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Roots and associated soil biota are key to the energy balance of plants since they consume substantial amounts of photosynthates and in turn provide resources that fuel photosynthesis. Due to spatiotemporal soil heterogeneity, single root systems are repeatedly exposed to soil conditions that either foster or limit plant growth. Knowledge concerning the root responses to repeated cycles of edaphic stress remain scarce but is indispensable to understand root foraging behaviour in soil. Quantification of the acclimative value of root responses to soil heterogeneity requires distinction between plastic (i.e., persistent) and elastic (i.e., reversible) root responses. We introduce energy balances as an approach to capture the effects of repeated edaphic stress cycles on crop growth and yield formation and to quantify the acclimative value of root responses to specific soil environments. Spatiotemporal soil heterogeneity and the resulting edaphic stress cycles can be decisive for crop growth. However, our understanding of the acclimative value of root responses to heterogeneous soil conditions remains limited. We outline a framework to evaluate the acclimative value of root responses that distinguishes between stress responses that are persistent and reversible upon stress release, termed 'plasticity' and 'elasticity', respectively. Using energy balances, we provide theoretical evidence that the advantage of plasticity over elasticity increases with the number of edaphic stress cycles and if responses lead to comparatively high energy gains. Our framework provides a conceptual basis for assessing the acclimative value of root responses to soil heterogeneity and can catalyse research on crop adaptations to heterogeneous belowground environments. [ABSTRACT FROM AUTHOR]

Weather extremes affect crop production. Remote sensing can help to detect crop damage and estimate lost yield due to weather extremes over large spatial extents. We propose a novel scalable method to predict in-season yield losses at the sub-field level and attribute these to weather extremes. To assess our method's potential, we conducted a proof-of-concept case study on winter cereal paddocks in South Australia using data from 2017 to 2022. To detect crop growth anomalies throughout the growing season, we aligned a two-band Enhanced Vegetation Index (EVI2) time series from Sentinel-2 with thermal time. The deviation between the expected and observed EVI2 time series was defined as the Crop Damage Index (CDI). We assessed the performance of the CDI within specific phenological windows to predict yield loss. Finally, by comparing instances of substantial increase in CDI with different extreme weather indicators, we explored which (combinations of) extreme weather events were likely responsible for the experienced yield reduction. We found that the use of thermal time diminished the temporal deviation of EVI2 time series between years, resulting in the effective construction of typical stress-free crop growth curves. Thermal-time-based EVI2 time series resulted in better prediction of yield reduction than those based on calendar dates. Yield reduction could be predicted before grain-filling (approximately two months before harvest) with an R2 of 0.83 for wheat and 0.91 for barley. Finally, the combined analysis of CDI curves and extreme weather indices allowed for timely detection of weather-related causes of crop damage, which also captured the spatial variations of crop damage attribution at sub-field level. The proposed framework provides a basis for early warning of crop damage and attributing the damage to weather extremes in near real-time, which should help to adopt appropriate crop protection strategies. [ABSTRACT FROM AUTHOR]

Accurately obtaining the spatial distribution and planting patterns of crops is very important for agricultural planning and food security. At present, time-series images have been proved to be an effective tool to characterize crop seasonal growth patterns, and identifying crop information by measuring the time-series similarity between unknown classes and known crop phenology curves is also considered to be a useful way. However, the existing methods of selecting feature ignore the connection between each phenological stage of crops and the unique growth rules of the whole phenology, which makes it difficult to select time-series spectral features that are potentially important for crop mapping. In order to make up for this problem, a Whole Phenology-based Spectral Feature Selection (WPS) method was proposed. The aim was to select the time-series feature sets with great differences among winter crops from a large number of spectral features, so as to improve the mapping accuracy of winter rapeseed and winter wheat. Firstly, spectral separability between all classes is calculated. Secondly, the key phenological periods of winter crops were selected according to the importance of temporal features, and the spectral feature sets with high separability were selected according to the key phenological periods. Finally, a Time-weighted Dynamic Time Warping (TWDTW) algorithm was used to generate the winter rapeseed and winter wheat maps of two cities in the middle and lower reaches of the Yangtze River. The mapping accuracy of the two crops is more than 92%, which matches the crop planting area well. The research shows that combining the WPS method with the TWDTW mapping method has great potential to accurately map crop types based on satellite time-series images. [ABSTRACT FROM AUTHOR]

[Display omitted] Field path planning provides the basis for the autonomous navigation of agricultural vehicles. Existing path planning approaches are constrained to a 2D plane, disregarding the impact of terrain factors on navigation tasks. Furthermore, these methods generate non-global paths, as evidenced by their inability to accommodate the headland turning area while performing path planning based on the actual crop growth in the farmland. Low-altitude remote sensing technology, characterized by its high spatial resolution, timely data acquisition, and strong terrain perception, holds great potential for application in autonomous navigation. This study aims to integrate low-altitude remote sensing technology with path-planning tasks for agricultural vehicles by constructing oblique photography models of fields. The objective is to achieve global 3D path planning leveraging advanced methodologies rooted in deep learning and image processing. Four main steps were included in the proposed method. Low-altitude remote sensing models of field blocks were constructed first. Secondly, the models were converted to image patches for dataset establishment. Thirdly, primary crop row identification was conducted by semantic segmentation. Finally, global 3D paths covering the farmland and headland areas were generated. Tea fields in hilly areas were used to test the algorithm. Experiments revealed that the proposed method could be adapted to different field shapes, row numbers, row spacing, and vehicle turning radii. The proposed method uses deep learning and image processing as the primary technical tools but goes beyond traditional crop row detection to form a global path-planning strategy. In addition, the elevation information enabled by the 3D detection manner allows agricultural vehicles to gain a comprehensive understanding of both their own position and that of their destination. The dataset and the code are available at: https://github.com/ZeroHeading/Global-3D-path-generation.git. [ABSTRACT FROM AUTHOR]

Accurate large-scale crop yield estimation under climate variability is essential to understanding the dynamics of global food security. The deep learning method has shown well performance for crop yield estimation because of its high capacity for temporal pattern recognition. However, most existing deep learning models were usually based on multi-source time series with weekly or coarse temporal resolutions, which simplified the dynamics of crop growth progress. How to effectively learn the long-term and short-term crop response from the remotely sensed and agro-meteorological time series at daily intervals remains challenging. In this study, a hierarchical deep learning model, named Daily DeepCropNet (DDCN), has been developed to process daily time series of satellite-derived vegetation indices and climatic variables for corn yield estimation. The DDCN model was a three-level pyramid structure that incorporated the corn phenology durations to extract crop growth patterns from the daily inputs. The Transformer and Long Short-Term Memory (LSTM) were implemented as a temporal filter in each level to capture the short-term crop growth patterns. The temporal patterns were hierarchically transferred from weekly to growing season aggregation to learn long-term dependency. The experiment was conducted at county level over the US Corn Belt from 2006 to 2020. LSTM, Transformer, and Random Forest (RF) models were built for comparison. The input analysis showed that the daily time series of climatic variables provided more temporal fluctuations than weekly aggregated inputs. The DDCN model provided the highest accuracy for end-of-season yield estimation under normal climate conditions in 2018–2020 (RMSE = 0.86 Mg ha−1) and extreme heat and drought conditions in 2012 (RMSE = 1.17 Mg ha−1). Further analyses indicated that the DDCN model had the potential for capturing the short-duration stresses. We also found that the DDCN model identified the silking phase as the key growth stage and achieved higher accuracy than other models for in-season yield estimation. The results showed that features extracted by the DDCN model had higher correlation coefficients with corn yield for the upper levels. By evaluating the contributions of different variables, we found that vegetation indices yield higher impact than climatic variables for yield estimation under both normal and stressful climate conditions. This study provided a promising approach to extracting the temporal pattern of crop growth at daily time steps. The DDCN model could be further integrated with the process-based model to achieve the fusion of knowledge and data for explainable AI in agricultural system modeling. [ABSTRACT FROM AUTHOR]

Aluminum (Al) toxicity is a crucial limiting factor for crop growth in acid soils. Recently, Liu et al. demonstrated that the root microbiota of rice modulates the responses to Al toxicity and phosphorus limitation, offering intriguing insights into microbiome function and opening new research opportunities. [ABSTRACT FROM AUTHOR]

• Biochar is environmentally friendly approach to improve crop growth • Biochar can remediate potentially toxic metal-contaminated soils. • Biochar application results in the higher soil enzyme activities and essential nutrients. Application of biochar is an effective and ecologically friendly approach to boost plant growth by activating the enzymatic defense system and improving soil health in metal-polluted soils. Present study aimed at evaluating the various concentrations of soil-applied maize straw biochar (control, 1, 3, 5, 7, and 10% w/w) to improve potato growth by the mediation of antioxidant defense system and soil health. Results showed that application of biochar in soil significantly enhanced plant growth by optimizing chlorophyll, enzymatic antioxidants, and soil enzymatic activities. Biochar application at a 7% (w/w) registered better performance as compared to control among all the treatments. Optimum rate of biochar significantly increased the leaf, stem, and tuber quality attributes by decreasing the accumulation of proline, malondialdehyde content, and bioaccumulation of Cu, Pb, Cd, and As in various plant parts of potato plant. The higher soil enzyme activities and essential nutrients were observed when 7% biochar was applied. Thus biochar could better remediate potentially toxic metal-contaminated soils and promote potato crop yield in times when food security is becoming a serious concern due to climate change. In crux, maize straw biochar addition in soil is useful for better plant growth, improvement in antioxidative defense system, and better quality of potato. [ABSTRACT FROM AUTHOR]

The plant-associated microbiome plays a crucial role in improving plant health by increasing crop resilience to environmental stressors. Nanomaterials can be used to engineer plant microbiome composition and function in a more precise manner, thereby improving plant and soil health under diverse environmental conditions. Synergistic application of nano-agrochemicals and the plant-beneficial microbiome can help plants to counter biotic and abiotic stresses by triggering distinct morphophysiological and genetic mechanisms. Numerous knowledge gaps should be addressed to ensure the safety and efficacy of commercial application of nano-enabled plant-associated microbiome engineering. The plant-associated microbiome is known to be a critical component for crop growth, nutrient acquisition, resistance to pathogens, and abiotic stress tolerance. Conventional approaches have been attempted to manipulate the plant–soil microbiome to improve plant performance; however, several issues have arisen, such as collateral negative impacts on microbiota composition. The lack of reliability and robustness of conventional techniques warrants efforts to develop novel alternative strategies. Nano-enabled approaches have emerged as promising platforms for enhancing agricultural sustainability and global food security. Specifically, the use of engineered nanomaterials (ENMs) as nanoscale agrochemicals has great potential to modulate the plant-associated microbiome. We review the dynamic interplay between nano-agrochemicals and the plant-associated microbiome for the safe development and use of nano-enabled microbiome engineering. [ABSTRACT FROM AUTHOR]

