Crop Yield Losses Research Articles

Prediction of the global occurrence of maize diseases and estimation of yield loss under climate change.

Climate change significantly impacts global maize production via yield reduction, posing a threat to global food security. Disease-related crop damage reduces quality and yield and results in economic losses. However, the occurrence of diseases caused by climate change, and thus crop yield loss, has not been given much attention. This study aims to investigate the potential impact of six major diseases on maize yield loss over the next 20 to 80 years under climate change. To this end, the Maximum Entropy model was implemented, based on Coupled Model Intercomparison Project 6 data. The results indicated that temperature and precipitation are identified as primary limiting factors for disease onset. Southern corn rust was projected to be the most severe disease in the future; with a few of the combined occurrence of all the selected diseases covered in this study were predicted to progressively worsen over time. Yield losses caused by diseases varied per continent, with North America facing the highest loss, followed by Asia, South America, Europe, Africa, and Oceania. This study provides a basis for regional projections and global control of maize diseases under future climate conditions. © 2024 Society of Chemical Industry.

Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM

Crop diseases can significantly affect various aspects of crop cultivation, including crop yield, quality, production costs, and crop loss. The utilization of modern technologies such as image analysis via machine learning techniques enables early and precise detection of crop diseases, hence empowering farmers to effectively manage and avoid the occurrence of crop diseases. The proposed methodology involves the use of modified MobileNetV3Large model deployed on edge device for real-time monitoring of grape leaf disease while reducing computational memory demands and ensuring satisfactory classification performance. To enhance applicability of MobileNetV3Large, custom layers consisting of two dense layers were added, each followed by a dropout layer, helped mitigate overfitting and ensured that the model remains efficient. Comparisons among other models showed that the proposed model outperformed those with an average train and test accuracy of 99.66% and 99.42%, with a precision, recall, and F1 score of approximately 99.42%. The model was deployed on an edge device (Nvidia Jetson Nano) using a custom developed GUI app and predicted from both saved and real-time data with high confidence values. Grad-CAM visualization was used to identify and represent image areas that affect the convolutional neural network (CNN) classification decision-making process with high accuracy. This research contributes to the development of plant disease classification technologies for edge devices, which have the potential to enhance the ability of autonomous farming for farmers, agronomists, and researchers to monitor and mitigate plant diseases efficiently and effectively, with a positive impact on global food security.

Examining Crop Yield Losses in Iğdır Plain Irrigation Systems in Türkiye Amidst Water Constraints

Water stands as a crucial component of agricultural production. This study aims to propose water efficiency measures in agriculture as an essential element for climate change adaptation. In this study, yield losses in staple crops in agricultural regions are analyzed by using the most suitable methodologies, particularly in agriculture-reliant developing nations. Furthermore, this study seeks to determine the financial consequences of such losses. The methodology applied for this purpose was implemented in Türkiye’s Iğdır Plain, selected as the study site. As the first step, the yields of the first three most cultivated products in each product group were assessed under normal climatic conditions in terms of their crop water requirements and irrigation water requirements. Subsequently, the irrigation water supply was reduced by 10%, and the resulting yield losses were calculated per hectare. Then, the overall crop losses after applying the 10% water constraint were determined in the total cultivation area. Among the crops cultivated in the region, the analysis reveals that clover from the field crops category exhibits the highest water dependence, while apricot demonstrates the least reliance on water resources. As a result, the recommended crop rotation for the Iğdır Plain under water constraints comprises wheat, apricot, watermelon, maize, melon, apple, tomato, peach, and clover. The following measures are proposed to ensure sustainable use of water resources and reduce exposure to climate change: increasing the water transmission efficiency and water use efficiency in irrigation areas, allocating more space to water-stress-resistant crops in the crop pattern in basins, and substituting crops requiring excessive water with less water-dependent crops.

Exploring the nematicidal mechanisms and control efficiencies of oxalic acid producing Aspergillus tubingensis WF01 against root-knot nematodes.

