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In the context of India's evolving economy, where agriculture remains a vital yet limited sector, the challenge is to enhance productivity on finite cultivable land. This research addresses this imperative by introducing the Crop Yield Prediction Using Deep Learning (CYPBL) model, leveraging the Bi-Directional Long Short-Term Memory (Bi-LSTM) algorithm. Employing soil health and crop yield data from the Government of India, the CYPBL model is implemented through PySpark for scalability. Featuring 20 LSTM layers with a 12 × 1 input shape, including a bidirectional LSTM layer, the model achieves exceptional accuracy at 99.5 percent on test data. With a focus on real-time data, a batch size of 1 ensures optimal responsiveness. Beyond technological advancements, CYPBL emerges as a groundbreaking soil health monitoring system, bridging the gap between experts and farmers. By empowering farmers with insights into soil conditions, crop suitability, and improvement strategies, this research paves the way for data-driven, responsive agriculture in India and globally, contributing to the pressing issue of global food security. [ABSTRACT FROM AUTHOR]

Over 50% of India's population relies on crop growing, making it the foundation of the country's economy. Differences in weather, temperature, and other environmental factors are now a significant threat to the continued success of agriculture. This makes it difficult to predict the crop yield appropriately. Hence, computer-based models are believed to be more predictive in analyzing crop yields. Machine learning (ML) and deep learning (DL) models play a vital role as the supporting tool for Crop Yield Prediction (CYP), which includes assisting decisions on which crops to plant and the measures to be taken for the growth of crops. This paper aims to propose a deep ensemble model for predicting crop yields more accurately. The steps followed are: Improved Log scaling-based pre-processing of data. Then, from the preprocessed data, the input features like higher-order features, Enhanced entropy-based features and correlation-based features are derived. Further, the optimal features are elected using the Integrated Bird Swarm and BOA (IBS-BOA) algorithm. Based on the selected features, prediction is done by the Deep Ensemble Model (DMO, Bi-GRU and CNN). Also, one of the models of Ensemble classifiers, CNN is trained by the IBS-BOA algorithm. Finally, the performance of the proposed DEC-IBSBOA is evaluated concerning varied error factors namely, MEAE, MAE, MALE, MAPE, ME and MSE. For the suggested system minimum error is obtained for MAE (~ 1.0) when compared to the other existing methods such as Bi-GRU, DMO, CNN, PLMDC, SVM, LSTM, SSTNN, EC + WOA, EC + BES, EC + BOA, EC + BMO and EC + BSA. [ABSTRACT FROM AUTHOR]

This research investigated the green gram crop by employing a time series of Sentinel-1A Single Look Complex (SLC) and Sentinel-2 datasets, which corresponded to different phenological growth stages of the crop. Field campaigns were conducted on the acquisition dates of Sentinel-1A datasets for the entire crop cycle to estimate crop height and soil moisture. In line with these observations, temporal variations in 10 salient Synthetic Aperture Radar (SAR) parameters, obtained from Sentinel-1A datasets, are systematically analysed, which elucidated the importance of dual-polarized SAR parameters in the diverse phenological growth stages of the crop. Moreover, the Normalized Difference Vegetation Index (NDVI) values from Sentinel-2 datasets, estimated at various green gram growth stages, are categorized into initial (NDVI $ \le $ ≤ 0.5), intermediate (0.5 $ \lt $ < NDVI $ \lt $ < 0.8), and maximum growth of the crop (NDVI $ \ge $ ≥ 0.8), which are then aligned with corresponding SAR and ground-measured parameters. The inversion model development and sensitivity analysis were carried out to correlate ground-measured parameters with SAR parameters across various growth stages based on the categorization of NDVI values as well as for the entire growing season of the crop. Backscattering intensities are found to have good sensitivity to crop height at its intermediate growth (r $ \ge $ ≥ 0.75). Among vegetation indices, the Polarimetric Radar Vegetation Index (PRVI) showed significant sensitivity to crop height in its intermediate growth stage (r $ \approx $ ≈ 0.77). H-A-Alpha decomposition parameters displayed notable sensitivity to soil moisture in the initial and maximum growth stages of green gram. This study demonstrates the effectiveness of Sentinel-1 dual-polarized SAR parameters in monitoring green gram phenological growth stages. The parameters show substantial sensitivity to both green gram height and soil moisture. [ABSTRACT FROM AUTHOR]

Pinellia ternata (Thunb.) Breit is an important traditional Chinese medicine. In North China, conventional flat planting of P. ternate is prone to root rot during the rainy season, leading to severe yield loss. Variations in planting patterns (e.g., ridge planting) can effectively alleviate this situation. However, the relationship between planting patterns and the changes induced by rhizosphere microbiome still needs to be determined. In this study, we clarified the effect of ridge planting on the yield of P. ternata and rhizosphere microbial community using high-throughput amplicon sequencing of 16S rRNA. Field experiments showed that ridge planting could increase the yield of P. ternata by 72.69% compared with flat planting. The high-throughput sequencing results demonstrated that fungal and bacterial communities in rhizosphere siols of flat and ridge planting showed obvious difference in diversity, structure, relative abundance, and community composition. The fungal phyla Zygomycota, Basidiomycota, Glomeromycota, and the bacterial phyla Chlamydiae, Tenericutes, and Hydrogenedentes were present in a higher relative abundance in the rhizosphere of ridge planting. Adonis multivariate analysis of variance results showed that 29 bacterial genera were significantly up/down-regulated, and only 4 fungal genera were changed considerably in ridge planting soil, indicating that the bacterial community composition varied significantly between the two treatments. Correlation analysis revealed that the yield of P. ternata was positively correlated with fungal genera Emericellopsis while negatively correlated with bacterial genera Acetobacter, Iamia, and fungal genera Thielavia. Overall, this study showed that ridge cropping significantly impacts the diversity and composition of the rhizosphere microbiome. It creates an environment favorable for crop growth and can be an effective planting strategy for P. ternata in areas with irrigation and high monsoon rainfall in North China. [ABSTRACT FROM AUTHOR]

The performance of three widely used thermal radiation models, the P-1 model, the surface-to-surface (S2S) model and the Discrete-Ordinates (DO) model, were evaluated for simulation temperature in Chinses solar greenhouse. The thermal radiation models were evaluated by comparing the numerical results with experimental data at representative points in the CSG. For indoor rear wall, the indoor soil and indoor air, the models showed good agreement between the experimental data and the simulated results correspond to P1, S2S and DO respectively. This work provides information for simulate greenhouse temperature and use specific radiation models for the most suitable thermal environment for crop growth. [ABSTRACT FROM AUTHOR]

The objective of modern agriculture includes environmental sustainability, low production costs, and higher productivity. Biostimulants are composed of humic acids, hormones, algae extracts, plant growth-promoting bacteria, amino acids, and a chain of peptides. Black gram and soybean crops are of immense economic significance. The growth and yield of these crops are limited by supply of nutrients. The potentiation of the early growth of plants by treating seeds with these substances has been one of the adopted alternatives, which often promote physiological changes that stimulate growth. The field experiment was conducted with randomized complete block design and replicated four times. Effects of different biostimulants at different concentrations as foliar spray and seed treatment were examined on growth, yield parameters, and yield of black gram (TAU-1) and soybean (JS-335) during kharif 2020 and 2021. Results showed that foliar application of Quantis biostimulant @ 5 ml L−1 recorded higher growth, yield parameters, and seed yield (897 kg ha−1), biological yield (1209 kg ha−1), production efficiency (13.00 kg day−1 ha−1) in black gram and seed treatment with Epivio Energy @ 2 ml kg−1 fetched higher seed yield (1403 kg ha−1), biological yield (2914 kg ha−1), production efficiency (16.13 kg day−1 ha−1) along with higher growth and yield parameters in soybean. It can be concluded that they will help in achieving higher growth and yield of black gram and soybean. [ABSTRACT FROM AUTHOR]

