Ontogeny Research Articles

Utilizing deep learning via computer vision for agricultural production quality control: jackfruit growth stage identification

Utilizing deep learning via computer vision for agricultural production quality control: jackfruit growth stage identification

Modeling Mechanical Properties of Titanium Scaffolds with Variable Microporosity

The article introduces a two‐level finite element model for metallic scaffolds with porosity at both design and material levels. Despite several additive manufacturing methods producing structures with controlled hierarchical porosity, their functional properties remain largely unknown, hindering industrial utilization. This article examines how material microporosity affects the mechanical properties of a scaffold prepared by direct ink writing from pure titanium with dimensions typical for orthopedic implants. The study focuses on the compressive response of scaffolds with microporosity ranging from 0.05 to 0.65. The article demonstrates the practical application of the model by estimating the effective Young's modulus and the relative length of the fatigue crack initiation stage. Tensile plastic strains at critical sites exhibit a delocalization from around micropores followed by relocalization into thinning interpore walls with increasing microporosity, resulting in the highest fracture strain predicted for microporosities between 0.2 and 0.3. These strains enable the estimation of the length of the fatigue crack initiation stage, which proves to be very short for all microporosities. This emphasizes the crucial role of the crack growth stage in scaffold fatigue life and confirms the potential for additional experiments on scaffolds with microporosities exceeding 0.15 to enhance their fatigue resistance.

Transfer learning by VGG-16 with convolutional neural network for paddy leaf disease classification

ABSTRACT One of the most recent areas of research in agriculture is the detection along with classification of diseases from plant leaf images. Since rice is the most widely consumed staple food worldwide, it is crucial to increase paddy production’s quality and quantity. In the production of paddy, early detection of pests and diseases at various growth stages is very important. Utilising image processing scheme to spot diseases of agricultural plants will lessen the need for farmers to safeguard agricultural products. The use of VGG-16 along with a Convolutional Neural Network (CNN) for the classification and recognition of paddy leaf diseases is proposed in this work. The rice plant leaves images taken from the kaggle dataset is utilised for the purpose of image acquisition. The Gaussian filter is used in pre-processing. The clustering technique is utilised for the segmentation of the diseased part, the normal part, and the surroundings. The proposed model is then utilised for disease classification. This study classifies 10 categories of paddy images. VGG16, InceptionV3, MobileNetV2, and ResNet-152, among other transfer learning methods, are evaluated and contrasted with the experimental results. The outcome accuracy of the proposed model achieves of nearly 99.9%.

Poor Survival Rate of Eastern Gray Kangaroos (Macropus giganteus) Affected by Chronic Phalaris Toxicity.

Chronic phalaris toxicity (CPT) is a neurological disease caused by animals ingesting toxins produced by early growth stages of Phalaris aquatica, a pasture plant introduced to the southeastern regions of Australia postcolonization. Little is known about the clinical progression of CPT in wildlife, as incidents are sporadic and predominantly reported when animals are in the end stages of disease and in a poor welfare state. We studied a cohort of 35 eastern gray kangaroos (Macropus giganteus) affected by CPT to clarify clinical prognosis and survival rates. Kangaroos were captured in May, June, and July of 2022 at Plenty Gorge Parklands, Victoria, Australia. Each animal was radiotracked for 180 d, clinical progression and disease outcomes monitored twice a week. By the conclusion of the study, 24 animals had died (19 by euthanasia due to deterioration, five found dead). Ten animals survived, with two demonstrating a reduction in clinical signs and eight showing full resolution of clinical signs. One animal was disqualified from the study. The overall survival rate was 29.4% (95% confidence interval 17.5-49.5%). The survival duration of animals that died ranged from 5 to 133 d. There was no difference in survival rate based on sex (P=0.2), age class (P=0.49) or the month of capture (P=0.49). These results suggest that CPT is an important health and welfare concern for at-risk macropod populations, with high case-fatality rates and prolonged clinical durations. Further research to manage the disease via methods such as reducing Phalaris aquatica plant coverage and preventative treatments for animals is warranted to reduce disease incidences and improve disease outcomes in wildlife populations.

