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The main objective of this study is to determine the impact of climate change and natural disasters on GDP growth rate. In order to examine long run association between climate change, natural disasters and GDP, an Auto Regressive Distributed Lag (ARDL) model and correlation matrix were employed, using 20 years data spanning from 2001 to 2021. The focus of the study was to investigate into how CO2 emission causes climate change and climate change, in turn, causes natural disasters and, in turn, natural disasters cause human and crop losses as well as high inflation. The empirical evidence reveals that climate change, CO2 emissions and natural disasters have negative association with GDP because natural disasters in the shape of earthquakes, heavy rains, and floods cause huge human and crop losses, have negative impact on GDP. The variables, agriculture production and employment, have positive and significant association with GDP growth because increase in agriculture production and employment will definitely have significant and positive impact on GDP growth in the long run. The study suggests to build resilient technologies to cope with climate change, control CO2 emissions and inflation as these three variables have negative impact on GDP growth in the long run. This study significantly contributes by highlighting the link between climate change and natural disasters and their consequential negative impact on GDP growth. [ABSTRACT FROM AUTHOR]

Crop lodging is recognised as one of the yield‐limiting factors in agricultural production. Therefore, better understanding to improve lodging resistance and to prevent lodging‐induced losses in agronomic crops is necessary. Besides yield losses, lodging severely affects the crop harvesting process and increases the production cost. However, achieving the objective of higher crop yields and yield quality without increasing lodging risk is quite challenging. To this end, it is essential to interpret the underlying mechanism of plant stem buckling and failure of root anchorage and optimise the fundamental trade‐off between lodging resistance and yield performance in agronomic crops. In the present review, we made an effort to discuss recent and innovative research insights that guarantee greater lodging resistance along with advanced lodging prevention strategies while sustaining higher crop yield and yield quality. [ABSTRACT FROM AUTHOR]

Meloidogyne spp. are the most devastating plant‐parasitic nematodes affecting tomato worldwide. Although resistant cultivars and rootstocks are used, selection for virulence occurs in the pathogen. Consequently, using other resistance sources, such as Solanumtorvum, could improve resistance durability. Several experiments in microplots and plastic greenhouses were carried out to determine the potential use of S. torvum as a tomato rootstock to protect against M. incognita and M. javanica. In microplots, the relationship between nematode density at transplanting (Pi) and multiplication rate did not differ between Meloidogyne species in either ungrafted or grafted tomato. However, maximum multiplication rate and maximum density on grafted tomato were 1.27% and 2.93% those on ungrafted, respectively. The grafted tomato plants yielded between 2.9 and 7.5 more times than the ungrafted plants at Pi ≥ 100 eggs + J2s per 100 cm3 of soil, but no differences were observed in plastic greenhouse where a large amount of scion‐rooting occurred. In microplots, the quality of the tomato fruits of ungrafted and grafted plants was affected by the Pi. In parallel, some pot experiments were conducted on S. torvum and susceptible eggplant to determine the putative selection for nematode virulence to S. torvum and the nematode fitness cost. These showed that the nematode subpopulations infected and reproduced less on S. torvum than on eggplant. However, the female fertility was only reduced after development of three or four subpopulations on S. torvum. Finally, a histopathological study showed that nematode infection and development in S. torvum was delayed compared to eggplant. [ABSTRACT FROM AUTHOR]

In response to the pressing agricultural concern posed by insect pests, leading to substantial crop losses and compounded by the challenges of distinguishing between similar species, this study presents an innovative solution using convolutional neural networks (CNNs) for rapid and accurate insect species recognition, addressing the agricultural challenge of insect pests and species differentiation. Initially, six pre-trained CNN base models (VGG16, VGG19, ResNet50, Inception-V3, Xception, and MobileNet) are fine-tuned and perform classification on our unique dataset from Kerala, India, called KSDAgriPest dataset with 33 insect classes. Later, four best-performing base models, VGG16, Inception-V3, Xception, and MobileNet, were modified and retrained using appropriate transfer learning and fine-tuning strategies and are ensembled via all possible combinations of three base models using genetic algorithm (GA) optimized weighted voting, is called GAEnsemble and the generated models are called Ensemble Variants (EV). In the final stage, two top-performing EVs are ensembled. The proposed "Genetic Algorithm-based Ensemble of Ensemble" (GA2Ensemble) achieves an impressive 99.34% accuracy on the KSDAgriPest dataset and competitive results on other datasets (DO: 98.99%, SMALL: 96.21%, IP102: 69.56%). GA2Ensemble proves effective for insect pest species identification, particularly on challenging datasets. [ABSTRACT FROM AUTHOR]

A 14‐year‐old, female, blue‐fronted Amazon parrot was referred for episodes of regurgitation. The differential diagnosis mainly included bornavirus infection, infectious or traumatic ingluvitis, gastro‐oesophageal reflux, gastritis/enteritis, heavy metal poisoning, metabolic disease (severe hepatic or renal disease), coelomitis, digestive neoplasia, extra‐digestive masses, organomegaly or foreign body. No improvement was noted with symptomatic treatment. Radiographs, avian bornavirus polymerase chain reaction and serology, biochemistry, haematology, crop cytology and ultrasonography failed to provide a definitive diagnosis. Lethargy, weight loss and crop stasis after 1 month motivated computed tomography, which revealed focal thickening of the oesophageal wall. Fine‐needle aspiration under computed tomography guidance of the mass was non‐diagnostic, and the bird died during recovery from anaesthesia. Oesophageal squamous cell carcinoma was confirmed by histopathology. This case illustrates the benefit of advanced diagnostic imaging to investigate chronic refractory regurgitations and describes the use of computed tomography‐guided fine‐needle aspiration in an avian patient. [ABSTRACT FROM AUTHOR]

Phytopathogens can continuously cause serious losses worldwide to crop, fruit, and vegetable yields. The excessive use of conventional fungicides in pathogen management has raised severe environmental and health side effects and induced fungicide-resistant pathogen strains. RNA-based spray-induced gene silencing (SIGS) has emerged as an ecologically sustainable pathogen control approach. Here, we introduce the SIGS mechanism in plant pathogens, summarize the application of SIGS in controlling plant pathogens, and highlight the major considerations of SIGS. In addition, we propose the future perspectives of SIGS in crop protection and disease management. [ABSTRACT FROM AUTHOR]

Agriculture is important for the economy of any country, and India is considered to be an agricultural country. One of the primary goals of agriculture is to produce disease-free crops. Since ancient times, farmers and other planting specialists have had to contend with a variety of problems and current agricultural constraints, such as widespread cotton diseases. There is a great need for a rapid, efficient, economical, and reliable approach to diagnosing cotton infection in the agri-informatics area, as severe cotton disease may result in the loss of grain crops. This paper presents an advanced method that automates the detection and classification of diseased cotton leaves and plants through deep learning techniques applied to images. To address the challenge of supervised image classification, we employ a bagging ensemble technique consisting of five transfer learning models: InceptionV3, InceptionResNetV2, VGG16, MobileNet, and Xception. This ensemble approach was adopted to significantly improve the performance of each individual mode. The ETL-NET framework we introduced was thoroughly evaluated using two publicly accessible datasets. Specifically, it achieved an impressive accuracy rate of 99.48% and a sensitivity rate of 99% when applied to binary datasets. Additionally, on the multi-class dataset, the framework achieved an accuracy rate of 98.52% and a sensitivity rate of 99%. Our method outperformed the state-of-the-art techniques and displayed comparatively better results. Remarkably, our approach demonstrated even higher performance than widely used ensemble techniques, generally considered benchmarks in the field. [ABSTRACT FROM AUTHOR]

