# Exploring how milk production, body weight and body condition dynamics affect reproductive success after artificial insemination in dairy goats

Nicolas Gafsi, Olivier Martin, F. Bidan, Bénédicte Grimard, Laurence Puillet

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## To cite this version:

Nicolas Gafsi, Olivier Martin, F. Bidan, Bénédicte Grimard, Laurence Puillet. Exploring how milk production, body weight and body condition dynamics affect reproductive success after artificial insemination in dairy goats. Theriogenology, 2025, 234, pp.83-91. 10.1016/j.theriogenology.2024.12.005. hal-04847967

HAL Id: hal-04847967

https://hal.inrae.fr/hal-04847967v1

Submitted on 22 May 2025

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Original Research Article

# Exploring how milk production, body weight and body condition dynamics affect reproductive success after artificial insemination in dairy goats

N. Gafsi<sup>a,b,*</sup>, O. Martin<sup>a</sup>, F. Bidan<sup>b</sup>, B. Grimard<sup>c</sup>, L. Puillet<sup>a</sup>

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ARTICLE INFO

Keywords:

Lactation

Body reserves

Artificial insemination

Productive curve

Phenotyping

ABSTRACT

In the context of agroecological transition, breeding females with robust reproductive performance, leading to prolonged lactation sequences, is valuable for farmers. This study aimed to explore the relationship between artificial insemination (AI) success and phenotypic lactation curves that serve as proxies for key biological functions in Alpine and Saanen goats. Using data from two French experimental farms (1996–2021), the study analyzed time series data on milk yield (MY), body weight (BW), and sternal body condition score (BCS_S). These data were modeled at the lactation scale to characterize dynamic profiles and create clusters. Each phenotypic lactation curve was evaluated with three levels of detail: cluster membership, synthetic indicators, and model parameters. To investigate AI success, three datasets were used: 638 lactations with complete MY, BW, and BCS_S data; 1359 lactations with MY and BW; and separate sets with 1731 MY and 795 BCS_S records. A mixed logistic regression model (year as a random effect) assessed the relationship between AI success and phenotypic lactation curve characteristics. Results showed that for primiparous goats, AI success was influenced by MY clusters (p < 0.05), while in multiparous goats, MY and BCS_S clusters did not influence AI success. However, indicators such as persistency (p < 0.001) and BW repletion speed (p < 0.001) were significant. Overall, the lactation curve shape was more important to AI success than milk production level, offering insights for enhancing reproductive performance in dairy goats.

# 1. Introduction

In French dairy goat systems, predicting a female's success or failure at reproduction is of great interest to help the farmers in their decision to manage females in a classical 10-month lactation system or in an extended lactation system. In fact, reproductive disorders are the third cause of culling [1] and 176,595 goats had an extended lactation for reproductive failure between 1998 and 2016 (i.e., 16.4 % of goats in the French national performance control with at least one extended lactation in their productive life) [2]. Understanding the variability in reproductive success will help to better manage the diversity of production curves in a herd.

Variability in reproductive success depends on the complex dynamic interplay between milk production and body reserves management. These interactions have been extensively studied in dairy cows [3–7].

Genetic selection for increased milk production in dairy cows has led to reproductive issues, including higher embryonic loss, shorter oestrus,

and reduced conception due to prolonged negative energy balance. The shape of the lactation curve also affects fertility, with persistent profiles linked to early embryonic death and non-persistent profiles to late embryonic death [4]. In dairy goats, the longer interval between parturition and first mating (7 months vs. 60 days in cows) may alter priorities among functions.

In goats, studies have demonstrated the effects of animal and environmental factors on fertility after artificial insemination (AI), such as breed, parity, stage of lactation at AI, breeding season, failure at previous AI, milk production level around AI [8–10]. Other studies have shown relationships between the level of body reserves (BCS at mating) and reproductive performance (e.g., oestrus duration and ovulation rate) [11–13].

Moreover, with climate change and a breeding season based on summer breading for seasonal production systems, heat stress must be considered as an environmental factor that can affect fertility. Indeed, dairy goats under extreme heat stress conditions showed lower

<footnote><sup>a</sup> Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120, Palaiseau, France</footnote>

<footnote><sup>b</sup> Institut de l'Elevage, F-75595, Paris, France</footnote>

<footnote><sup>c</sup> Université Paris-Saclay, INRAE, ENVA UMR BREED, F-78350, Jouy-en-Josas, France</footnote>

<footnote>* Corresponding author. Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120, Palaiseau, France.
E-mail address: nicolas.gafsi@outlook.fr (N. Gafsi).</footnote>
reproductive performance [14]. To our knowledge, no studies have considered criteria related to milk production and body reserves in a broad approach, allowing the identification of the best predictors to characterize reproductive success in dairy goats. In a previous study, we showed that there was a diversity of associations between phenotypic lactation curves of milk yield (MY), body weight (BW) and body condition score (BCS), suggesting a diversity of energy partitioning between these functions [15]. Therefore, the objective of this study was to assess the relationship between AI success and phenotypic lactation curves with three levels of increasing detail: cluster membership, synthetic indicators, and model parameters.

## 2. Material and methods

### 2.1. Data and reproductive management

Data came from two experimental farms: Le Pradel (agricultural high school Olivier de Serres) located in the French department of Ardeche (44° 34' 58.4364" N; 4° 29' 53.2068" E) and the MoSAR experimental farm (INRAE- AgroParisTech) located in the French department of Yvelines (48° 50' 31.4801" N; 1° 56' 56.5843" E). For the MoSAR experimental farm, goats were housed indoor, all year round, in free straw stalls. The herd was composed of half Alpine breed and half Saanen breed.

They were milked twice a day (07:00 and 15:00) and milk yield was recorded at each milking using an automatic device designed for milk recording in small ruminants developed by INRAE (European patent no. 94916284.6). They received a total mixed ration (TMR) (with 10–15 % feed refusals) based mainly on forages (hay, dry lucerne, and beet pulp) and supplemented with commercial concentrates (about 20 %) and minerals-vitamins-bicarbonate (1 %). All feeds were offered *ad libitum* in a collective trough twice daily after milking. The milking parlour was equipped with a walk-over scale, which provided body weights at each milking, and they were averaged to give daily BW (after excluding outliers greater than + 25 % of previous BW measurement). The farm had a seasonal management system with a kidding period between January and February. In August, selected goats were inseminated with frozen semen after oestrus induction and synchronization. Synchronization involved inserting a vaginal sponge impregnated with progestogen (20–45 mg fluorogestone acetate) for 11 days. Goats received an intramuscular injection of PGF2α (50 µg cloprostenol) and 400–500 IU of eCG 48 h before sponge removal. AI was performed 43–45 h after sponge removal [16]. If AI was unsuccessful the non-pregnant goats were then bred by natural mating with the other non-experimental goats. During the breeding season in August, all goats received a hormonal treatment and were inseminated during the same month. For the past 15 years, goats have been hand-mated in small groups of 10–12 per day for 6–7 days, resulting in an average kidding rate of 85 %.

For Le Pradel farm, goats were grazing on cultivated pastures (grass and lucerne), with a rotational system (1–2 ha plots). The herd only has the Alpine breed. The daily grazing was on average 6–7h/day during the early and late grazing periods of the year and 10–12 h/day during periods of high grass production. On average, goats were grazing over a 150-day period per year. When not grazing, goats are housed indoors in free straw stalls. Forages are distributed *ad libitum* in a collective trough after milking. Concentrates and dehydrated alfalfa pellets are distributed with an automatic feeder. Goats were miked twice a day (06:30 and 16:00). Milk production was recorded individually using the national performance control method and body weight was recorded individually every month. The farm uses a seasonal management system with a kidding period between January and February. In August, selected goats were inseminated with frozen semen after oestrus induction and synchronization. The treatment protocol was the as that used by the MoSAR experimental farm. If AI was unsuccessful the non-pregnant goats were then bred by natural mating with the other non-experimental goats. Males were introduced 18 days after AI. Males remained with the

females until October to mate the goats returning to heat after unsuccessful AI and the goats that were not inseminated.

