Morphological and Comparative Transcriptome Analysis of Three Species of Five-Needle Pines: Insights Into Phenotypic Evolution and Phylogeny

Pinus koraiensis, Pinus sibirica, and Pinus pumila are the major five-needle pines in northeast China, with substantial economic and ecological values. The phenotypic variation, environmental adaptability and evolutionary relationships of these three five-needle pines remain largely undecided. It is therefore important to study their genetic differentiation and evolutionary history. To obtain more genetic information, the needle transcriptomes of the three five-needle pines were sequenced and assembled. To explore the relationship of sequence information and adaptation to a high mountain environment, data on needle morphological traits [needle length (NL), needle width (NW), needle thickness (NT), and fascicle width (FW)] and 19 climatic variables describing the patterns and intensity of temperature and precipitation at six natural populations were recorded. Geographic coordinates of altitude, latitude, and longitude were also obtained. The needle morphological data was combined with transcriptome information, location, and climate data, for a comparative analysis of the three five-needle pines. We found significant differences for needle traits among the populations of the three five-needle pine species. Transcriptome analysis showed that the phenotypic variation and environmental adaptation of the needles of P. koraiensis, P. sibirica, and P. pumila were related to photosynthesis, respiration, and metabolites. Analysis of orthologs from 11 Pinus species indicated a closer genetic relationship between P. koraiensis and P. sibirica compared to P. pumila. Our study lays a foundation for genetic improvement of these five-needle pines and provides insights into the adaptation and evolution of Pinus species

The band gap and nonlinear optical susceptibility of SrSn1-xVxO3 films

Perovskite-type oxide SrSn1-xVxO3 thin films with different concentrations x = 0.1–0.9 were fabricated by using pulsed-laser deposition, and the effects of V doping on the structure, optical band gap and the third-order optical nonlinearity were systematically investigated. With the increase of x value, the lattice parameters of SrSn1-xVxO3 decrease from 3.997 to 3.862 Å gradually, while the optical band gaps firstly increase and then decrease with boundary at x = 0.3. The third-order nonlinear optical responses were studied via the z-scan technique. The closed-aperture measurements show a negative nonlinear refractive index n2, and the open-aperture measurements demonstrate a saturable absorption β. Both the n2 and β responses vary with the increase of V doping level. The metal-oxygen chemical bond along with the localized V5+Sn2+V5+ complex contribute to the enhancement of optical nonlinearity, and the highest value of third-order susceptibility χ(3) is observed in SrSn0.5V0.5O3 film

Evaluation of Multiple Satellite Precipitation Products and Their Use in Hydrological Modelling over the Luanhe River Basin, China

Satellite precipitation products are unique sources of precipitation measurement that overcome spatial and temporal limitations, but their precision differs in specific catchments and climate zones. The purpose of this study is to evaluate the precipitation data derived from the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, TRMM 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products over the Luanhe River basin, North China, from 2001 to 2012. Subsequently, we further explore the performances of these products in hydrological models using the Soil and Water Assessment Tool (SWAT) model with parameter and prediction uncertainty analyses. The results show that 3B42 and 3B42RT overestimate precipitation, with BIAs values of 20.17% and 62.80%, respectively, while PERSIANN underestimates precipitation with a BIAs of −6.38%. Overall, 3B42 has the smallest RMSE and MAE and the highest CC values on both daily and monthly scales and performs better than PERSIANN, followed by 3B42RT. The results of the hydrological evaluation suggest that precipitation is a critical source of uncertainty in the SWAT model, and different precipitation values result in parameter uncertainty, which propagates to prediction and water resource management uncertainties. The 3B42 product shows the best hydrological performance, while PERSIANN shows unsatisfactory hydrological performance. Therefore, 3B42 performs better than the other two satellite precipitation products over the study area

Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence

Cotton yield estimation is of great practical significance to producers, allowing them to make rational management decisions. At present, crop yield estimation methods mainly comprise traditional agricultural yield estimation methods, which have many shortcomings. As an ideal “probe” for detecting crop photosynthesis, sun-induced chlorophyll fluorescence (SIF) can directly reflect the dynamics of actual crop photosynthesis and has the potential to predict crop yield, in order to realize cotton yield estimation based on canopy SIF. In this study, we set up field trials with different nitrogen fertilizer gradients. The changes of canopy SIF and the physiological parameters of cotton in different growth periods were analyzed. To investigate the effects of LAI and AGB on canopy SIF estimation of cotton yield, four algorithms, Ada Boost (Adaptive Boosting), Bagging (Bootstrap Aggregating), RF (Random Forest), and BPNN (Backpropagation Neural Network), were used to construct cotton yield estimation models based on the SIF and SIFy (the normalization of SIF by incident photosynthetically active radiation) for different time and growth periods. The results include the following: (1) The effects of the leaf area index (LAI) and aboveground biomass (AGB) on cotton canopy SIF and cotton yield were similar. The correlation coefficients of LAI and AGB with cotton yield and SIF were significantly positively correlated with each other starting from the budding period, reaching the maximum at the flowering and boll period, and decreasing at the boll period; (2) In different monitoring time periods, the R2 of the cotton yield estimation model established based on SIF and SIFy showed a gradual increase from 10:00 to 14:00 and a gradual decrease from 15:00 to 19:00, while the optimal observation time was from 14:00 to 15:00. The R2 increased with the progression of growth from the budding period to the flowering and boll period and decreased at the boll period, while the optimum growth period was the flowering and boll period; (3) Compared to SIF, SIFy has a superior estimation of yield. The best yield estimation model based on the RF algorithm (R2 = 0.9612, RMSE = 66.27 kg·ha−1, RPD = 4.264) was found in the canopy SIFy of the flowering and boll period at 14:00–15:00, followed by the model utilizing the Bagging algorithm (R2 = 0.8898) and Ada Boost algorithm (R2 = 0.8796). In summary, SIFy eliminates the effect of PAR (photosynthetically active radiation) on SIF and can further improve the estimation of SIF production. This study provides empirical support for SIF estimation of cotton yield and methodological and modeling support for the accurate estimation of cotton yield