ABSTRACT The Mediator complex is a multisubunit transcription coregulator that transfers regulatory signals from different transcription factors to RNA polymerase II (Pol II) to control Pol II‐dependent transcription in eukaryotes. Studies on Arabidopsis Mediator subunits have revealed their unique or overlapping functions in various aspects of plant growth, stress adaptation and metabolite homeostasis. Therefore, the utilization of the plant Mediator complex for crop improvement has been of great interest. Advances in genome editing and sequencing techniques have expedited the characterization of Mediator subunits in economically important crops such as tomato, rice, wheat, soybean, sugarcane, pea, chickpea, rapeseed and hop. In this review, we summarize recent progress in understanding the molecular mechanisms of how the Mediator complex regulates crop growth, development and adaptation to environmental stress. We also discuss the conserved and diverse functions of the Mediator complex in different plant species. In addition, we propose several future research directions to deepen our understanding of the important roles of Mediator subunits and their interacting proteins, which would provide promising targets for genetic modification to develop new cultivars with desirable agronomic traits. [ABSTRACT FROM AUTHOR]

ABSTRACT Drought is one of the most severe environmental factors limiting plant growth and crop yield, necessitating the identification of genes that enhance drought resistance for crop improvement. Through screening an ethyl methyl sulfonate‐mutagenized rice mutant library, we isolated the PEG tolerance mutant 97‐1 (ptm97‐1), which displays enhanced resistance to osmotic and drought stress, and increased yield under drought conditions. A point mutation in OsMATE6 was identified as being associated with the drought‐resistant phenotype of ptm97‐1. The role of OsMATE6 in conferring drought resistance was confirmed by additional OsMATE6 knockout mutants. OsMATE6 is expressed in guard cells, shoots and roots and the OsMATE6‐GFP fusion protein predominantly localizes to the plasma membrane. Our ABA efflux assays suggest that OsMATE6 functions as an ABA efflux transporter; mutant protoplasts exhibited a slower ABA release rate compared to the wild type. We hypothesize that OsMATE6 regulates ABA levels in guard cells, influencing stomatal closure and enhancing drought resistance. Notably, OsMATE6 knockout mutants demonstrated greater yields under field drought conditions compared to wild‐type plants, highlighting OsMATE6 as a promising candidate for improving crop drought resistance. [ABSTRACT FROM AUTHOR]

The timely and accurate acquisition of crop-growth information is a prerequisite for implementing intelligent crop-growth management, and portable multispectral imaging devices offer reliable tools for monitoring field-scale crop growth. To meet the demand for obtaining crop spectra information over a wide band range and to achieve the real-time interpretation of multiple growth characteristics, we developed a novel portable snapshot multispectral imaging crop-growth sensor (PSMICGS) based on the spectral sensing of crop growth. A wide-band co-optical path imaging system utilizing mosaic filter spectroscopy combined with dichroic mirror beam separation is designed to acquire crop spectra information over a wide band range and enhance the device's portability and integration. Additionally, a sensor information and crop growth monitoring model, coupled with a processor system based on an embedded control module, is developed to enable the real-time interpretation of the aboveground biomass (AGB) and leaf area index (LAI) of rice and wheat. Field experiments showed that the prediction models for rice AGB and LAI, constructed using the PSMICGS, had determination coefficients (R²) of 0.7 and root mean square error (RMSE) values of 1.611 t/ha and 1.051, respectively. For wheat, the AGB and LAI prediction models had R² values of 0.72 and 0.76, respectively, and RMSE values of 1.711 t/ha and 0.773, respectively. In summary, this research provides a foundational tool for monitoring field-scale crop growth, which is important for promoting highquality and high-yield crops. [ABSTRACT FROM AUTHOR]

Nitrogen is an essential nutrient for plants and a major determinant of plant growth and crop yield. Plants acquire nitrogen mainly in the form of nitrate and ammonium. Both nitrogen sources affect plant responses and signaling pathways in a different way, but these signaling pathways interact, complicating the study of nitrogen responses. Extensive transcriptome analyses and the construction of gene regulatory networks, mainly in response to nitrate, have significantly advanced our understanding of nitrogen signaling and responses in model plants and crops. In this study, we aimed to generate a more comprehensive gene regulatory network for the major crop, rice, by incorporating the interactions between ammonium and nitrate. To achieve this, we assessed transcriptome changes in rice roots and shoots over an extensive time course under single or combined applications of the two nitrogen sources. This dataset enabled us to construct a holistic co-expression network and identify potential key regulators of nitrogen responses. Next to known transcription factors, we identified multiple new candidates, including the transcription factors OsRLI and OsEIL1, which we demonstrated to induce the primary nitrate-responsive genes OsNRT1.1b and OsNIR1. Our network thus serves as a valuable resource to obtain novel insights in nitrogen signaling. [ABSTRACT FROM AUTHOR]

Introduction: Exogenous melatonin (MT) can promote horticultural crops growth under stress conditions. Methods: In this study, the effects of exogenous MT on the accumulation of selenium (Se) in grape were studied under Se stress. Results and discussion: Under Se stress, exogenous MT increased the biomass, content of photosynthetic pigments and antioxidant enzyme activity of grapevines. Compared with Se treatment, MT increased the root biomass, shoot biomass, chlorophyll a content, chlorophyll b content, carotenoids, superoxide dismutase activity, and peroxidase activity by 18.11%, 7.71%, 25.70%, 25.00%, 25.93%, 5.73%, and 9.41%, respectively. Additionally, MT increased the contents of gibberellin, auxin, and MT in grapevines under Se stress, while it decreased the content of abscisic acid. MT increased the contents of total Se, organic Se and inorganic Se in grapevines. Compared with Se treatment, MT increased the contents of total Se in the roots and shoots by 48.82% and 135.66%, respectively. A transcriptome sequencing analysis revealed that MT primarily regulated the cellular, metabolic, and bioregulatory processes of grapevine under Se stress, and the differentially expressed genes (DEGs) were primarily enriched in pathways, such as aminoacyl-tRNA biosynthesis, spliceosome, and flavonoid biosynthesis. These involved nine DEGs and nine metabolic pathways in total. Moreover, a field experiment showed that MT increased the content of Se in grapes and improved their quality. Therefore, MT can alleviate the stress of Se in grapevines and promote their growth and the accumulation of Se. [ABSTRACT FROM AUTHOR]

Studies are being conducted to develop strategies to reduce the adverse effects of salinity stress. In the present study, it was aimed to determine the interactive effects of salinity stress with biochar on plant growth-the physiological and biochemical attributes of forage peas (Pisum sativum ssp. arvense L.). Salt applications were carried out with irrigation water at concentrations of 0, 25, 50, 75, and 100 mM NaCl. The experiment was conducted using a randomized complete block design with three applications [control: 0 (B0), 2.5% biochar (B1), and 5% biochar (B2)], five salt doses [0 (S0), 25 (S1), 50 (S2), 75 (S3), and 100 (S4) mM NaCl], and three replications, arranged in a 3 × 5 factorial arrangement. In the salt-stressed environment, the highest plant height (18.75 cm) and stem diameter (1.71 mm) in forage pea seedlings were obtained with the application of B1. The root fresh (0.59 g/plant) and dry weight (0.36 g/plant) were determined to be the highest in the B1 application, both in non-saline and saline environments. A decrease in plant chlorophyll content in forage pea plants was observed parallel to the increasing salt levels. Specifically, lower H2O2, MDA, and proline content were determined at all salt levels with biochar applications, while in the B0 application these values were recorded at the highest levels. Furthermore, in the study, it was observed that the CAT, POD, and SOD enzyme activities were at their lowest levels at all salt levels with the biochar application, while in the B0 application, these values were determined to be at the highest levels. There was a significant decrease in plant mineral content, excluding Cl and Na, parallel to the increasing salt levels. The findings of the study indicate that biochar amendment can enhance forage peas' growth by modulating the plant physiology and biochemistry under salt stress. Considering the plant growth parameters, no significant difference was detected between 2.5% and 5% biochar application. Therefore, application of 2.5 biochar may be recommended. [ABSTRACT FROM AUTHOR]

