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Nitrogen (N) is one of the most crucial elements for crop yields. N management is incredibly important because it's a major component of crop productivity. It is challenging to control due to the large field variability of the innate soil N and the differences in crop demand at various crop growth stages. Additionally, N's partial factor production is deteriorating day by day. Furthermore, crop, cultivar, and environmental factors affect how much N is needed. Therefore, crop production depends heavily on applying N at the right rate for the crop's needs. Precision N management increases N use efficiency while reducing N loss from runoff, ammonia volatilization, and leaching. By providing N during crucial growth phases, real-time N management increases use efficiency by applying N based on need at the appropriate time and amount. One aspect of real-time N management is the application of the leaf color chart (LCC). The LCC is a relatively basic, low-cost, portable, and user-friendly gadget that contrasts the color of the leaf with the device's color hues. Because farmers typically prefer dark-colored leaves, they apply N regardless of need, which poses serious risks to the ecosystem and contaminates the surface and groundwater. The sustainability of output is threatened by the declining soil health caused by increased fertilizer application. Therefore, for small and marginal farmers, employing a leaf color chart for site-specific N management is a more practical solution. [ABSTRACT FROM AUTHOR]

A three-year multi-location trial on the farmer's field was conducted in West Bengal, India to evaluate the impact of integrated nutrient management (INM) on the performance of kharif rice cv. Swarna Sub 1. The experiment employed a randomized block design (RBD) across eleven farmers' fields with similar soil properties, texture, topography, and historical fertilizer application patterns. Three treatments were tested: T1 – common practice of applying N:P:K @ 55:32:27 kg ha−1, T2 – soil test-based application (STA) of ∼75% recommended N dose through chemical fertilizer + ∼ 25% through Azolla, and T3 – soil test-based application of ∼ 75% recommended N dose through chemical fertilizer + 20% N from vermi-compost + 5% N through Azolla. The result of the experiment revealed that the T3 exhibited significant improvements in plant height, leaf area index (LAI), effective tillers meter−2, number of panicles plant−1, and 1000-grain weight compared to T2 and T1. Among the different nutrient management practices, the T3 recorded maximum rice grain productivity than the T1, and T2, Further, the benefit-cost ratio was noticed significantly higher under T3 (1.29) followed by T2 (1.27) and T1 (1.23). The soil organic carbon (SOC), available nutrient was enhanced by the soil test-based application of ∼75% recommended N dose through chemical fertilizer + 20% N from vermicompost + 5% N through Azolla. Thus, our study indicated that the balanced nutrient application through combined sources (T3) could improve crop growth; and sustain rice productivity, besides enhancing the farm income, and soil different soil properties. [ABSTRACT FROM AUTHOR]

Plant growth promoting rhizobacteria (PGPR) as microbial biostimulant promotes growth and productivity of crops under drought. However, their commercial utility is constrained by their instability and low viability in field trials. Immobilization or encapsulation of PGPR biostimulant aims to provide physical protection from stressful environment and ensure a high rate of rhizospheric colonization. In this work, we immobilized Acinetobacter calcoaceticus AC01 in 2% alginate by ionic gelation process, and evaluated for in vitro and in vivo studies. The rhizobacteria was characterized for osmotic stress, indole, phosphate solubilization, siderophore, gibberellic acid, salicylic acid, alginate and Exopolysaccharide (EPS). The encapsulated PGPR AC01 exhibited optimal moisture content of 3.57%, expansion rate of 80.62%, 95% embedding efficiency and diameter of 1.2–1.4 mm. The viability of AC01 in microcapsules remained 107CFU/g during 90 days of storage. The encapsulated PGPR biostimulant were also evaluated in Arachis hypogaea L. for drought mitigation. The inclusion of rhizobacterial microcapsules considerably induced maximum growth with significantly increased plant height, biomass, nodule count, relative water content, membrane stability index and osmolyte accumulation under drought conditions compared to non-encapsulated bacteria. Our results demonstrate that bio-encapsulation of Acinetobacter sp. AC01 in alginate matrix is a potential alternative for alleviating drought and a promising delivery system for agricultural applications. [ABSTRACT FROM AUTHOR]

Elevated near-surface ozone (O3) concentrations have surpassed the tolerance limits of plants, significantly impacting crop growth and yield. To mitigate ozone pollution, plants must evolve a rapid and effective defense mechanism to alleviate ozone-induced damage. DNA methylation, as one of the most crucial epigenetic modifications, plays a pivotal role in maintaining gene stability, regulating gene expression, and enhancing plant resilience to environmental stressors. However, the epigenetic response of plants to O3 stress, particularly DNA methylation variations and their intergenerational transmission, remains poorly understood. This study aims to explore the epigenetic mechanisms underlying plant responses to ozone stress across generations and to identify potential epigenetic modification sites or genes crucial in response to ozone stress. Using Open Top Chambers (OTCs), we simulated ozone conditions and subjected foxtail millet to continuous ozone stress at 200 nmol mol-1 for two consecutive generations (S0 and S1). Results revealed that under highconcentration ozone stress, foxtail millet leaves exhibited symptoms ranging from yellowing and curling to desiccation, but the damage in the S1 generation was not more severe than that in the S0 generation. Methylation Sensitive Amplified Polymorphism (MSAP) analysis of the two generations indicated that ozone stress-induced methylation variations ranging from 10.82% to 13.59%, with demethylation events ranged from 0.52% to 5.58%, while hypermethylation occurred between 0.35% and 2.76%. Reproductive growth stages were more sensitive to ozone than vegetative stages. Notably, the S1 generation exhibited widespread demethylation variations, primarily at CNG sites, compared to S0 under similar stress conditions. The inheritance pattern between S0 and S1 generations was mainly of the A-A-B-A type. By recovering and sequencing methylation variant bands, we identified six stress-related differential amplification sequences, implicating these variants in various biological processes. These findings underscore the potential significance of DNA methylation variations as a critical mechanism in plants' response to ozone stress, providing theoretical insights and references for a comprehensive understanding of plant adaptation mechanisms to ozone stress and the epigenetic role of DNA methylation in abiotic stress regulation. [ABSTRACT FROM AUTHOR]

Accurately counting the number of sorghum seedlings from images captured by unmanned aerial vehicles (UAV) is useful for identifying sorghum varieties with high seedling emergence rates in breeding programs. The traditional method is manual counting, which is time-consuming and laborious. Recently, UAV have been widely used for crop growth monitoring because of their low cost, and their ability to collect high-resolution images and other data non-destructively. However, estimating the number of sorghum seedlings is challenging because of the complexity of field environments. The aim of this study was to test three models for counting sorghum seedlings rapidly and automatically from redgreen-blue (RGB) images captured at different flight altitudes by a UAV. The three models were a machine learning approach (Support Vector Machines, SVM) and two deep learning approaches (YOLOv5 and YOLOv8). The robustness of the models was verified using RGB images collected at different heights. The R² values of the model outputs for images captured at heights of 15 m, 30 m, and 45 m were, respectively, (SVM: 0.67, 0.57, 0.51), (YOLOv5: 0.76, 0.57, 0.56), and (YOLOv8: 0.93, 0.90, 0.71). Therefore, the YOLOv8 model was most accurate in estimating the number of sorghum seedlings. The results indicate that UAV images combined with an appropriate model can be effective for large-scale counting of sorghum seedlings. This method will be a useful tool for sorghum phenotyping. [ABSTRACT FROM AUTHOR]

