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Prioritizing options for multi-objective agricultural development through the Positive Deviance approach.

ABSTRACT: Agricultural development must integrate multiple objectives at the same time, including food security, income, and environmental sustainability. To help achieve these objectives, development practitioners need to prioritize concrete livelihood practices to promote to rural households. But trade-offs between objectives can lead to dilemmas in selecting practices. In addition, heterogeneity among farming households requires targeting different strategies to different types of households. Existing diversity of household resources and activities, however, may also bear solutions. We explored a new, empirical research method that identifies promising options for multi-objective development by focusing on existing cases of strong multi-dimensional household performance. The "Positive Deviance" approach signifies identifying locally viable livelihood practices from diverse households that achieve stronger performance than comparable households in the same area. These practices are promising for other local households in comparable resource contexts. The approach has been used in other domains, such as child nutrition, but has not yet been fully implemented for agricultural development with a focus on the simultaneous achievement of multiple objectives. To test our adapted version of the Positive Deviance approach, we used a quantitative survey of over 500 rural households in South-Eastern Tanzania. We identified 54 households with outstanding relative performance regarding five key development dimensions (food security, income, nutrition, environmental sustainability, and social equity). We found that, compared to other households with similar resource levels, these "positive deviants" performed strongest for food security, but only slightly better for social equity. We then re-visited a diverse sub-sample for qualitative interviews, and identified 14 uncommon, "deviant" practices that plausibly contributed to the households' superior outcomes. We illustrate how these practices can inform specific recommendations of practices for other local households in comparable resource contexts. The study demonstrates how, with the Positive Deviance approach, empirical observations of individual, outstanding households can inform discussions about locally viable agricultural development solutions in diverse household context.

SUBMITTER: Steinke J

PROVIDER: S-EPMC6388925 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Agricultural landscapes provide many functions simultaneously including food production, regulation of water and regulation of greenhouse gases. Thus, it is challenging to make land management decisions, particularly transformative changes, that improve on one function without unintended consequences for other functions. To make informed decisions the trade-offs between different landscape functions must be considered. Here, we use a multi-objective optimization algorithm with a model of crop production that also simulates environmental effects such as nitrous oxide emissions to identify trade-off frontiers and associated possibilities for agricultural management. Trade-offs are identified in three soil types, using wheat production in the UK as an example, then the trade-off for combined management of the three soils is considered. The optimization algorithm identifies trade-offs between different objectives and allows them to be visualised. For example, we observed a highly non-linear trade-off between wheat yield and nitrous oxide emissions, illustrating where small changes might have a large impact. We used a cluster analysis to identify distinct management strategies with similar management actions and use these clusters to link the trade-off curves to possibilities for management. There were more possible strategies for achieving desirable environmental outcomes and remaining profitable when the management of different soil types was considered together. Interestingly, it was on the soil capable of the highest potential profit that lower profit strategies were identified as useful for combined management. Meanwhile, to maintain average profitability across the soils, it was necessary to maximise the profit from the soil with the lowest potential profit. These results are somewhat counterintuitive and so the range of strategies supplied by the model could be used to stimulate discussion amongst stakeholders. In particular, as some key objectives can be met in different ways, stakeholders could discuss the impact of these management strategies on other objectives not quantified by the model.

Project description:BackgroundIn the field of computational biology, analyzing complex data helps to extract relevant biological information. Sample classification of gene expression data is one such popular bio-data analysis technique. However, the presence of a large number of irrelevant/redundant genes in expression data makes a sample classification algorithm working inefficiently. Feature selection is one such high-dimensionality reduction technique that helps to maximize the effectiveness of any sample classification algorithm. Recent advances in biotechnology have improved the biological data to include multi-modal or multiple views. Different 'omics' resources capture various equally important biological properties of entities. However, most of the existing feature selection methodologies are biased towards considering only one out of multiple biological resources. Consequently, some crucial aspects of available biological knowledge may get ignored, which could further improve feature selection efficiency.ResultsIn this present work, we have proposed a Consensus Multi-View Multi-objective Clustering-based feature selection algorithm called CMVMC. Three controlled genomic and proteomic resources like gene expression, Gene Ontology (GO), and protein-protein interaction network (PPIN) are utilized to build two independent views. The concept of multi-objective consensus clustering has been applied within our proposed gene selection method to satisfy both incorporated views. Gene expression data sets of Multiple tissues and Yeast from two different organisms (Homo Sapiens and Saccharomyces cerevisiae, respectively) are chosen for experimental purposes. As the end-product of CMVMC, a reduced set of relevant and non-redundant genes are found for each chosen data set. These genes finally participate in an effective sample classification.ConclusionsThe experimental study on chosen data sets shows that our proposed feature-selection method improves the sample classification accuracy and reduces the gene-space up to a significant level. In the case of Multiple Tissues data set, CMVMC reduces the number of genes (features) from 5565 to 41, with 92.73% of sample classification accuracy. For Yeast data set, the number of genes got reduced to 10 from 2884, with 95.84% sample classification accuracy. Two internal cluster validity indices - Silhouette and Davies-Bouldin (DB) and one external validity index Classification Accuracy (CA) are chosen for comparative study. Reported results are further validated through well-known biological significance test and visualization tool.

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Prioritizing options for multi-objective agricultural development through the Positive Deviance approach.

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