The information of the temporal and spatial distribution of irrigation events is of great significance for protecting the cultivated land quality and coping with climate change. Irrigation information can traditionally be captured using farmland water conservancy construction and field investigation. However, there is a great variation in the time and space of irrigation information from the different regions, due to the various water sources, planting structures, irrigation technology, water transmission, and measurement facilities. At the same time, remote sensing can be expected to timely and accurately monitor the ground surface all day. The previous research focused mainly on the crop growing season without considering the influence of different vegetation coverage on the irrigation remote sensing signals. Therefore, it is very necessary to detect the year-round events of farmland irrigation using remote sensing, particularly for different vegetation coverage. In this study, the farmland irrigation events were detected in the arid areas of northwest China using remote sensing. The research area was taken as Daman Super Station in Ganzhou District, Zhangye City, Gansu Province, China. The comparison was also performed on the conventional, near-infrared band reflectivity with MOD09GA optical remote sensing data, segmented PDI differential threshold model, microwave moisture products, and the combination of PDI differential threshold and microwave moisture products. An optimal scheme was achieved in the remote sensing detection suitable for all-year farmland irrigation events with different vegetation coverage. The performance was finally tested from the space-time dimension. The results show that there was a strong negative linear correlation between the PDI difference threshold and NDVI. Thus, a segmented PDI difference threshold model was constructed in 2016; The highest accuracy of irrigation events was achieved in the microwave moisture products combined with the PDI differential threshold. There was a close relationship between the soil surface water after irrigation and the response of vegetation to the soil water supply. The content of soil water reached saturation after irrigation. But the soil surface water continued to decrease and then returned to the pre-irrigation water content in about three days, due mainly to the evaporation. Furthermore, the microwave data was more sensitive to the surface soil moisture than the vegetation, indicating the dominant soil dielectrics. The high time resolution was realized to timely and effectively capture the occurrence of irrigation events. However, the electromagnetic wave energy was attenuated by crops with the increase in crop coverage. The water status of the soil surface failed to be monitored. Therefore, it was suitable for the case of no crops or low vegetation coverage. Moreover, the time resolution was low in the optical data. The content of soil surface water was captured in time, but it was also sensitive to the vegetation. Plant growth was subjected to water stress before irrigation. Once the soil water supply was sufficient, a significant difference was found in the vegetation water content and leaf structure, indicating the vegetation spectral in the near-infrared and red bands before irrigation. Optical data was used to indirectly monitor the irrigation events using these spectral. In particular, microwave water products were compensated for the irrigation monitoring the vigorous growth of crops or high coverage. The best performance was achieved in dry years, followed by normal years. The regional inversion showed that the distribution map of irrigation times made by microwave moisture products combined with the PDI differential threshold was consistent with the actual situation. The farmland with low irrigation capacity was distributed mainly on the edge of Zhangye Basin, which was far from the river, indicating the greater risk of climate change. Therefore, it was suitable to use microwave moisture products combined with PDI differential threshold in the detection and application of irrigation events in arid areas of northwest China. The findings can provide a theoretical basis for the efficient acquisition of actual irrigation information, cultivated land quality protection, and response to climate change. [ABSTRACT FROM AUTHOR]

Profiled metabolites from 5-donor plants; Mucuna pruriens, L. (Mc), Cymbopogon nardus L. (C), Desmodium uncinatum Jacq. DC. (D), (Zea mays, L. (Mz) and New Rice for Africa 1 (NERICA 1) were Terpenoids and Phenols. NERICA 1 (R) is a cross of Oryza sativa and Oryza glaberrima. C produced 10 terpenoids + 1 ester, R and D each released 6 Terpenoids + 3 Phenols. Mc produced 2 Terpenoids + 4 Phenols and 5 Terpenoids + 4 Phenols were profiled from Mz. We adopted a completely randomised block design with 3 replicates and eight combinations of 3 leaf+ stem+root powders namely; Mc+D+C, Mz+Mc+C, RDC, Mc+D+C, Mz+R+Mc, Mz+D+Mc, R+Mc+C and Mz+R+C, mixed with 1.5 kg soil (15, 30 and 45 %, w/w). Controls had no powders. Mixtures were placed in plastic pots and 20 seeds of Biden pilosa L. were sown in each pot. Every 2 days, 200 ml of water was applied to all treatments for 60 days. Mz+Mc controlled B. pilosa and volunteer weeds relative to R+C and C+D. Weed germination and biomass reduced with increased powder doses. Volunteer monocot weeds were more inhibited than dicots. High potential exists for functional allelopathy using allelochemicals in the donor plants to develop bio-herbicides. [ABSTRACT FROM AUTHOR]

Maize, being a nutritious and multipurpose crop requires proper nutrition (N, P, and K) and standardization of sowing time to enhance productivity and profitability under changing climate scenario. Therefore, a field experiment was conducted during monsoon season of 2021 and 2022 at agricultural farm, Lovely Professional University, Punjab, in split plot design with four fertilizer levels (125, 100, 75, and 50% recommended dose of fertilizers; RDF) in main plots and four sowing dates (15 and 30 of June and July) in subplots, replicated thrice. Maize sown on 30 June and fertilized with 125% RDF ensured higher growth attributes throughout crop growth. Besides, comparatively greater yield attributes as well as grain yield (8.22 t ha−1), stover yield (11.86 t ha−1), and harvest index (40.9%) were observed from that treatment combination, and it also recorded higher quality protein and nutrient uptakes but less residual soil fertility. Maize grown on 30 July under application of 50% RDF was the poorest yielder. Further, production economics and energy analysis showed similar trend and indicated that maize grown on 30 June under application of 125% RDF recorded maximum gross return (₹168275 ha−1), net return (₹109565 ha−1), and benefit-cost ratio (2.87) as well as energy output (269084 MJ ha−1) and net energy gain (250750.3 MJ ha−1). [ABSTRACT FROM AUTHOR]

Summary: Boron (B) is crucial for plant growth and development. B deficiency can impair numerous physiological and metabolic processes, particularly in root development and pollen germination, seriously impeding crop growth and yield. However, the molecular mechanism underlying boron signal perception and signal transduction is rather limited.In this study, we discovered that CPK10, a calcium‐dependent protein kinase in the CPK family, has the strongest interaction with the boron transporter BOR1. Mutations in CPK10 led to growth and root development defects under B‐deficiency conditions, while constitutively active CPK10 enhanced plant tolerance to B deficiency.Furthermore, we found that CPK10 interacted with and phosphorylated BOR1 at the Ser689 residue. Through various biochemical analyses and complementation of B transport in yeast and plants, we revealed that Ser689 of BOR1 is important for its transport activity.In summary, these findings highlight the significance of the CPK10‐BOR1 signaling pathway in maintaining B homeostasis in plants and provide targets for the genetic improvement of crop tolerance to B‐deficiency stress. See also the Commentary on this article by Huang, 243: 1633–1635. [ABSTRACT FROM AUTHOR]

