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Plant height (PH) is associated with lodging resistance and planting density, which is regulated by a complicated gene network. In this study, we identified a spontaneous dwarfing mutation in maize, m30, with decreased internode number and length but increased internode diameter. A candidate gene, ZmCYP90D1, which encodes a member of the cytochrome P450 family, was isolated by map-based cloning. ZmCYP90D1 was constitutively expressed and showed highest expression in basal internodes, and its protein was targeted to the nucleus. A G-to-A substitution was identified to be the causal mutation, which resulted in a truncated protein in m30. Loss of function of ZmCYP90D1 changed expression of hormone-responsive genes, in particular brassinosteroid (BR)-responsive genes which is mainly involved in cell cycle regulation and cell wall extension and modification in plants. The concentration of typhasterol (TY), a downstream intermediate of ZmCYP90D1 in the BR pathway, was reduced. A haplotype conferring dwarfing without reducing yield was identified. ZmCYP90D1 was inferred to influence plant height and stalk diameter via hormone-mediated cell division and cell growth via the BR pathway.

ABSTRACT The geometric characteristics, physical parameters and contact parameters of coated cotton seeds were obtained through theoretical analysis and experimental measurements. A discrete element model reflecting the intrinsic parameters of cotton seeds was established, the contact parameters of coated cotton seeds were calibrated using Discrete Element Method (DEM), and the significant factors affecting the stacking angle were screened using the Plackett-Burman method. The optimal ranges of the parameters were determined by the steepest ascent method, and a second-order regression model of the stacking angle and significant parameters was established by a central composite design. The following optimal values were obtained for cotton seeds: collision recovery (0.38), static friction (0.57) and rolling friction (0.19) coefficients. The average value of the stacking angle obtained by simulations with the best combination of parameters was 30.52°, the result of the simulation test was 30.91°, and the relative error was 1.278%. It is shown that the discrete element particle model and calibration parameters of cotton seed are closer to the actual situation, which can provide theoretical reference for analyzing the precise seeding of cotton seed and optimizing the structural parameters of seed disperser.

The real-world crowns of broadleaf tree species feature green leaves surrounding branches, resulting in leaf spatial aggregation effect in the crown. However, the impact of such leaf spatial aggregation on chlorophyll content retrieval has not yet been determined. This study investigated the effect of leaf spatial aggregation on chlorophyll content retrieval in two distinct apple orchards with open canopies. The “PROSPECT + LESS” model was used for canopy reflectance simulation, and 25 hyperspectral vegetation indices (VIs) were analyzed to identify universal VIs for various leaf aggregations. Sensitivity analysis was conducted to evaluate the impact of leaf aggregation on the relationships between VIs and chlorophyll content. An artificial neural network regression algorithm was used to retrieve chlorophyll content by reversing the radiative transfer model (RTMs). The results show that leaf aggregation significantly affects the relationships between VIs and chlorophyll content as a result of the variability in the ratio of photosynthetic vegetation pixels to background pixels captured by the sensor at the top of the canopy. TCARI/OSAVI was found to be resistant to confounding factors (e.g., leaf area index and dry matter content) and maintained stable relationships with chlorophyll content. Leaf spatial aggregation had a significant impact on chlorophyll content retrieval, especially when leaves were highly aggregated. In such cases, the spectral variation driven by the photosynthetic vegetation was masked by the background, leading to a large divergence between simulated and observed spectra. Low to moderate levels of leaf aggregation, on the other hand, provided accurate chlorophyll content retrieval in both apple orchards (R2 = 0.49 to 0.67). In conclusion, when using 3D RTMs to retrieve chlorophyll content, it is recommended to configure low to moderate levels of leaf aggregation to ensure high accuracy and efficiency.

Leaf area profiles (LAP) represent the green leaf area per unit ground area distributed with the vertical leaf layer, which is a key trait for guiding nutrition diagnosis, crop management and crop breeding. However, passive mono-angle optical sensors don't have direction information on vertical LAP, which makes spectral remote sensing can't capture the canopy-scale vertical leaf area information from top to bottom. To meet this challenge, we present a modeling framework to decipher maize vertical LAP from spectral imagery data. It first employed a hybrid method to derive LAI from the spectral imagery, and then configured a bell-shaped function to decipher the vertical LAP. We conducted a five-year field experiment in critical growth stages to test the ability of the proposed method. Results showed great disagreements between vegetative and reproductive stages. Such differences impacted the leaf area development and the largest leaf layer for LAP modeling. The proposed method considered these two phenological stage to improve the LAP estimation. The performance of this method was assessed by comparing the derived vertical LAP with measurements over different planting years and maize grain production fields. Results showed robust canopy-level modeling for vertical LAP (RMSE = 0.083–0.094 m2/m2). This study highlights that this method extends the ability of passive optical remote sensing to derive vertical information. This method is a valuable and effective remote-sensing approach for deriving vertical LAP over maize canopy scale, also has potential reference value for other vegetation with similar vertical structure.