MY, BW and sternal BCS (BCS_S) data were recorded over the period 1996–2020. BCS_S was retained due to higher data availability and the sternal site was a better proxy of body fatness in dairy goats than lumbar site (Inra, 2018). Data included 1442 goats (1132 Alpine; 310 Saanen) with 4068 lactations. For each lactation, time series data for each phenotype were fitted with different models (Table .1) adapted to recording frequency (monthly, weekly, daily), following a method developed in a previous paper [15] to characterize their dynamics. The MY, BW and BCS_S curves were then summarized into synthetic indicators (see Fig. 1, which outlines the methodology developed in the previous paper). Principal component analysis and agglomerative hierarchical clustering analysis were performed separately for MY, BW and BCS_S curves. For MY and BW, clustering was performed per parity (primiparous vs. multiparous), while for BCS, clustering was performed by pooling parities together (see Fig. 2).

### 2.2. A three-level analysis of reproductive success

The clustering approach allowed us to characterize the overall shape of each phenotypic curve of MY, BW and BCS_S over the lactations with three levels of increasing detail: cluster membership, synthetic indicators, and model parameters (Figure. 1 and 2). In the present paper, three different data subsets were considered according to data availability, allowing us to exploit these different levels of detail to investigate the relationship between phenotypic lactation curves and reproductive success. The first dataset contained 638 lactations with full information on MY, BW and BCS_S. This first dataset was used to study the effects of the 3 phenotypes (effect of cluster membership for MY, BW and BCS_S) on reproductive success. The second dataset contained 1359 lactations with full information on MY and BW. This second dataset was used to study the effects of the synthetic indicators of MY and BW on reproductive success. The third dataset was divided into two datasets containing 1731 lactations for MY and 795 lactations for BCS_S. This latter data set was used to investigate the specific effect of MY and BCS_S model parameters on reproductive success. For BW, due to the difference in data frequency on the two farms, two different models were used

<caption>**Table 1**</caption>

Models used to fit raw data to obtain phenotypic curves of MY, BW and BCS for the whole lactation.

<table><thead><tr><th>Raw data<sup>a</sup></th><th>Model used to fit raw data</th><th>Parameters<sup>b</sup></th></tr></thead><tbody><tr><td>MY</td><td>Perturbed lactation model (Supplementary Material Gafsi et al., 2024)</td><td>a, b, c, d, e, N<sub>p</sub>, Loss</td></tr><tr><td>BW</td><td>Unperturbed weight model [36]</td><td>m<sub>1</sub>, m<sub>2</sub>, n<sub>1</sub>, n<sub>2</sub>, h<sub>1</sub>, h<sub>2</sub>, k<sub>1</sub>, k<sub>2</sub></td></tr><tr><td></td><td></td><td>B<sub>M</sub>, k<sub>E</sub>, k<sub>L</sub>, t<sub>1</sub>, L</td></tr><tr><td>BCS</td><td>[37]</td><td>B<sub>M</sub>, k<sub>E</sub>, k<sub>L</sub>, t<sub>1</sub>, L</td></tr></tbody></table>

<footnote><sup>a</sup> MY = milk yield; BW = body weight; BCS = body condition score.</footnote>

<footnote><sup>b</sup> Revised Wood model parameters: a = mean level at the beginning of lactation; b = rate of milk increase before the peak; c = decrease rate after the peak; d = time of late decrease of milk; e = time of half decrease of milk. Loss = difference between total milk production calculated with unperturbed model and perturbed model, N<sub>p</sub> = number of perturbations; m<sub>1</sub> = asymptotic mature body weight (in kg) at parturition p; m<sub>2</sub> = asymptotic mature body weight (in kg) at parturition p + 1; n<sub>1</sub> = time of nadir (i.e., min weight) in days after parturition p; k<sub>1</sub> = intensity of depletion (ranging from 0 to 1) after parturition p; h<sub>1</sub>: = time of half repletion in days after nadir after parturition p: n<sub>2</sub> = time of nadir (i. e., min weight) in days after parturition p + 1; k<sub>2</sub> = intensity of depletion (ranging from 0 to 1) after parturition p + 1; h<sub>2</sub> = time of half repletion in days after nadir after parturition p + 1; B<sub>M</sub> = plateau value in middle stage (in kg BW or BCS) k<sub>E</sub> = slope in early stage (in kg BW/d or BCS/d); k<sub>L</sub> = slope in late stage (in kg BW/d or BCS/d); t<sub>1</sub> = upper bound of early stage (in d); L = length of the middle stage (in d).</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 1. Scheme that represented the whole methodology used in the paper with: (1) the different steps used to characterize phenotypic lactation curves of milk yield (MY), body weight (BW), and body condition score (BCS), and (2) the different steps used to investigate artificial insemination (AI) success with different levels of detail. To characterize phenotypic lactation curves three steps were used. The first step was to fit the raw data with models adapted to the recording frequency (monthly, weekly, daily) to obtain phenotypic lactation curves: MY was fitted with the perturbed lactation model (PLM) (Supplementary Material Gafsi et al. (2024)), BW was fitted with two models: the unperturbed weight model (UWM) [35] and the Grossman model [37], BCS was fitted with the Grossman model. In the second step, MY, BW and BCS curves were then summarized into synthetic indicators with level and dynamic indicators: MY: SumMY = sum of daily milk yield values over 250 days; MYpeak = highest milk yield value; MY210 = milk yield value at 210 days; Peak time = time of the highest milk yield value; Persistency = (MY250-MY150)/MY150) x100; BW: BWk = body weight at kidding; BWmin = minimum body weight; BW210 = body weight at 210 days; Dep_speedk→30 = (BW30 - BWk)/30; Rep_speed180→210 = (BW210 - BW180)/30; BCS: BCSSk = Sternal BCS at kidding; BCSmin = Minimum sternal BCS; BCSSk = Sternal BCS at 210 days; Dep_speedSk→30 = (BCS30 - BCSSk)/30; Rep_speedS180→210 = (BCS210 - BCSS180)/30. In the final step principal component analysis (PCA) and agglomerative hierarchical clustering (AHC) were performed on synthetic indicators separately for MY, BW and BCS curves to obtain clusters of phenotypic lactation curves.</figcaption></figure>

to characterize the phenotypic lactation curves [15]. So, BW model parameters were not used in this last dataset.

## 2.3. Assessment of the reproductive success and heat stress

Because natural mating management was not the same in the two farms, only AI animals were studied. Reproductive success, as a binary outcome (1: AI success; 0: AI failure), was derived from the length of the interval between AI and the following kidding date: intervals less than 160 days were interpreted as an AI success and intervals greater than 160 days were interpreted as an AI failure [9,17]. If there were no following kidding dates, it was interpreted as an AI failure. Overall fertility considering AI success and subsequent natural mating success was not studied.

Weather data from locations close to the two experimental farms were used as a proxy for environmental conditions. Therefore, a French national database SAFRAN (web application SICLIMA) including daily average temperature, minimum, maximum temperature, and relative humidity was used to calculate the Temperature-Humidity Index (THI). The THI combined the ambient temperature (T, °C) and relative humidity (RH, %) and was described by the [18] as:

$$THI = (1,8 \times T + 32) - [(0,55 - 0,0055 \times RH) \times (1,8 \times T - 26,8)]$$

## 2.4. Statistical analysis

The statistical unit considered was the AI event. Mixed logistic regressions were performed using RStudio with the lme4 package (version 2023.06.01) to analyze the relationship between AI success and (1)

phenotypic lactation curve cluster membership, (2) phenotypic lactation curve synthetic indicators, and (3) phenotypic lactation curve model parameters. For all models, factors commonly known to affect AI success were considered, such as parity (1 = primiparous vs. 2+ = multiparous), breed (Alpine vs. Saanen), stage of lactation at AI calculated as the interval between kidding and AI, and AI success during the previous campaign (0,1, non-concerned (NC)) [9]. The maximum daily THI values at AI were also considered (<80 vs. ≥80). A threshold of 80 was used because goats start to experience heat stress when the THI is higher than 80 [19]. All the variables used in the different approaches are described in Tables S1–S4 (see Supplementary material). All the quantitative variables were classified in two ways.

