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Avocado is one of the most commercialized and profitable fruits in the international market. Its cultivation and production are centered in countries characterized by tropical and subtropical climatic conditions, many of them with emerging economies. Moreover, the use of technology is key to agricultural production improvement strategies. Using avocado crop data to forecast the potential impacts of biotic and abiotic factors, combined with smart farming technologies, growers can apply measures during a single production phase to reduce the risks caused by pests and weather variations. Therefore, this paper aims to distinguish the most relevant variables related to agroclimatic and phytosanitary events in avocado crops, their incidence on production and risk management, as well as the emerging technologies used for the identification and analysis of pests and diseases in avocados. A scientific literature search was performed, and the first search found 608 studies, and once the screening process was applied, 37 papers were included in this review. In the results, three research questions were answered that described the pests and diseases with high impact on avocado production, along with the data sources and the principal enabling technologies used in the identification of agroclimatic and phytosanitary events in avocados. Some challenges and trends in the parameterization of the technology in field conditions for data collection are also highlighted. [ABSTRACT FROM AUTHOR]

The accurate identification of crop aphids is an important aspect of improving agricultural productivity. Aphids are characterised by small targets and a body colour similar to their surroundings. Even the most advanced detectors can experience problems such as low detection accuracy and a high number of missed detections. In this paper, a multi-stream target detection model is proposed for fast and accurate detection of crop aphids in complex backgrounds. First, inspired by the human visual system, we propose a bionic attention (BA) approach. Unlike previous strategies, we do not improve the model but input additional category labels as bionic information streams into the network at the network input stage to support mainstream recognition, which improves the detection effect. In addition, through web crawling and manual screening, we construct an aphid dataset containing 2059 images, named IP_Aphids, based on IP102. In this paper, we combine the proposed BA with a number of classical target detection models, including YOLOv5s, YOLOv7-tiny, YOLOv8n, SSD, and faster R-CNN, and we also compare the BA with the classical attention mechanisms CBAM and SE. This approach allows the effectiveness of the method to be evaluated from multiple perspectives. The results show that combining the bionic information flow of BA with the original mainstream information flow as inputs to the five target detection models improves the mAP by 2.2%, 7%, 2.7%, 1.78%, and 1.12%, respectively, compared to the models using only the original inputs. In addition, the mAP of the YOLOv8n_BA model is higher than that of the YOLOv8n, YOLOv8n_CBAM, and YOLOv8n_SE models by 4.6%, 3.3%, and 2.7%, respectively. This indicates that the BA proposed in this study is significantly better than the classical attention to improve crop aphid detection, which provides a reference for crop aphid-related research. [ABSTRACT FROM AUTHOR]

The article discusses the importance of crop tolerance to both abiotic and biotic stresses in agricultural research. Abiotic stresses include environmental factors like drought and extreme temperatures, while biotic stresses involve pests, diseases, and pathogens. Understanding and enhancing crop tolerance to these stresses is crucial for global food security and sustainable crop production. The article highlights various research papers from different countries that focus on topics such as abiotic stress (salinity, drought) and biotic stress (pests, diseases). The research papers explore different strategies, including breeding and genetic engineering, to enhance crop tolerance. [Extracted from the article]

Apples are susceptible to infection by various pathogens during growth, which induces various leaf diseases and thus affects apple quality and yield. The timely and accurate identification of apple leaf diseases is essential to ensure the high-quality development of the apple industry. In practical applications in orchards, the complex background in which apple leaves are located poses certain difficulties for the identification of leaf diseases. Therefore, this paper suggests a novel approach to identifying and classifying apple leaf diseases in complex backgrounds. First, we used a bilateral filter-based MSRCR algorithm (BF-MSRCR) to pre-process the images, aiming to highlight the color and texture features of leaves and to reduce the difficulty of extracting leaf disease features with subsequent networks. Then, BAM-Net, with ConvNext-T as the backbone network, was designed to achieve an accurate classification of apple leaf diseases. In this network, we used the aggregate coordinate attention mechanism (ACAM) to strengthen the network's attention to disease feature regions and to suppress the interference of redundant background information. Then, the multi-scale feature refinement module (MFRM) was used to further identify deeper disease features and to improve the network's ability to discriminate between similar disease features. In our self-made complex background apple leaf disease dataset, the proposed method achieved 95.64% accuracy, 95.62% precision, 95.89% recall, and a 95.25% F1-score. Compared with existing methods, BAM-Net has higher disease recognition accuracy and classification results. It is worth mentioning that BAM-Net still performs well when applied to the task of the leaf disease identification of other crops in the PlantVillage public dataset. This indicates that BAM-Net has good generalization ability. Therefore, the method proposed in this paper can be helpful for apple disease control in modern agriculture, and it also provides a new reference for the disease identification of other crops. [ABSTRACT FROM AUTHOR]

Planting cover crops can improve soil health and help to sustain agricultural crop yields. In northern climates where corn (Zea mays L.) and soybean (Glycine max L.) are grown, cover crop biomass production can be low. This has led to people investigating the potential of interseeding cover crops into the growing main crop. This paper sought to determine biomass production and the benefit to grain yields, weed control and soil properties from interseeding cover crops into corn and soybean. This review included 70 studies published prior to 15 March 2024. Interseeded cover crops that were winter-hardy such as cereal rye (Secale cereale L.) produced more biomass in the spring (1.04 Mg ha−1 average biomass production) than any of the interseeded cover crops did in fall (0.35 Mg ha−1 average biomass production), primarily at crop row spacings of 76 cm. Factors that affected cover crop biomass production were crop stage, planting method, tillage practice, irrigation and row spacing. There was not a consistent widely planted cover crop species that produced the most biomass. Interseeded cover crops reduced weed biomass by 46% compared to weed control and generally did not affect crop grain yields when planted after V4 crop stage. Interseeded cover crops reduced soil nitrate concentration but generally did not affect other soil properties including soil water content. However, most of these studies planted cover crops at the same site for less than three years. Early interseeded cover crops generally did not perform better than interseeded winter-hardy cover crops planted around crop physiological maturity. [ABSTRACT FROM AUTHOR]

Avocado production has boomed worldwide in recent years, and Spain, including the Canary Islands, has been no exception. The number of avocado growers in the region has increased significantly as growers recognize the potential of this crop. However, several species of spider mites (Tetranychidae) pose a risk to this crop, with the genus Oligonychus being the most diverse and having the greatest economic impact. In particular, Oligonychus perseae (Tuttle, Baker and Abbatiello) has been reported as the one mainly responsible for the economic damage in major avocado-producing regions worldwide. In this paper, we aim to present an overview of the studies conducted and the measures implemented to mitigate the impact of O. perseae after its arrival in the Canary Islands. Our objective is to provide a detailed description of the current status of this pest (O. perseae), with special attention to its situation 17 years after its first appearance in avocado crops in the Canary Islands. In doing so, we aim to provide valuable insights and knowledge to understand and manage better the challenges posed by O. perseae in this region. [ABSTRACT FROM AUTHOR]

This document, titled "Smart Strategies and Technologies for Sustainability and Biodiversity in Herbaceous and Horticultural Crops," discusses the current trends in modern farming systems that focus on improving the sustainability and biodiversity of agroecosystems. The document highlights various innovative agronomical strategies and new technologies that can help farmers reduce chemical inputs, preserve soil and water quality, decrease emissions, prevent pollution, and lower energy demand. The special issue presented in the document includes 12 papers, with 5 focusing on tree crops/orchards and 7 on herbaceous crops. The papers cover topics such as innovative techniques for tree crops, weed control methods, conservation agriculture, organic farming, cover crops, intercropping, and precision farming technologies. The document concludes that these strategies and technologies can effectively enhance sustainability and biodiversity in herbaceous and horticultural crops in different farming scenarios. [Extracted from the article]

