Biologyarticle2026-08-01

INCOME TRANSFORMATION THROUGH GOAT REARING: A PRE–POST IMPACT ASSESSMENT OF NATIONAL LIVESTOCK MISSION AND OTHER GOVERNMENT SPONSERED SCHEMES BENEFICIARIES IN REWA DISTRICT, MADHYA PRADESH, INDIA

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Abstract

Purpose: Small-ruminant rearing is widely promoted as a pro-poor livelihood in rain-fed rural India, yet rigorous, effect-size-based evidence on the magnitude of household economic change under the National Livestock Mission (NLM) and other government schemes for goat rearing remains limited. This study quantifies the economic impact of NLM-supported goat rearing on beneficiary households during 2023–24 in Rewa district, Madhya Pradesh. Design/methodology: A self-controlled, pre–post design was applied to a sample of 300 beneficiaries. Five household economic variables — annual goat income, annual expenditure, annual net income, total herd size, and household size — were compared between the pre-scheme and post-scheme states using paired-samples t-tests (df = 299). Practical significance was assessed with Cohen's d and 95% confidence intervals. Findings: All five variables changed significantly (p < 0.01). Mean annual net income rose from Rs. 17,497 to Rs. 60,998 — an approximately 3.5-fold increase (t = 48.849; d = 2.82). Annual goat income increased 4.4-fold (t = 56.614; d = 3.27), while expenditure rose in parallel (t = 72.258; d = 4.17), indicating productive rather than consumptive spending. Herd size grew modestly and heterogeneously (t = 6.124; d = 0.35), reflecting uneven expansion across households. Originality/value: By pairing significance testing with effect sizes and confidence intervals on a full 300-household sample, the study moves beyond “statistically significant” claims to establish the scale and robustness of income transformation, and identifies asset-count heterogeneity as a target for more inclusive program design.

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View paper (DOI)Open access versionOpenAlexZenodo (CERN European Organization for Nuclear Research)Published 2026-08-01

Authors: Komal Chand Dwivedi1*, Kumud Shrivastava2, Nitesh Kumar3