Rumen fluid pretreatment promotes anaerobic methane production: revealing microbial dynamics driving increased acid yield from different concentrations of corn straw.

In this work, the corn straw (CS) with concentrations of 3%, 6%, and 9% (w/v) were pretreated by rumen fluid (RF) and then used for batched mesophilic biogas production. The results showed that after a 6-day pretreatment, volatile fatty acid (VFAs) production of 3.78, 8.27, and 10.4 g/L could be found in 3%, 6%, and 9%, respectively. When concerning with biogas production, the highest accumulative methane production of 149.1 mL CH4/g volatile solid was achieved by 6% pretreated CS, which was 22% and 45% higher than 3% and 9%, respectively. Also, it was 3.6 times higher than the same concentration of unpretreated CS. The results of the microbial community structure analysis revealed that the 6% CS pretreatment not only maintained a microbial community with the highest richness and diversity, but also exhibited the highest relative abundance of Firmicutes (45%) and Euryarchaeota (3.9%). This high abundance was conducive to its elevated production of VFAs and methane. These findings provide scientific reference for the utilization of CS and support the development of agricultural waste resource utilization and environmental protection.

Quantifying the Individual and Combined Effects of Short-Term Heat Stress at Booting and Flowering Stages on Nonstructural Carbohydrates Remobilization in Rice.

Rice production is threatened by climate change, particularly heat stress (HS). Nonstructural carbohydrates (NSCs) remobilization is a key physiological mechanism that allows rice plants to cope with HS. To investigate the impact of short-term HS on the remobilization of nonstructural carbohydrates (NSCs) in rice, two cultivars (Huaidao-5 and Wuyunjing-24) were subjected to varying temperature regimes: 32/22/27 °C as the control treatment, alongside 40/30/35 °C and 44/34/39 °C, for durations of 2 and 4 days during the booting, flowering, and combined stages (booting + flowering) within phytotrons across the years 2016 and 2017. The findings revealed that the stem's NSC concentration increased, while the panicle's NSCs concentration, the efficiency of NSCs translocation from the stem, and the stem NSC contribution to grain yield exhibited a consistent decline. Additionally, sugar and starch concentrations increased in leaves and stems during late grain filling and maturity stages, while in panicles, the starch concentration decreased and sugar concentration increased. The heat-tolerant cultivar, Wuyunjing-24, exhibited higher panicle NSC accumulation under HS than the heat-sensitive cultivar, Huaidao-5, which had more stem NSC accumulation. The flowering stage was the most vulnerable to HS, followed by the combined and booting stages. Heat degree days (HDDs) were utilized to quantify the effects of HS on NSC accumulation and translocation, revealing that the flowering stage was the most affected. These findings suggest that severe HS makes the stem the primary carbohydrate storage sink, and alleviation under combined HS aids in evaluating NSC accumulation, benefiting breeders in developing heat-tolerant rice varieties.

Low nitrogen priming improves nitrogen uptake and assimilation adaptation to nitrogen deficit stress in wheat seedling.

MAIN CONCLUSION: Early-stage low nitrogen priming promotes root growth and delays leaf senescence through gene expression, enhancing nitrogen absorption and assimilation in wheat seedlings, thereby alleviating growth inhibition under nitrogen deficit stress and supporting normal seedling development. Verifying the strategies to reduce the amount of nitrogen (N) fertilizer while maintaining high crop yields is important for improving crop N use efficiency (NUE) and protecting the environment. To determine whether low N (LN) priming (LNP) can alleviate the impact of N-deficit stress on the growth of wheat seedlings and improve their tolerance to N-deficit stress, we conducted hydroponic experiments using two wheat cultivars, Yangmai 158 (YM158, LN tolerant) and Zaoyangmai (ZYM, LN sensitive) to study the effects of LNP on wheat seedlings under N-deficit stress. N-deficit stress decreased the plant dry weight, leaf area, and leaf N content (LNC), while LNP could significantly reduce this reduction. Distinct sensitivities to N-deficit stress were observed between the wheat cultivars, with ZYM showing an early decrease in leaf N content compared to YM158, which exhibited a late-stage reduction. LNP promoted root growth, expanded N uptake area, and upregulated the expression of TaNRT1.1, TaNRT2.1, and TaNRT2.2 in wheat seedlings, suggesting that LNP can enhance root N uptake capacity to increase N accumulation in plants. In addition, LNP improved the activity of glutamine synthase (GS) to enhance the capacity of N assimilation of plants. The relative expression of TaGS1 in the lower leaves of priming and stress (PS) was lower than that of no priming and stress (NS) after LNP, indicating that the rate of N transfer from the lower leaves to the upper leaves became slower after LNP, which alleviated the senescence of the lower leaves. The relative expression of TaGS2 was significantly increased, which might be related to the enhanced photorespiratory ammonia assimilation capacity after LNP, which reduced the N loss and maintained higher LNC. Therefore, LNP in the early stage can improve the N absorption and assimilation ability and maintain the normal N supply to alleviate the inhibition of N-deficit stress in wheat seedlings.