Millions of people around the world have iron deficiency, which is one of several important nutritional disorders. Despite recent advances in the prevention and treatment of these deficiencies, it is estimated that over two billion individuals worldwide are at risk for vitamin A, iodine, and/or iron insufficiency. Many major health concerns can result from micronutrient deficits. Lack of iron, folate, B12, and A may cause anemia. Anemia causes weariness, weakness, breathlessness, and disorientation due to low red blood cell or hemoglobin levels. Biofortification, a way to make staple foods with more micronutrients, could help lower malnutrition. Plant growth-promoting rhizobacteria (PGPR) can boost the amount of iron in edible plant tissues by making it easier for iron to get to those tissues. A pot experiment was conducted at ISES, University of Agriculture, Faisalabad, to evaluate the impact of well-characterized rhizobacterial strains (Acinetobacter calcoaceticus and Bacillus simplex) alone and in combination with L-tryptophan (a plant growth regulator (PGR)) on agronomic, tuber, and nutritional attributes of potato. Results revealed that inoculation significantly enhanced the iron content of potato tubers compared to control plants. When rhizobacteria, L-tryptophan, and iron were added to the soil, the number of tubers grew by 68.24% compared to the control plants where neither PGPR nor PGR was applied. Biofortifying potatoes with microbes can help people get more micronutrients, especially in countries with few resources. These results showed that PGPR can help plants get more Fe from the soil if more Fe is added to the soil. These results provide support for the hypothesis that microbial aided biofortification in crops might reduce human micronutrient insufficiency, particularly in areas with little resources. [ABSTRACT FROM AUTHOR]

With the development of point cloud-based telemetry technology in recent years, the point cloud data of large field scenes acquired by various sensors have been applied to farmland boundary division, crop growth monitor, area surveying, etc. However, the large field point cloud will cost huge amounts of computational resources in the following transmission, storage and processing, which make it more important to simplify the field point cloud appropriately. In light of limitation of existing algorithms in point cloud simplification of large field scenes, we propose a novel feature-preserved simplification algorithm for large field point cloud data. By introducing the average local entropy as the threshold for area division, our algorithm effectively solves the problem of fuzzy boundary division, as well as preserving the field features and reducing the simplification errors. In view of the problem that the evaluation of current simplification algorithm is mainly focused on qualitative assessment, a quantitative evaluation index for the point cloud simplification is proposed by employing the point-average local entropy, which takes both model retention and simplification efficiency into account. Finally, comparable experiments are performed on four sets of point clouds. The results show that, compared with the statistics of six typical algorithms, the proposed algorithm increases the local entropy by 0.029%, 0.146% and 0.088% on our datasets, and increases 0.029% on the public dataset. The method accurately evaluates the simplification effect. Additionally, the surface area change rate is also used to further evaluate the performance of proposed algorithm, and the quantitative evaluation index is lower than others, which verify the advantages of proposed algorithm in feature protection and large field simplification. [ABSTRACT FROM AUTHOR]

The growing population in the face of water scarcity inevitably necessitates the quest for alternative sources of irrigation water, which integrates them with irrigation strategies for improved agricultural productivity to meet the Sustainable Development Goals. A three-year field experiment was conducted in 2017, 2018, and 2019 to investigate the effect of water quality (reclaimed water (RW) and clean water (CW)), irrigation techniques (subsurface drip irrigation (SDI) and furrow irrigation (FUI)), irrigation methods (full irrigation (FI) and alternate partial root-zone irrigation (APRI) (70% ETc)), and their interactions on the fresh fruit yield (FY), irrigation water use efficiency (IWUE), and nitrogen use efficiency (NUE) of tomatoes. Further, electrical conductivity (EC), pH, and organic matter (OM) of soil were evaluated. The experiments were undertaken over three growing spring seasons in a greenhouse at the Chinese Academy of Agricultural Sciences in Henan Province, China. Throughout the three years of this study, the yield, the IWUE, and the NUE values of all treatments under RW were higher than those corresponding values under CW. The trend was the same under SDI as it was under FUI. Statistical analyses revealed that there was no significant effect (P > 0.05) of water quality, irrigation technique, and irrigation methods on the soil EC, pH, and OM over the three years. In addition, the interaction between the different experimental factors over the three years of the study was not significant. In conclusion, the application of RW under SDI can result in saving CW and increasing productivity without any negative effect on the investigated soil properties. Furthermore, when RW-SDI is used in conjunction with APRI, it can result in increasing IWUE. [ABSTRACT FROM AUTHOR]

Crop classification is important for monitoring crop growth and ensuring national food security. Crop plots have a complex spatial planting structure with a certain degree of fragmentation, which leads to mixing of different crops with unclear borders, in turn affecting the ability of the classification model to recognize and distinguish between crops. To address the above problems, this paper proposes a crop classification method based on multi-source optical remote sensing data, which can extract crop information more accurately by taking advantage of the multi-spectral features and different spatial resolutions of different optical data. Firstly, spectral bands and vegetation indexes are extracted from Sentinel-2B and Jilin Gaofen (JLGF02B). Then, a Gemel Convolutional Neural Network (G-CNN) is constructed to fully mine and integrate the feature information at different scales for crop classification. Finally, the method is compared with classic deep learning algorithms. The result shows that the proposed G-CNN algorithm gets the best results with an overall accuracy OA of 96.5% and a Kappa coefficient of 94.7%. The G-CNN model provides a new idea and method for crop classification using multi-scale optical data. [ABSTRACT FROM AUTHOR]

The use of supplemental light has been proven to be effective in growing plants in controlled environments. These light sources have been proven effective in influencing both, developmental and phytochemical pathways. Among artificial/supplemental lights, light emitting diodes (LEDs) offer several advantages and unique properties. Consequently, the use of this novel technology has gained popularity in indoor plant cultivation. In this review, recent achievements and progress related to the preharvest application of LED technology are discussed, starting from the selection of appropriate LED wavelengths to varying photoperiod to enhance crop growth, yield, and phytochemical concentrations. The review further summarises the recent developments in the use of LED technology for cultivating crops hydroponically and the economic implications of LED usage is briefly discussed. Lastly, the future prospects and research directions are presented. [ABSTRACT FROM AUTHOR]

Far-red lights (FRs), with a wavelength range between 700 and 800 nm, have substantial impacts on plant growth, especially horticultural crops. Previous studies showed conflicting results on the effects of FRs on vegetable growth and quality. Therefore, we conducted a meta-analysis on the influence of FRs on vegetable growth, aiming to provide a comprehensive overview of their effects on the growth and nutritional indicators of vegetables. A total of 207 independent studies from 55 literature sources were analyzed. The results showed that FR treatment had significant effects on most growth indicators, including increasing the fresh weight (+25.27%), dry weight (+21.99%), plant height (+81.87%), stem diameter (+12.91%), leaf area (+18.57%), as well as reducing the content of chlorophyll (−11.88%) and soluble protein (−11.66%), while increasing soluble sugar content (+19.12%). Further subgroup analysis based on various factors revealed significant differences in the effects of FR on different physiological indicators, such as FR intensity, plant species, duration of FR exposure, and the ratio of red light to FR. In general, moderate FR treatment is beneficial for vegetable growth. This study provides important references and guidelines for optimizing the application of FR in the future. [ABSTRACT FROM AUTHOR]

Fluxapyroxad, a persistent fungicide in soil, was investigated for differences in residue dissipation in Chinese cabbage and spring scallion through the application methods of soil, foliar, and systemic treatment. Soil application of 0.4% granule fluxapyroxad resulted in residues up to 0.09 mg kg−1 in the scallion, while it did not contribute to the residues in the harvested cabbage. The 50% dissipation time (DT50) of fluxapyroxad in the scallion was 6.8 days. The residues from systemic treatment were highly correlated with foliar application in both the cabbage and the scallion, and the initial residue and DT50 values were similar for foliar and systemic treatments. In comparing the residues from the systemic treatments between the two crops, the initial residue was 3.11 and 0.22 mg kg−1 in the cabbage and the scallion after the systemic treatment, respectively. The DT50 values were 2.6 and 12.2 days in the cabbage and the scallion, respectively. The theoretical dilution effect due to crop growth was higher for the cabbage (4-fold) than for the scallion (1.2-fold), and the half-lives of fluxapyroxad without considering the dilution effect were 6.4 days in the cabbage and 17.8 days in the scallion. Thus, the residue difference was drastically reduced after 14 days from the last treatment. [ABSTRACT FROM AUTHOR]