Climate change and variability have threatened rainfed agriculture by affecting the livelihoods of rural communities in Ethiopia. The study area, Gassera District, is among the high-potential crop production areas of the Bale Zone and is severely impacted by recurrent droughts resulting from climate change. This study evaluated smallholder farmers' perceptions of significant climate change and its effects on food crop production across the agroecological zones of the Gassera District. A cross-sectional survey design was employed to collect data from 444 farm households via multistage random sampling techniques. Multiple linear regression (MLR) models were used for the data analysis. The results revealed that 98.5% of the interviewed farmers were aware of climate variability and that 51.6% understood its impact to a reasonable extent. However, over half of the farmers did not perceive climate change as the greatest threat to their livelihood. Most farmers experienced rain becoming more erratic, starting late, and ending early as medium climatic factors (60.4%, 68.1%, and 66.2%, respectively), affecting their crop production. The results revealed that rainfall had a negative and insignificant decreasing trend (2.92 mm/year). The annual mean temperature exhibited a positive and statistically significant increasing trend (Ρ < 0.01). Crop production is positively and linearly correlated with the amount of annual rainfall at the Ρ ≤ 0.05 level of significance. The findings revealed that the greatest crop yield loss was associated with lowland agroecology. The MLR results revealed that farmer agroecology, age, sex, and chemical use had substantial impacts on crop yield loss. We urge farmers to understand the long-term effects of climate change on their livelihoods. [ABSTRACT FROM AUTHOR]

Subsurface (or tile) drainage offers a valuable solution for enhancing crop productivity in poorly drained soils. However, this practice is also associated with significant nutrient leaching, which can contribute to water quality problems at the regional scale. This research presents the findings from a 4-year tile depth and spacing study in central Illinois that included three drain spacings (12.2, 18.3, and 24.4 m) and two drain depths (0.8 and 1.1 m) implemented in six plots under the corn and soybean rotation system (plots CS-1 and CS-3: 12.2 m spacing and 1.1 m depth, plots CS-2 and CS-4: 24.4 m spacing and 1.1 m depth, and plots CS-5 and CS-6 18.3 m spacing and 0.8 m depth). Our observations indicate that drain flow and NO3-N losses were higher in plots with narrower drain spacings, while plots with wider drain spacing showed reduced drain flow and NO3-N losses. Specifically, plots set up with drain spacings of 18.3 m and 24.4 m showed significant reductions in drain flow compared to plots featuring a 12.2 m drain spacing. Likewise, plots characterized by 18.3 m and 24.4 m drain spacings (except CS-4) showed better NO3-N retention and lower leaching losses than those with 12.2 m spacing (CS-1 and CS-3). Crop yield results over a 3-year period indicated that CS-2 (wider spacing plot) showed the highest productivity, with up to 13.6% higher yield compared to other plots. Furthermore, when comparing plots with the same drainage designs, CS-2 and CS-4 showed 5.1% to 2.6% higher corn yield (3-year average) compared to CS-1 and CS-3, and CS-5 and CS-6, respectively. Overall, a wider drainage system showed the capacity to export lower nutrient levels while concurrently enhancing productivity. These findings represent that optimizing tile drainage systems can effectively reduce nitrate losses while increasing crop productivity. [ABSTRACT FROM AUTHOR]

Sustainable pest management requires growers and regional land managers to consider the relationships among pest management practices, pest and natural enemy communities, crop loss, and multi-scalar habitat complexity. However, the causal links among these variables, in particular potential interactions between landscape and local-scale habitat complexity, remain underexplored. In the context of organically managed strawberry crops in California's Central Coast, we tested the independent effects of landscape and local habitat complexity gradients on arthropod communities and crop loss using a piecewise structural equation model (PSEM). We found that landscape-scale woody habitat proportion indirectly decreased crop loss through its positive effect on natural enemy abundance, while grassland proportion had the opposite effect due to its association with an important strawberry pest (Lygus spp.). We detected a pattern suggesting that on-farm diversification practices are most effective at reducing crop loss at an intermediate level (26%) of woody habitat proportion. Both organic-compliant insecticide application and tractor vacuuming negatively impacted natural enemies, and therefore had qualified effects on crop loss. Our study shows the key roles of native woodlands and natural enemy communities in reducing crop loss and highlights the importance of managing habitat complexity at both landscape and local scales. [ABSTRACT FROM AUTHOR]

Background/Objectives: Heterodera avenae is a highly significant plant-parasitic nematode, causing severe economic losses to global crop production each year. Trichoderma species have been found to parasitize nematodes and control them by producing enzymes that degrade eggshells. The T. longibrachiatum T6 (T6) strain has been demonstrated the parasitic and lethal effects on H. avenae cysts and eggs, associated with the increased serine protease activity and trypsin-like serine protease gene (PRA1) expression. Methods: Our present study aimed to purify the recombinant PRA1 protease through a prokaryotic expression system and identify its nematicidal activity. Results: The recombinant PRA1 protease was identified as S1 family trypsin-like serine protease, with a molecular weight of 43.16 kDa. The purified soluble protease exhibited the optimal activity at 35 °C and pH 8.0, and also demonstrating higher hydrolytic ability toward casein and skimmed milk. Meanwhile, the Ca2+ and Mg2+ enhanced its activity, while the inhibitor PMSF significantly reduced it. The contents of H. avenae eggs leaked out after treatment with the recombinant PRA1 protease, with egg hatching inhibition and relative hatching inhibition rates at 70.60% and 66.58%, respectively. In contrast, there was no sign of content dissolution, and embryos developed normally in the control group. Conclusions: Our present study revealed that the PRA1 protease of T6 strain has a lethal effect on H. avenae eggs, which providing a theoretical basis for developing biocontrol agents to control nematodes. [ABSTRACT FROM AUTHOR]

Crop yield losses have escalated worldwide due to extreme and/or prolonged drought periods as well as insufficient irrigation. In the cold and arid regions of northern China, the difficulty of straw decomposition is a regional problem, which is undoubtedly aggravated by the increasingly serious drought. Thus, the combination of a reasonable grain feed rotation mode and tillage mode of straw returning is an effective measure to solve this problem. This study set up a grain forage rotation (in the first year, it is used for grain production, and the straw is returned to the field after the grains are harvested; when the entire plant is harvested in the second year, it is used as feed) and two treatments for straw returning and no straw returning to the land characterized by two different water degresses (include drought stress and normal irrigation). To clarify the impact of returning straw to the field on the soil improvement and drought tolerance of various maize varieties, the effects of straw returning on the agronomic traits, hormones, osmotic adjustment substance content, whole plant fresh weight, and dry weight of six different maize varieties (MC703, A2636, DK 159, ZQ 1, 303 × 178, and ZJ 330) under drought stress were analyzed. The data showed the following: (1) After straw returning, the soil moisture content increased by 3.55–14.70% and the soil bulk density decreased by 0.73–5.23% under the drought stress treatment, and the soil physical condition became closer to the ideal value. (2) After straw returning, the plant height, ear height, stem diameter, fresh weight, and dry weight of the maize whole plant under the drought stress treatment increased by 0.95–7.70%, 4.70–15.71%, 0.31–9.33%, 2.67–36.23%, and 3.72–38.60%, respectively. (3) After straw returning, the abscisic acid, ethylene, soluble sugar, and soluble protein in the leaves under the drought stress treatment decreased by 7.92–21.42%, 8.03–3.37%, 0.63–22.98%, and 2.37–9.17%, respectively. Straw returning significantly alleviated the damage caused by drought stress to the growth and development of maize, and promoted the growth and development of maize under the drought treatment. The results of this study provide a theoretical basis for a high and stable yield of grain crops in arid areas. [ABSTRACT FROM AUTHOR]

