Hostname: page-component-8448b6f56d-cfpbc Total loading time: 0 Render date: 2024-04-20T03:28:11.766Z Has data issue: false hasContentIssue false

Organic system vs. conventional – a Bayesian analysis of Polish potato post-registration trials

Published online by Cambridge University Press:  25 January 2023

M. Przystalski*
Affiliation:
Research Centre for Cultivar Testing, 63-022 Słupia Wielka 34, Poland
T. Lenartowicz
Affiliation:
Research Centre for Cultivar Testing, 63-022 Słupia Wielka 34, Poland
*
Author for correspondence: M. Przystalski, E-mail: marprzyst@gmail.com

Abstract

Interest in organic agriculture worldwide is growing and is mainly supported by a strong consumer interest. In the literature, a lot of attention has been paid to comparing organic and conventional systems, on studying the yield gap between the two systems and, how to reduce it. In the present work, based on the results from Polish organic and conventional series of field trials carried out in 2019–2021, organic and conventional systems were compared in terms of potato tuber yield. Moreover, we propose a Bayesian approach to the variety × environment × system data set and describe Bayesian counterparts of two stability measures. Using this methodology, we identify the most stable and highest tuber yielding varieties in the Polish potato organic and conventional series of field trials. It is shown that the tuber yield in the organic system was approx. 44% lower than the tuber yield in the conventional system. Moreover, varieties Tajfun and Otolia were the most stable and highest yielding varieties in the organic system, whereas in the conventional system, the variety Jurek was the most stable and highest yielding variety among the tested varieties. In the present work, the use of the Bayesian approach allowed us to calculate the probability that the mean of a given variety in given system exceeds the mean of control varieties in that system.

Type
Crops and Soils Research Paper
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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

Mention of trade names or commercial products in this article is solely for the purpose of providing scientific information and does not imply recommendation or endorsement by the Research Centre for Cultivar Testing.