Root-knot nematodes (RKN; Meloidogyne spp.) are among the highly prevalent and significantly detrimental pathogens that cause severe economic and yield losses in crops. Currently, control of RKN primarily relies on the application of chemical nematicides but it has environmental and public health concerns, which open new doors for alternative methods in the form of biological control. In this study, we investigated the nematicidal and attractive activities of an endophytic strain WF01 against Meloidogyne incognita in concentration-dependent experiments. The active nematicidal metabolite was extracted in the WF01 crude extract through the Sephadex column, and its structure was identified by nuclear magnetic resonance and mass spectrometry data. The strain WF01 was identified as Aspergillus tubingensis based on morphological and molecular characteristics. The nematicidal and attractive metabolite of A. tubingensis WF01 was identified as oxalic acid (OA), which showed solid nematicidal activity against M. incognita, having LC50 of 27.48 μg ml-1. The Nsy-1 of AWC and Odr-7 of AWA were the primary neuron genes for Caenorhabditis elegans to detect OA. Under greenhouse, WF01 broth and 200 μg ml-1 OA could effectively suppress the disease caused by M. incognita on tomatoes respectively with control efficiency (CE) of 62.5% and 70.83%, and promote plant growth. In the field, WF01-WP and 8% OA-WP formulations showed moderate CEs of 51.25%-61.47% against RKN in tomato and tobacco. The combined application of WF01 and OA resulted in excellent CEs of 66.83% and 69.34% toward RKN in tomato and tobacco, respectively. Furthermore, the application of WF01 broth or OA significantly suppressed the infection of J2s in tomatoes by upregulating the expression levels of the genes (PAL, C4H, HCT, and F5H) related to lignin synthesis, and strengthened root lignification. Altogether, our results demonstrated that A. tubingensis WF01 exhibited multiple weapons to control RKN mediated by producing OA to lure and kill RKN in a concentration-dependent manner and strengthen root lignification. This fungus could serve as an environmental bio-nematicide for managing the diseases caused by RKN.

Contaminants in shrimp probiotics - a potential emerging threat to food security

Probiotics are used widely in shrimp aquaculture to improve growth and prevent disease; however, for the most part their safety and efficacy remains largely unknown. Shrimp aquaculture may be vulnerable to the negative effects of unsafe and ineffective commercial products, such as the transfer of shrimp and human pathogens and antibiotic resistance genes. Shrimp health may also be adversely affected by the use of these products, potentially leading to crop yield losses. Here, we used 16S amplicon sequencing to identify the bacterial genera present in commercial shrimp probiotics and assess this against their listed product content. We identified the presence of additional genera to the labelled contents including Escherichia/Shigella and Enterococcus that may pose a risk of disease to animals and potentially humans that come into contact with these products, as well as potentially acting as carriers for virulence and antimicrobial resistance genes. Our results suggest that some commercial shrimp probiotics may be misleading to consumers and potentially unsafe for shrimp, people and the environment. We highlight the risks that contaminated commercial probiotics may pose to food security and present a series of safety and efficacy considerations to support the sustainable use of commercial shrimp probiotics.

Spatially refined salinity hazard analysis in gypsum‐affected irrigated soils

AbstractThe global extent of salt‐affected agricultural land, 20% of which is deemed gypsiferous, results in billions of dollars of annual economic loss, a serious problem deserving of attention. However, the analysis of gypsiferous saline soils, such as in the irrigated Lower Arkansas River Valley (LARV) of Colorado, can result in an inflated estimation of soil salinity when using the traditional soil saturated paste extract electrical conductivity (ECe), leading to inaccurate crop yield loss predictions and misguided decisions for remediation. Sparingly soluble gypsum (CaSO4 2H2O) in these soils dissolves more readily during laboratory preparation of saturated paste extracts because of excess soil water dilution coupled with sample disturbance. We present a pragmatic linear‐regression approach to correct for this phenomenon, calibrated using two adapted methods for correcting ECe on an individual sample basis. The novel approach used electrical conductivity of pore water samples from saline fields to evaluate the accuracy of the correction methods. The approach was applied on soil samples from two surface‐irrigated, saline fields in the LARV, which were mapped using electromagnetic induction data and analysis of covariance linear regression, calibrated for ECe and ECe corrected for excess gypsum dissoultion (ECeg). Average ECeg values are as much as 26% lower than uncorrected ECe in gypsum‐biased portions of the fields. Estimation of corn salinity hazard in these gypsum‐affected areas using ECeg in lieu of ECe in a traditional yield response function generated mean relative yield values that are higher by up to 13 percentage points. We discuss lessons learned and suggest enhancements to the techniques.