Agronomic biofortification with iron (Fe) in tomatoes has emerged as a promising strategy to combat global iron deficiency. Iron is essential for human health and plays a critical role in the growth and development of crops. This study aimed to determine the optimal dose of Fe for tomato biofortification and assess its impact on its growth, yield and quality attributes, particularly the fruit’s Fe content. A greenhouse experiment was conducted using tomato hybrids Sandal and Sahel, applying Fe foliar treatments at concentrations of 0, 1, 3, 6 and 9 mM Fe per plant. The results demonstrated that foliar application of 6 mM Fe significantly enhanced the tomato’s growth, yield and quality, while markedly increasing the fruit’s Fe content to 30.07 and 34.1 mg kg−1 in Sandal and Sahel hybrids, respectively. These values were substantially higher than those of the control treatment with Fe contents of 14.77 and 15.0 mg kg−1, respectively. However, higher doses of Fe (> 6 mM Fe per plant) showed adverse effects on growth, yield, and quality. Foliar application of iron proved an effective and economical solution, readily integrated into commercial tomato production, offering farmers a practical method to produce Fe-biofortified tomatoes and combat iron deficiency-linked malnutrition. [ABSTRACT FROM AUTHOR]

The use of beneficial microbes is hitherto known and constantly increasing in agriculture due to their positive impact on crop growth and yield, and their minimal negative impact on the environment. The objective of this study was to evaluate the impact of eight Trichoderma strains of diverse origin on crop growth and yield of onion under field conditions. The identity of the strains used in the current study was confirmed by ITS and Tef1 gene sequencing. Field experiments were conducted in the Rabi season for 2 years (2020-21, and 2022-23) to evaluate the effect of the application of eight different Trichoderma strains that were applied individually and separately as eight different treatments (T1-T8) in experimental plots. In the plant growth promotion assay conducted in vitro, all strains showed the ability to produce IAA (indole-3-acetic acid), with levels ranging from 23.52 µg/mL (T6) to 45.54 μg/mL (T3). Our results revealed that Trichoderma treated experimental plots displayed better growth indices (plant height, pseudostem diameter), RWC (Relative water content), leaf chlorophyll content, and yield-attributing features like biomass (bulb and root dry mass), bulb diameter, and harvested bulb yield compared to the untreated control plants. In terms of yield, the T2 strain exhibited the highest bulb yield consistently for both the years (2020-21 and 2022-23) followed by T3 being statistically at par with T5. Among all the evaluated Trichoderma strains, the strain T2 (OGRDT2) and T3 (GRDT1), taxonomically identified as Trichoderma longibrachiatum, registered bulb yield of 32.24 t/ha and 30.76 t/ha, respectively while T5 (GRDT3), identified as Trichoderma asperellum, registered 30.55 t/ha average yield for 2 years compared to 24.08 t/ha average yield recorded for untreated control plants with an increase of 34, 28 and 27%, respectively. Based on our findings, it is concluded that the T. longibrachiatum strains OGRDT2 (T2) and GRDT1 (T3), T. asperellum strain GRDT3 (T5) are the best inducers of the onion crop growth and yield in the Rabi season and would be explored further for its commercial application in onion farming. [ABSTRACT FROM AUTHOR]

The success of Variable Rate Application (VRA) techniques is closely linked to the algorithm used to calculate the different fertilizer rates. In this study, we proposed an algorithm based on the integration between some estimated agronomic inputs and crop radiometric data acquired by using a multispectral sensor. Generally, VRA algorithms are evaluated by comparing the yields, but they can often be affected by factors acting in the final phase of the crop cycle and not dependent on the fertilization treatments. Therefore, we decided to compare our algorithm (ALG) versus the traditional application of fertilizer (TRD) by evaluating the crop growth 1.5 months after the fertilization time. The algorithm was tested on a sorghum crop under organic farming, managed with or without manure. The saving of N obtained with ALG was equal to 14 and 5 kg ha− 1 (-14 and − 10% for the non-manure and fertilized treatments, respectively). The NDVI values acquired after fertilization showed a remarkable reduction of relative standard deviation for ALG system (from 22 to 9% and from 34 to 14% for manured and not manured, respectively), which was not found for TRD system (from 16 to 17% and from 29 to 18% for manured and not manured, respectively). The above ground biomass produced was statistically equivalent for the two systems in the manured plots and significant higher for ALG in not-manured plots (+ 0.74 t ha− 1 of dm, equal to + 23%). Finally, the indices calculated to evaluate the Nitrogen Use Efficiency (NUE) were consistently better in the ALG theses. [ABSTRACT FROM AUTHOR]

Organic farming is gaining recognition as a viable alternative to conventional methods, promising soil health preservation and sustained crop productivity with economic benefits. This study evaluated the physiological, growth, and yield responses of the PSB Rc18 rice variety and appraised its economic feasibility under different production systems. The experiment was laid out in Randomized Complete Block Design (RCBD) with four replications and three treatments: T1-best bet organic production system, T2-farmers' organic production system in Leyte, and T3-farmers' conventional production system in Leyte. The crop growth rate (CGR) of PSB Rc18 remained consistent across the different systems. However, the Net Assimilation Rate (NAR) peaked significantly between 42-56 days after transplanting (DAT) in the T2. Additionally, the Leaf Area Index (LAI) in T1 was comparable to that of T3. Rice grown under T1 reached heading and maturation earlier than T3. Although T3 produced the highest fresh straw, most productive tillers, and heaviest total biomass, the grain yield was similar across all production systems. Economically, T2 outperformed with a superior benefit-cost ratio of $0.55 and $0.94 per USD invested, considering both regular and premium prices for organic palay. These findings highlight organic farming practices' economic and agronomic viability, suggesting that promoting organic farming can be a beneficial alternative to conventional methods in Leyte. This study underscores the potential for integrating organic practices to enhance sustainability and economic outcomes in rice production, making both T1 and T2 significant options for farmers in Eastern Visayas. [ABSTRACT FROM AUTHOR]

High temperature during early grain‐filling stage is one of the serious abiotic stresses limiting maize yield in the North China Plain. Nitrogen (N) fertiliser has an important role in promoting crop growth, especially under abiotic stresses. However, its contribution to alleviating heat stress (HS) inhibition on maize photosynthesis during early grain‐filling stage is still unclear. Experiments with three N rates (LN, low nitrogen; MN, medium nitrogen; HN, high nitrogen) and two temperature (HS, heat stress; CK, ambient temperature as control) regimes were conducted to examine the effects of increasing N supply on photosynthesis, N assimilation, antioxidant system, and hormones homeostasis of maize during early grain‐filling stage using two maize hybrids Xianyu335 (XY335, heat‐sensitive) and Zhengdan (ZD958, heat‐tolerant). HS negatively affected photosynthesis of both two hybrids, exhibited lower net photosynthetic rate, chlorophyll content and activities of Rubisco and phosphoenolpyruvate carboxylase (PEPC) compared with CK, and then decreased dry matter accumulation of maize, with a lesser extent for ZD958 than XY335. However, increasing N supply alleviated the adverse effects of HS on maize photosynthesis due to improved N assimilation capacity. Under HS condition, greater N content and higher activities of glutamine synthetase and glutamate synthase in maize ear leaf were found in treatment of HN compared with LN and MN. HN with higher N assimilation capacity directly increased the net photosynthetic rate due to improved chlorophyll content, activities of Rubisco and PEPC and antioxidant capacity. HS‐induced abscisic acid (ABA) accumulation was also repressed by HN, and then enhanced the stomatal conductance and transpiration rate to maintain higher photosynthetic capacity compared with LN and MN. Moreover, the positive effects of increasing N supply on maize photosynthesis under HS condition exhibited a larger extent for XY335 than ZD958. As a result of improved photosynthesis and N assimilation capacity by adequate N supply, maize accumulated more biomass under HS, especially for heat‐sensitive hybrid. [ABSTRACT FROM AUTHOR]