A method for small-sized wheat seedlings detection: from annotation mode to model construction.

The number of seedlings is an important indicator that reflects the size of the wheat population during the seedling stage. Researchers increasingly use deep learning to detect and count wheat seedlings from unmanned aerial vehicle (UAV) images. However, due to the small size and diverse postures of wheat seedlings, it can be challenging to estimate their numbers accurately during the seedling stage. In most related works in wheat seedling detection, they label the whole plant, often resulting in a higher proportion of soil background within the annotated bounding boxes. This imbalance between wheat seedlings and soil background in the annotated bounding boxes decreases the detection performance. This study proposes a wheat seedling detection method based on a local annotation instead of a global annotation. Moreover, the detection model is also improved by replacing convolutional and pooling layers with the Space-to-depth Conv module and adding a micro-scale detection layer in the YOLOv5 head network to better extract small-scale features in these small annotation boxes. The optimization of the detection model can reduce the number of error detections caused by leaf occlusion between wheat seedlings and the small size of wheat seedlings. The results show that the proposed method achieves a detection accuracy of 90.1%, outperforming other state-of-the-art detection methods. The proposed method provides a reference for future wheat seedling detection and yield prediction.

A low red/far-red ratio restricts nitrogen assimilation by inhibiting nitrate reductase associated with downregulated TaNR1.2 and upregulated TaPIL5 in wheat (Triticum aestivum L.).

Understanding the physiological mechanism underlying nitrogen levels response to a low red/far-red ratio (R/FR) can provide new insights for optimizing wheat yield potential but has been not well documented. This study focused on the changes in nitrogen levels, nitrogen assimilation and nitrate uptake in wheat plants grown with and without additional far-red light. A low R/FR reduced wheat nitrogen accumulation and grain yield compared with the control. The levels of total nitrogen, free amino acid and ammonium were decreased in leaves but nitrate content was temporarily increased under a low R/FR. The nitrate reductase (NR) activity in leaves was more sensitive to a low R/FR than glutamine synthetase, glutamate synthase, glutamic oxalacetic transaminase and glutamic-pyruvic transaminase. Further analysis showed that a low R/FR had little effect on the NR activation state but reduced the level of NR protein and the expression of encoding gene TaNR1.2. Interestingly, a low R/FR rapidly induced TaPIL5 expression rather than TaHY5 and other members of TaPILs in wheat, suggesting that TaPIL5 was the key transcription factor response to a low R/FR in wheat and might be involved in the downregulation of TaNR1.2 expression. Besides, a low R/FR downregulated the expression of TaNR1.2 in leaves earlier than that of TaNRT1.1/1.2/1.5/1.8 in roots, which highlights the importance of NR and nitrogen assimilation in response to a low R/FR. Our results provide revelatory evidence that restricted nitrate reductase associated with downregulated TaNR1.2 and upregulated TaPIL5 mediate the suppression of nitrogen assimilation under a low R/FR in wheat.

Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR.