• Chromium (Cr) at 20 mg kg−1 displayed encouraging effects on the physiology of Mentha arvensis. • Cr at 80 mg kg−1 showed deleterious effects on plants. • Excessive Cr accumulation destabilizes plant's growth, photosynthesis and physiological markers. • Cr at higher concentration diminishes the essential oil production including menthol content. Chromium (Cr) is the seventh most toxic heavy metal on the earth's crust. Crop growth, development, and yield are greatly hindered by the massive accumulation of Cr that discharges from various industries and mines in agricultural land. Chromium can mediate phytotoxicity either via direct interaction with multiple plant components and metabolic pathways or by promoting reactive oxygen species (ROS) generation. Thus, this investigation evaluated the effect of Cr on growth, physiological and oxidative stress, and other parameters, including essential oil (EO) constituents of Mentha arvensis L. plants. The soil was treated with various concentrations of Cr (10, 20, 40, 60, and 80 mg kg−1), except the control. In this study, 20 mg kg−1 of Cr concentration was beneficial in increasing plant growth via the enhancement of biomass, photosynthesis, biochemical markers and EO constituents in M. arvensis. The data demonstrated that the morphological and physiological parameters were negatively correlated with high Cr concentrations (above 20 mg kg−1). It has been hypothesized that Cr poisoning leads to oxidative damage, as evidenced by decreases in photosynthetic rate, chlorophyll, and stomatal characteristics, as well as increases in lipid peroxidation and ROS. The accumulation of Cr positively correlates with the enhancement of proline and antioxidant activities of M. arvensis. Thus, we concluded that excess Cr causes redox imbalance by uplifting the level of lipid peroxidation and ROS by destabilizing photosynthetic machinery and other physiological and morphological markers in plants. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) remote sensing technology has the advantage of all-weather observation and can acquire time-series images with crop growth period, which has great potential for applications such as crop phenology analysis. However, available studies primarily focus on conducting statistical and crop growth analyses based on the polarization or backscatter intensities of SAR images, and the exploration of polarization scattering information in SAR images is not sufficient. To comprehensively reflect the polarization characteristics and scattering mechanisms of crop at different growth stages, we propose a new method for extracting vegetation descriptors from Sentinel-1 dual-polarimetric SAR data. The method combines the backscattering intensity and polarization decomposition information to construct a normalized index q , which is used to generate three vegetation descriptors: the co-pol purity parameter (m cp ), the pseudo-scattering angle (θ cp ), and the pseudo-scattering entropy (H cp ). Further, a novel unsupervised clustering framework, founded on H cp and θ cp , has been proposed. This framework establishes six zones (named as Z1 to Z6) representing distinct physical scattering mechanisms, and by statistically sampling point data, it can determine the growth stage of the crops For validating the performance of the proposed vegetation descriptors and clustering framework, we conducted a three-year experiment using four crops from two publicly available datasets, namely wheat and canola from the Carman in Canada (Test site-1), corn and soybeans from Iowa in the United States (Test site-2). The experimental results indicate that m cp , θ cp , and H cp exhibit regular changes at different growth stages of crops from planting to maturity, with m cp and θ cp gradually decreasing while H cp gradually increasing. Within the entire phenology window, θ cp changes by approximately 42°, while both m cp and θ cp varies by about 0.9, and the sampling points shift from the Z2 to the Z5 zone. The vegetation descriptors are highly sensitive to the growth status of crops, and the clustering framework can also effectively respond to different growth stages of vegetation. Furthermore, the vegetation descriptors and clustering framework proposed in this study have the potential for extended application to different crop types and other polarimetric SAR data sources. [ABSTRACT FROM AUTHOR]

Crop planting timing is critical in regulating environmental conditions of crop growth throughout the season, and is an essential parameter in crop simulation models for estimating dry matter accumulation and yields. Accurate planting date information is key to characterizing crop growing dynamics under varying farming practices and facilitating agricultural adaptation to climate change. To date, the main methods to acquire planting dates include field survey methods, weather-dependent methods, and remote sensing phenological detecting methods. However, it is still challenging to effectively estimate the crop planting dates at field levels due to the lack of appropriate field-level modeling design as well as the dearth of ground planting reference data. In our study, we develop a novel CropSow modeling framework to estimate field-level planting dates by integrating the remote sensing phenological detecting method with the crop growth model. The remote sensing phenological detecting method is devised to retrieve the critical crop phenological metrics of farm fields from remote sensing time series, which are then integrated into the crop growth model for field planting date estimation in consideration of soil-crop-atmosphere continuum. CropSow leverages the rich physiological knowledge embedded in the crop growth model to scalably interpret satellite observations under a variety of environmental and management conditions for field-level planting date retrievals. With corn in Illinois, US as a case study, the developed CropSow outperforms three advanced benchmark models (i.e., the remote sensing accumulative growing degree day method, the weather-dependent method, and the shape model) in crop planting date estimation at the field level, with R square higher than 0.68, root mean square error (RMSE) lower than 10 days, and mean bias error (MBE) around 5 days from 2016 to 2020. It achieves better generalization performance than the benchmark models, as well as stronger adaptability to abnormal weather conditions with more robust performance in estimating the planting dates of farm fields. CropSow holds considerable promise to extrapolate over space and time for estimating the timing of crop planting of individual farm fields at large scales. [ABSTRACT FROM AUTHOR]

The green leaf area index (LAI) is an important structural parameter that can be used for monitoring plant transpiration and carbon cycling over large spatial extents. LAI is also critical for understanding biophysical processes and predicting crop productivity. However, the retrieval of wheat green LAI based on canopy reflectance is always affected by the background material. For example, in the Yangtze River Basin of China, rice residues are typically returned to rice–wheat rotation fields. Thus the background for observing wheat canopy varies from a mixture of traditional wheat-soil components to wheat-residue or wheat-residue-soil, posing a significant challenge for accurate LAI estimation at the early stages of crop growth. Furthermore, when current vegetation indices (VIs) are used to estimate LAI, leaf chlorophyll content (LCC) and field moisture are two additional confounding factors. To resolve these issues, we propose a novel residue-soil adjusted red edge difference index (RSARE), which draws on concepts from a simple physical algorithm, i.e., linear spectral mixture analysis (LSMA). The results of field, satellite and modeling experiments demonstrate that the RSARE-LAI model is generally not sensitive to LCC, or to variations in residue-soil and traditional soil backgrounds, and estimates LAI with high accuracy ( RMSE val = 0.55, RRMSE val =20.71%). In comparison to traditional VI-LAI maps, RASRE-LAI maps are less sensitive to confounding factors associated with the background components, field moisture, LCC, and both inter-annual and geographical differences. This novel residue-soil background-resistant RSARE particularly improves the accuracy of wheat LAI estimation at low LAI levels, thus facilitating large-scale mapping of LAI in the early growing season. [ABSTRACT FROM AUTHOR]

Accurate crop type mapping is essential for crop growth monitoring and yield estimation. Recently, various machine learning methods have been increasingly used for crop type mapping, but they often lose their validity when directly applied to other regions and years due to differences in the distribution of source and target data, that is, domain shift. To address the problem, we developed a deep adaptation crop classification network (DACCN) based on the idea of unsupervised domain adaptation (UDA). The proposed DACCN mainly consists of two parts, a feature extractor that converts the original input into high-level representations, and a domain aligner where the discrepancy between source and target distributions is measured using multiple kernel variant of maximum mean discrepancy (MK-MMD). Four states in the United States (U.S.) Corn Belt and two provinces in northeastern China were used as study areas, where samples used for model building and accuracy evaluation were collected based on time-series Sentinel-2 imagery and reference maps in 2018 and 2019. Then, three experiments were designed to verify the transferability of DACCN across space, time, and space–time, respectively. In each experiment, the proposed DACCN was compared to deep crop classification network (DCCN), a model with a similar structure to DACCN but without the domain adaptation mechanism, and two machine learning methods, random forest (RF) and support vector machines (SVM). The experimental results showed that DACCN outperformed other models in most transfer cases with overall classification accuracies ranging from 0.835 to 0.922. The DACCN also performed better in spatially continuous mapping with its predicted crop type maps more consistent with the reference ones. As an innovative application of transfer learning in crop type mapping, the methodology proposed in this study effectively addressed the problem of missing labels in target domains and alleviated the negative impact of domain shift. [ABSTRACT FROM AUTHOR]

A commonly held misconception is that weeds reduce crop yield primarily because of resource competition. Weed presence reduces crop yield regardless of resource availability based on the timing of the critical period for weed control, and resource supplementation, and weed density studies. Weeds alter developmental trajectories of crops early in the growing season that often result in reduced yields. Signals produced by weeds that alter crop growth include light quality alterations, soil-borne chemicals, and/or volatile chemicals. Weed signals elicit a stress response in crops that may suppress growth through repression of the TARGET OF RAPAMYCIN (TOR) signaling system. Identification of weed-inducible genes and promoters will provide tools to investigate mechanisms controlling crop–weed interactions and develop weed-tolerant crops. Direct competition for resources is generally considered the primary mechanism for weed-induced yield loss. A re-evaluation of physiological evidence suggests weeds initially impact crop growth and development through resource-independent interference. We suggest weed perception by crops induce a shift in crop development, before resources become limited, which ultimately reduce crop yield, even if weeds are subsequently removed. We present the mechanisms by which crops perceive and respond to weeds and discuss the technologies used to identify these mechanisms. These data lead to a fundamental paradigm shift in our understanding of how weeds reduce crop yield and suggest new research directions and opportunities to manipulate or engineer crops and cropping systems to reduce weed-induced yield losses. [ABSTRACT FROM AUTHOR]

