Using a positive deviance approach to inform farming systems redesign: A case study from Bihar, India

Toorop, Roos Adelhart; Ceccarelli, Viviana; Bijarniya, Deepak; Jat, M.L.; Jat, Rajkumar; López-Ridaura, Santiago; Groot, J.C.J.

doi:10.1016/j.agsy.2020.102942

Cited by 19 publications

(10 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This subsection describes how positive deviants were identified through the Paretooptimality ranking technique in a sample of 794 smallholder dairy farms. The Paretooptimality ranking technique is an objective and quantitative approach with which it is possible to isolate positive deviants in a population on multiple performance indicators simultaneously without a bias [8,9]. This technique is not sensitive to multi-collinearity and avoids bias in the identification of the positive deviants.…”

Section: Identification Of Positive Deviants Using Pareto-optimality ...mentioning

confidence: 99%

“…The technique is implemented without subjective weighting to avoid bias. The pioneering application of the Pareto-optimality ranking technique was by Goldberg [24] and later by others in ecology, agriculture and livestock studies [7][8][9], but longitudinal data on smallholder dairy farming systems have not been used. Paretooptimality ranking software was freely accessed [8].…”

Section: Identification Of Positive Deviants Using Pareto-optimality ...mentioning

confidence: 99%

“…This process makes the indicator distributions comparable despite being originally of different units and scales. The resultant performance scores for the 794 sample farms were subjected to the Pareto-optimality ranking algorithm [8]. The procedure allows for the choice of direction, whether to maximise or to minimise the indicator variable.…”

Section: Identification Of Positive Deviants Using Pareto-optimality ...mentioning

confidence: 99%

“…The identification of positive deviants in a population to inform one's choice of ameliorative practices for managing environmental stresses in a locality has been applied in community health, ecology, agriculture and livestock [7,8]. In identifying positive deviants, researchers have mostly applied subjective approaches, involving peers and expert knowledge dialogues, participatory ranking and snowballing sampling [7,8]. The data sources are cross-sectional surveys complemented with expert knowledge typologies and peer judgement to construct farm clusters.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Identifying Positive Deviant Farms Using Pareto-Optimality Ranking Technique to Assess Productivity and Livelihood Benefits in Smallholder Dairy Farming under Contrasting Stressful Environments in Tanzania

Shija

Mwai

Migwi

et al. 2022

World

View full text Add to dashboard Cite

In smallholder dairy-cattle farming, identifying positive deviants that attain outstanding performance can inform targeted improvements in typical, comparable farms under similar environmental stresses. Mostly, positive deviants are identified subjectively, introducing bias and limiting generalisation. The aim of the study was to objectively identify positive deviant farms using the Pareto-optimality ranking technique in a sample of smallholder dairy farms under contrasting stressful environments in Tanzania to test the hypothesis that positive deviant farms that simultaneously outperform typical farms in multiple performance indicators also outperform in yield gap, productivity and livelihood benefits. The selection criteria set five performance indicators: energy balance, disease-incidence density, daily milk yield, age at first calving and calving interval. Findings proved the hypothesis. A few farms (27: 3.4%) emerged as positive deviants, outperforming typical farms in yield gap, productivity and livelihood benefits. The estimated yield gap in typical farms was 76.88% under low-stress environments and 48.04% under high-stress environments. On average, total cash income, gross margins and total benefits in dairy farming were higher in positive deviants than in typical farms in both low- and high-stress environments. These results show that the Pareto-optimality ranking technique applied in a large population objectively identified a few positive deviant farms that attained higher productivity and livelihood benefits in both low- and high-stress environments. However, positive deviants invested more in inputs. With positive deviant farms objectively identified, it is possible to characterise management practices that they deploy differently from typical farms and learn lessons to inform the uptake of best practices and extension messages to be directed to improving dairy management.

show abstract

Section: Identification Of Positive Deviants Using Pareto-optimality ...mentioning

confidence: 99%

Section: Identification Of Positive Deviants Using Pareto-optimality ...mentioning

confidence: 99%

Section: Identification Of Positive Deviants Using Pareto-optimality ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identifying Positive Deviant Farms Using Pareto-Optimality Ranking Technique to Assess Productivity and Livelihood Benefits in Smallholder Dairy Farming under Contrasting Stressful Environments in Tanzania

