Yield Loss Research Articles

Impact of new maize variety adoption on yield and fertilizer input in China: Implications for sustainable food and agriculture

CONTEXTCrop variety adoption is considered as one of the main strategies for farmers to enhance productivity under stress brought by the climate change, with less environment impacts. It holds an upstream position in the whole life cycle of agricultural clean production, compared to the field management and agricultural non-point source pollution treatment. OBJECTIVEThis paper aims to (i) assess the impact of new maize variety adoption on maize yield and fertilizer input; (ii) analyze the main determinants of farmers' new maize variety choice in the context of climate change, and (iii) identify the heterogeneity of small-scale and large-scale farmers in maize variety choice and its impacts. METHODSBased on plot-level data collected from three major grain producing provinces in China, we employ a treatment effects model to correct the potential bias resulting from the farmers' self-selection and estimate the direct impacts of farmers' new maize variety adoption and the main determinants of such adoption. RESULTS AND CONCLUSIONSThe empirical results indicate that the adoption of new maize varieties significantly increases maize yield by 9.4% but fertilizer use by 16.6 kg/mu (1 mu = 1/15 ha) as well, and this trade-off between economic impacts and environmental impacts is more pronounced among small-scale farmers. However, in the face of natural disasters, adopting new maize varieties results in a significant reduction in yield losses by 1.4% and a decrease in fertilizer input for natural disaster management by 2.45 kg/mu, as compared to the adoption of old varieties. What's more, our findings suggest that a higher frequency of pest and disease disasters significantly increases the probability of new maize variety adoption by farmers, while a higher frequency of climate disasters significantly decreases the probability. SIGNIFICANCEThe estimated impacts of new maize varieties point out the need of synergistically optimized resistance and nutrient use efficiency for crop breeding, which have important policy implications for sustainable food and agriculture. Also, the significant factor analysis contributes to the proposal of feasible ways to promote the farmers' adoption behavior.

Sharing the burden: Cabbage stem flea beetle pest pressure and crop damage are lower in rapeseed fields surrounded by other rapeseed crops

The cabbage stem flea beetle (Psylliodes chrysocephala) is a significant pest of rapeseed (Brassica napus). Feeding by adult P. chrysocephala can cause severe leaf damage and larval infestation can reduce stem strength, both of which impact crop growth and development, causing substantial yield losses and economic damage. The structure of the agricultural landscape can regulate herbivorous pest populations through top-down and bottom-up processes. This has shown promise in regulating the populations of other herbivorous pests, but remains relatively unexplored for P. chrysocephala. Here we investigate how the structure of the agricultural landscape influences P. chrysocephala abundance (pest pressure) and associated crop damage. We also examine the effect of the landscape on natural enemies and their ability to regulate P. chrysocephala populations. We show that P. chrysocephala populations are primarily regulated through bottom-up processes. We identify adjacency to another rapeseed crop and the total proportion of rapeseed grown in the landscape as key factors influencing beetle pressure, crop damage, and larval infestation, but find no effect of host crop proportions grown in the previous year at the examined scales up to 1 km surrounding focal crops. We also observe positive effects of crop heterogeneity and semi-natural habitat proportions on natural enemy abundance and diversity; however, these increases had no direct impact on P. chrysocephala. Bottom-up processes appear to contribute to herbivorous pest regulation by diluting beetles in the landscape, and could represent an important mechanism for sustainably managing pest populations by adapting the proportions and neighbourhoods of rapeseed crops at small to large spatial scales.

Evaluating Pollination and Weed Control Strategies under Mesotunnel Systems for Organic Muskmelon Production in Iowa

