Factors affecting hematological parameters in Creole goats on the Southern Coast of Peru.

Background and Aim: Hematological parameters are critical indicators of health and physiological status in goats. This study aimed to evaluate the effects of location, feeding regimen, age, and body condition score (BCS) on hematological parameters in Creole goats reared under extensive systems on the southern coast of Peru and to establish context-specific reference values. Materials and Methods: A total of 111 multiparous goats from nine herds were assessed. Red blood cell (RBC) (RBC, hematocrit, hemoglobin [HGB], mean corpuscular volume [MCV], mean corpuscular hemoglobin [MCH], and MCH concentration [MCHC]) and white blood cell (WBC) (WBC, lymphocytes, monocytes [MON], neutrophils [NEU], and eosinophils [EOS]) parameters were determined using a veterinary hematology analyzer. Robust linear regression models with MM-estimation were applied, with model selection based on Akaike information criterion, Bayesian information criterion, and root mean square error. Correlation analyses and hierarchical clustering were also performed to explore inter-parameter relationships. Results: Significant interindividual variation was noted, particularly among leukocyte indices (EOS, MON, NEU; coefficient of variation >50%). In contrast, MCH and MCV exhibited low variability. The geographic location was not statistically significant, suggesting environmental homogeneity across the sites. Dietary composition and BCS significantly influenced several hematological indices. Goats with higher BCS and mixed alfalfa-residue diets exhibited increased HGB and RBC counts, whereas younger goats showed higher MCHC values. Predictive equations were generated to estimate hematological values under specific management conditions. Conclusion: This study highlights the relevance of age, nutritional status, and body condition in modulating hematological values in Creole goats. The derived models and reference values can inform localized diagnostic criteria and enhance decision-making in goat health management under extensive systems. Future studies should incorporate seasonal, sex-based, and longitudinal analyses to refine predictive accuracy.