Characterization of pectolytic bacteria causing soft rot on sugar beet in the west of Iran

AbstractBacterial soft rots are amongst the most economically important diseases that cause significant yield losses in various crops. A clear identification of the causal agents is required to develop management strategies. In 2022 and 2023, after the widespread occurrence of soft rot disease in sugar beet fields in the Firuzan region, Hamedan Province, Iran, the current study was conducted to identify and characterize the causal agents. A total of 38 bacterial isolates were obtained from sugar beet plants with soft rot symptoms and subjected to biochemical and phenotypical characterization and pathogenicity tests on healthy slices from potato tubers and sugar beet taproots. Twelve isolates from this study along with the type strain Pectobacterium carotovorum ICMP 5702 were selected for subsequent screening by BOX‐PCR. Six isolates were chosen for sequencing the 16SrRNA gene and phylogenetic analysis. In BLAST analyses, the pathogenic isolates S2, S9, S22, S11, S23 and 111 showed the highest similarity values (≥99%) with the reference sequences of Raoultella terrigena, Klebsiella michiganensis, Erwinia (Winslowiella) iniecta, Pantoea agglomerans and P. carotovorum, respectively. These isolates induced soft rot and maceration on plant materials from five species (bell pepper fruits, garlic cloves and slices from sugar beet taproot, potato tubers and carrot roots) 72 h after inoculation. In the greenhouse experiment, they caused soft rot symptoms in the taproots of 2‐month‐old sugar beet plants 3 weeks after inoculation. These soft rot agents had Gram‐negative, catalase‐positive and oxidase‐negative reactions, grew under both aerobic and anaerobic conditions, tolerated and grew at 37°C and 5% NaCl. In general, the results of this research showed that in addition to Pectobacterium, four other genera from the order Enterobacterales including Erwinia, Pantoea, Klebsiella and Raoultella, are amongst the causes of soft rot in sugar beet in Firuzan region of Hamedan.

The root-knot nematode effector MiEFF12 targets the host ER quality control system to suppress immune responses and allow parasitism.

Root-knot nematodes (RKNs) are microscopic parasitic worms able to infest the roots of thousands of plant species, causing massive crop yield losses worldwide. They evade the plant's immune system and manipulate plant cell physiology and metabolism to transform a few root cells into giant cells, which serve as feeding sites for the nematode. RKN parasitism is facilitated by the secretion in planta of effector molecules, mostly proteins that hijack host cellular processes. We describe here a conserved RKN-specific effector, effector 12 (EFF12), that is synthesized exclusively in the oesophageal glands of the nematode, and we demonstrate its function in parasitism. In the plant, MiEFF12 localizes to the endoplasmic reticulum (ER). A combination of RNA-sequencing analysis and immunity-suppression bioassays revealed the contribution of MiEFF12 to the modulation of host immunity. Yeast two-hybrid, split luciferase and co-immunoprecipitation approaches identified an essential component of the ER quality control system, the Solanum lycopersicum plant bap-like (PBL), and basic leucine zipper 60 (BZIP60) proteins as host targets of MiEFF12. Finally, silencing the PBL genes in Nicotiana benthamiana decreased susceptibility to Meloidogyne incognita infection. Our results suggest that EFF12 manipulates PBL function to modify plant immune responses to allow parasitism.

Integration analysis of miRNA-mRNA pairs between two contrasting genotypes reveals the molecular mechanism of jujube (Ziziphus jujuba Mill.) response to high-temperature stress

