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Global warming has adverse effects on crop growth, development and yield production. It is of great theoretical and practical significance to evaluate the seed heat tolerance of different crops and to mine the related genes for improving the tolerance of crops to high temperature stress. In order to study the differences of seed heat tolerance in different crops, the seeds of different crop varieties or parental lines were used as materials, and the germination potential and germination percentage were investigated after h, h, h, h and h of heat treatment under about 100 °C. The results showed that there were significant differences in seed heat tolerance in different crops. The general trend was that the germination potential and germination percentage decreased continuously with the extension of treatment time. The seeds of sesame and partial rapeseed varieties had the strongest heat tolerance, and the germination potential and germination percentages after hours heat treatment were still over %. The heat tolerance of wheat, rice, barley and safflower is moderate, and the heat tolerance of peanut seeds is the weakest, the germination potential and germination percentages after h treatment were less than %. In order to explore heat tolerance-related genes of rapeseed, Brassica napus lines were used as materials and treated at 100°C for 8h. And 60 K Illumina SNP array were used to conduct genome-wide association analysis of germination potential and germination percentage in two environments. As a result, 11 SNPs associated with germination potential and SNPs associated with germination percentage were detected. A total of heat tolerance-related genes were screened within the candidate interval, and some homologous genes of the genes have been reported to play an important role in heat tolerance response of other plants. [ABSTRACT FROM AUTHOR]

【Objective】Mining domestic satellite image data information to rapidly and accurately obtain the types of crop planting structures, to provide references for optimizing agricultural production layout.【Methods】 The random forest algorithm model was used in combination with sample data to analyze the importance of spectral features, vegetation index features, and texture features of the images. The optimal feature combinations based on GF-6 images were acquired by evaluating classification accuracy. The selected features were then used in object-oriented classification research（with smoothness of 0.5 and compactness of 0.3 as parameters and 10 as steps, setting a total of 11 different segmentation scales ranging from 40 to 140）, to obtain the spatial distribution of the main crop planting structure in the research area.【Results】The GVI, EVI, Nir, GI, GNDVI, and Green features obtained by feature optimization method could effectively reduce data redundancy in crop classification and improve classification efficiency. Among the 11 segmentation scales set in the research area farmland, the optimal segmentation scale was 100. The segmentation results retained the integrity of the plot and reflected the heterogeneity of different crop types. The classification accuracy based on object-oriented classification method reached 96.2%, with a Kappa coefficient of 0.944, which was 5.3 percentage points higher than pixel based classification accuracy.【Conclusion】Using domestically produced GF-6 images as the data source and employing the feature optimization object-oriented classification method could effectively improve classification accuracy and serve as an effective means for crop planting structure monitoring. [ABSTRACT FROM AUTHOR]

【Objective】To grasp the research progress on crop pests and diseases identification.【Methods】Using the literature on crop pests and diseases identification from Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases as data sources, statistical analysis and bibliometric methods were used to analyze the research situation in the field of crop pests and diseases identification during 2000 to 2022 from the perspectives of literature publication volume, publication country, publication institution, research scholars, and research hotspots.【Results】2 214 English literature related to crop pests and diseases identification were retrieved. Since 2014, the publication volume increased continuously. The publication volume during 2014 to 2022 accounted for 85.0% of the total publication volume. 2 390 Chinese literature were retrieved, with a steady increase in publication volume from 2000 to 2012. After 2013, despite large fluctuations, there was an overall upward trend.China and the United States accounted for 41.3% of the total publication volume on crop pests and diseases identification research.In terms of collaboration and communication, China had close research collaboration with the United States and some Asian and European countries. The Chinese Academy of Sciences (90 publications) had the highest English literature publication volume, while the National Agricultural Technology Promotion Service Center (47 publications) had the highest Chinese literature publication volume.HUANG Wenjiang had significant research results both domestically and internationally (45 publications) . The main international research hotspots in the field of crop pests and diseases identification were the establishment of identification models and data acquisition.Domestic research hotspots underwent a transformation from manual recognition through expert systems to model optimization.【Conclusion】The current research momentum on crop pests and diseases identification domestically and internationally are still strong, with different research focuses.Intentionally, the research systems in model optimization and data acquisition methods are relatively mature. But there are still huge developmental potentials that can be excavated. Domestically, the research on intelligent recognition started relatively late but developed rapidly. In the future, the application of deep learning new algorithms may become a new research hotspot in the field of crop pets and diseases identification. [ABSTRACT FROM AUTHOR]

High soil evaporation levels are a major contributor to loss of soil moisture in arid and semiarid regions globally. Therefore, it is important to use effective measures to slow the evaporation from farmland soils. We applied various amounts of straw biochar (BC) in a soil column experiment and a field experiment to study the influence of BC on soil evaporation and moisture content, respectively, to improve the water use efficiency of cultivated soil in arid areas. The addition of BC reduced soil evaporation and delayed water loss from the soil by evaporation. In the field experiment, cumulative evaporation in the treatments declined by 9.58% (Bo-10), 10.95% (Bo-30), and 4.2% (Bo-50) compared with that in the control group, demonstrating that 30 t/hm2 BC is the most effective at suppressing soil evaporation. BC also delayed the time required for the soil moisture content to drop to field capacity and increased the upward transport of water from the deeper soil layers at night. Data from continuous monitoring of moisture content for 3 days during each growth period revealed that the increases in moisture replenishment were 18.52–79.62% at the seedling stage, 55.81–202.38% at the jointing stage, 270.83–587.5% at the tassel stage, and 6.66–61.64% at the maturation stage; hence, BC was shown to work best at the tassel stage. [ABSTRACT FROM AUTHOR]