Nitrate (NO 3 –) and ammonium (NH 4 +) are two main inorganic nitrogen (N) sources during crop growth. Here, we enhanced the expression of OsAMT1.1 , which encodes a NH 4 + transporter, using the NO 3 –-inducible promoter of OsNAR2.1 and an ubiquitin promoter in transgenic rice plants. Under field condition of 120 kg /hm2 N, agronomic N use efficiency, N recovery efficiency and N transport efficiency, and grain yield of the pOsNAR2.1:OsAMT1.1 transgenic lines were increased compared with those of the wild type (WT) and the pUbi:OsAMT1.1 transgenic plants. Under 2.0 mmol/L NO 3 – + 0.5 mmol/L NH 4 + and 0.5 mmol/L NO 3 – + 2.0 mmol/L NH 4 + conditions of hydroponic culture, compared with the WT, both biomass and total N content were increased in the pOsNAR2.1:OsAMT1.1 transgenic lines. However, biomass was significantly reduced in pUbi:OsAMT1.1 transgenic plants under 0.5 mmol/L NO 3 – + 2.0 mmol/L NH 4 + condition. The lines expressing pOsNAR2.1:OsAMT1.1 exhibited increased OsAMT1.1 expression and 15NH 4 + influx in roots under both 2.0 mmol/L NO 3 – + 0.5 mmol/L NH 4 + and 0.5 mmol/L NO 3 – + 2.0 mmol/L NH 4 + conditions. Our study showed that expression of OsAMT1.1 can be promoted when driven by the OsNAR2.1 promoter, especially under high-level nitrate condition, leading to enhancement of NH 4 + uptake, N use efficiency and grain yield. [ABSTRACT FROM AUTHOR]

High phosphorus steel slag and carbonized rice husk are two common wastes characterized by high generation and low secondary use values. Through the reduction of high phosphorus steel slag by biomass, both wastes were fully utilized, thus reducing the negative impact on the environment. In this study, variables such as temperature, time, and amount of reactants were changed to determine the optimal conditions for the reaction of steel slag with carbonized rice husk at high temperatures. The actual amount of reducing agent consumed during the reduction was significantly greater than that predicted by theoretical calculations. Adding three carbon equivalent of carbonized rice husk and maintaining at 1500°C for 30 min could remove 79.25% of P 2 O 5 in the slag. By modeling the material cycle in which high phosphorus steel slag was treated with biomass, the product could be used for crop growth. Meanwhile, the reduced iron and residual steel slag can be used to make steel again, thereby leading to a sharp reduction in fossil fuel usage and greenhouse gas emissions in this process. [Display omitted] [ABSTRACT FROM AUTHOR]

Heavy metal pollution in the soil is a serious threat to crop growth and human health. Metallothionein (MT) is a low molecular weight protein that is rich in cysteine, which can effectively alleviate the toxicity of heavy metals in plants. In this study, a novel metallothionein encoding gene, NtMT2F , was cloned from the Cd-hyperaccumulator tobacco and heterologously expressed in E. coli and A. thaliana to verify its biological function. Recombinant E. coli incubated with NtMT2F effectively resisted heavy metal stress, particularly Cd. The recombinant strain grew significantly faster and had a higher content of Cd than the control. Mutations in the C-terminal Cys residues of NtMT2F significantly reduced its ability to chelate heavy metals. The overexpression of NtMT2F significantly enhanced resistance to Cd toxicity in transgenic A. thaliana. The germination rate, root length, and fresh weight of transgenic plants under Cd stress were higher than those of the wild type (WT). The contents of hydrogen peroxide (H 2 O 2) and malondialdehyde (MDA) were lower than those of the WT. In addition, the activities of anti-peroxidase enzymes including glutathione reductase (GR), catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD), were significantly increased in the transgenic plants. The results of this study indicate that NtMT2F significantly improved the tolerance of microorganisms and plants to Cd and could be an important candidate protein for phytoremediation. • Heterologous expression of NtMT2F enhances the resistance of E. coli to heavy metal toxicity. • NtMT2F promotes the accumulation of heavy metals in E. coli. • Point mutations of the Cys residue at the C-terminal result in a significant decrease in the ability of NtMT2F to chelate heavy metals. • Overexpression of NtMT2F enhances transgenic Arabidopsis against Cd stress. [ABSTRACT FROM AUTHOR]

Estimating soil and plant nutrients are essential for crop growth and yield production. Quantifying nutrients by conventional methods is a time-consuming and hectic process. The hyperspectral imagery and multispectral sensors mounted on the UAV (Unmanned Aerial Vehicle) are costlier. This study used open-source remote sensing imagery and obtained ground truth data from the Punjab region to estimate nine nutrients and soil textures: Nitrogen (N), Phosphorous (P), Carbon (C), Ammonium (N-NH 4), Nitrate Nitrogen (N-NO 3), Calcium (Ca), Magnesium (Mg), Clay and Silt. To remove the noise from the dataset, this study applied DOS1 atmospheric correction and compared it with the raw dataset with four ensemble machine learning methods: Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Random Forest Regressing (RFR), and Adaptive Boosting (ADA) for estimation of soil texture/nutrients content. The study has three main purposes; firstly, it focuses on the less studied agriculture-intensive Punjab region. Secondly, it analyses the impact of preprocessed vs raw multispectral images. Finally, it explores the correlation of multispectral images and different soil parameters via different machine learning techniques. Results of this study suggest that the raw dataset is in general significantly better than the DOS1 atmospheric correction. Also, some parameters have better correlation with the image data compared to others. This potential approach can estimate the soil texture/nutrient content in the same region. [ABSTRACT FROM AUTHOR]

Evaluation of crop growth parameters is crucial to understand the crop phenology and its relation with Synthetic Aperture Radar (SAR) scattering mechanism. The study explores the potential of dual polarization Sentinel–1 backscatter parameters to investigate its sensitivity with various biophysical parameters of paddy crop. Temporal profile of crop growth parameters such as fresh biomass, dry biomass, vegetation water content (VWC) and plant height of paddy crop are assessed with respect to SAR parameters such as VV, VH, VH/VV and Radar Vegetation Index (RVI). Temporal Sentinel–1A SAR data during June to November 2018 was considered for this study. Regression analysis using linear and logarithmic models reveal that the sensitivity of Sentinel–1A backscatter parameters with crop biophysical parameters is best when the crop is at early vegetation stage i.e. between tillering to booting stages with maximum R2 value of 0.68 for liner regression with VH and 0.69 for multiple regression for crop height followed by other biophysical parameters. At later vegetation stage i.e. ripening stage maximum R2 value of 0.52 for liner regression with VH and multiple regression for crop biomass is observed. There is no significant correlation for peak vegetation stage. The results are indicative for the potential use of dual-polarimetric SAR parameters as input parameters for multiple regression models to improve the prediction of biophysical parameters at different phenolgical stages. [ABSTRACT FROM AUTHOR]

• Characterization of the planting and harvesting planning problem and its modelling for medicinal plants. • Conceptual framework to characterize the crop planting and harvesting planning problem. • Model to plan planting and harvest of crops with variable molecules concentration and minimum time between harvests. • Model validation through the application to a natural food supplement supply chain case study. • Economic unfairness among farmers eliminated while respecting supply chain costs and concentration of plant molecules. The planting and harvesting of medicinal plants have characteristics that differentiate them from other crop types and complicate their planning. For example, drug processors do not require large quantities of product to be harvested but have a high concentration of active molecules. There is no evidence for any optimization tool to support the planting and harvesting of such plants. Given this sector's importance and its impact on populations' health, it is necessary to develop solutions to increase the sustainability of their supply chains. This paper aims to bridge this gap by proposing a conceptual framework to characterize a crop planting and harvesting planning problem, and a multi-objective optimization model for the planning of planting, harvesting, post-harvesting, distribution and storage of medicinal plants with variable concentrations of molecules and minimum time between harvests. The model optimizes three objectives aligned with sustainability: supply chain costs, concentration of molecules in plants, farmers' perceived economic unfairness. It is validated by its application to a case study of medicinal plants in the Basilicata region (Italy). The ε-constraint method is used to obtain 11 non dominated solutions showing the possibility of eliminating farmers' perception of economic unfairness by maintaining similar values for supply chain costs and concentrations of active molecules when planning the production of medicinal plants. Finally, the TOPSIS method is applied to select the best plan to be implemented into the supply chain. [ABSTRACT FROM AUTHOR]

Poor fertilizer management and intensive tillage have increased production costs, lowered productivity, and significantly depleted soil nutrients. Although nutrient management options (NMOs) within crop establishment options (CEOs) based maize production system (MPS) is seldom explored, CEOs is increasingly advocated to tackle problems with soil health, food security, and climate change. Developing and implementing effective NMOs is needed for improving system sustainability, profitability, and productivity. We evaluated the effects of CEOs and NMOs on nutrient acquisition, profitability, and maize productivity in the Northwestern Indo-Gangetic Plains (IGPs) of India during 2018–2019. In this study, four CEOs treatment [(i) conventional tillage without residue {CT–R}, (ii) conventional tillage with residue {CT+R}, (iii) permanent raised bed without residue {PRB–R}, and (iv) permanent raised bed with residue {PRB+R}], were kept in the main plot and three NMOs [(i) soil test-based recommendation {STBR}, (ii) nutrient expert-based recommendation {NE}, and (iii) NE with GreenSeeker {NE+GS} were tested in subplots. The results showed that the crop growth metrics, including plant height, dry matter accumulation, leaf area index, and crop growth rate, were significantly greater at PRB+R comparing to treatments. Additionally, PRB+R resulted in the shortest time to 50 % and 75 % silking, indicating enhanced crop development. NMOs significantly improved crop growth parameters. The NE+GS treatment recorded higher plant height (145.8–149.2 cm and 222.3–224.8 cm), dry matter accumulation (195.5–198.4 g/m² and 408.4–412.0 g/m²), leaf area index (2.45–2.48 and 3.24–3.30), and crop growth rate (6.50 and 7.10 g/m²/day). PRB+R showed the shortest silking times (60.5 and 62.2 days). PRB+R also attained the maximum maize yield (6.23 and 6.26 t/ha), by a 17.82 % and 17.57 % increase over CT–R in 2018 and 2019. The NE+GS treatment resulted in the highest maize productivity, with additional yield gains over NE alone and STBR. The lowest cultivation cost ($513.87/ha and $513.97 /ha), highest net return ($1028.91/ha and $1083.60/ha), and best benefit-cost ratio (2.00 and 2.11) were observed with PRB–R, while gross returns ($1573.78/ha and $1630.42/ha) had highest in PRB+R. The NE+GS option achieved higher gross returns ($1544.73/ha and $1599.37/ha), net returns ($918.29/ha and $977.30/ha), and benefit-cost ratios (1.47 and 1.57) with lower cultivation costs ($626.43/ha and $622.06/ha) compared to NE and STBR. The PRB+R and NE+GS combination had found highest nutrient uptake (N, P, K) in grain and straw, highlighting their effectiveness in nutrient management. Overall, our findings recommend adopting PRB+R and NE+GS to optimize maize production system productivity and profitability, ensuring agricultural sustainability and resilience to adverse climatic conditions in Northwest India. [Display omitted] • PRB+R and NE+GS optimize maize yields and nutrient uptake in NW India. • PRB+R boosts maize yield by 17.8 %, enhancing resilience to climate stress. • NE+GS increases maize productivity, profitability, and nutrient efficiency. • PRB+R and NE+GS enhance maize growth, yield, and profitability in NW India. [ABSTRACT FROM AUTHOR]

