PENGARUH BIOCHAR COATED UREA TERHADAP PERTUMBUHAN DAN HASIL BAWANG MERAH (Allium cepa var. aggregatum L.) DI TANAH BERPASIR
Nurlaili Sudarwati; Slamet Supriyadi; Fahmi Arief Rahman; Erick Yuhardi · 2026 · JTSL (Jurnal Tanah dan Sumberdaya Lahan)
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
This study was conducted to evaluate the effect of chemical fertilizer coated with biochar on the growth of shallots (Allium ascalonicum L.) in sandy soil. The research was carried out at the Greenhouse of the Agroecotechnology Department, Faculty of Agriculture, Trunojoyo University of Madura, located in Telang Village, Kamal District, Bangkalan Regency, Madura, at an altitude of approximately 5 meters above sea level, from August to December 2023. A non-factorial Completely Randomized Design (CRD) was used, consisting of six treatments: P0 (control), P1 (recommended fertilizer), P2 (80% urea
Abstract by Nurlaili Sudarwati; Slamet Supriyadi; Fahmi Arief Rahman; Erick Yuhardi, JTSL (Jurnal Tanah dan Sumberdaya Lahan) (2026) — 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 10.21776/ub.jtsl.2026.013.1.7