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Influence of experimental design on decentralized, on-farm evaluation of populations: a simulation study

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Abstract

Participatory plant breeding (PPB) has received much attention in recent decades for its ability to develop varieties adapted to the diversity of farm conditions and to farmers’ needs and practices. Specific methodological issues arise when working with on-farm experiments, one being the implementation of an experimental design that matches farmers’ constraints and objectives, while allowing for accurate statistical analyses of the data. We took the example of a French PPB case on bread wheat, in which farmers, facilitators and researchers have co-constructed an experimental design that meets their needs, but is very unbalanced and required the development of Bayesian statistical models to compare populations on-farm, over environments and analyze their sensitivity to environments. Through a simulation study, we investigated the effects of different characteristics of the experimental design on the behavior of two of these Bayesian models to identify the range of values that are most appropriate and give recommendations for decentralized experiments. We analyzed the estimates obtained by the models using different simulated datasets that differed by the values of the experimental design’s parameters. While within-environment population effects were well estimated even with few replicated controls, replicating populations of interest rather than controls within environments and including enough environments provided more power to detect significant differences. Population effects and sensitivities over environments were mainly impacted by the number of replications of populations across environments, therefore effort should be made to repeat populations in more environments if the aim is to characterize their behavior under various environmental conditions.

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References

  • Altieri M, Koohafkan P (2013) Strengthening resilience of farming systems: a prerequisite for sustainable agricultural production. Wake up before it is too late: make agriculture truly sustainable now for food security in a changing climate. UNCTAD Trade and Environment Review, Geneva, pp 56–60

  • Altieri MA, Nicholls CI, Henao A, Lana MA (2015) Agroecology and the design of climate change-resilient farming systems. Agron Sustain Dev 35(3):869–890. https://doi.org/10.1007/s13593-015-0285-2

    Article  Google Scholar 

  • Annicchiarico P (2007) Wide-versus specific-adaptation strategy for lucerne breeding in northern Italy. Theor Appl Genet 114(4):647–657. https://doi.org/10.1007/s00122-006-0465-1

    Article  CAS  PubMed  Google Scholar 

  • Annicchiarico P, Chiapparino E, Perenzin M (2010) Response of common wheat varieties to organic and conventional production systems across Italian locations, and implications for selection. Field Crops Res 116(3):230–238. https://doi.org/10.1016/j.fcr.2009.12.012

    Article  Google Scholar 

  • Aw-Hassan A, Mazid A, Salahieh H (2008) The role of informal farmer-to-farmer seed distribution in diffusion of new barley varieties in Syria. Exp Agric 44(3):413–431. https://doi.org/10.1017/S001447970800642X

    Article  Google Scholar 

  • Azaïs JM, Monod H, Bailey RA (1998) The influence of design on validity and efficiency of neighbour methods. Biometrics 54(4):1374–1387

    Article  Google Scholar 

  • Barot S, Allard V, Cantarel A, Enjalbert J, Gauffreteau A, Goldringer I, Lata JC, Le Roux X, Niboyet A, Porcher E (2017) Designing mixtures of varieties for multifunctional agriculture with the help of ecology. A review. Agron Sustain Dev 37(2):13. https://doi.org/10.1007/s13593-017-0418-x

    Article  Google Scholar 

  • Bellon MR, Reeves J (eds) (2002) Quantitative analysis of data from participatory methods in plant breeding. CIMMYT, Texcoco

    Google Scholar 

  • Besag J, Higdon D (1999) Bayesian analysis of agricultural field experiments. R Stat Soc 61(4):691–746. https://doi.org/10.1111/1467-9868.00201

    Article  Google Scholar 

  • van Bueren EL, Jones S, Tamm L, Murphy K, Myers J, Leifert C, Messmer M (2011) The need to breed crop varieties suitable for organic farming, using wheat, tomato and broccoli as examples: a review. NJAS - Wagening J Life Sci 58(3–4):193–205. https://doi.org/10.1016/j.njas.2010.04.001

    Article  Google Scholar 

  • van Bueren ETL, Struik PC, Jacobsen E (2002) Ecological concepts in organic farming and their consequences for an organic crop ideotype. NJAS - Wagening J Life Sci 50(1):1–26. https://doi.org/10.1016/S1573-5214(02)80001-X

