Mean-Variance Versus Mean–expected Shortfall Models: An Application to Wheat Variety Selection

Sukcharoen, Kunlapath; Leatham, David J.

doi:10.1017/aae.2016.8

Cited by 9 publications

(12 citation statements)

References 26 publications

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“…Results can, therefore, be directly translated into actionable information for climate adaptation on the ground. The findings can serve to create variety portfolios that diminish climate risk ( 22 ), can feed into climate information services in combination with seasonal forecasts ( 28 ), and can become part of decentralized plant breeding strategies for climate adaptation ( 8 ). Combining the tricot trial data with other data could generate additional insights into variety performance and acceptability as influenced by environmental ( 11 ), socioeconomic ( 29 ), and genomic ( 30 ) factors.…”

Section: Discussionmentioning

confidence: 99%

“…These two metrics produced divergent variety recommendations in all three cases (indicated in bold in Table 3 ). In principle, risk analysis for variety choice is also possible without explicit climatic analysis, but this produces results that are difficult to interpret in terms of climatic causality and requires trials during a large number of seasons to avoid sampling bias and to characterize probability distributions accurately ( 22 ).…”

Section: Improving Variety Recommendationsmentioning

confidence: 99%

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Crop variety management for climate adaptation supported by citizen science

Etten¹,

Sousa

Aguilar³

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

129

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Crop adaptation to climate change requires accelerated crop variety introduction accompanied by recommendations to help farmers match the best variety with their field contexts. Existing approaches to generate these recommendations lack scalability and predictivity in marginal production environments. We tested if crowdsourced citizen science can address this challenge, producing empirical data across geographic space that, in aggregate, can characterize varietal climatic responses. We present the results of 12,409 farmer-managed experimental plots of common bean (Phaseolus vulgarisL.) in Nicaragua, durum wheat (Triticum durumDesf.) in Ethiopia, and bread wheat (Triticum aestivumL.) in India. Farmers collaborated as citizen scientists, each ranking the performance of three varieties randomly assigned from a larger set. We show that the approach can register known specific effects of climate variation on varietal performance. The prediction of variety performance from seasonal climatic variables was generalizable across growing seasons. We show that these analyses can improve variety recommendations in four aspects: reduction of climate bias, incorporation of seasonal climate forecasts, risk analysis, and geographic extrapolation. Variety recommendations derived from the citizen science trials led to important differences with previous recommendations.

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Section: Discussionmentioning

confidence: 99%

Section: Improving Variety Recommendationsmentioning

confidence: 99%

Crop variety management for climate adaptation supported by citizen science

Etten¹,

Sousa

Aguilar³

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

129

View full text Add to dashboard Cite

show abstract

“…The evaluations mentioned above focus on average performance of varieties, but other approaches focus on the variation in performance across seasons to assess farmers’ risks. It has been shown that multi-environmental trial data from several seasons can be used to propose variety portfolios to reduce risk and maximize farmers’ profits (Nalley et al 2009 ; Nalley and Barkley 2010 ; Sukcharoen and Leatham 2016 ). These studies all focus on yield as the main evaluation criterion.…”

Section: Analysis Of Different Types Of Datamentioning

confidence: 99%

Data synthesis for crop variety evaluation. A review

Brown

Bergh

Bruin

et al. 2020

Agron. Sustain. Dev.

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Crop varieties should fulfill multiple requirements, including agronomic performance and product quality. Variety evaluations depend on data generated from field trials and sensory analyses, performed with different levels of participation from farmers and consumers. Such multi-faceted variety evaluation is expensive and time-consuming; hence, any use of these data should be optimized. Data synthesis can help to take advantage of existing and new data, combining data from different sources and combining it with expert knowledge to produce new information and understanding that supports decision-making. Data synthesis for crop variety evaluation can partly build on extant experiences and methods, but it also requires methodological innovation. We review the elements required to achieve data synthesis for crop variety evaluation, including (1) data types required for crop variety evaluation, (2) main challenges in data management and integration, (3) main global initiatives aiming to solve those challenges, (4) current statistical approaches to combine data for crop variety evaluation and (5) existing data synthesis methods used in evaluation of varieties to combine different datasets from multiple data sources. We conclude that currently available methods have the potential to overcome existing barriers to data synthesis and could set in motion a virtuous cycle that will encourage researchers to share data and collaborate on data-driven research.

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“…"coherent" state-of-the-art risk metric in finance [29,30], which has already been applied in agriculture to generate variety portfolio recommendations [31].…”

Section: Potential Contribution Of the Minimum Regret Modelmentioning

confidence: 99%

Analysing Agricultural Diversification as a Risk Management Strategy with the Minimum Regret Model

Etten¹

2019

Preprint

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Diversification is an important strategy for managing risk in agricultural systems. Risk analysis can help to support farmers’ diversification strategies, but existing analytical methods are complicated and little used. The minimum regret model helps to fill this gap. It provides a simple, transparent calculation procedure that can be executed with existing spreadsheet software. Regret is an important heuristic in the behavioural sciences and regret-based models are used in finance. The article presents the model with a numerical example. It also presents a framework to compare minimum regret portfolios with two limit cases (maximum utility and minimax regret). A case study illustrates the use of the model and the comparative framework.

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Mean-Variance Versus Mean–expected Shortfall Models: An Application to Wheat Variety Selection

Cited by 9 publications

References 26 publications

Crop variety management for climate adaptation supported by citizen science

Crop variety management for climate adaptation supported by citizen science

Data synthesis for crop variety evaluation. A review

Analysing Agricultural Diversification as a Risk Management Strategy with the Minimum Regret Model

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