INFLUENCE OF NITROGEN SOURCES ON GROWTH, YIELD, SOME MACRO AND MICRONUTRIENTS CONTENT OF MAIZE PLANT (Zea mays, L.) IN SALT AFFECTED SOILS
M.M.A. Bader; H.Z. Abd El-Salam; M.R. Mohamed; S.H. Sarhan · 2004 · Journal of Soil Sciences and Agricultural Engineering
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Two field experiments were conducted at the Experimental Station of Agricultural Research Station at Tag EI·Ezz Dakahlia Governorate during 2000 and 2001 seasons. The used soil was salt affected one. The research aims to study the effect of N-NO'3, N-NH+4 or N-urea on yield and some characters of maize plant as well as macro-micronutrients content in ear leaves at maturity stage (physiological stage) and seeds. Single hybrid CS 10 of maize was used as testing plant within two seasons (2000 and 2001) and the soil salinity levels were 1.69, 5.3 and 11.10 dS/m in 2000, and 1.69, 5.43 and 11.10 ds
Abstract by M.M.A. Bader; H.Z. Abd El-Salam; M.R. Mohamed; S.H. Sarhan, Journal of Soil Sciences and Agricultural Engineering (2004) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
t-Test at the Probe Level: An Alternative Method to Identify Statistically Significant Genes for Microarray Data
Negative / Null Result ReportMeteorological Causes of the Secular Variations in Observed Extreme Precipitation Events for the Conterminous United States
Negative / Null Result ReportThe Next Generation of Sepsis Clinical Trial Designs
Negative / Null Result ReportAnalysis of DNA Methylation in Young People: Limited Evidence for an Association Between Victimization Stress and Epigenetic Variation in Blood
Negative / Null Result ReportStudy preregistration: an early example and analysis.
Negative / Null Result ReportInsights Into LSTM Fully Convolutional Networks for Time Series Classification
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: DOAJ · DOI 10.21608/jssae.2004.240847