Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-13T19:52:08.617Z Has data issue: false hasContentIssue false
  • English
  • Français

Adaptability analysis in a participatory variety trial of organic vegetable crops

Published online by Cambridge University Press:  08 January 2019

Alexandra Lyon Open the ORCID record for Alexandra Lyon [Opens in a new window] ,
William Tracy Open the ORCID record for William Tracy [Opens in a new window] ,
Micaela Colley ,
Patrick Culbert ,
Michael Mazourek Open the ORCID record for Michael Mazourek [Opens in a new window] ,
James Myers Open the ORCID record for James Myers [Opens in a new window] ,
Jared Zystro  and
Erin M. Silva
Alexandra Lyon*
Affiliation:
University of Wisconsin-Madison, Madison, WI, USA University of British Columbia, Vancouver, BC, Canada
William Tracy
Affiliation:
University of Wisconsin-Madison, Madison, WI, USA
Micaela Colley
Affiliation:
Wageningen University, Wageningen, Netherlands
Patrick Culbert
Affiliation:
University of British Columbia, Vancouver, BC, Canada
Michael Mazourek
Affiliation:
Cornell University, Ithaca, NY, USA
James Myers
Affiliation:
Oregon State University, Corvallis, OR, USA
Jared Zystro
Affiliation:
University of Wisconsin-Madison, Madison, WI, USA
Erin M. Silva
Affiliation:
University of Wisconsin-Madison, Madison, WI, USA
*
Author for correspondence: Alexandra Lyon, E-mail: alexandra.lyon@ubc.ca
Get access Rights & Permissions [Opens in a new window]

Abstract

Successful organic farming requires crop varieties that are resilient to environmental variability. Assessing variety performance across the range of conditions represented on working farms is vital to developing such varieties; however, data collected from on-farm, participatory trials can be difficult to both collect and interpret. To assess the utility of data arising from participatory trialing efforts, we examined the performance of butternut squash (Cucurbita moschata L.), broccoli (Brassica oleracea L.) and carrot (Daucus carota L.) varieties grown in diverse organic production environments in participatory trials in Oregon, Washington, Wisconsin and New York using adaptability analysis (regression of variety means on environmental index). Patterns of adaptation varied across varieties, with some demonstrating broad adaptation and others showing specific adaptation to low- or high-yielding environments. Selection of varieties with broad vs specific adaptation should be guided by farmers’ risk tolerance and on-farm environmental variation. Adaptability analysis was appropriate for continuous variables (e.g., yield traits), but less so for ordinal variables and quality traits such as flavor and appearance, which can be vitally important in organic vegetable crop variety selection. The relative advantages of adaptability analysis and additive main effects and multiplicative interactions are also discussed in relation to on-farm trial networks. This work demonstrated the unique challenges presented by extensive participatory vegetable trialing efforts, which, as compared to grain crops, require novel approaches to facilitating farmer participation as well as data collection and analysis. Efficient, precise and reliable methods for evaluating quality related traits in these crops would allow researchers to assess stability and adaptation across a wider range of traits, providing advantages for effective plant breeding and trialing activities within the organic sector.

Keywords

On-farm researchorganic agricultureparticipatory variety selectionseed systemsvegetable production
Type
Research Paper
Information
Renewable Agriculture and Food Systems , Volume 35 , Issue 3 , June 2020 , pp. 296 - 312
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

Current address: University of British Columbia, 2357 Main Mall, Vancouver, BC, V6 T 1Z4, Canada.