The effect of amending "barren" sandy and clay soils with different organic origins on soil organic carbon (SOC) accumulation and permeability is not well known. Therefore, proposing desirable practices to improve their qualities and fertility is a major challenge. To improve soil organic carbon, permeability (PR) and productivity, the two barren soil textures were amended with 0.39 and 0.78 tha−1 (ton per hectare) of manure and lignite, respectively, and irrigated with treated wastewater. During the four years of experimentation, the ranges of increase in SOC of sandy and clay soils were 1.9–3.87 kgm−2, and 0.29–1.40 kgm−2, respectively, and in PR of 35.2–69.3% in sandy soils and 39.3–74.8% in clay soils. These improvements have a major effect on nutrient cycling, significantly improving water and nutrient availability to roots and plants. The levels of improvement increase with the amounts of organic compounds introduced, especially in the clay soil amended with 0.78 tha−1. The proportions of mineralized (lost) SOC were 35.3–62.3% in the sandy soils and 1.0–3.1% in the clay soils. The maximum crop weights produced were approximately 41.2 gm−2 in sandy soils and 1152 gm−2 in clay soils. Thus, amendment of barren sandy and clay soils by all three sources of organic carbon was beneficial, especially for the clay soil. achieved the desired objectives. The gain in clay soils is significantly greater than in sandy soils, which require further study. The type of practice desired by farmers will depend on cost and crop yield. Laboratoire Géoressources. Univerversité de Carthage-Centre des Recherches et Technologies des Eaux, B.P. 273, Soliman 8020 (Tunisia). [ABSTRACT FROM AUTHOR]

The excessive use of chemical substances such as pesticides, herbicides, and fungicides, along with other harmful practices, has disrupted the beneficial microflora in the soil. These harmful effects extend beyond the soil and can lead to the spread of dangerous diseases in both humans and animals. Pesticides are frequently applied in modern agriculture to boost crop production and meet the demands of a growing population. However, the accumulation of pesticides in the soil poses a threat to both the soil itself and the overall ecosystem. As pesticides enter the food chain, groundwater, and marine water, they have direct or indirect impacts on human health and negatively affect terrestrial and aquatic life. These chemical compounds contribute to various biological and microscopic contaminants, resulting in detrimental environmental consequences. Fortunately, certain microorganisms possess natural tolerance to pesticides and can survive under adverse conditions. Diazotrophs, which are nitrogen-fixing bacteria, have also been found to play a role in the biodegradation of toxic compounds released from pesticides, thereby contributing to a healthier environment. This review focuses on the essential microbial community involved in pesticide degradation, with particular emphasis on Diazotrophs. It explores their classification, mechanisms of pesticide degradation, and regulation under stress conditions. Understanding the characteristics of these essential microbes (Diazotrophs) operating in stressful environments is crucial for pesticide degradation. The study highlights the significant role of microbial diversity in the breakdown of xenobiotic pesticide compounds in the soil, emphasising the importance of an eco-friendly approach to improve agricultural practices, sustain soil fertility, promote crop production, and protect ecotoxicology. [ABSTRACT FROM AUTHOR]

Leaf senescence is a natural phenomenon that occurs during the aging process of plants and is influenced by various internal and external factors. These factors encompass plant hormones, as well as environmental pressures such as inadequate nutrients, drought, darkness, high salinity, and extreme temperatures. Abiotic stresses accelerate leaf senescence, resulting in reduced photosynthetic efficiency, yield, and quality. Gaining a comprehensive understanding of the molecular mechanisms underlying leaf senescence in response to abiotic stresses is imperative to enhance the resilience and productivity of crops in unfavorable environments. In recent years, substantial advancements have been made in the study of leaf senescence, particularly regarding the identification of pivotal genes and transcription factors involved in this process. Nevertheless, challenges remain, including the necessity for further exploration of the intricate regulatory network governing leaf senescence and the development of effective strategies for manipulating genes in crops. This manuscript provides an overview of the molecular mechanisms that trigger leaf senescence under abiotic stresses, along with strategies to enhance stress tolerance and improve crop yield and quality by delaying leaf senescence. Furthermore, this review also highlighted the challenges associated with leaf senescence research and proposes potential solutions. [ABSTRACT FROM AUTHOR]