BACKGROUND: The metrics for assessing the yield of crops in the field include the number of ears per unit area, the grain number per ear, and the thousand-grain weight. Typically, the ear number per unit area contributes the most to the yield. However, calculation of the ear number tends to rely on traditional manual counting, which is inefficient, labour intensive, inaccurate, and lacking in objectivity. In this study, two novel extraction algorithms for the estimation of the wheat ear number were developed based on the use of terrestrial laser scanning (TLS) in conjunction with the density-based spatial clustering (DBSC) algorithm based on the normal and the voxel-based regional growth (VBRG) algorithm. The DBSC involves two steps: (1) segmentation of the point clouds using differences in the normal vectors and (2) clustering of the segmented point clouds using a density clustering algorithm to calculate the ear number. The VBRG involves three steps: (1) voxelization of the point clouds, (2) construction of the topological relationships between the voxels as a connected region using the k-dimensional tree, and (3) detection of the wheat ears in the connected areas using a regional growth algorithm. RESULTS: The results demonstrated that DBSC and VBRG were promising in estimating the number of ears for different cultivars, planting densities, N fertilization rates, and growth stages of wheat (RMSE = 76 ~ 114 ears/m2, rRMSE = 18.62 ~ 27.96%, r = 0.76 ~ 0.84). Comparing the performance of the two algorithms, the overall accuracy of the DBSC (RMSE = 76 ears/m2, rRMSE = 18.62%, r = 0.84) was better than that of the VBRG (RMSE = 114 ears/m2, rRMSE = 27.96%, r = 0.76). It was found that with the DBSC, the calculation in points as units permitted more detailed information to be retained, and this method was more suitable for estimation of the wheat ear number in the field. CONCLUSIONS: The algorithms adopted in this study provide new approaches for non-destructive measurement and efficient acquisition of the ear number in the assessment of the wheat yield phenotype.

Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet.

Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.

SPSI: A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery.

Rapid and accurate estimation of panicle number per unit ground area (PNPA) in winter wheat before heading is crucial to evaluate yield potential and regulate crop growth for increasing the final yield. The accuracies of existing methods were low for estimating PNPA with remotely sensed data acquired before heading since the spectral saturation and background effects were ignored. This study proposed a spectral-textural PNPA sensitive index (SPSI) from unmanned aerial vehicle (UAV) multispectral imagery for reducing the spectral saturation and improving PNPA estimation in winter wheat before heading. The effect of background materials on PNPA estimated by textural indices (TIs) was examined, and the composite index SPSI was constructed by integrating the optimal spectral index (SI) and TI. Subsequently, the performance of SPSI was evaluated in comparison with other indices (SI and TIs). The results demonstrated that green-pixel TIs yielded better performances than all-pixel TIs apart from TI[HOM], TI[ENT], and TI[SEM] among all indices from 8 types of textural features. SPSI, which was calculated by the formula DATT[850,730,675] + NDTICOR[850,730], exhibited the highest overall accuracies for any date in any dataset in comparison with DATT[850,730,675], TINDRE[MEA], and NDTICOR[850,730]. For the unified models assembling 2 experimental datasets, the RV2 values of SPSI increased by 0.11 to 0.23, and both RMSE and RRMSE decreased by 16.43% to 38.79% as compared to the suboptimal index on each date. These findings indicated that the SPSI is valuable in reducing the spectral saturation and has great potential to better estimate PNPA using high-resolution satellite imagery.

Improving the predictions of leaf photosynthesis during and after short-term heat stress with current rice models.

In response to increasing global warming, extreme heat stress significantly alters photosynthetic production. While numerous studies have investigated the temperature effects on photosynthesis, factors like vapour pressure deficit (VPD), leaf nitrogen, and feedback of sink limitation during and after extreme heat stress remain underexplored. This study assessed photosynthesis calculations in seven rice growth models using observed maximum photosynthetic rate (Pmax ) during and after short-term extreme heat stress in multi-year environment-controlled experiments. Biochemical models (FvCB-type) outperformed light response curve-based models (LRC-type) when incorporating observed leaf nitrogen, photosynthetically active radiation, temperatures, and intercellular CO2 concentration (Ci ) as inputs. Prediction uncertainty during heat stress treatment primarily resulted from variation in temperatures and Ci . Improving FVPD (the slope for the linear effect of VPD on Ci /Ca ) to be temperature-dependent, rather than constant as in original models, significantly improved Ci prediction accuracy under heat stress. Leaf nitrogen response functions led to model variation in leaf photosynthesis predictions after heat stress, which was mitigated by calibrated nitrogen response functions based on active photosynthetic nitrogen. Additionally, accounting for observed differences in carbohydrate accumulation between panicles and stems during grain filling improved the feedback of sink limitation, reducing Ci overestimation under heat stress treatments.