Bacterial wilt of cucurbits, caused by Erwinia tracheiphila, is spread by spotted (Diabrotica undeimpunctata howardi) and striped (Acalymma vittatum) cucumber beetles and results in major losses for US cucurbit (Cucurbitaceae spp.) growers. Organic growers of muskmelon (Cucumis melo) lack reliable control measures against bacterial wilt. During previous field trials in Iowa, USA, a system called mesotunnels, which are 3.5-ft-tall barriers covered with a nylon mesh insect netting, resulted in a higher marketable yield of organic ‘Athena’ muskmelon than low tunnels or noncovered plots. However, satisfactory pollination and weed control are challenging in mesotunnels because the netting covers the crop for most or all of the growing season, and economic feasibility of these systems has not been determined. Consequently, two field trials conducted in Iowa from 2020 to 2022 evaluated strategies to ensure pollination under mesotunnels in commercial-scale plots, assess effectiveness of teff (Eragrostis tef) as a living mulch for weed control in mesotunnel systems, and compare the profitability of the treatment options for organic ‘Athena’ muskmelon. The treatments used during the pollination trial were as follows: full season, in which mesotunnels remained sealed all season and bumble bees (Bombus impatiens) were added at the start of bloom for pollination; open ends, wherein both ends of the tunnels were opened at the start of bloom then reclosed 2 weeks later; and on-off-on, in which nets were removed at the start of bloom and then reinstalled 2 weeks later. The full-season treatment had significantly higher marketable yield than the other treatments in two of three trial years. Plants with the full season and open ends treatments had a bacterial wilt incidence <2.5% across all three years and similar numbers of cucumber beetles, whereas plants with the on-off-on treatment had an average bacterial wilt incidence of 11.0% and significantly more cucumber beetles. The open ends treatment had fewer bee visits to ‘Athena’ muskmelon flowers than the other treatments. In the 2-year (2021–22) weed management trial, treatments applied to the furrow between plastic-mulched rows were as follows: landscape fabric; teff seeded at 4 lb/acre and mowed 3 weeks after seeding; teff seeded at 4 lb/acre and not mowed; a control with bare ground where weeds were mowed 3 weeks after transplanting; and a bare ground control with no mowing. The landscape fabric and mowed teff treatments had statistically similar marketable yield, and mowing appeared to minimize yield losses compared with nonmowed treatments. The landscape fabric had no weeds, followed by mowed teff, mowed bare ground, and nonmowed teff. Nonmowed bare ground had the highest weed biomass. The partial budget and cost-efficiency ratio analysis indicated that the full-season treatment was the most cost-efficient pollination option for mesotunnel systems. An economic analysis of the weed management strategies showed that using teff as a living mulch in the furrows between organic ‘Athena’ muskmelon rows, coupled with timely mowing to suppress its growth, can generate revenue comparable to that of landscape fabric. Our findings suggest that organic ‘Athena’ muskmelon growers in Iowa may gain the greatest yield and soil quality benefits when mesotunnels are kept closed for the entire season, bumble bees are used for pollination, and teff (mowed 3 weeks after seeding) is used to control weeds in the furrows. Further trials integrating these pollination and weed management strategies would help validate a comprehensive approach to organic ‘Athena’ muskmelon production under mesotunnels.

Validation and optimization of the loop-mediated isothermal amplification (LAMP) technique for rapid detection of wheat stripe mosaic virus, a wheat-infecting pathogen

BackgroundWheat stripe mosaic virus (WhSMV) is a significant wheat pathogen that causes substantial yield losses in Brazil and other countries. Although several detection methods are available, reliable and efficient tools for on-site WhSMV detection are currently lacking. In this study, a Loop-Mediated Isothermal Amplification (LAMP) method was developed for rapid and reliable field detection of WhSMV. We designed WhSMV-specific primers for the LAMP assay and optimized reaction conditions for increased sensitivity and specificity using infected plant samples. ResultsWe have developed a diagnostic method utilizing the Loop-Mediated Isothermal Amplification (LAMP) technique capable of rapidly and reliably detecting WhSMV. The LAMP assay has been optimized to enhance sensitivity, specificity, and cost-effectiveness. ConclusionThe LAMP assay described here represents a valuable tool for early WhSMV detection, serving to mitigate the adverse economic and social impacts of this viral pathogen. By enabling swift and accurate identification, this assay can significantly improve the sustainability of cereal production systems, safeguarding crop yields against the detrimental effects of WhSMV.