With global warming, high temperature (HT) has become one of the most common abiotic stresses resulting in significant crop yield losses, especially for jujube (Ziziphus jujuba Mill.), an important temperate economic crop cultivated worldwide. This study aims to explore the coping mechanism of jujube to HT stress at the transcriptional and post-transcriptional levels, including identifying differentially expressed miRNAs and mRNAs as well as elucidating the critical pathways involved. High-throughput sequencing analyses of miRNA and mRNA were performed on jujube leaves, which were collected from “Fucumi” (heat-tolerant) and “Junzao” (heat-sensitive) cultivars subjected to HT stress (42 °C) for 0, 1, 3, 5, and 7 days, respectively. The results showed that 45 known miRNAs, 482 novel miRNAs, and 13,884 differentially expressed mRNAs (DEMs) were identified. Among them, integrated analysis of miRNA target genes prediction and mRNA-seq obtained 1306 differentially expressed miRNAs-mRNAs pairs, including 484, 769, and 865 DEMIs-DEMs pairs discovered in “Fucuimi”, “Junzao” and two genotypes comparative groups, respectively. Furthermore, functional enrichment analysis of 1306 DEMs revealed that plant-pathogen interaction, starch and sucrose metabolism, spliceosome, and plant hormone signal transduction were crucial pathways in jujube leaves response to HT stress. The constructed miRNA-mRNA network, composed of 20 DEMIs and 33 DEMs, displayed significant differently expressions between these two genotypes. This study further proved the regulatory role of miRNAs in the response to HT stress in plants and will provide a theoretical foundation for the innovation and cultivation of heat-tolerant varieties.

Risk assessment, fitness cost and transcriptome analysis of cyantraniliprole resistance in Spodoptera frugiperda

Spodoptera frugiperda is a notorious invasive pest causing substantial yield losses of crops and has developed resistance to various types of insecticides. In this study, a cyantraniliprole-resistant strain, SfCYAN-R, was obtained from a susceptible strain, SfCYAN-S, after 13 generations of selection with cyantraniliprole. The fitness cost in SfCYAN-R strain was evaluated, and the putative resistance-related genes were explored by RNA-seq analysis. The results showed that SfCYAN-R strain developed 23.97-fold resistance to cyantraniliprole with the realistic heritability of 0.127. The development time of eggs, larvae, prepupae and pupae in SfCYAN-R strain was significantly prolonged than that in SfCYAN-S strain, but no difference in pupation rate, emergence rate and female fecundity was observed between SfCYAN-R and SfCYAN-S strains. Comparative gene expression analysis between SfCYAN-R and SfCYAN-S strains identified 776 significant differentially expressed genes (DEGs), among which several DEGs associated with xenobiotic metabolism were upregulated in SfCYAN-R strain. These results provide insights into the resistance mechanisms of cyantraniliprole and would be helpful for resistance management of S. frugiperda.

Edeine B1 produced by Brevibacillus brevis reduces the virulence of a plant pathogenic fungus by inhibiting mitochondrial respiration.

Plant pathogenic fungi cause serious diseases, which result in the loss of crop yields and reduce the quality of crops worldwide. To counteract the escalating risks of chemical fungicides, interest in biological control agents to manage plant diseases has significantly increased. In this study, we comprehensively screened microbial culture filtrates using a yeast screening system to find microbes exhibiting respiratory inhibition activity. Consequently, we found a soil-borne microbe Brevibacillus brevis HK544 strain exhibiting a respiration inhibitory activity and identified edeine B1 (EB1) from the culture filtrate of HK544 as the active compound of the respiration inhibition activity. Furthermore, against a plant pathogenic fungus Fusarium graminearum, our results showed that EB1 has effects on multiple aspects of respiration with the downregulation of most of the mitochondrial-related genes based on transcriptome analysis, differential EB1-sensitivity from targeted mutagenesis, and the synergistic effects of EB1 with electron transport chain complex inhibitors. With the promising plant disease control efficacy of B. brevis HK544 producing EB1, our results suggest that B. brevis HK544 has potential as a biocontrol agent for Fusarium head blight.IMPORTANCEAs a necrotrophic fungus, Fusarium graminearum is a highly destructive pathogen causing severe diseases in cereal crops and mycotoxin contamination in grains. Although chemical control is considered the primary approach to control plant disease caused by F. graminearum, fungicide-resistant strains have been detected in the field after long-term continuous application of fungicides. Moreover, applying chemical fungicides that trigger mycotoxin biosynthesis is a great concern for many researchers. Biocontrol of Fusarium head blight (FHB) by biological control agents (BCAs) represents an alternative approach and could be used as part of the integrated management of FHB and mycotoxin production. The most extensive studies on bacterial BCAs-fungal communications in agroecosystems have focused on antibiosis. Although many BCAs in agricultural ecology have already been used for fungal disease control, the molecular mechanisms of antibiotics produced by BCAs remain to be elucidated. Here, we found a potential BCA (Brevibacillus brevis HK544) with a strong antifungal activity based on the respiration inhibition activity with its active compound edeine B1 (EB1). Furthermore, our results showed that EB1 secreted by HK544 suppresses the expression of the mitochondria-related genes of F. graminearum, subsequently suppressing fungal development and the virulence of F. graminearum. In addition, EB1 exhibited a synergism with complex I inhibitors such as rotenone and fenazaquin. Our work extends our understanding of how B. brevis HK544 exhibits antifungal activity and suggests that the B. brevis HK544 strain could be a valuable source for developing new crop protectants to control F. graminearum.