Shija

Mwai

Migwi

et al. 2022

World

View full text Add to dashboard Cite

show abstract

“…The idea is to find solutions for the farms in order to minimise the global warming consequences [65]. The practices associated with CSA are recognized as intensive agricultural techniques compatible with a sustainable development [66]. They are included, also, in silvo-aquaculture [67] and integrated aquaculture [68] systems, in a context called as Agriculture 4.0 [69].…”

Section: Environmental Impacts and Climate Changementioning

confidence: 99%

Integrated-Smart Agriculture: Contexts and Assumptions for a Broader Concept

Martinho

Guiné

2021

Agronomy

View full text Add to dashboard Cite

The innovative technologies developed in the different fields of science (nanotechnology, artificial intelligence, genetic modification, etc.) opened new and infinite possibilities for the several stakeholders that carry out their activities in the different economic sectors. For agriculture, these new approaches are particularly relevant and may bring interesting contributions, considering the specificities of the sector, often dealing with contexts of land abandonment and narrow profit margins. Nonetheless, the question in these unstopped evolutions is about the interlinkages with sustainability. In this context, the objectives of this study are to highlight the main insights from the available scientific literature about the interrelationships between the new trends in the agriculture and the sustainability. To achieve these aims, a search on the Web of Science Core Collection (WoS) and Scopus databases was carried out, on 15 May 2021, for the topics ‘smart agriculture’ and ‘sustainability’. A total of 231 documents (102 from WoS and 129 from Scopus) were obtained, remaining 155 documents after removing the duplicated, which were surveyed through systematic review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach. As main insights, the concerns of the researchers with the impacts on the sustainability from the transformations in the farming organization are highlighted. On the other hand, it was shown the relevance and the new opportunities, including in terms of food supply, arising from the precision agriculture, agricultural intelligence, vertical/urban farming, circular economy, internet of things, and crowdfarming. We suggest the new and wider concept of ‘integrated-smart agriculture’, better than ‘climate-smart agriculture’.

show abstract

Eastern Australian Farmers Managing and Thinking Differently: Innovative Adaptation Cycles

McKenzie,

Joyce,

Zander

et al. 2023

Environmental Management

View full text Add to dashboard Cite

The uncertainty of climate change is a significant challenge prompting Australian farmers to create different thinking and different management systems that ensure sustained farm business viability and continuity, particularly in extreme environments. The purpose of this study was to explore the conditions and adaptive processes for managing farm resilience and cyclic adaptation pathways, in response to climate change. A positive deviance sample of farmers was interviewed, and data was collected from a cohort of twenty-two climate change innovators across Eastern Australia. Grounded theory analysis of data identified three processes and two transactional maps of climate change adaptation, in this under studied farmer cohort. The development of the transactional maps found the resilience and preparedness processes as adaptive learning responses to the stressors of climate change. The processes of managing the business and resources were identified as markers of preparedness and resilience that ensured business viability and continuity. Farmers prepared for climate change through transforming make-over processes as an adaptive learning response to climate challenges. Mapping the cycle of adaptation identified the processes of socio-cognitive agency, learning from feedback and consequences, and contextual variables as critical elements of adaptation. The intervening socio-ecological processes of intelligence gathering and influencing, and socio-cognitive precursors, were found to regulate the adaptation cycle. The cycle was found to have both incremental and transformative transmission processes, and intervening processes of climate and contextual variables. The changing patterns and extremes of climate change were found to impact the growing season, and its potential, as unique variables that demand farm adaptation. Ultimately, this study identified potential points of influence for leveraging preparedness behaviours.

show abstract

Using a positive deviance approach to inform farming systems redesign: A case study from Bihar, India

Cited by 19 publications

References 65 publications

Identifying Positive Deviant Farms Using Pareto-Optimality Ranking Technique to Assess Productivity and Livelihood Benefits in Smallholder Dairy Farming under Contrasting Stressful Environments in Tanzania

Identifying Positive Deviant Farms Using Pareto-Optimality Ranking Technique to Assess Productivity and Livelihood Benefits in Smallholder Dairy Farming under Contrasting Stressful Environments in Tanzania

Integrated-Smart Agriculture: Contexts and Assumptions for a Broader Concept

Eastern Australian Farmers Managing and Thinking Differently: Innovative Adaptation Cycles

Contact Info

Product

Resources

About