The responses of eco‐physiological processes such as leaf expansion, plant transpiration and senescence to soil water deficit have been reported to be genotype‐dependent in different crops. To study such responses in soybean (Glycine max. (L.) Merr.), a 2‐year (2017 and 2021) outdoor pot experiment was carried out on the Heliaphen automated phenotyping platform at INRAE in Toulouse (France). Six soybean cultivars (Sultana‐MG 000, ES Pallador‐MG I, Isidor‐MG I, Santana‐MG I/II, Blancas‐MG II and Ecudor‐MG II) belonging to four maturity groups (MG) commonly grown in Europe were subjected to progressive soil water deficit from the reproductive stage R1 for 17 and 23 days in 2017 and 2021, respectively. The fraction of transpirable soil water (FTSW) was used as an indicator of soil water deficit. Non‐linear regression was used to calculate FTSWt, that is, the FTSW threshold for which the rate of the eco‐physiological process in stressed plants starts to diverge from a reference value. According to FTSWt, the three eco‐physiological processes showed significant differences in sensitivity to water deficit: leaf expansion exhibits the highest sensitivity and the widest range (FTSWt: 0.44–0.93), followed by plant transpiration (FTSWt: 0.17–0.56), with leaf senescence showing the narrowest range (FTSWt: 0.05–0.16). Among six cultivars, regarding leaf expansion, Cvs Santana (FTSWt = 0.48 in 2017; FTSWt = 0.44 in 2021), Blancas (FTSWt = 0.51 in 2017; FTSWt = 0.48 in 2021) and Ecudor (FTSWt = 0.46 in 2017; FTSWt = 0.52 in 2021) in late MGs (I/II to II) exhibited higher tolerance to soil drying. Conversely, the cv. Sultana in the earliest MG (000) showed the highest sensitivity (FTSWt = 0.91 in 2017; FTSWt = 0.93 in 2021) to water deficit. However, concerning the FTSWt values for plant transpiration (0.17–0.56 in 2017; 0.19–0.31 in 2021) and senescence (0.05–0.16 in 2017; 0.06–0.16 in 2021), their range did not demonstrate a correlated trend with the MG. In addition, a negative linear correlation was observed between values of FTSWt of normalised leaf expansion at the whole‐plant level (NLE) and specific leaf area (SLA) measured on irrigated plants for both years. This suggests that genotypes with high values of SLA could be associated with higher tolerance of leaf expansion to soil water deficit. Such a non‐destructive phenotyping method under outdoor conditions could bring new information to variety testing process and provide paths for integrating genotypic variability into crop growth models used for simulating soybean eco‐physiological responses to water deficit across the plant, field and even regional scales. [ABSTRACT FROM AUTHOR]

Hydroponic wastewater recovery provides a green alternative to chemical fertilizers by recycling nutrients, yet transitioning to closed systems introduces problems such as pathogen proliferation and high sterilization costs. These challenges, along with the need for regular water analysis to correct nutrient imbalances, can hinder crop growth and increase expenses. However, hydroponic wastewater is also beneficial for capturing harmful emissions like CO2, NOx, and NH3, thanks to its rich mineral content. As it reacts with air pollutants, it produces compounds like Ca(NO3)2 and NH4NO3, turning pollutants into useful fertilizers. This study designed a novel hydroponic wastewater recovery process for CO2, NOx, and NH3 utilization from crop residue burning gas. through the following procedures: (1) ion separation of hydroponic wastewater, (2) NOx capture and utilization using precipitated Ca(OH)2, (3) NH3 capture and utilization using separated NO3− and SO42−, and (4) CO2 capture and utilization using KOH, Ca(OH)2 and Mg(OH)2. A process model was developed, and the economic feasibility of the proposed system was determined by calculating the total annualized cost. The result shows that approximately 85% of NOx, 99% of CO2, and 99% of NH3 were captured and utilized. Importantly, the total annualized cost of the system could decrease by 70% compared with that of the base process. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to explore the effects of fertilization and biochar addition on soil respiration (Rs), environmental factors and vegetation index, and the effects of biotic factors and abiotic factors on soil respiration, and to provide the reference for the assessment of crop growth and carbon emission in farmland ecosystem. [Methods] In a sorghum-corn rotation agroecosystem of Taiyuan basin after a continuous 5-year (2011-2015) fertilization and biochar additon with 4 treatments (control: CK, biochar: B: inorganic fertilizer: INF and inorganic fertilizer + biochar: INF+B), based on periodic observations of Rs biological and abiotic factors for 2 consecutive years (2015-2016), we analyzed the effects of the 4 treatments on Rs and its relationship with biological and abiotic factors. [Results] (1) Compared with CK, B, INF and INF+B reduced soil temperature (2.8%~4.2%), and increased temperature sensitivity (Q10) of soil respiration (4.5%~20.2%) and soil respiration rate (0.5%~8.8%), increased the enhanced vegetation index (EVI) by 1.5%~6.2%, difference vegetation index (DVI) by 0.7%~6.0%, normalized vegetation index (NDVI) by 1.2%~5.4%, respectively; but the influence on these factors did not reach a significant level (p>0.05). (2) The effects of B, INF and INF+B on Rs differed in different growth stages. B, INF and INF+B increased the Rs in the early growth stage, while the Rs of CK treatment was higher than that in other treatments (except B treatment in the grouting stage) (p<0.05) in the later growth stage, resulting in no significant difference in Rs among all treatments in the whole growing stage. (3) Among the three vegetation indices, the seasonal variation of DVI showed the highest interpretation of the seasonal variation in Rs at 75.5%. Soil temperature in 10 cm depth (T10) and soil moisture over 0-10 cm depth (W.) explained 30.8% and 20.1% of the seasonal variation of Rs, respectively. From the R² and AIC values, the three-factor model with T10, Ws and DVI as independent variables predicted the seasonal change of Rs well. [Conclusion] There was no significant difference in Rs between the four treatments in whole sorghum, and Rs did not increase by fertilization and biochar addition. The results provided the basis for the relationship between inorganic fertilizer and biochar application on soil carbon emission, soil improvement and crop growth. [ABSTRACT FROM AUTHOR]