Effects of pre-anthesis low-temperature stress on the mineral components in wheat grains.

Introduction: The nutritional value of wheat is important to human health. Despite minerals being essential nutrients for the human body, they are often neglected in consideration of the nutritional quality of cereal grains. Extreme low-temperature events have become more frequent due to the current environmental unpredictability, and it is yet unknown how the mineral components in grains are affected by low temperature. Methods: To provide valuable information for enhancing the nutritional quality of wheat under potential climatic conditions, we treated different cold-sensitive wheat cultivars at four low-temperature levels during the individual and combined stages of jointing and booting in controlled-environment phytotrons. Results and Discussion: In general, the contents of P, K, Ca, and Zn in the cold-sensitive cultivar (Yangmai16) and K in the cold-tolerant cultivar (Xumai30) were enhanced by low temperature. However, the accumulation of minerals in mature grains was reduced under low-temperature treatment, except for P, Ca, and Zn in Yangmai16. In addition, the mineral content and accumulation in Yangmai16 (except for Fe) were more susceptible to low temperature during the combined stages, while the mineral content and accumulation of K, Fe, and Zn in Xumai30 were more susceptible to low temperature during the booting stage. Moreover, Yangmai16 under extremely low temperatures (T3 and T4) during booting and Xumai30 under all low-temperature treatments during the combined stages had lower comprehensive evaluation values. These findings offer a crucial reference for enhancing the nutritional quality of wheat grains under climate change.

Interaction of Genotype, Environment, and Management on Organ-Specific Critical Nitrogen Dilution Curve in Wheat.

The organ-specific critical nitrogen (Nc) dilution curves are widely thought to represent a new approach for crop nitrogen (N) nutrition diagnosis, N management, and crop modeling. The Nc dilution curve can be described by a power function (Nc = A1·W-A2), while parameters A1 and A2 control the starting point and slope. This study aimed to investigate the uncertainty and drivers of organ-specific curves under different conditions. By using hierarchical Bayesian theory, parameters A1 and A2 of the organ-specific Nc dilution curves for wheat were derived and evaluated under 14 different genotype × environment × management (G × E × M) N fertilizer experiments. Our results show that parameters A1 and A2 are highly correlated. Although the variation of parameter A1 was less than that of A2, the values of both parameters can change significantly in response to G × E × M. Nitrogen nutrition index (NNI) calculated using organ-specific Nc is in general consistent with NNI estimated with overall shoot Nc, indicating that a simple organ-specific Nc dilution curve may be used for wheat N diagnosis to assist N management. However, the significant differences in organ-specific Nc dilution curves across G × E × M conditions imply potential errors in Nc and crop N demand estimated using a general Nc dilution curve in crop models, highlighting a clear need for improvement in Nc calculations in such models. Our results provide new insights into how to improve modeling of crop nitrogen-biomass relations and N management practices under G × E × M.

Extreme low-temperature events can alleviate micronutrient deficiencies while increasing potential health risks from heavy metals in rice.

