The goal of the current study was to identify the growth stage of the DK8031 maize cultivated in Embu County, Kenya at which the quantity of leaves and plant height best indicate the production of grain and biomass. Irrigation levels and nitrogen rates were designated as the main and sub-plot treatments, respectively, in this RCBD study’s split plot layout. With a maize grain yield of 3,968 kg/ha, which was just slightly higher than the observed output of 3,966 kg/ha, the MLR models provided extremely strong predictions of grain production in Season II compared to Season I, making them promising for application in maize farming in Embu County, Kenya. Grain yields were monitored during harvest, and data were kept using SAS and Microsoft Excel (V9.0). While using single factor regression, the best model fits had a correlation coefficient (r) and coefficient of determination (R2) in the eighth week; when using multilinear regression functions, the best model fits had a correlation coefficient (r) and coefficient of determination (R2). The coefficients for the later crop were frequently much lower. Multifactor regression models using the same agronomic traits suggest that the greatest fit for forecasting yield is feasible six weeks after the crop is sown. This is important because it gives the maize producer a chance to decide early on whether to keep upping, cutting back, or discontinuing new crop resource inputs. A move like this promotes resource-efficient maize cultivation for home or commercial usage.
Charles Nyambane Onyari,
Water and Agricultural Resources Management, University of Embu, P.O.Box 6-60100, Embu, Kenya.
Please see the link here: https://stm.bookpi.org/CTAS-V8/article/view/7259
Keywords: Yield assessment, soil fertility, biomass yield, Kenya.