In an effort to find alternatives to paraquat for weed control and basal foliage removal in hops (Hummus lupulus L.), due to regulatory and safety concerns, a study was conducted across Oregon and Washington in 2020 and 2021. The study compared the efficacy of tiafenacil and tolpyralate against a nontreated control and carfentrazone. Applications were made early when hops were 2‐ to 3‐m tall (early), and at over 4 m (late). Tiafenacil showed 55%–85% effectiveness in basal foliage control, slightly higher than carfentrazone, and did not cause crop injury. Tiafenacil at 50 and 100 g a.i. ha−1 controlled Lolium multiflorum (Lam.) Husnot (63%). The mixture of tiafenacil and tolpyralate controlled 80% of L. multiflorum, Cirsium arvense L., and Bassia Scoparia (L.) A.J. Scott. In all cases, control was followed by weed regrowth. No signs of crop injury were observed in any of the studies with tiafenacil. Early applications of tiafenacil reduced yield between 0% and 40% relative to the nontreated control. Late applications of tolpyralate and tiafenacil did not significantly reduce yield. Tolpyralate was as effective as carfentrazone for weed control and basal foliage removal. Early applications of tolpyralate reduced plant height and yield relative to the nontreated control and consistently induced phytochemical injury. Tolpyralate yield reductions ranged from 0% to 84% relative to the nontreated control, depending on the trial. A mixture of tolpyralate and tiafenacil was the most effective treatment tested. We conclude that both tiafenacil and tolpyralate are safe for sucker applications in hops, but careful timing is needed to reduce the risk of injury with tolpyralate. The mixture of tiafenacil and tolpyralate can improve weed control comparable to available options. Core Ideas: Tiafenacil and tolpyralate effectively control basal foliage and important weeds in hops.Early applications of tolpyralate and tiafenacil can cause yield loss or crop injury.Tiafenacil and tolpyralate herbicides could replace paraquat for basal foliage removal in hops. [ABSTRACT FROM AUTHOR]

Huanglongbing is a severe citrus disease that causes significant tree and crop losses worldwide. It is caused by three Candidatus liberibacter species and spread by psyllids and infected budwood. Various methods have been used to diagnose and understand HLB, including recent advances in molecular and biochemical assays that explore the pathogen's mode of action and its impact on the host plant. Characterization is essential for developing sustainable HLB management strategies. Nanotechnology, particularly nano sensors and metal nanoparticles, shows potential for precise disease diagnosis and control. Additionally, antibiotics, nanomaterials, and genetic engineering techniques like transgenesis offer promising avenues for mitigating HLB. These diverse approaches, from conventional to cutting-edge, contribute to developing integrated HLB management strategies for sustainable citrus cultivation. The review highlights the significant advancements in conventional and advanced molecular and biochemical characterization of HLB, aiding in early detection and understanding of the infection mechanism. It emphasizes the multidimensional efforts required to characterize disease and devise innovative management strategies. As the citrus industry faces unprecedented challenges, exploring new frontiers in HLB research provides hope for sustainable solutions and a resilient future for global citrus cultivation. [ABSTRACT FROM AUTHOR]

Halyomorpha halys is an invasive stink bug, native to east Asia, responsible for extensive economic loss in numerous crops. Since several factors can influence the severity of the recorded injury level, we performed field and semi‐field experiments in commercial apple orchards to monitor their interaction. To establish how position (both in the orchard and on the canopy), management and cultivar interact in determining pre‐harvest injury levels, more than 100,000 apples, distributed over 106 orchard blocks, were examined in the field. Apples located higher in the canopy, on edge rows and in orchards with organic management had a higher number of external injuries and such factors were more relevant when occurring in combination, and on mid and late‐season cultivars. Exclusion cages were used to assess if the injury severity changed with the apple growth stage and with the life stage of H. halys, exposing Red delicious apples to adults and nymphs for 48 hours, from May to July. Early‐season injuries did not evolve in distinct depressions nor in wide areas with necrotic tissue. Later in season, the injury severity was higher and similar for adults and older nymphs (fourth and fifth instars), with the development of depressions externally and necrotic tissue internally. Thus, even a low number of individuals could cause severe damage, over a short exposure period, when it happened near full maturation. The combined results offer new insights to support the development of informed and sustainable control strategies. [ABSTRACT FROM AUTHOR]

Sensor technologies were integrated into a commercial sensor-guided hoeing system to counteract the force of gravity and reduce crop damage caused by the offset of hoeing in maize fields on sloping terrains. For this study, a hoe was equipped with a contact disc, sensors, an electric cylinder, and a decision support system. The offset of the hoe could be compensated in real time based on the automatic adjustment angle of the support wheel. In maize, three field experiments were conducted over two years to evaluate the system on three different slope gradients (between 4 and 12°). Plant populations were measured in each plot one day before and during hoeing to evaluate crop damage. However, for support wheel angle, Slope Compensation Intensity (SCI) 2 and 3, there were no significant crop plant losses in any trials. As a result, there was no hoe drifting during the sensor-based guidance along the rows. It has been verified that the development presented is functional and can counteract the force of gravity on slopes. This development aims to optimise the use of precision mechanical weed control and support farmers during hoeing on hilly terrain. • Tractor and machine are pushed down the slope by downhill force. • The hoe is not parallel to the tractor and plant losses will occur. • The offset of a hoe can be compensated by the developed slope-compensation system. • Crop losses can be compensated on hilly fields. [ABSTRACT FROM AUTHOR]

Satellite remote sensing images contain complex and diverse ground object information and the images exhibit spatial multi-scale characteristics, making the panoptic segmentation of satellite remote sensing images a highly challenging task. Due to the lack of large-scale annotated datasets for panoramic segmentation, existing methods still suffer from weak model generalization capabilities. To mitigate this issue, this paper leverages the advantages of the Segment Anything Model (SAM), which can segment any object in remote sensing images without requiring any annotations and proposes a high-resolution remote sensing image panoptic segmentation method called Remote Sensing Panoptic Segmentation SAM (RSPS-SAM). Firstly, to address the problem of global information loss caused by cropping large remote sensing images for training, a Batch Attention Pyramid was designed to extract multi-scale features from remote sensing images and capture long-range contextual information between cropped patches, thereby enhancing the semantic understanding of remote sensing images. Secondly, we constructed a Mask Decoder to address the limitation of SAM requiring manual input prompts and its inability to output category information. This decoder utilized mask-based attention for mask segmentation, enabling automatic prompt generation and category prediction of segmented objects. Finally, the effectiveness of the proposed method was validated on the high-resolution remote sensing image airport scene dataset RSAPS-ASD. The results demonstrate that the proposed method achieves segmentation and recognition of foreground instances and background regions in high-resolution remote sensing images without the need for prompt input, while providing smooth segmentation boundaries with a panoptic segmentation quality (PQ) of 57.2, outperforming current mainstream methods. [ABSTRACT FROM AUTHOR]

The pathogenic watermelon mosaic virus (WMV) causes severe crop losses and threatens cucurbits production worldwide. This study presents the first sequenced genome of WMV from Iraq and the second from Iran. The near-complete genome sequences of Iranian (In) and Iraqi (Iq) isolates were, respectively, 9999 and 10,041 nucleotides in length, excluding the 3′-terminal poly(A) tail. WMV-Iq and WMV-In shared nucleotide identities of 92.61–96.32% with those of 30 worldwide representative WMV isolates in GenBank and identities of 93.04–93.90% with a previously reported Iranian isolate IR02-54. Phylogenetic analysis based on the genomic sequences clustered all 32 isolates into seven groups (G1–G7), in which WMV-Iq and WMV-In clustered alongside 12 isolates from Asia, Europe, and America in group G1. Phylogenetic analysis of nucleotide sequences of the coat protein gene of WMV isolates resolved into five phylogroups (I–V) and both isolates, WMV-Iq and WMV-In, clustered together with isolates in group I and contain the N-terminal KEA motif, which is the characteristic of group I isolates. Our results show that WMV-Iq and WMV-In are recombinant isolates with multiple recombination events detected across the genome. Symptom intensity of WMV isolates in experimental hosts was variable and depended upon the host species, virulence of isolates, and environmental conditions. [ABSTRACT FROM AUTHOR]