First report of Helicotylenchus dihystera parasitizing Lady Tankerville orchids (Phaius tankervilleae) in Vietnam.

Lady Tankerville, a rare orchid species (Phaius tankervilleae (Banks ex L'Hér.) Blume) in Vietnam, not only enhances the aesthetic value of the surroundings with its enchanting blooms but also holds high economic value (Aver'janov & Averyanova, 2003). Investigating the necrosis symptom on the root system and declined Lady Tankerville in the market in Hai Duong province in Vietnam from 11/2020 to 12/2021, we discovered a substantial infestation (24/24 plants were infected) of a spiral nematode, Helicotylenchus sp. The nematode population was extracted from the rhizosphere soil, roots, and stems of a single orchid using the modified Baermann tray technique (Whitehead & Hemming, 1965), for thorough characterization. The average nematode density was measured at 525±85 (350-670) nematodes/250 cm3 of soil, 202±56 (198-264) nematodes/15 g of roots, and 80±15 (72-95) nematodes/15 g of stem. Two hundred nematodes from the same population were inoculated into another healthy orchid for preservation and further infection tests. This species was morphologically identified as Helicotylenchus dihystera according to the key of Fotedar and Kaul (1985) and the description of Sher (1966). Morphometric measurements of the females (n = 12) are as follows: body length = 632-725 (681 ± 32) µm; a = 24-34 (28 ± 3); b = 5.1-7.2 (6.0 ± 0.6); b' = 4.3-6.4 (5.3 ± 0.6); c = 33-46 (39 ± 5); c' = 1.1-1.4 (1.2 ± 0.1); V = 62-78 (65 ± 4)%; O = 30 -49 (39 ± 6); stylet length = 21-26 (24 ± 2) µm; DGO = 9.2-12.4 (10.8 ± 1.1) µm. To validate morphological observations, molecular analyses of the ITS (Vrain et al., 1992) and the D2-D3 of 28S rRNA (Subbotin et al., 2006) were conducted. The ITS and D2-D3 sequences from this study (accession number: PP060444 and PP033748) exhibited the highest similarity of 99.8% and 100% to the sequence of H. dihystera in GenBank (KM506885 and MW023215), respectively. The infection test took place in a greenhouse at 28 ± 2℃. Three-month-old Phaius tankervilleae (n=8) were individually grown in 15 x 15 cm deep pots filled with sterilized sand and inoculated with 500 gravid females of H. dihystera (Rashid & Azad, 2013). Two noninoculated plants served as controls. After 60 days of inoculation, symptoms such as sunspots and root necrosis, observed in the market, appeared in the tested plants (Fig. S1). The presence and diagnosis of H. dihystera infestation in soil, roots, and stems across growth stages of orchids indicates the nematode to be an obligate parasite. The nematodes penetrate the roots, causing characteristic necrotic lesions initially yellow, then turn reddish-brown to black, along with brown root flecks in discolored tissues. Heavy infestations post-flowering led to extensive necrosis, distortion, and decay of the roots. The average reproduction factor (final population/initial population) of H. dihystera in this study was 22.2. Control plants remained symptom-free. Notably, 100% of tested plants were infected, highlighting the severe impact of H. dihystera. The nematodes were successfully reisolated and identified as H. dihystera through molecular analyses (accession number: PP060615 (ITS) and PP033748 (D2-D3)), reaffirming its identity. In addition to 32 host plants in Vietnam (Nguyen et al., 2023), our study reveals a strain of H. dihystera parasitizing Lady Tankerville orchids with a relatively high reproduction factor and infection rate. This marks the first reported instance of H. dihystera parasitizing Lady Tankerville orchids in Vietnam, emphasizing the need for proactive measures to protect this plant.

A Study on Genetically Engineered Foods: Need, Benefits, Risk, and Current Knowledge.