    Article  Google Scholar 

  • Carson Y, Maria A (1997) Simulation optimization: methods and applications. In: Proceedings of the 29th conference on winter simulation, IEEE Computer Society, Washington, DC, USA, WSC ’97, pp 118–126. https://doi.org/10.1145/268437.268460

  • Ceccarelli S (1989) Wide adaptation: how wide? Euphytica 40(3):197–205. https://doi.org/10.1007/BF00024512

    Article  Google Scholar 

  • Ceccarelli S (2012) Plant breeding with farmers—a technical manual. ICARDA, Aleppo

    Google Scholar 

  • Ceccarelli S (2015) Efficiency of plant breeding. Crop Sci 55(1):87. https://doi.org/10.2135/cropsci2014.02.0158

    Article  Google Scholar 

  • Ceccarelli S, Grando S (2007) Decentralized-participatory plant breeding: an example of demand driven research. Euphytica 155(3):349–360. https://doi.org/10.1007/s10681-006-9336-8

    Article  Google Scholar 

  • Chaloner K, Verdinelli I (1995) Bayesian experimental design: a review. Stat Sci 10(3):273–304

    Article  Google Scholar 

  • Coomes OT, McGuire SJ, Garine E, Caillon S, McKey D, Demeulenaere E, Jarvis D, Aistara G, Barnaud A, Clouvel P, Emperaire L, Louafi S, Martin P, Massol F, Pautasso M, Violon C, Wencélius J (2015) Farmer seed networks make a limited contribution to agriculture? Four common misconceptions. Food Policy 56:41–50. https://doi.org/10.1016/j.foodpol.2015.07.008

    Article  Google Scholar 

  • Cotes JM, Crossa J, Sanches A, Cornelius PL (2006) A Bayesian approach for assessing the stability of genotypes. Crop Sci 46(6):2654–2665. https://doi.org/10.2135/cropsci2006.04.0227

    Article  Google Scholar 

  • Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 11(4):381–393. https://doi.org/10.1198/108571106X154443

    Article  Google Scholar 

  • David O (1994) Balanced block designs under interactive linear models. J Stat Plan Inference 39(1):33–41

    Article  Google Scholar 

  • Desclaux D, Nolot JM, Chiffoleau Y, Gozé E, Leclerc C (2008) Changes in the concept of genotype x environment interactions to fit agriculture diversification and decentralized participatory plant breeding: pluridisciplinary point of view. Euphytica 163(3):533–546. https://doi.org/10.1007/s10681-008-9717-2

    Article  Google Scholar 

  • Digby PGN (1979) Modified joint regression analysis for incomplete variety x environment data. J Agric Sci 93(01):81. https://doi.org/10.1017/S0021859600086159

    Article  Google Scholar 

  • Finckh M, Gacek E, Goyeau H, Lannou C, Merz U, Mundt C, Munk L, Nadziak J, Newton A, de Vallavieille-Pope C (2000) Cereal variety and species mixtures in practice, with emphasis on disease resistance. Agronomie 20(7):813–837

    Article  Google Scholar 

  • Finlay K, Wilkinson G (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agric Res 14(6):742–754

    Article  Google Scholar 

  • Gelman A, Rubin D (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4):457–511. https://doi.org/10.1214/ss/1177011136

    Article  Google Scholar 

  • Humphries S, Rosas JC, Gómez M, Jiménez J, Sierra F, Gallardo O, Avila C, Barahona M (2015) Synergies at the interface of farmer-scientist partnerships: agricultural innovation through participatory research and plant breeding in Honduras. Agric Food Secur 4(1):27. https://doi.org/10.1186/s40066-015-0046-0

    Article  Google Scholar 

  • Kempton RA, Fox PN, Cerezo M (2012) Statistical methods for plant variety evaluation. Springer, Berlin

    Google Scholar 

  • Kleinknecht K, Möhring J, Laidig F, Meyer U, Piepho H (2016) A simulation-based approach for evaluating the efficiency of multienvironment trial designs. Crop Sci 56(5):2237. https://doi.org/10.2135/cropsci2015.07.0405