Despite global warming, extreme low-temperature stress (LTS) events pose a significant threat to rice production (especially in East Asia) that can significantly impact micronutrient and heavy metal elements in rice. With two billion people worldwide facing micronutrient deficiencies (MNDs) and widespread heavy metal pollution in rice, understanding these impacts is crucial. We conducted detailed extreme LTS experiments with two rice (Oryza sativa L.) cultivars (Huaidao 5 and Nanjing 46) grown under four temperature levels (from 21/27 °C to 6/12 °C) and three LTS durations (three, six, and nine days). We observed significant interaction effects for LTS at different growth stages, durations and temperature levels on the contents and accumulation of mineral elements. The contents of most mineral elements (such Fe, Zn, As, Cu, and Cd) increased significantly under severe LTS at flowering, but decreased under LTS at the grain-filling stage. The accumulations of all mineral elements decreased at the three growth stages under LTS due to decreased grain weight. The contents and accumulation of mineral elements were more sensitive to LTS at the peak flowering stage than at the other two stages. Furthermore, the contents of most mineral elements in Nanjing 46 show larger variation under LTS compared to Huaidao 5. Accumulated cold degree days (ACDD, °C·d) were found to be suitable for quantifying the effects of LTS on the relative contents and accumulations of mineral elements. LTS at the flowering stage will help alleviate MNDs, but may also increase potential health risks from heavy metals. These results provide valuable insights for evaluating future climate change impacts on rice grain quality and potential health risks from heavy metals.

Effect of High-Molecular Weight Glutenin Subunits (HMW-GSs) on Gluten Polymerization during Biscuit Making: Insights from Experimental and Molecular Dynamics Simulation Study.

The effect of high-molecular weight glutenin subunits (HMW-GSs) on gluten polymerization during biscuit making was investigated using a set of HMW-GS deletion lines. Results showed that the deletion of HMW-GSs improved the biscuit quality compared with the wild type (WT), especially in x-type HMW-GS deletion lines. Slight gluten depolymerization was observed during dough mixing, while progressive gluten polymerization occurred during biscuit baking. The deletion of HMW-GSs suppressed the polymerization of glutenin and gliadin compared with the WT during biscuit baking, especially in x-type HMW-GS deletion lines. These actions resulted in less elevation of the intermolecular ß-sheet and ordered α-helix and altering the disulfide (SS) conformation to a less stable conformation in HMW-GS deletion lines compared with the WT during baking. Molecular dynamics simulation analysis further demonstrated that x-type HMW-GSs had higher thermal stability compared with y-type HMW-GSs during heating.

Improving multi-scale detection layers in the deep learning network for wheat spike detection based on interpretive analysis.

BACKGROUND: Detecting and counting wheat spikes is essential for predicting and measuring wheat yield. However, current wheat spike detection researches often directly apply the new network structure. There are few studies that can combine the prior knowledge of wheat spike size characteristics to design a suitable wheat spike detection model. It remains unclear whether the complex detection layers of the network play their intended role. RESULTS: This study proposes an interpretive analysis method for quantitatively evaluating the role of three-scale detection layers in a deep learning-based wheat spike detection model. The attention scores in each detection layer of the YOLOv5 network are calculated using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm, which compares the prior labeled wheat spike bounding boxes with the attention areas of the network. By refining the multi-scale detection layers using the attention scores, a better wheat spike detection network is obtained. The experiments on the Global Wheat Head Detection (GWHD) dataset show that the large-scale detection layer performs poorly, while the medium-scale detection layer performs best among the three-scale detection layers. Consequently, the large-scale detection layer is removed, a micro-scale detection layer is added, and the feature extraction ability in the medium-scale detection layer is enhanced. The refined model increases the detection accuracy and reduces the network complexity by decreasing the network parameters. CONCLUSION: The proposed interpretive analysis method to evaluate the contribution of different detection layers in the wheat spike detection network and provide a correct network improvement scheme. The findings of this study will offer a useful reference for future applications of deep network refinement in this field.

A Portable Pull-Out Soil Profile Moisture Sensor Based on High-Frequency Capacitance.

Soil profile moisture is a crucial parameter of agricultural irrigation. To meet the demand of soil profile moisture, simple fast-sensing, and low-cost in situ detection, a portable pull-out soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of a moisture-sensing probe and a data processing unit. The probe converts soil moisture into a frequency signal using an electromagnetic field. The data processing unit was designed for signal detection and transmitting moisture content data to a smartphone app. The data processing unit and the probe are connected by a tie rod with adjustable length, which can be moved up and down to measure the moisture content of different soil layers. According to indoor tests, the maximum detection height for the sensor was 130 mm, the maximum detection radius was 96 mm, and the degree of fitting (R2) of the constructed moisture measurement model was 0.972. In the verification tests, the root mean square error (RMSE) of the measured value of the sensor was 0.02 m3/m3, the mean bias error (MBE) was ±0.009 m3/m3, and the maximum error was ±0.039 m3/m3. According to the results, the sensor, which features a wide detection range and good accuracy, is well suited for the portable measurement of soil profile moisture.