Accurate and timely information on winter wheat spatial distribution is essential for food security and environmental sustainability. However, high-quality nation-wide winter wheat products at high resolutions are still scarce around the world, and the approaches for winter wheat mapping are generally constrained by the lack of sufficient and representative training data. In this study, a knowledge-based approach based on spectral and polarization information from critical stages of winter wheat, was proposed to extract high-quality training data of winter wheat, thereby supporting winter wheat mapping with machine learning classifiers. Additionally, classification model trained by the generated training data was transferred across years to achieve the in-season mapping of winter wheat. Two-year classification scenarios based on the automated training data generation (ATDG) or model transfer (MT) were designed to evaluate the quality of automatically generated training data, the performance of model transfer, the contribution of optical and radar data, and the earliest timing for winter wheat mapping over China. With the ATDG and MT, the first 10-m resolution maps of winter wheat over China (ChinaWheat10) were produced for three consecutive years (2020 & 2021 by ATDG; 2021 & 2022 by MT). For ATDG and MT, the combined features of Sentinel-1 and Sentinel-2 yielded the highest overall accuracies with the random forest classifier. Specifically, winter wheat mapping with the ATDG achieved the highest F1-score of 0.94 for both 2020 and 2021. The MT reached a comparable F1-score of 0.94 and 0.93 for 2021 and 2022, and winter wheat maps with the F1-score of 0.93 and 0.92 could be produced as early as April (two months ahead of harvesting). Besides well-delineated winter wheat parcels, the estimated areas of ChinaWheat10 aligned well with the agricultural census data at the provincial (R2 ≥ 0.95) and municipal (R2 ≥ 0.91) levels. These findings suggest the proposed approaches have a great potential for accurate, cost-effective and high-resolution in-season mapping of winter wheat over large regions. [ABSTRACT FROM AUTHOR]

The transition from conventional arable towards silvoarable systems can increase the delivery of ecosystem services. Nevertheless, the assessment of crop yield under agroforestry condition is crucial to evaluate of the reliability of these systems and to increase the knowledge base needed to support their design. Although the feasibility of poplar short rotation coppice (SRC) silvoarable alley-cropping systems has been widely investigated, few studies have addressed the agronomic response of crops intercropped with poplar SRC in narrow alleys, especially in Mediterranean environments. Thus, this paper treats the effects of SRC poplar rows on soybean and sorghum productivity in a 2-year rotation implemented in an alley-cropping system. A field experiment was carried out in 2018 and 2019 with the objective of measuring and evaluating effects of light availability variation, as affected by the growing rate of 2-year coppice cycle poplar SRC rows, and the soil characteristics on soybean-sorghum and sorghum–soybean rotations. Above-ground biomass, grain yield and crops residue showed a significant reduction in the tree–crop interface up to 74% and sorghum proved to be less tolerant to light reduction compared to soybean. Our results demonstrated that light is the most important factor for sorghum cultivation, despite grain yield was also influenced by the soil characteristics such as pH, while soybean is affected also by soil moisture and water retention capacity. The design of crop rotation in an SRC-based agroforestry system needs to consider the different agronomic performance of different crops and the harvest cycle of tree rows. [ABSTRACT FROM AUTHOR]

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Rice (Oryza sativa) is a staple food crop for over half of the world's population. However, drought as a result of climate change has led to increased soil salinity, thereby reducing agricultural potential, especially rice nutritional compositions and biochemical properties. Nevertheless, soil management by using suitable fertilizers might be able to improve rice quality even though these rice samples were grown in soil with a high degree of salinity. This study investigated nutritional compositions, phenolic contents, and antioxidant activities of twenty-five rainfed rice samples in Khao Dawk Mali 105 (KDML105) and Rice Department 15 (RD15) varieties grown in soil with different degrees of salinity. The soil, however, had been improved by the usage of fertilizer at the tillering and booting stages. Results indicated that all rice samples exhibited similar nutrients, total phenolic contents (TPCs), and antioxidant potentials, suggesting that appropriate fertilizer could improve rice qualities. Principle Component Analysis (PCA) and Pearson correlation results suggested that regardless of rice varieties, organic matter (OM) and soil potassium (Ks) showed a very strong positive correlation with protein and minerals (Ca, Na, K, and Fe), while opposite results were observed with soil pH. Moderate to very weak correlations were also observed between soil parameters and TPCs, as well as between soil parameters and antioxidant activities. The received information will be useful for the future development of appropriate fertilizer usage in salt-tolerant rice with particular nutritional quality. [ABSTRACT FROM AUTHOR]

Agriculture is always needed by every human and responsible for the economic growth of a country. Developed countries likewise America, Japan, China are leading and making other countries too dependent on their technologies. But developing countries like India are expecting a lot of new technological innovations in the field of agriculture. Innovations may be in the form of smart machines, automation systems, sensorbased instruments, etc. and an advantage for society. In this paper, we have proposed Recommending and Predicting Crop Yield using Smart Machine Learning Algorithm (SMLA). The proposed algorithm namely SMLA is compared with other traditional algorithms to predict crop yield. In comparison to other algorithms the proposed algorithm works efficiently and produces 95% accuracy. [ABSTRACT FROM AUTHOR]

