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 (PO43--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, PO43--P and K+-K in cotton petioles during important growth periods was established, and predictive yield models for NO3--N, PO43--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, PO43--P and K+-K content of petiole at important growth stages was significantly positively correlated with yield. Further, the variation range of NO3--N, PO43--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, PO43--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.
ABSTRACT
In order to elucidate the effects of different nitrogen (N), phosphorus (P), and potassium (K) fertilization timing sequences management on nutrient absorption and utilization in drip irrigation cotton, field experiments were conducted from 2020 to 2021. There are six timing sequences management methods for NPK fertilization (S1-S6: 1/3Time N-1/3Time PK-1/3Time W, 1/3Time PK-1/3Time N-1/3Time W, 1/2Time NPK-1/2Time W, 1/4Time W-1/4Time N-1/4Time PK-1/4Time W, 1/3Time W-1/3Time NPK-1/3Time W), among which S6 is the current management method for field fertilization timing sequences, and S7 is the non N. The results showed that during the main growth stage, S5 accumulated more nitrate nitrogen (NO3--N) and ammonium nitrogen (NH4+-N) content in soil between 20 and 40 cm, and accumulated more available phosphorus content in soil between 5-15 cm and 15-25 cm, S5 reducing N leaching and increasing P mobility. It is recommended to change the timing sequences management method of NPK fertilization for drip irrigation cotton to 1/4Time W-1/4Time PK-1/4Time N-1/4Time W, which is beneficial for plant nutrient absorption and utilization while reducing environmental pollution.
ABSTRACT
BACKGROUND: Estimation of nitrate nitrogen (NO3--N) content in petioles is one of the key approaches for monitoring nitrogen (N) nutrition in crops. Rapid, non-destructive, and accurate evaluation of NO3--N contents in cotton petioles under drip irrigation is of great significance. METHODS: In this study, we discussed the use of hyperspectral data to estimate NO3--N contents in cotton petioles under drip irrigation at different N treatments and growth stages. The correlations among trilateral parameters and six vegetation indices and petiole NO3--N contents were first investigated, after which a traditional regression model for petioles NO3--N content was established. A wavelet neural network (WNN) model for estimating petiole NO3--N content was also established. In addition, the performance of WNN was compared to those of random forest (RF), radial basis function neural network (RBF) and back propagation neural network (BP). RESULTS: Between the blue edge amplitude (Db) and blue edge area (SDb) of the blue edge parameters was the optimal index for the estimation model of petiole NO3--N content. We found that the prediction results of the blue edge parameters and WNN were 7.3% higher than the coefficient of determination (R2) of the first derivative vegetation index and WNN. Root mean square error (RMSE) and mean absolute error (MAE) were 25.2% and 30.9% lower than first derivative vegetation, respectively, and the performance was better than that of RF, RBF and BP. CONCLUSIONS: An inexpensive approach consisting of the WNN algorithm and blue edge parameters can be used to enhance the accuracy of NO3--N content estimation in cotton petioles under drip irrigation.