- Some variables may have a quadratic relationship with AI success, suggesting better AI success at a certain threshold, and lower AI success beyond that threshold. For these variables, the classification was based on the raw distribution.

- The other quantitative variables were divided into quartiles of the three groups with 33 % of the population in each group.

For all variables, one group was designated as the reference category for odds ratio (OR = 1). An OR greater than one indicates an increased likelihood, and an OR less than one indicates an opposite association. The confidence interval (CI) indicated the level of uncertainty around the effect measure. If the CI crossed 1 (e.g., 95%CI [0.85; 1.60]), this indicated no statistical difference for the studied variables. The first approach was based on phenotypic lactation curves of MY, BW, and BCS. Univariate analysis was performed on the phenotypic lactation curves
<figure><img src="image_5.png" /><figcaption>Fig. 2. Clusters of phenotypic lactation curves estimated after principal component analysis and hierarchical clustering analysis on synthetic indicators for milk yield ((a) primiparous, (b) multiparous), body weight ((c) primiparous, (d) multiparous) and sternal body condition score ((e) all parities together); $Y_{L}^{p.} = Low milk yield$ and low persistency cluster for primiparous; $Y_{M.} = Medium milk yield and low persistency cluster for primiparous; $Y_{H}^{p.} = High milk yield and medium persistency cluster for primiparous; $Y_{H}^{m.} = High milk yield and medium persistency cluster for multiparous; $W_{L.}^{p.} = Low body weight and low depletion cluster for primiparous; $W_{H.}^{p.} = High body weight and high depletion cluster for primiparous; $W_{H.}^{m.} = High body weight and low depletion cluster for multiparous; $W_{H.}^{m.} = High body weight and low depletion cluster for multiparous; $ST_{M.} = Medium sternal body condition score and depletion cluster; $ST_{M} = Medium sternal body condition score and low depletion cluster; $ST_{H.} = High sternal body condition score and depletion cluster) (Adapted from Gafsi et al. (2024).</figcaption></figure>

and common factors to assess their relationship with AI success. Variables associated with AI success (p-value <0.20) were retained to build the final models. Models were performed by parity based on the methodology used to create the phenotypic lactation curves [15]. The second approach was based on the synthetic indicators of MY and BW. Univariate analysis was performed on the synthetic indicators and common factors to assess their relationship with AI success. Variables related to AI success (p-value <0.20) were retained in the second step of the analysis. These variables were submitted to a forward and backward stepwise selection procedure to retain the best independent variables with the lowest Akaike’s information criterion (AIC). The last approach was based on the model parameters for MY and BCS, since the same models were used in both farms. The same methodology was used as for the second approach. For all approaches, at least the stage of lactation at

AI was forced as an adjustment variable in all models. Stage of lactation at AI is one of the main factors affecting fertility at AI [9]. Given our approach using phenotypic lactation curves, it is essential to adjust all variables for the stage of lactation at AI. Analysis of variance was performed for each model to assess the global effect of each variable on AI success. The significance threshold was set at 0.05. A trend was considered with $0.05 \le p-value \le 0.10$.

For all approaches, the p-values were adjusted using Tukey’s test for multiple comparisons.

For all approaches, the model used was:

$$Y_{ij} = \text{Log} \left( \frac{p_{ij}}{1 - p_{ij}} \right) = X_{ij}\beta + u_{j}$$
$$u_j \sim N(0, \sigma_u^2)$$

where the outcome $Y_{ij}$ is a binary variable representing the AI success for a goat $i$ in the year $j$. $X_{ij}$ is a matrix containing all adjustments (i.e., variables generated by the fitting procedure) and independent variables, $\beta$ is the vector of coefficients associated with these variables, $u_j$ is the random effect for the year $j$. The random effect $u_j$ was assumed to follow a normal distribution with a mean of 0 and a variance of $\sigma_u^2$.

# 3. Results

## 3.1. Description of goats according to dataset used

The mean characteristics of goats are shown in Table 2. During the period 1996–2021, goats were always inseminated in August (between August 6, 1996 and August 17, 2021). On average, goats had a sternal BCS around AI of 3.0. Among the factors known to affect AI success, more than 50 % of the goats were inseminated between 200 and 215 days of lactation (Table 2 and Tables S1–S4). In the present study, primiparous goats represented 44 %–48 % of the goats (Tables S1–S4). In the different datasets, the Alpine breed represented 89.0 %–95.0 % of the animals (Tables S1–S4). The percentage of goats that failed AI in the previous campaign accounted for 2.8 %–3.5 % of the herd (Tables S1–S4). In the different datasets, fertility at AI ranged from 67.1 % to 71.7 %.

## 3.2. Relationship between AI success and clusters of phenotypic lactation curves

The relationships between the variables studied and AI success are shown in Table 3 for primiparous goats and Table 4 for multiparous goats. For primiparous goats, fertility at AI was 74.1 %. Breed, AI success in the previous campaign, maximum THI values at AI, and BW clusters were not retained after the univariate analysis ($p > 0.20$; Supplementary Table S1). Goats in the low milk yield and low persistency cluster ($Y_{L+}^m$) were more likely to succeed at AI than goats in the low milk yield and high persistency cluster ($Y_{H+}^m$) ($p < 0.05$). Stage of lactation at AI and sternal BCS clusters were not associated with AI success. For multiparous goats, fertility at AI was 69.6 %. Breed, AI success during the previous campaign, and BW clusters were not retained after the univariate analysis ($p > 0.20$; Supplementary Table S1). Stage of lactation at AI, MY clusters and BCS_S clusters were not associated with fertility.

## 3.3. Relationship between AI success and synthetic indicators of phenotypic lactation curves

The relationships between the variables studied and AI success are shown in Table 5. Parity, breed, AI success during the previous campaign, MY\_peak, MY\_210, SumMY, Peak time, BW\_k, BW\_210, maximum THI values at AI, and Dep\_speed\_k>30 were not retained after the

<caption>**Table 3**</caption>

Relationship between adjustment variables, phenotypic lactation curves, and AI success in primiparous dairy goats (n = 309 lactations).

<table><thead><tr><th>Variables</th><th>n</th><th>Fertility (%)</th><th>OR<sup>a</sup></th><th>CI<sup>a</sup></th><th>p-value</th></tr></thead><tbody><tr><td colspan="6"><strong>Lactation stage<sub>at AI</sub> (d)</strong></td></tr><tr><td>&lt;200</td><td>47</td><td>74.6</td><td>-</td><td>-</td><td></td></tr><tr><td>[200; 215]</td><td>232</td><td>74.7</td><td>1.00</td><td>[0.44; 2.28]</td><td>&gt;0.90</td></tr><tr><td>&ge;215</td><td>30</td><td>77.9</td><td>1.20</td><td>[0.38; 3.77]</td><td>&gt;0.90</td></tr><tr><td colspan="6"><strong>Clusters of MY<sup>b</sup></strong></td></tr><tr><td>Y<sub>L+<sup>m</sup></sub></td><td>47</td><td>88.8</td><td>-</td><td>-</td><td></td></tr><tr><td>Y<sub>H+<sup>m</sup></sub></td><td>100</td><td>70.5</td><td>0.30</td><td>[0.10; 0.90]</td><td>0.14</td></tr><tr><td>Y<sub>L+<sup>m</sup></sub></td><td>42</td><td>60.7</td><td>0.19</td><td>[0.06; 0.62]</td><td>0.03</td></tr><tr><td>Y<sub>H+<sup>m</sup></sub></td><td>120</td><td>76.6</td><td>0.41</td><td>[0.15; 1.17]</td><td>0.34</td></tr><tr><td colspan="6"><strong>Clusters of BCS_S<sup>b</sup></strong></td></tr><tr><td>ST<sub>M</sub></td><td>149</td><td>80.5</td><td>-</td><td>-</td><td></td></tr><tr><td>ST<sub>H+</sub></td><td>43</td><td>75.4</td><td>0.74</td><td>[0.31; 1.79]</td><td>0.78</td></tr><tr><td>ST<sub>M+</sub></td><td>117</td><td>70.7</td><td>0.58</td><td>[0.31; 1.09]</td><td>0.21</td></tr></tbody></table>

<sup>a</sup> OR = Odds Ratio, CI = Confidence Interval.