References

Abidin, PE, van Eeuwijk, F, Stam, P, Struik, PC, Malosetti, M, Mwanga, RO, Odongo, B, Hermann, M and Carey, EE (2005) Adaptation and stability analysis of sweet potato varieties for low-input systems in Uganda. Plant Breeding 124, 491–497.CrossRefGoogle Scholar
Ashby, J (1990) Evaluating Technology with Farmers. Cali, CO: Centro Internacional de Agricultura Tropical (CIAT).Google Scholar
Baenziger, PS, Salah, I, Little, RS, Santra, DK, Regassa, T and Wang, MY (2011) Structuring an efficient organic wheat breeding program. Sustainability 3, 1190–1205.CrossRefGoogle Scholar
Beans, C (2017) Science and culture: vegetable breeders turn to chefs for flavor boost. The Proceedings of the National Academy of Sciences 114, 10506–10508.CrossRefGoogle ScholarPubMed
Becker, HC and Leon, J (1988) Stability analysis in plant breeding. Plant Breeding 101, 1–23.CrossRefGoogle Scholar
Bellon, MR, Berthaud, JB, Smale, M, Aguirre, JA, Taba, S, Aragón, F, Díaz, J and Castro, H (2003) Participatory landrace selection for on-farm conservation: an example from the Central Valleys of Oaxaca, Mexico. Genetic Resources and Crop Evolution 50, 401–416.CrossRefGoogle Scholar
Bradley, JP, Knittle, KH and Troyer, AF (1988) Statistical methods in seed corn product selection. Journal of Production Agriculture 1, 34.CrossRefGoogle Scholar
Burger, H, Schloen, M, Schmidt, W and Geiger, HH (2008) Quantitative genetic studies on breeding maize for adaptation to organic farming. Euphytica 163, 501–510.CrossRefGoogle Scholar
Ceccarelli, S (1994) Specific adaptation and breeding for marginal conditions. Euphytica 77, 205–219.CrossRefGoogle Scholar
Ceccarelli, S, Grando, S and Hamblin, J (1992) Relationship between barley grain yield measured in low- and high-yielding environments. Euphytica 64, 49–58.Google Scholar
Ceccarelli, S, Erskine, W, Hamblin, J and Grando, S (1994) Genotype by environment interaction and international breeding programmes. Journal of Experimental Agriculture 30, 177–187.CrossRefGoogle Scholar
Ceccarelli, S, Grando, S, Singh, M, Michael, M, Shikho, A, Al Issa, M, Al Saleh, A, Kaleonjy, G, Al Ghanem, SM, Al Hasan, AL et al. (2003) A methodological study on participatory barley breeding II. Response to selection. Euphytica 133, 185–200.CrossRefGoogle Scholar
Chable, V, Conseil, M, Serpolay, E and Lagadec, F (2008) Organic varieties for cauliflowers and cabbages in Brittany: from genetic resources to participatory plant breeding. Euphytica 164, 521–529.CrossRefGoogle Scholar
Coe, R (2002) Analyzing ranking and rating data from participatory on-farm trials. In Bellon, MR and Reeves, J (eds), Quantitative Analysis of Data from Participatory Methods in Plant Breeding. Mexico, DF: CIMMYT, pp. 44–65.Google Scholar
Dawson, JC, Rivière, P, Berthellot, J-F, Mercier, F, Kochko, P, Galic, N, Pin, S, Serpolay, E, Thomas, M, Giuliano, S and Goldringer, I (2011) Collaborative plant breeding for organic agricultural systems in developed countries. Sustainability 3, 1206–1223.CrossRefGoogle Scholar
de Mendiburu, F (2017) Statistical Procedures for Agricultural Research using R. La Molina, Lima: Universidad Nacional Agraria.Google Scholar
Drinkwater, LE, Letourneau, DK, Workneh, F, van Bruggen, AHC and Shennan, C (1995) Fundamental differences between conventional and Organic Tomato Agroecosystems in California. Ecological Applications 5, 1098–1112.CrossRefGoogle Scholar
Eberhart, SA and Russell, WA (1966) Stability parameters for comparing varieties. Crop Science 6.CrossRefGoogle Scholar
Entz, MH, Kirk, AP, Vaisman, I, Fox, SL, Fetch, JM, Hobson, D, Jensen, HR and Rabinowicz, J (2015) Farmer participation in plant breeding for Canadian organic crop production: implications for adaptation to climate uncertainty. Procedia Environmental Sciences, Agriculture and Climate Change—Adapting Crops to Increased Uncertainty (AGRI 2015) 29, 238–239.CrossRefGoogle Scholar