Food requirements have always been a top priority, and with the exponential growth of the human population, there is an increasing need for large quantities of food. Traditional cultivation methods are not able to meet the current demand for food products. One significant challenge is the shortened shelf-life of naturally occurring food items, which directly contributes to food scarcity. Contaminating substances such as weeds and pests play a crucial role in this issue. In response, researchers have introduced genetically engineered (GE) food as a potential solution. These food products are typically created by adding or replacing genes in the DNA of naturally occurring foods. GE foods offer various advantages, including increased quality and quantity of food production, adaptability to various climatic conditions, modification of vitamin and mineral levels, and prolonged shelf life. They address the major concerns of global food scarcity and food security. However, the techniques used in the production of GE foods may not be universally acceptable due to the genetic alteration of animal genes into plants or vice versa. Additionally, their unique nature necessitates further long-term studies. This study delves into the procedures and growth stages of DNA sequencing, covering the benefits, risks, industrial relevance, current knowledge, and future challenges of GE foods. GE foods have the potential to extend the shelf life of food items, alleviate food shortages, and fulfill the current nutritional food demand.

Lipidomic Analysis Reveals Branched-Chain and Cyclic Fatty Acids from Angomonas deanei Grown under Different Nutritional and Physiological Conditions

Angomonas deanei belongs to Trypanosomatidae family, a family of parasites that only infect insects. It hosts a bacterial endosymbiont in a mutualistic relationship, constituting an excellent model for studying organelle origin and cellular evolution. A lipidomic approach, which allows for a comprehensive analysis of all lipids in a biological system (lipidome), is a useful tool for identifying and measuring different expression patterns of lipid classes. The present study applied GC-MS and NMR techniques, coupled with principal component analysis (PCA), in order to perform a comparative lipidomic study of wild and aposymbiotic A. deanei grown in the presence or absence of FBS. Unusual contents of branched-chain iso C17:0 and C19:0-cis-9,10 and-11,12 fatty acids were identified in A. deanei cultures, and it was interesting to note that their content slightly decreased at the log phase culture, indicating that in the latter growth stages the cell must promote the remodeling of lipid synthesis in order to maintain the fluidity of the membrane. The combination of analytical techniques used in this work allowed for the detection and characterization of lipids and relevant contributors in a variety of A. deanei growth conditions.

Metabolomic profiles and differential metabolites of volatile components in Citrus aurantium Changshan-huyou pericarp during different growth and development stages

Citrus fruits possess a distinctive aroma and flavor, with Citrus aurantium Changshan-huyou (CACH) standing out due to their considerable edible and medicinal value. However, the volatile components (VOCs) in the CACH pericarp (CP) remain underexplored. In this study, gas chromatography–mass spectrometry (GC–MS) was utilized to qualitatively analyze VOCs in 27 CP samples across different growth stages. A total of 544 VOCs were identified, including 91 terpenoids. The types, quantities and distributions of VOCs were conducted. Detailed discussions on the major terpenoids in CP were also presented. A metabolomics approach combining multivariate statistical analysis with univariate analysis was employed for screening the differential metabolites. The study provides comprehensive insights into the VOCs in CP and citrus plants. Moreover, it delivers the first in-depth analysis of differential metabolites in CP throughout the entire CACH growth and development process, laying a foundation for ongoing research and development of the VOCs in CP.

Effect of Transient Waterlogging Stress on Growth, Physiology and Yield of Cowpea (Vigna unguiculata L.) in Semi-Arid Region

Cowpea cultivation during rainy season is highly affected by the waterlogging stress due to unpredicted high-intensity rains. The studies on assessment of waterlogging effect on different growth stages of cowpea are necessary for planning mitigation strategies. Hence study was conducted during kharif (June to September) 2022 under factorial randomized block design (FRCBD) set up. The first factor consisted of seven waterlogging durations (3 to 15 days), and second factor was three growth stages of cowpea (15 DAE; Days after emergence, 25 DAE and at 50% flowering). The results revealed that regardless of growth stages, growth and yield attributes were drastically decreased with increased duration of waterlogging. The highest plant height (25.07 cm), number of branches plant–1 (5.33) and leaf area (205.27 cm2 plant–1), and number of pods plant–1 (4.27), pod length (15.24 cm), number of seeds pod–1 (14.27), grain (6.27 g plant–1) and haulm yield (15.62 g plant–1) were recorded with 3 days of waterlogging, whereas lowest values were reported with 15 days of waterlogging. Regarding growth stages, highest growth, and yield attributes were recorded with waterlogging during 50% flowering, followed by 25 DAE and 15 DAE. Moreover, the correlation study indicated that physiological parameters such as leaf protein content (r = 0.95) and Normalized Difference Vegetation Index NDVI (r = 0.97) were positively related to grain yield. It was found that, cowpea is sensitive to high-intensity waterlogging (beyond 3–5 days) especially during the early growth stage (15 DAE).