<sup>b</sup> $Y_{L+<sup>m</sup>}^m$ = Low milk yield and high persistency cluster for primiparous; $Y_{H+<sup>m</sup>}^m$ = Low milk yield and low persistency cluster for primiparous; $Y_{L+<sup>m</sup>}^H$ = Medium milk yield and low persistency cluster for primiparous; $Y_{H+<sup>m</sup>}^H$ = High milk yield and medium persistency cluster for primiparous; $ST_{M+<sup>m</sup>}^m$ = Medium sternal body condition score and depletion cluster; $ST_{M+<sup>m</sup>}^H$ = Medium sternal body condition score and low depletion cluster; $ST_{H+<sup>m</sup>}^m$ = High sternal body condition score and depletion cluster.

<caption>**Table 4**</caption>

Relationship between adjustment variables, phenotypic lactation curves, and AI success in multiparous dairy goats (n = 329 lactations).

<table><thead><tr><th>Variables</th><th>n</th><th>Fertility (%)</th><th>OR<sup>a</sup></th><th>CI<sup>a</sup></th><th>p-value</th></tr></thead><tbody><tr><td colspan="6"><strong>Lactation stage<sub>at AI</sub> (d)</strong></td></tr><tr><td>&lt;200</td><td>21</td><td>52.6</td><td>-</td><td>-</td><td></td></tr><tr><td>[200; 215]</td><td>211</td><td>74.8</td><td>2.67</td><td>[0.88; 8.09]</td><td>0.19</td></tr><tr><td>&ge;215</td><td>97</td><td>64.9</td><td>1.66</td><td>[0.52; 5.32]</td><td>0.67</td></tr><tr><td colspan="6"><strong>Clusters of MY<sup>b</sup></strong></td></tr><tr><td>Y<sub>M<sup>m</sup></sub></td><td>90</td><td>70.3</td><td>-</td><td>-</td><td></td></tr><tr><td>Y<sub>H<sup>m</sup></sub></td><td>113</td><td>60.8</td><td>0.66</td><td>[0.34; 1.27]</td><td>0.43</td></tr><tr><td>Y<sub>M<sup>m</sup></sub></td><td>126</td><td>62.3</td><td>0.70</td><td>[0.36; 1.36]</td><td>0.54</td></tr><tr><td colspan="6"><strong>Clusters of BCS_S<sup>b</sup></strong></td></tr><tr><td>ST<sub>M</sub></td><td>145</td><td>67.6</td><td>-</td><td>-</td><td></td></tr><tr><td>ST<sub>H+</sub></td><td>108</td><td>63.1</td><td>0.82</td><td>[0.46; 1.47]</td><td>0.78</td></tr><tr><td>ST<sub>M+</sub></td><td>76</td><td>62.9</td><td>0.81</td><td>[0.42; 1.57]</td><td>0.81</td></tr></tbody></table>

<sup>a</sup> OR = Odds Ratio, CI = Confidence Interval.

<sup>b</sup> $Y_{M<sup>m</sup>+<sup>m</sup>}^m$ = Medium milk yield and high persistency cluster for multiparous; $Y_{H<sup>m</sup>+<sup>m</sup>}^m$ = Medium milk yield and a low persistency cluster for multiparous; $Y_{H<sup>m</sup>+<sup>m</sup>}^H$ = High milk yield and a medium persistency cluster for multiparous; $ST_{M<sup>m</sup>+<sup>m</sup>}^m$ = Medium sternal body condition score and depletion cluster; $ST_{M<sup>m</sup>+<sup>m</sup>}^H$ = Medium sternal body condition score and low depletion cluster; $ST_{H<sup>m</sup>+<sup>m</sup>}^m$ = High sternal body condition score and depletion cluster.

<caption>**Table 2**</caption>

Individual goat characteristics according to dataset used.

<table><thead><tr><th rowspan="3"></th><th colspan="2">Clusters of phenotypic lactation curves</th><th colspan="2">Synthetic indicators</th><th colspan="2">MY model parameters</th><th colspan="2">BCS_S model parameters</th></tr><tr><th colspan="2">n = 638</th><th colspan="2">n = 1359</th><th colspan="2">n = 1731</th><th colspan="2">n = 795</th></tr><tr><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th></tr></thead><tbody><tr><td>Parity</td><td>2.0</td><td>1.2</td><td>2.1</td><td>1.3</td><td>2.0</td><td>1.2</td><td>2.0</td><td>1.2</td></tr><tr><td>Lactation stage<sub>at AI</sub> (d)</td><td>209.2</td><td>8.4</td><td>207.8</td><td>10.6</td><td>208.1</td><td>11.0</td><td>209.7</td><td>9.1</td></tr><tr><td>BCS_S<sub>210</sub> ([0–5] scale)<sup>a</sup></td><td>3.0</td><td>0.3</td><td>3.0</td><td>0.3</td><td>3.0</td><td>0.3</td><td>3.0</td><td>0.3</td></tr><tr><td>BW<sub>210</sub> (kg)</td><td>58.5</td><td>9.2</td><td>60.2</td><td>10.1</td><td>60.2</td><td>10.1</td><td>58.7</td><td>9.3</td></tr><tr><td>MY<sub>210</sub> (kg/d)</td><td>3.2</td><td>0.7</td><td>3.1</td><td>0.7</td><td>3.1</td><td>0.7</td><td>3.2</td><td>0.7</td></tr><tr><td>SumMY(kg)</td><td>914.1</td><td>184.1</td><td>900.8</td><td>182.0</td><td>903.2</td><td>186.4</td><td>917.6</td><td>189.1</td></tr><tr><td>AI fertility (%)</td><td>71.7</td><td>0.5</td><td>67.6</td><td>0.5</td><td>67.1</td><td>0.5</td><td>69.4</td><td>0.5</td></tr></tbody></table>

<sup>a</sup> SumMY = sum of daily milk yield values over 250 days; MY<sub>210</sub> = milk yield value at 210 days; BW<sub>210</sub> = body weight at 210 days; BCS_S<sub>210</sub> = sternal BCS at 210 days.
<caption>**Table 5**</caption>

Relationship between adjustment variables, synthetic indicators, and AI success in dairy goats (n = 1359 lactations).

