Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-21T16:43:31.619Z Has data issue: false hasContentIssue false
  • English
  • Français

REML IS AN EFFECTIVE ANALYSIS FOR MIXED MODELLING OF UNBALANCED ON-FARM VARIETAL TRIALS

Published online by Cambridge University Press:  01 January 2009

D. S. VIRK ,
D. B. PANDIT ,
M. A. SUFIAN ,
F. AHMED ,
M. A. B. SIDDIQUE ,
M. A. SAMAD ,
M. M. RAHMAN ,
M. M. ISLAM ,
G. ORTIZ-FERRARA  and
K. D. JOSHI
...Show all authors
D. S. VIRK*
Affiliation:
CAZS-Natural Resources, College of Natural Sciences, Bangor University, Wales, UK
D. B. PANDIT
Affiliation:
Wheat Research Centre, Bangladesh Agricultural Research Institute (BARI), Nashipur, Dinajpur, Bangladesh
M. A. SUFIAN
Affiliation:
Wheat Research Centre, Bangladesh Agricultural Research Institute (BARI), Nashipur, Dinajpur, Bangladesh
F. AHMED
Affiliation:
Regional Agricultural Research Station, BARI, Jamalpur, Bangladesh
M. A. B. SIDDIQUE
Affiliation:
Regional Agricultural Research Station, BARI, Jessore
M. A. SAMAD
Affiliation:
Wheat Testing Station, BARI, Joydebpur, Gazipur
M. M. RAHMAN
Affiliation:
Regional Wheat Research Centre, BARI, Shyampur, Rajshahi
M. M. ISLAM
Affiliation:
Wheat Research Centre, Bangladesh Agricultural Research Institute (BARI), Nashipur, Dinajpur, Bangladesh
G. ORTIZ-FERRARA
Affiliation:
International Center for Maize and Wheat Improvement (CIMMYT) South Asia Regional Program, Kathmandu, Nepal
K. D. JOSHI
Affiliation:
CAZS-Natural Resources, CIMMYT South Asia Regional Program Office, Kathmandu, Nepal
J. R. WITCOMBE
Affiliation:
CAZS-Natural Resources, College of Natural Sciences, Bangor University, Wales, UK
*
*Corresponding author: d.s.virk@bangor.ac.uk
Get access Rights & Permissions [Opens in a new window]

Summary

On-farm participatory varietal selection (PVS) trials are often of two types: mother trials (with all of the entries) and baby trials (each having one, or very few of the entries from the mother trials). We conducted PVS trials on 17 wheat varieties in 12 villages of four districts of Bangladesh over three years but the data were highly unbalanced. Both quantitative and qualitative traits were measured in the on-farm trials. The factors in the trials were both fixed effects (varieties and districts) and random (years and farmers). We used the residual or restricted maximum likelihood (REML) analysis for the mixed model for quantitative traits. For qualitative data on farmers' perceptions, logistic regression procedures were used that are equally applicable to balanced and unbalanced data sets. The REML analysis provided adjusted mean values for quantitative traits for all the varieties, for the mother and baby trials separately, using the data from all years and all locations. It identified varieties BAW 1006 and BAW 1008 that yielded 19–30% more than the control Kanchan and also had a higher 1000-grain weight, were at least as early to flower and had a high overall ranking by farmers in the mother trials. The logistic regression analysis of perception data agreed with the results of the REML analysis as these varieties were most preferred by farmers for grain yield, earlier maturity and better chapatti making quality. The less labour-intensive method of recording qualitative perceptions can usefully replace actual yield measurements, particularly when validated by other participatory measures such as intended and actual adoption. In 2005, BAW 1006 was released as BARI Gom 23 or Bijoy and BAW 1008 as BARI Gom 24 or Prodip for the whole of Bangladesh. The validity of the results of the REML analysis was confirmed by the high early adoption trends of the identified varieties. Since REML is an effective analysis for unbalanced PVS trial data using a mixed model, its wider use by researchers would increase the value of the PVS process.

Type
Research Article
Information
Experimental Agriculture , Volume 45 , Issue 1 , January 2009 , pp. 77 - 91
Copyright
Copyright © Cambridge University Press 2008

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.)