Identification of metal tolerance proteins (MTP) and their gene expression under drought stress in potato (Solanum tuberosum L.)

Metal tolerance proteins (MTPs) are as metal efflux transporters, existing extensively at all plant sections and play significant roles in regulation of the metal levels in biological processes. In the current study, phylogenetic relationships, gene structures, conserved motifs, and StMTP domains were analyzed. Here, 12 MTP genes in S. tuberosum were detected and categorized in three major clusters namely Fe/Zn-MTP, Zn-MTP, and Mn-MTP and seven groups (1, 5, 6, 7, 8, 9, and 11) according to phylogenetic relationships. Based on in silico and qPCR analysis, all of StMTPs included a cation diffusion facilitator (CDF) domains and the putative Mn-MTP harbored the ZT-dimer. An evolutionary analysis indicated that StMTP genes had undergone gene duplication leading to gene loss and gene expansion events. Analysis of transcription factor binding sites (TFBS) and microRNA in promoter region and coding sequence of StMTP genes revealed the presence of 5312 putative TFBS and 13 StmiRNAs. The analysis of promoter regions of StMTP genes possess various frequencies of TFBS, illustrating various responses in different growth and developmental stages as well as under abiotic stress. Expression profile analysis revealed that the StMTP9 were up-regulated in leaves and stem, while, StMTP8 up-regulated in leaves. Both genes down-regulated in tubers, roots as well as under drought stress. These results will provide a better insight for functional characterization of StMTP genes and can be helpful to elucidate the biological structure of their genes in potato.

Optimizing UAV spray parameters to improve precise control of tobacco pests at different growth stages.

Unmanned aerial vehicles (UAVs) for pesticide application show promising potential in tobacco pest management. However, the impact of flight parameters on spray efficacy requires further investigation. Three field experiments were conducted from the rosette to the maturation stage of tobacco to systematically assess spray efficacy under varying flight heights, speeds, and application volumes. Using a multi-index weight analysis method, optimal operational parameter combinations for different tobacco growth stages were evaluated and compared with backpack electric sprayers. For the rosette stage, the recommended parameter is a flight speed of 5 m s-1, a flight height of 2 m, and a liquid application volume of 30 L hm-2; during the vigorous growth stage, the suggested parameter includes a flight speed of 3 m s-1, a flight height of 2 m, and a liquid application volume of 22.5 L hm-2. In the maturing stage, optimal parameter consists of a flight speed of 3 m s-1, a flight height of 3.5 m, and a liquid application volume of 30 L hm-2. Furthermore, UAV spraying achieves higher droplet deposition on both sides of tobacco leaves compared to traditional electric backpack sprayers. Adjusting UAV spraying parameters for different tobacco growth stages is crucial. These results can provide the methods for the precise control technology of tobacco pests at different growth stages. © 2024 Society of Chemical Industry.