Integration of climate information in farm-level adaptation decision is crucial to manage climate change induced risks. The Government of Kenya is mandated to manage and disseminate climate information to enhance adaptation to climate change. However, the use of climate information on farm-level adaptation has not been explored. The purpose of this study was to: identify the climate information accessed by farmers, establish the association between using climate information and the adaptation strategies adopted, examine the factors influencing adaptation decisions, find out how climate information were used and the benefits and constraints of using the information. Data was collected from 432 households randomly selected from semi-arid areas in Machakos, Makueni and Kitui counties. The Multivariate probit model was used to analyse data. The results showed that farmers accessed short and medium-term forecasts; decadal forecasts and agrometeorological advisories. There was a significant association between using climate information and the climate change adaptation strategies adopted by the farmers. Demographic, socio-economic and institutional factors significantly influenced farmers' adaptation decisions. Farmers used the climate information for tactical, short-term farm operations. Benefits of using climate information were manifested in reduced crop loss, increased yield and farm revenue. However, farmers' use of climate information was constraint by economic and technical factors. The study recommends that the Government enforces policies that empowers farmers to access the requisite farm inputs and facilitate the provision of agrometeorological advisories to increase local-level utility of climate information. The Government should formulate and implement policies that strengthen the provision of timely, simplified and adequately actionable climate information that suit farmers' climate change adaptation needs. [ABSTRACT FROM AUTHOR]

BACKGROUND: Developing herbicide‐resistant (HR) crop cultivars is an efficient way to control weeds and minimize crop yield losses. However, widespread and long‐term herbicide application has led to the evolution of resistant weeds. Here, we established a resistant (R) E. indica population, collected from imidazolinone‐resistant rice cultivar fields. RESULTS: The R population evolved 4.5‐fold resistance to imazamox. Acetolactate synthase (ALS) gene sequencing and ALS activity assays excluded the effect of target‐site resistance in this population. P450 inhibitor malathion pretreatment significantly reversed resistance to imazamox. RNA sequencing showed that a P450 gene CYP81A104 was expressed higher in R versus susceptible (S) plants. Arabidopsis overexpressing CYP81A104 showed resistance to ALS inhibitors (imazamox, tribenuron‐methyl, penoxsulam and flucarbazone‐sodium), PSII inhibitor (bentazone), hydroxyphenyl pyruvate dioxygenase inhibitor (mesotrione) and auxin mimics (MCPA), which was generally consistent with the results presented in the R population. CONCLUSION: This study confirmed that the CYP81A104 gene endowed resistance to multiherbicides with different modes‐of‐action. Our findings provide an insight into the molecular characteristics of resistance and contribute to formulating an appropriate strategy for weed management in HR crops. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: 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. RESULTS: 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. CONCLUSION: This study provides a basis for regional projections and global control of maize diseases under future climate conditions. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

ABSTRACT Drought is one of the most devastating causes of yield losses in crops like maize, and the anticipated increases in severity and duration of drought spells due to climate change pose an imminent threat to agricultural productivity. To understand the drought response, phenotypic and molecular studies are typically performed at a given time point after drought onset, representing a steady‐state adaptation response. Because growth is a dynamic process, we monitored the drought response with high temporal resolution and examined cellular and transcriptomic changes after rehydration at 4 and 6 days after leaf four appearance. These data showed that division zone activity is a determinant for full organ growth recovery upon rehydration. Moreover, a prolonged maintenance of cell division by the ectopic expression of PLASTOCHRON1 extends the ability to resume growth after rehydration. The transcriptome analysis indicated that GROWTH‐REGULATING FACTORS (GRFs) affect leaf growth by impacting cell division duration, which was confirmed by a prolonged recovery potential of the GRF1‐overexpression line after rehydration. Finally, we used a multiplex genome editing approach to evaluate the most promising differentially expressed genes from the transcriptome study and as such narrowed down the gene space from 40 to seven genes for future functional characterization. [ABSTRACT FROM AUTHOR]

Background: As identified by the research, it is imperative to develop effective ways to address the pressing problem of disease and pest susceptibility in chili agriculture and secure sustainable crop yield. The research examines the impact of various growing microclimates, watering regimens, and chili cultivars on disease incidence, pest attacks, and yield loss. Results: The study, which took place over a season, used a randomized complete block design to evaluate how well Tanjung, Unpad, and Osaka cultivars performed in four different watering regimens (100, 75, 50, and 25% ETc) and different microclimates (greenhouse, rain shelter, screen house, and open field). The findings exhibited that watering regimens and microclimates greatly influenced disease and pest occurrence, but cultivars had a minimal effect on these variables. Disease and pest attack rates were highest in the open field and lowest in the screen house. A correlation was found between lower disease and pest incidence and optimal irrigation levels (75% and 100% ETc). At lower watering regimens of 25% ETc and in the open field, yield loss was the greatest. Conclusion: The results emphasize how crucial controlled environments and appropriate irrigation techniques are to reducing crop loss and increasing production. Enhancing watering regimens and implementing screen house cultivation are two strategies for improving the productivity and sustainability of chili output. [ABSTRACT FROM AUTHOR]

Salinity causes widespread crop loss and prompts plants to adapt through changes in gene expression. In this study, we aimed to investigate the function of the non-tandem CCCH zinc-finger (non-TZF) protein gene AtC3H3 in response to salt stress in Arabidopsis. AtC3H3, a gene from the non-TZF gene family known for its RNA-binding and RNase activities, was up-regulated under osmotic stress, such as high salt and drought. When overexpressed in Arabidopsis, AtC3H3 improved tolerance to salt stress, but not drought stress. The expression of well-known abscisic acid (ABA)-dependent salt stress-responsive genes, namely Responsive to Desiccation 29B (RD29B), RD22, and Responsive to ABA 18 (RAB18), and representative ABA-independent salt stress-responsive genes, namely Dehydration-Responsive Element Binding protein 2A (DREB2A) and DREB2B, was significantly higher in AtC3H3-overexpressing transgenic plants (AtC3H3 OXs) than in wild-type plants (WT) under NaCl treatment, indicating its significance in both ABA-dependent and -independent signal transduction pathways. mRNA-sequencing (mRNA-Seq) analysis using NaCl-treated WT and AtC3H3 OXs revealed no potential target mRNAs for the RNase function of AtC3H3, suggesting that the potential targets of AtC3H3 might be noncoding RNAs and not mRNAs. Through this study, we conclusively demonstrated that AtC3H3 plays a crucial role in salt stress tolerance by influencing the expression of salt stress-responsive genes. These findings offer new insights into plant stress response mechanisms and suggest potential strategies for improving crop resilience to salinity stress. [ABSTRACT FROM AUTHOR]

Bacterial soft rots are among the most economically important diseases that cause significant yield losses in various crops. During 2022 and 2023, widespread occurrence of soft rot disease was observed in sugar beet fields in the Firuzan region, Hamedan Province, Iran. From the sugar beet plants with soft rot symptoms, a bacterial isolate was obtained that was obligately aerobic, negative for Gram, oxidase, levan, and arginine dihydrolase tests, and positive for pectinase, urease and hypersensitivity reaction on tobacco. Koch's postulates confirmed its pathogenicity by developing soft rot symptoms in sugar beet plants. The 16SrRNA and rpoD sequences from this isolate were most closely related to Pseudomonas punonensis LMT03 with 99.8% and 94.7% identity, respectively and then to Pseudomonas argentinensis LMG22563 (= CH01) with 99.4% and 94.3% identity values. To our knowledge, this is the first report of a Pseudomonas sp. strain causing soft rot disease in sugar beet worldwide. [ABSTRACT FROM AUTHOR]

