Precisely controlling and predicting nitrogen release rate of urea–formaldehyde nanocomposite fertilizer for efficient nutrient management

Abstract

Achieving precise control over the nutrient release rate of biodegradable polymer fertilizers, such as urea–formaldehyde (UF), is a crucial aim in agricultural research, with significant implications for both crop yield and environmental sustainability. This study addresses this aim by developing a nanocomposite UF/monopotassium phosphate (MKP) fertilizer through a combination of solution mixing, polycondensation, and free water evaporation. The methodology employed in this study ensures the successful nano-sizing of MKP, achieved through a continuous embedding coprecipitation effect, hydrogen-bonding interaction between matrix UF and MKP, and the cage effect of UF macromolecular chains. The study's significant finding is that the nitrogen release behavior of the UF matrix can be controlled by adjusting the amount of added MKP. Furthermore, this study introduced a predictive model, developed using machine learning algorithms, which can accurately estimate the nitrogen release rate of UF/MKP in loamy sand soil in northern China (R² = 0.9946). This model's application was confirmed through tomato cultivation experiments, which showed improved crop yield and nutrient utilization efficiency when treated with the model-predicted UF/MKP, as compared to a physically mixed system of pure UF and MKP. This study's findings are significant as they offer a more precise fertilizer preparation process and fertilization strategies for different crops, thereby contributing to the coordinated development of crop yield and soil environmental quality. The study also identifies areas for further research, particularly in exploring the applicability of the developed predictive model in different soil types and for different crop types.