Improving winter wheat water use efficiency in the North China Plain (NCP), China is essential in light of current irrigation water shortages. In this study, the AquaCrop model was used to calibrate, and validate winter wheat crop performance under various planting dates and irrigation application rates. All experiments were conducted at the Xiaotangshan experimental site in Beijing, China, during seasons of 2008/2009, 2009/2010, 2010/2011 and 2011/2012. This model was first calibrated using data from 2008/2009 and 2009/2010, and subsequently validated using data from 2010/2011 and 2011/2012. The results showed that the simulated canopy cover (CC), biomass yield (BY) and grain yield (GY) were consistent with the measured CC, BY and GY, with corresponding coefficients of determination (R2) of 0.93, 0.91 and 0.93, respectively. In addition, relationships between BY, GY and transpiration (T), (R2 = 0.57 and 0.71, respectively) was observed. These results suggest that frequent irrigation with a small amount of water significantly improved BY and GY. Collectively, these results indicate that the AquaCrop model can be used in the evaluation of various winter wheat irrigation strategies. The AquaCrop model predicted winter wheat CC, BY and GY with acceptable accuracy. Therefore, we concluded that AquaCrop is a useful decision-making tool for use in efforts to optimize wheat winter planting dates, and irrigation strategies.

Highly biocompatible biomimetic nanoparticles for antibiotic-resistant bacteria infection by photothermal therapy.

Chlorophyll content is an important indicator for assessing crop health and predicting crop yield. It is possible that chlorophyll content (CC) was quickly and non-destructively estimated by remote sensing. The objective of the experiment was to develop precision agricultural practices for predicting CC of wheat. In this study, we compared some spectral parameters (SPs) and CC with the determination coefficient (R2), and combined these SPs by stepwise regression methods. The results indicated that the 1.45SIPI-1.05PSRI, the R2 value was 0.6589 and corresponding the root mean square error (RMSE) was 1.463, and it can be used to improve the prediction accuracy of CC.

Nitrogen is a crucial parameter in maintaining crop health and predicting crop yield. It is possible that leaf nitrogen concentration (LNC) was quickly and non-destructively estimated by remote sensing method. The objective of this experiment was to develop a sensitive spectral index for monitoring LNC in wheat based on HJ-CCD data. In this study, we assessed several common spectral indices based on stepwise regression methods with the determination coefficient (R2) and root mean square error (RMSE). The results indicated that compared to other spectral index, the spectral index of 1.65SIPI-1.35PSRI was the most positively related to LNC (R2 = 0.6328, p<0.01), and the model for monitoring LNC based on 1.65SIPI-1.35PSRI was with the RMSE of 0.685 g.m-2 (p<0.01), and was significantly better than the models adopting other spectral indices. In conclusion, this study confirmed the feasibility of utilizing 1.65SIPI-1.35PSRI derived from airborne remotely sensed data to monitor LNC in wheat.