Monitoring future irrigation water demand as a part of agricultural interventions is crucial to ensure food security. In this study, the impact of climate change on paddy cultivation in Brunei is investigated, focusing on the Wasan rice scheme. This research aims to project irrigation water requirement (IWR) and crop water requirement (CWR) or the main and off season using the WEAP-MABIA model. Historical data analysis over the past 30 years and future projections up to 2100 are employed to assess potential impacts. An ensemble of statistically downscaled climate models, based on seven CMIP6 GCMs under shared socioeconomic pathways (SSPs) (SSP245, SSP370, and SSP585), was utilised to project the IWR and CWR. Using downscaled CMIP6 data, three future periods were bias-corrected using quantile delta mapping (QDM) for 2020–2046 (near future), 2047–2073 (mid future), and 2074–2100 (far future). The WEAP-MABIA model utilises a dual crop coefficient approach to evaluate crop evapotranspiration (ETc), a critical factor in computing IWR. Results indicate that changes in future temperatures will lead to higher average ETc. Consequently, this results in elevated demands in irrigation water during the off season, and it is especially prominent in high-emission scenarios (SSP370 and SSP585). While the main season experiences a relatively stable or slightly increasing IWR trend, the off season consistently shows a decreasing trend in IWR. Moreover, the off season benefits from substantial rainfall increases, effectively reducing IWR despite the rise in both maximum and minimum temperatures. This study also highlights some recommendations for implementing possible improvements in irrigation management to address the effects of climate change on rice cultivation in the region. Future investigation should focus on enhancing crop yield predictions under climate change by integrating a dynamic crop growth model that adjusts for changing crop coefficient (Kc) values. [ABSTRACT FROM AUTHOR]

Croplands have been the focus of substantial investigation due to their considerable potential for sequestering carbon. Understanding the potential for soil organic carbon (SOC) sequestration and necessary management strategies will be enabled with accurate process-based models. Accurately representing crop growth and agricultural practices will be critical for realistic SOC modeling. The MEMS 2 model incorporates a current understanding of SOC formation and stabilization, measurable SOC pools, and deep SOC dynamics and is seen as a highly promising tool to inform management intervention for SOC sequestration. Thus far, MEMS 2 has been developed to represent grasslands. In this study, we further developed MEMS 2 to model annual grain crops and common agricultural practices, such as irrigation, fertilization, harvesting, and tillage. Using four Ameriflux sites, we demonstrated an accurate simulation of crop growth and development. Model performance was strong for simulating aboveground biomass (index of agreement [d] range of 0.89–0.98) and green leaf area index (d from 0.90 to 0.96) across corn, soybean, and winter wheat. Good agreement with observations was also achieved for net ecosystem CO2 exchange (d from 0.90 to 0.96), evapotranspiration (d from 0.91 to 0.94), and soil temperature (d of 0.96), while discrepancy with the available soil water content data remain (d from 0.14 to 0.81 at four depths to 100 cm). While we will continue model testing and improvement, MEMS 2 (version 2.14) has now demonstrated its ability to effectively simulate the growth of common grain crops and practices [ABSTRACT FROM AUTHOR]

Microalgae have unique properties that allow them to thrive in unconventional spaces, making them suitable for areas that are not normally suitable for crop growth. This is due to their ability to multiply rapidly, grow easily and adapt to different environments at low cost. As a result, the present study aims to analyze the potential of microalgae as a source of agricultural nutrition, as well as the health benefits they can provide. The following research was conducted on an experimental scale using a closed system (photobioreactor) for the cultivation of microalgae, its physicochemical characterization of the cultures and dry biomass. It can be observed the percentages of 11 N; 1.4 P; 0.3 K and its micronutrients are essential for plant growth, since microalgae-based biofertilizers are considered as a sustainable, cost-effective and environmentally friendly alternative to chemical fertilizers. The use of microalgae biomass as a biofertilizer in agriculture can increase fertility, reduce soil erosion and nutrient loss, and improve soil quality over time. It also benefits plants, vegetables and greens, as it contains nitrogen, phosphorus and potassium, which are necessary for growth. [ABSTRACT FROM AUTHOR]

The current investigation endeavors to evaluate the prevalence of stubble burning in India, with a special focus on the state of Punjab. The study emphasizes the enormity of stubble burning by examining farm fire incidents, pollutant emissions, its detrimental impacts. It supports the effective management of crop residue along with proposing alternatives to stubble burning. The article conveys the message that stubble burning can result in deleterious effects on the environment, human health, crop growth, natural ecosystems, visibility, and physical infrastructure. The key solutions lie in education, functional literacy, a heightened awareness of environmental laws, rights and duties, stringent governance, and socially responsible public, promoting adherence to the National Green Tribunal's guidelines for managing crop residue and enlightening farmers about the ill effects of stubble burning on animal, soil, human health, crop biodiversity, and climate change. The available data of districts of Punjab indicates the recent waning trend in stubble burning, thus heralding a positive indication of environmental preservation. Decreased stubble burning is the reward of untiring government initiatives, support and subsidies, awareness programs, advanced research and technology, and enforcement of stringent regulations combined with recognition of the deleterious environmental impacts of stubble burning. This research article indicates that there is still a need for efforts to be made to eliminate stubble burning altogether. [ABSTRACT FROM AUTHOR]

Sustainable and substantial crop production is essential globally, especially considering the increasing population. To achieve this, selecting appropriate crops and applying necessary fertilizers are pivotal for ensuring satisfactory crop growth and productivity. Farmers have relied heavily on intuition when choosing which crops to cultivate and suitable fertilizers to use in a given season. However, this traditional approach often needs to consider the significant impact of current environmental and soil conditions on crop growth and yield. Overlooking these factors can have far-reaching consequences, impacting not just individual farmers and their households but also the entire agricultural sector. The integration of machine learning offers a promising avenue for addressing these challenges and providing practical solutions. The core contribution of this research lies in proposing a unified framework termed Machine Learning-enabled Crop Selection and Fertilizer Recommendation (ML-CSFR). This framework's primary objective is to predict appropriate crops accurately and subsequently suggest corresponding fertilizers based on specific agricultural conditions. The initial phase involves the identification of proper crops for individual farmlands, considering local input variables. This phase employs artificial neural networks (ANN) to filter crops effectively using the available choices. The next phase utilizes soil and environmental parameters to anticipate the optimal fertilizer for the selected crops. This phase leverages the XGBoost (XGB) model to predict the most suitable fertilizers accurately. This two-phase approach ensures a comprehensive and effective recommendation system for enhancing agricultural outcomes. Experimental results demonstrate the effectiveness of this framework, achieving an accuracy score of 99.10% using ANN and 97.66% for XGB. The framework's capability to deliver tailored recommendations for individual farms and its potential to integrate real-time sensor data positions it as an effective tool for improving agricultural decision-making. [ABSTRACT FROM AUTHOR]

In today's society, keeping our gardens attractive is complex, especially if you need more time to care for them. This can cause the plant to wilt if it is not watered occasionally to keep the soil moist. In summer, this problem tends to get worse because the temperature tends to rise and reach high degrees. The objective is to design an automatic and manual irrigation system with a humidity detector through hardware programming and free software to solve this. The necessary components will be identified and selected, humidity thresholds will be established, and the adoption of technologies such as internet of things (IoT), Arduino, and humidity sensors will be promoted to solve the problem in automated irrigation systems. The technical specifications of the components are described, and the circuit design is presented. A programming algorithm will be developed to control the frequency and duration of irrigation, as well as the state of the water pump. Implementing the automated system will allow precise water supply control, contributing to the healthy growth of plants and crops in green areas. [ABSTRACT FROM AUTHOR]

