Validating the Classification of Smallholder Dairy Farming Systems Based on Herd Genetic Structure and Access to Breeding Services

Gizaw, Solomon; Abera, M.; Muluye, M.; Aliy, Mohammed; Alemayehu, Kefyalew; Tegegne, Azage

doi:10.4236/as.2017.87041

Cited by 9 publications

(13 citation statements)

References 4 publications

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“…The latter approach involves use of various supervised and unsupervised algorithms to study, analyze, model, and predict trends in smallholder production systems. Recently, unsupervised learning algorithms have been applied in various studies to understand production systems [11, 12]. Some of the more popular unsupervised algorithms include hierarchical clustering, nonhierarchical clustering (K-means), unsupervised neural network algorithms (Self-Organizing Maps), Naïve Bayes and fuzzy clustering algorithms.…”

Section: Introductionmentioning

confidence: 99%

Application of Multiple Unsupervised Models to Validate Clusters Robustness in Characterizing Smallholder Dairy Farmers

Nyambo

Luhanga

Yonah

et al. 2019

The Scientific World Journal

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The heterogeneity of smallholder dairy production systems complicates service provision, information sharing, and dissemination of new technologies, especially those needed to maximize productivity and profitability. In order to obtain homogenous groups within which interventions can be made, it is necessary to define clusters of farmers who undertake similar management activities. This paper explores robustness of production cluster definition using various unsupervised learning algorithms to assess the best approach to define clusters. Data were collected from 8179 smallholder dairy farms in Ethiopia and Tanzania. From a total of 500 variables, selection of the 35 variables used in defining production clusters and household membership to these clusters was determined by Principal Component Analysis and domain expert knowledge. Three clustering algorithms, K-means, fuzzy, and Self-Organizing Maps (SOM), were compared in terms of their grouping consistency and prediction accuracy. The model with the least household reallocation between clusters for training and testing data was deemed the most robust. Prediction accuracy was obtained by fitting a model with fixed effects model including production clusters on milk yield, sales, and choice of breeding method. Results indicated that, for the Ethiopian dataset, clusters derived from the fuzzy algorithm had the highest predictive power (77% for milk yield and 48% for milk sales), while for the Tanzania data, clusters derived from Self-Organizing Maps were the best performing. The average cluster membership reallocation was 15%, 12%, and 34% for K-means, SOM, and fuzzy, respectively, for households in Ethiopia. Based on the divergent performance of the various algorithms evaluated, it is evident that, despite similar information being available for the study populations, the uniqueness of the data from each country provided an over-riding influence on cluster robustness and prediction accuracy. The results obtained in this study demonstrate the difficulty of generalizing model application and use across countries and production systems, despite seemingly similar information being collected.

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Section: Introductionmentioning

confidence: 99%

Application of Multiple Unsupervised Models to Validate Clusters Robustness in Characterizing Smallholder Dairy Farmers

Nyambo

Luhanga

Yonah

et al. 2019

The Scientific World Journal

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“…Classes obtained by using CM and DA were the same. Use of DA is also supported by Gizaw et al [16], in which farming systems developed based on demographic data were validated. Validated classification of smallholder dairy farmers placed farmers into three categories (urban, rural, and periurban systems).…”

Section: Deterministic Methods For Smallholder Farmers' Characterimentioning

confidence: 95%

“…Validated classification of smallholder dairy farmers placed farmers into three categories (urban, rural, and periurban systems). According to Gizaw et al [16], gaps left by the demographic based classification includes valid statistical analyses to support the classification, missing farm topologies in the different systems and within system homogeneity to support aggregation. Apart from characterization, understanding determinants of variation in herd structure within systems was an important aspect in the Gizaw et al [16] work.…”

Section: Deterministic Methods For Smallholder Farmers' Characterimentioning

confidence: 99%

“…According to Gizaw et al [16], gaps left by the demographic based classification includes valid statistical analyses to support the classification, missing farm topologies in the different systems and within system homogeneity to support aggregation. Apart from characterization, understanding determinants of variation in herd structure within systems was an important aspect in the Gizaw et al [16] work. A linear combination of four predictor variables was considered, including local, low, and high grade cross-bred cattle.…”