Damages to crops happen due to natural calamities, irregular fertilization, improper treatment, etc. Estimation of this damage is important in order to plan and execute corrective action strategies. To perform this estimation with high accuracy, both satellite and near-field images are needed. Satellite images assists in evaluation of damages due to natural calamities, while near-field images assist in evaluation of damage due to plant diseases. Separate models are designed for processing these images, which limits their correlative analysis; and thereby reduces overall accuracy of damage detection. To remove this drawback, this text proposes a deep convolutional network (DCN) design that integrates both near-field and far-field images in order to perform effective correlation. Moreover, design of an integrated model would assist researchers to identify and resolve dataset-specific performance gaps. The proposed model analyses different datasets, and estimates performance of context-sensitive classification methods. These are integrated to improve efficiency for multimodal augmented image classification deployments via correlative analysis. This analysis allows the system to predict crop-damages with higher efficiency than individual models. The model is trained for detection of areas which are infected by natural calamities, thereby assisting farm experts to undertake corrective measures based on specific area. Results of proposed model are compared with some of the recently developed state-of-the-art methods, and it is observed that the former model achieves 10% better accuracy, 8% better precision and 5% better recall performance. This evaluation is done on a large number of datasets, thereby assisting in model validation and estimating its usage for multiple type of crops. This text also recommends future research directions that can be undertaken for performance improvement of the underlying model. [ABSTRACT FROM AUTHOR]

Main conclusion: This review provides a direction for crop quality improvement and ideas for further research on the application of CRISPR/Cas9 gene editing technology for crop improvement. Various important crops, such as wheat, rice, soybean and tomato, are among the main sources of food and energy for humans. Breeders have long attempted to improve crop yield and quality through traditional breeding methods such as crossbreeding. However, crop breeding progress has been slow due to the limitations of traditional breeding methods. In recent years, clustered regularly spaced short palindromic repeat (CRISPR)/Cas9 gene editing technology has been continuously developed. And with the refinement of crop genome data, CRISPR/Cas9 technology has enabled significant breakthroughs in editing specific genes of crops due to its accuracy and efficiency. Precise editing of certain key genes in crops by means of CRISPR/Cas9 technology has improved crop quality and yield and has become a popular strategy for many breeders to focus on and adopt. In this paper, the present status and achievements of CRISPR/Cas9 gene technology as applied to the improvement of quality in several crops are reviewed. In addition, the shortcomings, challenges and development prospects of CRISPR/Cas9 gene editing technology are discussed. [ABSTRACT FROM AUTHOR]

Pollinators provide crucial ecosystem services, i.e., pollination, which determines crops' reproductive fitness and yield. As pollinators decline, flowering crops might face pollination-deficit stress depending on their dependence on pollinators and pollinator availability. Here, we assessed the dependency of some crops (belonging to diverse plant families) on biotic pollinators based on their maximum reproductive potential in a supplementary pollination treatment and minimum reproductive success in a pollinator exclusion treatment. Additionally, we determined the pollen transfer limitation of the crops in open field conditions. We also determined the influence of the different reproductive traits with the index of dependency of crops on pollinators (IDP) and the coefficient of pollination deficit (D). Based on the values of IDP, members of Cucurbitaceae are obligatorily dependent on pollinators for their fruit set. Members of Brassicaceae and Rutaceae are highly reliant on pollinators. A few crops, like Lablab purpureus and Nigella sativa, are less dependent on pollinators. In open field conditions, most crops have a low pollination deficit, some without pollen transfer limitations, and only a few crops (Citrus × limon and Citrus maxima) show a higher pollination deficit. The IDP is negatively influenced by the pollen–ovule ratio, which also negatively affects the pollination deficit of the crops. This study will be useful in understanding and mitigating the effects of pollinator losses, as well as in choosing crops (those under pollination deficit stress and largely dependent on pollinators for fruit set) for supplemental pollination services to increase agricultural production. [ABSTRACT FROM AUTHOR]

Agriculture serves as the mainstay of India's economy, bearing a vital responsibility in nourishing an expanding populace. The thriving of this sector is contingent upon numerous variables, among which the choice of the optimal crop plays a pivotal role. The advent of Machine Learning (ML) has engendered a transformative impact on the agricultural sector by facilitating the prediction of suitable crops, contingent on soil attributes. This study undertakes the examination of diverse ML algorithms, encompassing Decision Tree, Linear Regression, Naïve Bayes, Random Forest, Extreme Gradient Boosting (XGBoost), and Support Vector Machine, to assess their efficacy in recommending optimal crops based on soil parameters. The parameters under consideration include Phosphorus, Nitrogen, Potassium, Electrical Conductivity, pH, Organic Carbon, Boron, Iron, Zinc, Copper, Manganese, and Sulphur. The crop recommendations are focused on Rice, Cotton, and Jowar for the Kurnool district of Andhra Pradesh, India. Among the assessed models, it was observed that the XGBoost model surpassed others in terms of accuracy in determining the most suitable crop for the given soil parameters. The experimental findings substantiate the precision of the model in forecasting the apt crop, thereby underscoring the immense potential of ML in the agricultural domain. This investigation signifies a considerable stride towards optimal crop recommendation, thereby increasing the potential for enhanced yield and profitability. Through the incorporation of technological innovation, agriculture can be rendered more efficient, cost-effective, and sustainable, thus laying the groundwork for a promising future. [ABSTRACT FROM AUTHOR]

Biodiversity is one of the most used expressions in the biological sciences and has become a household word. Biodiversity is defined as all hereditarily based variation at all levels of organization from genes within a single population or species to the species comprising all or a part of a local community and finally to the communities themselves that compose the living parts of multifarious ecosystems of the world. The study of biodiversity became most important in the areas of systematics, ecology and in related fields of biology. Even physical scientists, geographers and artists were drawn into discussions. The subject has become important and today we have biodiversity science, biodiversity studies as popular disciplines, and a research institute on biodiversity, the so-called Biodiversity International. This review which is based on my personal experiences is aimed at understanding the concept of biodiversity, historical background, including its potential benefits, and emerging complexity involved with the implementation of free germplasm exchanges.