Biomonitoring of biocontrol across the full annual cycle in temperate climates: Post‐harvest, winter and early‐season interaction data and methodological considerations for its collection

Abstract Conservation biocontrol, the regulation of crop pests by naturally occurring biocontrol agents (e.g., predators and parasitoids), is predominantly monitored throughout periods of primary crop growth when pests exert the most observable impact on yields. Pest‐focused agricultural biomonitoring often overlooks post‐harvest, winter and even early‐season biocontrol, despite the significant predator–pest interactions during these periods that profoundly affect pest abundance and, consequently, crop yields. Rapid advances in biomonitoring, particularly in the detection of predator–pest interactions that underpin biocontrol, provide an opportunity to reconsider how and when we monitor these interactions. Advances in agricultural biomonitoring must transcend methodological innovation and encompass conceptual changes in the monitoring of agricultural systems. Here, we assess existing evidence supporting the importance of periods beyond primary crop growth for biocontrol and how predator–pest interactions are likely to evolve during these periods, subsequently influencing pest population dynamics during the primary crop growth period. We advocate for a greater concerted effort to establish continuous monitoring of biocontrol interactions, particularly beyond primary crop growth periods in temperate climates. To facilitate this, we also summarise the methodological approaches that can make it possible and explore how extending sampling across the full annual cycle might impact the practicalities and outcomes of these approaches. Year‐round monitoring of biocontrol interactions, both in crops and adjacent semi‐natural habitats, will provide a previously intractable understanding of predator–pest dynamics, offering significant potential to enhance our ability to optimise and manipulate these systems. This would manifest in reduced crop yield losses, pest infestation rates and disease transmission, with concomitant long‐term financial, environmental and land‐use benefits.

Viperin-like proteins interfere with RNA viruses in plants.

Plant viruses cause substantial losses in crop yield and quality; therefore, devising new, robust strategies to counter viral infections has important implications for agriculture. Virus inhibitory protein endoplasmic reticulum-associated interferon-inducible (Viperin) proteins are conserved antiviral proteins. Here, we identified a set of Viperin and Viperin-like proteins from multiple species and tested whether they could interfere with RNA viruses in planta. Our data from transient and stable overexpression of these proteins in Nicotiana benthamiana reveal varying levels of interference against the RNA viruses tobacco mosaic virus (TMV), turnip mosaic virus (TuMV), and potato virus x (PVX). Harnessing the potential of these proteins represents a novel avenue in plant antiviral approaches, offering a broader and more effective spectrum for application in plant biotechnology and agriculture. Identifying these proteins opens new avenues for engineering a broad range of resistance to protect crop plants against viral pathogens.

DeepLeaf: Automated Plant Disease Diagnosis using Deep Learning Approach

Automated diagnosis of plant diseases is critical for ensuring food security and agricultural sustainability. In this study, we propose DeepLeaf, a novel deep-learning framework for the automated recognition and classification of plant diseases. DeepLeaf leverages convolutional neural networks to analyze plant leaf images and accurately identify disease symptoms. The framework is trained on a large dataset of annotated images, encompassing a wide range of plant species and disease types. Through extensive experimentation, we demonstrate the effectiveness of DeepLeaf in accurately diagnosing plant diseases across diverse environmental conditions and varying degrees of disease severity. Our results show that DeepLeaf achieves high accuracy and robustness, outperforming traditional methods and commercial systems in speed and reliability. Furthermore, DeepLeaf is designed to be easily deployable in real-world agricultural settings, enabling farmers and agronomists to identify and mitigate plant diseases quickly, thus refining crop yield and dropping economic losses.