Traditionally, 100 % phosphorus (P) fertilizer application as a band at various depths before sowing significantly influenced crop root growth and yield by reducing P fixation and optimizing its spatial distribution. However, with the advent of drip fertigation in Xinjiang, China, P fertilization practices have shifted from 100 % basal to a combination of basal and fertigation for enhanced P nutrition in cotton. Despite this, the impact of pre-sowing P band application on cotton growth under drip fertigation remains unclear. This study aimed to determine the optimal P fertilizer banding depth for cotton under a drip fertigation system. Field trials were conducted comparing different basal P fertilizer application depths (5 cm, 15 cm, and 25 cm, denoted as D5, D15, and D25, respectively) with 50 % of the P rate and the remaining 50 % applied as topdressing via drip fertilization. A control (CK) involving 50 % broadcasted P fertilizer and 50 % topdressed P was included. The study focused on the effects of P application depth on soil P availability, root growth patterns, P utilization, and cotton yield. At the boll opening stage, the D15 treatment exhibited a significant 18.69 %-49.76 % increase in available phosphorus in the 10–40 cm soil layer compared to the CK. During the peak boll to boll opening stage, the D15 treatment significantly outperformed the CK in terms of total root biomass density (11.62 %-17.54 %), total root length (16.75 %-24.81 %), total root surface area (23.07 %-37.59 %), and total root volume (20.69 %-26.23 %). Moreover, root activity and growth parameters were notably higher in the D15 treatment within the 10–40 cm soil layer. Applying 50 % of the P fertilizer as a band at a 15 cm depth before planting drip-irrigated cotton is optimal. This practice enhances soil P availability, stimulates root growth and distribution, and ultimately improves P utilization and cotton yield. Banding P fertilizer at a 15 cm depth in combination with drip fertigation demonstrates superior yield benefits. This technology offers a novel approach to fertilizer application, enhancing nutrient use efficiency and crop productivity in drip-irrigated systems. • Optimal phosphorus depth (15 cm) increased soil available phosphorus in 10–40 cm soil layer. • Optimal phosphorus depth (15 cm) promoted root growth and optimized root distribution in 10–40 cm soil layer. • Optimal phosphorus depth (15 cm) increased both phosphorus use efficiency and seed cotton yeild. [ABSTRACT FROM AUTHOR]

Plant N nutrition and preharvest stem nonstructural carbohydrates (NSCs) greatly influence ratoon crop yield in a rice ratoon-rice system. However, their physiological relationships haven't been unraveled. We designed this study to test whether greater rice regeneration ability due to preharvest N application is accompanied by increased stem reserves, or whether plant N nutrition and stem reserves independently influence regeneration. First, we evaluated ratooning of crops that receive N after the main crop's late reproductive stage. Second, we imposed shade to decrease light intensity by 64 % 69 % during grain filling of the main crop, and measured the effects of N application on NSCs of the main crop and ratoon crop growths in 2 years. N applied at 5 days after heading of the main crop consistently increased the regeneration ability and ratoon crop yield under non-shaded condition. It did not increase the regeneration ability under heavy shade, when only small amounts of stem NSCs accumulated. Without shade, N application at 5 days after heading increased the concentration of stem NSCs in only one of the two years, whereas the regeneration ability and ratoon crop yield increased in both years. Our results suggest that the increase in ratoon crop yield with preharvest N application requires more than a threshold amount of stem NSCs before the main crop is harvested. However, the preharvest N application can also promote tiller regeneration without further accumulation of stem reserves. N management for ratoon crops therefore depends on light conditions and the main crop's stem reserves. The relationships between plant N, stem reserves, and regeneration ability revealed here will support improved N management for ratoon rice cultivation. • Effects of preharvest N × shading on ratoon crop growths were examined in rice. • N applied at 5 d after the main crop's heading increased ratoon crop yield. • It didn't increase ratoon crop yield when stem reserves were scarce under shade. • Without shade, it didn't increase amounts of stem reserves but promoted ratooning. [ABSTRACT FROM AUTHOR]

This study sought to corroborate microwave remote sensing and simulation models to efficiently delineate groundnut cultivation area and to estimate the yield by integration. Near real-time information on crop acreage and yield estimation is essential for making policy decisions. S1A SAR data were downloaded for entire crop growth period of groundnut during Kharif monsoon seasons (June – October) of 2019 and 2020 and were processed using MAPSCAPE RIICE software to extract groundnut cultivated area in the study districts of Tamil Nadu. Spectral dB curve groundnut generated using multi-date Sentinel 1 A SAR data showed a minimum at sowing, reached a peak at the pod development stage and decreased after that towards maturity. Groundnut area map was generated with a classification accuracy of 85.2 and 84.8 per cent with a kappa coefficient of 0.70, and total groundnut area of 104343 and 116199 ha was mapped during Kharif monsoon season 2019 and 2020, respectively. The mean agreement of 75.01 and 84.94 per cent was observed between DSSAT model simulated LAI and observed LAI at thirty monitoring locations in the study area during Kharif monsoon season 2019 and 2020, respectively, whereas agreement for yield was 82.11 and 83.70 per cent with RMSE of less than 20 per cent. Spatial distribution of groundnut LAI and yield was estimated by assimilating dB from satellite image and from DSSAT model, respectively. The estimated mean spatial LAI was 2.81 and 3.52, whereas mean spatial pod yield was 2124 and 2195 Kg ha−1 during Kharif monsoon season 2019 and 2020, respectively with RMSE of less than 20 per cent and R2 for integrating satellite products and simulation model for spatial estimates during both the year was >0.70, it shows the fitness of products towards increased accuracy of estimation. [Display omitted] • Accurate and instantaneous information on acreage and yield is essential for policy-making, crop insurance, and sustainable management. Due to estimation accuracy, the multi-date Sentinel 1 A Synthetic Aperture Radar data estimates groundnut area and maximum leaf area derived from backscattering which can be integrated into DSSAT CROPGRO Peanut model for generating yields at a spatial scale well before harvest of crop for quick decision making. [ABSTRACT FROM AUTHOR]

Soda saline-alkali stress severely hampers rice growth, nitrogen use efficiency, and yield formation. Biochar addition has been recognized as a potential solution to mitigate the adverse effects of saline-alkali stress on crops. Similarly, optimal agronomic measures are known to optimize crop growth conditions and enhance yield formation and resource utilization efficiency. Despite this knowledge, there is limited understanding of the combined effects of optimizing agronomic measures and biochar addition on ionic accumulation, nitrogen use efficiency, and rice yield in soda saline-alkali paddy fields. In this study, a 3-year field experiment was undertaken to evaluate the combined effects of optimal agronomic measures and biochar application on rice physiological properties, nitrogen use efficiency, and yield under five agronomic treatments: zero-fertilizer control (CK), farmers' practice (FP), high-yield and high-efficiency management (OPT1), super-high-yield management (OPT2), high-yield and high-efficiency management combined with biochar (OPT1+B), and super-high-yield management combined with biochar (OPT2+B). The results demonstrate that treatments of optimal agronomic practices combined with biochar application (OPT2+B and OPT1+B) effectively reduced leaf Na+ concentration, Na+/K+ ratio, abscisic acid (ABA) concentration, and malondialdehyde (MDA) concentration, while enhancing leaf K+ concentration, improving leaf water status, and reducing relative electrical leakage over three years. Furthermore, these combined treatments positively influenced the enzyme activities of nitrate reductase (NR), glutamine synthetase (GS), glutamate synthase (GOGAT), and leaf nitrogen content (LN), as well as SPAD values. Additionally, the average nitrogen agronomic use efficiency (AEn) increased by 154.71 %, 109.81 %, 50.67 %, and 32.60 % in OPT1+B, OPT2+B, OPT1, and OPT2, respectively, compared to FP, while nitrogen physiological use efficiency (PEn) decreased by 64.03 %, 58.56 %, 29.46 %, and 21.81 %. The average grain yield (GY) increased by 311.42 %, 302.86 %, 196.57 %, 178.86 %, and 133.72 % in OPT2+B, OPT1+B, OPT2, and OPT1, respectively, compared to FP. AEn exhibited positive correlations with K+, leaf water status (LWS), NR, GS, GOGAT, LN, SPAD, and GY. These findings will offer new insights into the sustainable development and utilization of soda saline-alkali lands. • Optimal agronomic practices combined with biochar decrease leaf Na+ concentration and Na+/K+ ratio. • Optimal agronomic practices combined with biochar show an ability to alleviate saline-alkali stress. • Optimal agronomic practices combined with biochar showed a sustained ability to improve NR, GS, GOGAT, and LN. • Improvement of stress and enzyme activity of N metabolism acted on increase AEn and yield of rice. [ABSTRACT FROM AUTHOR]