Nanotechnology represents an innovative approach to ameliorating abiotic stress in oilseed crops, with the application of iron oxide nanoparticles (FeO−NPs) gaining notable popularity recently. Therefore, we have utilized FeO−NPs as an alleviating agent on an oilseed crop, specifically rapeseed (Brassica napus L.), grown in soil with varying levels of arsenic (As). This study investigates various growth−related attributes, the efficiency of the photosynthetic machinery, indicators of oxidative stress, and responses of both enzymatic and non-enzymatic antioxidants, along with their specific gene expression, sugar content, organic acids exudation pattern and As accumulation in different parts of the plant. Our findings indicated that soil contaminated with As reduced crop growth, photosynthetic efficiency, and nutritional status in plants, while simultaneously enhancing oxidative stress indicators, organic acid exudation, activity of both enzymatic and non-enzymatic antioxidants and their related gene expressions, and endogenous As content in the shoots and roots of B. napus. Moreover, increasing levels of As in the soil caused a signifcant increase in proline and organic acids exudation pattern. However, the exogenous application of FeO−NPs enhanced plant growth and the photosynthetic rate in B. napus by boosting the antioxidant system and mineral status, and by reducing the concentrations of oxidative stress biomarkers, organic acids, and As accumulation in both roots and shoots. Hence, this study suggests that seed priming with FeO−NPs is an effective technique that can be employed to fortify nutrients and mitigate metal toxicity in areas polluted with metals. [ABSTRACT FROM AUTHOR]

Salt stress has become one of the most widespread abiotic stresses globally, negatively affecting crop growth, development, and yield. Rice is an important economic crop affected by salt stress. This study used Huanghuazhan ('HHZ') as the test material to investigate the mitigating effect of spraying Hemin (alone) or with Hb and ZnPP (in combination) on the oxidative damage induced by 100 mM NaCl solution. The results showed that Hemin treatment maintained the growth of rice seedlings under NaCl stress and improved shoot (plant height, stem base width, leaf area) and root (root length, root surface area, root volume, average root diameter and number of root tips) morphological parameters. In addition, exogenous Hemin increased root activity, raised the content of various photosynthetic pigments, improved leaf structure, and increased the area of vascular bundles, which sustained photosynthesis and promoted the accumulation of biomass. Furthermore, Hemin reduced the accumulation of malondialdehyde (MDA) and hydrogen peroxide (H2O2), by activating the activities of various antioxidant enzymes and increasing the content of non-enzymatic antioxidants. While ZnPP (a specific inhibitor of heme oxygenase-1 (HO-1)) and Hb (CO scavenger) reversed the positive regulation of Hemin, and the indexes (biomass of rice seedlings, root activity, and the activity of superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX)) somewhat decreased. In conclusion, this study demonstrated that Hemin reduced ROS accumulation, maintained leaf structure and photosynthetic pigment content, and mitigated the adverse effects of oxidative damage caused by NaCl stress in rice through improving the antioxidant capacity of leaves and roots. This research provides a theoretical basis for Hemin to regulate salt tolerance in other crops. However, the molecular mechanisms involved in Hemin regulation need to be further explored. [ABSTRACT FROM AUTHOR]

Fertilizer recommendation is the crucial factor to be considered in automation of agricultural predictions. Fertilizer fill the necessary portion of any farming region. There are some micronutrients and macro nutrients which need to be given to crops for proper growth. If fertilization is not done to an optimum level, it may badly harm the soil quality and crop health, so optimum fertilization is important. In this paper we discuss fertilizer and nutrient recommender, where we have used a new feature selection methodology. We have shown the difference between two implementation cases considering presence and absence of feature ranking and selection. Feature ranking and selection has clearly increased the efficiency of the fertilizer & nutrient recommender in our work from 85% to 98%. Feature selection & raking has been introduced with random forest approach. [ABSTRACT FROM AUTHOR]

Plants are subjected to various biotic and abiotic stresses that significantly impact their growth and productivity. To achieve balanced crop growth and yield, including for leafy vegetables, the continuous application of micronutrient is crucial. This study investigates the effects of different concentrations of copper sulphate (0, 75, 125, and 175 ppm) on the morphological and biochemical features of Spinacia oleracea and Avena sativa. Morphological parameters such as plant height, leaf area, root length, and fresh and dry weights were optimized at a concentration of 75 ppm copper sulfate. At this concentration, chlorophyll a & b levels increased significantly in Spinacia oleracea (462.9 and 249.8 휇푔/푔), and Avena sativa (404.7 and 437.63휇푔/푔). However, carotenoid content and sugar levels in Spinacia oleracea were negatively affected, while sugar content in Avena sativa increased at 125 ppm (941.6 µg/ml). Protein content increased in Spinacia oleracea (75 ppm, 180.3 µg/ml) but decreased in Avena sativa. Phenol content peaked in both plants at 75 ppm (362.2 and 244.5 µg/ml). Higher concentrations (175 ppm) of copper sulfate reduced plant productivity and health. Plants exposed to control and optimal concentrations (75 and 125 ppm) of copper sulpate exhibited the best health and growth compared to those subjected to higher concentrations. Maximum plant height, leaf area, root length, fresh and dry weights were observed at lower concentrations (75 and 125 ppm) of copper sulfate, while higher concentrations caused toxicity. Optimal copper sulfate levels enhanced chlorophyll a, chlorophyll b, total chlorophyll, protein, and phenol contents but inhibited sugar and carotenoid contents in both Spinacia oleracea and Avena sativa. Overall, increased copper sulfate treatment adversely affected the growth parameters and biochemical profiles of these plants. [ABSTRACT FROM AUTHOR]

Introduction: Optimizing the dynamics of daylily (Hemerocallis citrina Baroni) growth under various planting patterns is critical for enhancing production efficiency. This study presents a comprehensive model to simulate daylily growth and optimize planting patterns to maximize bud yield while minimizing land resource utilization. Methods: The model incorporates source-sink relationship specific to daylilies into physiological process modeling, considering environmental factors such as micro-light and temperature climate, and CO2 concentration. Spatial factors, including planting pattern, row spacing, plant spacing, and plant density were examined for their impact on light interception, photosynthesis, and resource efficiency. Employing partial least square path modeling (PLS-PM), we analyzed the interrelations and causal relationships between planting configurations and physiological traits of daylily canopy leaves and buds. Through in situ simulations of 36 planting scenarios, we identified an optimal configuration (Scenario ID5) with a density of 83,000 plants·ha-1, row spacing of 0.8 m, and equidistant planting with a plant spacing of 0.15 m. Results and discussion: Our research findings indicate that increased Wide +Narrow row spacing can enhance yield to a certain extent. Although planting patterns influence daylily yield, their overall impact is relatively minor, and there is no clear pattern regarding the impact of plant spacing on individual plant yield. This modeling approach provides valuable insights into daylily plant growth dynamics and planting patterns optimization, offering practical guidance for both farmers and policymakers to enhance daylily productivity while minimizing land use. [ABSTRACT FROM AUTHOR]

Salinity is one of the most detrimental factors for the growth performance and productivity of crops worldwide. Therefore, understanding crop responses or growth potentials and their effectiveness in salinity mitigation is highly important for the selection of salinity-tolerant plant varieties. In this study, the effects of salinity at various stress levels (0 mM, 50 mM, 100 mM, and 150 mM NaCl) on the morphological, physiological, and biochemical parameters of three soybean varieties ('Afigat', 'Gishama', and 'Pawi-2') were investigated. The results showed that salinity significantly reduced morphological traits including plant height, number of leaves per plant, stem thickness, shoot and root length, and fresh and dry weight. This reduction was more prominent in the 'Afigat' variety for all of these traits except shoot and root length. The concentrations of chlorophyll a and b decreased with increasing salinity. In addition, salinity significantly increased leaf electrolyte leakage (EL), lipid peroxidation, proline accumulation, and phenol and flavonoid content. The 'Pawi-2' variety was more tolerant than the other studied varieties in terms of membrane stability (less EL and a low malondialdehyde content) and proline, phenol, and flavonoid accumulation. Therefore, 'Pawi-2' may be considered as the most salt-tolerant variety in comparison with the other studied soybean varieties. Further complementary studies in field conditions including anatomical parameters are needed to confirm these findings. [ABSTRACT FROM AUTHOR]