<table><thead><tr><th>Variables</th><th>n</th><th>Fertility (%)</th><th>OR<sup>a</sup></th><th>CI<sup>a</sup></th><th>p-value</th></tr></thead><tbody><tr><td>Lactation stage<sub>at AI</sub> (d)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;200</td><td>274</td><td>59.0</td><td>–</td><td>–</td><td></td></tr><tr><td>[200; 215 [</td><td>757</td><td>73.3</td><td>1.91</td><td>[1.38; 2.65]</td><td>&lt;0.001</td></tr><tr><td>≥215</td><td>328</td><td>68.4</td><td>1.51</td><td>[1.03; 2.20]</td><td>0.086</td></tr><tr><td>Persistency (%)<sup>b</sup></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;36.7</td><td>444</td><td>75.6</td><td>–</td><td>–</td><td></td></tr><tr><td>[-36.7; -26.1 [</td><td>461</td><td>71.9</td><td>0.83</td><td>[0.60; 1.13]</td><td>0.46</td></tr><tr><td>≥26.1</td><td>454</td><td>52.0</td><td>0.35</td><td>[0.25; 0.48]</td><td>&lt;0.001</td></tr><tr><td>Rep_speed<sub>180→210</sub> (kg/d)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;0.03</td><td>478</td><td>61.2</td><td>–</td><td>–</td><td></td></tr><tr><td>[0.03; 0.05 [</td><td>332</td><td>65.9</td><td>1.23</td><td>[0.90; 1.68]</td><td>0.40</td></tr><tr><td>≥0.05</td><td>549</td><td>73.7</td><td>1.78</td><td>[1.31; 2.41]</td><td>&lt;0.001</td></tr><tr><td>BW<sub>min</sub> (kg)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;50.0</td><td>451</td><td>71.3</td><td>–</td><td>–</td><td></td></tr><tr><td>[50.0; 60.0 [</td><td>448</td><td>66.7</td><td>0.80</td><td>[0.59; 1.09]</td><td>0.34</td></tr><tr><td>≥60.0</td><td>460</td><td>63.2</td><td>0.69</td><td>[0.50; 0.96]</td><td>0.068</td></tr></tbody></table>

univariate analysis (p > 0.20; Supplementary Table S2). AI success was significantly lower for goats inseminated before 200 days postpartum than for goats inseminated between 200 days and 215 days postpartum (p < 0.001). AI success tended to be lower for goats inseminated before 200 days postpartum than for goats inseminated after 215 days postpartum (p = 0.086). AI success was significantly lower for goats with high persistency (≥-26.1 %) than for goats with low persistency (<-36.7 %) (p < 0.001). AI success was significantly lower for goats with a slower repletion of BW (<0.03 kg/d) than for goats with a faster repletion of BW (≥0.05 kg/d) (p < 0.001). AI success tended to be lower for goats with a BW at 210 days greater than or equal to 60 kg than for goats with a BW lower than 50 kg (p = 0.068), but the overall effect was not significant (p = 0.08).

### 3.4. Relationship between AI success and MY model parameters

The relationships between the variables studied and the MY parameters are shown in Table 6. Breed, AI success during the previous campaign, maximum THI values at AI, parameters a, b and Loss were not retained after the univariate analysis (p > 0.20; Supplementary Table S3). AI success was lower for multiparous than for primiparous goats (p < 0.001). AI success was significantly lower for goats inseminated before 200 days postpartum than for goats inseminated between 200 days and 215 days (p < 0.001) and after 215 days (p < 0.05). AI success decreased significantly when goats had a slower decrease in milk production after the peak (parameter c ≥ 0.0034) than for goats with a higher decrease in milk production after the peak (parameter c < 0.0026) (p < 0.001). AI success was significantly lower for goats with a delayed time of late decrease (parameter d ≥ 219.4 days) than for goats with an earlier time of late decrease (parameter d < 183.4 days) (p < 0.001). AI success was significantly lower for goats with a delayed time of half decrease (parameter e ≥ 118.4 days) than for goats with an earlier time of half decrease (parameter e < 92.8 days) (p = 0.002). AI success tended to be lower for goats with a number of perturbations (parameter N<sub>p</sub>) lower than 6 by lactation than for goats with a number of

<caption>**Table 6**</caption>

Relationship between adjustment variables, MY parameters, and AI success in dairy goats (n = 1731 lactations).

<table><thead><tr><th>Variables</th><th>n</th><th>Fertility (%)</th><th>OR<sup>a</sup></th><th>CI<sup>a</sup></th><th>p-value</th></tr></thead><tbody><tr><td>Parity</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1</td><td>758</td><td>73.2</td><td>–</td><td>–</td><td></td></tr><tr><td>2+</td><td>973</td><td>65.5</td><td>0.69</td><td>[0.54; 0.89]</td><td>0.003</td></tr><tr><td>Lactation stage<sub>at AI</sub> (d)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;200</td><td>348</td><td>60.3</td><td>–</td><td>–</td><td></td></tr><tr><td>[200; 215 [</td><td>903</td><td>74.8</td><td>1.95</td><td>[1.44; 2.63]</td><td>&lt;0.001</td></tr><tr><td>≥215</td><td>480</td><td>72.3</td><td>1.72</td><td>[1.21; 2.44]</td><td>0.008</td></tr><tr><td>c<sup>b</sup></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;0.0026</td><td>570</td><td>64.3</td><td>–</td><td>–</td><td></td></tr><tr><td>[0.0026; 0.0034 [</td><td>563</td><td>67.3</td><td>1.14</td><td>[0.88; 1.48]</td><td>0.58</td></tr><tr><td>≥0.0034</td><td>598</td><td>76.1</td><td>1.77</td><td>[1.34; 2.34]</td><td>&lt;0.001</td></tr><tr><td>d</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;183.4</td><td>573</td><td>75.9</td><td>–</td><td>–</td><td></td></tr><tr><td>[183.4; 219.4 [</td><td>568</td><td>76.1</td><td>1.01</td><td>[0.74; 1.38]</td><td>&gt;0.90</td></tr><tr><td>≥219.4</td><td>590</td><td>54.1</td><td>0.37</td><td>[0.27; 0.52]</td><td>&lt;0.001</td></tr><tr><td>e</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;92.8</td><td>570</td><td>73.1</td><td>–</td><td>–</td><td></td></tr><tr><td>[92.8; 118.4 [</td><td>574</td><td>73.5</td><td>1.02</td><td>[0.78; 1.34]</td><td>&gt;0.90</td></tr><tr><td>≥118.4</td><td>587</td><td>60.9</td><td>0.57</td><td>[0.42; 0.79]</td><td>0.002</td></tr><tr><td>N<sub>p</sub><sup>c</sup></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;6</td><td>555</td><td>67.3</td><td>–</td><td>–</td><td></td></tr><tr><td>[6; 8 [</td><td>406</td><td>74.0</td><td>1.38</td><td>[1.03; 1.86]</td><td>0.081</td></tr><tr><td>≥8</td><td>770</td><td>66.8</td><td>0.98</td><td>[0.76; 1.26]</td><td>&gt;0.90</td></tr></tbody></table>

perturbations between 6 and 8 by lactation (p = 0.081).

### 3.5. Relationship between AI success and BCS model parameters

The relationships between the variables studied and the sternal BCS parameters are shown in Table 7. Parity, breed, AI success in the previous campaign, maximum THI values at AI, parameters k<sub>E</sub>, k<sub>L</sub>, t<sub>1</sub>, and L were not retained after the univariate analysis (p > 0.20; Supplementary

<caption>**Table 7**</caption>

Relationship between adjustment variables, sternal BCS parameters, and AI success in dairy goats (n = 795 lactations).

