Nitrogen (N) and potassium (K) are key elements for crop growth, yet studies on the impact of N–K interactions on plant N and K status and yield are lacking. This study aimed to develop effective indicators for diagnosing N and K nutrition and predicting the yield of wheat under N–K interactions based on the theoretical framework of a critical nutrient dilution curve. A 4-year N–K interaction experiment involving three wheat cultivars was employed for building and validating nutrient indices (NIs) based on the critical N dilution curve (CNDC) and the critical K dilution curve (CKDC). In addition, relevant data from the literature were collected for supplementary validation. The results revealed that changes in parameter A1 of the critical K dilution curves (CKDCs) can reflect the impact of nitrogen application on K absorption and utilization. However, the difference in K nutrition index (KNI) values calculated by CKDC under different N levels was not significant. Based on the aboveground biomass (AGB), a universal CKDC was established and defined as Kc=3.63AGB–0.37 under N–K interactions. The results showed that the direct effects of N or K deficiency on crops could be quantified by the N–K interaction index (NKI) calculated by integrating CNDC and CKDC, and the changes in crop growth in response to proportional N and K concentrations could be determined by NKI as well. In addition, topdressing N fertilizer at the jointing stage significantly improved the N–K interaction effect on the N nutrition index (NNI) and NKI at the booting stage (P<0.05), but it had no significant N–K interaction effect on the KNI. All indicators at the heading stage demonstrated the best predictive capability for relative yield (RY) compared to other stages. Compared with NNI and KNI, the prediction accuracy of yield with NKI improved by 11.63 and 17.44%, respectively. The NKI has better performance in diagnosing N and K nutrition and predicting yield under N–K interactions than either NNI or KNI. This result enhances our understanding of the effects of N–K interactions on wheat growth and has important applications for improving the accuracy of N and K nutrition diagnosis and yield prediction.
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The objective of this paper was to clearly demonstrate the effects of powdery mildew on photosynthetic characteristics of winter wheat and to establish a model for simulating effects of powdery mildew stress on wheat photosynthetic productivity.
To clarify the effects of powdery mildew on wheat photosynthetic characteristics, the powdery mildew experiments of wheat were conducted under two initial inoculation degrees of wheat powdery mildew at jointing and booting stages. On this basis, a prediction model of wheat powdery mildew severity was established to quantify the physiological effects of powdery mildew on wheat. And then, based on the single leaf net photosynthetic rate (Pn) and leaf area index (LAI), the wheat powdery mildew severity prediction model was coupled with the crop growth model (WheatGrow).
Under the stress of powdery mildew, Pn and LAI showed a decreasing trend. Compared with the control (CK), the averaged Pn and LAI decreased by 18.81% and 23.41%, respectively. Moreover, the effects of stages of powdery mildew on Pn and LAI were more serious than the initial inoculation degrees. In general, the development of wheat powdery mildew in the field had obvious gentle period, exponential outbreak period and stable period, and the temporal dynamic characteristics of wheat powdery mildew epidemic under each treatment accorded with Logistic function. Therefore, based on the Logistic fitting results, the wheat powdery mildew disease stress factor was established to reflect the stress effects of powdery mildew on wheat physiological indexes. In addition, based on the photosynthesis productivity sub-model of WheatGrow and the effect factor of wheat powdery mildew severity, the algorithms to simulate the effects of powdery mildew on Pn and LAI were established, and then the improved WheatGrow model was estimated by using the powdery mildew experimental datasets in independent years.
The integrated model with powdery mildew stress algorithms was better than the original WheatGrow model in predicting Pn, LAI, aboveground biomass and yield under powdery mildew stress condition, with the simulation accuracy improved by 53.29%, 43.61%, 60.09% and 67.57%, respectively. The improved model could provide the digital tool and technical support for prediction of wheat powdery mildew severity and the quantitative evaluation of wheat yield loss.
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