Existing Condition Analysis and Business Models Identification of Rice Farmer Corporation in Subang Regency

Abstract The transformation of rice farmers from farmer groups to a corporate business entity necessitates their ability to meet rice productivity targets and implement a sustainable business model. This study aimed to analyze the current conditions of rice farmer corporations, including their rice production performance, added value, and business models. Initially, the performance of these corporations was assessed by comparing it to rice productivity targets. Subsequently, the Hayami method was used to analyze the added value generated by the farmers. Furthermore, the existing business model of the rice farmer corporation was described, and the canvas business model method was employed to identify elements that impact the sustainability of the rice business. The research was conducted in the rice agro-industry center locations in Subang Regency. Rice productivity still lower than target, it is crucial to prioritize post-harvest handling as it plays a vital role in minimizing crop yield losses. The findings of the value-added analysis conducted within the company obtained is good and indicate that both processing procedures have the potential to create additional value. In order for this business activity to function effectively, certain resources are required. To ensure the stability of this business, partnerships are essential, benefiting both the corporation and its partners. Moreover, the development of a business model is expected to enhance the role of rice farmer corporations in promoting the growth of national rice production.

Comparative proteomic and metabolomic analyses reveal stress responses of hemp to salinity.

Integrated omics analyses outline the cellular and metabolic events of hemp plants in response to salt stress and highlight several photosynthesis and energy metabolism related pathways as key regulatory points. Soil salinity affects many physiological processes of plants and leads to crop yield losses worldwide. For hemp, a crop that is valued for multiple aspects, such as its medical compounds, fibre, and seed, a comprehensive understanding of its salt stress responses is a prerequisite for resistance breeding and tailoring its agronomic performance to suit certain industrial applications. Here, we first observed the phenotype of salt-stressed hemp plants and found that under NaCl treatment, hemp plants displayed pronounced growth defects, as indicated by the significantly reduced average height, number of leaves, and chlorophyll content. Next, we conducted comparative proteomics and metabolomics to dissect the complex salt-stress response mechanisms. A total of 314 proteins and 649 metabolites were identified to be differentially behaving upon NaCl treatment. Functional classification and enrichment analysis unravelled that many differential proteins were proteases associated with photosynthesis. Through metabolic pathway enrichment, several energy-related pathways were found to be altered, such as the biosynthesis and degradation of branched-chain amino acids, and our network analysis showed that many ribosomal proteins were involved in these metabolic adaptations. Taken together, for hemp plants, influences on chloroplast function probably represent a major toxic effect of salinity, and modulating several energy-producing pathways possibly through translational regulation is presumably a key protective mechanism against the negative impacts. Our data and analyses provide insights into our understanding of hemp's stress biology and may lay a foundation for future functional genomics studies.

Identification and Functional Analysis of KH Family Genes Associated with Salt Stress in Rice.

Salinity stress has a great impact on crop growth and productivity and is one of the major factors responsible for crop yield losses. The K-homologous (KH) family proteins play vital roles in regulating plant development and responding to abiotic stress in plants. However, the systematic characterization of the KH family in rice is still lacking. In this study, we performed genome-wide identification and functional analysis of KH family genes and identified a total of 31 KH genes in rice. According to the homologs of KH genes in Arabidopsis thaliana, we constructed a phylogenetic tree with 61 KH genes containing 31 KH genes in Oryza sativa and 30 KH genes in Arabidopsis thaliana and separated them into three major groups. In silico tissue expression analysis showed that the OsKH genes are constitutively expressed. The qRT-PCR results revealed that eight OsKH genes responded strongly to salt stresses, and OsKH12 exhibited the strongest decrease in expression level, which was selected for further study. We generated the Oskh12-knockout mutant via the CRISPR/Cas9 genome-editing method. Further stress treatment and biochemical assays confirmed that Oskh12 mutant was more salt-sensitive than Nip and the expression of several key salt-tolerant genes in Oskh12 was significantly reduced. Taken together, our results shed light on the understanding of the KH family and provide a theoretical basis for future abiotic stress studies in rice.

Plant growth-promoting rhizobacteria modulate induced corn defense against Spodoptera litura (Lepidoptera: Noctuidae).