• A novel regional-scale hydrological model for drainage districts is developed. • The spatiotemporal exchange between surface water and groundwater are evaluated. • Impacts of controlled drainage on groundwater recharge to root layer are quantified. • Impact mechanism of controlled drainage on regional crop water stress is identified. Controlled drainage (CD) has emerged as an effective strategy to prevent field waterlogging and mitigate drought during crop growth by altering the hydrological process, while few studies have quantified its effect on groundwater-surface water interactions and groundwater recharge at a regional scale. In this study, a new model is developed for the coupled numerical simulation of water flow in ditches, soil, and underground aquifers under CD conditions. The domain is partitioned into horizontal sub-areas and two vertical layers, with ditches discretized into segments based on groundwater flow grids. The Saint-Venant equations, Richards equation, and MODFLOW are applied to describe water movement processes in ditches, soil, and underground aquifers, respectively. The proposed model is calibrated and validated using five years of observations of ditch water levels (DWL) and groundwater levels, demonstrating excellent agreement with observed data. Subsequently, water balance components of the shallow aquifer below 1 m depth (GW), 1 m of root layer soil profile (SW), and ditch water (DW) under seven scenarios with different control schemes during flood and dry seasons are analyzed to quantify variations in hydrological processes caused by CD. The results show that CD significantly impacts water within SW, GW, and DW, soil evaporation, infiltration, and net recharge from GW to SW (GtoS) by altering regional groundwater table depth (GWT) through the exchange between DW and GW (DtoG and GtoD). GWT and DW show moderate correlation with GtoD, while their correlation with DtoG varies significantly under different rainfall conditions. Smaller precipitation results that precipitation and GWT have a higher linear correlation with water balance components during the dry season compared to the flood season. The correlation values (r) between transpiration (T) and GWT range from −0.99 to 0.96 and −1 to −0.85 during the flood and dry seasons, respectively, indicating a pronounced influence of CD on crop growth. On average, a 1 m increase in DWL control scheme leads to a 0.51 m increase in DWL, and regional GWT decreases by 0.35 m and 0.24 m during flood and dry seasons, respectively. Furthermore, for every 1 m decrease in GWT, net GtoS increases by 36.1 mm and 28.1 mm, respectively, resulting in an increase in SW by 13.9 mm and 11.8 mm, respectively. Considering the varying main crop water stress and the impact of CD under different rainfall conditions, an appropriate CD scheme needs to be adjusted accordingly. These findings provide a quantitative reference for the effect of CD on regional hydrological process and serve as a typical case for similar areas aiming to implement CD strategies for waterlogging and drought control. [ABSTRACT FROM AUTHOR]

Microplastics (MPs), which are widely dispersed in terrestrial environments, threaten crop growth and human food security. However, plant accumulation and phytotoxicity related to the size effects of MPs remain insufficiently explored. This study investigated the accumulation and toxicity of two sizes of MPs on Capsicum annuum Linn. (C. annuum) through fluorescence tracing and antioxidant defense system assessment. The results revealed that the size of MPs significantly impacts their accumulation characteristics in C. annuum roots, leading to variations in toxic mechanisms, including oxidative stress and damage. Smaller MPs and higher exposure concentrations result in more pronounced growth inhibition. C. annuum roots have a critical size threshold for the absorption of MPs of approximately 1.2 μm. MPs that enter the root tissue exhibit an aggregated form, with smaller-sized MPs displaying a greater degree of aggregation. MP exposure induces oxidative stress in root tissues, with high concentrations of smaller MPs causing lipid peroxidation. Analysis of the IBR values revealed that C. annuum roots utilize ascorbic acid (ASA) to prevent oxidative damage caused by larger MPs. Conversely, smaller MPs primarily induce superoxide dismutase (SOD) and glutathione (GSH). These results emphasize the significant impact of MP size on plant antioxidant defense response mechanisms, laying the foundation for further investigating the implications for human health. [Display omitted] • Microplastics (MPs) can reduce the height and fresh weight of Capsicum annuum Linn. (C. annuum). • The critical size of MPs entering the roots of C. annuum is approximately 1.2 μm. • In C. annuum roots, smaller MPs are more prone to aggregation. • Small and large MPs induce distinct oxidative stress response mechanisms. [ABSTRACT FROM AUTHOR]

Water stress, a significant abiotic stressor, significantly hampers crop growth and yield, posing threat to food security. Despite the promising potential of nanoparticles (NPs) in enhancing plant stress tolerance, the precise mechanisms underlying the alleviation of water stress using O -Carboxymethyl chitosan nanoparticles (O -CMC-NPs) in maize remain elusive. In this study, we synthesized O -CMC-NPs and delved into their capacity to mitigate water stress (waterlogging and drought) in maize seedlings. Structural characterization revealed spherical O -CMC-NPs with a size of approximately 200 nm. These NPs accumulated near the seed embryo and root tip, resulting in a substantial increase in fresh and dry weights. The application of O -CMC-NPs to water-stressed maize seedlings remarkedly elevated the chlorophyll content and activity of various antioxidant enzymes, including superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and polyphenol oxidase (PPO). The malondialdehyde (MDA) content was significantly reduced compared to the untreated control. Additionally, the expression of stress-responsive genes, such as ZmSOD , ZmCAT, ZmPOD , ZmTIFY , ZmACO , ZmPYL2 , ZmNF-YC12, and ZmEREB180 , were significantly upregulated in the O -CMC-NPs treated seedlings. These findings unveil the novel role of O -CMC-NPs in enhancing plant stress tolerance, suggesting their potential application in safeguarding maize seedlings under water stress conditions and facilitating the recovery from oxidative damage. [Display omitted] • We determined the role of O -CMC-NPs in water stress mitigation in maize. • Priming with O -CMC-NPs boosted maize seeds' germination, vitality, and growth traits. • O -CMC-NPs improved chlorophyll, antioxidants, and reduced lipid peroxidation. • The expression of stress-responsive genes were upregulated by O -CMC-NPs, providing • Insights into the water stress tolerance mechanisms. [ABSTRACT FROM AUTHOR]

Nitrogen addition is commonly used to remediate total petroleum hydrocarbons (TPH) in petroleum-contaminated soils. However, acceptable exogenous nitrogen dosages and their utilization efficiency for the degradation of hydrocarbons in oil-polluted soils are not well understood. This study compared the hydrocarbon bioremediation capacity by applying different doses of NH 4 Cl as a stimulant in soils contaminated with TPH at 8553 and 17090 mg/kg. The results showed acceptable exogenous nitrogen levels ranging from 60 to 360 mg N/kg soil, and the optimal nitrogen dosage for TPH remediation was 136 mg N/kg in soils with different TPH concentrations. The nitrogen availability efficiency (NAE) and nitrogen polarization factor availability (NPFA) in the 136 mg N/kg addition treatments were 6.69 and 20.47 mg/mg in 8533 mg/kg TPH-polluted soil, and 6.03 and 31.11 mg/mg in 17090 mg/kg TPH-polluted soil, respectively. Metagenomic analysis revealed that the application of 136 mg/kg nitrogen facilitated ammonia oxidation and nitrite reduction to nitric oxide, and induced soil microorganisms to undergo regulatory or adaptive changes in energy supply and metabolic state, which could aid in restoring the ecological functions of petroleum-contaminated soils. These findings underscore that 136 mg/kg of nitrogen dosage application is optimal for remediation of petroleum-contaminated soils irrespective of the TPH concentrations. This exogenous nitrogen application dosage for TPH remediation aligns with the nitrogen requirements for crop growth in agriculture. [Display omitted] • Acceptable nitrogen levels ranging from 60 to 360 mg N/kg, regardless of contamination levels. • The optimal nitrogen dose for petroleum hydrocarbon bioremediation was 136 mg N /kg. • The application of 136 mg N /kg enhances the ecological functioning in oil contaminated soils. • Heightened N transformation and utilization at the optimal biostimulate dosage. • The consistent nitrogen acceptance dosages with nitrogen requirements in agriculture. [ABSTRACT FROM AUTHOR]

• China provincial annual nitrogen (N) use is assessed from 1985 to 2020. • An improved scheme for assessing the contribution of synthetic N and soil fertility to crop growth is proposed. • Crop N uptake is modeled directly with N input components by using Random Forest. • The response of crop N uptake and NUE to the regulation of N inputs is simulated. Improving nitrogen (N) management are crucial for food security and ecological protection in China and globally. Assessing the contribution and influence characteristics of N input components on crop growth is a key component. There are challenges in conducting such assessments at large regional scales, particularly in developing models that fit regional N inputs and crop N uptake. An improved scheme that fit regional N inputs and crop N uptake with consideration of spatial heterogeneity of cultivation conditions was proposed. Based on the division of homogeneous cultivation conditions zones, linear models and Random Forest models were developed to assess the contribution and influence characteristics of N input components on crop growth at the provincial scale in mainland China. And synthetic N contribution rate (SNCR) and soil fertility N contribution (SFNC) were innovatively proposed to represent the contribution of fertilizer and soil fertility to crop growth. The results showed that the overall N use efficiency ranged from 35 % to 55 % in 1985–2020, and higher NUE could be seen in major grain producing provinces. The SNCR generally declined, with a spatial pattern higher in the southwest, northwest, and northeast regions. The SFNC showed an increasing trend, with a spatial pattern higher in the northeast than southeast than west. For each N input component, Synthetic N input was critical in the northwest, southwest regions and north China, positively affecting crop N uptake. The improved scheme, which incorporated considerations for spatial heterogeneity, demonstrated superior accuracy compared to the model fitted by year, increasing from 0.27 (0.11) to 0.42 (0.47). Over the past 35 years, the crop N uptake and N surplus of mainland China had experienced a process of extensive-unsustainable-sustainable-conservative pattern changes. The response of crop N uptake and N use efficiency to the regulation of N inputs in typical regions was simulated, providing reference for provincial N inputs regulation in China. This study can provide support for the design of N management strategies in China to reduce N pollution, and the method can provide guidance for other regions to assess N use in different scales. [ABSTRACT FROM AUTHOR]

