# Prospects for Data Collection to Optimise Kid Rearing in Dutch Dairy Goat Herds

**Authors:** Eveline Dijkstra, Inge Santman-Berends, Tara de Haan, Gerdien van Schaik, René van den Brom, Arjan Stegeman

PMC · DOI: 10.3390/ani15111653 · 2025-06-03

## TL;DR

This study developed and tested indicators to monitor young goat rearing in Dutch farms, finding that data collection can improve management but needs better tools.

## Contribution

The study introduces a set of practical, farm-tested indicators for kid rearing and highlights the need for digital tools to streamline data collection.

## Key findings

- Significant variation in rearing outcomes was observed across farms, particularly in birth weights and postweaning growth.
- Birth weight was identified as a key predictor for average daily gain, and weaning strategies strongly influenced postweaning performance.
- Data collection was found to be labor-intensive and challenging during the kidding season, prompting a call for digital solutions.

## Abstract

Good management of young dairy goats is essential for healthy and productive herds, but farmers often lack practical tools to monitor and improve rearing practices. This study actively involved farmers, veterinarians, and researchers to develop a set of measurable indicators related to early life care, such as birth weight, colostrum intake, growth, and kid survival. These indicators were then tested as proof of principle on five Dutch commercial dairy goat farms, with data collected from over 700 kids from birth to mating age. The results showed the added value of the collection of these data, revealing considerable differences between farms in how kids were raised and how they developed, particularly after weaning. Nevertheless, it also showed that the collection of the developed indicators was challenging, labour-intensive, and not always feasible, especially during the kidding season, which is the most labour-intensive period on a dairy goat farm. Farmers acknowledged the added value of the indicators but expressed a strong need for a digital tool to simplify data collection and interpretation. This study demonstrates that individual-level data can offer important insights into rearing performance and support more informed management decisions. Additionally, these findings highlight the potential of tailored, data-driven approaches to improve young stock care and support more sustainable dairy goat farming practices.

Optimising kid rearing is essential for sustainable dairy goat farming, yet validated parameters and practical benchmark data are lacking. This study aimed to develop and evaluate a set of key performance indicators (KPIs) for monitoring kid-rearing practices through a participatory approach. Researchers, veterinarians and five dairy goat farms participated developed a prototype set of KPIs covering birth, colostrum management, average daily gain (ADG), and mortality, which were stratified across four rearing phases: perinatal (first 48 h), postnatal (birth to weaning), postweaning (weaning to 12 weeks), and final rearing (12 weeks to mating). The set of KPIs was subsequently tested for its added value but also for its feasibility in practice on the five participating farms as proof of principle. On these farms, data were gathered over a six-month period (June 2020–January 2021), combining routine census data with on-farm records. Only three out of five farms returned complete datasets encompassing data from 715 kids. Results revealed significant variation in rearing outcomes across farms, particularly in birth weights and postweaning growth. Birth weight emerged as a key predictor for ADG, while differences in weaning strategies had the greatest impact on postweaning performance. Although the farmers acknowledged the added value of the developed KPIs, collection of these data during the kidding season was challenging and required further automation to simplify data collection on the farm. This study demonstrates the feasibility and value of individual-level data collection in dairy goat systems and underscores the need for practical tools to support routine monitoring and data-driven management.

## Full-text entities

- **Diseases:** injury to (MESH:D014947), ID (MESH:C537985), deaths (MESH:D003643), CAE (MESH:D004660), stillbirths (MESH:D050497), Weight gain (MESH:D015430), CL (MESH:D008199)
- **Chemicals:** ADG (-), NH3 (MESH:D000641), CO2 (MESH:D002245), Water (MESH:D014867)
- **Species:** Homo sapiens (human, species) [taxon 9606], Bos taurus (bovine, species) [taxon 9913], Capra hircus (domestic goat, species) [taxon 9925]

## Figures

3 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12153765/full.md

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Source: https://tomesphere.com/paper/PMC12153765