Drought stress is a major threat leading to global plant and crop losses in the context of the climate change crisis. Brassinosteroids (BRs) are plant steroid hormones, and the BR signaling mechanism in plant development has been well elucidated. Nevertheless, the specific mechanisms of BR signaling in drought stress are still unclear. Here, we identify a novel Arabidopsis gene, BRZ INSENSITIVE LONG HYPOCOTYL 9 (BIL9), which promotes plant growth via BR signaling. Overexpression of BIL9 enhances drought and mannitol stress resistance and increases the expression of drought-responsive genes. BIL9 protein is induced by dehydration and interacts with the HD-Zip IV transcription factor HOMEODOMAIN GLABROUS 11 (HDG11), which is known to promote plant resistance to drought stress, in vitro and in vivo. BIL9 enhanced the transcriptional activity of HDG11 for drought-stress-resistant genes. BIL9 is a novel BR signaling factor that enhances both plant growth and plant drought resistance. [ABSTRACT FROM AUTHOR]

Spodoptera frugiperda (J. E. Smith) is an invasive deleterious pest that causes huge economic losses to various crops, especially maize worldwide. Therefore, the insecticidal and antifeedant activities of lemongrass, Cymbopogon citratus, and peppermint, Mentha piperita essential oils (EOs) against the third larval instar of S. frugiperda were tested under laboratory conditions to find safer alternative approaches to managing S. frugiperda. The impact of sublethal concentrations (LC10 & LC30) of tested EOs on the biological parameters and the activities of detoxifying enzymes of S. frugiperda were also evaluated. Their chemical composition was identified using gas chromatography-mass spectrometry (GC–MS). The major compounds in C. citratus EO were d-limonene (45.06%), β-citral (10.30%), and α-citral (9.90%); whereas, in M. piperita EO were menthol (32.03%), menthone (30.18%), and p-menthan-3-one (11.53%). Bioassay results revealed that C. citratus (LC50= 725.2 mg/L) exhibited more toxicity on S. frugiperda larvae than M. piperita (LC50= 1024.2 mg/L) after 48 h of exposure. Both EOs revealed remarkable antifeedant effects, with the feeding deterrence index ranging from 30.67-43.06% against S. frugiperda. Sublethal concentrations of the tested EOs resulted in prolonged larval and pupal durations, reduced pupal weight of females and males, and decreased pupation and adult emergence percentages, compared to the control. The activities of carboxylesterases and glutathione S-transferase enzymes in S. frugiperda were dramatically suppressed, compared to the control, with dose-dependent effects. These results suggest that M. piperita and C. citratus EOs may be used to manage S. frugiperda. [ABSTRACT FROM AUTHOR]

Furoviruses are bipartite viruses causing mosaic symptoms and stunting in cereals. Infection with these viruses can lead to severe crop losses. The virus species Furovirus tritici with soil-borne wheat mosaic virus (SBWMV), Furovirus cerealis with soil-borne cereal mosaic virus (SBCMV) and Furovirus japonicum with Japanese soil-borne wheat mosaic virus (JSBWMV) and French barley mosaic virus (FBMV) as members are biologically and genetically closely related. Here, we develop SYBR green-based real-time quantitative RT-PCR assays to detect and quantify the RNA1 and RNA2 of the three virus species. Using experimental data in combination with Tm-value prediction and analysis of primer and amplicon sequences, we determine the capacity of our method to discriminate between the different viruses and evaluate its genericity to detect different isolates within the same virus species. We demonstrate that our method is suitable for discriminating between the different virus species and allows for the detection of different virus isolates. However, JSBWMV RNA1 primers may amplify SBWMV samples, bearing a risk for false positive detection with this primer. We also test the efficiency of polyclonal antibodies to detect the different viruses by ELISA and suggest that ELISA may be applied as a first screening to identify the virus. The real-time qRT-PCR assays developed provide the possibility to screen for quantitative disease resistance against SBCMV, SBWMV and JSBWMV. Moreover, with our method, we hope to promote research to unravel yet unresolved questions with respect to furovirus–host interaction concerning host range and resistance as well as regarding the role of multipartite genomes. [ABSTRACT FROM AUTHOR]

Spiroplasma citri, the causal agent of citrus stubborn disease (CSD), causes significant losses in citrus crops. An efficient pathogen detection system is critical for epidemiology studies, particularly when a large sample size is involved. In this study, we report the development of an immunomolecular assay, immunocapture real-time polymerase chain reaction (IC-qPCR), targeting the spiralin gene for direct detection of S. citri without DNA isolation. This method can use either plant sample extracts or media in which S. citri was cultivated. The IC-qPCR protocol demonstrated a limit of detection for pure S. citri culture at a Ct value of 36.523 with a 103-fold dilution factor, making it equally sensitive as qPCR, which exhibited signal disappearance at a 10–3 dilution (Ct value of 37.484). In contrast, the immunological double-antibody sandwich enzyme-linked immunosorbent assay (DAS-ELISA) test produced positive results up to a 10–2 dilution only. For S. citri-infected citrus samples, the established IC-qPCR protocol had a limit of detection at 36.46 Ct with a 1/64-fold dilution factor, matching the sensitivity of qPCR, where signal disappearance occurred at a 1/64 dilution (Ct value of 37.21). On the other hand, the immunological DAS-ELISA test yielded positive results only up to a 1/16 dilution, with optical density (OD) values of 0.364 and 0.113 for 1/16 and 1/32 dilutions, respectively. The IC-qPCR assay shows no cross-reaction for any other highly related spiroplasma species and bacteria affecting citrus trees including Candidatus liberibacter, Xylella fastidiosa, and Xanthomonas campestris pv. citri. Therefore, IC-qPCR assay provides an alternative quick and very sensitive method to screening S. citri, with the advantage of not requiring any concentration or DNA purification steps while still allowing an accurate diagnosis of CSD. [ABSTRACT FROM AUTHOR]

The intersection of the COVID-19 pandemic with other crises can amplify vulnerabilities and push communities further into poverty. In low-income countries, the dual impacts of COVID-19 and extreme weather events, along with multidimensional poverty and structural vulnerabilities in agriculture can decimate farmer livelihoods. This study aims to understand the effects of individual and compounding crises (COVID-19, cyclones, and vanilla price collapse) on smallholder vanilla farmers and local coping strategies in Madagascar, one of the world's largest vanilla producers and poorest countries. We used semi-structured and scenario-based interviews across two case study villages with contrasting enforcement of forest regulations. We found that the impact of the pandemic, combined with the cyclone event, disrupted livelihoods, resulting in income losses and food security challenges that exacerbated farmer vulnerabilities. Sixty eight percent of households reported crop losses due to strong winds and heavy rainfall brought by cyclone Enawo in 2017. The COVID-19 outbreak struck the region just as the residents were recovering from the effects of the cyclone. COVID-19-related travel restrictions in the aftermath of the cyclone took a substantial economic toll, with 54.1% of respondents experiencing a decline in earnings, and 17% facing a total loss of income due to the imposed lockdown. The decline in vanilla prices at the onset of 2020 had a far-reaching additional impact, affecting not only farmers but also residents who rely on other sources of income. Local communities reported using the forest resources more frequently as a safety net during crises in the village with more lenient regulations. This study underscores the importance of understanding the interconnectedness and compounding impacts of cascading crises on food security and natural resource use. We highlight the need for a comprehensive approach to increasing farmer resilience, particularly for those reliant on global market crops such as vanilla. [ABSTRACT FROM AUTHOR]