Finlay, KW and Wilkinson, GN (1963) The analysis of adaptation in a plant-breeding programme. Crop & Pasture Science 14, 742–754.CrossRefGoogle Scholar
Francis, TR and Kannenberg, LW (1978) Yield stability studies in short-season maize. I. A descriptive method for grouping genotypes. Canadian Journal of Plant Science 58, 1029–1034.CrossRefGoogle Scholar
Frossard, D (2002) How farmer-scientist cooperation is devalued and revalued: a Philippine example. In Cleveland, DA and Soleri, D (eds), Farmers, Scientists and Plant Breeding: Integrating Knowledge and Practice. New York, NY: CABI Publishing, pp. 137–160.CrossRefGoogle Scholar
Gauch, HG (2006) Statistical analysis of yield trials by AMMI and GGE. Crop Science 46, 1488–1500.CrossRefGoogle Scholar
Haussmann, BIG, Fred Rattunde, H, Weltzien-Rattunde, E, Traoré, PSC, vom Brocke, K and Parzies, HK (2012) Breeding strategies for adaptation of pearl millet and Sorghum to climate variability and change in West Africa. Journal of Agronomy and Crop Science. n/a–n/a. doi: https://doi.org/10.1111/j.1439-037X.2012.00526.x.CrossRefGoogle Scholar
Healy, GK, Emerson, BJ and Dawson, JC (2017) Tomato variety trials for productivity and quality in organic hoop house versus open field management. Renewable Agriculture and Food Systems 32, 562–572.CrossRefGoogle Scholar
Hildebrand, PG and Russell, JT (1996) Adaptability Analysis: a Method for the Design, Analysis, and Interpretation of on-Farm Research-Extension, 1st Edn.Ames: Iowa State University Press.Google Scholar
Kirk, AP, Fox, SL and Entz, MH (2012) Comparison of organic and conventional selection environments for spring wheat: comparison of organic and conventional selection environments. Plant Breeding 131, 687–694.CrossRefGoogle Scholar
Kissing Kucek, L, Dyck, E, Russell, J, Clark, L, Hamelman, J, Burns-Leader, S, Senders, S, Jones, J, Benscher, D, Davis, M, Roth, G, Zwinger, S, Sorrells, ME and Dawson, JC (2017) Evaluation of wheat and emmer varieties for artisanal baking, pasta making, and sensory quality. Journal of Cereal Science 74, 19–27.CrossRefGoogle Scholar
Lammerts van Bueren, ET and Myers, JR (2012) Organic crop breeding: integrating organic agricultural approaches and traditional and modern plant breeding methods. In van Bueren, ETL and Myers, JR (eds), Organic Crop Breeding. Wiley-Blackwell, pp. 1–13.CrossRefGoogle Scholar
Lin, C-S, Binns, MR and Lefkovitch, LP (1986) Stability analysis: where do we stand? Crop Science 26, 894–900.CrossRefGoogle Scholar
Lyon, AH, Silva, E, Zystro, J and Bell, M (2015) Seed and plant breeding for Wisconsin's organic vegetable sector: understanding farmers’ needs. Agroecology and Sustainable Food Systems 39, 601–624.CrossRefGoogle Scholar
Mazourek, M, Moriarty, G, Glos, M, Fink, M, Kreitinger, M, Henderson, E, Palmer, G, Chickering, A, Rumore, DL, Kean, D, Myers, JR, Murphy, JF, Kramer, C and Jahn, M (2009) ‘Peacework’: a cucumber mosaic virus-resistant early red bell pepper for organic systems. HortScience 44, 1464–1467.CrossRefGoogle Scholar
Murphy, KM, Lammer, D, Lyon, S, Carter, B and Jones, SS (2005) Breeding for organic and Low-input farming systems: an evolutionary–participatory breeding method for inbred cereal grains. Renewable Agriculture and Food Systems 20, 48–55.CrossRefGoogle Scholar
Murphy, KM, Campbell, KG, Lyon, SR and Jones, SS (2007) Evidence of varietal adaptation to organic farming systems. Field Crops Research 102, 172–177.CrossRefGoogle Scholar
Murphy, SE, Lee, EA, Woodrow, L, Seguin, P, Kumar, J, Rajcan, I and Ablett, GR (2009) Genotype×environment interaction and stability for isoflavone content in soybean. Crop Science 49, 1313–1321.CrossRefGoogle Scholar