BACKGROUND: Aerobic fermentation always suffers from nitrogen loss and low humification degree. The objective of this study was to investigate the promotion of phosphate buffer on organic matter degradation and precursor polymerization into humus (HS) in aerobic fermentation, and to analyze the key roles played by different precursors. In order to achieve this, sludge aerobic fermentation tests were conducted on control (CK), phosphate buffer addition treatment (KP) and potassium chloride addition treatment (K). RESULTS: The HS content of KP treatment exhibited a notable increase compared to the CK and K treatments, with a maximum increase of 38.29%. In addition, phosphate addition improved the nitrogen retention capacity and the complexity of the HS structure. Phosphate buffer enhanced both the polyphenol and Maillard humification pathways by promoting the condensation of precursors (polysaccharides, reducing sugars, polyphenols, amino acids and proteins). Among these precursors, reducing sugars, amino acids and proteins were identified as the key driving precursors of phosphate. CONCLUSIONS: Phosphate, as an exogenous additive to the fermentation system, reduces nitrogen loss while promoting precursor polymerization to form HS, which benefits the improvement of soil fertility and crop growth upon organic fertilizer application. © 2024 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

BACKGROUND: Low recovery of conventional fertilizers remains a significant bottleneck for maize production globally. In particular, with phosphate fertilization, zinc (Zn) is prone to precipitation in soil, reducing recovery of both phosphorus (P) and Zn by maize. RESULTS: The present study was designed to investigate the synergistic effect of zinc oxide (ZnO) nanoparticles (NPs) and P on maize crop growth, yield, and nutrient uptake under ZnO seed coating and foliar application in a randomized complete block design. However, plants were subjected to two ZnO NPs levels (0.5 and 12 kg ha−1) amended with two P levels (45 and 90 kg ha−1). ZnO NPs, especially in the form of foliar application, with a P dose of 90 Kg ha−1 significantly (P < 0.05) improved maize crop growth, yield, and nutrient uptake compared with control. In comparison with the control group, plants grown in these conditions absorbed higher levels of Zn and P. Zn uptake rose to 16.34 g ha−1, 137.88 g ha−1, and 166.89 g ha−1 in roots, grains, and stover respectively, and P uptake increased to 0.80 mg kg−1, 10.066 mg kg−1, and 12.17 mg kg−1 respectively. Additionally, seed emergence rate, plant height, and cob length increased by up to 2%, 1177 cm2, and 3.3 cm respectively compared with control. Furthermore, Zn use efficiency was increased up to 38.55% in ZnO NPs foliar application. CONCLUSIONS: Application of ZnO NPs at 0.5 kg ha−1 in the form of foliar application with 90 kg ha−1 P dose produced a more pronounced increment in the parameters studied than ZnO NPs seed coating did. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Optimizing biochar application is vital for enhancing crop production and ensuring sustainable agricultural production. A 3‐year field experiment was established to explore the effects of varying the biochar application rate (BAR) on crop growth, quality, productivity and yields. BAR was set at 0, 10, 50 and 100 t ha−1 in 2018; 0, 10, 25, 50 and 100 t ha−1 in 2019; and 0, 10, 25 and 30 t ha−1 in 2020. Crop quality and growth status and production were evaluated using the dynamic technique for order preference by similarity to ideal solution with the entropy weighted method (DTOPSIS‐EW), principal component analysis (PCA), membership function analysis (MFA), gray relation analysis (GRA) and the fuzzy Borda combination evaluation method. RESULTS: Low‐dose BAR (≤ 25 t ha−1 for cotton; ≤ 50 t ha−1 for sugar beet) effectively increased biomass, plant height, leaf area index (LAI), water and fertility (N, P and K) productivities, and yield. Biochar application increased the salt absorption and sugar content in sugar beet, with the most notable increases being 116.45% and 20.35%, respectively. Conversely, BAR had no significant effect on cotton fiber quality. The GRA method was the most appropriate for assessing crop growth and quality. The most indicative parameters for reflecting cotton and sugarbeet growth and quality status were biomass and LAI. The 10 t ha−1 BAR consistently produced the highest scores and was the most economically viable option, as evaluated by DTOPSIS‐EW. CONCLUSION: The optimal biochar application strategy for improving cotton and sugar beet cultivation in Xinjiang, China, is 10 t ha−1 biochar applied continuously. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

AbstractWidespread multi-nutrient deficiency and water stress are the major yield-limiting factors in alkaline soil. Inorganic nutrient application in alkaline soil is ineffective for green gram due to the antagonistic interaction with alkalinity. In this setting, a balanced supply of foliar nutrients is a viable strategy to improve green gram nutrient utility and productivity in an alkaline environment. Field experiments were conducted for two consecutive years during Rabi 2019 and 2020. Water-soluble NPK (2%), 1% multi-micronutrient and 1% humic acid foliar nutrients in five combinations were applied at 30 and 45 days after sowing. The effect of multi-nutrient and humic acid foliar spray on green gram growth, physiology, nutrient accumulation, and yield were measured by adopting the standard protocol. Green gram seed yield increased by 20 per cent on the application of water soluble fertilizers 2% and liquid multi-micronutrients 1% with 1% humic acid. The same set of treatments augmented leaf area (428 cm2 plant−1), dry matter production (2087 kg ha−1), SPAD value (46.4), nitrogen (36.6 kg ha−1), phosphorous (4.9 kg ha−1), potassium (46.5 kg ha−1), and micronutrients uptake. Foliar application of multi-nutrients reduces nutrient stress and aids in the promotion of green gram crop growth and production in alkaline soil. [ABSTRACT FROM AUTHOR]

Saline soils pose significant challenges to global agricultural productivity, hindering crop growth and efficiency. Despite various mitigation strategies, the issue persists, underscoring the need for innovative and sustainable solutions. One promising approach involves leveraging microorganisms and their plant interactions to reclaim saline soils and bolster crop yields. This review highlights pioneering and recent advancements in utilizing multi-traits Trichoderma and Bacillus species as potent promoters of plant growth and health. It examines the multifaceted impacts of saline stress on plants and microbes, elucidating their physiological and molecular responses. Additionally, it delves into the role of ACC deaminase in mitigating plant ethylene levels by Trichoderma and Bacillus species. Although there are several studies on Trichoderma-Bacillus, much remains to be understood about their synergistic relationships and their potential as auxiliaries in the phytoremediation of saline soils, which is why this work addresses these challenges. [ABSTRACT FROM AUTHOR]

Sorghum is a crop that is susceptible to low temperatures. However, due to the extreme global temperatures, new genotypes have been bred to adapt better to cold climates. Therefore, to better understand the metabolic and molecular differences that may contribute to the improved cold tolerance of these genotypes, it is essential to investigate whether specific molecules play a key role in determining the degree of sorghum tolerance. Furthermore, identifying genes associated with cold tolerance may assist in the development of more robust and productive sorghum cultivars. Here, we analysed the changes in selected elements of the antioxidant metabolism, genes, and miRNAs in two sorghum varieties with different degrees of tolerance to cold stress at different times of exposure to cold stress. The high expression of SbCBF6 in response to cold treatment in early time in the tolerant variety suggests its involvement in the cold response, thereby facilitating a better adaptation of the plant to cold conditions. MiR398 and miR394 showed contrasting behaviour in both varieties in low temperatures, resulting in interesting miRNAs in the degree of sorghum tolerance. MiR319, a marker for the selection of cold‐tolerant cultivars, presents a decrease, suggesting that the better capacity to repress the expression of several members of TCP family of transcription factor genes might be involved in better tolerance to cold stress. This study shows the changes of molecules of interest in response to cold stress in sorghum, marking the difference between cold‐susceptible and cold‐tolerant sorghum plants, which can contribute to selecting tolerant genotypes, allowing new strategies to enhance crop productivity and food security, in regions where low temperatures significantly constrain crop growth and yield. [ABSTRACT FROM AUTHOR]