Cotton (Gossypium hirsutum) is one of the most important crops and is the main source of fiber for the textile industry, but its productivity is still hampered by several challenges, such as pests, diseases and abiotic stresses. This scenario has increased interest in achieving cotton events with greater productivity and sustainability through biotechnological approaches. An essential component of these strategies is the controlled expression of the gene of interest, suggesting that promoters are a key element. These promoters are generally divided into three types: constitutive, spatiotemporal, and inducible. However, to date, this diversity of promoter activity has not been as widely explored in cotton improvement. In this review, we provide an overview of cotton promoters that can be used to achieve fine-tuning of expression, facilitating decision-making and improving the ability to develop desirable traits in cotton plants. In addition, we present new approaches to identify promoters that may be useful for the development of new tools for cotton improvement.How to cite: Atella AL, Grossi-de-Sá MF, Alves-Ferreira M. Cotton promoters for controlled gene expression. Electron J Biotechnol 2023;62. https://doi.org/10.1016/j.ejbt.2022.12.002.

To study the development characteristics of fruit plants and to create favorable conditions for the plant, it is necessary to know the phylogenetic development of that plant. The individual development of plants is called ontogenesis. This development begins with the formation of the zygote and ends with its complete destruction. Stems and branches become thicker as a result of the growth of cambium tissue cells in plants. Suitable soil and climate conditions are necessary for the normal development and growth of fruit plants. Plants go through 4 main stages of growth and development during the period of activity: rapid growth, weak development; poor growth, rapid development; rapid growth, rapid development and poor growth, poor development. [ABSTRACT FROM AUTHOR]

Agriculture is important for ecology. The early detection and treatment of agricultural crop diseases are meaningful and challenging tasks in agriculture. Currently, the identification of plant diseases relies on manual detection, which has the disadvantages of long operation time and low efficiency, ultimately impacting the crop yield and quality. To overcome these disadvantages, we propose a new object detection method named “Plant Leaf Detection transformer with Improved deNoising anchOr boxes (PL-DINO)”. This method incorporates a Convolutional Block Attention Module (CBAM) into the ResNet50 backbone network. With the assistance of the CBAM block, the representative features can be effectively extracted from leaf images. Next, an EQualization Loss (EQL) is employed to address the problem of class imbalance in the relevant datasets. The proposed PL-DINO is evaluated using the publicly available PlantDoc dataset. Experimental results demonstrate the superiority of PL-DINO over the related advanced approaches. Specifically, PL-DINO achieves a mean average precision of 70.3%, surpassing conventional object detection algorithms such as Faster R-CNN and YOLOv7 for leaf disease detection in natural environments. In brief, PL-DINO offers a practical technology for smart agriculture and ecological monitoring.

In response to the adverse effects of climate change-induced frequent extreme disasters on agricultural production and supply stability, this study develops a comprehensive agricultural disaster prevention system based on current adaptation strategies for mitigating agricultural meteorological disasters. The primary goal is to enhance disaster preparedness and recovery through three core platforms: a fine-scale weather forecast service system, a crop disaster early warning system, and an agricultural information service platform for disasters. The results show that every major agricultural production township in Taiwan now has dedicated agricultural weather stations and access to refined weather forecasts. Additionally, a disaster prevention calendar for 76 important crops is established, integrating cultivation management practices and critical disaster thresholds for different growth periods. Utilizing this calendar, the crop disaster early warning system can provide timely disaster-related information and pre-disaster prevention assistance to farmers through various information dissemination tools. As a disaster approaches, the agricultural information service platform for disasters provides updates on current crop growth conditions. This service not only pinpoints areas at higher risk of disasters and vulnerable crop types but also offers mitigation suggestions to prevent potential damage. Administrative efficiency is then improved with a response mechanism incorporating drones and image analysis for early disaster detection and rapid response. In summary, the collaborative efforts outlined in this study demonstrate a proactive approach to agricultural disaster prevention. By leveraging technological advancements and interdisciplinary cooperation, the aim is to safeguard agricultural livelihoods and ensure food security in the face of climate-induced challenges.

