Main Article Content
Cotton (Gossypium hirsutum L.) is an important cash crop in Zambia. The national seed cotton yield (SCY) per hectare ranges from 200-500 kg/ ha as compared to the potential of up to 2500 kg/ ha. Understanding the specific performance of several genotypes across different environments is an option, which may maximize specific genotypic performance. In addition, the performance of specific environments if known concerning specific and mean genotypic performance may guide the breeding approaches to these environments. This study therefore investigated the presence of SCY mega-production environments in Zambia and delineated the environments and identified the ideal test environment capable of discriminating yield differences among genotypes. Thirty (30) genotypes, were planted following a 6 x 5 lattice design with three replications in seven environments of Zambia. Additive main effects and multiplicative interaction (AMMI) model and genotype plus genotype by environment (GGE) biplot were used to explore the genotype by genotypic environmental interaction (GEI). Three mega environments (M1, M2 and M3) were identified. Genotype G27, G26 and G28 were the best performing genotypes in M1, M2 and M3 with overall mean SCY of 1416, 1320 and 960 kg/ ha respectively. Among the locations, Masumba was identified as an ideal test environment with mean SCY of 1249 kg/ ha. Therefore, testing seed cotton genotypic yield and selecting desirable genotypes in Masumba may be sufficient for evaluation.
CIRCOT. Utilisation of cotton seed by-products. Mumbai, India; 2019.
Dareka A, Reddy AA. Cotton pricing forecasting in major producing states. Econominc Affairs New Delhi Publishers. 2017;62:373-378.
FAO. Production countries by commodity; 2017.
Available:http://www.fao.org/faostat (Accessed on 23/01/2019)
Cotton Development Trust. Cotton Year Report, Mazabuka, Zambia; 2014.
Mutale CE, Munyinda K, Tembo L. Tagging, mapping and identification of the QTLs associated with phosphorous utilization for phosphorous- limiting soils in tropical maize. Journal of Genetics, Genomics and Plant Breeding. 2020;4:47-53.
Mbwando A, Lungu DM, Tryphone GM, Tembo L. Nature of resistance of cowpea Alectra vogelii infestation. African Crop Science Journal. 2016;24:389-395.
Crossa JV, Eeuwijk FA, Jiang C, Edmeades GO, Hoisington D. Interpreting genotype x environment in tropical maize using linked molecular markers and environmental covariables. Theoretical Applied Genetics. 1999;99:611-625.
Bbebe N, Siamasonta B, Lungu DM. Combining ability among interspecific (G. hirsutum x G. barbadense) and mutation derived lines of cotton in fiber quality and agronomic traits. Ruforum Biennial Conference. 2010;2:417-420.
Riaz M, Farooq J, Ahmed S, Amin M, Chatta WS, Ayoub M, Kainth RA. Stability analysis of different cotton genotypes under normal and water deficit conditions. Journal of Integrative Agriculture. 2018;17:3-9.
Payne RW, Murray DA, Harding RA, Baird DB, Soutars DM. An introduction to Genstat for Windows VSN International. 13th Edition: Hemel Hempstead, UK; 2010.
Gauch HGG, Piepho HP, Annicchiarico P. Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Science. 2008;4:6–8.
Yan W, Kang MS. GGE Biplot analysis: A graphical tool for breeders, geneticists and agronomists. CRC Press, Florida, USA; 2020.
Oladosu Y, Rafii MY, Magaji U, Abdullah N, Ramli A, Hussin G. Assessing the representative and discriminative ability of test environments for rice breeding in Malaysia using GGE Biplot. International Journal of Scientific and Technology Research. 2017;6:8-16.
Tembo L, Asea G, Gibson PT, Okori P. Quantitative trait loci for resistance to Stenocarpella maydis and Fusarium graminearum cob rots in tropical maize. Journal of Crop Improvement. 2014;2:1– 8.
Tembo L. Effect in hydroponics of nitrogen and aluminium toxicity on tropical maize. Asian Research Journal of Agriculture. 2018;9:1-7.
Ndhlela T, Herselman L, Magorokosho C, Setimela P, Mutimaamba C, Labuschagne M. Genotype x environment interaction of maize grain yield using AMMI biplots. Crop Science. 2014;4:1-6.
McDermott B, Coe R. An easy introduction to biplots for multi-environment trials. World Agroforestry Centre. 2012;1:1-6.
Tukamuhabwa PA, Siimwe MN, Abasirye M, Kabayi P, Maphosa M. Genotype by environment interaction of advanced generation soybean lines for grain yield in Uganda. African Crop Science. 2012;20: 105-117.
Ali Y, Aslam Z, Hussain F. Genotype and environment interaction effect on yield of cotton under naturally salt stress condition. International Journal Environmental Science Technology. 2018;2:1‒7.