• Legacy and recent data were combined to improve SOM mapping accuracy. • 1D-CNN exhibited superior performance for SOM prediction than RF and PLSR. • Similar spatial distribution patterns were found for legacy and recent periods. • SOM showed an average increase of 5.90 g/kg from legacy to recent periods. Soil organic matter (SOM) is critical for soil fertility, crop growth, and plays an important role in the global carbon cycle and climate change. Therefore, spatial prediction of SOM is important to rational soil resource utilization, agricultural production, and ecological environment management. However, large-area SOM mapping research heavily relies on legacy soil data, and large-scale recent SOM mapping may not be possible or have lower accuracy due to limited or less recent data availability. In this study, we aimed to improve SOM prediction and mapping accuracy by combining legacy data with limited recent data. Three models, namely, partial least squares regression (PLSR), random forest (RF), and one-dimensional convolutional neural network (1D-CNN), were applied and compared. The results showed that combining legacy and recent data effectively improved SOM prediction accuracy compared to using only recent data. Among the three modeling methods, 1D-CNN exhibited superior performance, with an averaged determination coefficient of the prediction (R 2) of 0.58, a root mean square error (RMSE) of 4.56 g/kg, and a ratio of performance to interquartile distance (RPIQ) of 2.05. The predicted SOM content for both legacy (1980 s) and recent (2010 s) periods showed similar spatial distribution patterns throughout the Huanghuaihai Plain. Generally, there was a noticeable trend of increasing SOM content from northwest to southeast, with higher values observed in Jiangsu and lower values concentrated in Henan, Hebei, and Shandong regions within the study area. Over time, SOM contents in the Huanghuaihai Plain showed an increasing trend, with an average increase of 5.90 g/kg from legacy to recent period. This study provides a promising approach for improving SOM prediction and mapping accuracy at large scales, particularly when recent data availability is limited. [ABSTRACT FROM AUTHOR]

Ridge–furrow with plastic film mulching (RFPFM) is quite successful under dryland agriculture. However, its suitability for sunflower (Helianthus annuus L.) production with different plastic film mulching techniques such as ridge–furrow fully covered with plastic film mulching (FM) and half mulching (HM), in combination with appropriate nitrogen (N) fertilizer management remains uncertain. The current research investigated the performance of sunflower crop under three planting patterns (HM, FM and CP, i.e., conventional planting on flat land without plastic film mulching) and two N doses (120 and 240 kg N ha–1) in northwest China. The objectives were to evaluate the soil hydrothermal conditions, crop performance, water productivity (WP), N harvest index (NHI) and seed yield, under different combinations of these treatments. The results showed that both HM and FM had 1.8 °C higher topsoil temperature during seedling stage and soil moisture (+21 % and +30 %) during the whole growth stage, respectively, when compared to CP. The favorable hydrothermal conditions under HM and FM accelerated the seedling emergence and shortened the crop growth duration by nearly 5 days, and further improved seed yield (+23 and +33 %), WP (+26 and +36 %), and N uptake (+20 and +24 %), compared to CP. A comparison between FM and HM treatments did not show a significant increase in WP or yield for the former, despite improved soil hydrothermal conditions. In addition, increasing N application rate from 120 to 240 kg N ha–1 did not lead to a consistent and significant effect on total N uptake, WP and seed yield. Overall, the present study suggests that HM, when coupled with a reduced N dose, can replace FM in sunflower production. This approach reduces the use of plastic film and N fertilizer, while efficiently conserving water, enhancing WP and improving seed yield of sunflower under dry and semi–humid conditions. • Plastic mulching enhanced sunflower yield and water productivity due to improved soil moisture. • The higher N application rate did not increase sunflower yield and water productivity. • The yield of half mulched ridge-furrow plots (HM) was at par with fully mulched ridge-furrows. • HM can be recommended to reduce plastic film usage without compromising yield or water productivity. [ABSTRACT FROM AUTHOR]

Timely and accurate plant growth monitoring is crucial for precision crop management. Traditional remote sensing methods use a single agronomic parameter to evaluate crop growth status (GST), limiting accuracy. To develop a comprehensive growth monitoring index (CGMI) based on multiple parameters. A two-year field experiment in the Mosuwan Reclamation Region of Xinjiang, China was conducted to collect parameter characterization data for cotton growth, including leaf area index, canopy chlorophyll content, above-ground biomass, and boll numbers, and their contributions and interrelationships in relation to yield were analyzed. Entropy and game theory weighting methods were used to establish the CGMI EWM and CGMI GT , and a sequential forward selection algorithm (SFS) was used to screen the most effective remote-sensing monitoring feature variables for the different reproductive stages. Partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR) were used to develop an optimal model to comprehensively monitor cotton growth and draw a spatial distribution map. CGMI EWM and CGMI GT could effectively reflect GST. The correlation between CGMI GT and yield based on a game theory combination weighting method was significantly higher than that between yield and each agronomic parameter. The correlation between CGMI GT and yield (r = 0.75) was slightly higher at the initial boll stage than that of CGMI EWM (r = 0.73), whereas at the initial boll-opening stage, the correlation between CGMIGT and yield (r = 0.74) was significantly higher than that of CGMI EWM (r = 0.63). The weight coefficients used to construct the CGMI GT exhibited stable performance in different years. Feature variables were selected to monitor the comprehensive growth of cotton at different stages based on the SFS algorithm. PLSR, RF, and SVR were used to estimate CGMI GT. The RF algorithm had the best estimation performance in both the initial boll and initial boll-opening stages (R² = 0.63, root mean square error (RMSE) = 0.086, RE = 19.8 % vs. R² = 0.56, RMSE = 0.107, RE = 24.1 %). A comprehensive cotton growth distribution map in the Mosuwan Reclamation Region was drawn using the optimal model, and growth was comprehensively evaluated. Areas with good cotton growth were concentrated in the north, and there was a decreasing trend from north to south. We provide a new comprehensive evaluation tool for cotton growth status large-scale, real-time monitoring. Our results promote differentiated management, improve crop yield prediction accuracy, and aid in the formulation of cotton price strategies. [ABSTRACT FROM AUTHOR]

The essential role of water-conducting xylem tissue in plant growth and crop yield is well-established. However, the molecular mechanisms underlying xylem formation and its unique functionality, which is acquired post-mortem, remain poorly understood. Recent advancements in genetic tools and model systems have significantly enhanced the ability to microscopically study xylem development, particularly its distinctive cell wall patterning. Early molecular mechanisms enabling pattern formation have been elucidated and validated through computational models. Despite these advancements, numerous questions remain unresolved but are approachable with current methodologies. This mini-review takes in the latest research findings in xylem cell wall synthesis and patterning and highlights prospective directions for future investigations. [ABSTRACT FROM AUTHOR]

Aluminum (Al) stress is a significant issue in acidic soils, severely affecting crop growth and yield. Rice is notably resilient to Al toxicity, yet the internal tolerance mechanisms remain inadequately addressed. Here, we examined the role of OsTIP2;1, a tonoplast-bound intrinsic protein (TIP), in rice's internal Al detoxification. Our findings reveal that OsTIP2;1 expression was quickly and explicitly activated by Al ions in roots but not in shoots. The OsTIP2;1-GFP protein localizes to the tonoplast in plant and yeast cells. Non-functional ostip2;1 rice mutants were more vulnerable to Al toxicity. In the roots, the ostip2;1 mutants exhibited considerably lower levels of Al in the cell sap, primarily the vacuolar contents, than in the wild-type plant. Moreover, the ostip2;1 mutants showed reduced Al accumulation in the roots but increased translocation to the shoots. Heterologous expression of tonoplast-localized OsTIP2;1 in yeast led to enhanced Al tolerance, suggesting that OsTIP2;1 facilitates Al sequestration to the vacuole. These findings indicate that OsTIP2;1 mediates internal detoxification by transporting Al into the vacuole in the root and restricting its transport to above-ground tissues, thus contributing to Al resistance in rice. [Display omitted] • OsTIP2;1 expression is induced explicitly by aluminum (Al) in rice roots. • OsTIP2; 1 regulates the distribution of Al in rice by sequestration to the root vacuole. • OsTIP2;1 contributes significantly to Al tolerance mechanisms in rice. [ABSTRACT FROM AUTHOR]

Climate change will bring the interaction of stresses such as increased temperature and drought under high [CO 2 ] conditions. This is likely to impact on crop growth and productivity. This study aimed to (i) determine the response of barley water relations to vegetative and anthesis drought periods under triple interaction conditions, (ii) test the possibility to prime barley plants for drought, and (iii) analyse the involvement of aquaporins in (i) and (ii). The water status of barley was not affected by drought at the vegetative stage, regardless of the environmental conditions. At the anthesis stage, when the water shortage period was more severe, barley plants growing under combined elevated CO 2 and temperature conditions were able to maintain a better water status compared with plants grown under current conditions. Elevated CO 2 and temperature conditions reduced the stomatal conductance and slowed down the plant water flow through a root-leaf hydraulic conductivity coordination. Leaf HvPIP2;1 and HvTIP1 ; 1 aquaporins seemed to play a key role regulating barley's water flow, while leaf and root HvPIP2;5 provided basic level of water flow. At anthesis drought and under future combined conditions, plants showed a reduced cell dehydration and decrease in leaf relative water content compared with plants grown under current conditions. Exposure to a previous drought did not prime the water status of barley plants to a subsequent drought, but instead worsened the response under future conditions. This was due to an imbalance between the roots versus shoot development. • The water status of barley is not affected by vegetative drought. • However, it is affected by anthesis drought but eased by elevated [CO2] × temperature. • Subsequent drought periods worsen barley water status at elevated [CO2] × temperature. • These events are tuned by isoform-specific changes in gene expression of aquaporins. [ABSTRACT FROM AUTHOR]

Accurate estimation of wheat maturity date (MD) is helpful to make reasonable harvest planning and guarantee crop yield and quality. In this study, wheat phenology extracted from satellite images was assimilated into WheatGrow model to develop wheat maturity date estimation model. Theoretical uncertainty was introduced into assimilation system as the error covariance matrix of remote sensing observations, which improved the performance of maturity date estimation model. Compared with the simulated maturity date of crop growth model and assimilation system combined with the constant uncertainty (Assimilation1), the accuracy of assimilation system combined with the theoretical uncertainty (Assimilation2) was higher (r = 0.81, RMSE = 4.5 d). Assimilation2 has better performance and robustness in different years and different subregions. The mean relative errors between the estimated values of Assimilation2 and the observations were generally small and concentrated in the range of −5 % to 5 %. The estimated maturity date showed latitude variation in spatial distribution in the Huang-Huai-Hai Plain (HHHP). In addition, the trend of wheat maturity date from 2001 to 2020 in the central region of HHHP was significant (p < 0.05), and the mean change rate of maturity date reached 3–6 d/10a. However, the overall change trend of maturity date in the HHHP was not significant. Temperature was main driver affecting the spatiotemporal variation of wheat maturity date. The regional wheat maturity date estimation model can provide technical support for wheat maturity date estimation at regional scale. • Maturity date estimation model was developed. • Considering theoretical uncertainty can improve estimation of maturity date. • Assimilation system can capture spatiotemporal variability of maturity date. • Temperature is negatively correlated with maturity date. [ABSTRACT FROM AUTHOR]