Estimation of Nitrogen Content Based on the Hyperspectral Vegetation Indexes of Interannual and Multi-Temporal in Cotton

Crop nitrogen is an efficient index for estimating crop yield. Using hyperspectral information to monitor nitrogen in cotton information in real time can help guide cotton cultivation. In this study, we used drip-irrigation cotton in Xinjiang as the research object and employed various nitrogen treatments to explore the correlation between hyperspectral vegetation indexes and leaf nitrogen concentration (LNC) and the canopy nitrogen density (CND) of cotton in different growth periods and interannual. We employed 30 published hyperspectral vegetation indexes obtained through spectral monitoring in 2019 and 2020 to screen for hyperspectral vegetation indexes highly correlated with the nitrogen in cotton indexes. Based on the same group of hyperspectral vegetation indexes, interannual and multi-temporal nitrogen estimation models of cotton were established using three modeling methods: simple multiple linear regression (MLR), partial least-squares regression (PLSR), and support vector regression (SVR). The results showed the following: (1) The correlations between LNC and CND and vegetation index in individual growth periods of cotton were lower than those for the entire growth period. The correlations between hyperspectral vegetation indexes and cotton LNC, CND, leaf area index (LAI), and aboveground biomass (AGB), were significantly different between years and varieties. The relatively stable indexes between vegetation and LNC were TCARI, PRI, CCRI, and SRI-2, and the absolute values of correlation were 0.251~0.387, 0.239~0.422, 0.245~0.387, and 0.357~0.533. In addition, the correlation between CIred-edge and REIlinear and group indicators (CND, AGB, and LAI) was more stable. (2) In the models established by MLR, PLSR, and SVR, the R2 value from the SVR method was higher in the estimation model based on the entire growth period data and LNC and CND. (3) Using the same group of selected hyperspectral vegetation indexes to estimate nitrogen in cotton in different growth stages, the accuracy of the estimation model of canopy nitrogen density (CND) was higher than that of the estimation model for leaf nitrogen concentration. The canopy nitrogen density most stable model was established by MLR at the flowering and boll stages and the full-boll stage with R2 = 0.532~0.665. This study explored the application potential of hyperspectral vegetation indexes to the nitrogen of drip-irrigated cotton, and the results provide a theoretical basis for hyperspectral monitoring for crop nutrients and canopy structure

Genetic Mapping and Analysis of a Compact Plant Architecture and Precocious Mutant in Upland Cotton

With the promotion and popularization of machine cotton-picking, more and more attention has been paid to the selection of early-maturity varieties with compact plant architecture. The type of fruit branch is one of the most important factors affecting plant architecture and early maturity of cotton. Heredity analysis of the cotton fruit branch is beneficial to the breeding of machine-picked cotton. Phenotype analysis showed that the types of fruit branches in cotton are controlled by a single recessive gene. Using an F2 population crossed with Huaxin102 (normal branch) and 04N-11 (nulliplex branch), BSA (Bulked Segregant Analysis) resequencing analysis and GhNB gene cloning in 04N-11, and allelic testing, showed that fruit branch type was controlled by the GhNB gene, located on chromosome D07. Ghnb5, a new recessive genotype of GhNB, was found in 04N-11. Through candidate gene association analysis, SNP 20_15811516_SNV was found to be associated with plant architecture and early maturity in the Xinjiang natural population. The GhNB gene, which is related to early maturity and the plant architecture of cotton, is a branch-type gene of cotton. The 20_15811516_SNV marker, obtained from the Xinjiang natural population, was used for the assisted breeding of machine-picked cotton varieties

Monolithically-grained perovskite solar cell with Mortise-Tenon structure for charge extraction balance

Abstract Although the power conversion efficiency values of perovskite solar cells continue to be refreshed, it is still far from the theoretical Shockley-Queisser limit. Two major issues need to be addressed, including disorder crystallization of perovskite and unbalanced interface charge extraction, which limit further improvements in device efficiency. Herein, we develop a thermally polymerized additive as the polymer template in the perovskite film, which can form monolithic perovskite grain and a unique “Mortise-Tenon” structure after spin-coating hole-transport layer. Importantly, the suppressed non-radiative recombination and balanced interface charge extraction benefit from high-quality perovskite crystals and Mortise-Tenon structure, resulting in enhanced open-circuit voltage and fill-factor of the device. The PSCs achieve certified efficiency of 24.55% and maintain >95% initial efficiency over 1100 h in accordance with the ISOS-L-2 protocol, as well as excellent endurance according to the ISOS-D-3 accelerated aging test