Turfloop constitutes an area in Mankweng, situated in the Limpopo Province of South Africa, where several villages are home to smallholder farmers who specialize in vegetable farming. Among the most crucial crops cultivated in this region is tomato, a fruit that has gained significant commercial importance due to its high demand and versatility in various culinary applications. To determine the relationship of plant-parasitic nematodes and soil physicochemical properties, soil samples were collected from tomato fields. Overall, our results showed that seven nematode genera were in the tomato fields. There was a significant positive correlation between the number of Meloidogyne and potassium (r = 0.903) and a negative correlation with Na (r = −0.684). In contrast, there was no association between the number of Meloidogyne spp. and the pH and texture of the fields. The number of Criconema in the field was negatively correlated with pH (r = −0.732). Soil texture percentages, including clay (r = 0.744), sand (r = −0.744), and silt (r = 0.706), were only correlated with the number of Criconema. The number of dagger nematodes, Xiphinema, was only correlated negatively with NH4+ (r = −0.589) and positively with boron (B) (r = 0.779). None of the soil variables were correlated with the number of Pratylenchus. The principal component analysis (PCA) placed soil samples of tomatoes together, in which the number of Meloidogyne was not correlated to any soil sample site. In conclusion, plant-parasitic nematodes that were associated with tomatoes are of high economic importance as they can reduce the yield. Criconema was found to be sensitive to the soil’s physicochemical properties. In addition, Helicotylenchus was found in all soil samples. Our results suggest that the plant-parasitic nematodes in tomatoes have high diversity with the potential to reduce crop production.

Drought stress is one of the key factors restricting crop yield. The beneficial effects of exogenous proline on crop growth under drought stress have been demonstrated in maize, rice, and other crops. However, little is known about its effects on wheat under drought stress. Especially, the water-holding capacity of leaves were overlooked in most studies. Therefore, a barrel experiment was conducted with wheat at two drought levels (severe drought: 45% field capacity, mild drought: 60% field capacity), and three proline-spraying levels (0 mM, 25 mM, and 50 mM). Meanwhile, a control with no stress and no proline application was set. The anatomical features, water-holding capacity, antioxidant capacity, and proline content of flag leaves as well as grain yields were measured. The results showed that drought stress increased the activity of catalase and peroxidase and the content of proline in flag leaves, lessened the content of chlorophyll, deformed leaf veins, and decreased the grain yield. Exogenous proline could regulate the osmotic-regulation substance content, chlorophyll content, antioxidant enzyme activity, water-holding capacity, and tissue structure of wheat flag leaves under drought stress, ultimately alleviating the impact of drought stress on wheat yield. The application of proline (25 mM and 50 mM) increased the yield by 2.88% and 10.81% under mild drought and 33.90% and 52.88% under severe drought compared to wheat without proline spray, respectively.

Abstract The crop-livestock farming system in paddy fields is integrated between rice crops and beef cattle (rice-beef cattle CLFS). Rice plants will produce straw and rice bran that can be used for animal feed, and livestock will produce compost/manure that can be used for rice nutrition. Some other benefits of implementing CLFS are increasing crop productivity, increasing the value of farmers' income, farming cost efficiency, and preserving the environment. The probability of the sustainability of CLFS application in paddy fields can be measured by calculating the system proximity index ratio using the SPIR formula. This research aims to demonstrate the accuracy of the SPIR formula in predicting the probability of CLFS sustainability in paddy fields. This study compares CLFS programs in several regions in Indonesia. It uses an independent sample t-test analysis tool with one-way rejection criteria to see the difference in SPIR scores on sustainable and unsustainable CLFS. The results showed that the SPIR formula could predict the sustainability of the CLFS program in Indonesia. However, further studies are needed to prove its ability to predict CLFS sustainability in other parts of the world.

Crop models are valuable tools for examining the interactions of cultivar characteristics, environment, and management practices, and how they affect crop growth and development. The difficulty in finding all the data needed to set up a simulation can often deter potential users from utilizing a crop model. Model interfaces are necessary to make these complex tools accessible to end-users who may lack the expertise needed to work with the models directly, but who would benefit from the information generated by the models. As crop models vary in terms of input and output structures, there is no one universally compatible interface, so different crop model suites require their own interface. CLASSIM is a graphical user interface (GUI) for a suite of models developed by USDA-ARS. The interface provides a simplified tool to assemble input data for a model simulation. Web access to online databases for soil and weather data simplifies finding site-related data. The user is guided step by step to build the necessary input files. A database structure facilitates data entry, storage, and retrieval. The use of Structured Query Language (SQL) for data retrieval allows for advanced analysis and visualization outside of the interface. We describe the features of the GUI and provide examples of various operations. Model outputs are provided in tables and graphs to allow for visualization and analysis. Two-dimensional contour plots of soil processes are provided to visualize output from the two-dimensional finite element soil model. CLASSIM also allows users to set up single- and multiple-season runs for maize, cotton, soybean, potato, and fallow treatments, and is expected to be expanded to more crops in the future. A user-friendly GUI is essential for making simulation tools accessible to the broad range of researchers and practitioners working to solve the agricultural system challenges.