Model parameters in mechanistic process-based crop models are broadly classified into general parameters (environmental, management, and parameters related to soil processes), species-specific parameters, and cultivar-dependent parameters. While general and species-specific parameters typically remain unchanged, identifying cultivar-dependent parameters is crucial during calibration. With the continuous development of new cultivars, updating cultivar-specific parameters in crop models is necessary. However, gathering data on the behavior of these newer cultivars and calibrating the model to accommodate them is challenging. Currently, there is no standard approach for determining cultivar-specific parameters in crop models due to the difficulty of identifying variations in the growth and development process in newer cultivars, as well as the variations in the model structure, correlations between sub-model components and between different parameters in the crop models. The present study develops a standard methodology for determining cultivar-dependent parameters based on experiments and incorporating them into process-based crop growth models. The developed methodology is demonstrated using the cotton crop and the GOSSYM, a mechanistic process-based cotton crop simulation model. Experiments are conducted on 40 major cotton cultivars grown in the USA to quantify their growth and development under the same environmental and management conditions. Cultivar-dependent parameters are derived and integrated into the GOSSYM model based on the experimental data. By establishing a standardized methodology and demonstrating its application in the context of cotton and the GOSSYM model, this study provides practical guidance for incorporating cultivar-dependent parameters into crop simulation models. Researchers and agronomists can effectively utilize this methodology to integrate new cultivars into their crop models. • Experimental analysis of major growth and development features of 40 recent cotton cultivars in the USA. • A new methodology for determining cultivar-dependent parameters for newer cotton cultivars in crop models. • Adaptable methodology for determining cultivar-dependent parameters in other crop models. [ABSTRACT FROM AUTHOR]

Ensuring sustainable agriculture is crucial amidst global challenges, demanding effective methods to enhance soil health and nutrient cycling. Microbial inoculants, particularly arbuscular mycorrhizal (AM) fungi, offer promising solutions. However, concerns persist regarding the efficacy and quality control of commercial products. Past work assessing commercial inoculants have not controlled for fertilizers added to individual products when assessing product effects under typical use. This study examines twenty-three mycorrhizal inoculants using conventions of organic production to shed light on differences between laboratory grown fungi, commercial products, and field soil. Employing a comprehensive approach, inoculants were assessed through spore enumeration, root infection potential, and crop growth response. The results uncover significant shortcomings in many commercial products compared to laboratory grown fungi. Key findings include discrepancies of up to 100 % in reported propagule counts versus spore concentrations, insufficient root colonization by commercial inoculants, and contamination by fungal plant pathogens, particularly Olpidium , in products. Moreover, while laboratory grown fungi exhibited superior symbiotic relationships with host plants due to increased colonization abilities and crop benefit, commercial inoculants often failed to deliver significant growth benefits when fertilizers are controlled for. These findings highlight the urgent need for improved standards and practices within the commercial inoculant industry. • In agriculture, enhancing soil health is vital for sustainability. • Colonization and crop growth were assessed for twenty-three mycorrhizal inoculants. • Laboratory grown fungi outperformed commercial products and field soil. • Commercial products had poor root colonization and fungal pathogen contamination. • These findings stress the need for standards in the commercial inoculant industry. [ABSTRACT FROM AUTHOR]

Soil salinization poses a major constraint to crop productivity worldwide. Cultivated soybean (Glycine max) is a major oilseed crop with moderate salt tolerance. Accumulating evidence indicates that plant growth-promoting rhizobacteria (PGPR) can promote crop growth and reduce the negative impacts of salt stress on crops. In the present study, we isolated DY22, a newly identified strain of Ensifer sesbaniae , from saline soil and determined that it showed high tolerance to 4 % NaCl. DY22 treatment enhanced the tolerance of soybean to salt stress compared to salt-treated control seedlings. We analyzed the mechanism underlying DY22-mediated salt tolerance in soybean via physiological, biochemical, and transcriptomic analyses. DY22 inoculation significantly increased chlorophyll accumulation and soluble sugar and proline contents in soybean under salt stress. Moreover, inoculation with DY22 enhanced antioxidant enzyme activity and reduced malondialdehyde contents compared with non-inoculation treatment under salt stress. Transcriptomic analysis revealed 8911 differentially expressed genes (4412 upregulated and 4499 downregulated) in DY22-inoculated plants under high-salinity conditions compared with the salt-treated control. GO and KEGG pathway analysis suggested that DY22 affects the transcriptional responses of genes involved in photosynthesis, oxidation-reduction, and plant hormone-mediated pathways, especially auxin, jasmonic acid, and abscisic acid signaling under salt stress. Overall, these findings highlight the important contribution of DY22 in mitigating the deleterious effects of salinity on soybean growth and development and provide valuable insights into the mechanisms underlying plant–microbes interactions. • Ensifer se. DY22 had great ability of salt tolerance. • Stain DY22 could improve soybean adaptation to salt stress. • Strain DY22 increased the accumulation of compatible osmolytes and enhanced antioxidant enzyme activity in soybean. • Stain DY22 regulated genes related to plant hormones signal transduction under salt stress. [ABSTRACT FROM AUTHOR]

Salinity is a prevalent soil issue that reduces crop growth by a combination of osmotic stress and ion-specific toxicity. The crop response to salt stress is generally described in terms of a two phase model in which growth is initially reduced by osmotic stress and then Na+ toxicity. However, the relative importance of these mechanisms to salt stress is still not well understood. In this study a high-resolution image capture and analysis system was used to monitor shoot growth of faba bean plants non-destructively, while gas exchange measurements were used to examine the effects on photosynthesis in order to gain an understanding of the aforementioned stress mechanisms. The results of this soil-based study suggested that responses of crop growth to salinity stress depend on its severity: osmotic stress was the predominant cause of reduced growth at high levels of salinity, while specific-ion toxicity was more important under mild stress. We showed that the tolerant faba bean variety used dual mechanisms of ion exclusion and osmotic tolerance compared with the sensitive variety. Analysis of photosynthetic responses indicated that the extent to which stomatal closure affects photosynthetic capacity is indicated by the magnitude of the reduction in intercellular CO 2 concentration. • Non-destructive measurements of plant growth enabled quantification of osmotic and ionic effects of salt stress. • The relative importance of osmotic stress varies with the severity and duration of salt stress. • A dual mechanism of ion exclusion and osmotic tolerance is essential to improve salt tolerance. [ABSTRACT FROM AUTHOR]

• First sequential global sensitivity analysis through multiple seasons for tomato models. • Weather variability in a location substantially impacts the importance of a parameter. • Relevant for low-tech greenhouses given their vulnerability to extreme weather. • Parameters' sensitivity analyses should be performed for multiple seasons and years. • Parameters representing temperature effects were very important in all models. Fresh tomatoes are an important component of a healthy diet, and their cultivation is often facilitated by protected structures, especially in harsh climates. While greenhouse tomato models have potential applications in controlling these environments, they have not been extensively evaluated. This study aims to characterize the sources of uncertainty in such tomato models, particularly in greenhouse tomatoes grown in low-technology environments. These environments, where optimal environmental conditions may not be taken for granted, are often overlooked in existing studies. We used three tomato models: two designed for protected environments (the Reduced State Tomgro model and the model developed by Vanthoor et al.) and one was developed for field growth, the Simple model. We conducted the first sequential global sensitivity analysis for different weather conditions in tomato models. This approach allowed for uncovering the most important parameters through growth and ensuring the parameters chosen as most important would likely be important in multiple seasons and not only the season in which growth was observed. For instance, for the models and locations studied, intra- and inter-annual weather variability affected parameter importance more than which location was evaluated. These results informed our choice of parameters to be calibrated, and the preliminary evaluation of model performance suggested that both models developed for protected environments may be adequate to represent indeterminate growth in both locations assessed. For the Simple model, some adaptations are required, such as in the radiation use efficiency and a continuous growth of new leaves to compensate for senescence. The uncertainty analysis, also derived from the assessment in different weather conditions, showed that errors in the estimates of the most important parameters, which are often related to environmental factors, such as the fruit abortion temperature threshold for the Reduced Tomgro model or the threshold temperature for crop growth inhibition for the Vanthoor model, impact the outcome substantially. These aspects need to be considered when using these models for low-technology greenhouses, since these environments do not ensure optimal temperatures. Therefore, parameter importance determination and calibration should be performed with data that comprise these more extreme conditions, which cannot be mitigated by technologies like ventilation or evaporative cooling. [ABSTRACT FROM AUTHOR]

• Double time-domain multi-layer closed-loop control method for greenhouse. • The NSGA-II algorithm improves the temperature control accuracy by 6.18 %. • The energy saving optimization strategy reduces energy consumption by 14.4%. • The benefit priority control strategy improves the economic benefit by 19.3%. • Design a greenhouse microclimate environment monitoring system. Aiming at the time difference problem of the slow process of physiological and ecological changes lagging behind the fast process of greenhouse microclimate environmental changes during crop growth, this paper proposes a greenhouse environment dual time domain multi-layered closed-loop control method, which can accurately control greenhouse crop environmental factors while reducing energy consumption and improve economic efficiency. The overall structure is divided into a greenhouse environment control layer and a crop growth control layer. The greenhouse environment control layer 'feeds back' the difference between indoor environmental factors and set environmental factors to the input terminal, and uses a Multi-objective Particle Swarm Optimization Algorithm, Non-dominated Sorting Genetic Algorithm, and Non-dominated Sorting Genetic Algorithm-II to optimize and solve the greenhouse microclimate environment model and greenhouse energy consumption model. The results show that the Non-dominated Sorting Genetic Algorithm-II optimization algorithm reduces energy consumption by 21.75%, increases control accuracy by 6.18%, and has faster convergence speed and higher accuracy. The crop growth control layer aims at maximizing yield, conducts in-depth research on the relationship between environmental factors and tomato yield, and adopts energy-saving optimization and benefit priority control strategies respectively. The energy-saving optimization control strategy saves 14.4% of energy while maintaining the suitable growth of tomatoes. Although the energy consumption of the efficiency-first regulation strategy increased slightly, the economic benefits increased by 19.3%, which has a certain reference significance for scientific guidance of agricultural production. [ABSTRACT FROM AUTHOR]