Supply chain disruptions, a pandemic, and war in Ukraine have exposed faultlines in a globalised food system that depends on a few staple crops grown in a few exporting regions and transported to consumers around the world. In the UK, just three crops, (wheat, barley, and oilseed rape), account for 75 per cent of the UK's 4.5 million hectares of arable land whilst the country imports around half its food—nearly 40 per cent—from just four EU countries (The Netherlands, Ireland, Germany, and France). Poor diets contribute to one in seven deaths in the UK, 63 per cent of the population is overweight or obese and health inequality is increasing between the poorest and most affluent regions. The food security and health of the UK population is therefore dependent on a small number of locally grown crops, vulnerable supply chains, and an unhealthy, obesogenic diet. The UK food system must diversify if it is to become food and nutritionally secure, meet its climate and biodiversity goals and have a healthy and active population. Climate-resilient and nutritious underutilised crops can help diversify the UK agrifood system, but research and investment in them is sporadic, piecemeal, and unfocused. In this paper, we compare two approaches to identifying potentially suitable underutilised crops for the UK. The first, based on UK Department for Environment, Food and Rural Affairs (Defra) Project CH0224, was delivered through literature and database searches and the expertise of growers, advisers, breeders, seed suppliers, processors, traders, and researchers. The second used the CropBASE digital knowledge base for underutilised crops. The two approaches produced no single crop that was common to both shortlists. We propose that the analytical and predictive tools derived from CropBASE could be combined with local knowledge and expertise from the Defra project to provide a common framework for the identification of underutilised crops that are best suited to local UK circumstances now and in climates of the future. [ABSTRACT FROM AUTHOR]

Paddy leaf diseases encompass a range of ailments affecting rice plants' leaves, arising from factors like bacteria, fungi, viruses, and environmental stress. Precision agriculture leverages technologies for enhanced crop production, with disease detection being a vital element. Prompt identification of diseases in paddy leaves is critical for curtailing their propagation and reducing crop damage. However, manually diagnosing paddy diseases in regions with vast agricultural areas and limited experts proves immensely difficult. The utilization of machine learning (ML) and deep learning (DL) for diagnosing diseases in agricultural crops appears to be effective and well-suited for widespread application. These ML/DL methods cannot ensure data privacy, as they involve sharing training data with a central server, overlooking competitive and regulatory considerations. As a solution, federated learning (FL) aims to facilitate decentralized training to tackle the identified limitations of centralized training. This paper utilizes the FL approach for the classification of rice-leaf diseases. The manuscript presents an effective approach for rice-leaf disease classification with a federated architecture, ensuring data privacy. We have compiled an unbalanced dataset of rice-leaf disease images, categorized into four diseases with their respective image counts: bacterial blight (1584), brown spot (1440), blast (1600), and tungro (1308). The proposed method, called federated transfer learning (F-TL), maintains privacy for all connected devices using a decentralized client-server setup. Both IID (independent and identically distributed) and non-IID datasets were utilized for testing the F-TL framework after preprocessing. Initially, we conducted an effectiveness analysis of CNN and eight transfer learning models for rice-leaf disease classification. Among them, MobileNetV2 and EfficientNetB3 outperformed the other transfer-learned models. Subsequently, we trained these models using both IID and non-IID datasets in a federated learning environment. The framework's performance was assessed through diverse scenarios, comparing it with traditional and federated learning models. The evaluation considered metrics like validation accuracy, loss as well as resource utilization such as CPU and RAM. EfficientNetB3 excelled in training, achieving 99% accuracy with 0.1 loss for both IID and non-IID datasets. MobilenetV2 showed slightly lower training accuracy at 98% (IID) and 90% (non-IID) with losses of 0.4 and 0.6, respectively. In evaluation, EfficientNetB3 maintained 99% accuracy with 0.1 loss for both datasets, while MobilenetV2 achieved 90% (IID) and 97% (non-IID) accuracy with losses of 0.6 and 0.2, respectively. Results indicated the F-TL framework's superiority over traditional distributed deep-learning classifiers, demonstrating its effectiveness in both single and multiclient instances. Notably, the framework's strengths lie in its cost-effectiveness and data-privacy assurance for resource-constrained edge devices, positioning it as a valuable alternative for rice-leaf disease classification compared to existing tools. [ABSTRACT FROM AUTHOR]

Timely and accurate extraction of crop planting structure information is of great importance for food security and sustainable agricultural development. However, long time series data with high spatial resolution have a much larger data volume, which seriously limits the quality and efficiency of the application of remote sensing to agriculture in complex crop rotation areas. To address this problem, this paper takes Lujiang County, a typical complex crop rotation region in the middle and lower reaches of the Yangtze River in China as an example, and proposes utilizing the Google Earth Engine (GEE) platform to extract the Normalized Difference Vegetation Index (NDVI), Normalized Difference Yellowness Index (NDYI) and Vertical-Horizontal Polarization (VH) time series sets of the whole planting year, and combining the Simple Non-Iterative Clustering (SNIC) multi-scale segmentation with the Support Vector Machine (SVM) and Random Forest (RF) algorithms to realize the fast and high-quality planting information of the main crop rotation patterns in the complex rotation region. The results show that by combining time series and object-oriented methods, SVM leads to better improvement than RF, with its overall accuracy and Kappa coefficient increasing by 4.44% and 0.0612, respectively, but RF is more suitable for extracting the planting structure in complex crop rotation areas. The RF algorithm combined with time series object-oriented extraction (OB + T + RF) achieved the highest accuracy, with an overall accuracy and Kappa coefficient of 98.93% and 0.9854, respectively. When compared to the pixel-oriented approach combined with the Support Vector Machine algorithm based on multi-temporal data (PB + M + SVM), the proposed method effectively reduces the presence of salt-and-pepper noise in the results, resulting in an improvement of 6.14% in overall accuracy and 0.0846 in Kappa coefficient. The research results can provide a new idea and a reliable reference method for obtaining crop planting structure information efficiently and accurately in complex crop rotation areas. [ABSTRACT FROM AUTHOR]

With respect to the optimal ripening time of the fruit, in recent decades there have been hundreds of works that study the optimum time for fruit harvesting based on its compositional and organoleptic characteristics [[1], [3]]. These original research papers can be grouped into two categories: (1) Evolution of compounds and characteristics of crops during ripening [[21], [23]]; (2) Evolution of compounds and characteristics of crops after harvest [[25], [27]]. Evolution of Compounds and Characteristics of Crops during Ripening In the first article, Szalóki et al. [[21]] determined the threshability of four local temperate japonica rice varieties during the ripening phase over three consecutive years with the application of an improved threshing meter. In this work, special emphasis has been placed on the details related to the changes that occur in vitamin C, phenolic compounds, flavonoids, glycosides, and volatile compounds, as well as the antioxidant activity during the drying process of fruits and plants. [Extracted from the article]

Crop nutrient biochemical information (mainly including chlorophyll class and nutrient elements mainly nitrogen, phosphorus and potassium) is an important basis for revealing crop growth and development patterns and their relationship with the environment. Hyperspectral technology has been rapidly developed and applied in crop nutrient biochemical information monitoring research. This paper firstly describes the theoretical basis of hyperspectral technology for monitoring crop nutrients and biochemical information. Then, the research progress of hyperspectral technology in monitoring nutrient and biochemical information of crops in different growth periods or different growth environments is outlined. Meanwhile, the shortcomings of the current technology in these research directions and the future research trends are discussed. Finally, the modeling methods for building crop nutrient biochemical information monitoring models by applying hyperspectral data are systematically outlined. And the effects of different spectral pre-processing methods, spectral effective information extraction methods and modeling algorithms on the accuracy of monitoring models are analyzed. On this basis, the challenges and prospects of hyperspectral technology in monitoring crop nutrient biochemical information are presented, aiming to provide relevant theoretical basis and technical reference for the research related to monitoring and inversion of crop physiological parameters based on hyperspectral technology. [ABSTRACT FROM AUTHOR]

They concluded that the greatest tuber yield and N use efficiency were achieved at a N rate of 150-180 kg ha SP -1 sp with 2-3 splits, a fertilization depth of 15-25 cm, and a planting date of 25 April to 10 May. Yuan et al. [[5]] conducted a long-term field experiment to investigate the effects of crop rotation and biochar-based fertilizer application on the crop yield, soil attribute, crop quality, and agronomic traits in 2014-2020. [Extracted from the article]