This article describes our study on the alignment of two complementary knowledge graphs useful in agriculture: the thesaurus of cultivated plants in France named French Crop Usage (FCU) and the French national taxonomic repository TAXREF for fauna, flora, and fungi. FCU describes the usages of plants in agriculture: “tomatoes” are crops used for human food, and “grapevines” are crops used for human beverage. TAXREF describes biological taxa and associated scientific names: for example, a tomato species may be “Solanum lycopersicum” or a grapevine species may be “Vitis vinifera”. Both knowledge graphs contain vernacular names of plants but those names are ambiguous. Thus, a group of agricultural experts produced some mappings from FCU crops to TAXREF taxa. Moreover, new RDF properties have been defined to declare those new types of mapping relations between plant descriptions. The metadata for the mappings and the mapping set are encoded with the Simple Standard for Sharing Ontological Mappings (SSSOM), a new model which, among other qualities, offers means to report on provenance of particular interest for this study. The produced mappings are available for download in Recherche Data Gouv, the federated national platform for research data in France.

Stripe rust of wheat is commonly known as yellow rust disease of wheat (Triticum aestivum L.) caused by Puccinia triticina. This disease is among the most important diseases of wheat globally. Present review presents the basic and recent information regarding the epidemiology of stripe rust with signs and symptoms of disease on an infected crop. Identification and control of the disease is becoming challenging due to lack of information, thus present review had been summarized for the understanding of local farmers and experts. Yellow rust is the foliar disease of wheat, fungal spores of P. triticina are also termed as air born disease as causative agents of disease spread through wind. Pathogens of rust disease are the potential source of reducing yield and production. These causative agents are host specific and had a great potential to reduce yield around 70-100% in case of susceptible wheat variety. Pathogenic strains of stripe rust infect the green tissues of crops, chances of disease infection are equal at any-time of one leaf stage of crop. However, the symptoms of disease appear after one week of an infection while sporulation starts just after two weeks of infection having optimum conditions of an environment. Just after disease attacks, tiny, yellow- to orange-colored rust pustules appear on an infected area which is called as uredia, having thousands of uredinio spores in each uredium, which is not visible through naked eye. These spores are usually yellow to orange in color and are powdery in nature. The type of symptoms appearance on an infected crop depends upon the nature of resistance in specific crop. Cultivation of resistant wheat varieties with the applications of effective fungicides are the possible solutions of these pathogenic spores. However, selection of suitable wheat variety and the cultivation of crop at right time keeping in mind the other environmental factors such as temperature and moisture would be important for wheat cultivation. [ABSTRACT FROM AUTHOR]

The maize field environment is complex. Weeds and maize have similar colors and may overlap, and lighting and weather conditions vary. Thus, many methods for the automated differentiation of maize and weeds achieve poor segmentation or cannot be used in real time. In this paper, a weed recognition model based on improved Swin-Unet is proposed. The model first performs semantic segmentation of maize seedlings and uses the resulting mask to identify weeds. U-Net acts as the semantic segmentation framework, and a Swin transformer module is introduced to improve performance. DropBlock regularization, which randomly hides some blocks in crop feature maps, is applied to enhance the generalization ability of the model. Finally, weed areas are identified and segmented with the aid of an improved morphological processing algorithm. The DeepLabv3+, PSANet, Mask R-CNN, original Swin-Unet, and proposed models are trained on a dataset of maize seedling images. The proposed Swin-Unet model outperforms the others, achieving a mean intersection over union of 92.75%, mean pixel accuracy of 95.57%, and inference speed of 15.1 FPS. Our model could be used for accurate, real-time segmentation of crops and weeds and as a reference for the development of intelligent agricultural equipment. [ABSTRACT FROM AUTHOR]

India is an agriculture-based country, agro-homoeopathy is one of the newest approaches in homoeopathic and agricultural research. Agriculture is a significant source of income in India, and it bears a significant economic burden in order to provide a livelihood. The ergot is one of the common diseases seen in cereal crop caused by fungus Claviceps purpurea. Soil was prepared with 2.5kg Loam soil + 500g Coco peat + 500g Cow dunk + 500g NPK + 500g Urea and Potash + 500g DAP and distributed across the seven plastic trays, each measuring 30cm x 15cm x 18cm (L xW xD). Thirty wheat seeds of LOK-1 variety were planted in three rows, four centimetres apart from one another, with a total of ten seeds in each row. After inoculation of the fungal pathogen Claviceps purpurea on the kernel of wheat crop, the treatment was started with homoeopathic medicine Secale cornutum with its various potencies (Q, 30C, 200C and 1M). The control group were treated with Crapton fungicide. The ethyl alcohol was used as negative control in this study. The treatment was given twice a day for 14 days. After 14 days of treatment, there were no any antifungal activity observed on wheat crop trays treated with Secale cornutum (Q, 30C, 200C, 1M), and ethyl alcohol. Accordingly, Secale cornutum, does not revealed antifungal activity on pathogenesis of Claviceps purpure consequently. Further studies are needed to identify homoeopathic medicines that have antifungal effects on Claviceps purpure in wheat crop pathology. [ABSTRACT FROM AUTHOR]

This article aims to explain the benefits of implementing a protected cultivation system for farmers in the San Quintin Valley (in Baja California) dedicated to the production of vegetables, particularly tomato. The methodology involved two steps: collection of theoretical information and data collection through a questionnaire applied to eight tomato companies in the San Quintin Valley during 2020. As results, it was found that most of them opt for cultivation with greenhouse for the production of the products, but it is very difficult to obtain government resources. It is concluded that there are still limitations in the acquisition of government support and it is for this reason that most use the capital of their own company and the financing of a bank for their implementation. [ABSTRACT FROM AUTHOR]

