Invited review: Helping dairy farmers to improve economic performance utilizing data-driving decision support tools

Cabrera, Víctor E.

doi:10.1017/s1751731117001665

Cited by 23 publications

(12 citation statements)

References 21 publications

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“…With dairy farming being a dynamic and integrated production system, decision making to optimize management and farm profitability is complex (Cabrera, 2018). The effect of income maximization per unit of physical output in dairy production (e.g., milk) is often the focus over maximization of production per cow (Houben, 1995).…”

Section: Lpl Length Of Productive Life Presented Bymentioning

confidence: 99%

Invited review: Academic and applied approach to evaluating longevity in dairy cows

Schuster

Barkema

Vries

et al. 2020

Journal of Dairy Science

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Section: Lpl Length Of Productive Life Presented Bymentioning

confidence: 99%

Invited review: Academic and applied approach to evaluating longevity in dairy cows

Schuster

Barkema

Vries

et al. 2020

Journal of Dairy Science

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“…Cows with a long productive life span typically exhibit good reproductive performance, few health problems, and efficient and consistent milk production. A dairy cow typically only starts to make profit for the farmer during her second lactation, and she reaches her full production potential as late as in her third lactation (Cabrera, 2018). Early culling and short longevity thus clearly have a negative influence on the economic efficiency of the herd.…”

Section: Introductionmentioning

confidence: 99%

Productive life span and resilience rank can be predicted from on-farm first-parity sensor time series but not using a common equation across farms

Adriaens

Friggens

Ouweltjes³

et al. 2020

Journal of Dairy Science

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A dairy cow's lifetime resilience and her ability to recalve gain importance on dairy farms, as they affect all aspects of the sustainability of the dairy industry. Many modern farms today have milk meters and activity sensors that accurately measure yield and activity at a high frequency for monitoring purposes. We hypothesized that these same sensors can be used for precision phenotyping of complex traits such as lifetime resilience or productive life span. The objective of this study was to investigate whether lifetime resilience and productive life span of dairy cows can be predicted using sensor-derived proxies of first-parity sensor data. We used a data set from 27 Belgian and British dairy farms with an automated milking system containing at least 5 yr of successive measurements. All of these farms had milk meter data available, and 13 of these farms were also equipped with activity sensors. This subset was used to investigate the added value of activity meters to improve the model's prediction accuracy. To rank cows for lifetime resilience, a score was attributed to each cow based on her number of calvings, her 305-d milk yield, her age at first calving, her calving intervals, and the DIM at the moment of culling, taking her entire lifetime into account. Next, this lifetime resilience score was used to rank the cows within their herd, resulting in a lifetime resilience ranking. Based on this ranking, cows were classified in a low (last third), moderate (middle third), or high (first third) resilience category within farm. In total, 45 biologically sound sensor features were defined from the time series data, including measures of variability, lactation curve shape, milk yield perturbations, activity spikes indicating es-trous events, and activity dynamics representing health events (e.g., drops in daily activity). These features, calculated on first-lactation data, were used to predict the lifetime resilience rank and, thus, to predict the classification within the herd (low, moderate, or high). Using a specific linear regression model progressively including features stepwise selected at farm level (cutoff P-value of 0.2), classification performances were between 35.9 and 70.0% (46.7 ± 8.0, mean ± SD) for milk yield features only, and between 46.7 and 84.0% (55.5 ± 12.1, mean ± SD) for lactation and activity features together. This is, respectively, 13.7 and 22.2% higher than what random classification would give. Moreover, using these individual farm models, only 3.5 and 2.3% of cows were classified high when they were actually low, or vice versa, whereas respectively 91.8 and 94.1% of wrongly classified animals were predicted in an adjacent category. The sensor features retained in the prediction equation of the individual farms differed across farms, which demonstrates the variability in culling and management strategies across farms and within farms over time. This lack of a common model structure across farms suggests the need to consider local (and evidence-based) culling management rules when deve...

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“…La trayectoria de la curva de lactación es esencial para establecer el manejo optimo en el manejo nutricional, reproductivo y sanitario de la vaca lechera moderna para hacer los modelos de producción más eficientes fisiológicamente y alcanzar una mayor rentabilidad económica (Cabrera, 2018).…”

Section: Introductionunclassified

“…Los modelos regresivos lactacionales predicen la trayectoria de la producción de leche y contribuyen con la adecuación del manejo y el aprovechamiento de la capacidad productiva en las fincas lecheras (Cabrera, 2018). Por ende, los registros biolactacionales son esenciales para evaluar la capacidad funcional de las vacas y definir el manejo integral; en especial en el medio trópico, donde hay limitantes nutricionales y el estrés térmico que reducen simultáneamente la productividad lechera (Araúz, 2010).…”

Section: Introductionunclassified

Perfil De La Curva De Lactación Y Producción Láctea Comercial en Vacas Cebuínas Y Cruzadas en Fincas Lecheras Con Baja Tecnología en El Trópico

Araúz

Araúz²,

Montezuma

2020

Investig. agropecu.

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La curva de lactación y la producción de leche comercial fueron evaluadas en 250 vacas multíparas cebuínas y cebuínas x europeo tipo leche en catorce fincas lecheras Grado C; bajo pastoreo, estrés calórico moderado y un ordeño manual diario con ternero en la época lluviosa en el trópico húmedo de bajura; donde el estrés calórico diurno máximo ocurrió a la 01 PM (HR 76.71%, TAbs 32.7°C, RSD 676.3 kcal/m2hr, ITH 86.59 e ITHRSDVVmax 92.35). La producción comercial acumulada y la longitud lactacional en el grupo cebuíno resultó en 692.07 kg en 177 días; ½ Bos taurus tipo leche x ½Cebú 1267.35 kg en 213 días y en las ¾ Bos taurus tipo leche x ¼ Cebú 2012.16 kg en 262 días (P<.01). El perfil típico de la curva de lactación mostró una producción diaria inicial, máxima y final en las cebuínas de 3.42, 4.62 a los 14 días y 2.23 kg/vaca (P<.05); 5.63, 8.63 a los 26 días y 3.27 kg/vaca (P<.01) en las ½ Bos taurus tipo leche x ½Cebú y 7.50, 10.95 a los 34 días y 4.40 kg/vaca (P<.01 en las 3/4Bos taurus x ¼ Cebú). La curva de lactación en las cebuínas fue Y(kg/vaca-día)=13.09572121X-0.0733187e-0.005064242X (R2=0.38); en las ½Cebú x ½ Bos taurus Y(kg/vaca-día)=8.5935X0.13048e-0.005115067X (R2=0.68) y en las ¾ Bos taurus lechero x ¼Cebú Y(kg/vaca-día)=12.7632X0.24051e-0.007117078X (R2=0.72). La producción lechera fue potenciada con el mayor aporte genético tipo leche en los cruces; sin embargo, el entorno ambiental y el manejo afectaron la curva de lactación y redujeron la habilidad lechera fenotípica en todos los grupos genéticos en el trópico húmedo.

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Invited review: Helping dairy farmers to improve economic performance utilizing data-driving decision support tools

Cited by 23 publications

References 21 publications

Invited review: Academic and applied approach to evaluating longevity in dairy cows

Invited review: Academic and applied approach to evaluating longevity in dairy cows

Productive life span and resilience rank can be predicted from on-farm first-parity sensor time series but not using a common equation across farms

Perfil De La Curva De Lactación Y Producción Láctea Comercial en Vacas Cebuínas Y Cruzadas en Fincas Lecheras Con Baja Tecnología en El Trópico

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