Compensation of cotton yield by nitrogen fertilizer in non-mulched fields with deficit drip irrigation

Irrigation and fertilization are the main measures used by farmers to maintain crop yield and quality, especially in arid and semi-arid regions. To investigate the compensation of cotton yield loss due to deficit drip irrigation in non-mulched fields by increasing nitrogen (N) fertilizer application, a split-plot experiment with irrigation amount as the main plot and N application as the subplot was carried out in southern Xinjiang, China. The experiment included two irrigation amounts of 450 mm (deficit irrigation, W1) and 540 mm (full irrigation, W2) and three N application rates of 150 kg ha−1 (F1), 225 kg ha−1 (F2), and 300 kg ha−1 (F3). From the cotton seedling stage, ET0 (reference evapotranspiration) was calculated from daily meteorological data and irrigation was applied when the cumulative ET0-P reached 45 ± 3 mm. The results showed that irrigation and fertilization significantly affected soil moisture and total N, which in turn affected cotton growth, biomass formation, and yield (P<0.05). Soil water storage (SWS) increased by 15.9% on average under W2 compared to W1. F2 and F3 decreased SWS by 5.0%-5.1% and 7.7%-10.9% respectively, compared to F1. Total soil nitrogen in W2 was on average 13.3% lower than W1, and F2 and F3 were 4.4%-11.1% and 11.3%-16.5% lower than F1, respectively. Aboveground biomass increased significantly with increasing irrigation and N amounts and was 47.9% higher in W2F3 than in W1F1. However, the increased biomass was not translated into yield and excessive accumulation of root and stem biomass reduced cotton yield. Under full irrigation (W2), cotton yield increased and then decreased with increasing N application. Cotton yield in F2 increased by 3.4% and 8.6% compared to F1 and F3, respectively. Under deficit irrigation (W1), N fertilizer compensation increased with increasing N application, and cotton yield in F3 increased by 10.3% and 15.0% compared to F1 and F2, respectively (P<0.05). TOPSIS analysis showed that W1F3 had the highest rank, followed by W1F2. The results indicated that W1F3, irrigation amount of 450 mm and N application rate of 300 kg ha−1, could be proposed for cotton fields under non-mulched drip irrigation in southern Xinjiang, China.

Effects of alternate wetting and drying irrigation on yield, water-saving, and emission reduction in rice fields: A global meta-analysis

With limited natural resources and uncertain climate, increasing food production must mitigate the environmental impacts. Rice, a water-intensive grain crop, contributes significantly to greenhouse gas (GHG) emissions in agricultural systems. Alternate Wetting and Drying (AWD) Irrigation stands out as the prevailing water-saving irrigation technique in rice fields worldwide. Nonetheless, the effects of AWD on yield variation, water conservation, and emissions reduction as well as the synergies and trade-offs between the effects remain unclear. Here a global meta-analysis of 437 literature sources covering 93% of global rice production was conducted to quantify changes in yield, water utilization, and GHG emissions following AWD implementation. Results show that, on a global scale, AWD reduces irrigation water usage by 33.88% and enhances water use efficiency, irrigation water use efficiency, and water productivity by 20.27%, 47.58%, and 29.63%, respectively. However, there is a minor decrease in yield by 1.56%. Notably, AWD leads to a noteworthy 47.47% reduction in methane emissions, counterbalanced by a 52.20% rise in N2O emissions, ultimately resulting in a decrease in global warming potential (GWP) and greenhouse gas intensity (GHGI) by 39.38% and 38.06%, respectively. The AWD threshold is the most significant predictive factor, adopting a suitable threshold can greatly improve the accuracy of AWD implementation. Soil properties also play a pivotal role, with pH levels > 7 or soil organic carbon (SOC) < 12 g/kg correlating to more pronounced yield losses. Implementing reasonable field management measures, such as suitable rice varieties and straw incorporation can effectively increase rice productivity. Approximately 60.36% of the data falls within the "win-win" zone, demonstrating simultaneous in yield increases, water conservation, and emissions reduction. AWD is more suitable for acidic soils with high SOC (>12 g/kg) and a light texture. This study introduces novel insights for analyzing the synergies and trade-offs within the food–water–climate nexus.