References

Annicchiarico, P (1992) Cultivar adaptation and recommendation from alfalfa trials in northern Italy. Journal of Genetics and Breeding 46, 269278.Google Scholar
Annicchiarico, P (2002) Genotype × Environment Interactions: Challenges and Opportunities for Plant Breeding and Cultivar Recommendations, vol. 1. Rome: FAO.Google Scholar
Azevedo, CF, Nascimento, M, Carvalho, IR, Nascimento, ACC, Ferreira de Almeida, HC, Cruz, CD and Gonzalez da Silva, JA (2022) Updated knowledge in the estimation of genetics parameters: a Bayesian approach in white oat (Avena sativa L.). Euphytica 218, 113.CrossRefGoogle Scholar
Bernardo Júnior, LAY, da Silva, CP, de Oliveira, LA, Nuvunga, JJ, Pires, LPM, Von Pinho, RG and Balestre, M (2018) AMMI Bayesian models to study stability and adaptability in maize. Agronomy Journal 110, 17651776.CrossRefGoogle Scholar
Bocianowski, J and Liersch, A (2021) Multi-environmental evaluation of winter oilseed rape genotypic performance using mixed models. Euphytica 217, 1–9.CrossRefGoogle Scholar
Caliński, T, Czajka, S, Kaczmarek, Z, Krajewski, P, Pilarczyk, W, Siatkowski, I and Siatkowski, M (2017) On mixed model analysis of multi-environment variety trials: a reconsideration of the one-stage and the two-stage models and analyses. Statistical Papers 58, 433465.CrossRefGoogle Scholar
Colombari Filho, JM, de Resende, MDV, de Morais, OP, de Castro, AP, Guimaraes, EP, Pereira, JA, Utumi, MM and Breseghello, F (2013) Upland rice breeding in Brazil: a simultaneous genotypic evaluation of stability, adaptability and grain yield. Euphytica 192, 117129.CrossRefGoogle Scholar
Cowles, MK and Carlin, BP (1996) Markov chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical Association 91, 833904.CrossRefGoogle Scholar
Crossa, J, Perez-Elizalde, S, Jarquin, D, Cotes, JM, Viele, K, Liu, G and Cornelius, PL (2011) Bayesian Estimation of additive main effects and multiplicative interaction model. Crop Science 51, 14581469.CrossRefGoogle Scholar
Damesa, TM, Möhring, J, Worku, M and Piepho, HP (2017) One step at a time: stage-wise analysis of series of experiments. Agronomy Journal 109, 845857.CrossRefGoogle Scholar
de Oliveira, LA, da Silva, CP, Nuvunga, JJ, da Silva, AQ and Balestre, M (2016) Bayesian GGE biplot models applied to maize multi-environment trials. Genetics and Molecular Research 15, 121.CrossRefGoogle Scholar
de Oliveira, TRA, de Carvalho, HWL, Nascimento, M, Costa, EFN, do Amaral Junior, AT, Gravina, GDA and de Carvalho Filho, JLS (2018) The Eberhart and Russell's Bayesian method used as an instrument to select maize hybrids. Euphytica 214, 19.CrossRefGoogle Scholar
De Ponti, T, Rijk, B and Van Ittersum, MK (2012) The crop yield gap between organic and conventional agriculture. Agricultural Systems 108, 19.CrossRefGoogle Scholar
de Valpine, P, Turek, D, Paciorek, CJ, Anderson-Bergman, C, Lang, DT and Bodik, R (2017) Programming with models: writing statistical algorithms for general model structures with NIMBLE. Journal of Computational and Graphical Statistics 26, 403413.CrossRefGoogle Scholar
de Valpine, P, Paciorek, C, Turek, D, Michaud, N, Anderson-Bergman, C, Obermeyer, F, Wehrhahn Cortes, C, Rodrìguez, A, Lang, DT and Paganin, S (2022) NIMBLE User Manual. R package manual version 0.12.2. Available at https://r-nimble.org (Assessed 09.06.2022).Google Scholar
Derejko, A, Studnicki, M, Wójcik-Gront, E and Gacek, E (2020) Adaptive grain yield patterns of Triticale (Triticosecale Wittmack) cultivars in six regions of Poland. Agronomy 10, 114.CrossRefGoogle Scholar
Dias, PC, Xavier, A, de Resende, MDV, Barbosa, MHP, Biernaski, FA and Estopa, RA (2018) Genetic evaluation of Pinus taeda clones from somatic embryogenesis and their genotype × environment interaction. Crop Breeding and Applied Biotechnology 18, 5564.CrossRefGoogle Scholar
Dias, KOG, dos Santos, JPR, Krause, MD, Piepho, HP, Guimarães, LJM, Pastina, MM and Garcia, AAF (2022) Leveraging probability concepts for cultivar recommendation in multi-environment trials. Theoretical and Applied Genetics 135, 13851399.CrossRefGoogle ScholarPubMed
Dierauer, H, Gelencsér, T and Klaiss, M (2021) Sortenliste Biokartoffeln. Available at https://www.fibl.\newline.org/en/shop-en/1041-biokartoffeln (Assessed 16.02.2022).Google Scholar
Digby, PGN (1979) Modified joint regression analysis for incomplete variety × environment data. The Journal of Agricultural Sciences 93, 8186.Google Scholar
Eberhart, SA and Russell, WA (1966) Stability parameters for comparing varieties. Crop Science 6, 3640.CrossRefGoogle Scholar
Edwards, JW and Jannink, JL (2006) Bayesian modeling of heterogeneous error and genotype × environment interaction variances. Crop Science 46, 820833.CrossRefGoogle Scholar
Edwards, JW and Orellana, M (2015) Increasing selection response by Bayesian modeling of heterogeneous environmental variances. Crop Science 55, 556563.CrossRefGoogle Scholar
Eskridge, KM (1990) Selection of stable cultivars using a safety-first rule. Crop Science 30, 369374.CrossRefGoogle Scholar