Section: Deterministic Methods For Smallholder Farmers' Characterimentioning

confidence: 99%

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A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies

Nyambo

Luhanga

Yonah

2019

The Scientific World Journal

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Characterization of smallholder farmers has been conducted in various researches by using machine learning algorithms, participatory and expert-based methods. All approaches used end up with the development of some subgroups known as farm typologies. The main purpose of this paper is to highlight the main approaches used to characterize smallholder farmers, presenting the pros and cons of the approaches. By understanding the nature and key advantages of the reviewed approaches, the paper recommends a hybrid approach towards having predictive farm typologies. Search of relevant research articles published between 2007 and 2018 was done on ScienceDirect and Google Scholar. By using a generated search query, 20 research articles related to characterization of smallholder farmers were retained. Cluster-based algorithms appeared to be the mostly used in characterizing smallholder farmers. However, being highly unpredictable and inconsistent, use of clustering methods calls in for a discussion on how well the developed farm typologies can be used to predict future trends of the farmers. A thorough discussion is presented and recommends use of supervised models to validate unsupervised models. In order to achieve predictive farm typologies, three stages in characterization are recommended as tested in smallholder dairy farmers datasets: (a) develop farm types from a comparative analysis of more than two unsupervised learning algorithms by using training models, (b) assess the training models’ robustness in predicting farm types for a testing dataset, and (c) assess the predictive power of the developed farm types from each algorithm by predicting the trend of several response variables.

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“…This is a cross-sectional descriptive study where the data were collected during October 2017 to December 2017 from field visits in the ten adjacent villages (periurban) to CMA (Gizaw et al, 2017), the second biggest metro area of Bangladesh. These ten villages mostly fit the per-urban villages of Chittagong Metro City as they are the only villages situated at the very entrance of the City namely Chikondondi, Shikarpur, Burirchor (Hathazari Upazilla); Urkirchar, Noapara (Raojan Upazilla); Gomdandi (Boalkhali Upazilla); Char Pathorghata, Shikalbaha, Kolagaon (Patiya Upazilla) and Salimpur (Sitakund Upazilla).…”

Section: Methodology Study Area and Durationmentioning

confidence: 99%

Socio-economic, farm and technological characteristics of the peri-urban small and marginal dairy farmers of Chittagong metro area, Bangladesh

2019

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The paper depicted the salient features of the socio-economic, farm and technological background and the existing problems of the peri-urban small and marginal dairy farmers of Chittagong Metro Area (CMA), Bangladesh. Data were collected using a pre-tested structured questionnaire. Snowball sampling method was used to gather socioeconomic, farm and technological data. Both descriptive statistics and mathematical analyses were used for analyzing the data. The study finds the peri-urban small and marginal dairy farmers are not fully dependent on dairy farming for income and young people are less interested in dairy farming as well. Better education status, less profitability, unfair and unstable price, etc. might be the reasons behind this circumstance. Though the farmers get lesser price for milk, the milk price is higher in urban market. Interestingly, 62.7%, 52.9% and 51.0% of the farmer families have smartphone, facebook and internet users respectively. Based on the findings, the study recommends that technology-based market linkage could be created by any government/non-government development partner between the cluster-based farmers’ group and the urban consumers for ensuring fair price for milk. SAARC J. Agri., 17(1): 77-91 (2019)

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Validating the Classification of Smallholder Dairy Farming Systems Based on Herd Genetic Structure and Access to Breeding Services

Cited by 9 publications

References 4 publications

Application of Multiple Unsupervised Models to Validate Clusters Robustness in Characterizing Smallholder Dairy Farmers

Application of Multiple Unsupervised Models to Validate Clusters Robustness in Characterizing Smallholder Dairy Farmers

A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies

Socio-economic, farm and technological characteristics of the peri-urban small and marginal dairy farmers of Chittagong metro area, Bangladesh

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