Variation models of maize ear production in relation to morphological elements
Abstract
The variation in the weight of maize ears was analyzed in relation to certain morphological parameters. The experiment was carried out within ARDS Lovrin. The biological material was represented by 20 corn hybrids obtained at ARDS Suceava. In the framework of the experiment and the study, the hybrids were named SV1 to SV20. The ear length (EL), the number of rows per ear (RN), the number of grains per row (GNR) and the weight of the ear sample (EW) were determined. Variable values of the considered parameters were recorded, EL = 16.00 – 20.30±0.29 cm, RN = 12.00 – 17.00±0.29, GNR = 23.50 – 46.00±1.09, and EW = 0.543 – 1.304±0.048 kg. Low variability was recorded in the case of EL (CV = 7.5504), RN (CV = 9.0508) and GNR (CV = 13.2078) parameters, and moderate variability was recorded in the case of EW (CV = 23.6016). The variation of EW was described by equations and graphic models under conditions of statistical safety. Morphological parameters EL and GNR facilitated the estimation of EW variation under higher statistical safety conditions, compared to other parameters considered in the study. In the case of the variation models based on EL and GNR, statistical safety parameters p = 0.0023, and RMSE = 0.1146 resulted, in comparison with the other analyzed parameters (RMSE = 0.1860 in the case of EL with RN; RMSE = 0.1197 in the case of RN with GNR).
References
Agapie A.L., Horablaga M.N., Vacariu B., Eremi O., Sala F. (2024). Biometric parameters in the characterization of ears in a collection of corn genotypes. Life Science and Sustainable Development, 5(1): 44-51.
Cui J., Cui Z., Lu Y., Lv X., Cao Q., Hou Y., Yang X., Gu Y. (2022). Maize grain yield enhancement in modern hybrids associated with greater stalk lodging resistance at a high planting density: a case study in northeast China. Scientific Reports, 12(1): 14647.
Hammer Ø., Harper D.A.T., Ryan P.D. (2001). PAST: Paleontological Statistics software package for education and data analysis. Palaeontologia Electronica, 4(1): 1-9.
Liang X., Ye J., Li X., Tang Z., Zhang X., Li W., Yan J., Yang W. (2021). A high-throughput and low-cost maize ear traits scorer. Molecular Breeding, 41(2): 17.
Luo Y., Zhang M., Liu Y., Liu J., Li W., Chen G., Peng Y., Jin M., Wei W., Jian L., Yan J., Fernie A.R., Yan J. (2022). Genetic variation in YIGE1 contributes to ear length and grain yield in maize. New Phytologist, 234(2): 513-526.
Ma Z., Wang J., Wen S., Ren J., Hui H., Huang Y., Yang J., Zhao B., Liu B., Gao Z. (2024). Evaluation of maize hybrids for resistance to ear rot caused by dominant fusarium species in Northeast China. Agronomy, 14: 855.
Mandić V., Ðordević S., Brankov M., Živković V., Lazarević M., Keškić T., Krnjaja V. (2024). Response of yield formation of maize hybrids to different planting densities. Agriculture, 14: 351
Meier U. (2001). Growth stages of mono-and dicotyledonous plants e BBCH monograph. Federal Biological Research Centre for Agriculture and Forestry, 158 pp.
Mendes-Moreira P.M.R., Mendes-Moreira J., Fernandes A., Andrade E., Hallauer A.R., Pêgo S.E., Vaz Patto M.C. (2014). Is ear value an effective indicator for maize yield evaluation? Field Crops Research, 161: 75-86.
Mueller S.M., Messina C.D., Vyn T.J. (2019). The role of the exponential and linear phases of maize (Zea mays L.) ear growth for determination of kernel number and kernel weight. European Journal of Agronomy, 111: 125939.
Ortez O.A., McMechan A.J., Hoegemeyer T., Ciampitti I.A., Nielsen R., Thomison P.R., Elmore R.W. (2022). Abnormal ear development in corn: A review. Agronomy Journal, 114(2): 1168-1183.
Oury V., Leroux T., Turc O., Chapuis R., Palaffre C., Tardieu F., Prado S.A., Welcker C., Lacube S. (2022). Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits. Plant Methods, 18: 96.
Petkevichius S., Shpokas L., Kutzbach H.-D. (2008). Investigation of the maize ear threshing process. Biosystems Engineering, 99(4): 532-539.
Rossini M.A., Curin F., Otegui M.E. (2023). Ear reproductive development components associated with kernel set in maize: Breeding effects under contrasting environments. Field Crops Research, 304: 109150.
Sala F., Rujescu C., Feher A. (2019). Assessment model for the imbalance in N and PK fertilization for maize: Case study for the western part of Romania. Romanian Agricultural Research, 36: 143-153.
Wang J., Zhao S., Zhang Y., Lu X., Du J., Wang C., Wen W., Guo X., Zhao C. (2023). Investigating the genetic basis of maize ear characteristics: A comprehensive genome-wide study utilizing high-throughput phenotypic measurement method and system. Frontiers in Plant Science, 14: 1248446.
Wang Y., Luo Y., Guo X., Li Y., Yan J., Shao W., Wei W., Wei X., Yang T., Chen J., Chen L., Ding Q., Bai M., Zhuo L., Li L., Jackson D., Zhang Z., Xu X., Yan J., Liu H., Liu L., Yang N. (2024). A spatial transcriptome map of the developing maize ear. Nature Plants, 10: 815-827.
Wolfram, Research, Inc., Mathematica, Version 12.1, Champaign, IL (2020).
Ye D., Chen J., Yu Z., Sun Y., Gao W., Wang X., Zhang R., Zaib-Un-Nisa Su, D., Atif Muneer M. (2023). Optimal plant density improves sweet maize fresh ear yield without compromising grain carbohydrate concentration. Agronomy, 13(11): 2830.