This study evaluated the impact of differential sowing windows and improved weed management strategies on weed dynamics, productivity, and economic viability of direct drum seeded rice (Oryza sativa L.) in the temperate agro-ecosystem of Kashmir. A two-year field experiment was conducted utilizing a split-plot design with two sowing dates (May 10 and June 3) as main plots and six weed management practices as sub-plots. The earlier sowing date (May 10) resulted in significantly enhanced leaf area index, crop growth rate, relative growth rate, net assimilation rate, and grain and straw yields compared to the later sowing (June 3). Among weed management treatments, four mechanized conoweedings (equivalent to weed-free conditions) and sequential application of bensulfuron methyl + pretilachlor (60 and 600 g a.i. ha−1) as pre-emergence followed by 2,4-D (0.75 kg a.i. ha−1) as post-emergence demonstrated superior efficacy in weed suppression and augmentation of crop growth parameters and yield attributes. These treatments also exhibited the lowest weed index and highest benefit–cost ratio. The May 10 sowing, coupled with efficacious weed control measures, significantly reduced weed density and biomass while concomitantly improving nutrient uptake and economic returns. The results indicate that adopting a May 10 sowing date for direct seeded rice, in conjunction with either four conoweedings or the aforementioned sequential herbicide application, can optimize agronomic productivity and economic profitability under the temperate conditions of Kashmir. The study aided in choosing the best sowing window and efficient weed management strategy for attaining higher productivity and profitability of direct seeded rice in temperate conditions. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a key variable for monitoring crop growth. Compared with traditional measurement methods, unmanned aerial vehicle (UAV) remote sensing offers a cost-effective and efficient approach for rapidly obtaining crop LAI. Although there is extensive research on rice LAI estimation, many studies suffer from the limitations of models that are only applicable to specific scenarios with unclear applicability conditions. In this study, we selected commonly used RGB and multispectral (Ms) data sources, which contain three channels of color information and five multi-band information, respectively, combined with five different spatial resolutions of data at intervals of 20–100 m. We evaluated the effectiveness of models using single- and multi-feature variables for LAI estimation in rice. In addition, texture and coverage features other than spectra were introduced to further analyze their effects on the inversion accuracy of the LAI. The results show that the accuracy of the model established with multi-variables under single features is significantly higher than that of the model established with single variables under single features. The best results were obtained using the RFR (random forest regression) model, in which the model's R2 is 0.675 and RMSE is 0.886 for multi-feature VIs at 40 m. Compared with the analysis results of Ms and RGB data at different heights, the accuracy of Ms data estimation results fluctuates slightly and is less sensitive to spatial resolution, while the accuracy of the results based on RGB data gradually decreases with the increase in height. The estimation accuracies of both Ms and RGB data were improved by adding texture features and coverage features, and their R2 improved by 9.1% and 7.3% on average. The best estimation heights (spatial resolution) of the two data sources were 40 m (2.2 cm) and 20 m (0.4 cm), with R2 of 0.724 and 0.673, and RMSE of 0.810 and 0.881. This study provides an important reference for the estimation of rice LAI based on RGB and Ms data acquired using the UAV platform. [ABSTRACT FROM AUTHOR]

In contrast to prolonged exposure to high temperatures, investigating short-term high-temperature stress can provide insights into the impact of varying heat stress durations on plant development and soil nutrient dynamics, which is crucial for advancing ecological agriculture. In this study, five heating temperatures were set at 200°C, 250°C, 300°C, 350°C, and 400°C, along with five heating time gradients of 6s, 10s, 14s, 18s, and 20s, including a control. A total of 26 treatment groups were analyzed, focusing on maize growth parameters and soil indicators. Principal component analysis was used for comprehensive evaluation. The results showed that high-temperature treatments with different heating times significantly influenced maize growth and soil properties. For instance, the treatment of 300°C+6s resulted in the longest total root length, while 200°C+6s led to the highest average root diameter. Plant height and leaf length were notably increased with the treatment of 400°C+6s. Most treatments resulted in decreased soil pH and organic matter content. Notably, the treatment of 350°C+16s showed the highest available phosphorus content, reaching 24.0 mg/kg, an increase of 4.5 mg/kg compared to the control. The study found that the average levels of active organic carbon and peroxidase were 1.26 mg/g and 3.91 mg/g, respectively. Additionally, the average mass fractions of clay, silt, and sand particles were 8.99%, 66.75%, and 24.26%, respectively. Through principal component analysis, six principal components were able to extract 19 indicators from the 26 treatments, covering 86.129% of the information. It was observed that 16 treatment methods performed better than the control in terms of soil comprehensive quality. The optimal treatment temperature and time identified for improving soil physicochemical properties and crop growth were 300°C+6s. These findings can be used to guide agricultural management and soil improvement practices, ultimately enhancing field productivity and providing valuable insights for sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Understanding the complex interactions between genotype-environment dynamics is fundamental for optimizing crop improvement. However, traditional phenotyping methods limit assessments to the end of the growing season, restricting continuous crop monitoring. To address this limitation, we developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking. Leveraging multi-scan terrestrial laser scanning (TLS), we captured high-resolution 3D LiDAR data in a cotton breeding field across various stages of the growing season to generate fourdimensional (4D) crop models, seamlessly integrating spatial and temporal dimensions. Our registration procedure involved an initial pairwise terrain-based matching for rough alignment, followed by a bird's-eye view adjustment for fine registration. Point clouds collected throughout nine sessions across the growing season were successfully registered both spatially and temporally, with average registration errors of approximately 3 cm. We used the generated 4D models to monitor canopy height (CH) and volume (CV) for eleven cotton genotypes over two months. The consistent height reference established via our spatiotemporal registration process enabled precise estimations of CH (R² = 0.95, RMSE = 7.6 cm). Additionally, we analyzed the relationship between CV and the interception of photosynthetically active radiation (IPARf), finding that it followed a curve with exponential saturation, consistent with theoretical models, with a standard error of regression (SER) of 11%. In addition, we compared mathematical models from the Richards family of sigmoid curves for crop growth modeling, finding that the logistic model effectively captured CH and CV evolution, aiding in identifying significant genotype differences. Our novel TLS-based digital phenotyping methodology enhances precision and efficiency in field phenotyping over time, advancing plant phenomics and empowering efficient decision-making for crop improvement efforts. [ABSTRACT FROM AUTHOR]

Introduction: Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects. Methods: In this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits. Results: The results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used. Discussion: These results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances. [ABSTRACT FROM AUTHOR]

Cadmium (Cd) is a harmful metal in soil, and reducing Cd accumulation in plants has become a vital prerequisite for maintaining food safety. Phosphate-solubilizing bacteria (PSB) can not only improve plant growth but also inhibit the transportation of metals to roots. However, data on gene expression in PSB Burkholderia sp. strain 'N3' and grafted watermelon plants dealing with Cd remain to be elucidated. In this study, core genes and metabolic pathways of strain 'N3' and grafted plants were analyzed by Illumina sequencing. Results showed that 356 and 2527 genes were upregulated in 'N3' and grafted watermelon plants, respectively, whereas 514 and 1540 genes were downregulated in 'N3' and grafted watermelon plants, respectively. Gene ontology enrichment analysis showed that signal transduction, inorganic ion transport, cell motility, amino acid transport, and metabolism pathways were marked in 'N3'. However, pathways such as secondary metabolite biosynthesis, oxidation–reduction process, electron transfer activity, and channel regulator activity were marked in the grafted plants. Six genes related to pentose phosphate, glycolysis, and gluconeogenesis metabolism were upregulated in the grafted plants. This study paves the way for developing potential strategies to improve plant growth under Cd toxicity. [ABSTRACT FROM AUTHOR]