Waterlogging is a critical abiotic stress increasing in importance due to more intense, erratic rainfall associated with climate change. Waterlogging leads to significant yield losses in sensitive crops, such as soybean (Glycine max [L.] Merr.). Identifying soybean genotypes and traits associated with better waterlogging tolerance is of high interest. We assessed the response of six soybean genotypes, selected from a field screening of over 190 genotypes, to 10 days of waterlogging at the R1 (onset of flowering) and R4 (grain filling) stages. We evaluated yield and its components, as well as shoot and root dry weights (DW) at the end of the waterlogging treatments and at maturity, along with morphological traits such as plant branch number, stem diameter and plant height. By integrating all these traits, a waterlogging tolerance index (WTI) was calculated for each genotype to rank their sensitivity. The WTI showed variations among genotypes from 0.61 to 0.77, indicating genotypic variation in response to waterlogging. Greater reductions in root DW compared to shoot DW were observed immediately after waterlogging. By maturity, shoot DW of waterlogged plants was more severely reduced than root DW in all genotypes. Despite similar DW losses at R1 and R4 at physiological maturity, seed number per plant and 100‐seed weight responses differed between the treatments. Genotypes that performed well under control conditions suffered significant yield reductions of 70%–85% after waterlogging, mainly due to fewer fertile nodes and seeds per pod, with some also experiencing a notable decrease in 100‐seed weight. In contrast, other genotypes had milder responses, with less severe reductions in seed and pod traits. Identifying breeding soybean genotypes tolerant to waterlogging during reproductive stages that maintain the number of fertile nodes and pods per node without changes in seeds per pod could significantly mitigate yield losses from waterlogging. [ABSTRACT FROM AUTHOR]

This study quantitatively evaluates the effectiveness of three main options for the adaptation of crop farming to climate change (i.e., shift of planting dates, increase/addition of irrigation, and resilient hybrids/cultivars) in Greece, a country in southern Europe. The potential effect of each option on the yields of several crops in all Greek regions is estimated for 2021–2040 and 2041–2060 and compared with those under the historical local climate of 1986–2005, by using agronomic and statistical regression models, and data from different climatic simulations and climate change scenarios. Our results reveal that all the adaptation options examined have the potential to significantly reduce crop yield losses occurring under no adaptation, particularly during 2021–2040 when for many regions and crops more than half of the losses can be compensated for. Notably, in some cases during this period, the measures examined resulted in crop yields that are higher than those under the historical climate. However, the effectiveness of the measures diminished significantly in 2041–2060 under very adverse climate change conditions, highlighting the dynamic nature of adaptation. Assessing the effectiveness of combined adaptation options and evaluating additional criteria (e.g., feasibility) represent essential areas for future research. [ABSTRACT FROM AUTHOR]

The prevalence of apples as the most widely consumed fruit globally does not exempt them from storage-related issues, resulting in substantial harvest losses. A prominent concern is the development of rot due to various factors during storage. This research endeavors to examine the influence of agrotechnological methods on the longevity of apples and the incidence of rot throughout storage. Apple trees (Malus domestica Borkh. cv. Rubin) grafted on dwarfing rootstocks P60 were planted in 2010 in single rows with a spacing of 1.25 m between trees and 3.5 m between rows. Eight combinations of different growth control measures (manual, mechanical pruning, spraying, trunk cutting) were selected for the experiment. The implementation of mechanical pruning, in conjunction with trunk cutting and Ca-prohexadione spraying, as well as summer pruning, detrimentally impacted the shelf life of apples. Examination of the storage period revealed a loss of 33–40% of the crop due to rot. Conversely, manual pruning sustained a consistent level of phenolic compounds throughout the storage period. Other pruning methods resulted in a notable increase in phenolic compounds, ranging from 67% to a two-fold rise compared to the compounds present at harvest. However, the integration of mechanical pruning with subsequent manual pruning not only significantly augmented the yield of apples but also yielded a shelf life akin to that of manually pruned apples. Following the analysis of the results, it is advisable to conduct mechanical pruning of the apples intended for storage along with supplementary manual pruning. [ABSTRACT FROM AUTHOR]

Recently, the decline in plant species, loss of crop yields, and reduced efficacy of herbal medicines due to environmental issues and biotic stresses have garnered significant attention. Developing smart devices for plant health monitoring is essential for early intervention, timely adjustment of the growing environment to combat environmental and pest stresses, and to promote robust plant growth, biodiversity, ecological balance, and sustainable agriculture. Flexible wearable sensors with the advantages of superior shape adaptation, excellent biocompatibility, and high integration have emerged as one of the most promising avenues for plant health monitoring. Here, recent advances in flexible wearable sensors based on monitoring different types of plant signals are summarized. The discussion focuses on the constituent materials, fabrication methods, and sensing mechanisms of each type of wearable sensor. In addition, the challenges and potential strategies are summarized for plant sensing development, including energy supply, materials and preparation, signal transmission, and data analysis. [ABSTRACT FROM AUTHOR]

Pseudotheraptus devastans (Dist.), a coreid bug, feeds on pods causing extensive deformation of the pods and clumping of beans, resulting in reduced yields. This pest was considered important in the 1960s and was more prevalent on hybrid cocoa in the Eastern region of Ghana. With the increase in the use of hybrid planting materials across the entire cocoa landscape in Ghana, information on distribution and population dynamics of the pest is needed to aid effective management. The distribution survey involving 1,740 farms indicated that the pest is present in all the cocoa‐growing regions. P. devastans numbers and associated damage pods were assessed monthly on 35 farms (100 trees per farm) using the visual hand‐height sampling method from 2018 to 2021. The P. devastans population peaked in April, followed by a decline from June with a minor peak in August. Pest numbers and damaged pods were significantly different for the different months but not for the regions. No significant relationship was observed between P. devastans numbers sampled to hand‐height and their associated damage pods. This suggests that damage can occur regardless of the quantity of pests. Regular pest monitoring in cocoa farms is needed to ensure that pest damage is spotted early enough to avoid crop loss since P. devastans numbers do not necessarily correlate with damage. [ABSTRACT FROM AUTHOR]

Background: Viral diseases of sweet potatoes are causing severe crop losses worldwide. More than 30 viruses have been identified to infect sweet potatoes among which the sweet potato latent virus (SPLV), sweet potato mild speckling virus (SPMSV), sweet potato virus G (SPVG) and sweet potato virus 2 (SPV2) have been recognized as distinct species of the genus Potyvirus in the family Potyviridae. The sweet potato virus 2 (SPV2) is a primary pathogen affecting sweet potato crops. Methods: In this study, we detected an SPV2 isolate (named SPV2-LN) in Ipomoea nil in China. The complete genomic sequence of SPV2-LN was obtained using sequencing of small RNAs, RT-PCR, and RACE amplification. The codon usage, phylogeny, recombination analysis and selective pressure analysis were assessed on the SPV2-LN genome. Results: The complete genome of SPV2-LN consisted of 10,606 nt (GenBank No. OR842902), encoding 3425 amino acids. There were 28 codons in the SPV2-LN genome with a relative synonymous codon usage (RSCU) value greater than 1, of which 21 end in A/U. Among the 12 proteins of SPV2, P3 and P3N-PIPO exhibited the highest variability in their amino acid sequences, while P1 was the most conserved, with an amino acid sequence identity of 87-95.3%. The phylogenetic analysis showed that 21 SPV2 isolates were clustered into four groups, and SPV2-LN was clustered together with isolate yu-17-47 (MK778808) in group IV. Recombination analysis indicated no major recombination sites in SPV2-LN. Selective pressure analysis showed dN/dS of the 12 proteins of SPV2 were less than 1, indicating that all were undergoing negative selection, except for P1N-PISPO. Conclusion: This study identified a sweet potato virus, SPV2-LN, in Ipomoea nil. Sequence identities and genome analysis showed high similarity between our isolate and a Chinese isolate, yu-17-47, isolated from sweet potato. These results will provide a theoretical basis for understanding the genetic evolution and viral spread of SPV2. [ABSTRACT FROM AUTHOR]