Spectral sensor‐based device for real‐time detection and severity estimation of groundnut bud necrosis virus in tomato

AbstractA machine learning‐based approach was utilized to develop a device for groundnut bud necrosis virus (GBNV) disease severity detection and estimation in tomato plants (Solanum lycopersicum L.). The study involved inoculating tomato plants with GBNV, monitoring changes in morphological and spectral characteristics, evaluating machine learning algorithms (decision tree [DT] classifier) for analysis and classification of disease severity, and developing and validating a device for disease detection and severity estimation. Spectral data analysis revealed distinct patterns in reflectance, with notable peaks observed in the 680 and 760 nm bands, while reflectance remained low and constant beyond 900 nm. Machine learning techniques, specifically a DT model, were employed to classify disease severity based on spectral data with high accuracy (95.01% training accuracy and 93.65% testing accuracy). The model identified the near‐infrared band as highly correlated (correlation coefficient of 0.82) with disease severity. Furthermore, a compact handheld device integrating a spectral sensor, organic light‐emitting diode display, and Raspberry Pi 3B was developed for real‐time disease severity estimation. The device demonstrated robust performance, accurately predicting disease severity at different growth stages, even in the absence of visible symptoms. Additionally, disease severity percentages obtained via reverse transcription polymerase chain reaction were used to validate the accuracy of the device's estimations. Its responsive nature, with estimated response times ranging from milliseconds to seconds, facilitates timely interventions in agricultural settings. Overall, this interdisciplinary approach, combining spectral analysis, machine learning, and device development, presents a promising solution for efficient disease monitoring and management in agriculture, contributing to enhanced crop health and food security.

An Innovative Prototype to Measure Carbon Dioxide, Ethylene, and Nitric and Nitrous Oxides in the Soil Air

ABSTRACT Agricultural intensification has increased the consumption of inputs, mainly fertilizers, which may increase emissions of greenhouse gases (GHG). The improvement of our knowledge on GHG dynamics in the soil is mandatory to optimize the use of fertilizers and reduce environmental impacts. We developed an electronic sensor prototype based on Arduino to monitor concentrations of carbon dioxide (CO2), ethylene (C2H4), ammonia (NH3), and nitrogen oxides (N2O/NOx) in the soil air in a greenhouse experiment with soybean (Glycine max L.) under two treatments: single inoculation (Bradyrhizobium spp.) or triple co-inoculation (Bradyrhizobium spp. Azospirillum brasilense and Pseudomonas fluorescens) in a completely randomized design. One-way analysis of variance, as well as correlation between plant traits at 35 days of emergence and the average daily concentration of gases in the soil air were performed. Although not affecting plant growth or yield-related traits, co-inoculation increased CO2, while decreased NOx concentrations compared with single inoculation, whereas C2H4 emission changed with the plant growth stage. Although co-inoculation increased respiration, decreased N losses by denitrification, mitigating the total emissions of GHG. The prototype showed to be efficient for monitoring GHG across the experiment at a low cost and easy use, contributing for monitoring GHG concentrations in the soil air.

Influence of Lateral Length and Residual Chlorine Concentration on Soil Nitrogen and Soil Enzyme Activities under Drip Irrigation with Secondary Sewage Effluent

Chlorination has been demonstrated to be an effective method for the prevention and reduction in emitter biological blockage in drip irrigation systems. The injected chlorine dose is generally determined by chlorine concentration at the terminal of the laterals which may lead to a high concentration of residual chlorine at the front of the system and bring detrimental impact to the soil environment and growth of crops, especially in large irrigation units with long laterals. A two-season experiment was undertaken to determine the effects of chlorine disinfection and lateral length on soil NO3-N, enzyme activities, and maize yield under wastewater drip irrigation. The experiments were constructed with lateral lengths ranging from 20 to 80 m and injection chlorine concentrations at the end of laterals ranging from 2 to 6 mg L−1. Lateral length had a major impact on enzyme activities in the partial growth stage, although the effects of lateral length and chlorine concentration on soil nitrogen content and maize yield were negligible. Enzyme activity and soil nitrogen concentration decreased throughout the lateral length as a result of chlorination. A higher detrimental impact on soil characteristics and maize yield is probably going to result from a high concentration of chlorine and a long lateral length mode. The maize yield peaked at L80 with a concentration of 2 mg L−1 chlorine, while it peaked at L40 with 6 mg L−1 chlorine concentration. A chlorination scheme with long lateral length and low chlorine concentration is recommended in consideration of maize yield and minimizing potential negative effects on the soil environment.

