African Journal of Applied Statistics

Crop yield estimation at district level for agricultural seasons 2014 in Rwanda

Innocent NGARUYE, Dietrich VON ROSEN, and Martin SINGULL

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In this paper, we discuss an application of Small Area Estimation (SAE) techniques under a multivariate linear regression model for repeated measures data to produce district level estimates of crop yield for beans which comprise two varieties, bush beans and climbing beans in Rwanda during agricultural seasons 2014. By using the micro data of National Institute of Statistics of Rwanda (NISR) obtained from the Seasonal Agricultural Survey (SAS) 2014 combined with the data from the Crop Assessment Survey 2013, we derive efficient estimates which show considerable gain. The considered model and its estimates may be useful for policy-makers or for further analyses.


Dans cet article, nous abordons une application des techniques d'estimation sur petits domaines en utilisant le model de régression linéaire multivariée pour des données de mesures répétées en vue de produire des estimations du rendement de récolte de haricots bruts et haricots grimpants pour les saisons agriculturales 2014 au Rwanda. En utilisant les microdonn´ees de l'Institut National des Statistiques au Rwanda (NISR) obtenues de l'ênquete agricultural saisonier combinées avec les données de l'ênquete d'évaluation des cultures, année 2013, nous déduisons des estimations efficaces avec un gain considérable. Le modèle considéré et ses estimations peuvent être utiles aux décideurs politiques ou à une analyse plus approfondie.

Article information

Afr. J. Appl. Stat., Volume 3, Number 1 (2016), 69-90.

First available in Project Euclid: 16 May 2019

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Digital Object Identifier

Primary: 62F10: Point estimation 62H12: Estimation 62D05: Sampling theory, sample surveys

crop yield model-based estimates multivariate linear model small area estimation


NGARUYE, Innocent; VON ROSEN, Dietrich; SINGULL, Martin. Crop yield estimation at district level for agricultural seasons 2014 in Rwanda. Afr. J. Appl. Stat. 3 (2016), no. 1, 69--90. doi:10.16929/ajas/2016.69.203.

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