Horticulture, a critical component of agriculture, encounters various challenges, including crop loss stemming from factors like pests, diseases, adverse weather conditions, and inefficient farming practices. The introduction of advanced technologies such as robotics and artificial intelligence (AI) holds great promise in mitigating crop losses and bolstering productivity in the field of horticulture. Robotic systems have been devised to automate labor‐intensive tasks involved in horticulture, such as harvesting, pruning, and weeding. Equipped with sensors, cameras, and intelligent algorithms, these robots are capable of identifying ripe fruits, detecting and removing weeds, and performing precise pruning operations. For example, Peixoto et al. in 2015 employed fuzzy systems to create a model for controlling soybean aphids, significantly improving the timing of predator release and enhancing integrated pest management (IPM). By reducing the reliance on human labor and enhancing operational efficiency, the integration of robotic solutions contribute to the minimization of crop losses and the augmentation of yields. In horticulture crop loss reduction, AI plays a vital role when coupled with machine learning algorithms. By analyzing extensive volumes of data encompassing weather patterns, soil conditions, and occurrences of pests and diseases, AI systems can provide farmers with real‐time insights and predictive models. This allows for proactive decision‐making regarding optimal timing for pesticide application, irrigation scheduling, and disease detection. Consequently, farmers can adopt preventive measures, minimizing losses and optimizing resource utilization. For instance, Ji et al. in 2007 developed an artificial neural network (ANN)‐based system for rice yield prediction in Fujian, China, improving accuracy over traditional models. Moreover, AI‐powered imaging techniques, such as computer vision, enable the early detection of diseases, pests, and nutrient deficiencies in plants. Early detection empowers farmers to take prompt action, averting the further spread of diseases and minimizing crop losses. Tobal and Mokthar in 2014 pioneered an AI‐assisted image processing method for weed identification, introducing an evolutionary ANN to optimize neural parameters using a genetic algorithm. However, the implementation of these technologies face challenges such as high initial costs, the need for technical expertise, and the integration of various data sources. Additionally, small‐scale farmers may find it difficult to adopt these technologies due to financial and infrastructural constraints. By harnessing the potential of robotics and AI, the horticulture sector can overcome challenges related to crop losses caused by pests, diseases, adverse weather conditions, and inefficient farming practices. These technological applications offer a pathway to enhanced productivity, reduced losses, and greater sustainability in horticulture. As we move forward, it is imperative to continue advancing and integrating these technologies, fostering innovation and collaboration between technology developers and the farming community. [ABSTRACT FROM AUTHOR]

This study proposed a framework using artificial neural networks (ANNs), the designed residual regression method, and probabilistic indexing, for drought sensitivity shift analysis, associated with future yield loss risk in Anthropocene. The temporal assessment of the drought index (standardized precipitation evapotranspiration index, SPEI) illustrated prevalent occurrence of mild to moderate drought with a fluctuating trend. Furthermore, the optimal ANN model, with R2 = 0.86 for training and 0.7 for testing, was used to project yield for future analysis. The projected spatial changes in drought sensitivity patterns suggest most of the area will experience an increased sensitivity to drought in the far future (2036–2050). The estimated probability loss under the different risk levels defined by the yield reduction rate was 6.2–55.5%. Furthermore, the spatial distribution of the designed risk index (IR) revealed the districts with the highest probability of vulnerability and showed a probable increase in future vulnerability. [ABSTRACT FROM AUTHOR]

The productivity of the soybean crop (Glycine max) can be limited by several factors, including diseases. In the integrated management of diseases, one of the strategies used is chemical control. The aim of this experiment was to evaluate the influence of spray nozzle types on the chemical control of fungal diseases in soybean plants. The experimental design was randomised blocks, with six treatments and four replications. The treatments consisted of control (without fungicide application on soybean plants) and application of fungicides with different spray nozzles: hollow cone jet, double flat jet, extended range flat jet, wide angle flat jet and air induction flat jet. The sprayer used was the backpack pressurised by carbon dioxide CO2. The experiment was installed in the Campos Gerais region (Paraná, Brazil); during three cropping seasons. The evaluated variables were incidence and severity, spray quality (covered area, droplet density, and relative amplitude) and yield components. It was concluded that the chemical control of fungal diseases in soybean plants reduced the proliferation of pathogens and losses in crop yield. There were significant changes in spraying quality in all analysed variables. There were no differences among the spray nozzle types regarding the analysis of incidence, severity and soybean yield components. [ABSTRACT FROM AUTHOR]

Striga hermonthica (Delile) Benth is a root parasitic weed that causes significant crop losses in cereals in the sub‐Saharan Africa. Host resistance is a sustainable management approach; however, Striga resistance in maize is rare. Furthermore, hybrids, preferred for high yield and other agronomic traits are more susceptible than local landraces. We sought to determine the responses of popular maize hybrids against Striga infection using in vitro germination and rhizotron assays. We screened 16 maize hybrids commonly cultivated in Kenya against two common Striga ecotypes (Kibos and Alupe) by measuring their (i) ability to induce the germination of S. hermonthica seeds in an in vitro system (pre‐attachment resistance) and (ii) ability to resist the attachment and establishment of effective parasitism (post‐attachment resistance). Results showed that two maize hybrids H614D and H629 exhibit both pre‐ and post‐attachment resistance comparable to a Striga resistant landrace KSTP'94 used in this study as a resistant check. Other hybrids displayed moderate Striga resistance; DK8031 (pre‐attachment resistance) and Duma43, phb30G19 and WH507 (post‐attachment resistance). Striga virulence, defined as aggressiveness of the parasite—measured by response to germination and host colonisation, was higher for the Kibos ecotype compared to the Alupe ecotype. However, the resistance pattern of maize genotypes remained consistent across the different Striga ecotypes. These results provide a basis for informed decisions on cultivation of various maize hybrids under Striga infestation in western Kenya. [ABSTRACT FROM AUTHOR]

Crop losses to pests were the main obstacle to food security globally. Pest control was a laudable exercise, but the exercise could be hindered by the inevitable adjustment between pest reductions, operation costs as well as impacts on the environment and human health. The pest could be controlled by many methods, but biological control was the most popular technique because it addressed inevitable trade-offs between costs and side effects. In this paper, a mathematical model was developed to quantify intricate biological procedures in the context of biological control using prey-predator mechanisms. Three equilibrium points (one trivial and two non-trivial) were derived, and the stability of each equilibrium point was examined. The stability results indicated that the adoption of biological control might neutralize pest infestation but the situation might not persist (unstable trivial equilibrium). It was also discovered that pest control through biological means might fail if the predator was wrongly selected or if the population of the predator vanished while the pest remained in existence (unstable non-trivial equilibrium). The analytical results were finally justified by a means of simulation via a computer-in-built maple program. [ABSTRACT FROM AUTHOR]

Plant‐environment interactions, particularly biotic stress, are increasingly essential for global food security due to crop losses in the dynamic environment. Therefore, understanding plant responses to biotic stress is vital to mitigate damage. Beneficial microorganisms and their association with plants can reduce the damage associated with plant pathogens. One such group is PGPR (Plant growth‐promoting rhizobacteria), which influences plant immunity significantly by interacting with biotic stress factors and plant signalling compounds. This review explores the types, metabolism, and mechanisms of action of PGPR, including their enzyme pathways and the signalling compounds secreted by PGPR that modulate gene and protein expression during plant defence. Furthermore, the review will delve into the crosstalk between PGPR and other plant growth regulators and signalling compounds, elucidating the physiological, biochemical, and molecular insights into PGPR's impact on plants under multiple biotic stresses, including interactions with fungi, bacteria, and viruses. Overall, the review comprehensively adds to our knowledge about PGPR's role in plant immunity and its application for agricultural resilience and food security. [ABSTRACT FROM AUTHOR]