Cadmium (Cd) is a non-essential, highly phytotoxic metal and damages ramie plant growth and development even at low concentrations. Ramie is one of the most significant crops in China, with excellent fiber quality and immense industrial importance. Planting Cd-tolerant ramie cultivars can prevent yield loss on contaminated soil. Previously, significant efforts have been made to develop Cd tolerance in ramie. However, the Cd tolerance mechanism is still not fully understood; hence, breeding industrial crops is critical to tackling the ongoing challenges. Cd tolerance is a complex genetic mechanism requiring high-level molecular studies to clarify the genes network. Genetic studies have identified several Cd-tolerant genes in ramie, which led to the development of several ramie cultivars suitable to grow on toxic soils; however, due to the continuous rise in Cd toxicity, potent molecular tools are critical in modern-day breeding programs. Genetic engineering, and transcriptome analysis have been used to develop abiotic stress tolerance in ramie, but QTL mapping and clustered regularly interspaced short palindromic repeats (CRISPR) are rarely studied. However, studies are still limited in addressing this issue. This review critically elaborated on using QTL mapping, transcriptomes, transcription factors, CRISPR/Cas9, and genetic engineering to enhance Cd tolerance in ramie. These genes/QTL should be transferred or edited into sensitive cultivars using genetic engineering or CRISPR/Cas9. CRISPR/Cas9 is highly recommended because it provides targeted gene editing in ramie, its use is limited and can address the research gaps, and it would revolutionize the field of agriculture. Limitations, gaps, and future potential are briefly discussed. This review paper presents new clues to help future researchers comprehensively understand Cd tolerance in ramie and develop tolerant cultivars for industrial purposes. [ABSTRACT FROM AUTHOR]

Crop row detection is one of the foundational and pivotal technologies of agricultural robots and autonomous vehicles for navigation, guidance, path planning, and automated farming in row crop fields. However, due to a complex and dynamic agricultural environment, crop row detection remains a challenging task. The surrounding background, such as weeds, trees, and stones, can interfere with crop appearance and increase the difficulty of detection. The detection accuracy of crop rows is also impacted by different growth stages, environmental conditions, curves, and occlusion. Therefore, appropriate sensors and multiple adaptable models are required to achieve high-precision crop row detection. This paper presents a comprehensive review of the methods and applications related to crop row detection for agricultural machinery navigation. Particular attention has been paid to the sensors and systems used for crop row detection to improve their perception and detection capabilities. The advantages and disadvantages of current mainstream crop row detection methods, including various traditional methods and deep learning frameworks, are also discussed and summarized. Additionally, the applications for different crop row detection tasks, including irrigation, harvesting, weeding, and spraying, in various agricultural scenarios, such as dryland, the paddy field, orchard, and greenhouse, are reported. [ABSTRACT FROM AUTHOR]

Continuous cropping increases disease severity and causes arrested development of chilli plants and the decrease of yield and quality. Hemp-chilli rotation cropping evidently eased chilli diseases, but the causation remains unknown. This paper investigated the disease index (DI) of chilli's anthracnose for hemp-chilli and continuous chilli cropping, the antifungal effect of water extract of hemp and chilli residues, and bacteria antagonistic to chilli anthracnose fungus from hemp and chilli rhizosphere. Hemp grown as a preceding crop decreased anthracnose DI from 35–39% for the continuous chilli to 14–15% for hemp-chilli rotation. Hemp residue water extract executed suppression of chilli anthracnose fungus and the efficiency increased as the extract concentration increased from 1% to 5%. Hemp extract concentration 5% gave a mean inhibition ratio (IR) of 32.34% to spore germination and IR 53.72% to mycelia growth, which was much greater than that of the chilli extract. Antagonistic bacteria isolated from the hemp rhizosphere evidently depressed the mycelia growth of the fungus with a mean IR 32.35%, while no antagonistic bacteria were obtained from the chilli rhizosphere. The stronger allelopathy of preceding hemp plants and antagonistic bacteria from the hemp rhizosphere synergistically suppressed the fungus growth and eased the disease in the succeeding chilli crop. [ABSTRACT FROM AUTHOR]

Coffee production and marketing is one of the main global commercial activities, but crop yields depend on several factors, among which plant health. The objective of this study was to evaluate the efficiency of spray droplet deposition in coffee crops grown in a mountain region, associated to the efficacy of the control of fungal diseases. The application efficiency, using an unmanned aerial vehicle (UAV), and the efficacy of the products applied were tested. Water-sensitive paper tags were used to analyze the application efficiency; agronomic efficiency, vegetative vigor, yield, and physiological parameters were used to determine the fungicide efficacy. Droplet coverage in the upper canopy layer using a pneumatic sprayer (28.70%) was 4.11-fold higher than that found in the same layer for application using a UAV (6.98%) at the rate of 15 L ha−1. The highest droplet depositions by using a UAV were found for the rate of 15 L ha−1: 1.60, 1.04, and 0.43 µL cm−2 in the upper, middle, and lower layers, respectively; the deposition in the upper layer with application using a pneumatic sprayer was 42.67 µL cm−2, and therefore, a 26.7-fold higher deposition. The results denote that the control of fungal diseases through fungicide applications using a UAV is efficient for mountain coffee crops. [ABSTRACT FROM AUTHOR]

In the past, chemical thinning dominated in fruit orchards. This paper for the special issue outlines alternatives to chemical thinning for crop load management (CLM) and its effect on fruit size, firmness, sugar, starch, and weight, indicating ripeness and fruit quality, yield, and alternate bearing. A total of 450 apple trees (Malus domestica Borkh., cv. 'Roter Boskoop'; six years old) on M9 rootstock were used at the Klein-Altendorf experimental station (50° N) of the University of Bonn, Germany. As the first alternatives, trees were mechanically blossom-thinned at the balloon stage (BBCH 59) with a rotor speed of 320 rpm or 380 rpm at 5 km/h tractor speed or were chemically thinned at the full bloom stage (BBCH 65) with ammonium thiosulfate (ATS), ethephon (ETH), and/or 6-benzyladenine (BA) at 10–12 mm fruit size (BBCH 71) after applying ATS/ETH. Flower clusters and/or cluster leaves (source) were manually removed to determine the optimum sink-source ratio to achieve different ratios of fruitlets (sink) relative to the leaves (source) at fruit set (BBCH 67–69). Un-thinned, adjacent trees served as the control. The majority of CLM methods improved fruit size and weight. Removing cluster leaves at fruit set increased fruit size and weight of the remaining fruit, which has not been observed before. The most effective treatment for fruit size and weight and return bloom was the 75% flower cluster and complete cluster leaf removal. Removal of more than 50% of flower clusters successfully improved return bloom, indicative of alternate bearing. The mechanical blossom thinning had a positive effect on fruit size and weight with a return bloom similar to that of removal of 50% flower clusters. [ABSTRACT FROM AUTHOR]

The growth of the Indonesian population has led to an increase in the demand for rice, which the country has yet to satisfy. Indonesia needs a comprehensive strategy that integrates meaningful efforts to increase its agricultural production. This study aims to review the examined trends in rice yield in Indonesia for 70 years after Indonesia's independence (1945–2016) followed by the identification of the application technology and factors that contribute to increasing rice yields to forecast sustainable food security scenarios up to 2030. This article reviews the results of research on rice production technology in Indonesia from 1945 to 2016, and the outlook for 2030. This paper examines the main points of the Indonesian transformation of rice technology: improvement of rice varieties, integrated crop management, innovations in agricultural machinery, and the Integrated Cropping Calendar Information System (ICCIS). We found that transformation has helped Indonesia increased its rice yields from 3 t ha−1 prior to 1961 to 4.6 t ha−1 in 1985, stagnated in 1990, and increased again in 2017 to 5.46 t ha−1. The increase in yield was sustained by an increase in the harvested area owing to cropping index (CI) innovation. Food security and sustainable development remain the primary goals of Indonesia's agricultural sector. The application of appropriate technologies and institutional innovations can assist Indonesia in achieving its food security. Therefore, the transformation of technological innovations will continue to be an essential driver of future agricultural growth, including greater use of crop varieties, machinery, and land/institutional reforms. [ABSTRACT FROM AUTHOR]

