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HEMERYTHRIN MOTIF-CONTAINING REALLY INTERESTING NEW GENE AND ZINC-FINGER PROTEIN1 (OsHRZ1) is a putative iron-binding sensor. However, it is unclear how OsHRZ1 transmits signals. In this study, we reveal that POSITIVE REGULATOR OF IRON HOMEOSTASIS1 (OsPRI1) interacts with OsHRZ1. A loss-of-function mutation to increased the sensitivity of plants to Fe-deficient conditions and down-regulated the expression of Fe-deficiency-responsive genes. Yeast one-hybrid and electrophoretic mobility shift assay results suggested that OsPRI1 binds to the and promoters. In vitro ubiquitination experiments indicated that OsPRI1 is ubiquitinated by OsHRZ1. Cell-free degradation assays revealed that the stability of OsPRI1 decreased in wild-type roots but increased in the mutant, suggesting OsHRZ1 is responsible for the instability of OsPRI1. The seedlings were insensitive to Fe-deficient conditions. When the mutation was introduced into mutants, the double mutant was more sensitive to Fe deficiency than the mutant. Additionally, the expression levels of Fe-deficiency-responsive genes were lower in the double mutant than in the mutant. Collectively, these results imply that OsPRI1, which is ubiquitinated by OsHRZ1, mediates rice responses to Fe deficiency by positively regulating and expression as part of the signal transduction cascade.

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Leaf area index (LAI) is a significant biophysical variable in the models of hydrology, climatology and crop growth. Rapid monitoring of LAI is critical in modern precision agriculture. Remote sensing (RS) on satellite, aerial and unmanned aerial vehicles (UAVs) has become a popular technique in monitoring crop LAI. Among them, UAVs are highly attractive to researchers and agriculturists. However, some of the UAVs vegetation index (VI)-derived LAI models have relatively low accuracy because of the limited number of multispectral bands, especially as they tend to saturate at the middle to high LAI levels, which are the LAI levels of high-yielding wheat crops in China. This study aims to effectively estimate wheat LAI with UAVs narrowband multispectral image (400-800 nm spectral regions, 10 cm resolution) under varying growth conditions during five critical growth stages, and to provide the potential technical support for optimizing the nitrogen fertilization. Results demonstrated that the newly developed LAI model with modified triangular vegetation index (MTVI 2 ) has better accuracy with higher coefficient of determination (R c 2 = 0.79, R v 2 = 0.80) and lower relative root mean squared error (RRMSE = 24%), and higher sensitivity under various LAI values (from 2 to 7), which will broaden the applied range of the new LAI model. Furthermore, this LAI model displayed stable performance under different sub-categories of growth stages, varieties, and eco-sites. In conclusion, this study could provide effective technical support to precisely monitor the crop growth with UAVs in various crop yield levels, which should prove helpful in family farm for the modern agriculture.

Abstract:The rapid and non-destructive monitoring of the canopy leaf nitrogen concentration (LNC) in crops is important for precise nitrogen (N) management. Nowadays, there is an urgent need to identify next-generation bio-physical variable retrieval algorithms that can be incorporated into an operational processing chain for hyperspectral satellite missions. We assessed six retrieval algorithms for estimating LNC from canopy reflectance of winter wheat in eight field experiments. These experiments represented variations in the N application rates, planting densities, ecological sites and cultivars and yielded a total of 821 samples from various places in Jiangsu, China over nine consecutive years. Based on the reflectance spectra and their first derivatives, six methods using different numbers of wavelengths were applied to construct predictive models for estimating wheat LNC, including continuum removal (CR), vegetation indices (VIs), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural networks (ANNs), and support vector machines (SVMs). To assess the performance of these six methods, we provided a systematic evaluation of the estimation OPEN ACCESS Remote Sens. 2015, 7 14940 accuracies using the six metrics that were the coefficients of determination for the calibration (R 2 C) and validation (R 2 V) sets, the root mean square errors of prediction (RMSEP) for the calibration and validation sets, the ratio of prediction to deviation (RPD), the computational efficiency (CE) and the complexity level (CL). The following results were obtained: (1) For the VIs method, SAVI(R1200, R705) produced a more accurate estimation of the LNC than other indices, with R² C, R² V, RMSEP, RPD and CE values of 0.844, 0.795, 0.384, 2.005 and 0.10 min, respectively; (2) For the SMLR, PLSR, ANNs and SVMs methods, the SVMs using the first derivative canopy spectra (SVM-FDS) offered the best accuracy in terms of R² C, R² V, RMSEP, RPD, and CE, at 0.96, 0.78, 0.37, 2.02, and 21.17, respectively; (3) The PLSR-FDS, ANN-OS and SVM-FDS methods yield similar accuracies if the CE and CL are not considered, however, ANNs and SVMs performed better on calibration set than the validation set which indicate that we should take more caution with the two methods for over-fitting. Except PLS method, the performance for most methods did not enhance when the spectrum were operated by the first derivative. Moreover, the evaluation of the robustness demonstrates that SVM method may be better suited than the other methods to cope with potential confounding factors for most varieties, ecological site and growth stage; (4) The prediction accuracy was found to be higher when more wavelengths were used, though at the cost of a lower CE. The findings are of interest to the remote sensing community for the development of improved inversion schemes for hyperspectral applications concerning other types of vegetation. The examples provided in this paper may also serve to illustrate the advantages and shortcomings...

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