The adoption of automated methods for the identification and assessment of tomato-related disorders is highly sought-after in the agriculture sector. Using this technology is crucial for reducing wasteful spending, increasing the efficiency of treatments, and ultimately growing more resilient crops by reducing losses in agricultural output and maximising the effectiveness of these processes. An automated method has been suggested for accurately identifying and classifying diseases using a single photograph. The described method for disease detection in tomato plants makes use of a computer vision-based technique. Image processing, ML, and deep learning are just a few of the methods that this strategy uses. The goal of this approach is to prevent tomato crops from being damaged by various illnesses by reducing the need of conventional procedures. Bacterial spot, early blight, late blight, leaf mould, spider mites, target spot, spotted spider mite, mosaic virus, and yellow leaf curl are all examples of these illnesses. The following ten diseases frequently strike tomato crops in India. By utilising picture segmentation in combination with the Enhanced OPTICS algorithm (EOPTICSA), the affected area of the tomato plant may be precisely detected and defined after image pre-processing procedures have been used. It may be necessary to look for certain visual signs in order to diagnose the previously mentioned illnesses. The primary goal of this study was to evaluate the efficacy of the EOPTICSA method for detecting diseases in plant leaves. To eliminate the geometric features associated with colour, texture, and leaf arrangement in the provided plant pictures, image segmentation and edge detection methods are employed. Using these methods allows us to achieve our goal. Various efficacy measures are used to assess and provide a technique recommendation. This research shows that when performance metrics are used to implement these strategies, the suggested strategy outperforms the current methods in terms of accuracy, precision, and F1-score. The process of detecting sickness involves several consecutive steps. Capturing images, segmenting them, detecting edges, and determining the infection's severity are all steps in this process. To accomplish the goal of recognising and categorising different types of diseases that might impact tomato plants, the method of transfer learning is employed. As soon as the problem is identified, it is recommended to take proactive measures to help individuals and organisations involved in agriculture address the effects of these disorders using appropriate measures. [ABSTRACT FROM AUTHOR]

Maize, Zea mays L. is an economic cereal in Egypt. Numerous pests infested maize, but only a few of them caused high crop losses, such as FAW, Spodoptera frugiperda. Therefore, this study is urgent to determine: the effect of chitin synthesis inhibitors on FAW larvae, and evaluate LC90 efficiency of CSIs on maize under the field. In this study, the mortality percentage increased with increasing exposure time to CSIs. Moreover, LC50 for hexaflumuron was low (0.487 mg/L), followed by flufenoxuron (0.5712 mg/L) on the 2nd larva. On the 4th larva, hexaflumuron and diflubenzuron were more lethal than other CSIs. LC90 of all CSIs ranged between 1.3815 5.0569; and 1.024514.6923 mg/L on the 2nd and 4th larvae, respectively. Results were elucidated that the toxicity index was low by novaluron and lufenuron treatments with high resistance ratios, respectively. However, it had a high value with diflubenzuron, followed by novaluron, with a few resistance ratios when compared with hexaflumuron, respectively. In field experiments, data showed that flufenoxuron, hexaflumuron and diflubenzuron could be used as recommended insecticides to FAW control, however, chlorfluazuron, lufenuron and novaluron were the lowest ones on maize. On the response of proteins, carbohydrates and lipids to CSIs, diflubenzuron, hexaflumuron and flufenoxuron caused the lowest significant reduction in protein. Wherever, the highest significant protein value was recorded with chlorfluazuron and lufenuron. The values of chitinase, phenoloxidase and carboxylesterase indicated a clear disturbance in physiological aspects for larvae. Now, CSIs are used for developing eco-friendly FAW management. [ABSTRACT FROM AUTHOR]

Soil salinity is a major contributor to crop yield losses. To improve our understanding of root responses to salinity, we developed and exploited a real-time salt-induced tilting assay. This assay follows root growth upon both gravitropic and salt challenges, revealing that root bending upon tilting is modulated by Na+ ions, but not by osmotic stress. Next, we measured this salt-specific response in 345 natural Arabidopsis (Arabidopsis thaliana) accessions and discovered a genetic locus, encoding the cell wall-modifying enzyme EXTENSIN ARABINOSE DEFICIENT TRANSFERASE (ExAD) that is associated with root bending in the presence of NaCl (hereafter salt). Extensins are a class of structural cell wall glycoproteins known as hydroxyproline (Hyp)-rich glycoproteins, which are posttranslationally modified by O-glycosylation, mostly involving Hyp-arabinosylation. We show that salt-induced ExAD-dependent Hyp-arabinosylation influences root bending responses and cell wall thickness. Roots of exad1 mutant seedlings, which lack Hyp-arabinosylation of extensin, displayed increased thickness of root epidermal cell walls and greater cell wall porosity. They also showed altered gravitropic root bending in salt conditions and a reduced salt-avoidance response. Our results suggest that extensin modification via Hyp-arabinosylation is a unique salt-specific cellular process required for the directional response of roots exposed to salinity. Salt induces EXTENSIN ARABINOSE DEFICIENT TRANSFERASE-dependent arabinosylation of hydroxyproline residues in extensin to modify cell wall structure and the gravitropism response of roots. [ABSTRACT FROM AUTHOR]

The global challenge of crop loss due to salt stress became increasingly significant, especially in the context of meeting the rising demands of a growing world population. This review focuses on the impact of salt stress on leguminous plants throughout their entire growth stages. Additionally, it provides a comprehensive overview of the molecular strategies employed to enhance the performance of legumes in saline environments. In addressing this issue, the review critically assesses recent advancements in bolstering legume salt stress tolerance through genetic engineering. This approach is acknowledged for its efficiency compared to traditional breeding methods, facilitating the transfer of desired genes without introducing extraneous genetic material from the donor organism. The review also examines the critical role of preventing ionic toxicity in transgenic leguminous plants by expressing foreign Na+/H+ antiporter genes and transcription factors. Furthermore, the review emphasizes the positive outcomes observed when introducing or overexpressing genes related to compatible solutes in transgenic legumes. These genetic modifications have proven effective in enhancing the tolerance of legumes to salinity-induced osmotic stress. Another aspect explored in the review is the improving of salt stress-induced oxidative stress management in various transgenic legume species. This is achieved through the expression of both enzymatic and non-enzymatic genes. Finally, the review explores the manipulation of candidate genes to improve nodule performance under salt stress. By identifying and modifying specific genes, researchers can pave the way for leguminous plants to thrive in salt-affected environments. [ABSTRACT FROM AUTHOR]

Simple Summary: The number of invasive pests has increased worldwide, mainly due to international trade. This problem is further exacerbated by climate change and the continued restrictions on the quantity and number of pesticides imposed by international authorities. Delottococcus aberiae, a mealybug native to South Africa, was first detected in Europe in 2009, specifically in the Valencia Community (Spain). The identification of its sex pheromone allowed the development of a monitoring network for this pest a decade later. This network facilitated the mapping of the affected citrus area and its subsequent expansion over time. The monitoring network has revealed that from 2019 until 2023, the citrus area affected by the pest has considerably increased. This network, in conjunction with fruit damage assessments conducted at the end of the crop cycle, has also allowed us to know that the damage to the fruit is independent of the maximum annual population level of the pest. The male catches obtained during the months of March to June, when fruit setting occurs, seem to be related to economic losses due to fruit damage. The invasive mealybug Delottococcus aberiae De Lotto (Hemiptera: Pseudococcidae) has rapidly spread in the Mediterranean basin since its detection in 2009 in the Valencia Community in eastern Spain. The use of sticky traps baited with its sex pheromone, (4,5,5-trimethyl-3-methylenecyclopent-1-en-1-yl)methyl acetate, has allowed to determine the geographical distribution of D. aberiae by means of the surveillance network described in the present work. The population monitoring of the pest over a five-year period (2019–2023) has revealed an increase from 31% to 70% of the affected citrus-growing area. The monitoring network has also allowed a better understanding of the pests' biological cycle throughout the year. The populations start growing from March to June and reach their maximum in July–August. During autumn, there is a gradual decline in the population. Although the highest annual populations were detected in 2022 and 2023, the greatest crop losses were recorded in 2020 and 2021, with mean values near 18%. Data suggest that the damage responsible for fruit deformation, and thus the economic losses, are related to the population levels in spring (April–May) rather than those in summer (July–August). The findings of this study can be valuable for future research and development of effective pest control strategies. [ABSTRACT FROM AUTHOR]