and at this level cow nutrients requirements were met. Using fuzzy model and applying all restriction, the least cost for one kilogram of total mixed ration was 1222.5 Rails, and at this level cow nutrients requirements were met. Using fuzzy model in compare to linear programming models, feed cost was reduced about 8 percentages. The result of this experiment guarantees the formulation of ration using fuzzy models can be used to reduce feed cost and obtain different ration that they may met dairy cow nutrients requirements over different situations. In addition, because of the results in an

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Lead Factors for Total Mixed Ration Formulation

Cited by 24 publications

References 2 publications

Grouping strategies and lead factors for ration formulation in milking ewes of the Chios breed

Grouping strategies and lead factors for ration formulation in milking ewes of the Chios breed

Variation in carbon footprint of milk due to management differences between Swedish dairy farms

Application Of Fuzzy Optimization In Diet Formulation

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