Simple Summary: Cotton leafroll dwarf virus (CLRDV) is capable of causing yield loss in cotton. Eight species of aphids have been reported to feed on cotton, but Aphis gossypii is the only known CLRDV vector in the United States (U.S.). Little is known about their distribution, abundance, and seasonal dynamics in the southern U.S. The epidemiological value of understanding this prompted a two-year study to monitor the populations of aphids that infest cotton fields throughout the southern U.S., where CLRDV has been reported. Aphis gossypii and Protaphis middletonii were the most abundant aphid species collected. Aphis craccivora, Macrosiphum euphorbiae, Myzus persicae, Rhopalosiphum rufiabdominale, and Smynthurodes betae were also detected in this study; however, their populations remained consistently low throughout the collection period. Results from this study presented novel information regarding the seasonal variation of the species and populations of aphids associated with cotton in the region. Cotton leafroll dwarf virus (CLRDV) is an emerging aphid-borne pathogen infecting cotton, Gossypium hirsutum L., in the southern United States (U.S.). The cotton aphid, Aphis gossypii Glover, infests cotton annually and is the only known vector to transmit CLRDV to cotton. Seven other species have been reported to feed on, but not often infest, cotton: Protaphis middletonii Thomas, Aphis craccivora Koch, Aphis fabae Scopoli, Macrosiphum euphorbiae Thomas, Myzus persicae Sulzer, Rhopalosiphum rufiabdominale Sasaki, and Smynthurodes betae Westwood. These seven have not been studied in cotton, but due to their potential epidemiological importance, an understanding of the intra- and inter-annual variations of these species is needed. In 2020 and 2021, aphids were monitored from North Carolina to Texas using pan traps around cotton fields. All of the species known to infest cotton, excluding A. fabae, were detected in this study. Protaphis middletonii and A. gossypii were the most abundant species identified. The five other species of aphids captured were consistently low throughout the study and, with the exception of R. rufiabdominale, were not detected at all locations. The abundance, distribution, and seasonal dynamics of cotton-infesting aphids across the southern U.S. are discussed. [ABSTRACT FROM AUTHOR]

From an economic point of view, combining various components in the same unit helps to diversify farmers' income. An attempt was made to determine the contribution of various components, adopted in an IFS unit, to the annual household income among the IFS units of Kerala during 2022. In this study, total annual household income was computed by summing up the gross income from all the on-farm sources. The study revealed that 52.78 per cent of respondents were found in the low-income category, followed by one-third in the medium-income category. Among the selected components, the contribution of income from the crop component, dairy component, and poultry component, was found to be low to medium, while the trend in income generation from fish component was from medium to low. In the case of apiculture and mushroom components, medium to high-income generation was also noticed. It was also found that farm size, experience in farming, economic motivation, training undergone, and herd size were positively correlated with the total farm income of households. [ABSTRACT FROM AUTHOR]

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The article examines the influence of physicochemical traits on yield depending on the variety and year of cultivation. Four common to Poland grape cultivars, i.e. 'Regent', 'Rondo', 'Seyval Blanc', and 'Solaris', were evaluated by analysing, among others, number of clusters per bush, their weight, number of berries, and the yield per hectare, number of woody shoots, weight of woody shoots, and the diameter of woody shoots. Energy and emission parameters were evaluated by conducting technical evaluation (lower heating value, ash content, volatile matters content, moisture content, fixed carbon) and elemental analysis (carbon, nitrogen, hydrogen, sulphur and oxygen contents) of one-year, two-year and three-year vine shoots. In addition, emission factors for CO, CO2, NOx, SO2 and dust were estimated. The study showed that there was no significant differences between years under study (2020, 2021 and 2022) and energy and emission parameters. It was observed that the highest LHV (lower heating value) occurred in the 'Regent' cultivar while the lowest level in the 'Rondo' cultivar. As regards energy-emission parameters, a significant influence of cultivar ('Solaris', 'Rondo', 'Seyval Blanc' and 'Regent') was shown on the parameters studied except for nitrogen content and NOx emission index. The interaction of year and cultivar showed no significant differences except for the moisture content. [ABSTRACT FROM AUTHOR]

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Classification of Indonesian crops is a critical task in developing farming and getting more understanding of agriculture. However, there is no clear task in classifying types of crops in Indonesia. Transfer learning has been used successfully in a variety of image classification applications. Thus, in this paper, we collected images of Indonesian crops from the internet randomly and proposed a classification by using transfer learning of deep learning with four pre-trained models: EffficientNet- B0, ResNet18, VGG19, and AlexNet. In the experiment, augmentation techniques such as random horizontal flip, random vertical flip, and random affine were utilized to prevent the network from overfitting. The result found that EfficientNet-B0 outperformed other models with an accuracy of 82.55. Then, the model struggled to distinguish between crops in the same family. According to the results, although transfer learning can work well to classify images of Indonesian agricultural crops, some improvements are still required to address existing issues. [ABSTRACT FROM AUTHOR]