Soil microbial inoculants are widely recognized as an environmentally friendly strategy for promoting crop growth and increasing productivity. However, research on utilizing the microbial resources from desert biological soil crusts to enhance crop growth remains relatively unexplored. In the present work, a bacterial strain designated AC1-8 with high levels of amylase, protease, and cellulase activity was isolated from cyanobacterial crusts of the Tengger Desert and identified as Bacillus cereus (CGMCC 1.60196). The refinement of the fermentation parameters of B. cereus CGMCC 1.60196 determined that the most effective medium for biomass production was composed of 5 g/L glucose, 22 g/L yeast extract and 15 g/L MgSO4, and the optimal culture conditions were pH 6.0, temperature 37°C, inoculation quantity 3% and agitation speed 240 rpm. Furthermore, the utilization of B. cereus CGMCC 1.60196 has resulted in substantial improvements in various growth parameters of maize seedlings, including shoot length, shoot fresh and dry weights, root fresh and dry weights, and the contents of chlorophyll a, chlorophyll b, and total chlorophyll. The most pronounced growth promotion was observed at an application concentration of 1 × 109 CFU/m2. These results suggest that the novel B. cereus strain, isolated from cyanobacterial crusts, can be regarded as an exemplary biological agent for soil improvement, capable of enhancing soil conditions, promoting crop cultivation and supporting food production. [ABSTRACT FROM AUTHOR]

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]

Pearl millet [Pennisetum glaucum (L.) R.Br.] is an essential subsistence cereal for food security in dryland farming systems of the semiarid tropics (e.g., in sub‐Saharan Africa) and has improved tolerance to drought, heat, and salinity stress compared to other domesticated cereals. Assessing the variation on phenology is critical toward devising effective adaptative management strategies for crop adaptation to current and future climate change. In this context, pearl millet presents a vast genetic diversity, exhibiting sensitivity to temperature and photoperiod. Hence, this study aims to describe the genotypic variability in the phenological responses of pearl millet to temperature and photoperiod, particularly affecting leaf number with implications on the overall total time to flowering. The dataset encompassed 21 publications from seven countries, with experiments conducted from 1965 to 2023, including three field studies from the United States. Broad variability has been reported for phyllochron values ranging from 45 to 111°Cd leaf−1, with a mean value of 67°Cd leaf−1. Thermal time to panicle initiation ranged from 340 to 594°C, but no response to photoperiod duration was found due to the nature of dataset. Maximum number of leaves per shoot ranged from 11 to 25, showing response (1.55–2.15 leaf h−1) to photoperiod due to variations in thermal time to flowering (from 875 to 1346°Cd). Thermal time to flowering increased ca. 323°Cd h−1 under day durations longer than 13.3 h, below which basic vegetative phase duration was close to 1033°Cd. Based on the Agricultural Production Systems sIMulator simulations, different combinations of the above responses (in silico cultivars) generated a great range of times to flowering (44–120 days) for locations in Senegal, Brazil, India, and United States. The findings of this study can help breeders to explore the phenological genetic variability of pearl millet and provide inputs for crop growth models to evaluate future in silico scenarios. [ABSTRACT FROM AUTHOR]

Essential mineral elements such as zinc and iron play a crucial role in maintaining crop growth and development, as well as ensuring human health. Foxtail millet is an ancient food crop rich in mineral elements and constitutes an important dietary supplement for nutrient-deficient populations. The ZIP (ZRT, IRT-like protein) transporters are primarily responsible for the absorption, transportation and accumulation of Zn, Fe and other metal ions in plants. Here, we identified 14 ZIP transporters in foxtail millet (SiZIP) and systematically characterized their phylogenetic relationships, expression characteristics, sequence variations, and responses to various abiotic stresses. As a result, SiZIPs display rich spatiotemporal expression characteristics in foxtail millet. Multiple SiZIPs demonstrated significant responses to Fe, Cd, Na, and K metal ions, as well as drought and cold stresses. Based on homologous comparisons, expression characteristics and previous studies, the functions of SiZIPs were predicted as being classified into several categories: absorption/efflux, transport/distribution and accumulation of metal ions. Simultaneously, a schematic diagram of SiZIP was drawn. In general, SiZIPs have diverse functions and extensively involve in the transport of metal ions and osmotic regulation under abiotic stresses. This work provides a fundamental framework for the transport and accumulation of mineral elements and will facilitate the quality improvement of foxtail millet. [ABSTRACT FROM AUTHOR]

Mulching is a widely used agricultural practice that can significantly affect crop growth, yield, and economic outcomes, particularly in regions with varying climatic conditions. The present study evaluated the influence of various mulching practices on the growth, yield, and economic viability of common bean (Phaseolus vulgaris L.) cultivation in Tanzania. The study was conducted across three sites in the eastern agro-ecological zone of Tanzania: Kipera (E4 200-1000 m.a.s.l.), Mgeta (E14 500-000 m.a.s.l.), and Ndole (E2 500-1200 m.a.s.l.). Four mulching treatments--polythene mulch, synthetic biodegradable mulch, rice husk mulch, and a control group--were applied to assess their effects on plant growth and yield components. Results revealed significant variations in growth parameters and yield components across sites. Notably, polythenemulch and synthetic biodegradablemulch consistently outperformed the other treatments. Polythene mulch resulted in an average plant height of 68.37cm, followed closely by synthetic biodegradable mulch at 68.26cm, both significantly (p < 0.05) taller than rice husk mulch (62.79cm) and the control (57.74cm). Canopy coverage was highest with polythene mulch at 61.7%, followed by synthetic biodegradable mulch at 60.5%. Grain yields did not differ significantly between synthetic biodegradable mulch (2.64 t ha-1) and polythene mulch (2.67 t ha-1). Economic analysis indicated that synthetic biodegradable mulch offers promising marginal returns (MR: Tshs. 3,787,450 or USD 1,469) and a benefit-cost ratio (BCR) of 1.91, compared to polythene mulch (MR: Tshs. 4,114,050 or USD 1,595, BCR: 2.06). These findings suggest that synthetic biodegradable mulch is a sustainable and economically viable option for enhancing common bean production across diverse agro-ecological settings in Tanzania. [ABSTRACT FROM AUTHOR]

The image integrity of real-time monitoring is crucial for monitoring crop growth, helping farmers and researchers improve production efficiency and crop yields. Unfortunately, existing schemes just focus on ground equipment and drone imaging, neglecting satellite networks in remote or extreme environments. Given that satellite internet features wide area coverage, we propose SEAIS, a secure and efficient agricultural image storage scheme combining blockchain and satellite networks. SEAIS presents the mathematical model of image processing and transmission based on satellite networks. Moreover, to ensure the integrity and authenticity of image data during pre-processing such as denoising and enhancement, SEAIS includes a secure agricultural image storage and verification method based on blockchain, homomorphic encryption, and zero-knowledge proof. Specifically, images are stored via IPFS, with hash values and metadata recorded on the blockchain, ensuring immutability and transparency. The simulation results show that SEAIS exhibits more stable and efficient processing times in extreme environments. Also, it maintains low on-chain storage overhead, enhancing scalability. [ABSTRACT FROM AUTHOR]