<table><thead><tr><th>Variables</th><th>n</th><th>Fertility (%)</th><th>OR<sup>a</sup></th><th>CI<sup>a</sup></th><th>p-value</th></tr></thead><tbody><tr><td>Lactation stage<sub>at AI</sub> (d)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;200</td><td>92</td><td>62.7</td><td>–</td><td>–</td><td></td></tr><tr><td>[200; 215 [</td><td>495</td><td>70.0</td><td>1.39</td><td>[0.84; 2.30]</td><td>0.41</td></tr><tr><td>≥215</td><td>208</td><td>59.5</td><td>0.87</td><td>[0.51; 1.51]</td><td>0.87</td></tr><tr><td>B<sub>M</sub><sup>b</sup></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;2.5</td><td>87</td><td>49.4</td><td>–</td><td>–</td><td></td></tr><tr><td>[2.5; 2.75 [</td><td>197</td><td>73.7</td><td>2.87</td><td>[1.63; 5.05]</td><td>&lt;0.001</td></tr><tr><td>≥2.75</td><td>511</td><td>67.8</td><td>2.15</td><td>[1.29; 3.57]</td><td>0.009</td></tr></tbody></table>

<footnote><sup>a</sup> OR = Odds Ratio, CI = Confidence Interval.</footnote>

<footnote><sup>b</sup> Persistency = (MY<sub>250</sub>-MY<sub>150</sub>/MY<sub>150</sub>) x100; BW<sub>min</sub> = minimum body weight; Rep_speed<sub>180→210</sub> = (BW<sub>210</sub> - BW<sub>180</sub>)/30.</footnote>

<footnote><sup>a</sup> OR = Odds Ratio, CI = Confidence Interval.</footnote>

<footnote><sup>b</sup> Revised Wood model parameters: c = decrease rate of milk production after the peak; d = time of late decrease of milk; e = time of half decrease of milk.</footnote>

<footnote><sup>c</sup> Perturbed lactation model parameters: N<sub>p</sub> = number of perturbations.</footnote>

<footnote><sup>a</sup> OR = Odds Ratio, CI = Confidence Interval.</footnote>

<footnote><sup>b</sup> Grossman parameters: B<sub>M</sub> = plateau value in middle stage (BCS) (i.e. nadir sternal BCS).</footnote>
Table S4). AI success was significantly lower for goats with a sternal BCS nadir (parameter $B_M$) lower than 2.5, than for goats with a sternal BCS nadir between 2.5 and 2.75 (p < 0.001), and a sternal BCS nadir higher or equal to 2.75 (p < 0.05).

# 4. Discussion

## 4.1. Factors usually known to affect AI success

The objective of the study was to analyze the relationship between AI success and (1) the clusters of phenotypic lactation curves, (2) the synthetic indicators of phenotypic lactation curves and (3) model parameters of phenotypic lactation curves. Fertility at AI was high and ranged from 67.1 % to 71.7 %, which is between 10.2 and 14.8 points higher than the average national fertility in 2022 (56.9 %) [20]. The breed was not retained in the final models. Alpine goats have a higher fertility at AI than Saanen goats [8,9]. This lack of breed effect, could be related to the fact that Saanen represented only 2.8 %–3.5 % of our experimental animals. Parity is one of the factors, which is known to affect AI success. In our study, parity was significantly related to AI success in the statistical model that included the MY model parameters. AI success was higher for primiparous than for multiparous goats. This does not agree with previous studies [8,9,21]. These aforementioned authors proposed different hypotheses. One hypothesis is related to reproductive management, with an interval between kidding and AI not adapted to primiparous goats [8].

Another hypothesis is that primiparous goats had additional needs for growth compared to multiparous goats, and growth is a priority function compared to reproduction [21]. In our study, parity was adjusted for confounding factors such as stage of lactation at AI and milk parameters. Primiparous goats usually have higher milk persistence than multiparous goats. The lower fertility of primiparous goats observed in the other studies might also be related to a confounding effect of persistency, which was considered in our study. Moreover, our results could be linked to good management practices with special care for young animals in the two experimental farms. The response to hormonal treatment could also decrease with increasing age due to repeated treatments [8]. Antibodies against PMSG are induced after repeated treatments with PMSG, which could reduce the effectiveness of the treatment [22].

For the stage of lactation at AI, goats inseminated before 200 days postpartum were less likely to succeed at AI than goats inseminated between 200 and 215 days and those inseminated later than 215 days. On the two farms, the recommendations were generally followed and a few goats were inseminated before 180 days or after 240 days [23]. The decrease in fertility in goats inseminated before 200 days could be related to higher milk production (see next chapter). Another explanation could be that some goats had later kidding dates than others due to reproductive challenges in the previous year. THI was not related to fertility in our study, although the threshold of 80 was exceeded on 16–19 % of the mating days. Artificial insemination success may be less dependent on heat stress than on natural mating. Heat stress is known to affect male [14,24] and individual activity, which is less important for fertility when AI is used.

## 4.2. Milk production

During lactation, the experimental goats produced more milk than the French national results (815 kg/lactation/year) [25]. For the relationship between fertility and milk production, we found consistent results among the three level of analysis (clusters, synthetic indicators, and model parameters). AI success was significantly associated with the MY clusters: primiparous goats in the low milk yield and low persistency cluster ($Y_L^+$) were more likely to succeed at AI than primiparous goats in the low milk yield and high persistency cluster ($Y_L^-$). Similarly, when we consider the indicators, goats with high persistency ($> -26.1$ %) were less

likely to succeed at AI than goats with low persistency ($<-36.7$ %). For the MY model parameters, the highest decrease in the rate of milk production after the peak (c) was associated with the best fertility. Other indicators (time of delayed decrease in milk production, time of half decrease of milk production, decrease rate after the peak, etc.) lead to the same conclusion: the shape of the lactation curve has a stronger effect on fertility than the level of milk production. This is particularly well illustrated in primiparous goats: the two groups $Y_L^+$ and $Y_L^-$ produced almost the same amount of milk over 250 days of lactation but fertility was higher when persistency was low. The effect of the shape of the lactation curve after the peak or the change in milk production around the breeding season on fertility has already been observed by Freret et al. [9]. They concluded that goats with an increase or a stable milk production around AI were less likely to become pregnant than goats with a decrease in milk production around AI. This relationship between fertility and the shape of the lactation curve is the opposite in dairy cows. Cows with “flatter” curves (high persistency) have higher fertility (lower risk of late embryonic death) [4]. This could be explained by the period of reproduction in the two species: in cows (60–90 days postpartum) and in goats (210–250 days postpartum). The priorities in nutrient and energy partitioning between milk production and reproduction may be different between these two periods. We can hypothesize that persistent goats still invest energy in milk production 210 days after kidding, which reduces reproductive efficiency [26]. This observation may have two implications: in systems where reproductive efficiency is a major concern, the selection for high milk yield at the beginning of lactation and low persistency after the peak of production might be an aim. If a short period between kidding is not required, goats with high persistency could be mated later after parturition. These goats can also be good candidates for an extended lactation: with high persistency they can easily maintain milk production.

## 4.3. Body condition score

For sternal BCS, results showed complementary information for cluster and model parameter analyses. Fertility was not significantly associated with sternal BCS clusters. This is probably related to the combined effect of the lack of statistical power ($n = 638$ lactations) and adjustment for MY clusters. We can see, however, that the $ST_M$ + goats had the lowest fertility for all parities. Moreover, with model parameters analysis, we found that the nadir of sternal BCS, $B_M$, was significantly associated with AI success. Goats with a nadir below 2.5 were less likely to succeed at AI compared to goats with a nadir between 2.5 and 2.75 or higher or equal to 2.75. These results are consistent with observations made in dairy sheep [12,27]. Sheep with a BCS greater than 3 at AI had higher lambing rates than sheep with a BCS less than 2. Moreover, Mellado et al. [28] reported a positive correlation between BCS around mating and kidding rates in dairy goats. Indeed, goats with a BCS lower than 1.5 were three times less likely to conceive compared to goats with a higher BCS. Globally, animals with a high BCS have higher reproductive efficiency than animals with a low BCS. BCS reflects an animal’s nutritional status. Undernutrition can affect several reproductive functions such as sex hormone production, oocyte quality and competence, fertilization, and early embryonic development [29]. Undernutrition can affect GnRH secretion from the pituitary gland, leading to a decrease in pulsatile secretion of LH and in the pre-ovulatory LH surge [29]. However, goats are provided with hormonal treatments that induced ovulation. In dairy cows, poorer AI fertility could be related to lack of a response to oestrus induction and synchronization treatment, poorer oocyte quality, and poorer uterine environment. Indeed, goats with a low BCS had lower concentrations of insulin-like growth factor I (IGF-I), which can affect follicular maturation, progesterone production from the corpus luteum and impair early embryonic development [30]. BCS level and dynamic could impair reproductive performance in natural mating with the buck [31,32]. The BCS level is more informative than dynamic indicators. It is important to monitor goats until they reach
their nadir BCS in order to decide which goat to inseminate and to exclude goats with a nadir BCS below 2.5. As BCS scoring is a subjective method and the variability between animals was low, this probably explains the lack of dynamic effects on reproductive performance.