Potential of Establishing the Universal Critical Nitrogen Dilution Curve for Japonica Rice.

Establishing the universal critical nitrogen (NC) dilution curve can assist in crop N diagnosis at the regional scale. This study conducted 10-year N fertilizer experiments in Yangtze River Reaches to establish universal NC dilution curves for Japonica rice based on simple data-mixing (SDM), random forest algorithm (RFA), and Bayesian hierarchical model (BHM), respectively. Results showed that parameters a and b were affected by the genetic and environmental conditions. Based on RFA, highly related factors of a (plant height, specific leaf area at tillering end, and maximum dry matter weight during vegetative growth period) and b (accumulated growing degree days at tillering end, stem-leaf ratio at tillering end, and maximum leaf area index during vegetative growth period) were successfully applied to establish the universal curve. In addition, representative values (most probable number [MPN]) were selected from posterior distributions obtained by the BHM approach to explore universal parameters a and b. The universal curves established by SDM, RFA, and BHM-MPN were verified to have a strong N diagnostic capacity (N nutrition index validation R 2 ≥ 0.81). In summary, compared with the SDM approach, RFA and BHM-MPN can greatly simplify the modeling process (e.g., defining N-limiting or non-N-limiting groups) while maintaining a good accuracy, which are more conducive to the application and promotion at the regional scale.

Influence of High-Molecular-Weight Glutenin Subunit on Components and Multiscale Structure of Gluten and Dough Quality in Soft Wheat.

A set of high-molecular-weight glutenin subunit (HMW-GS) deletion lines were used to investigate the influences of HMW-GS on wheat gluten, and dough properties were investigated using a set of HMW-GS deletion lines. Results showed that HMW-GS deletion significantly decreased the dough stability time, as well as viscoelastic moduli (G' and G″), compared with the wild type, where the deletion of x-type HMW-GSs (Ax1d, Bx7d, and Dy12d) decreased more than y-type HMW-GSs (By8d and Dy12d). The deletion of HMW-GS significantly decreased HMW-GS contents and increased α-/γ-gliadin contents. A proteomic study showed that the HMW-GS deletion down-regulated the HMW-GS, ß-amylase, serpins, and protein disulfide isomerase and up-regulated the LMW-GS, α/γ-gliadin, and α-amylase inhibitor. Meanwhile, HMW-GS deletion significantly decreased contents of ß-turn and ß-sheet. In addition, less energetically stable disulfide conformations (trans-gauche-gauche and trans-gauche-trans) were abundant in HMW-GS deletion lines. Furthermore, analysis of five HMW-GSs based on amino acid sequences proved that Dx2 and Bx7 had a more stable structure, followed by Ax1, then Dy12, and finally By8.

Improving the spatial and temporal estimation of ecosystem respiration using multi-source data and machine learning methods in a rainfed winter wheat cropland.

