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Herd Level Yield Gap Analysis in a Local Scale Dairy Farming System: A Practical Approach to Discriminate between Nutritional and Other Constraining Factors
SIMPLE SUMMARY: Improving feed efficiency is one of the keys to meet the production and economic needs of dairy farmers and reduce environmental impact; however, reaching this goal is a challenge. Our results showed that farms with low and average production and efficiency must improve their feed qu...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913683/ https://www.ncbi.nlm.nih.gov/pubmed/36766412 http://dx.doi.org/10.3390/ani13030523 |
Sumario: | SIMPLE SUMMARY: Improving feed efficiency is one of the keys to meet the production and economic needs of dairy farmers and reduce environmental impact; however, reaching this goal is a challenge. Our results showed that farms with low and average production and efficiency must improve their feed quality to increase the amount of feed digested by cows. In addition, low producing farms must eliminate the factors which reduce the conversion of digested feed into milk, such as negative health events, distress and management procedures. This study provides some indicators and a practical approach to help farmers understand their main limitations to reaching achievable milk yield. ABSTRACT: This study performed a yield gap analysis to help farmers understand whether their constraints were mainly due to nutritional factors or management and health issues. Twenty-nine farms were periodically evaluated. Milk yield (MY), dry matter intake (DMI), total mixed ration (TMR) composition and homogeneity index (HI), TMR digestibility, income over feed cost (IOFC), and MY summer–winter ratio (SWR) were collected. Farms were divided and compared according to the average annual MY: Low (L), Medium (M) and High (H), characterised by <31.1, 31.1–36.7 and >36.7 kg/head/day. An ANOVA mixed model and a stepwise regression to assess the relationship between nutritional variables and MY were run. H farms showed higher IOFC (p < 0.001), DMI (p = 0.006), DDM (p < 0.001), digestible crude protein (DCP, p = 0.019), HI (p = 0.09), SWR (p = 0.041) and lower HI coefficient of variation (p = 0.04). The conversion of DDM into milk was higher in H and M farms. Stepwise regression for MY selected DDM and CP (R(2) = 0.716, p < 0.05). M farms were mainly constrained by nutritional factors, whereas L farms were also affected by other factors such as those related to management and health. |
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