## 4.4. Body weight

For the BW synthetic indicators, goats with a slower repletion (BW variation between 180 and 210 days of lactation <0.03 kg/d) were less likely to succeed at AI than goats with a faster repletion (BW variation between 180 and 210 days of lactation ≥0.05 kg/d). This observation is consistent with published literature in dairy cows [33] and in dairy sheep [34]. Goats with a faster BW repletion probably had a faster return to a positive energy balance. In dairy sheep, positive energy balance restored normal pituitary activity (e.g., normal GnRH secretion, normal LH pulsatility, and increased FSH concentrations). Moreover, blood leptin and insulin concentrations increased, which could act directly on the ovary, resulting in an increased ovulation rate [35]. Finally, only the dynamics of BW between 180 days and 210 days were relevant to explain AI success. Indeed, the whole trajectory and the level of BW were not relevant to explain AI success. The level of BW captured the “size” of the goats and was not sufficient to explain AI success.

## 4.5. What is the best information to assess AI success?

This approach with three different levels of detail: cluster membership, synthetic indicators, and model parameters provided complementary information. Cluster membership and model parameters are relevant for scientists but meaningless for the dairy goat sector (i.e., technicians, farmers, etc.). Synthetic indicators are more relevant for the dairy goat sector because these indicators could be accessible on the farm. For scientists, the use of clusters or model parameters will depend on the data availability. Indeed, the high frequency of data collection used in this paper on MY, BW and BCS allowed lactation curve characterization. For high frequency MY records, the use of adapted models to characterize a curve of the whole lactation was relevant to target goats with a high persistency cluster. However, in commercial dairy goat farms, where MY is measured at most once a month, the use of models to characterize a curve is difficult. With our approach, we showed that the dynamics of milk after the peak (i.e., the persistency) is more important than the milk level. Measuring the slope of milk after the peak could be a good proxy to assess whether the female is maintaining production or not (i.e., delaying the ability to reproduce). Goats with high persistency can either be mated with males (natural mating or hand mating), where fertility results are better, or delayed for reproduction and used in an extended lactation system. For BW and BCS, the curve of the whole lactation was not relevant for assessing AI success. BW and BCS are not frequently measured on commercial dairy goat farms. For BW, we can advise farmers to monitor BW gain one month to two months before AI to target goats with a slower BW gain. For BCS, monitoring goats once a month from kidding to nadir might be sufficient to target goats with the lowest body reserves and decide which goats should be inseminated.

# 5. Conclusion

The results suggest that the effect of the shape of the lactation curve especially persistency rather than milk yield, is very important for AI success. Goats with high persistency are less likely to succeed at AI than goats with low persistency. Therefore, goats with high persistency prioritize milk production rather than reproduction, whereas goats with low persistency prioritize reproduction. Goats with a sternal BCS of less than 2.5 are less likely to succeed at AI than goats with a higher sternal BCS. To avoid adverse effects on reproductive performance, it is necessary to maintain sternal BCS above 2.5 during lactation. Finally, goats with a high repletion speed for BW are more likely to succeed at AI than those with a lower repletion speed. Of the three levels of detail,

only synthetic indicators are useful to farmers.

## CRediT authorship contribution statement

N. Gafsi: Writing – review & editing, Writing – original draft, Formal analysis, Data curation, Conceptualization. O. Martin: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. F. Bidan: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. B. Grimard: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. L. Puillet: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization.

## Ethics approval

Not applicable.

## Data and model availability statement

Script of statistical models is available in the Supplementary material and the data was deposited in an official repository: https://doi.org/10.57745/WCIBFV.

## Financial support statement

This work was funded by the French Livestock Institute and was supported by ANRT (Association Nationale de la Recherche et de la Technologie) with a CIFRE doctoral fellowship (2020/1547). This doctoral fellowship is a part of a research axis of a Technical Joint Unit called “Sustainable goat systems of tomorrow” (UMT SC3D).

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Nicolas GAFSI reports was provided by The French Livestock Institute. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We gratefully acknowledge the teams at the INRAE UMR 791 Modélisation Systémique Appliquée aux Ruminants (Ile-De-France, France) experimental farm and Le Pradel experimental farm (Ardeche, France) for the care of the animals and their work to provide the different dataset. We gratefully acknowledge Maxime Legris from l'Institut de l'Elevage (Ile-De-France, France) for his help on statistical support for this paper. We gratefully acknowledge Pr. Andrew Ponter for revising the English of this manuscript.

# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.theriogenology.2024.12.005.

## References

[1] Astruc J-M, de Boissieu C, Buisson D, Clément V, de Crémoux R, Doucet M, et al. RUSTIC - Vers une approche intégrée de la robustesse des petits ruminants. Innovations Agronomiques 2021;82:369-86.

[2] de Crémoux R, Legris M, et al. Clément V. Evaluation of careers and performances of goats with extended lactations. Small Rumin Res 2024;231(March 2023). https://doi.org/10.1016/j.smallrumres.2024.107198.

[3] Bedere N, Cutullic E, Delaby L, Garcia-Launay F, et al. Disenhaus C. Meta-analysis of the relationships between reproduction, milk yield and body condition score in dairy cows. Livest Sci 2018;210:73-84. https://doi.org/10.1016/j.livsci.2018.01.017. avr.
[4] Cutullic E, Delaby L, Gallard Y, et alDisenhaus C. Towards a better understanding of the respective effects of milk yield and body condition dynamics on reproduction in Holstein dairy cows. In: Animal. Elsevier; janv. 2012. p. 476–87. https://doi.org/10.1017/S175173111100173X.

[5] Friggens NC, Disenhaus C, et alPetit HV. Nutritional sub-fertility in the dairy cow: towards improved reproductive management through a better biological understanding. Animal : an international journal of animal bioscience 2010;4(7): 1197–213. https://doi.org/10.1017/S1751731109991601.

[6] Pryce JE, Royal MD, Garnsworthy PC, Mao et IL. « Fertility in the high-producing dairy cow. Livest Prod Sci 2004;86(1–3):125–35. https://doi.org/10.1016/S0301-6226(03)00145-3. mars.

[7] Roche JR, Macdonald KA, Burke CR, Lee JM, et al, alBerry DP. Associations among body condition score, body weight, and reproductive performance in seasonal-calving dairy cattle. J Dairy Sci 2007;90(1):376–91. https://doi.org/10.3168/jds.S0022-0302(07)72639-5. janv.

[8] Chanvallon A, De Cremoux R, Piacere A, et alRibaud D. « Evolution de la fertilité à l'IA chez les chèvres de 2004 à 2010. Rencontres Recherches Ruminants 2013;20: 75595.

[9] Freret S, Philippe P, Brun T, Legarto J, Clement V, Bidan F, et al. Facteurs de variation de la fertilité après insémination animale et lors des retours en chaleurs chez la chèvre : caractéristiques individuelles, production laitière et conduite alimentaire. Rencontres Recherches Ruminants 2018;(24):393–6.

[10] Furstoss V, David I, Fatat A, Boissard K, Clément V, Bodin et L. « Genetic and non-genetic factors related to the success of artificial insemination in dairy goats. Animal 2015;9(12):1935–42. https://doi.org/10.1017/S175173115001500.