Drones have been attracting significant attention in the field of agriculture. They can be used for various tasks such as spraying pesticides, monitoring pests, and assessing crop growth. Sensors are also widely used in agriculture to monitor environmental parameters such as soil moisture and temperature. Due to the high cost of communication infrastructure and radio-wave modules, the adoption of high-density sensing systems in agriculture is limited. To address this issue, we propose an agricultural sensor network system using drones and Optical Camera Communication (OCC). The idea is to transmit sensor data from LED panels mounted on sensor nodes and receive the data using a drone-mounted camera. This enables high-density sensing at low cost and can be deployed in areas with underdeveloped infrastructure and radio silence. We propose a trajectory control algorithm for the receiving drone to efficiently collect the sensor data. From computer simulations, we confirmed that the proposed algorithm reduces total flight time by 30 % compared to a shortest-path algorithm. We also conducted a preliminary experiment at a leaf mustard farm in Kamitonda-cho, Wakayama, Japan, to demonstrate the effectiveness of the proposed system. We collected 5178 images of LED panels with a drone-mounted camera to train YOLOv5 for object detection. With simple On–Off Keying (OOK) modulation, we achieved sufficiently low bit error rates (BERs) under 10 − 3 in the real-world environment. The experimental results show that the proposed system is applicable for drone-based sensor data collection in agriculture. [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 brassinosteroid (BR) receptor BRASSINOSTEROID-INSENSITIVE 1 (BRI1) plays a critical role in plant growth and development. Although much is known about how BR signaling regulates growth and development in many crop species, the role of StBRI1 in regulating potato (Solanum tuberosum) tuber development is not well understood. To address this question, a series of comprehensive genetic and biochemical methods were applied in this investigation. It was determined that StBRI1 and Solanum tuberosum PLASMA MEMBRANE (PM) PROTON ATPASE2 (PHA2), a PM-localized proton ATPase, play important roles in potato tuber development. The individual overexpression of StBRI1 and PHA2 led to a 22% and 25% increase in tuber yield per plant, respectively. Consistent with the genetic evidence, in vivo interaction analysis using double transgenic lines and PM H+-ATPase activity assays indicated that StBRI1 interacts with the C-terminus of PHA2, which restrains the intramolecular interaction of the PHA2 C-terminus with the PHA2 central loop to attenuate autoinhibition of PM H+-ATPase activity, resulting in increased PHA2 activity. Furthermore, the extent of PM H+-ATPase autoinhibition involving phosphorylation-dependent mechanisms corresponds to phosphorylation of the penultimate Thr residue (Thr-951) in PHA2. These results suggest that StBRI1 phosphorylates PHA2 and enhances its activity, which subsequently promotes tuber development. Altogether, our results uncover a BR–StBRI1–PHA2 module that regulates tuber development and suggest a prospective strategy for improving tuberous crop growth and increasing yield via the cell surface-based BR signaling pathway. The brassinosteroid receptor StBRI1 promotes potato tuber development by enhancing the activity of the plasma membrane H+-ATPase PHA2 by phosphorylating Thr-951. [ABSTRACT FROM AUTHOR]

Crop growth simulation models relate the soil–water-plant-atmosphere components to estimate the development and yield of plants in different scenarios, enabling the identification of efficient irrigation strategies. The aim of this study was to calibrate crop coefficients for a common bean cultivar (IAPAR 57) and assess the AquaCrop model's efficacy in simulating crop growth under different irrigation regimes (T0 – non-irrigated, T1—fully irrigated, and T2—deficit irrigated) and sowing dates (S1—March 21, S2—April 24, and S3—August 23). Successful calibration was achieved for crop seasons with suitable temperatures to crop growth (S1 and S3). However, during periods with suboptimal temperatures (April 24 season), coupled with reduced irrigation supply (T0 and T2), the AquaCrop model did not appropriately account for the combined effects of thermal and water stresses. Despite adjustments to stress coefficients, this led to an overestimation of crop growth and yield. In long-term simulations, the model successfully replicated the variability of crop water availability over cropping seasons, reflecting the impact of precipitation variations. It recommended irrigation strategies for the study region (irrigate at depletion of 120 and 170% of readily available water for sowing on March 21 and August 24, respectively) to achieve high crop yield (> 2,769 kg ha−1) and water productivity (1,050 to 1,445 kg m−3) with minimal application depths (< 150 mm). While acknowledging the need for improvements in thermal stress calculations, the AquaCrop model demonstrates promising utility in studies and applications where water availability significantly influences crop production. [ABSTRACT FROM AUTHOR]

Background: Pigeon pea (Cajanus cajan, Mayer) is the member of Asian Cajanus crop and also reffered as Tur and Arhar and commonly grown in Kharif session in Indian subcontinent.). In India, around 250 known species of insect pests have been identified to affect pigeon pea crops at different growth stages. The leaf webber and leaf roller are notable insect pests that pose a significant threat to pigeonpea, particularly during its vegetative stage. The leaf webber and leaf roller, among various insect pests, are notably visible and pose a threat to pigeonpea. These insects target the pigeonpea plant during its vegetative stage. Specifically, the leaf webber, identified as Grapholita (Pammene) critica, Meyrick (Lepidoptera: Tortricidae), has been acknowledged as a significant constraint affecting pigeonpea production. The aim of this investigation is to assess the relationship between the incidence of leaf roller and leaf webber and various abiotic factors, including temperature, relative humidity, wind velocity and rainfall. Methods: The research, spanning Kharif 2021-22 and 2022-23, at Entomological Research Block in BUAT, Banda. It aimed to observe leaf roller and leaf webber incidence in pigeon pea alongside weather conditions. 'IPA-203' pigeon pea variety was manually sown in 200 m2 plots on July 12, 2021 (first year) and July 18, 2022 (second year). Standard agronomic practices were adhered to and no insecticides were used on the crop. Result: Over the two-year observation period, the larval population of G. critica on pigeon pea plants commenced during the 34th SMW, peaking at 4.90 larvae plant-1 in the 45th SMW. Simultaneously, C. soyella infestation initiated in the 34th SMW, steadily increasing until the 42nd SMW, reaching its peak larval count at 3.03. [ABSTRACT FROM AUTHOR]

Background: Clusterbean (Cyamopsis tetragonoloba L.) is important commercial crop which belongs to family Leguminosae and has capacity to fix the atmospheric nitrogen. The nitrogen fixed by the legume crop is not enough to meet the demand of short duration crop like clusterbean. Besides, the experimental site falls under arid region where drought stress and poor distribution of rain during the crop growing cycle is common which affects the crop growth and productivity. Drawing on these insights, the study was planned to assess the performance of cluster bean with objectives to quantify water balance and to calculate suitable levels of irrigation and nitrogen. Methods: The experiment was conducted at research farm, Agricultural research sub-station, Hanumangarh, Swami Keshwanand Rajasthan Agricultural University, Bikaner during Kharif, 2016. Cluster bean variety RGC-1055 with seed rate of 16 kg ha-1 was planted with 3 levels of irrigation (100, 200 and 300 mm) and 4 levels of nitrogen (0, 20, 40 and 60 kg ha-1) and analyzed in split plot design with three replications. Result: Results showed by that the irrigation and nitrogen application rates significantly influenced the growth parameters, yield, nitrogen content and uptake by cluster bean. Irrigation level 200 mm significantly increased leaf area index at 60 DAS (2.62), dry matter accumulation (DMA) at 60 DAS and harvest (16.51 and 42.62 g plant-1), crop growth rate at 30-60 DAS and at harvest (0.36 and 0.62 g m-2 day-1), relative growth rate at 60- at harvest (1.59 g g-1 day-1), grain yield (1624 kg ha-1), straw yield (3645 kg ha-1) and biological yield (5270 kg ha-1) over irrigation level 100 mm, but statistically at par with irrigation level 300 mm. However, leaf area index at harvest (1.24) was highest at 300 mm irrigation level. Nitrogen level at 40 kg ha-1 significantly increased leaf area index at 30, 60 DAS and at harvest (0.19, 2.65 and 1.16), dry matter accumulation at 60 DAS and at harvest (19.62 and 49.58 g plant-1), crop growth rate at 30-60 DAS and 60-harvest (0.46 and 0.71 g m-2 day-1), relative growth rate at 30-60 DAS and 60-harvest (1.26 and 1.66 g g-1 day-1), grain yield (1668 kg ha-1), straw yield (3696 kg ha-1) and biological yield (5364 kg ha-1) over control. The highest nitrogen content and uptake in grain and straw was obtained with irrigation level at 300 mm and nitrogen level @ 60 kg ha-1. [ABSTRACT FROM AUTHOR]