Indoor hydroponic farming is an advanced cultivation technique with diverse sustainability benefits, such as facilitating local produce, minimizing transportation costs and emissions, and enabling year-round crop cultivation. To optimize crop growth for enhanced yield, improved crop quality, and reduced environmental footprint, precise monitoring and replenishment of essential nutrients within hydroponic systems is crucial. Current methods employed in most commercial farms for online nutrient supply monitoring is limited to pH and conductivity measurements. These techniques can only offer an indication of the overall change in the complex nutrient mixture and lack the capability to precisely identify the specific nutrient or quantify the nutrient content. Most of the existing techniques for measuring individual nutrient levels are expensive and invasive, necessitating sample preparation, frequent recalibration, and skilled personnel for operation. In this context, we propose and demonstrate a real-time, on-site monitoring system for the precise analysis of hydroponic nutrient supply based on laser-induced breakdown spectroscopy (LIBS). We also discuss the system design considerations, parametric optimizations, limit of detection (LOD), and limit of quantitation (LOQ) of key nutrient components such as potassium (K), sodium (Na), calcium (Ca), and magnesium (Mg), using the proposed approach. The detection range of the developed LIBS-based monitoring system can encompass the typical concentration range observed in hydroponic nutrient solutions used at agricultural farms. This technique offers rapid online monitoring of individual nutrient components, providing precise, real-time analysis and the potential to enable comprehensive automation capabilities for current and future hydroponic farms. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the dynamics of solar radiation and the morphological and physiological responses of bean and soybean plants grown in two integrated cultivation systems and under full sun. Two experiments were conducted in southern Brazil, one with beans (in-season) and the other with soybeans (off-season). The experimental design used was randomised blocks in a bifactorial 2 × 2 arrangement with an additional treatment, characterised by two forest arrangements (6 × 6 m and 12 × 12 m); two forest species (Peltophorum dubium and Eucalyptus urograndis); and full sun, with three repetitions. The transmissivity of solar radiation, intercepted solar radiation, leaf inclination angle, light extinction coefficient (k), and chlorophyll content (a, b, total, and a/b ratio) were measured. The dynamics of solar radiation were modified by the arrangement of trees and the canopy characteristics of the forest species. Shading significantly influenced the levels of chlorophyll a, b, and total chlorophyll, while no variation was observed in the angle of inclination of the leaves. It is recommended to cultivate beans intercropped with the forest species E. urograndis in the 12 × 12 intercropped arrangement, while for soybean cultivation, intercropping with the two forest species studied in the 12 × 12 intercropped arrangement is recommended. [ABSTRACT FROM AUTHOR]

The Philippines is primarily an agricultural country in which most Filipinos depend on the health of the soil for livelihood. Plastic pollution adversely affects agriculture by degrading soil quality, fertility, and crop growth. However, the prevalence of plastic particles such as mesoplastics (>5 mm to 25 mm) and microplastics (<5 mm) in agricultural soils in the Philippines remains poorly understood. This study investigated the prevalence of mesoplastics and microplastics in agricultural soils in Iligan City, Philippines. We collected soil samples from the six sampling stations in high-elevation and low-elevation sites. Mesoplastics and microplastics were examined through manual sorting, categorized based on color, shape, and size, and identified using Fourier-transform infrared spectroscopy (FTIR). Mesoplastics were found to be more prevalent, averaging 2.22 items/kg compared to microplastics 0.83 items/kg. Mesoplastics were mainly white-colored (42%), mostly fragmented (55%), 5–10 mm in size (55%), and were predominantly polypropylene (85%). Microplastics were mainly transparent (40%), composed of fragments and film shapes(46%), and 3–4 mm (93%) in size. Polyethylene was the abundant microplastic polymer type (47%). It was observed that the low-elevation sites had a higher abundance of plastic particles, stressing the need to address local factors in tackling plastic pollution. [ABSTRACT FROM AUTHOR]

Phosphorus (P) is the key to several structural molecules and catalyzes numerous biochemical reactions in plant body besides its involvement in energy transfer. Any deficit in P availability is likely to result in reduced RNA and protein content, inhibiting crop growth and development. Thus, availability of soil P is extremely crucial for plant growth especially in acid soils of India, where most of the fraction is bound to solid phase rendering their availability. The present communication deals with the isolation of elite phosphate-solubilizing bacterial (PSB) strains from the acid soils to work out their ability to improve the fertilizer P use efficiency in the acidic environment. Initially twenty-six bacteria were isolated from the acid soils of Northeastern India. Among them, ten bacteria were selected based on formation of halo zone in the Pikovskaya agar plate. In addition, these bacteria were able to solubilize insoluble zinc (Zn) and potassium (K). The isolates were subject to in vitro optimization for P solubilization under different insoluble P source utilization and at different pH and salinity conditions. Strains AN3, AN11, and AN21 exhibited significant solubilization of insoluble P, Zn, and K, and were identified as Streptomyces sp., Enterobacter sp., and Paraburkholderia caribensis. These three bacteria solubilized 206.53 to 254.08 µg mL−1 P, 79.7 to 177.55 µg mL−1 Zn, and 0.96 to 1.56 µg mL−1 K from insoluble minerals. Their performance was further evaluated in pot culture experiment using green gram as test crop. These three bacteria were found to improve P uptake and dry matter accumulation in green gram plant substantially. Seed bio-priming with the PSB strains enhanced the efficiency of added P fertilizer, resulting in a 1.40 to 1.52 times higher effectiveness compared to the control. On the whole, AN11 may be ranked as best inoculant for the acidic soils of Northeastern India. [ABSTRACT FROM AUTHOR]

Background Results Conclusion Maize, wheat, rice and soybean production are intimately linked to food security. Identifying the key factors affecting crop yields and determining the countries where increased irrigation and nitrogen application most effectively enhance yields are essential steps towards achieving sustainable development goals and ensuring food security. Identifying these areas is crucially dependent on yield gaps. However, the lack of comparability between different regions in current regional‐scale yield gap studies stems from varied methodologies. Moreover, global yield gap research, relying on statistical models and regression methods, tends to neglect the crop growth process. In this study, we used a random forest model, based on statistical and meteorological data, to pinpoint the key factors influencing crop yields. Subsequently, using unified yield data from the Inter‐Sectoral Impact Model Intercomparison Project (ISIMIP), derived from crop models simulations, we applied the yield gap method to calculate the potential yield increase for four crops across countries, under conditions of full irrigation and nitrogen application.Our research finds that nitrogen application is the main factor affecting yields globally, while irrigation plays a crucial role in the major producing countries. The countries with high potential for yield increases are located at the border between Africa and Eurasia. The global average yield of the four major crops increased 13.7–29.8% under full irrigation, 2.9–39.1% under full nitrogen application and 29.4–97.8% under both conditions.This study provides crucial insights into global crop yield changes and their determinants, which are highly important for global sustainable agriculture and food security efforts. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

AbstractPhosphorus (P) is the second most important essential macronutrient after nitrogen for plant growth and development. Even though P is plentiful in soil, it is largely inaccessible to plants as it is strongly bound with metal ions and form insoluble metal-P complexes. Therefore, different agrochemicals are used to improve the P availability in soils and mitigate the plant need for sustainable growth and productivity of crops. However, those agrochemicals deteriorate ecosystem integrity and adversely affect the soil health. Consequently, there is a need for a more economical and ecologically beneficial P bio-fortification method by phosphate-solubilizing microorganisms (PSMs). The characterization of phosphorus pools and the assessment of different microbial communities can help to improve our understanding of plant–microbe interaction and P availability. The objective of this study is to discuss the PSMs that facilitate P nutrition mechanisms, including the molecular aspects of P solubilization in soil, and to characterize their features that promote plant growth and development. We mainly focused on how PSMs constitute a fundamental resource for the release of P nutrient where described the several pathways of solubilization and mineralization. The use of PSMs is a viable resource for the future sustainable agriculture, mainly due to its application can helps to reduce the fertilizer dose of P and ultimately reduce the exploitation of fertilizer P and its resources. [ABSTRACT FROM AUTHOR]