    Article  Google Scholar 

  • Kobilinsky A, Monod H, Bailey RA (2017) Automatic generation of generalised regular factorial designs. Comput Stat Data Anal 113:311–329

    Article  Google Scholar 

  • Lian L, de los Campos G (2016) Fw: An r package for Finlay-Wilkinson regression that incorporates genomic/pedigree information and covariance structures between environments. G3: Genes Genomes Genet 6(3):589–597. https://doi.org/10.1534/g3.115.026328

    Article  Google Scholar 

  • Müller P (2005) Optimal design: simulation approaches. In: Dey DK, Rao CR (eds) Handbook of statistics, vol 25. Elsevier, Amsterdam, pp 509–518

    Google Scholar 

  • Murphy KM, Campbell KG, Lyon SR, Jones SS (2007) Evidence of varietal adaptation to organic farming systems. Field Crops Res 102(3):172–177. https://doi.org/10.1016/j.fcr.2007.03.011

    Article  Google Scholar 

  • Nabugoomu F, Kempton RA, Talbot M (1999) Analysis of series of trials where varieties differ in sensitivity to locations. J Agric Biol Environ Stat 4(3):310. https://doi.org/10.2307/1400388

    Article  Google Scholar 

  • Omer SO, Abdalla AWH, Mohammed MH, Singh M (2015) Bayesian estimation of genotype-by-environment interaction in sorghum variety trials. Commun Biometry Crop Sci 10(2):82–95

    Google Scholar 

  • O’Hara RB, Cano JM, Ovaskainen O, Teplitsky C, Alho JS (2008) Bayesian approaches in evolutionary quantitative genetics. J Evol Biol 21(4):949–957

    Article  Google Scholar 

  • Pautasso M, Aistara G, Barnaud A, Caillon S, Clouvel P, Coomes OT, Delêtre M, Demeulenaere E, De Santis P, Döring T, Eloy L, Emperaire L, Garine E, Goldringer I, Jarvis D, Joly HI, Leclerc C, Louafi S, Martin P, Massol F, McGuire S, McKey D, Padoch C, Soler C, Thomas M, Tramontini S (2013) Seed exchange networks for agrobiodiversity conservation. A review. Agron Sustain Dev 33(1):151–175. https://doi.org/10.1007/s13593-012-0089-6

    Article  Google Scholar 

  • Plummer M (2003) Jags: a program for analysis of bayesian graphical models using gibbs sampling

  • Plummer M (2016) rjags: Bayesian graphical models using MCMC. https://CRAN.R-project.org/package=rjags, r package version 4-6

  • R Core Team (2018) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

  • Rincent R, Kuhn E, Monod H, Oury FX, Rousset M, Allard V, Le Gouis J (2017) Optimization of multi-environment trials for genomic selection based on crop models. Theor Appl Genet 130(8):1735–1752

    Article  CAS  Google Scholar 

  • Rivière P (2014) Méthodologie de la sélection décentralisée et participative : un exemple sur le blé tendre. PhD thesis, Paris-Sud

  • Rivière P, Pin S, de Oliviera Y, David O, Dawson J, Wanner A, Heckmann R, Obbellianne S, Ronot B, Parizot S, Hyancinthe A, Dalmasso C, Baltassat R, Bochède A, Mailhe G, Caizergue F, Gascuel JS, Gasnier R, Berthellot JF, Baboulène J, Poilly C, Lavoyer R, Hernandez MP, Coulbeaut JM, Peloux F, Mouton A, Mercier F, Ranke O, Wittrish R, de Kochko P, Goldringer I (2013) Mise en place d’une méthodologie de sélection participative sur le blé tendre en France. Innov Agron 32:427–441

    Google Scholar 

  • Rivière P, Dawson JC, Goldringer I, David O (2015) Hierarchical Bayesian modeling for flexible experiments in decentralized participatory plant breeding. Crop Sci 55(3):1053. https://doi.org/10.2135/cropsci2014.07.0497

    Article  Google Scholar 

  • Rivière P, van Frank G, David O, Muñoz F (2017) PPBstats: an R package to perform analysis found within PPB programmes regarding network of seeds circulation, agronomic trials, organoleptic tests and molecular experiments. Version 0.23. URL https://github.com/priviere/PPBstats

  • Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008) Global sensitivity analysis: the primer. Wiley, Hoboken

    Google Scholar 

  • Silvey SD (1980) Optimal designs: an introduction to the theory for parameter estimation. Chapman and Hall, London

    Book  Google Scholar 

  • Simmonds NW (1991) Selection for local adaptation in a plant breeding programme. Theor Appl Genet 82(3):363–367

    Article  CAS  Google Scholar 

  • Singh M, Tavva S, Saharawat YS, Rizvi J (2018) A Bayesian assessment of productivity and risks to achieve target yields from improved Chickpea and Mung Bean varieties using on-farm trials in Afghanistan. Exp Agric 54(03):470–481. https://doi.org/10.1017/S0014479717000187

    Article  Google Scholar 

  • Sperling L, Ashby JA, Smith ME, Weltzien E, McGuire S (2001) A framework for analyzing participatory plant breeding approaches and results. Euphytica 122(3):439–450

    Article  Google Scholar 

  • Sun X, Peng T, Mumm RH (2011) The role and basics of computer simulation in support of critical decisions in plant breeding. Mol Breed 28(4):421–436. https://doi.org/10.1007/s11032-011-9630-6

    Article  Google Scholar 

  • Tekin E, Sabuncuoglu I (2004) Simulation optimization: a comprehensive review on theory and applications. IIE Trans 36(11):1067–1081. https://doi.org/10.1080/07408170490500654

    Article  Google Scholar 

  • Theobald CM, Talbot M, Nabugoomu F (2002) A bayesian approach to regional and local-area prediction from crop variety trials. J Agric Biol Environ Stat 7(3):403–419. https://doi.org/10.1198/108571102230

    Article  Google Scholar 

  • Thomas M, Dawson JC, Goldringer I, Bonneuil C (2011) Seed exchanges, a key to analyze crop diversity dynamics in farmer-led on-farm conservation. Genet Resources Crop Evol 58(3):321–338. https://doi.org/10.1007/s10722-011-9662-0

    Article  Google Scholar 

  • de Vallavieille-Pope C (2004) Management of disease resistance diversity of cultivars of a species in single fields: controlling epidemics. Comptes Rendus Biol 327(7):611–620. https://doi.org/10.1016/j.crvi.2003.11.014

    Article  Google Scholar 

  • Witcombe J, Yadavendra J (2014) How much evidence is needed before client-oriented breeding (COB) is institutionalised? Evidence from rice and maize in India. Field Crops Res 167:143–152. https://doi.org/10.1016/j.fcr.2014.06.022

    Article  Google Scholar 

  • Witcombe JR, Joshi A, Goyal SN (2003) Participatory plant breeding in maize: a case study from Gujarat, India. Euphytica 130(3):413–422

    Article  Google Scholar 

  • Østergård H, Finckh MR, Fontaine L, Goldringer I, Hoad SP, Kristensen K, van Bueren ETL, Mascher F, Munk L, Wolfe MS (2009) Time for a shift in crop production: embracing complexity through diversity at all levels. J Sci Food Agric 89(9):1439–1445. https://doi.org/10.1002/jsfa.3615

    Article  CAS  Google Scholar 

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Acknowledgements

We thank all the farmers and facilitators participating to the PPB project, as well as Alexandre Protat for assistance with programming the simulations. We also thank the reviewers for their helpful comments on improving the manuscript. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 633571 (DIVERSIFOOD).

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Authors and Affiliations

  1. GQE – Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France

    Gaëlle van Frank & Isabelle Goldringer

  2. Réseau Semences Paysannes, 47190, Aiguillon, France

    Pierre Rivière

  3. MaIAGE, INRA, Université Paris-Saclay, 78350, Jouy-en-Josas, France

    Olivier David

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  1. Gaëlle van Frank
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van Frank, G., Goldringer, I., Rivière, P. et al. Influence of experimental design on decentralized, on-farm evaluation of populations: a simulation study. Euphytica 215, 126 (2019). https://doi.org/10.1007/s10681-019-2447-9

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