Phosphorus is a major essential element in plants, and the absorption and transport of P are related to crop growth and productivity. Phosphate (Pi) is absorbed in the roots and transported to the shoot. Plants store surplus Pi in the vacuoles. The characteristics of Pi storage and turnover in various parts of the nodulated soybeans might be related to plant growth and P-use efficiency. This research focused on the changes in the Pi concentrations and Pi contents in each part of young soybean plants grown in Pi-sufficient (50 μM Pi) or Pi-deficient (0 μM Pi) conditions. Also, the Pi flux rate in xylem sap from roots to shoot was determined. The growth of the plants was the same after 7 days of Pi-sufficient and Pi-deficient treatments. During the Pi-deficient period, the Pi concentrations in the roots, leaves, and stems decreased significantly but did not deplete. The decrease in Pi concentration in nodules was much slower than the other parts. After the re-supply of 50 μM Pi in the solution, the Pi concentration increased only a little in each part of the Pi-deficient plants. The Pi concentration and Pi flux in the xylem sap quickly responded to the changes in the Pi concentration in the culture solution. [ABSTRACT FROM AUTHOR]

Using the case study of a sterile-type chilling disaster during the head-flowering phase of rice, this study presents a novel approach to constructing vulnerability curves that can overcome data limitations while also taking into account crop growth mechanisms. Meteorological data during 1990-2010 was used to generate sterile-type chilling scenarios at county scale, estimated rice yield losses through combinmg one crop model (MCWLA) and machine learning (XGBoost) method, finally developed sterile-type chilling vulnerability curves for each main rice-planting zone in China and estimated long-tenn historical (1961-2010) yield loss caused by sterile-type chilling disasters. The results showed that: (1) Machine learning could efiectively reproduce the estimation ability of crop model (RRMSE<6%, R²>0.93). (2) The sterile-type vulnerability decreased with decreasing latitude, and was weaker in growing seasons for late rice than that in early rice. (3) The historical yield loss was higher for single rice (1224kg-ha-1) than for double rice (early rice: 868kg-ha-1; late rice: 807kg-ha-1). [ABSTRACT FROM AUTHOR]

Crop monitoring with unmanned aerial vehicles (UAVs) has the potential to reduce field monitoring costs while increasing monitoring frequency and improving efficiency. However, the utilization of RGB-based UAV imagery for crop-specific monitoring, especially for sugarcane, remains limited. This work proposes a UAV platform with an RGB camera as a low-cost solution to monitor sugarcane fields, complementing the commonly used multi-spectral methods. This new approach optimizes the RGB vegetation indices for accurate prediction of sugarcane growth, providing many improvements in scalable crop-management methods. The images were captured by a DJI Mavic Pro drone. Four RGB vegetation indices (VIs) (GLI, VARI, GRVI, and MGRVI) and the crop surface model plant height (CSM_PH) were derived from the images. The fractional vegetation cover (FVC) values were compared by image classification. Sugarcane plant height predictions were generated using two machine learning (ML) algorithms—multiple linear regression (MLR) and random forest (RF)—which were compared across five predictor combinations (CSM_PH and four VIs). At the early stage, all VIs showed significantly lower values than later stages (p < 0.05), indicating an initial slow progression of crop growth. MGRVI achieved a classification accuracy of over 94% across all growth phases, outperforming traditional indices. Based on the feature rankings, VARI was the least sensitive parameter, showing the lowest correlation (r < 0.5) and mutual information (MI < 0.4). The results showed that the RF and MLR models provided better predictions for plant height. The best estimation results were observed withthe combination of CSM_PH and GLI utilizing RF model (R2 = 0.90, RMSE = 0.37 m, MAE = 0.27 m, and AIC = 21.93). This study revealed that VIs and the CSM_PH derived from RGB images captured by UAVs could be useful in monitoring sugarcane growth to boost crop productivity. [ABSTRACT FROM AUTHOR]

Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis) using an object-based image analysis approach. The methodology employs YOLOv8, a Grounding Detection Transformer with Improved Denoising Anchor Boxes (DINO), and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using aerial image datasets, which then facilitated the training of the Grounded Segment Anything Model framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study's findings utilized a multi-modal monitoring approach to highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. The results indicate consistent and comparable growth patterns between aerial images and ortho-mosaics, with significant periods of rapid expansion and minor fluctuations over time. The results also indicated a correlation between the time and method of observation which paves a future possibility of integration of such techniques aimed at increasing the accuracy in crop growth monitoring based on automatically derived temporal crop row segmentation masks. [ABSTRACT FROM AUTHOR]

To investigate the effects and mechanism of prolonged inorganic nitrogen (N) fertilization on the N-use efficiency of spring wheat (Triticum aestivum L.), a long-term study initiated in 2003 was conducted. The study analyzed how N fertilization affects dry matter translocation, N translocation, soil NO3-N, and N-use efficiency. Five different N-fertilizer rate treatments were tested: N0, N52.5, N105, N157.5, and N210, corresponding to annual N fertilizer doses of 0, 52.5, 105.0, 157.5, and 210.0 kg N ha−1, respectively. Results showed that increasing N-fertilizer rates significantly enhanced the two-year average dry matter accumulation amount (DMA) at maturity by 22.97–56.25% and pre-flowering crop growth rate (CGR) by 17.11–92.85%, with no significant increase beyond 105 kg N ha−1. However, no significant correlation was observed between the dry matter translocation efficiency (DTE) and wheat grain yield. Both insufficient and excessive N applications resulted in an imbalanced N distribution favoring vegetative growth over reproductive growth, thus negatively impacting N-use efficiency. At maturity, the N-fertilized treatments significantly increased the two-year average N accumulation amount (NAA) by 52.04–129.98%, with no further increase beyond 105 kg N ha−1. N fertilization also improved the two-year average N translocation efficiency (NTE) by 56.89–63.80% and the N contribution proportion (NCP) of wheat vegetative organs by 27.79–57.83%, peaking in the lower-N treatment (N52.5). However, high-N treatment (N210) led to an increase in NO3-N accumulation in the 0–100 cm soil layer, with an increase of 26.27% in 2018 and 122.44% in 2019. This higher soil NO3-N accumulation in the 0–100 cm layer decreased NHI, NUE, NAE, NPFP, and NMB. Additionally, N fertilization significantly reduced the two-year average N harvest index (NHI) by 9.89–12.85% and N utilization efficiency (NUE) by 11.14–20.79%, both decreasing with higher N application rates. The NAA followed the trend of anthesis > maturity > jointing. At the 105 kg N ha−1 rate, the highest N agronomic efficiency (NAE) (9.31 kg kg−1), N recovery efficiency (NRE) (38.32%), and N marginal benefit (NMB) (10.67 kg kg−1) were observed. Higher dry matter translocation amount (DTA) and N translocation amount (NTA) reduced NHI and NUE, whereas higher NTE improved NHI, NUE, and N partial factor productivity (NPFP). Overall, N fertilization enhanced N-use efficiency in spring wheat by improving N translocation rather than dry matter translocation. [ABSTRACT FROM AUTHOR]

Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby compensating for the limitations of in-situ observations and satellite remote sensing. However, previous research has primarily focused on SMC diagnostics for the entire crop growth period, often neglecting the development of targeted soil moisture modeling paradigms that account for the specific characteristics of the canopy and root zone at different growth stages. Furthermore, the variations in soil moisture status between fields, resulting from the hysteresis of water flow in irrigation channels at different levels, may influence the development of soil moisture modeling schemes, an area that has been seldom explored. In this study, SMC models based on UAV spectral information were constructed using Random Forest (RF) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithms. The soil moisture modeling paradigms (i.e., input–output mapping) under different growth stages and soil moisture conditions of summer maize were systematically compared and discussed, along with the corresponding physical interpretability. Our results showed that (1) the SMC modeling schemes differ significantly across the various growth stages, with distinct input–output mappings recommended for the early (i.e., jointing, tasselling, and silking stages), middle (i.e., blister and milk stages), and late (i.e., maturing stage) periods. (2) these machine learning-based models performed best at the jointing stage, while subsequently, their accuracy generally exhibited a downward trend as the maize grew. (3) the RF model demonstrates superior robustness in estimating soil moisture status across different fields (moisture conditions), achieving optimal estimation accuracy in fields with overall higher SMC in line with the PSO-SVM model. (4) unlike the RF model's robustness in spatial SMC diagnostics, the PSO-SVM model more reliably captured the temporal dynamics of SMC across different growth stages of summer maize. This study offers technical references for future modelers in UAV-based SMC modeling across various spatial and temporal conditions, addressing both the types of models as well as their input features. [ABSTRACT FROM AUTHOR]

The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence on the field, and poor adaptability of the model in traditional agricultural applications. Therefore, this study makes a systematic literature retrieval based on Web of Science, Scopus, Google Scholar, and PubMed databases, introduces in detail the assimilation strategies based on many new remote sensing data sources, such as satellite constellation, UAV, ground observation stations, and mobile platforms, and compares and analyzes the progress of assimilation models such as compulsion method, model parameter method, state update method, and Bayesian paradigm method. The results show that: (1) the new remote sensing platform data assimilation shows significant advantages in precision agriculture, especially in emerging satellite constellation remote sensing and UAV data assimilation. (2) SWAP model is the most widely used in simulating crop growth, while Aquacrop, WOFOST, and APSIM models have great potential for application. (3) Sequential assimilation strategy is the most widely used algorithm in the field of agricultural data assimilation, especially the ensemble Kalman filter algorithm, and hierarchical Bayesian assimilation strategy is considered to be a promising method. (4) Leaf area index (LAI) is considered to be the most preferred assimilation variable, and the study of soil moisture (SM) and vegetation index (VIs) has also been strengthened. In addition, the quality, resolution, and applicability of assimilation data sources are the key bottlenecks that affect the application of data assimilation in the development of precision agriculture. In the future, the development of data assimilation models tends to be more refined, diversified, and integrated. To sum up, this study can provide a comprehensive reference for agricultural monitoring, yield prediction, and crop early warning by using the data assimilation model. [ABSTRACT FROM AUTHOR]

In arid and semiarid regions, crop production has high irrigation water demands due to low precipitation. Efficient irrigation water management strategies can be developed using crop growth models to assess the effect of different irrigation management practices on crop productivity. The leaf area index (LAI) is an important growth parameter used in crop modeling. Measuring LAI requires specialized and expensive equipment not readily available for producers. Canopy cover (CC) and canopy height (CH) measurements, on the other hand, can be obtained with little effort using mobile devices and a ruler, respectively. The objective of this study was to determine the relationships between LAI, CC, and CH for fully and deficit-irrigated alfalfa (Medicago sativa L.). The LAI, CC, and CH measurements were obtained from an experiment conducted at the Valley Road Field Lab in Reno, Nevada, starting in the Fall of 2020. Three irrigation treatments were applied to two alfalfa varieties (Ladak II and Stratica): 100%, 80%, and 60% of full irrigation demands. Biweekly measurements of CC, CH, and LAI were collected during the growing seasons of 2021 and 2022. The dataset was randomly split into training and testing subsets. For the training subset, an exponential model and a simple linear regression (SLR) model were used to determine the individual relationship of CC and CH with LAI, respectively. Also, a multiple linear regression (MLR) model was implemented for the estimation of LAI with CC and CH as its predictors. The exponential model was fitted with a residual standard error (RSE) and coefficient of determination (R2) of 0.97 and 0.86, respectively. A lower performance was obtained for the SLR model (RSE = 1.03, R2 = 0.81). The MLR model (RSE = 0.82, R2 = 0.88) improved the performance achieved by the exponential and SLR models. The results of the testing indicated that the MLR performed better (RSE = 0.82, R2 = 0.88) than the exponential model (RSE = 0.97, R2 = 0.86) and the SLR model (RSE = 1.03, R2 = 0.82) in the estimation of LAI. The relationships obtained can be useful to estimate LAI when CC, CH, or both predictors are available and assist with the validation of data generated by crop growth models. [ABSTRACT FROM AUTHOR]

In recent years, the increase due to the rise in the high yielding verities led to the high-rate application of chemicals and pesticides in the soil. These chemical fertilizers give great responses to the farmers on a short-term basis, but in the long term, they harm the soil and human health by interfering in the food chain. The use of biofertilizers is a very good alternative for crop production in a sustainable and environment-friendly manner. Numerous bacteria and fungi can be used as biofertilizers for making the essential nutrients available to the plants which are associated with the rhizosphere of different crops, either symbiotically or non-symbiotically. Zinc plays a vital role in crop growth and achieving a great yield among the various macro and micronutrients. Zinc is responsible for Auxin synthesis, chlorophyll formation, protein metabolism, carbohydrate fixation, disease and stress tolerance. Zn deficiency is a global issue that gradually lowers crop output and productivity. Using zinc solubilizing microorganisms is one of the most effective sustainable approaches to achieving higher yield and restoring soil productivity. These microorganisms solubilize the available zinc pool in the soil so plants can easily uptake zinc. Bacillus subtilis, Thiobacillus thioxidans, Rhizobium, Pantoea sp., Gluconacetobacter, Saccharomyces sp. and PGPR (Plant Growth Promoting Rhizobacteria) are involved in zinc solubilizing process and boost the soluble zinc in the soil. Using zinc solubilizers can reduce the fertilizer requirement of the crop to about 25-50% in combination with inorganic zinc fertilizers, so it reduces overall fertilizer cost. [ABSTRACT FROM AUTHOR]