Crop yield prediction plays an important role in food security. Crop growth models can be used for this purpose, however, they require empirical parametrization. In this study, we show that remote sensing observations can be used to parameterize a crop growth model, providing crop growth monitoring and yield predictions, while mitigating the need for extensive field measurements and accounting for variability in crop varieties. For this purpose, we coupled the SCOPE radiative-transfer model with the state-rate crop model WOFOST. In this paper, we explain how the two models are coupled, assess the accuracy and present a global sensitivity analysis of the coupled model. To assess the accuracy of the coupled model in predicting growth and yield, experimental data of five potato varieties from two fields, in two different locations, in the Netherlands were used. Results show that simulated LAI had a good correlation with measured LAI for four out of the five varieties (R2 > 0.6), while RMSE was in the range 1.09–1.34 m2 m-2. Simulated leaf dry matter content showed similar results, with high accuracy for all varieties (R2 > 0.6) and RMSE between 310 and 440 kg ha-1. Finally, yield simulation showed promising results with an average difference of 1800 kg ha-1 between simulated and measured values. Based on the findings of this study, it is possible to conclude that a coupled WOFOST-SCOPE model has the potential to improve the application of remote sensing data for crop growth monitoring and yield prediction. • Remote sensing data assimilation improves crop growth simulations. • Remote sensing can reduce the dependency on destructive measurements. • Coupling SCOPE and WOFOST address the issue of retrieval continuity. • LAI and leaf weight significantly affect both growth and reflectance simulations. [ABSTRACT FROM AUTHOR]

Drought is known to alter the physiological responses in plants affecting their growth and development. Jute (Corchorus olitorius L.), an important lignocellulosic fibre crop, is susceptible to drought stress at early growth stage. Limited information on plants' response to early drought and its mitigation are available in jute. Here, we investigated early drought response in contrasting jute genotypes and focused on drought mitigation using different plant growth regulators to identify the best growth regulator and genotypic combination. Comparative effect of these plant growth regulators showed that 10 mM ascorbic acid aided in maximum drought recovery. Drought arrested biomass yield (72%) and plant height (16.3%), reduced leaf number (44.7%) and chlorophyll content (41.9%), affected nutrient (N, P, K, Fe and Ca) uptake and modified the cell wall composition reducing fibre productivity and quality. Drought enhanced biosynthesis of proline, ascorbic acid, flavonoids and antioxidants coupled with increase in endogenous ascorbic acid. Endogenous ascorbic acid exhibited significant correlation with more traits (25) under drought compared to control (11 traits). Exogenous ascorbic acid resulted in recovery of plant height (6.5%), fresh biomass (17.25%), dry biomass (6.0%), fibre cellulose (60.2%), total chlorophyll (31.2%), N (10.5%), P (18.1%), K (5.7%), Fe (44.6%), Ca (31.3%) and membrane stability (70.69%) whereas proline content was reduced up to 9.6 times. With exogenous ascorbic acid application, the endogenous ascorbic acid increased by 14.4% exhibiting significant positive correlation with 22 traits. The drought tolerance of genotypes, cv. JRO 204, is attributed to higher endogenous ascorbic acid, proline, nutrient content and anti-oxidants which contributed to higher cellulose in fibre and biomass. Hence, the cultivation of drought tolerant genotype supplemented with exogenous ascorbic acid can be a robust mitigation strategy for early drought management in jute. [Display omitted] • Jute shows approx. 16% plant height loss under drought at early growth stage. • Inherent high AsA content imparts jute a strong antioxidant machinery to combat drought. • Exogenous AsA (10 mM) improves growth by enhancing the plant height under drought. • Exogenous AsA improves nutrient uptake under drought. • Exogenous AsA improves the cellulose content and reduces lignin content in jute under drought. [ABSTRACT FROM AUTHOR]

Halogenated flame retardants in farmlands were observed to inhibit the growth of exposed crops. This study aimed to elucidate the mechanism of inhibition on rice by employing four representative polybrominated diphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs). The exposure to these contaminants at 200 nM led to a decrease of 0.63–0.95 fold in rice below-ground biomass and 0.49–0.66 fold in yield, and a corresponding 4%–10% increase in soluble sugars in leaves. PBDEs and PCBs were found to significantly disrupt the synthesis, decomposition, and transport of sugars in leaves, the three pivotal determinants of crop growth. Notably, these compounds promoted a 1.41- to 7.60-fold upregulation of the triose phosphate translocator, significantly enhancing soluble sugar synthesis. Conversely, a 0.45–0.97 fold downregulation was observed for sucrose transporters, thus impeding the leaf-to-shoot efflux of soluble sugars. Furthermore, PBDEs and PCBs were favorably bound to fructose-1,6-bisphosphate aldolase (FBA), inducing its substrate-specific dysfunction in fructose-1,6-diphosphate decomposition (3%–14%). Overall, PBDE and PCB exposure promoted a notable intracellular accumulation of soluble sugars in rice leaves, a typical symptom of plant diabetes, since the intensified synthesis of soluble sugars in leaves and the repressed decomposition and transportation of soluble sugars to other storage organs, thus impeding crop growth. This study provided an insightful understanding of the toxic effects and molecular mechanisms of halogenated flame retardants, highlighting their role in abnormal sugar accumulation and growth inhibition in crops and offering vital information for the risk assessment and administration of these compounds to guarantee the safety of agricultural products. [Display omitted] • PBDEs and PCBs increased leaf soluble sugars, reducing rice biomass and yield. • Synthesis, breakdown and transport of soluble sugars in leaves were disrupted. • Up-regulation of triose phosphate translocator promoted soluble sugars synthesis. • Down-regulation of sucrose transporters hindered soluble sugars transport. • Activity of fructose-1,6-bisphosphate aldolase was inhibited by PBDEs and PCBs. [ABSTRACT FROM AUTHOR]

Crop diseases pose as a major threat to global food security. Minimizing disease-induced damage during crop growth and optimizing crop yields are vital for agricultural sustainability. Therefore, advanced disease detection and prevention of such diseases are crucial and the detection must be prompt and efficient as it is essential for the implementation of appropriate control measures. In this work, a parallel deep learning framework based on deep feature fusion is developed to precisely identify the severity of crop diseases. The framework utilizes ResNet50 and Xception as separate branches for feature extraction. Convolutional layer weights are initialized through transfer learning techniques employing models pre-trained on the ImageNet dataset. A fine-tuning strategy is employed for the optimization of convolutional layers and the design of the top layer. This framework achieves an accuracy of 88.58% on the AI Challenger 2018 dataset, marking an enhancement of 2.8% and 13% over other influential deep learning models, and it also outperforms some recent works. Likewise, the framework exhibits a recognition accuracy of 99.53% on the PlantVillage dataset. Moreover, an Android-based application is developed to diagnose the severity of crop diseases in real-time. The diagnostic system swiftly procures results and provides control recommendations through the capture and upload of images to the platform. The advanced severity detection system reduces the expertise required from users, facilitating precise prevention and control measures whilst focusing on accessibility. This work aims to provide innovative approaches and solutions for disease detection in the agricultural field, utilizing artificial intelligence to enhance agricultural informatization and creating more sustainable farming methods. • Proposed a parallel deep learning framework for identification of crop diseases. • Improved framework performance in imbalanced data with focal loss function. • Conducted extensive experiments on large datasets to validate the framework. • Developed a real-time online mobile diagnosis app using the proposed framework. [ABSTRACT FROM AUTHOR]

Corbicula fluminea meal was added to a high-fat diet for eight weeks to determine its effects on growth, lipid metabolism, and intestinal microbiota in juvenile rice field eel (Monopterus albus). C. fluminea meal was made of dried and crushed soft parts of freshwater clam. The study included four diets: a normal fat diet (6.63% lipid, CON), a high fat diet (12.61% lipid, HFD), and a high fat diet supplemented with 1% or 2% C. fluminea meal (CFM1 and CFM2, respectively). The results showed that: (1) the growth performance was higher in the HFD, CFM1 and CFM2 group compared with the CON group, the hepatosomatic index (HSI) was significantly higher in fish fed the high-fat diet than in those fed other diets. (2) Compared with the CON group, The levels of serum triacylglycerol (TG), low-density lipoprotein cholesterol (LDL-C) contents, glutamate pyruvic transaminase (GPT), glutamic oxaloacetic transaminase (GOT) activities and the number of hepatic lipid droplets were significantly increased in the HFD group. The addition of C. fluminea meal both reduced liver damage and fat deposition. (3) The mRNA expressions of pparγ , fas , acc , me1 , srebp1 , g6pdh , hsl , lpl , cpt1, and cpt2 in the HFD group were significantly upregulated, while HFD supplemented with C. fluminea meal significantly downregulated the mRNA expressions of pparγ , fas , acc , me1 , srebp1 and g6pdh and significantly upregulated the mRNA expressions of hsl and lpl. (4) The alpha diversity of intestinal microbial community in the HFD group was higher than those in the CON and CFM1 groups. The beta diversity revealed the microbial composition differed in the HFD group from the CON and CFM1 groups. The abundance of Firmicutes in the HFD group increased significantly compared with the CON and CFM1 groups, while the abundances of Cetobacterium decreased. In conclusion, the high-fat diet resulted in abnormal liver fat deposits and intestinal microbiota dysfunction in M. albus , but HFD supplemented with 1% or 2% C. fluminea meal reduced these negative effects. • The effects of high-fat feed on lipid metabolism and intestinal flora of eels were revealed. • 1% or 2% C. fluminea meal can mitigated the negative effects of high-fat feeds on the liver. • 1% C. fluminea meal improving the intestinal flora structure of eel. • C. fluminea meal may play a vital role in liver protection and energy metabolism of eels. [ABSTRACT FROM AUTHOR]