Accuracy of estimates of milk production per lactation from limited test-day and recall data collected at smallholder dairy farms

Migose, S.A.; Linden, A. van der; Bebe, Bockline Omedo; Boer, I.J.M. de; Oosting, S.J.

doi:10.1016/j.livsci.2019.103911

Cited by 13 publications

(16 citation statements)

References 42 publications

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“…The fit of the linear and the non-linear equation (i.e., how well the model fits the data) was compared based on the average RMSE of the two models. This comparison showed a better fit of the linear model compared to the J&F model for all herds (average RMSE=721 kg/lactation vs 1056 kg/lactation), which is in line with results of a study by Migose et al (2020) in Kenyan herds.…”

Section: Y (T) = At + B (Equationsupporting

confidence: 89%

“…showing an increase in milk yield up to peak production, and subsequently a gradual decline until drying-off. In this study we chose the non-linear equation by Migose et al (2020) after Jenkins and Ferrell ('J&K';1984), which is a modified Wood's equation, and requires only two parameters that were available in the data collected in the present study: milk yield and days in milk. The J&F equation, therefore, is suited to estimate milk production per lactation if limited lactation data are available (Migose et al, 2020).…”

Section: Milk Yield (Cows In Herd Sample; V4-v7)mentioning

confidence: 99%

“…In this study we chose the non-linear equation by Migose et al (2020) after Jenkins and Ferrell ('J&K';1984), which is a modified Wood's equation, and requires only two parameters that were available in the data collected in the present study: milk yield and days in milk. The J&F equation, therefore, is suited to estimate milk production per lactation if limited lactation data are available (Migose et al, 2020). Cows with at least one milk yield record during the monitoring period (V4-V7) were included, i.e.…”

Section: Milk Yield (Cows In Herd Sample; V4-v7)mentioning

confidence: 99%

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Technical performance and carbon footprint of commercial dairy farms in South West Uganda

Vries

Twongyirwe

2022

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The aim of the present study was to evaluate technical performance and carbon footprint of 101 dairy farms participating the dairy development project SNV TIDE in south western Uganda. Results showed that average 24-h milk yield per lactating cow was 7.1 ± 2.0 (SD) kg/cow/d, and estimated annual milk yield was 1626 ± 614 kg/cow/y. Average calving interval was 483 ± 64 days, mortality rate of adult cows was 3.2 ± 4.8 % per farm, and average culling rate of adult cows was 18 ± 15 % per farm.With regard to the carbon footprint of farms, estimated average greenhouse gas (GHG) emission intensity was 2.1 kg CO2-eq per kg fat and protein correct milk and 13.6 kg CO2-eq per kg live weight.Most important sources of GHG emissions were rumen enteric fermentation (83%), followed by manure (11%), and feed production (5%). GHG emission intensity was lower in specialized farms than in mixed crop-livestock farms; lower in farms with supplemental feeding besides grazing than in farms with grazing only; and in farms with crossbreds than in farms with HF as predominant breed. GHG emission intensity was highest in Ntungamo district (2.6 kg CO2-eq/kg FPCM) and lowest in Kiruhura district (1.8 kg CO2-eq/kg FPCM).

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Section: Y (T) = At + B (Equationsupporting

confidence: 89%

Section: Milk Yield (Cows In Herd Sample; V4-v7)mentioning

confidence: 99%

Section: Milk Yield (Cows In Herd Sample; V4-v7)mentioning

confidence: 99%

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Technical performance and carbon footprint of commercial dairy farms in South West Uganda

Vries

Twongyirwe

2022

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“…Components of variance were estimated by restricted maximum likelihood (REML, e.g. McCulloch et al 2008). Facilities from R routine glmmTMB (Brooks et al 2017) were used for the calculations, i.e.…”

Section: Discussionmentioning

confidence: 99%

“…However, both cross-sectional observations and farmer recall could hamper an accurate assessment of the exact situation on a farm. For example, a study by Migose et al (2020) showed that assessment of milk yield based on farmers recall was less accurate than those based on recordings, while milk yield explains a significant part of the variation in GHG emission intensity (e.g. De Vries et al 2019;Wilkes et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Understanding variability in greenhouse gas emission estimates of smallholder dairy farms in Indonesia

Apdini

Zahra

Oosting

et al. 2021

Int J Life Cycle Assess

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Purpose Life cycle assessment studies on smallholder farms in tropical regions generally use data that is collected at one moment in time, which could hamper assessment of the exact situation. We assessed seasonal differences in greenhouse gas emissions (GHGEs) from Indonesian dairy farms by means of longitudinal observations and evaluated the implications of number of farm visits on the variance of the estimated GHGE per kg milk (GHGEI) for a single farm, and the population mean. Methods An LCA study was done on 32 smallholder dairy farms in the Lembang district area, West Java, Indonesia. Farm visits (FVs) were performed every 2 months throughout 1 year: FV1–FV3 (rainy season) and FV4–FV6 (dry season). GHGEs were assessed for all processes up to the farm-gate, including upstream processes (production and transportation of feed, fertiliser, fuel and electricity) and on-farm processes (keeping animals, manure management and forage cultivation). We compared means of GHGE per unit of fat-and-protein-corrected milk (FPCM) produced in the rainy and the dry season. We evaluated the implication of number of farm visits on the variance of the estimated GHGEI, and on the variance of GHGE from different processes. Results and discussion GHGEI was higher in the rainy (1.32 kg CO2-eq kg−1 FPCM) than in the dry (0.91 kg CO2-eq kg−1 FPCM) season (P < 0.05). The between farm variance was 0.025 kg CO2-eq kg−1 FPCM in both seasons. The within farm variance in the estimate for the single farm mean decreased from 0.69 (1 visit) to 0.027 (26 visits) kg CO2-eq kg−1 FPCM (rainy season), and from 0.32 to 0.012 kg CO2-eq kg−1 FPCM (dry season). The within farm variance in the estimate for the population mean was 0.02 (rainy) and 0.01 (dry) kg CO2-eq kg−1 FPCM (1 visit), and decreased with an increase in farm visits. Forage cultivation was the main source of between farm variance, enteric fermentation the main source of within farm variance. Conclusions The estimated GHGEI was significantly higher in the rainy than in the dry season. The main contribution to variability in GHGEI is due to variation between observations from visits to the same farm. This source of variability can be reduced by increasing the number of visits per farm. Estimates for variation within and between farms enable a more informed decision about the data collection procedure.

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Comparison of models for lactation curves of Holstein, Brown Swiss, and F1 crossbred cows under subtropical conditions

Hernández-Zamudio

Villagómez-Cortés

Murillo

et al. 2022

Trop Anim Health Prod

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Accuracy of estimates of milk production per lactation from limited test-day and recall data collected at smallholder dairy farms

Cited by 13 publications

References 42 publications

Technical performance and carbon footprint of commercial dairy farms in South West Uganda

Technical performance and carbon footprint of commercial dairy farms in South West Uganda

Understanding variability in greenhouse gas emission estimates of smallholder dairy farms in Indonesia

Comparison of models for lactation curves of Holstein, Brown Swiss, and F1 crossbred cows under subtropical conditions

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