A Review: CNN Based Plant Disease Detection

Abstract: Identifying plant diseases is the initial step toward mitigating losses in agricultural product yield and quantity. This work focuses on exploiting such technical breakthroughs, with an actual focus on plant disease detection. The system will analyze plant photos using cutting-edge image processing algorithms to precisely diagnose diseases, gauge the extent of damage, and make wise suggestions for necessary pesticides and nutrient supplements. The suggested remedy will identify particular nutrient deficits causing problems with crop health in addition to detecting the existence of illnesses. The system will aid farmers in making well-informed decisions about how to use nutrients and pesticides by integrating a thorough analysis. This will ultimately help to manage crop diseases in a timely and efficient manner, maximize agricultural yield, and reduce financial losses

Analysis of the insecticidal effect of botanical extracts on mealybugs (Phenacoccus solenopsis)

The Cotton mealybug, also known as Phenacoccus solenopsis, poses a significant threat to agricultural productivity, particularly in cotton cultivation, where infestations cause substantial yield losses. This study examined the effectiveness of botanical extracts from neem (Azadirachta indica), clove (Syzygium aromaticum), black pepper (Piper nigrum), and bay leaf (Laurus nobilis) in controlling P. solenopsis. Neem extract showed the highest mortality effect at 86.6% followed by clove (68.3%) and black pepper (58.3%). Bay leaf extract had the lowest mortality effect at 36.6%. These findings highlight the potential of neem, clove, and black pepper extracts as effective options for managing P. solenopsis infestations in agriculture.

Analysis of biological organic extract of plants in suppressing aphid (Myzus persicae) infestations

Myzus persicae commonly known as the peach-potato aphid, poses a significant threat to agricultural productivity, particularly in potato cultivation, where infestations lead to substantial yield losses and quality degradation. In this study, we investigated the potential of botanical extracts from neem (Azadirachta indica), clove (Syzygium aromaticum), black pepper (Piper nigrum), and bay leaf (Laurus nobilis) in controlling Myzus persicae infestations. Experimental trials were conducted to assess the mortality effects of these botanical extracts on Myzus persicae populations. Results revealed that neem extract exhibited the highest mortality effect, with an impressive 97.7% mortality rate. Clove and black pepper extracts followed closely, demonstrating mortality rates of 92.2% and 77.7%, respectively. In contrast, bay leaf extract exhibited the least mortality effect, with only 48.8% mortality observed.

Vertical and temporal flight patterns of coffee berry borer (Coleoptera: Curculionidae) in Hawaii.

Coffee berry borer (Hypothenemus hampei Ferrari)(Coleoptera: Curculionidae) is the most damaging insect pest of coffee worldwide, causing significant losses in coffee yields and quality. Knowledge of vertical and temporal flight patterns in coffee berry borer could be used to optimize spray timing and precision targeting of areas within the coffee tree, which may be more susceptible. In the present study, we estimated the vertical distribution of coffee berry borer females using traps set at 1-m intervals up to 5 m in height. We also quantified coffee berry borer infestation in the low, mid, and high canopy and documented fruit availability. Temporal flight patterns were estimated using timer traps, and correlation analyses were conducted to determine the relationship between the timing of daily flight and weather variables. Across the 4 study sites, we observed that 77%-84% of the trap catch was at 1 m, 11%-20% was at 2 m, and 1%-4% was at 3-5 m in height. Fruit infestation was significantly higher in the low branches (35%) relative to the high branches (17%). Flight height remained the same year-round, regardless of fruit availability. Coffee berry borer flew in low numbers during the day and night but peaked from 12 to 4 PM. Daily flight was positively correlated with an increase in air temperature and wind speed and negatively correlated with relative humidity. Findings from this study suggest that pesticide sprays should target low- to mid-level branches at 1-2 m in height and aim to be conducted in the early afternoon when coffee berry borer are actively flying and most vulnerable to chemical controls.

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.

Plant Disease Detection Using Deep Learning-Based Convolutional Neural Networks With Transfer Learning Algorithms

Plant diseases can have significant impacts on agricultural production, leading to significant losses in yield and quality. Early detection and diagnosis of crop diseases is essential for effective control and management. In this study, We present a novel approach for the automated detection of plant diseases using deep learning-based Convolutional Neural Networks (CNNs) coupled with Transfer Learning algorithms. Our study focuses on developing a robust and adaptable system capable of accurately identifying plant diseases from image data. By leveraging a comprehensive dataset comprising diverse plant species and disease types, our goal is to train the model to achieve high accuracy and real-time detection capabilities. The potential significance of this research lies in its potential to significantly improve agricultural practices by offering farmers a valuable tool for prompt disease diagnosis and management, ultimately leading to increased crop yields and sustainable farming practices. Keywords: CNN, Deep Learning, Plant Diseases detection, Disease Diagnosis, Agriculture.