Insecticide applications are typically being carried out with traditional manual spraying equipment in redgram, which leads to inadequate control of insects due to higher crop height. The modern deployment of tractor-drawn spray machines causes serious damage to the crop. In this connection, unmanned aerial vehicle (UAV) spray technology has great potential for precise insecticide application in redgram crops. One of the important machine parameters influencing droplet deposition and drift characteristics in UAV sprayers is downwash airflow generated by a multi-rotor propeller. A field experiment was carried out at the redgram research field (N11.01, E76.92), Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, during 2021–2022 to study the spray drift and deposition characteristics of an autonomous UAV sprayer. The Imidacloprid (a.i. 17.8SL) insecticide mixed with water in a ratio of 1 mL per liter was sprayed with a UAV sprayer. Water-sensitive paper samples were kept at upper, middle, and bottom positions on the leaves, and data were analyzed for the spray droplet size, deposition rate, droplet density, and area coverage both in target and non-target areas using Spray Deposit Scanner software. UAV spray droplet deposition rate (2.93 ± 0.17, 2.01 ± 0.08, and 2.21 ± 0.162.38 μL cm−2), droplet density (47 ± 4.04, 53 ± 3.61, and 52 ± 8.74 droplets cm−2), and area coverage (15.72 ± 0.39, 16.60 ± 0.71, and 14.99 ± 0.39%) were highest in the upper layer as compared to the middle layer (droplet deposition rate: 1.21 ± 0.08, 1.07 ± 0.03, and 0.77 ± 0.02 μL cm−2; droplet density: 42 ± 2.52, 43 ± 8.50, and 38 ± 2.52 droplets cm−2; area coverage: 10.95 ± 0.81, 11.22 ± 0.56, and 8.57 ± 0.44%) and bottom layer (droplet deposition rate: 0.41 ± 0.06, 0.35 ± 0.03, and 0.33 ± 0.03 μL cm−2; droplet density: 22 ± 4.36, 17 ± 3.51, and 19 ± 4.51 droplets cm−2; area coverage: 2.78 ± 0.29, 2.95 ± 0.45, and 2.46 ± 0.20%, respectively). In the spray drift test, there was a higher droplet deposition rate (1.63 ± 0.09, 1.93 ± 0.05, and 1.82 ± 0.06 μL cm−2), area coverage (14.40 ± 0.07, 17.54 ± 0.36, and 16.42 ± 0.30%), and droplet density (46 ± 3.61, 54 ± 2.08, and 45 ± 3.21 No's cm−2) in the target area as compared to the non-target area (droplet deposition rate: 0.88 ± 0.02, 0.46 ± 0.03, 0.22 ± 0.05, and 0.00 μL cm−2; droplet density: 23 ± 1.53, 11 ± 2.08, 6 ± 1.53, and 0.00 droplets cm−2; area coverage: 7.58 ± 0.34, 4.41 ± 0.19, 2.16 ± 0.05, and 0.00%, respectively), which may have been due to the downwash airflow produced by the multi-rotor propeller of the UAV sprayer. Finally, the UAV-based spraying technology results showed that the downwash air produced by the six-rotor propeller improved the penetrability of insecticide to crop leaves and led to a higher droplet deposition rate, droplet density, area coverage, and droplet penetrability on the upper layer, middle layer, and bottom layer of the plants. [ABSTRACT FROM AUTHOR]

Computer vision (CV) combined with a deep convolutional neural network (CNN) has emerged as a reliable analytical method to effectively characterize and quantify high-throughput phenotyping of different grain crops, including rice, wheat, corn, and soybean. In addition to the ability to rapidly obtain information on plant organs and abiotic stresses, and the ability to segment crops from weeds, such techniques have been used to detect pests and plant diseases and to identify grain varieties. The development of corresponding imaging systems to assess the phenotypic parameters, yield, and quality of crop plants will increase the confidence of stakeholders in grain crop cultivation, thereby bringing technical and economic benefits to advanced agriculture. Therefore, this paper provides a comprehensive review of CNNs in computer vision for grain crop phenotyping. It is meaningful to provide a review as a roadmap for future research in such a thriving research area. The CNN models (e.g., VGG, YOLO, and Faster R-CNN) used CV tasks including image classification, object detection, semantic segmentation, and instance segmentation, and the main results of recent studies on crop phenotype detection are discussed and summarized. Additionally, the challenges and future trends of the phenotyping techniques in grain crops are presented. [ABSTRACT FROM AUTHOR]

Considering that worldwide the amount of sludge from sewage treatment plants has increased, which through storage pollutes the environment, solutions must be found for its management. In this paper, through an analysis of studies from the literature, we present an ecological method of recovery of sewage sludge (SS) in agriculture through vermicomposting with Eisenia etida, Eisenia andrei, Eudrilus eugeniae and Perionyx excavatus earthworms, thus we analyzed the possibility that sewage sludge can be transformed from waste into quality fertilizer that benefits the soil, plants, and people, thus being able to replace chemical fertilizers which, if applied to the soil, can acidify and pollute the soil and agricultural crops. We observed that the total nitrogen content of the phosphorus increased. Through the vermicomposting process, organic substances are rapidly decomposed and nitrogen mineralization is accelerated. We studied the impact of dewatered sewage sludge vermicomposting on pH value, electrical conductivity, porosity, moisture content, nitrogen content, water retention capacity, metal content, and the development of agricultural crops, highlighting the positive impact of vermicompost application on the soil. Adding vermicompost to the soil has been observed to improve plant development. [ABSTRACT FROM AUTHOR]

Agricultural practices of smallholder farming systems of Latin America can play an important role in reducing their exposure to the risks associated with climate extremes. To date, however, there is no systematic analysis of scientific evidence for the extent to which these practices can provide the multiple benefits needed for smallholders to adapt to climate extremes. In this paper, we searched scientific databases to review scientific evidence of the benefit provided by twenty-six practices in crops commonly farmed by smallholders in the region and highly relevant for their food and nutrition security; namely, coffee, maize and beans. We reviewed scientific documents (n = 304) published in the period 1953–2021 to register evidence of the practices' effects on fifty-five benefits. Our analysis of these documents found measurement records (n = 924) largely based on field experiments (85%). Our results show strong evidence of the multiple benefits that some ecosystem-based practices (e.g., tree-based practices for coffee and no tillage for maize) can provide to support the adaptation to climate extremes of smallholder farming systems and enhance a farm's natural assets (e.g., biodiversity, water, soil). We also found that the majority of research on practices in the region focused more on the socioeconomic dimension (54%) rather than on the capacity of practices to improve the natural assets of a smallholder farmers or reduce the impact of climate extremes. Given these knowledge gaps, we discuss the importance of a renovated investment in research to address existing knowledge gaps. Our concluding suggestions for future research include the need for systematizing existing knowledge from different sources (e.g., peer-reviewed, gray literature, farmers, extension agencies, etc.), and to assess the extent to which these practices can provide multiple benefits for smallholder farming systems by improving their wellbeing, reducing their vulnerability to different hydroclimatic extremes while also contributing to ecosystem services provision at the landscape level. [ABSTRACT FROM AUTHOR]