Common cutworm, Spodoptera litura is an important pest of corn causing significant crop yield loss. Synthetic insecticides have mostly been used to combat this pest, raising human and environmental health concerns. Plant growth-promoting rhizobacteria (PGPR) could compensate for or augment the harmful effects of agrochemicals. Herein, we aimed to assess whether PGPR-induced defenses in corn plants impact the host-plant selection behavior of S. litura. Headspace volatile organic compounds were analyzed using gas chromatography-mass spectrometry. Larvae fed inoculated corn exhibited lower weights and relative growth rate than noninoculated plants. Under choice experiments, PGPR-treated plants significantly reduced percentage leaf damage area and oviposition rate compared to untreated plants. Volatile organic compound ratio emission varied significantly between control and PGPR treatments, which, in part, explains feeding and oviposition deterrence in PGPR-treated plants. The results demonstrate that PGPR inoculation can enhance corn resistance to S. litura, making it a promising candidate for crop protection strategies.

Cotton leaf curl disease (CLCuD) prediction modeling in upland cotton under different ecological conditions using machine learning tools

Cotton leaf curl disease (CLCuD) is a major threat to cotton production in Africa and South Asia. Due to the dearth of absolute CLCuD-resistant cultivars and effective Bemisia tabaci-vector management strategy, yield loss in cotton crops is witnessed regularly. To ensure the timely application of management practices there is a dire need for a reliable prediction model that can forecast the CLCuD with high speed and accuracy. To overcome this problem, we developed and compared the machine learning (ML) techniques- multiple linear regression (MLR), bootstrap forest (BSF), boosted tree (BST) and artificial neural network (ANN) to predict CLCuD under field conditions. We investigated disease and weather data collected from four locations (host spots) for a period of nine years (2011–12 to 2019–20). During each season/year, 10 ecological variables including CLCuD disease incidence, severity data from ∼8000 plants on four cultivars (2000 each), and ecological factors at each location were recorded. Temperature data were transformed to growing degree days (GDD) and used for modeling as one of the most dependent factors. All data sets for training and validation sets were divided into the ratio of 75:25 for machine learning models. Results indicated that the BSF model was the best ML model with the highest R-squared value in terms of CLCuD prediction accuracy (R2training = 0.81 and R2validation = 0.64) for CLCuD prediction. ANN model with 14 hidden nodes achieved a slightly low R-squared value (R2training = 0.80 and R2validation = 0.79) having an architecture of (9:14:1) whereas, the BST model achieved the lowest R-squared value (R2training = 0.71 and R2validation = 0.59). The testing and validation of activation functions and various training and validation sets indicated that the BSF model is the best ML model. This can provide technical support for CLCuD prediction under field conditions through the designed graphical user interface and further advocate the timely application of management interventions to boost cotton productivity in the region.

Morpho-physiological adaptations to weed competition impair green bean (Phaseolus vulgaris) ability to overcome moderate salt stress.

The two stresses of weed competition and salt salinity lead to crop yield losses and decline in the productivity of agricultural land. These constraints threaten the future of food production because weeds are more salt stress tolerant than most crops. Climate change will lead to an increase of soil salinity worldwide, and possibly exacerbate the competition between weeds and crops. This aspect has been scarcely investigated in the context of weed-crop competition. Therefore, we conducted a field experiment on green beans (Phaseolus vulgaris ) to investigate the combined impact of weed competition and salt stress on key morpho-physiological traits, and crop yield. We demonstrated that soil salinity shifted weed composition toward salt tolerant weed species (Portulaca oleracea and Cynodon dactylon ), while it reduced the presence of lower tolerance species. Weed competition activated adaptation responses in green bean such as reduced leaf mass per area and biomass allocation to the stem, unchanged stomatal density and instantaneous water use efficiency, which diverge from those that are typically observed as a consequence of salt stress. The morpho-physiological modifications caused by weeds is attributed to the alterations of light intensity and/or quality, further confirming the pivotal role of the light in crop response to weeds. We concluded that higher yield loss caused by combined salt stress and weed competition is due to impaired morpho-physiological responses, which highlights the negative interaction between salt stress and weed competition. This phenomenon will likely be more frequent in the future, and potentially reduce the efficacy of current weed control methods.