ABSTRACT To solve the current problems of poor seeding stability and complex maintenance of maize seed-metering devices, a clamp-holding precision maize-seed-metering device is designed. The structure and working principle of the seed-metering device are described, the optimum state of maize seed clamping is analyzed, and the range of each working area of the seed-metering device is determined. The profile and structural parameters of the limit guide plate are designed by analyzing the laws of motion of the seed-picking block. A mechanical model of maize seeds under the critical state in each working area of the seed-metering device is established. The influence of the seed-metering device's rotational speed and the clamping force of the seed-picking block on the seeding performance are analyzed. A physical prototype of the seed-metering device was made for bench tests and field verification tests. The qualified, multiple seeding, and missed seeding rates of the designed seed-metering device were 94.21%, 3.52%, and 2.27%, respectively, meeting the agronomic requirements of maize sowing. The study provides theoretical guidance for designing and optimizing precision maize-seed-metering devices.

Rapidly developing remote sensing techniques are shedding new light on large-scale crop growth status monitoring, especially in recent applications of unmanned aerial vehicles (UAVs). Many inversion models have been built to estimate crop growth variables. However, the present methods focused on building models for each single crop stage, and the features generally used in the models are vegetation indices (VI) or joint VI with data derived from UAV-based sensors (e.g., texture, RGB color information, or canopy height). It is obvious these models are either limited to a single stage or have an unstable performance across stages. To address these issues, this study selected four key wheat growth parameters for inversion: above-ground biomass (AGB), plant nitrogen accumulation (PNA) and concentration (PNC), and the nitrogen nutrition index (NNI). Crop data and multispectral data were acquired in five wheat growth stages. Then, the band reflectance and VI were obtained from multispectral data, along with the five stages that were recorded as phenology indicators (PIs) according to the stage of Zadok’s scale. These three types of data formed six combinations (C1–C6): C1 used all of the band reflectances, C2 used all VIs, C3 used bands and VIs, C4 used bands and PIs, C5 used VIs and PIs, and C6 used bands, Vis, and PIs. Some of the combinations were integrated with PIs to verify if PIs can improve the model accuracy. Random forest (RF) was used to build models with combinations of different parameters and evaluate the feature importance. The results showed that all models of different combinations have good performance in the modeling of crop parameters, such as R2 from 0.6 to 0.79 and NRMSE from 10.51 to 15.83%. Then, the model was optimized to understand the importance of PIs. The results showed that the combinations that integrated PIs showed better estimations and the potential of using PIs to minimize features while still achieving good predictions. Finally, the varied model results were evaluated to analyze their performances in different stages or fertilizer treatments. The results showed the models have good performances at different stages or treatments (R2 > 0.6). This paper provides a reference for monitoring and estimating wheat growth parameters based on UAV multispectral imagery and phenology information.

As a key functional trait, leaf photosynthetic pigment content (LPPC) plays an important role in the health status monitoring and yield estimation of apples. Hyperspectral features including vegetation indices (VIs) and derivatives are widely used in retrieving vegetation biophysical parameters. The fractional derivative spectral method shows great potential in retrieving LPPC. However, the performance of fractional derivatives and machine learning (ML) for retrieving apple LPPC still needs to be explored. The objective of this study is to test the capacity of using fractional derivative and ML methods to retrieve apple LPPC. Here, the hyperspectral data in the 400–2500 nm domains was used to calculate the fractional derivative order of 0.2–2, and then the sensitive bands were screened through feature dimensionality reduction to train ML to build the LPPC estimation model. Additionally, VIs-based ML methods and empirical regression models were developed to compare with the fractional derivative methods. The results showed that fractional derivative-driven ML methods have higher accuracy than the ML methods driven by the original spectra or vegetation index. The results also showed that the ML methods perform better than empirical regression models. Specifically, the best estimates of chlorophyll content and carotenoid content were achieved using support vector regression (SVR) at the derivative order of 0.2 (R2 = 0.78) and 0.4 (R2 = 0.75), respectively. The fractional derivative maintained a good universality in retrieving the LPPC of multiple phenological periods. Therefore, this study highlights that the fractional derivative and ML improved the estimation of apple LPPC.