Cereal grains and tubers are among the highly consumed staple foods globally; however, due to unfavorable weather conditions and the competition for natural resources, the major staple cereal crops, such as rice, are under production threat. On the other hand, the overuse of chemical fertilizers and pesticides to increase crop yield is deteriorating the growing environment for plants and animals, including humans. As such, sustainable management practices are the key method that can be employed to increase crop production without harming the environment. Plant growth-promoting bacteria (PGPB), such as the purple non-sulfur bacteria (PNSB), have recently gained much attention in crop production due to their ability to accumulate higher-value compounds that are highly beneficial to crops. Some of the major benefits PNSB holds are that it can fix atmospheric nitrogen, solubilize phosphate, remediate heavy metals, suppress methane emissions from waterlogged paddy fields, and assist in carbon sequestration. These benefits allow PNSB to be an important bacterium for improving plant growth and yield much more sustainably while benefiting the environment. This review article discusses the beneficial effects of PNSB on rice crop plants through careful screening of previous work in this area. The review also identifies the research gaps and suggests future research pathways to make PNSB an important bacteria for sustainable rice crop production. The review paper aims for the United Nation's sustainable development goal number two, "Zero Hunger," target 2.4, indicator 2.4.1, "Proportion of agricultural area under productive and sustainable agriculture". [ABSTRACT FROM AUTHOR]

Site-specific weed control offers a great potential for herbicide savings in agricultural crops without causing yield losses and additional weed management costs in the following years. Therefore, precision weed management is an efficient tool to meet the EU targets for pesticide reduction. This review summarizes different commercial technologies and prototypes for precision patch spraying and spot spraying. All the presented technologies have in common that they consist of three essential parts. (1) Sensors and classifiers for weed/crop detection, (2) Decision algorithms to decide whether weed control is needed and to determine a suitable type and rate of herbicide. Usually, decision algorithms are installed on a controller and (3) a precise sprayer with boom section control or single nozzle control. One point that differs between some of the techniques is the way the decision algorithms classify. They are based on different approaches. Green vegetation can be differentiated from soil and crop residues based on spectral information in the visible and near-infrared wavebands ("Green on Brown"). Those sensors can be applied for real-time on/off control of single nozzles to control weeds before sowing after conservation tillage and in the inter-row area of crops. More sophisticated imaging algorithms are used to classify weeds in crops ("Green on Green"). This paper will focus on Convolutional Neural Networks (CNN) for plant species identification. Alternatively, the position of each crop can be recorded during sowing/planting and afterward herbicides can be targeted to single weeds or larger patches of weeds if the economic weed threshold is exceeded. With a standardized protocol of data communication between sensor, controller and sprayer, the user can combine different sensors with different sprayers. In this review, an ISOBUS communication protocol is presented for a spot sprayer. Precision chemical weed control can be realized with tractor-mounted sprayers and autonomous robots. Commercial systems for both classes will be introduced and their economic and environmental benefits and limitations will be highlighted. Farmers ask for robust systems with less need for maintenance and flexible application in different crops. [ABSTRACT FROM AUTHOR]

Cucurbits are an important vegetable crop of the gourd family. Unfortunately, gummy stem blight (GSB) causes a major fungal disease on Cucurbitaceous vegetable crops. It is also known as black root when affecting fruits, and it is found all over the world. GSB is caused by the fungal pathogen Didymella bryoniae. Research efforts have investigated the different developmental stages and various parts of Cucurbits affected with this disease. In the present paper, we have completed a systematic review for the disease's symptomatic, pathogenic microbes, resistance resources, resistance inheritance regularity, molecular biology and genomic study of resistance gene and control method, etc., on Cucurbits. This review provides the background and rationale for future studies aiming to address the issues existing in gummy stem blight research and development. [ABSTRACT FROM AUTHOR]

Among the abiotic stresses, drought has been shown to have harmful effects on crop productivity worldwide, and this has affected arid and semiarid lands such as the Mediterranean Basin and Asian countries like Pakistan, due to rapid climate change scenarios. The individual climate-change-inducing stressors for plants are abiotic in nature [[2]], and they impose stress upon most plant species. Some abiotic plant stressors include drought, salinity, elevated CO SB 2 sb , and temperature (low and high), among others. After applying 200 µM of MEL, the leaf water, measured as the relative water content (RWC), leaf water content (LWC), and relative saturation deficit (RSD) increased by 1.9%, 100%, and 71.2%, respectively, during mild drought, and 17%, 133%, and 32% under severe drought. [Extracted from the article]

The transformation of spent coffee grounds (SCGs) into hydrochars has been extensively studied in recent years to explore their potential in biofortifying foods and mitigating the plant toxicity associated with SCGs. This study aimed to evaluate the effects of adding activated (ASCG and AH160) and functionalized SCGs, as well as SCG-derived hydrochars (ASCG-Fe and AH160-Fe), on cucumber production and plant iron content. To achieve this, SCGs and SCG-derived hydrochars activated and functionalized with Fe were incorporated into cucumber crops grown in a greenhouse over multiple harvests. Among the treatments, SCG-Fe proved to be the most promising for cucumber production, yielding an average of 25 kg of cumulative production per treatment across three harvests. Regarding iron content, the average results across all harvests showed that SCGs and functionalized SCG-hydrochars matched the performance of the commercial chelate (0.108 vs. 0.11 mg Fe/100 g fresh weight). However, in subsequent harvests, iron appeared to leach out, with the activated bio-products (ASCG and AH160) leaving the highest iron reserves in the soil. Additionally, the hydrochar activated at 160 °C demonstrated the highest utilization efficiency. In conclusion, the incorporation of SCG residues and second-generation residues (hydrochars) shows promise as agents for biofortifying cucumbers. [ABSTRACT FROM AUTHOR]

Three-wheeled agricultural robots possess the advantages of high flexibility, strong maneuverability, and low cost. They can adapt to various complex terrains and operational environments, making them highly valuable in the fields of crop planting, harvesting, irrigation, and more. However, the horizontal stability of the three-wheeled agricultural robot chassis is compromised when working in harsh terrain, significantly impacting the overall operational quality and safety. To address this issue, this study designed a leveling system based on active suspension and proposed a stepwise leveling method based on an adaptive dual-loop composite control strategy (ADLCCS-SLM). Firstly, in the overall control of the three-wheeled chassis, a stepwise leveling method (SLM) was introduced. This method allows for rapid leveling by incrementally adjusting one or two suspensions, effectively avoiding the complex interactions between suspension components encountered in traditional methods involving the simultaneous linkage of three suspensions. Next, in terms of suspension actuator control, an adaptive dual-loop composite control strategy (ADLCCS) was proposed. This strategy employs a dual-loop composite control both internally and externally and utilizes an improved adaptive genetic algorithm to adjust critical control parameters. This adaptation optimizes the chassis leveling performance across various road conditions. Finally, the effectiveness of the proposed ADLCCS-SLM was validated through simulation and experimental testing. The test results showed that the control effect of the proposed method was significant. Compared to the traditional multi-suspension linkage leveling method based on PID, the peak values of pitch angle and roll angle were reduced by 31.8% and 33.3%, respectively. [ABSTRACT FROM AUTHOR]

Utilizing the potential of crops to suppress weeds is an important strategy for sustainable management. Feathertop Rhodes grass (FTR) (Chloris virgata Sw.) is a problematic warm-season weed in Australia that has recently expanded into colder seasons. This study investigated the growth and seed production of FTR at two planting times (May and July) and three wheat planting densities (0, 82, and 164 plants m−2) using a neighborhood design over two years. In both years, the plant height, tiller production, and panicle production of FTR were lower in the presence of wheat compared to when wheat was absent. Surrounding FTR with wheat delayed its pinnacle emergence, varying from 1 to 6 days in the first year and 4 to 21 days in the second year, depending on the planting date and wheat density. During both years and planting times, wheat's presence caused approximately a 99% decrease in the dry matter and seed production of FTR compared to wheat's absence. Additionally, the wheat height, an important competitive factor, was higher at both densities in the May planting compared to the July planting. The emergence, establishment, and continuous production of FTR seeds throughout the year indicate that inadequate management could result in the further spread of this weed. Our findings suggest that adjusting the date and density of wheat planting could be a viable strategy for sustainably managing this weed during colder seasons. [ABSTRACT FROM AUTHOR]