Eskridge, KM and Mumm, RF (1992) Choosing plant cultivars based on the probability of outperforming a check. Theoretical and Applied Genetics 84, 494500.CrossRefGoogle ScholarPubMed
Finlay, K and Wilkinson, G (1963) The analysis of adaptation in a plant breeding programme. Australian Journal of Agricultural Research 14, 742754.CrossRefGoogle Scholar
Flis, B, Domański, L, Zimoch-Guzowska, E, Polgar, Z, Pousa, and Pawlak, A (2014) Stability analysis of agronomic traits in potato cultivars of different origin. American Journal of Potato Research 91, 404413.CrossRefGoogle Scholar
Gauch, HG (1992) Statistical Analysis of Regional Yield Trials. AMMI Analysis of Factorial Designs. New York: Elsevier.Google Scholar
Gelman, A and Rubin, DB (1992) Inference from iterative simulation using multiple sequences (with discussion). Statistical Science 7, 457511.CrossRefGoogle Scholar
Gelman, A, Carlin, JB, Stern, HS, Dunson, DB, Vehtari, A and Rubin, DB (2013) Bayesian Data Analysis, 3rd Edn. Boca Raton: Chapman & Hall/CRC.CrossRefGoogle Scholar
Hadfield, JD (2012) MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. Journal of Statistical Software 33, 122.Google Scholar
Hedges, LV and Olkin, I (1985) Statistical Methods for Meta-Analysis. Orlando: Academic Press.Google Scholar
Hoagland, C (2009) Impact of conventional and organic production on agronomic and end-use quality traits of winter wheat (MS thesis). Univ. of Nebraska, Lincoln.Google Scholar
Huang, A and Wand, MP (2013) Simple marginally noninformative prior distributions for covariance matrices. Bayesian Analysis 8, 439452.CrossRefGoogle Scholar
Josse, J, van Eeuwijk, F, Piepho, HP and Denis, JB (2014) Another look at Bayesian analysis of AMMI models for genotype-environment data. Journal of Agricultural, Biological and Environmental Statistics 19, 240257.Google Scholar
Kazimierczak, R, Średnicka-Tober, D, Hallmann, E, Kopczyńska, K and Zarzyńska, K (2019) The impact of organic vs. conventional agricultural practices on selected quality features of eight potato cultivars. Agronomy 9, 799.CrossRefGoogle Scholar
Kirk, AP, Fox, SL and Entz, MH (2012) Comparison of organic and conventional selection environments for spring wheat. Plant Breeding 131, 687694.CrossRefGoogle Scholar
Kucek, LK, Santantonio, N, Gauch, HG, Dawson, JC, Mallory, EB, Darby, HM and Sorrells, ME (2019) Genotype×environment interactions and stability in organic wheat. Crop Science 58, 18.Google Scholar
Lenartowicz, T, Piepho, HP and Przystalski, M (2020) Stability analysis of tuber yield and starch yield in mid-late and late maturing starch cultivars of potato (Solanum tuberosum). Potato Research 63, 179197.CrossRefGoogle Scholar
Lesur-Dumoulin, C, Malézieux, E, Ben-Ari, T, Langlais, C and Makowski, D (2017) Lower average yields but similar yield stability in organic versus conventional horticulture. A meta-analysis. Agronomy for Sustainable Development 37, 45.CrossRefGoogle Scholar
Lewandowski, D, Kurowicka, D and Joe, H (2009) Generating random correlation matrices based on vines and extended onion method. Journal of Multivariate Analysis 100, 19892001.CrossRefGoogle Scholar
Lian, L and 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, Genetics 6, 586597.Google Scholar
Lin, CS and Binns, MR (1988) A superiority measure of cultivar performance for cultivar × location data. Canadian Journal of Plant Sciences 68, 193198.CrossRefGoogle Scholar
Löschenberger, F, Fleck, A, Grausgruber, H, Hetzendorfer, H, Hof, G, Lafferty, J, Marn, M, Neumayer, A, Pfaffinger, G and Birschitzky, J (2008) Breeding for organic agriculture: the example of winter wheat in Austria. Euphytica 163, 469480.CrossRefGoogle Scholar
Lunn, D, Jackson, C, Best, N, Thomas, A and Spiegelhalter, D (2013) The BUGS Book. A Practical Introduction to Bayesian Analysis. Boca Raton: Chapman& Hall/CRC.Google Scholar
Mathew, B, Holand, AM, Koistinen, P, Léon, J and Sillanpää, MJ (2016) Reparametrization-based estimation of genetic parameters in multi-trait animal model using integrated nested Laplace approximation. Theoretical and Applied Genetics 129, 215225.CrossRefGoogle ScholarPubMed
Mead, R, Riley, J, Dear, K and Singh, SP (1986) Stability comparison of intercropping and monocropping systems. Biometrics 42, 253266.CrossRefGoogle Scholar
Murphy, KM, Campbell, KG, Lyon, SR and Jones, SS (2007) Evidence of varietal adaptation to organic farming systems. Field Crops Research 102, 172177.CrossRefGoogle Scholar
Nascimento, M, Nascimento, ACC, Fabyano Fonseca e Silva, FF, Teodoro, PE, Azevedo, CF, de Oliveira, TRA, do Amaral Junior, AT, Cruz, CD, Farias, FJC and de Carvalho, LP (2020) Bayesian Segmented regression model for adaptability and stability evaluation of cotton genotypes. Euphytica 216, article 30.CrossRefGoogle Scholar
Olivoto, T and Lúcio, ADC (2020) metan: an R package for multi-environment trial analysis. Methods in Ecology and Evolution 11, 783789.CrossRefGoogle Scholar
