Determination of the limiting Nutrient for Maize (Zea mays L.) Production in Yeki District, Southwest Ethiopia
Abstract
A wide range of agroecology of Ethiopia soil is deficient in both major and micronutrients. The new fertilizer type for crop production is formulated based on the nutrient deficiency. This location-specific fertilizer does not show crop yield variability when compared with the previously recommended NP fertilizer. Understanding the most yield-limiting nutrient in crop production under different agroecology is very important. Hence, the objective of this study was to identify the most yield-limiting nutrient and quantify the level of yield penalty in maize crops under three Locations in the Yeki district during the 2023 cropping season. Ten treatments constitute of control, recommended nitrogen and phosphorus (RNP), RNP +sulfur (S2), six nutrients (NPKSBZn), nitrogen omitted of fife nutrient (PKSBZn), phosphorus omitted of fife nutrient (NKSBZn), potassium omitted of fife nutrient (NPSBZn), sulfur omitted of fife nutrient (NPKBZn), boron omitted of fife nutrient (NPKSZn) and zinc omitted of fife nutrient (NPKSB) were arranged by randomized complete block design under three replication. Maize agronomic data were taken and analyzed by using R software 4.2.2. The mean difference of treatments was compared by LSD at a probability level of 5%. The pool means analysis of variance indicated that maize grain yield and yield components were significantly (p<0.05) influenced by different nutrient omissions. The highest maize grain yield penalty (42.19%) and (34.26%) were recorded under the control plot followed by nitrogen omitted plots respectively and the lowest (0.65%) from the previously recommended NP fertilizer applied plots. In the study area, the most yield reduction that showed more than 10% yield penalty of maize 34.26%, 23.20%, 19.92%, and 10.66% was due to N, S, P, and K omitted respectively. Since the study was conducted for one season further validation and demonstration for specific nutrients across multi-location and soil tests will be better to see more variability.
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DOI: https://doi.org/10.46676/ij-fanres.v%25vi%25i.482
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