article · Veterinary and Animal Science
A field evaluation in Burkina Faso assessed Jaɓnde, a digital decision-support tool designed to formulate dairy-cow feed rations for resource-constrained farming systems in sub-Saharan Africa. Forty volunteer dairy farmers in the Bobo-Dioulasso milkshed tested an Excel-based prototype of the tool across 82 lactating zebu and crossbred cows. Farmers demonstrated high fidelity in applying the recommended rations relative to actual feed supplied. Statistical analyses showed strong correlations between predicted and observed performance across most variables, alongside limited bias in milk production estimates. While crossbred cows maintained higher overall milk yields, indigenous zebu cows showed a greater relative gain in production, rising by 33 percent compared to an 8 percent increase in crossbred cows. More than 90 percent of participating farmers reported enhanced milk yields and expressed satisfaction with the tool during supervised field use.
Optimising livestock feed is critical for improving dairy productivity and rural livelihoods in resource-constrained settings. Providing accessible ration-formulation guidance tailored to local feeds and livestock breeds enables smallholders to enhance milk yields cost-effectively. Demonstrating that digital tools can accurately predict feeding outcomes helps build trust in data-driven farm management among farmers and extension services.
Jaɓnde is an applied decision-support tool targeted at smallholder dairy producers and agricultural advisors. While evaluated as an Excel prototype, a mobile version is already available online and via the Google Play Store for testing, indicating an advanced stage near practical deployment. Further field trials across diverse management systems and seasons remain necessary to establish its broader commercial robustness.
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Background and objectives Jaɓnde is a locally adapted ration formulation tool for resource-constrained dairy systems in sub-Saharan Africa. The version used in this study was the Excel-based prototype installed on a laptop. A mobile version, now available for free on the Google Play Store and online ( https://jabnde.cirad.fr/ ), is under testing. This preliminary, supervised field validation pursued three objectives: (i) to compare the technical and economic performance of zebu and crossbred cows under dairy farmer-implemented rations; (ii) to assess the implementation fidelity and the predictive performance of Jaɓnde ; and (iii) to assess dairy farmer satisfaction with the use of Jaɓnde. Methodology The tool was tested by 40 volunteer dairy farmers in the Bobo-Dioulasso milkshed (Burkina Faso) on 82 lactating cows (65 zebu, 17 crossbred). Implementation fidelity was assessed from the mean difference (predicted ration, R2 - actual ration, R3), and predictive performance from robust linear regression, reported overall and by cow type using R², the slope, and error indicators (mean absolute error, MAE; root mean square error, RMSE; mean bias error, MBE), complemented by Bland-Altman analyses. A structured survey assessed the satisfaction of dairy farmers. Results Dairy farmers implemented R3 ration close to R2 (mean differences -0.37 to +0.04 kg GM/cow/day across feed types). Predicted and actual values were strongly associated for most variables (R² > 0.70), though weaker for milk production among crossbred cows; bias remained limited (MBE = -0.27 L/d/cow for milk production). Agreement on milk production was the most independent performance indicator. Crossbred cows produced more milk (10.6 vs 1.2 L/d/cow), whereas the relative change was greater in zebu cows (33% vs 8% increase). Over 90% of dairy farmers reported increased production and were satisfied. Contributions This first field assessment is encouraging: under supervised conditions, farmers implemented Jaɓnde 's rations, predictive performance was promising for milk production, and satisfaction was high. Confirming its causal contribution and robustness will require controlled designs across more farms, seasons, and management systems.
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DOI: 10.1016/j.vas.2026.100837
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