Establishment of an NPK nutrient monitor system in yield-graded cotton petioles under drip irrigation

Abstract

Abstract Background The determination of nutrient content in the petiole is one of the important methods for achieving cotton fertilization management. The establishment of a monitoring system for the nutrient content of cotton petioles during important growth periods under drip irrigation is of great significance for achieving precise fertilization and environmental protection. Methods A total of 100 cotton fields with an annual yield of 4500–7500 kg/ha were selected among the main cotton-growing areas of Northern Xinjiang. The nitrate nitrogen (NO3 −–N), inorganic phosphorus (PO4 3−–P) and inorganic potassium (K+–K) content and yield of cotton petioles were recorded. Based on a yield of 6000 kg/ha as the dividing line, a two-level and yield-graded monitoring system for NO3 −–N, PO4 3−–P and K+–K in cotton petioles during important growth periods was established, and predictive yield models for NO3 −–N, PO4 3−–P and K+–K in petioles during important growth periods were established. Results The results showed found that the yields of the 100 cotton fields surveyed were normally distributed. Therefore, two yield grades were classified using 6000 kg/ha as a criterion. Under different yield-graded, the NO3 −–N, PO4 3−–P and K+–K content of petiole at important growth stages was significantly positively correlated with yield. Further, the variation range of NO3 −–N, PO4 3−–P and K+–K content in petioles could be used as a standard for yield-graded. In addition, a yield prediction model for the NO3 −–N, PO4 3−–P and K+–K content of petioles was developed. The SSO-BP validation model performed the best (R2 = 0.96, RMSE = 0.06 t/ha, MAE = 0.05 t/ha) in the full bud stage, which was 12.9% higher than the BP validation model. However, the RMSE and MAE were decreased by 86.7% and 88.1%, respectively. Conclusion The establishment of NPK nutrition monitor system of cotton petioles under drip irrigation based on yield-graded provides an important basis for nutrition monitor of cotton petiole under drip irrigation in Xinjiang. It also provides a new method for cotton yield prediction.

Keywords

• Petiole
• Cotton
• Nutritional monitor system
• Back propagation neural network
• Algorithm optimization