Exploring the potential of Bacillus for crop productivity and sustainable solution for combating rice false smut disease

Rice false smut, which is caused by the soil-borne fungal pathogen Ustilaginoidea virens (U. virens), is one of the most threatening diseases in most of the rice-growing countries including India that causes 0.5–75% yield loss, low seed germination, and a reduction in seed quality. The assessment of yield loss helps to understand the relevance of disease severity and facilitates the implementation of appropriate management strategies. This study aimed to mitigate biotic stress in rice by employing a rhizobacterial-based bioformulation, which possesses diverse capabilities as both a plant growth promoter and a biocontrol agent against U. virens. Rhizobacteria were isolated from the soil of the rice rhizospheres from the healthy plant of the false smut affected zone. Furthermore, they were identified as Bacillus strains: B. subtilis (BR_4), B. licheniformis (BU_7), B. licheniformis (BU_8), and B. vallismortis (KU_7) via sequencing. Isolates were screened for their biocontrol potential against U. virens under in vitro conditions. The antagonistic study revealed that B. vallismortis (KU_7) inhibited U. virens the most (44.6%), followed by B. subtilis BR_4 (41.4%), B. licheniformis BU_7 (39.8%), and B. licheniformis BU_8 (43.5%). Various biochemical and plant growth promoting attributes, such as phosphate and Zn solubilization, IAA, ammonium, siderophore, and chitinase production, were also investigated for all the selected isolates. Furthermore, the potential of the isolates was tested in both in vitro and field conditions by employing talc-based bioformulation through bio-priming and root treatment. The application of bioformulation revealed a 20% decrease in disease incidence in plants treated with B. vallismortis (KU_7), a 60.5% increase in the biological yield, and a 45% increase in the grain yield. This eco-friendly approach not only controlled the disease but also improved the grain quality and reduced the chaffiness.

Comparative analysis of different Karnal bunt disease prediction models developed by machine learning techniques for Punjab conditions.

Timely prediction of pathogen is important key factor to reduce the quality and yield losses. Wheat is major crop in northern part of India. In Punjab, wheat face challenge by different diseases so the study was conducted for two locations viz. Ludhiana and Bathinda. The information regarding the occurrence of Karnal bunt in 12 consecutive crop seasons (from 2009-10 to 2020-21) in Ludhiana district and in 9 crop seasons (from 2010-11 to 2018-19) in Bathinda district, was collected from the Wheat Section of the Department of Plant Breeding and Genetics at Punjab Agricultural University (PAU), located in Ludhiana. The study aims to investigate the adequacy of various methods of machine learning for prediction of Karnal bunt using meteorological data for different time period viz. February, March, 15 February to 15 March and overall period obtained from Department of Climate Change and Agricultural Meteorology, PAU, Ludhiana. The most intriguing outcome is that for each period, different disease prediction models performed well. The random forest regression (RF) for February month, support vector regression (SVR) for March month, SVR and BLASSO for 15 February to 15 March period and random forest for overall period surpassed the performance than other models. The Taylor diagram was created to assess the effectiveness of intricate models by comparing various metrics such as root mean square error (RMSE), root relative square error (RRSE), correlation coefficient (r), relative mean absolute error (MAE), modified D-index, and modified NSE. It allows for a comprehensive evaluation of these models' performance.

Cotton seed cultivar identification based on the fusion of spectral and textural features.