The investigation of ecosystem respiration (RE) and its vital influential factors along with the timely and accurate detection of spatiotemporal variations in RE are essential for guiding agricultural production planning. RE observation in the plot region is primarily based on the laborious chamber method. However, upscaling the spatial-temporal estimates of RE at the canopy scale is still challenging. The present study conducted a field experiment to determine RE using the chamber method. A multi-rotor unmanned aerial vehicle (UAV) equipped with a multispectral camera was employed to acquire the canopy spectral data of wheat during each RE test experiment. Moreover, the agronomic indicators of aboveground plant biomass, leaf area index, leaf dry mass as well as agrometeorological and soil data were measured simultaneously. The study analyzed the potential of multi-information for estimating RE at the field scale and proposed two strategies for RE estimation. In addition, a semiempirical, yet Lloyd and Taylor-based, remote sensing model (LT1-NIRV) was developed for estimating RE observed across different growth stages with a small margin of error (coefficient of determination [R2] = 0.60-0.64, root-mean-square error [RMSE] = 285.98-316.19 mg m-2 h-1). Further, five machine learning (ML) algorithms were utilized to independently estimate RE using two different datasets. The rigorous analyses, which included statistical comparison and cross-validation for estimating RE, confirmed that the XGBoost model, with the highest R2 and lowest RMSE (R2 = 0.88 and RMSE = 172.70 mg m-2 h-1), performed the best among the evaluated ML models. The LT1-NIRV model was less effective in estimating RE compared with the other ML models. Based on this comprehensive comparison analysis, the ML model can successfully estimate variations in wheat field RE using high-resolution UAV multispectral images and environmental factors from the wheat cropland system, thereby providing a valuable reference for monitoring and upscaling RE observations.

Low red/far-red ratio can induce cytokinin degradation resulting in the inhibition of tillering in wheat (Triticum aestivum L.).

Shoot branching is inhibited by a low red/far-red ratio (R/FR). Prior studies have shown that the R/FR suppressed Arabidopsis thaliana branching by promotes bud abscisic acid (ABA) accumulation directly. Given that wheat tiller buds are wrapped in leaf sheaths and may not respond rapidly to a R/FR, systemic cytokinin (CTK) may be more critical. Here, systemic hormonal signals including indole-3-acetic acid (IAA), gibberellins (GA) and CTK and bud ABA signals in wheat were tested under a low R/FR. The results showed that a low R/FR reduced the percentage of tiller occurrence of tiller IV and the tiller number per plant. The low R/FR did not rapidly induced ABA accumulation in the tiller IV because of the protection of the leaf sheath and had little effect on IAA content and signaling in the tiller nodes. The significant change in the CTK levels was observed earlier than those of other hormone (ABA, IAA and GA) and exogenous cytokinin restored the CTK levels and tiller number per plant under low R/FR conditions. Further analysis revealed that the decrease in cytokinin levels was mainly associated with upregulation of cytokinin degradation genes (TaCKX5, TaCKX11) in tiller nodes. In addition, exposure to a decreased R/FR upregulated the expression of GA biosynthesis genes (TaGA20ox1, TaGA3ox2), resulting in elevated GA levels, which might further promote CTK degradation in tiller nodes and inhibit tillering. Therefore, our results provide evidence that the enhancement of cytokinin degradation is a novel mechanism underlying the wheat tillering response to a low R/FR.

Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System.

Soil is characterized by high spatiotemporal variability due to the combined influence of internal and external factors. The most efficient approach for addressing spatial variability is the use of management zones (MZs). Common approaches for delineating MZs include K-means and fuzzy C-means cluster analysis algorithms. However, these clustering methods have been used to delineate MZs independent of the spatial dependence of soil variables. Thus, the accuracy of the clustering results has been limited. In this study, six soil variables (soil pH, total nitrogen, organic matter, available phosphorus, available potassium, and soil apparent electrical conductivity) were used to characterize the spatial variability within a representative village in Suining County, Jiangsu Province, China. Two variable reduction techniques (PCA, multivariate spatial analysis based on Moran's index; MULTISPATI-PCA) and three different clustering algorithms (fuzzy C-means clustering, iterative self-organizing data analysis techniques algorithm, and Gaussian mixture model; GMM) were used to optimize the MZ delineation. Different clustering model composites were evaluated using yield data collected after the wheat harvest in 2020. The results indicated that the variable reduction technologies in conjunction with clustering algorithms provided better performance in MZ delineation, with average silhouette coefficient (ASC) and variance reduction (VR) of 0.48-0.57, and 13.35-23.13%, respectively. Moreover, the MULTISPATI-PCA approach was more conducive to identifying variables requiring MZ delineation than traditional PCA methods. Combining MULTISPATI-PCA and the GMM algorithm yielded the greatest VR and ASC values in this study. These results can guide the optimization of MZ delineation in intensive agricultural systems, thus enabling more precise nutrient management.