A data-driven approach to optimizing nitrogen fertilization for cotton growth under saline soil conditions

¹ Bukhara State University, Bukhara, Uzbekistan
² Karshi State University, Karshi, Uzbekistan
³ Bukhara State Medical Institute named after Abu Ali ibn Sino, Faculty of Biomedicine, Bukhara, Uzbekistan

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

This study is part of a coordinated research effort investigating cotton responses to nitrogen fertilization across salinity gradients, complementing parallel studies on plant water relations, biomass accumulation, and yield structure. This study presents a large-scale field study investigating the effects of precision nitrogen fertilization on growth parameters of cotton (Gossypium spp.) grown across a soil salinity gradient. This study is consistent with the precision agriculture paradigm, specifically considering nutrient management and site-specific crop management (SSCM) to optimize yield under abiotic stress conditions. The research was conducted on meadow-alluvial soils classified as non-saline, slightly saline, moderately saline, and strongly saline, with biometric observations recorded on four consecutive dates. The study measured cotton main stem height and the number of leaves per plant across control, background fertilization (P175K125), and nitrogen-enriched treatments ranging from N100 to N350 kg/ha. Results indicated that in all salinity levels, nitrogen application significantly increased cotton stem height and leaf number compared to non-fertilized variants. The highest efficacy was observed in the N250P175K125 treatment. Although elevated salinity levels reduced these indicators overall, nitrogen application mitigated the negative effects of salinity and maintained superior plant development. The results confirm that nitrogen fertilization can partially offset the inhibitory effects of salinity stress on cotton, with optimal effects observed at 250–300 kg/ha nitrogen application. This research supports decision-making systems in precision agriculture by using agronomic data for sustainable soil and nutrient management under adverse conditions.