References

REFERENCES

Allan, E. and Rowlands, J. (2001). Mixed Models and Multilevel Data Structures in Agriculture. Statistical Service Centre, The University of Reading, UK.Google Scholar
Coe, R. (2002). Analyzing data from participatory on-farm trials. In Quantitative Analysis of Data from Participatory Methods in Plant Breeding, 18–34 (Eds Mauricio, R., and Reeves, J.). Mexico, DF: CIMMYT.Google Scholar
DeLacy, I. H., Basford, K. E., Cooper, M., Bull, J. K. and McLaren, C. G. (1996). Analysis of multi-environment trials – a historical perspective. In Plant Adaptation and Crop Improvement, 39–124 (Eds Cooper, M. and Hammer, G. L.). CAB International, Wallingford, Oxon, UK.Google Scholar
Eskridge, K. M. (1996). Analysis of multiple environmental trials using the probability of outperforming a check. In Genotype-by-environment Interaction, 283–308 (Eds Kang, M. S. and Gauch, H. G.). CRC Press, Boca Raton, FL, USA.Google Scholar
Ortiz-Ferrara, G., Chand, R., Bhatta, M. R., Mudwari, A., Thapa, D. B., Sufian, M. A., Saikia, T. P., Chatrath, R., Witcombe, J. R., Virk, D. S. and Sharma, R. C. (2007). Partnering with farmers to accelerate adoption of new technologies in South Asia to improve wheat productivity. Euphytica 157:399–407.CrossRefGoogle Scholar
Grisley, W. and Shamambo, M. (1993). An analysis of adoption and diffusion of Carioca beans in Zambia resulting from an experimental distribution of seed. Experimental Agriculture 29:379–386.CrossRefGoogle Scholar
Joshi, A. and Witcombe, J. R. (1996). Farmer participatory crop improvement. II. Participatory varietal selection, a case study in India. Experimental Agriculture 32:461–477.CrossRefGoogle Scholar
Littell, R. C., Nilliken, G. A., Stroup, W. W. and Wolfinger, R. D. (1996). SAS System for Mixed Models. SAS Institute Inc., Cary, NC, USA.Google Scholar
Omanya, G. O., Weltzein-Rattunde, E., Sogodogo, D., Sanogo, M., Hanssens, N., Guero, Y. and Zangre, R. (2007). Participatory varietal selection with improved pearl millet in west Africa. Experimental Agriculture 43:5–19.CrossRefGoogle Scholar
Piepho, H. P. (1994). Best linear unbiased prediction (BLUP) for regional yield trials: A comparison to additive main effects and multiplicative interactions (AMMI) analysis. Theoretical Applied Genetics 89:647–654.CrossRefGoogle ScholarPubMed
Searle, S. R., Casella, G. and McCulloch, C. E. (1992). Variance Components. Wiley, New York.CrossRefGoogle Scholar
Simmonds, N. W. (1979). Principles of Crop Improvement. London: English Language Book Society and Longman Group Ltd.Google Scholar
Snapp, S. (1999). Mother and baby trials: a novel trial design being tried out in Malawi. In TARGET. The Newsletter of the Soil Fertility Research Network for Maize-Based Cropping Systems in Malawi and Zimbabwe. Jan. 1999 issue. CIMMYT, Zimbabwe.Google Scholar
Snedecor, G. W. and Cochran, W. G. (1973). Statistical Methods, 6th edn. Iowa: Iowa State University Press.Google Scholar
Sperling, L. and Sceidegger, U. (1995). Participatory selection of beans in Rwanda: Results, Methods Institutional issues. International Institute for Environment and Development Gatekeeper Series No. 51. International Institute for Environmental and Development, London.Google Scholar
Sperling, L. and Loevinsohn, M. E. (1993). The dynamics of adoption: distribution and morality of bean varieties among small farmers in Rwanda. Agricultural Systems 41:441–453.CrossRefGoogle Scholar
Virk, D. S., Chakraborty, M., Ghosh, J. and Harris, D. (2006). Participatory evaluation of horsegram (Macrotyloma uniflorum) varieties and their on-station responses to on-farm seed priming in eastern India. Experimental Agriculture 42:411–425.CrossRefGoogle Scholar
Virk, D. S., Chakraborty, M., Ghosh, J., Prasad, S. C. and Witcombe, J. R. (2005). Increasing the client-orientation of maize breeding using farmer participation in eastern India. Experimental Agriculture 41:413–426.CrossRefGoogle Scholar
Virk, D. S. and Witcombe, J. R. (2008). Evaluating cultivars in unbalanced on-farm participatory trials. Field Crops Research 106:105–115.CrossRefGoogle Scholar
Witcombe, J. R. (1999). Do farmer participatory methods apply more to high potential areas than to marginal ones? Outlook on Agriculture 28:43–49.CrossRefGoogle Scholar
Witcombe, J. R. (2002). A mother and baby trial system. In Breeding Rainfed Rice for Drought-prone Environments: Integrating Conventional and Participatory Plant Breeding in South and South-east Asia. Proceedings of a DFID Plant Science Research Programme/IRRI Conference 12–15 March 2002, IRRI, Los Baños, Philippines, 79–89 (Eds J. R. Witcombe, L. B. Parr and G. N. Atlin). Department for International Development (DFID) Plant Science Research Programme (PSP), Centre for Arid Zone Studies (CAZS), Bangor and International Rice Research Institute (IRRI), Manila.Google Scholar
Witcombe, J. R., Joshi, A., Joshi, K. D. and Sthapit, B. R. (1996). Farmers participatory crop improvement. I. Varietals selection and breeding methods and their impacts on biodiversity. Experimental Agriculture 32:445–460.CrossRefGoogle Scholar
Witcombe, J. R., Joshi, K. D., Gyawali, S., Musa, A. A., Johansen, C., Virk, D. S. and Sthapit, B. R. 2005. Participatory Plant Breeding is better described as highly client-oriented plant breeding. I. Four indicators of client orientation in plant breeding. Experimental Agriculture 41:1–21.CrossRefGoogle Scholar
Witcombe, J. R., Petre, R., Jones, S. and Joshi, A. (1999). Farmer participatory crop improvement. IV. The spread and impact of a rice variety identified by participatory varietal selection. Experimental Agriculture 35:471–487.CrossRefGoogle Scholar