This document discusses the molecular regulation mechanisms of ripening, senescence, and stress resistance in fruits and vegetables. It highlights the biological changes that occur during the development and ripening of these crops, as well as the various factors that contribute to their production and quality. The document also explores the network of hormones, transcription factors, and epigenetic modifications involved in the development of fruits and vegetables, as well as the postharvest treatments used to preserve their quality. Several studies are presented that analyze the genetic regulation of specific crops, such as Brassica napus, loquat, olecranon honey peaches, eggplant, and litchi, providing insights into their growth, development, and response to pathogens. Additionally, a study on the use of selenium treatment to extend the shelf life of broccoli is discussed. The document concludes by emphasizing the need for further research to fully understand the complex molecular regulation mechanisms of fruits and vegetables. [Extracted from the article]

The rapid and accurate detection of weeds is the prerequisite and foundation for precision weeding, automation, and intelligent field operations. Due to the wide variety of weeds in the field and their significant morphological differences, most existing detection methods can only recognize major crops and weeds, with a pressing need to enhance accuracy. This study introduces a novel weed detection approach that integrates the GFPN (Green Feature Pyramid Network), Slide Loss, and multi-SEAM (Spatial and Enhancement Attention Modules) to enhance accuracy and improve efficiency. This approach recognizes crop seedlings utilizing an improved YOLO v8 algorithm, followed by the reverse detection of weeds through graphics processing technology. The experimental results demonstrated that the improved YOLO v8 model achieved remarkable performance, with an accuracy of 92.9%, a recall rate of 87.0%, and an F1 score of 90%. The detection speed was approximately 22.47 ms per image. And when shooting from a height ranging from 80 cm to 100 cm in the field test, the crop detection effect was the best. This reverse weed detection method addresses the challenges posed by weed diversity and complexities in image recognition modeling, thereby contributing to the enhancement of automated and intelligent weeding efficiency and quality. It also provides valuable technical support for precision weeding in farmland operations. [ABSTRACT FROM AUTHOR]

Insecticides based on baculoviruses have become an alternative for pest control in different agricultural crops. The aim of this study was to assess the biological activity of the bioinsecticide Cartugen™ (SfMNPV: Baculoviridae: Alphabaculovirus) on larvae of Spodoptera frugiperda J. E. Smith (Lepidoptera: Noctuidae) resistant to Bt corn expressing the insecticidal proteins Cry1A.105+Cry2Ab2+Cry1F. In addition, we assessed the efficiency of SfMNPV on S. frugiperda control in the field from natural infestation of the pest during two agricultural seasons. The results showed that no larvae survived 10 days after being inoculated with Bt and non-Bt corn leaves contaminated with 1.50 × 1010 occlusion bodies (OBs)/L (equivalent to the recommended dose of the product). However, when using doses equivalent to 50% (7.50 × 109 OBs/L) and 25% (3.75 × 109 OBs/L), the larval mortality ranged from 21.12% to 46.55%, respectively. Although larvae resistant to the Cry1A.105+Cry2Ab2+Cry1F proteins, when exposed to 50% of the SfMNPV dose (7.50 × 109 OBs/L), showed reductions in larval weight (52 to 67% reduction), pupal weight (32 to 59% reduction), and total fecundity (67 to 86% reduction) compared to the control. Furthermore, doses above 25% (3.75 × 109 OBs/L−1) caused a population decrease in the growth of the species in both Bt and non-Bt corn according to the fertility life table. In the field, at 7 and 10 DAA (days after application), corn plants sprayed with SfMNPV (1.50 × 1010 OBs/L) showed reductions in leaf damage according to the Davis scale. However, from 14 to 21 DAA, there was an increase in leaf damage in corn leaves from both treatments, with or without the application of SfMNPV. This shows that SfMNPV may be an important strategy in the integrated management and resistance management of S. frugiperda. [ABSTRACT FROM AUTHOR]

Accurate detection of early diseased plants is of great significance for high quality and high yield of crops, as well as cultivation management. Aiming at the low accuracy of the traditional deep learning model for disease diagnosis, a crop disease recognition method was proposed based on multi-source image fusion. In this study, the adzuki bean rust disease was taken as an example. First, color and thermal infrared images of healthy and diseased plants were collected, and the dynamic thresholding excess green index algorithm was applied to extract the color image of the canopy as the reference image, and the affine transformation was used to extract the thermal infrared image of the canopy. Then, the color image was fused with the thermal infrared image by using a linear weighting algorithm to constitute a multi-source fusion image. In addition, the sample was randomly divided into a training set, validation set, and test set according to the ratio of 7:2:1. Finally, the recognition model of adzuki bean rust disease was established based on a novel deep learning model (ResNet-ViT, RMT) combined with the improved attention mechanism and the Squeeze-Excitation channel attention mechanism. The results showed that the average recognition rate was 99.63%, the Macro-F1 was 99.67%, and the recognition time was 0.072 s. The research results realized the efficient and rapid recognition of adzuki bean rust and provided the theoretical basis and technical support for the disease diagnosis of crops and the effective field management. [ABSTRACT FROM AUTHOR]

Barley (Hordeum vulgare L.) and field bean (Vicia faba L. var minor) are often used for forage production in the Mediterranean environment. Their bromatological and productive characteristics are known when cultivated as sole crops, but if grown simultaneously in intercropping, the changes in their morphological and physiological characteristics could affect the quality and the nutrient yield of the resulting forages. In a two-year field research in Central Italy, we determined the bromatological traits and nutrient yields of barley and field bean, grown as sole crops or intercrops in a 1:1 additive design harvested at the heading and early dough stage with five nitrogen (N) rates (i.e., from 0 to 200 kg ha−1). Both intercropping and N fertilization increased the concentration of crude protein and fiber but decreased the general quality of the forage. However, the effects on nutrient yields were more marked; those of crude protein and total digestible nutrients increased by 46 and 29% with intercropping and by 49 and 46% with 150 kg N ha−1. Thus, we concluded that N fertilization should not exceed 50 kg ha−1 to maximize the relative feed value, while 150 kg ha−1 are suitable to boost nutrient yields. [ABSTRACT FROM AUTHOR]

The long-term use of chemical fertilizers poses a serious threat to crop productivity and soil quality. Organic fertilizers are used to improve the soil fertility and crop productivity. The application of organic fertilizers improves soil health and plant growth by improving the soil organic matter (SOM), soil structure, aggregate stability, nutrient uptake, water-holding capacity, cation exchange capacity, nutrient use efficiency and microbial activities of soil. The intensity of abiotic stress is continuously increasing, which is a serious threat to crop productivity and global food security. However, organic fertilizers have been reported to improve tolerance against drought, salinity, heat and heavy metal (HM) stresses. The application of organic fertilizer improves the leaf water status, nutrient uptake, nutrient homeostasis, synthesis of chlorophyll, osmolytes, hormones, secondary metabolites, antioxidant activities and gene expression, resulting in improved tolerance against drought, salinity, heat, and heavy metals. In the present review, we have discussed the ability of organic fertilizers to improve soil fertility, crop yield, and the nutrient use efficiency. We have also presented the various mechanisms through which organic fertilizers improve tolerance against drought, salinity, heat, and heavy metals. Therefore, this review will put forth new directions for researchers working on the use of organic materials to improve soil fertility, crop productivity and tolerance against abiotic stresses. [ABSTRACT FROM AUTHOR]

Accurate crop classification is of vital importance for agricultural water management. Most researchers have achieved crop classification by model optimization in the same temporal and regional domain by adjusting the value of input features. This study aims to improve the accuracy of crop classification across temporal and spatial domains. Sentinel-2 satellite imagery is employed for crop classification training and prediction in selected farming areas of Heilongjiang Province by calculating vegetation indices and constructing sequential input feature datasets. The HUNTS filtering method was used to mitigate the influence of cloud cover, which increased the stability and completeness of the input feature data across different years. To address the issue of shifts in the input feature values during cross-scale classification, this study proposes the hypothesis testing distribution method (HTDM). This method balances the distribution of input feature values in the test set even without knowing the crop distribution, thereby enhancing the accuracy of the classification test set. The results indicate that the HTDM significantly improves prediction accuracy in cases of substantial image quality variance. In 2022, the recognition accuracy for crop types at all farms processed by the HTDM was above 87%, showcasing the strong robustness of the HTDM. [ABSTRACT FROM AUTHOR]