Experimental study on nonlinear development process of oil-filled equipment fire under external fire source

Abstract The transformer is the key oil-filled equipment in the power system, and its fire behavior seriously affects the safe operation of the power grid. In this paper, in order to analyze the fire development process and combustion behavior of oil-filled equipment, a mesoscale model of transformer equipment was constructed, and fire simulation experiments of transformer equipment under the action of external ignition sources were conducted. The flame temperature, flame height, heat release rate, oil temperature, and pressure were measured. The experimental results show that the oil-filled equipment fire presents the characteristics of nonlinear development. The fire can be divided into three stages: ignition stage, stable growth stage, and combustion mutation stage. The transformer oil near the wall is pyrolyzed by the external heat source, and the combustible gas and transformer oil form a gas-liquid two-phase flow, which is the main reason for the nonlinear development of oil-filled equipment fire. The experimental results are of great significance for the safe operation and fire control of power system oil-filled equipment.

The evolution of sensitive periods beyond early ontogeny: Bridging theory and data

Abstract Sensitive periods, in which experiences have a large impact on phenotypic development, are most common early in ontogeny. Yet, they may also occur at later ontogenetic stages, such as adolescence. At present, however, we know little about why natural selection favours sensitive periods for some traits early in ontogeny and for others later in ontogeny. This article synthesizes recent mathematical models and empirical studies that explore sensitive periods beyond early ontogeny. Across mathematical models, we observe two general patterns. First, sensitive periods emerge beyond early ontogeny when an organism's uncertainty about the environment‐phenotype fit increases at later developmental stages. Second, sensitive periods emerge beyond early ontogeny when cues at later stages reduce this uncertainty more than earlier cues do. In the empirical literature, we observe that traits showing sensitive periods beyond early ontogeny tend to be social traits, particularly among mammals. Connecting theory to data, we hypothesize that mammals have evolved to expect reliable information from peers in adolescence to reduce uncertainty about the current and future social environment (e.g. social dominance, mate value). Finally, we highlight current gaps in our understanding and suggest future directions for strengthening bridges between empirical and theoretical studies of sensitive periods. Ultimately, we hope our synthesis will contribute towards an integrative science of sensitive periods across the biological and the social sciences. Read the free Plain Language Summary for this article on the Journal blog.

Effect of foliar fungicide application timing on corn yield across different water regimes

ABSTRACT Corn (Zea mays L.) is a major irrigated crop in the Texas High Plains (THP). Inadequate precipitation, declining groundwater resources, and inevitable seasonal drought in the area have led to a greater number of studies on crop hybrids, water regimes, and other yield-enhancing techniques. A four-year field study was conducted to investigate the effect of foliar fungicide timing on corn grain yield, grain moisture, and grain test weight under different irrigation regimes. A fungicide containing QoI + DMI was applied to four to six corn hybrids each year at various growth stages (V5, R1, and V5 + R1). For all years, corn plants were grown under three irrigation regimes to meet the seasonal evapotranspiration (ET) requirements of 50% (I50), 75% (I75), and 100% (I100). Hybrid response to the timing of fungicide application and irrigation regime was not consistent over the years. However, in 2 of the 4 years, timing of fungicide application resulted in yield differences among the hybrids, especially at I75 and L100. For those years, the fungicide applied at R1 resulted in a higher grain yield, grain moisture, and grain test weight followed by the fungicide applied at V5 + R1. Results showed that hybrids reacted differently to the foliar fungicide based on the availability of water under disease-free conditions. This study further revealed that irrigation water could be reduced from I100 to I75 with a limited (5.6% in 2011, 11.3% in 2012, 1.3% in 2013, and 0.3% in 2014) yield penalty in the THP region.