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

Mitochondrial protein translation that is essential for aerobic energy production includes four essential steps of the mitochondrial ribosome cycle, namely, initiation, elongation, termination of the polypeptide, and ribosome recycling. Translation termination initiates when a stop codon enters the A site of the mitochondrial ribosome where it is recognized by a dedicated peptide release factor (RF). However, RFs and mechanisms involved in translation in plant mitochondria, especially in monocotyledons, remain largely unknown. Here, we identified a crumpled kernel (crk5 allele) mutant, with significantly decreased kernel size, 100-kernel weight, and an embryo-lethal phenotype. The Crk5 allele was isolated using map-based cloning and found to encode a mitochondrial localization RF2a. As it is an ortholog of Arabidopsis mitochondrial RF2a, we named the gene ZmmtRF2a. ZmmtRF2a is missing the 5th-7th exons in the crk5 resulting in deletion of domains containing motifs GGQ and SPF that are essential for release activity of RF, mitochondrial ribosome binding, and stop codon recognition. Western blot and qRT-PCR analyses indicate that the crk5 mutation results in abnormal mitochondrion structure and function. Intriguingly, we observed a feedback loop in the crk5 with up-regulated transcript levels detected for several mitochondrial ribosome and mitochondrial-related components, in particular mitochondrial complexes CI, CIV, and a ribosome assembly related PPR. Together, our data support a crucial role for ZmmtRF2a in regulation of mitochondrial structure and function in maize. [ABSTRACT FROM AUTHOR]

[Objectives] This study was conducted to select superior maize combinations. [Methods] With the control maize variety Haihe 2 in the middle altitude area of Yunnan Province as the control, adopting a single-factor randomized block three-replicate experiment design, a one-year field cultivation experiment was carried out in Luyin Village, Yongfeng Town, Zhaoyang District, Zhaotong City. [Results] A total of 11 new superior maize combinations including ZT-1 and ZT-2 were obtained. Through comprehensive comparison analysis with Haihe 2 (CK), we selected six superior combinations of ZT-1, ZT-3, ZT-4, ZT-6, ZT-7 and ZT-9, which showed overall performance better than the control Haihe 2 (CK) in terms of resistance, yield, and ear traits. [Conclusions] The selected 6 superior combinations have promotion value, and are recommended for higher-level trials and demonstrations. [ABSTRACT FROM AUTHOR]

[Objectives J This study was conducted to select superior maize combinations. [Methods J With the control maize variety Haihe 2 in the middle altitude area of Yunnan Province as the control, adopting a single-factor randomized block three-replicate experiment design, a one-year field cultivation experiment was carried out in Luyin Village, Yongfeng Town, Zhaoyang District, Zhaotong City. [Results] A total of 11 new superior maize combinations including ZT-1 and ZT-2 were obtained. Through comprehensive comparison analysis with Haihe 2 (CK), we selected six superior combinations of ZT-1, ZT-3, ZT 4, ZT-6, ZT-7 and ZT-9, which showed overall performance better than the control Haihe 2 (CK) in terms of resistance, yield, and ear traits. [ Conclusions]The selected 6 superior combinations have promotion value, and are recommended for higher-level trials and demonstrations. [ABSTRACT FROM AUTHOR]

About 40 000 inhabitants migrated from a high-risk area of esophageal squamous cell carcinoma (ESCC) to a low-risk area of esophageal cancer 40 years ago. Little is known about the change in the mortality in esophageal cancer among these immigrants. This study examined the impact of changing environments on esophageal cancer by comparing age-standardized mortality rates of immigrant group to the rates of native population (natives who live in high cancer location and have never moved) and host populations (hosts who live in low cancer location and have never moved people). All ESCC deaths taking place during 1999–2004 among the migrant, native, and host populations were identified by retrospective population-based screening. Direct age-adjusted mortality rates were calculated by using the China population of year 2000 as standard population. From 1999–2004, the average annual age-adjusted mortality of ESCC for the migrant, native, and host population was 61.6/100 000, 59.7/100 000, and 6.7/100 000, respectively. No decreasing tendency was found in mortality rate of ESCC in the population of young immigrants. The mortality rate of ESCC of migrants remained high even they had been living in the low endemic region for 40 years. This study strongly suggested that genetic susceptibility, rather than environment exposure, is responsible for the high risk of ESCC in the migrants. [ABSTRACT FROM AUTHOR]