Algae are currently used in many areas, including dietetics, pharmacy, cosmetology and to increase the nutritional value of food and animal feed due to their chemical composition. They are a source of extremely valuable molecules, including polyunsaturated fatty acids and pigments. Algae are also a valuable source of protein and almost all essential vitamins. They are rich in pigments such as chlorophyll, carotenoids and phycobiliproteins. These substances have a wide range of commercial applications. Due to its very intensive use, the demand for this plant raw material is constantly growing. Therefore, the methods of growing and harvesting algae are constantly improved in order to maximize the cultivation effect while minimizing costs and energy inputs. Future research should focus on improving algae cultivation and harvesting systems, with an emphasis on the possibility of genetic modifications that would allow even more efficient algae cultivation. This review summarizes methods of micro- and macroalgae cultivation, the chemical composition of selected algae species, which are important from the pharmaceutical, dietary and cosmetic points of view and therapeutic and dietary applications of compounds derived from different algae species. The key conclusion drawn from this article is that algae are an extremely valuable raw material, rich in numerous macro- and micronutrients necessary for humans, the acquisition of which is part of the currently important strategy of ecological policy for obtaining raw materials for various industries. [ABSTRACT FROM AUTHOR]

The Special Issue "Adaptation of Crops to the Environment under Climate Change: Physiological and Agronomic Strategies—Volume II" compiles eleven original research articles that address strategies to cope with climate change in agriculture. The articles cover topics such as increasing water stress tolerance, selecting heat-resistant cultivars, and comparing growth conditions in response to salt stress. The issue includes contributions from various countries, providing diverse approaches and strategies. The research findings highlight the importance of sustainable and climate-resistant practices in agriculture to mitigate the negative effects of climate change and ensure food security. [Extracted from the article]

This paper rethinks the governance of genebanks in a social and political context that has significantly evolved since their establishment. The theoretical basis for the paper is the commons conceptual framework in relation to both seed and plant genetic resources. This framework is applied to question the current policy ecosystem of genetic research and breeding and explore different collective governance models. The concept of crop diversity management system (CDMS) commons is proposed as the new foundation for a more holistic and inclusive framework for crop diversity management, that covers a broad range of concerns and requires different actors. The paper presents a multi-stakeholder process established within the context of the two recent projects CoEx and Dynaversity, imagining possible collective arrangements to overcome existing deadlocks, foster collective learning, and design collaborative relationships among genebanks, researchers, and farmers' civil society organizations involved in crop diversity management. [ABSTRACT FROM AUTHOR]

The comprehensive evaluation of crop germplasm serves to scientifically and objectively assess the quality of different genetic accessions against certain standards. Here, we propose an optimized approach to enhance the result's stability when assessing salt tolerance in crop germplasm. This protocol was applied to a case study involving 249 tomato genotypes, systematically refining the processes involved in constructing an evaluation index system, data preprocessing, statistical method selection, and weight calculation. The optimization process reduced the system variance of salt tolerance evaluation results and achieved an 85.42% concordance with a classical approach, across a tomato population covering 241 genotypes, suggesting the improved stability and high accuracy of the optimized protocol. Moreover, an 83.82% consistency rate between pre- and post-optimization results also suggested the high accuracy of the optimized protocol. The enhanced stability was further confirmed by a secondary validation on a subpopulation (covering 39 genotypes), which demonstrated a consistency rate of 83.87% between the two populations. The study identified 8.43% of the evaluated germplasm as salt-tolerant accessions, providing valuable parental materials for breeding programs. The findings underscore the potential of our protocol for the precise identification of stress-resistant germplasm, contributing to the development of stress-tolerant crop varieties. [ABSTRACT FROM AUTHOR]

The changing global climate, characterized by rising surface air temperatures, shifting precipitation patterns, and heightened occurrences of extreme weather events, is anticipated to profoundly impact the environment, economy, and society worldwide. This impact is particularly acute in African nations like Angola, where crucial sectors, such as agriculture, rely heavily on climate variability and exhibit limited adaptive capacity. Given that the majority of Angola's agriculture is rain-fed and serves as a vital source of livelihood for the populace, the country is especially vulnerable to climate change, particularly in its southern region. Climate change has caused severe damage in Angola, especially in the southern part of the country, where the worst droughts in decades have affected over 3.81 million people, resulting in food and water shortages. Between 2005 and 2017, climate-related disasters cost the country about 1.2 billion US dollars, further exacerbating the economic and social challenges faced by the population. This study presents a systematic review of the effects of climate change on agriculture in Angola, with a focus on the southern region. Employing the PRISMA2020 methodology, the review examined 431 documents from databases such as Scopus and Web Science, spanning from 1996 to 2023, with 63 meeting inclusion criteria. The review reveals a paucity of research on the short and long-term impacts of climate change on Angolan agriculture. Projections indicate a rise in temperatures and a general decrease in precipitation, with the southern region experiencing a more pronounced decline. Agricultural productivity may suffer significantly, with models suggesting a potential 7% reduction by 2050. [ABSTRACT FROM AUTHOR]

Garlic (Allium sativum) is an important economic crop in China. In terms of using remote sensing technology to identify it, there is still room for improvement, and the high-precision classification of garlic has become an important issue. However, to the best of our knowledge, few studies have focused on garlic area mapping. Here, we propose a method for identifying garlic crops using samples and a multi-feature dataset under limited conditions. The results indicate the following: (1) In the land-use classification of the Erhai Lake Basin, the importance ranking of the characteristic bands, from high to low, is as follows: spectral features, vegetation features, texture features, and terrain features. (2) The random forest method based on feature selection demonstrates high accuracy in land-use classification within the Erhai Lake Basin in Yunnan Province. The overall classification accuracy reached 95.79%, with a Kappa coefficient of 0.95. (3) From 1999 to 2023, the expansion of garlic cultivation in the Erhai Lake Basin showed a trend of initially strengthening from north to south, which was followed by weakening. The vertical development of garlic cultivation reached saturation, showing a slow trend toward horizontal expansion between 2005 and 2018. The planting distributions in various townships in the Erhai Lake Basin gradually shifted from relatively uniform distributions to upstream development. This study utilized the Google Earth Engine (GEE) cloud computing platform and machine learning algorithms to compensate for the lack of statistical data on garlic cultivation in the Erhai Lake Basin. Moreover, it accurately, rapidly, and efficiently extracted planting information, demonstrating significant potential for practical applications. [ABSTRACT FROM AUTHOR]

Yellow nutsedge (Cyperus esculentus L.) is cultivated worldwide due to its agricultural and biotechnological potential. In Brazil, it is considered a weed, and we lack studies on its cultivation. Overcoming tuber dormancy is crucial for propagation. This study aimed to assess various dormancy-breaking methods' effects on tubers and initial plant development. The treatments included gibberellic acid immersion, ethylene exposure, purple nutsedge extract immersion, temperature conditioning, scarification, and bud cutting, along with a control. Scarification resulted in the shortest emergence time (0.904 days) and fastest emergence speed (5.092 tubers/day). Plant development was minimally affected by the treatments, with scarification and gibberellic acid (100 mg L−1) resulting in taller plants (1.19–1.23 times higher than the control). The conditioning at 4 °C and 70 °C proved to be less effective in breaking dormancy. Purple nutsedge extract immersion and bud cutting hindered plant growth. Scarification emerged as the most effective dormancy-breaking method. This study provides insights into the cultivation of yellow nutsedge in Brazil, highlighting the effectiveness of scarification in improving tuber germination and the early growth stages of plants. [ABSTRACT FROM AUTHOR]

This document explores the impact of nitrogen fertilizer on crop yields and quality in winter wheat and potatoes. It found that increasing rates of nitrogen fertilizer resulted in higher grain yields and improved protein and gluten content in winter wheat. Late-season nitrogen application was also found to be important for wheat yield and quality. In potatoes, the use of sulfur in combination with nitrogen fertilizer increased tuber yield and nitrogen use efficiency. The document also discusses the role of inorganic nitrogen in soil and its impact on greenhouse gas emissions. The findings emphasize the importance of optimizing fertilizer application to maximize crop productivity while minimizing environmental impacts. [Extracted from the article]