Orellana, M, Edwards, J and Carriquiry, A (2014) Heterogeneous variances in multi-environment yield trials for corn hybrids. Crop Science 54, 10481056.CrossRefGoogle Scholar
Pedersen, TM (2012) Organic VCU Testing. Current status in 16 European Countries. Aarhus, Denmark: Knowledge Centre for Agriculture (SEGES).Google Scholar
Piepho, HP (1996) A simplified procedure for comparing the stability of cropping systems. Biometrics 52, 315320.CrossRefGoogle Scholar
Piepho, HP (1998) Methods for comparing the yield stability of cropping systems. Journal of Agronomy and Crop Science 180, 193213.CrossRefGoogle Scholar
Piepho, HP (1999) Stability analysis using the SAS system. Agronomy Journal 91, 154160.CrossRefGoogle Scholar
Piepho, HP (2000) Exact confidence limits for covariate-dependent risk in cultivar trials. Journal of Agricultural, Biological and Environmental Statistics 5, 202213.CrossRefGoogle Scholar
Plummer, M, Best, N, Cowles, K and Vines, K (2006) CODA: convergence diagnosis and output analysis for MCMC. RNews 6, 711.Google Scholar
Ponisio, LC, M'Gonigle, LK, Mace, KC, Palomino, J, de Valpine, P and Kremen, C (2015) Diversification practices reduce organic to conventional yield gap. Proceedings of the Royal Society B 282, 20141396.CrossRefGoogle ScholarPubMed
Przystalski, M and Lenartowicz, L (2020) Yielding stability of early maturing potato varieties: Bayesian analysis. The Journal of Agricultural Science 158, 564573.CrossRefGoogle Scholar
Przystalski, M, Osman, A, Thiemt, EM, Rolland, B, Ericson, L, Østergård, H, Levy, L, Wolfe, M, Büchse, A, Piepho, HP and Krajewski, P (2008) Comparing the performance of cereal winter wheat varieties in organic and non-organic systems in different European countries. Euphytica 163, 417433.CrossRefGoogle Scholar
Rakszegi, M, Mikó, P, Löschenberger, F, Hiltbrunner, J, Aebi, R, Knapp, S, Tremmel-Bede, K, Megyeri, M, Kovács, G, Molnár-Láng, M, Vida, G, Láng, L and Bedő, Z (2016) Comparison of quality parameters of wheat varieties with different breeding origin under organic and low-input conventional conditions. Journal of Cereal Science 69, 297305.CrossRefGoogle Scholar
Reid, TA, Yang, R-C, Salmon, DF, Navabi, A and Spaner, D (2011) Realized gains from selection for spring wheat grain yield are different in conventional and organically managed systems. Euphytica 177, 253266.Google Scholar
Resende, MDV (2007) Matematica e estatistica na Analise de Experimentos e no Melhoramento Genetico. Colombo: Embrapa Florestas.Google Scholar
Schrama, M, de Haan, JJ, Kroonen, M, Verstegen, H and Van der Putten, WH (2018) Crop yield gap and stability in organic and conventional farming systems. Agriculture, Ecosystems and Environment 256, 123130.Google Scholar
Shah, A, Askegaard, M, Rasmussen, IA, Jimenez, EMC and Olesen, JE (2017) Productivity of organic and conventional arable cropping systems in long-term experiments in Denmark. European Journal of Agronomy 90, 1222.CrossRefGoogle Scholar
Shukla, GK (1972) Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29, 237245.CrossRefGoogle ScholarPubMed
Silva, FF, Viana, JMS, Faria, VR and de Resende, M (2013) Bayesian inference of mixed models in quantitative genetics of crop species. Theoretical and Applied Genetics 126, 17491761.CrossRefGoogle Scholar
Spiegelhalter, DJ, Best, NG, Carlin, BP and Van der Linde, A (2002) Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society Series B 64, 583689.CrossRefGoogle Scholar
Theobald, CM and Talbot, M (2002) The Bayesian choice of crop variety and fertilizer dose. Journal of the Royal Statistical Society Series C – Applied Statistics 51, 2336.CrossRefGoogle Scholar
Theobald, CM and Talbot, M (2004) Bayesian selection of fertilizer level when crop price depends on quality. Computational Statistics and Data Analysis 47, 867880.CrossRefGoogle Scholar
Theobald, CM, Talbot, M and Nabugoomu, F (2002) Bayesian approach to regional and local-area prediction from crop variety trials. Journal of Agricultural, Biological and Environmental Statistics 7, 403419.CrossRefGoogle Scholar
Theobald, CM, Roberts, AMI, Talbot, M and Spink, JM (2006) Estimation of economically optimum seed rates for winter wheat from series of trials. The Journal of Agricultural Science 144, 303316.Google Scholar
Willer, H, Trávníček, J, Meier, C and Schlatter, B (eds) (2022) The World of Organic Agriculture. Statistics and Emerging Trends 2022. Research Institute of Organic Agriculture FiBL, Frick, and IFOAM – Organics International, Bonn. (Assessed 16.02.2022).Google Scholar
Yan, W and Kang, MS (2003) GGE Biplot Analysis: A Graphical Tool for Breeders, Genetists and Agronomists. Boca Raton: CRC Press.Google Scholar
Youngflesh, C (2018) MCMCvis: tools to visualize, manipulate, and summarize MCMC output. Journal of Open Source Software 3, 640.CrossRefGoogle Scholar
Supplementary material: File

Przystalski and Lenartowicz supplementary material

Supplements S1-S8
Download Przystalski and Lenartowicz supplementary material(File)
File 4.8 MB