The nitrogen nutrition index (NNI) is a reliable indicator for diagnosing crop nitrogen (N) status.  However, there is currently no specific vegetation index for the NNI inversion across multiple growth periods.  To overcome the limitations of the traditional direct NNI inversion method (NNI_T1) of the vegetation index and traditional indirect NNI inversion method (NNI_T2) by inverting intermediate variables including the aboveground dry biomass (AGB) and plant N concentration (PNC), this study proposed a new NNI remote sensing index (NNI_RS).  A remote-sensing-based critical N dilution curve (N_{c_RS}) was set up directly from two vegetation indices and then used to calculate NNIRS.  Field data including AGB, PNC, and canopy hyperspectral data were collected over four growing seasons (2012–2013 (Exp.1), 2013–2014 (Exp. 2), 2014–2015 (Exp. 3), 2015–2016 (Exp. 4)) in Beijing, China.  All experimental datasets were cross-validated to each of the NNI models (NNI_T1, NNI_T2 and NNI_RS).  The results showed that: (1) the NNI_RS models were represented by the standardized leaf area index determining index (sLAIDI) and the red-edge chlorophyll index (CI_{red edge}) in the form of NNI_RS=CI_{red edge}/(a×sLAIDI^b), where “a” equals 2.06, 2.10, 2.08 and 2.02 and “b” equals 0.66, 0.73, 0.67 and 0.62 when the modeling set data came from Exp.1/2/4, Exp.1/2/3, Exp.1/3/4, and Exp.2/3/4, respectively; (2) the NNI_RS models achieved better performance than the other two NNI revised methods, and the ranges of R² and RMSE were 0.50–0.82 and 0.12–0.14, respectively; (3) when the remaining data were used for verification, the NNI_RS models also showed good stability, with RMSE values of 0.09, 0.18, 0.13 and 0.10, respectively.  Therefore, it is concluded that the NNI_RS method is promising for the remote assessment of crop N status.

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale.  To verify this method, we applied the ensemble Kalman filter (EnKF) to assimilate the leaf area index (LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model.  From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi.  We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat.  We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI.  With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat.  We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error (RMSE) by 0.43 and 0.29 m² m^–2, respectively, based on the measured LAI.  The assimilation improved the estimation accuracy of the LAI time series.  The highest determination coefficient (R₂) was 0.8627 and the lowest RMSE was 472.92 kg ha^–1 in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements.  The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with
high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.

Sixteen cotton cultivars widely planted in China were sowed under five different drought concentrations (0, 2.5, 5, 7.5, and 10%) using PEG₆₀₀₀ to screen the indices of drought resistance identification and explore the drought resistance of different cotton cultivars.  Eighteen physiological indices including root, stem, and leaf water contents (RWC, SWC, and LWC), net photosynthetic rate (P_n), the maximum photochemical quantum yield (F_v/F_m), the actual photochemical quantum yield (Φ_PSII), non-photochemical quenching coefficient (NPQ), leaf water potential (LWP), osmotic potential (Ψs), leaf relative conductivity (REC), leaf proline content (Pro), leaf and root soluble protein contents (LSPC and RSPC), leaf and root malondialdehyde (MDA) contents (LMDA and RMDA), root superoxide dismutase, peroxidase, and catalase activities (RSOD, RPOD, and RCAT) were measured.  Results indicated the 18 physiological indices can be converted into five or six independent comprehensive indices by principal component analysis, and nine typical indices (F_v/F_m, SWC, LWP, Pro, LMDA, RSPC, RMDA, RSOD, and RCAT) screened out by a stepwise regression method could be utilized to evaluate the drought resistance.  Moreover, the 16 cotton cultivars were divided into four types: drought sensitive, drought weak sensitive, moderate drought resistant, and drought resistant types.  The resistance ability of two selected cotton cultivars (drought resistant cultivar, Dexiamian 1; drought sensitive cultivar, Yuzaomian 9110) with contrasting drought sensitivities were further verified by pot experiment.  Results showed that the responses of final cotton biomass, yield, and yield composition to drought were significantly different between the two cultivars.  In conclusion, drought resistant cultivar Dexiamian 1 and drought sensitive cultivar Yuzaomian 9110 were screened through hydroponics experiment, which can be used as ideal experimental materials to study the mechanism of different cotton cultivars with contrasting drought sensitivities in response to drought stress.

 

Peanut (Arachis hypogaea L.) is an important oil crop globally and high oil content is one of the major targets in peanut breeding programs.  Previous studies indicated that the osmotic pressure (OP) of the leaves of peanut plants subjected to drought stress was negatively correlated with kernel oil content.  Based on this knowledge, we established a practical and reliable method for creating new peanut varieties with high oil content using in vitro mutagenesis and directional OP-based selection.  Using embryonic leaflets of peanut variety Huayu 20 as explants, pingyangmycin (PYM) as the mutagen, and hydroxyproline (HYP) as the OP regulator, we developed 15 HYP-tolerant regenerated plants.  For each regenerated plant, we selected offspring with oil content>55% (relative to 49.5% for Huayu 20).  We developed and released three new peanut varieties with high yield and high oil content from the offspring of the HYP-tolerant regenerated plants.  The three new varieties were named as Yuhua 4, Yuhua 9 and Yuhua 14 and their oil contents were 57.7, 61.1 and 59.3%, respectively.  The results indicate that in vitro mutagenesis with PYM followed by directed screening with HYP is a useful approach for breeding peanut varieties with high oil contents.

Sweetpotato, Ipomoea batatas (L.) Lam., is a globally important food crop and usually grown on arid- and semi-arid lands.  Therefore, investigating the molecular mechanism of drought tolerance will provide important information for the improvement of drought tolerance in this crop.  In this study, transcriptome analysis of the drought-tolerant sweetpotato line Xushu 55-2 was conducted on Illumina HiSeq 2500 platform.  A total of 86.69 Gb clean data were generated and assembled into 2 671 693 contigs, 222 073 transcripts, and 73 636 unigenes.  In total, 11 359 differentially expressed genes (DEGs) were identified after PEG6000 treatment, in which 7 666 were up-regulated and 3 693 were down-regulated.  Of the 11 359 DEGs, 10 192 DEGs were annotated in at least one database, and the remaining 1 167 DEGs were unknown.  Abscisic acid (ABA), ethylene (ETH), and jasmonic acid (JA) signalling pathways play a major role in drought tolerance of sweetpotato.  Drought-inducible transcription factors were identified, some of which have been reported to be associated with drought tolerance and others are unknown in plants.  In addition, 7 643 SSRs were detected.  This study not only reveals insights into the molecular mechanism of drought tolerance in sweetpotato but also provides the candidate genes involved in drought tolerance of this crop.