Myers, J, McKenzie, L, Mazourek, M, Tracy, W, Shelton, A and Navazio, J (2012 a) Breeding peas, sweet corn, broccoli, winter squash and carrots as part of the Northern Organic Vegetable Improvement Collaborative (NOVIC). in: Strengthening Community Seed Systems. Presented at the Strengthening Community Seed Systems. Proceedings of the 6th Organic Seed Growers Conference, Port Townsend, Washington, USA, 19–21 January 2012. Organic Seed Alliance, pp. 44–45.Google Scholar
Myers, J, McKenzie, L and Voorrips, RE (2012 b) Brassicas: breeding cole crops for organic agriculture. In van Bueren, ETL and Myers, JR (eds), Organic Crop Breeding. Wiley-Blackwell, pp. 251–262.Google Scholar
Pessarakli, M (ed.) (2016) Handbook of Cucurbits. Boca Raton: CRC Press.CrossRefGoogle Scholar
Prairie Road Organic Seed (2018) Zucchini: Dark Star [www Document]. Prairie Road Organic Seed: Northern Grown. Available at https://www.prairieroadorganic.co/products/new-dark-star-zucchini-certified-organic-seed-1-packet-25-seeds (Accessed 11 August 18).Google Scholar
Przystalski, M, Osman, A, Thiemt, EM, Rolland, B, Ericson, L, Østergård, H, Levy, L, Wolfe, M, Büchse, A, Piepho, H-P and Krajewski, P (2008) Comparing the performance of cereal varieties in organic and non-organic cropping systems in different European countries. Euphytica 163, 417–433.CrossRefGoogle Scholar
R Core Team (2014) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Raman, A, Ladha, JK, Kumar, V, Sharma, S and Piepho, HP (2011) Stability analysis of farmer participatory trials for conservation agriculture using mixed models. Field Crops Research 121, 450–459.CrossRefGoogle Scholar
Renaud, ENC, Lammerts van Bueren, ET, Paulo, MJ, van Eeuwijk, FA, Juvik, JA, Hutton, MG and Myers, JR (2014) Broccoli cultivar performance under organic and conventional management systems and implications for crop improvement. Crop Science 54, 1539.CrossRefGoogle Scholar
SAS Institute, Inc. (2000) SAS 9.4. Cary, North Carolina: SAS Institute, Inc.Google Scholar
Shelton, A and Tracy, W (2015) Recurrent selection and participatory plant breeding for improvement of Two organic open-pollinated sweet corn (Zea mays L.) populations. Sustainability 7, 5139–5152.CrossRefGoogle Scholar
Shukla, GK (1972) Some statistical aspects of partitioning genotype environmental components of variability. Heredity 29, 237–245.CrossRefGoogle ScholarPubMed
Singh, S, Terán, H, Lema, M and Hayes, R (2011) Selection for dry bean yield on-station versus on-farm conventional and organic production systems. Crop Science 51, 621.CrossRefGoogle Scholar
Snapp, S (2002) Quantifying farmer evaluation of technologies: the mother and baby trial design. In Bellon, MR and Reeves, J (eds), Quantitative Analysis of Data from Participatory Methods in Plant Breeding. Mexico: CIMMYT, pp. 9–17.Google Scholar
Soleri, D and Cleveland, DA (2002) Understanding farmers’ knowledge as the basis for collaboration with plant breeders: methodological development and examples from ongoing research in Mexico, Syria, Cuba and Nepal. Farmers Sci. Plant Breed. Integrating Knowl. Pract. 16–60.CrossRefGoogle Scholar
Tollenaar, M and Lee, EA (2002) Yield potential, yield stability and stress tolerance in maize. Field Crops Research 75, 161–169; Preface.CrossRefGoogle Scholar
Wolfe, MS, Baresel, JP, Desclaux, D, Goldringer, I, Hoad, S, Kovacs, G, Löschenberger, F, Miedaner, T, Østergård, H and Lammerts van Bueren, ET (2008) Developments in breeding cereals for organic agriculture. Euphytica 163, 323–346.CrossRefGoogle Scholar
Yan, W (2014) Crop Variety Trials: Data Management and Analysis. Somerset, NJ, USA: John Wiley & Sons, Incorporated.CrossRefGoogle Scholar
Yan, W and Kang, MS (2003) GGE biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists. Boca Raton, FL: CRC Press.Google Scholar
Yan, W, Kang, MS, Ma, B, Woods, S and Cornelius, PL (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Science 47, 643–653.CrossRefGoogle Scholar
Supplementary material: File

Lyon et al. supplementary material

Lyon et al. supplementary material 1

Download Lyon et al. supplementary material(File)
File 60.7 KB