Perennial grains have been proposed as a soil-healthy alternative to annual grains. Intermediate wheatgrass (Thinopyrum intermedium), whose seed is currently sold under the trade name Kernza®, and silflower (Silphium integrifolium), which is in the early stages of domestication at The Land Institute in Central Kansas, lack characterization for their deficiency symptoms. This has complicated attempts to assess the causes of visible stress on plants in the field and the greenhouse. By growing Th. intermedium and S. integrifolium in a set of hydroponic solutions, each containing all but one selected nutrient—including nitrogen, phosphorous, potassium, calcium, magnesium, sulfur, iron, boron, zinc, copper, molybdenum, and manganese—we were able to assess the effects of twelve different nutrient deficiencies across the two species. Visible symptoms were described and documented via photographs. The effects of the deficiencies on height, leaf biomass, root biomass, gas exchange and photosynthesis (silflower), and resin production (silflower) were measured. Calcium, nitrogen, and potassium were found to alter growth responses in intermediate wheatgrass; in silflower, growth, resin production, and photosynthetic traits were affected by many nutrient deficient treatments. Our results suggest that further work addressing how symptoms might look at the time of flowering, seed production, and in the field at different concentrations of key nutrients would help ongoing plant-breeding efforts. [ABSTRACT FROM AUTHOR]

The scientific aim of this article is to investigate the potential benefits of implementing a multi-cropping system, specifically focusing on the incorporation of caraway, to improve soil agrochemical and biological properties, prevent soil degradation and erosion, and ultimately enhance soil quality and health to better adapt to climate change. This study aims to provide valuable insights into the comparative analysis of various soil parameters and biological indicators to showcase the promising perspectives and importance of perennial crop production for improving soil quality and agricultural sustainability. These crops are designed to provide multiple benefits simultaneously, including improved yields, enhanced ecosystem services, and reduced environmental effects. However, an integrated assessment of their overall effects on the agroecosystem is crucial to understand their potential benefits and trade-offs. The field experiment was conducted over three consecutive vegetative seasons (2017 to 2021) at the Experimental Station of Vytautas Magnus University Agriculture Academy (VMU AA) in Kaunas district, Lithuania. The experimental site is located at 54°53′7.5″ N latitude and 23°50′18.11″ E longitude. The treatments within a replicate were multi-cropping systems of sole crops (spring barley (1), spring wheat (2), pea (3), caraway (4)), binary crops (spring barley–caraway (5), spring wheat–caraway (6), pea–caraway (7)), and trinary crops (spring barley–caraway–white clover (8), spring wheat–caraway–white clover (9), pea–caraway–white clover (10)) crops. However, an integrated assessment of their impact on the agroecosystem is needed to understand their potential benefits and processes. To determine the complex interactions between indicators, the interrelationships between indicators, and the strength of impacts, this study applied an integrated assessment approach using the comprehensive assessment index (CEI). The CEI values showed that integrating caraway (Carum carvi L.) into multi-cropping systems can have several positive effects. The effect of the binary spring barley and caraway and the trinary spring barley, caraway, and white clover crops on the agroecosystem is positively higher than that of the other comparative sole, binary, and trinary crops. Caraway, after spring wheat together with white clover, has a higher positive effect on the agroecosystem than caraway without white clover. Specifically, this study addresses key aspects, such as soil health, nutrient cycling, weed management, and overall agricultural sustainability, within the context of multi-cropping practices. By evaluating the effects of these cropping systems on soil agrochemical properties and ecosystem dynamics, the research provides valuable insights into sustainable agricultural practices that promote environmental conservation and long-term soil health. [ABSTRACT FROM AUTHOR]

The intensive cultivation of olive trees and grapevines in the Mediterranean region not only results in large yields but also generate wastes, with high restrictions on their impact on people's well-being and the environment. The current study sought to investigate the potential use of olive-mill waste (OW), grape-mill waste (GW) and their mixtures (OW + GW) at different levels (0%, 5%, 10% and 20% v/v) for partial peat substitution in the production of carnation (Dianthus caryophyllus L.) plants. The presence of OW, GW and OW + GW wastes raised the pH, the electrical conductivity, the content of organic matter and mineral content in substrate mixtures, while they decreased the total porosity and the available free air. The use of OW had more negative impacts than GW, while the OW + GW mixture alleviated, to some extent, the negative OW impacts. The use of high levels of residues decreased plant growth, chlorophyll content and mineral accumulation in plant tissue due to inappropriate growing media properties. The increased OW presence caused oxidative stress to the plants, as verified by the increased malondialdehyde and hydrogen peroxide content. This resulted in an upsurge in the total phenolics. However, GW presence did not impact any oxidative stress. It can be suggested that 10% OW, 10% GW or 20% OW + GW can be used in growing media, as they resulted in suitable plant growth. To ensure sufficient yields, nevertheless, the growing media's characteristics also need to be enhanced. [ABSTRACT FROM AUTHOR]

The effects of the co-location of energy production from a photovoltaic (PV) plant and aromatic crops (thyme, oregano, and Greek mountain tea) in a hot and dry environment have been investigated in Enel Green Power PV plant located in Kourtesi (Greece). The study was aimed at evaluating the influence of the PV plant on microclimate, on soil temperature and water potential, and on the crops' growth in a testing area (the corridors between two modules rows) compared to a full sunlight control area. The net radiation and of the wind speed recorded in the testing area showed a 44% and 38% reduction respectively, compared to the control area, while air temperature and relative humidity did not show any difference. Among crop/soil monitored parameters, cumulative reference evapotranspiration (ET0) and the average soil temperature were found 29% and 8% lower in the testing area, conversely the soil water potential (SWP) was 34% higher compared to control area. No significant differences in the plant growth have been detected between testing and control areas, except for oregano whose weight was higher in testing area. The results suggest that in hot and dry climatic conditions, the reduction of climatic stress could compensate the decrease of photosynthesis due to the shading effect of the PV modules, thus not damaging crops' growth. [ABSTRACT FROM AUTHOR]

Salinity adversely affects plant growth and productivity worldwide. To overcome salinity and other abiotic stresses, safe, ecofriendly biofertilizers that stimulate productivity have been experiencing rising demand, alongside decreasing use of mineral fertilizers. The purpose of this study was to examine changes in the growth, yield, physiological and biochemical parameters of wheat crop as a response to two potassium-solubilizing microorganisms (KSMs), Trichoderma asperellum and Bacillus circulans, with 50% or 75% of the recommended amount of K fertilizer (potassium sulphate), combined with no or 25% feldspar as well as 1.5% potassium sulphate (K-leaf) as foliar application, and all treatments were compared with a control treatment under salt-affected soil conditions, during two winter seasons in 2019–2020 and 2020–2021. The randomized complete block design (RCBD) was used to set up the experiment. Results showed that the vegetative growth, yield, physiological and biochemical parameters were affected under salt-affected soil conditions. Nevertheless, inoculation with T. asperellum and B. circulans with potassium application mitigated the deleterious effect of salt-affected soil conditions by improving growth parameters, photosynthetic pigment content, antioxidant enzymes (superoxide dismutase, ascorbate peroxidase, catalase and peroxidase) content, total soluble sugars, relative water content, potassium content in leaves, grains yield, and some biochemical constituents in the grains and straw. Meanwhile, these treatments decreased proline content, Na+ content in leaves, and the Na+/K+ ratio as compared to the control treatment. The most pronounced treatment, inoculation by B. circulans with 1.5% K-leaf as foliar application, significantly increased grain yield by 16.41% relative to the control treatment. It could be concluded that inoculating wheat with KSMs T. asperellum and B. circulans with 1.5% K-leaf as foliar application will increase wheat salinity tolerance and wheat productivity and decrease the detrimental effect of salinity on wheat growth and grain yield quantity and quality under salt-affected soils conditions. [ABSTRACT FROM AUTHOR]