Construction of a High-Resolution Waterlogging Disaster Monitoring Framework Based on the APSIM Model: A Case Study of Jingzhou and Bengbu

This study investigates waterlogging disasters in winter wheat using the Agricultural Production Systems Simulator (APSIM) model. This research explores the effects of soil hypoxia on wheat root systems and the tolerance of wheat at different growth stages to waterlogging, proposing a model to quantify the degree of waterlogging in wheat. Remote sensing data on soil moisture and wheat distribution are utilized to establish a monitoring system for waterlogging disasters specific to winter wheat. The analysis focused on affected areas in Bengbu and Jingzhou. Experimental results from 2017 to 2022 indicate that the predominant levels of waterlogging disasters in Bengbu and Jingzhou were moderate and mild, with the proportion of mild waterlogging ranging from 30.1% to 39.3% and moderate waterlogging from 14.8% to 25.6%. A combined analysis of multi-source remote sensing data reveals the key roles of precipitation, evapotranspiration, and altitude in waterlogging disasters. This study highlights regional disparities in the distribution of waterlogging disaster risks, providing new strategies and tools for precise assessment of waterlogging disasters.

In-season dynamic diagnosis of maize nitrogen status across the growing season by integrating proximal sensing and crop growth modeling

Efficient and accurate in-season diagnosis of crop nitrogen (N) status is crucially important for precision N management. The main objective of this study was to develop a strategy for in-season dynamic diagnosis of maize (Zea mays L.) N status across the growing season by integrating proximal sensing and crop growth modeling. In this study, we integrated plant N concentration (PNC) derived from leaf fluorescence sensor data and aboveground biomass (AGB) based on the best-performing spectral index calculated from active canopy reflectance sensor data with simulated PNC and AGB using a crop growth model, DSSAT-CERES-Maize, for dynamic in-season maize N status diagnosis across the growing season. The results confirmed the applicability of leaf fluorescence sensing for PNC estimation and active canopy reflectance sensing for AGB estimation, respectively. The calibrated DSSAT CERES-Maize model performed well for simulating AGB (R2 = 0.96), which could be used for calculating the N status indicator, N nutrition index (NNI). However, the model did not perform satisfactorily for PNC simulation, with significant discrepancies between the simulated and measured PNC values. The data integration method using both proximal sensing and crop growth modeling produced accurate predictions of NNI (R2 = 0.95) and N status diagnostic outcomes (Kappa statistics = 0.64) for key growth stages in this study and could be used to simulate maize N status across the growing season, showing the potential for in-season dynamic N status diagnosis and management decision support. More studies are needed to further improve this approach by multi-sensor and multi-source data fusion using machine learning models.

Seasonal precipitation distribution determines ecosystem CO2 and H2O exchange by regulating spring soil water–salt dynamics in a brackish wetland

Abstract The intensification of the global hydrological cycle is anticipated to increase the variability of precipitation patterns. Brackish wetlands respond to changes in precipitation patterns by regulating the absorption and release of CO2 and H2O to maintain the stability of ecosystem functions. However, there is limited understanding of how the inter‐seasonal precipitation distribution (SPD) affects ecosystem CO2 and H2O exchange compared with annual precipitation totals. Here, we conducted four consecutive years of field experiments in a brackish wetland, manipulating the proportion of precipitation across different seasons while maintaining a constant annual precipitation total. We utilized five inter‐SPD proportions (+73%, +56%, control (CK), −56%, −73%) to examine the effects of SPD on ecosystem CO2 and H2O exchange. Our findings revealed that the annual ecosystem CO2 and H2O fluxes showed a trend of decreasing with the decrease in spring precipitation distribution. Among them, the annual net ecosystem CO2 exchange, evapotranspiration, carbon use efficiency and water use efficiency were shown to be more sensitive to decrease in spring precipitation distribution and increase in summer and autumn precipitation distribution. This negative asymmetric response pattern suggests that annual ecosystem CO2 and H2O exchange is primarily governed by seasonal precipitation variability, with spring soil water–salt dynamics identified as the key driver. Therefore, this association can be explained by the fact that drought of the early growth stage exacerbates soil salinization and inhibits vegetation colonization and growth, thereby greatly impairing the annual CO2–H2O exchange capacity of brackish wetlands. Our results emphasized that the spring extreme precipitation‐induced soil water–salt conditions will greatly influence CO2 and H2O exchange in brackish wetlands in the future. These findings are crucial for improving predictions of the carbon sequestration and water‐holding capacity of brackish wetlands. Read the free Plain Language Summary for this article on the Journal blog.