The mixing of cotton seeds of different cultivars and qualities can lead to differences in growth conditions and make field management difficult. In particular, except for yield loss, it can also lead to inconsistent cotton quality and poor textile product quality, causing huge economic losses to farmers and the cotton processing industry. However, traditional cultivar identification methods for cotton seeds are time-consuming, labor-intensive, and cumbersome, which cannot meet the needs of modern agriculture and modern cotton processing industry. Therefore, there is an urgent need for a fast, accurate, and non-destructive method for identifying cotton seed cultivars. In this study, hyperspectral images (397.32 nm-1003.58 nm) of five cotton cultivars, namely Jinke 20, Jinke 21, Xinluzao 64, Xinluzao 74, and Zhongmiansuo 5, were captured using a Specim IQ camera, and then the average spectral information of seeds of each cultivar was used for spectral analysis, aiming to estab-lish a cotton seed cultivar identification model. Due to the presence of many obvious noises in the < 400 nm and > 1000 nm regions of the collected spectral data, spectra from 400 nm to 1000 nm were selected as the representative spectra of the seed samples. Then, various denoising techniques, including Savitzky-Golay (SG), Standard Normal Variate (SNV), and First Derivative (FD), were applied individually and in combination to improve the quality of the spectra. Additionally, a successive projections algorithm (SPA) was employed for spectral feature selection. Based on the full-band spectra, a Partial Least Squares-Discriminant Analysis (PLS-DA) model was established. Furthermore, spectral features and textural features were fused to create Random Forest (RF), Convolutional Neural Network (CNN), and Extreme Learning Machine (ELM) identification models. The results showed that: (1) The SNV-FD preprocessing method showed the optimal denoising performance. (2) SPA highlighted the near-infrared region (800-1000 nm), red region (620-700 nm), and blue-green region (420-570 nm) for identifying cotton cultivar. (3) The fusion of spectral features and textural features did not consistently improve the accuracy of all modeling strategies, suggesting the need for further research on appropriate modeling strategies. (4) The ELM model had the highest cotton cultivar identification accuracy, with an accuracy of 100% for the training set and 98.89% for the test set. In conclusion, this study successfully developed a highly accurate cotton seed cultivar identification model (ELM model). This study provides a new method for the rapid and non-destructive identification of cotton seed cultivars, which will help ensure the cultivar consistency of seeds used in cotton planting, and improve the overall quality and yield of cotton.

Characterization of Quantitative Trait Loci for Leaf Rust Resistance in the Uzbekistani Wheat Landrace Teremai Bugdai.

Leaf rust, caused by Puccinia triticina, is a major cause of wheat yield losses globally, and novel leaf rust resistance genes are needed to enhance wheat leaf rust resistance. Teremai Bugdai is a landrace from Uzebekistan that is highly resistant to many races of P. triticina in the United States. To unravel leaf rust resistance loci in Teremai Bugdai, a recombinant inbred line (RIL) population of Teremai Bugdai × TAM 110 was evaluated for response to P. triticina race Pt54-1 (TNBGJ) and genotyped using single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS). Quantitative trait loci (QTL) analysis using 5,130 high-quality GBS-SNPs revealed three QTLs, QLr-Stars-2DS, QLr-Stars-6BL, and QLr.Stars-7BL, for leaf rust resistance in two experiments. QLr-Stars-2DS, which is either a new Lr2 allele or a new resistance locus, was delimited to an ∼19.47-Mb interval between 46.4 and 65.9 Mb on 2DS and explained 31.3 and 33.2% of the phenotypic variance in the two experiments. QLr-Stars-6BL was mapped in an ∼84.0-kb interval between 719.48 and 719.56 Mb on 6BL, accounting for 33 to 36.8% of the phenotypic variance in two experiments. QLr.Stars-7BL was placed in a 350-kb interval between 762.41 and 762.76 Mb on 7BL and explained 4.4 to 5.3% of the phenotypic variance. Nine GBS-SNPs flanking these QTLs were converted to kompetitive allele specific PCR (KASP) markers, and these markers can be used to facilitate their introgression into locally adapted wheat lines.

Development of an Efficient CRISPR/Cas9 System in Fusarium verticillioides and Its Application in Reducing Mycotoxin Contamination.

Fusarium verticillioides (F. verticillioides) is a globally recognized and highly impactful fungal pathogen of maize, causing yield losses and producing harmful mycotoxins that pose a threat to human and animal health. However, the genetic tools available for studying this crucial fungus are currently limited in comparison to other important fungal pathogens. To address this, an efficient CRISPR/Cas9 genome editing system based on an autonomously replicating plasmid with an AMA1 sequence was established in this study. First, gene disruption of pyrG and pyrE via nonhomologous end-joining (NHEJ) pathway was successfully achieved, with efficiency ranging from 66 to 100%. Second, precise gene deletions were achieved with remarkable efficiency using a dual sgRNA expression strategy. Third, the developed genome editing system can be applied to generate designer chromosomes in F. verticillioides, as evidenced by the deletion of a crucial 38 kb fragment required for fumonisin biosynthesis. Fourth, the pyrG recycling system has been established and successfully applied in F. verticillioides. Lastly, the developed ΔFUM1 and ΔFUM mutants can serve as biocontrol agents to reduce the fumonisin B1 (FB1) contamination produced by the toxigenic strain. Taken together, these significant advancements in genetic manipulation and biocontrol strategies provide valuable tools for studying and mitigating the impact of F. verticillioides on maize crops.