[11] Branca A. « Effet de l'état corporel sur différents paramètres reproductifs chez la chèvre Sarde après insémination artificielle. In: Ben Salem H, Nefzaoui A, Morand-Fehr P, editors. Seminar of the sub-network on nutrition of the FAO-CIHEAM inter-regional cooperative research and development network on sheep and goats, 2001/ 11/08-10, hammamet (Tunisia), vol. 77; 2004. p. 73–7. Nutrition and feeding strategies of sheep and goats under harsh climates. Zaragoza : CIHEAM, 2004. pp. 73–77. (Options Méditerranéennes : Série A. Séminaires Méditerranéens; n. 59. 9.

[12] Forcada F, Abecia JA, et alSierra I. Seasonal changes in oestrus activity and ovulation rate in Rasa Aragonesa ewes maintained at two different body condition levels. Small Rumin Res sept. 1992;8(4):313–24. https://doi.org/10.1016/0921-4488(92)90212-M.

[13] Santiago-Miramontes MAD, Malpaux B, et alDelgadillo JA. Body condition is associated with a shorter breeding season and reduced ovulation rate in subtropical goats. Anim Reprod Sci 2009;114(1–3):175–82. https://doi.org/10.1016/j.anireprosci.2008.09.001.

[14] Adjassin JS, Assani AS, Bani AA, Worogo HSS, Alabi CDA, Assogba BGC, et al. Impact of heat stress on reproductive performances in dairy goats under tropical sub-humid environment. Heliyon 2022;8(2):e08971. https://doi.org/10.1016/j.heliyon.2022.e08971. févr.

[15] Gafsi N, Martin O, Bidan F, Grimard B, et alPuillet L. « Diversity of performance patterns in dairy goats: multi-scale analysis of the lactation curves of milk yield, body condition score and body weight. Peer Community Journal 2024;4(e85). https://doi.org/10.24072/pci.

[16] Fatat A, Pellicer-Rubio MT, et alLeboeuf B. Reproductive cycle of goats. Anim Reprod Sci avr. 2011;124(3–4):211–9. https://doi.org/10.1016/j.anireprosci.2010.08.029.

[17] Amiri S, Puillet L, Huau C, Fassier T, Rupp R, et al, alFriggens NC. Analysis of reproduction success, growth and milk trajectories and response to nutritional challenge in two Alpine goat lines selected on divergent longevity. Animal 2023;17 (11):101004. https://doi.org/10.1016/j.animal.2023.101004.

[18] NRC. A guide to environmental research on animals. National Academy of Sciences; 1971.

[19] Silanikove N, et alKoluman DN. Impact of climate change on the dairy industry in temperate zones: predictions on the overall negative impact and on the positive role of dairy goats in adaptation to earth warming. Small Rumin Res 2015;123(1): 27–34. https://doi.org/10.1016/j.smallrumres.2014.11.005.

[20] Institut de l'élevage-Capgènes, « Campagne d'ia 2022 - statistiques nationales. 2022. p. 2016–7.

[21] Mellado M, Valdéz R, García JE, López R, et alRodríguez A. Factors affecting the reproductive performance of goats under intensive conditions in a hot arid environment. Small Rumin Res 2006;63(1–2):110–8. https://doi.org/10.1016/j.smallrumres.2005.02.016. mai.

[22] Roy F, Maurey M-C, Combes B, Vaiman D, Cribiu EP, Lantier I, et al. The negative effect of repeated equine chorionic gonadotropin treatment on subsequent fertility in alpine goats is due to a humoral immune response involving the major histocompatibility complex 1. Biol Reprod 1999;60:805–13 [En ligne]. Disponible sur: https://academic.oup.com/biolreprod/article/60/4/805/2740831.

[23] GRC. Le choix des chèvres pour l' insémination Fiche 2c PRINCIPE DE TRI 4 étapes de tri. 2023.

[24] Gupta M, Kumar S, Dangi S, et alJangir B. Physiological, biochemical and molecular responses to thermal stress in goats. Int. J. Livestock Res. 2013;3(2):27. https://doi.org/10.5455/ijlr.20130502081121.

[25] Institut de l'élevage. Résultats de Contrôle laitier Caprin. 2022. p. 7823–30.

[26] Friggens NC. Body lipid reserves and the reproductive cycle: towards a better understanding. Livest Prod Sci 2003;83(52nd):219–36. https://doi.org/10.1016/S0301-6226(03)00111-8. Annual EAAP Meeting.

[27] Fidelle F, Arranz JM, Landagaray F, Sallato O, Mendez HI, Soulas C, et al. Un partenariat technique avec les éleveurs de brebis laitières dans les Pyrénées Atlantiques pour adapter les pratiques et réussir l'insémination animale. Rencontres Recherches Ruminants 2015;22(1):203–6.

[28] Mellado M, Valdez R, Lara LM, et alGarcía JE. « Risk factors involved in conception, abortion, and kidding rates of goats under extensive conditions. Small Rumin Res oct. 2010;55(1–3):191–8. https://doi.org/10.1016/J SMALLRUMRES.2003.10.016.

[29] Sejan V, Maurya VP, Naqvi SMK, Kumar D, Joshi et A. Effect of induced body condition score differences on physiological response, productive and reproductive performance of Malpura ewes kept in a hot, semi-arid environment. J Anim Physiol Anim Nutr avr. 2010;94(2):154–61. https://doi.org/10.1111/j.1439-0396.2008.00896.x.

[30] Leroy JLMR, Soom AV, Opsomer G, et alBols PEJ. The consequences of metabolic changes in high-yielding dairy cows on oocyte and embryo quality. Animal 2008;2 (8):1120–7. https://doi.org/10.1017/S1751731108002383.

[31] Gallego-Calvo L, Gatica MC, Guzmán JL, et alZarazaga LA. Reproductive performance response to the male effect in goats is improved when doe live weight/body condition score is increasing. Anim Reprod Sci mai 2015;156:51–7. https://doi.org/10.1016/j.anireprosci.2015.03.001.

[32] Veliz-Deras FG, Meza-Herrera CA, De Santiago-Miramontes A, Santos-Alvarado A, Bustamante-Andrade JA, Flores-Salas JM, et al. An enhanced body condition improved sexual behavior, ovarian structure and function, and reproductive fitness in rangeland-crossbred dairy goats. Agriculture juin 2023;13(7):1337. https://doi.org/10.3390/agriculture13071337.

[33] Buckley F, O'Sullivan K, Mee JF, Evans RD, et alDillon P. Relationships among milk yield, body condition, cow weight, and reproduction in spring-calved Holstein-Friesians. J Dairy Sci 2003;86(7):2308–19. https://doi.org/10.3168/JDS.S0022-0302(03)73823-5.

[34] Viñoles C. Effect of nutrition on follicle development and ovulation rate in the Ewe. Departm. Clinical Chem. 2003;PhD:120.

[35] Scaramuzzi RJ, Campbell BK, Downing JA, Kendall NR, Khalid M, Muñoz-Gutiérrez M, et al. A review of the effects of supplementary nutrition in the Ewe on the concentrations of reproductive and metabolic hormones and the mechanisms that regulate folliculogenesis and ovulation rate. In: Reproduction nutrition development. Reprod Nutr Dev, juil; 2006. p. 339–54. https://doi.org/10.1051/rnd:2006016.

[36] Martin O, et alBen Abdelkrim A. Detecting perturbations in dairy cows liveweight trajectories. In: 70th EAAP annual meeting, ghent, Belgium (hal-03190731); 2019.

[37] Grossman M, Hartz SM, et alKoops WJ. Persistency of lactation yield: a novel approach. J Dairy Sci 1999;82(10):2192–7. https://doi.org/10.3168/jds.S0022-0302(99)75464-0.