The accurate and rapid estimation of canopy nitrogen content (CNC) in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture. However, the determination of CNC from field sampling data for leaf area index (LAI), canopy photosynthetic pigments (CPP; including chlorophyll a, chlorophyll b and carotenoids) and leaf nitrogen concentration (LNC) can be time-consuming and costly. Here we evaluated the use of high-precision unmanned aerial vehicle (UAV) multispectral imagery for estimating the LAI, CPP and CNC of winter wheat over the whole growth period. A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. Six machine learning methods, i.e., multiple stepwise regression (MSR), support vector regression (SVR), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were compared for the retrieval of winter wheat LAI, CPP and CNC values, and a double-layer model was proposed for estimating CNC based on LAI and CPP. The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. The proposed double-layer models (R²=0.67–0.89, RMSE=13.63–23.71 mg g^–1, MAE=10.75–17.59 mg g^–1) performed better than the direct inversion models (R²=0.61– 0.80, RMSE=18.01–25.12 mg g^–1, MAE=12.96–18.88 mg g^–1) in estimating winter wheat CNC. The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs (R²=0.89, RMSE=13.63 mg g^–1, MAE=10.75 mg g^–1). The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.

Maize (Zea mays L.) is one of the three major food crops and an important source of carbohydrates for maintaining food security around the world.  Plant height (H), stem diameter (SD), leaf area index (LAI) and dry matter (DM) are important growth parameters that influence maize production.  However, the combined effect of temperature and light on maize growth is rarely considered in crop growth models.  Ten maize growth models based on the modified logistic growth equation (Mlog) and the Mitscherlich growth equation (Mit) were proposed to simulate the H, SD, LAI and DM of maize under different mulching practices based on experimental data from 2015–2018.  Either the accumulative growing degree-days (AGDD), helio thermal units (HTU), photothermal units (PTU) or photoperiod thermal units (PPTU, first proposed here) was used as a single driving factor in the models; or AGDD was combined with either accumulative actual solar hours (ASS), accumulative photoperiod response (APR, first proposed here) or accumulative maximum possible sunshine hours (ADL) as the dual driving factors in the models.  The model performances were evaluated using seven statistical indicators and a global performance index.  The results showed that the three mulching practices significantly increased the maize growth rates and the maximum values of the growth curves compared with non-mulching.  Among the four single factor-driven models, the overall performance of the Mlog_PTU Model was the best, followed by the Mlog_AGDD Model.  The Mlog_PPTU Model was better than the Mlog_AGDD Model in simulating SD and LAI.  Among the 10 models, the overall performance of the Mlog_AGDD–APR Model was the best, followed by the Mlog_AGDD–ASS Model.  Specifically, the Mlog_AGDD–APR Model performed the best in simulating H and LAI, while the Mlog_AGDD–ADL and Mlog_AGDD–ASS models performed the best in simulating SD and DM, respectively.  In conclusion, the modified logistic growth equations with AGDD and either APR, ASS or ADL as the dual driving factors outperformed the commonly used modified logistic growth model with AGDD as a single driving factor in simulating maize growth.

We conducted a two-year study of deficit irrigation impact on peach yield and quality in semi-arid northwest China.  Over two years, four-year-old peach trees were irrigated at 100, 75, 50 and 25% of peach evapotranspiration (ET_c), here, ET_c= Coefficient (K_c)×Local reference evapotranspiration (ET_o).  During the April-July fruit production season we measured root zone soil water depletion, sap flow velocity, net photosynthetic rate (P_n), transpiration rate (T_r), stomatal conductance (G_s), water use efficiency (WUE=P_n/T_r), fruit quality, and yield under a mobile rain-out shelter.  Increased soil water depletion reasonably mirrored decreasing irrigation rates both years, causing progressively greater water stress.  Progressive water stress lowered G_s, which in turn translated into lower T_r as measured by sap flow.  However, mild deficit irrigation (75% ET_c) constricted T_r more than P_n.  P_n was not different between 100 and 75% ET_c treatments in both years, and it decreased only 5–8% in June with higher temperature than that in May with cooler temperature.  Concurrently under 75% ET_c treatment, T_r was reduced, and WUE was up to 13% higher than that under 100% ET_c treatment.  While total fruit yield was not different under the two treatments, because 75% ET_c treatment had fewer but larger fruit than 100% ET_c trees, suggesting mild water stress thinned fruit load.  By contrast, sharply decreased T_r and P_n of the driest treatments (50 and 25% ET_c) increased WUE, but less carbon uptake impacted total fruit yield, resulting 13 and 33% lower yield compared to that of 100% ET_c treatment.  Irrigation rates affected fruit quality, particularly between the 100 and 75% ET_c trees.  Fewer but larger fruit in the mildly water stressed  trees (75% ET_c) resulted in more soluble solids and vitamin C, firmer fruit, and improved sugar:acid ratio and fruit color compared to the 100% ET_c treatment.  Overall, trees deficit irrigated at 75% ET_c maintained yield while improving fruit quality and using less water. 

To better interpret summer maize stomatal conductance (g_s) variation under conditions of changing water status at different growth stages, three water stress indicators, soil water content (SWC), leaf-air temperature difference (?T) and leaf level water stress index (CWSI_L) were employed in Jarvis model, which were J_S, J_T and J_C models respectively.  Measurements of g_s were conducted in a summer maize field experiment during the year 2012–2013.  In the insufficient irrigation experiment, three levels of irrigation amount were applied at four different growth stages of summer maize.  We constructed three scenarios to evaluate the performance of the three water stress indicators for estimating maize g_s in a modified Jarvis model.  Results showed that J_T and J_C models had better simulation accuracy than the J_S model, especially at the late growth stage (Scenario 1) or considering the plant recovery compensation effects (Scenario 2).  Scenario 3 indicated that the more environmental factors were adopted, the better prediction performance would be for J_S model.  While for J_T model, two environmental factors (photosynthesis active radiation (PAR), and vapor pressure deficit (VPD)) seemed good enough to obtain a reliable simulation.  When there were insufficient environmental data, CWSI_L would be the best option.  This study can be useful to understand the response of plant stomatal to changing water conditions and will further facilitate the application of the Jarvis model in various environments.