BUDDHIMAN KRISHI ANUVADHANAM: A New Hope for Better Agriculture System

The Agriculture Monitoring System addresses pressing challenges in contemporary farming, notably ineffective soil moisture management and persistent threats of animal intrusion. Recent statistics indicate a global agricultural loss of 20% due to suboptimal soil moisture practices and animal-related damages. Existing monitoring systems fall short in providing a holistic solution, resulting in significant economic losses for farmers. In response, the proposed system integrates advanced technologies, including precise soil moisture sensors, infrared (IR) sensors for animal detection, and real-time visual monitoring via an ESP32 camera. Farmers can also monitor and control irrigation pumps and electric shock generators through their smartphones. This comprehensive approach aims to minimize yield losses, optimize resource utilization, and empower farmers with actionable insights for informed decision-making. By bridging critical gaps in agricultural monitoring and protection, the Agriculture Monitoring System emerges as a transformative solution with the potential to revolutionize and fortify global farming practices

Identification and fungicides sensitivity evaluation of the causal agent of cobweb disease on Lyophyllum decastes in China

BackgroundCobweb disease is a fungal disease that commonly affects the cultivation and production of edible mushrooms, leading to serious yield and economic losses. It is considered a major fungal disease in the realm of edible mushrooms. The symptoms of cobweb disease were found during the cultivation of Lyophyllum decastes. This study aimed to identify the causative pathogen of cobweb disease and evaluate effective fungicides, providing valuable insights for field control and management of L. decastes cobweb disease.ResultsThe causal agent of cobweb disease was isolated from samples infected and identified as Cladobotryum mycophilum based on morphological and cultural characteristics, as well as multi-locus phylogeny analysis (ITS, RPB1, RPB2, and TEF1-α). Pathogenicity tests further confirmed C. mycophilum as the responsible pathogen for this condition. Among the selected fungicides, Prochloraz-manganese chloride complex, Trifloxystrobin, tebuconazole, and Difenoconazole exhibited significant inhibitory effects on the pathogen’s mycelium, with EC50 values of 0.076 µg/mL, 0.173 µg/mL, and 0.364 µg/mL, respectively. These fungicides can serve as references for future field control of cobweb disease in L. decastes.ConclusionThis study is the first report of C. mycophilum as the causing agent of cobweb disease in L. decastes in China. Notably, Prochloraz-manganese chloride complex demonstrated the strongest inhibitory efficacy against C. mycophilum.

Potential of undersown species identity versus diversity to manage disease in crops

Abstract In the absence of chemical control with its negative side effects, fungal pathogens can cause large yield losses, requiring us to develop agroecosystems that are inherently disease resistant. Grassland biodiversity experiments often find plant species diversity to reduce pathogen pressure, but whether incorporating high biodiversity levels in agricultural fields have similar effects remains largely unknown. We tested if undersown plant species diversity could reduce barley disease, and whether the effect was mediated through above‐ or below‐ground mechanisms, by combining an agricultural field trial with a soil transplant experiment. As predicted, barley disease decreased in the presence of undersown plants. Undersown species richness had no effect, but their abundance led to early season disease reduction. Above‐ground mechanisms underpinned this disease reduction. Barley yield slightly decreased with increasing undersown species richness, and undersown species varied in their impact on yield. We identified two undersown species, Trifolium repens and T. hybridum, that contributed most to disease reduction and had the potential to increase barley yield. Furthermore, our results indicate that above‐ground mechanisms caused this. We show that agroecosystem functioning can be improved without trade‐offs on yield by targeted selection of undersown species. Read the free Plain Language Summary for this article on the Journal blog.