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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]

Crop diseases and pests are important factors restricting agricultural production. Traditional pest detection methods are mainly targeted at a single pest species, which is difficult to meet the needs of multi-target identification and rapid response in real scenes. Therefore, this paper improves the YOLOv8n model for efficient multi-target pest detection. Two feature enhancement modules, EMSFEM and AFEM_SIE, are proposed in this paper. The EMSFEM module enriches the model's receptive field through the combination of multi-scale asymmetric convolution kernel and different expansion rates and can better extract the width, height, texture, and edge information of the target. The AFEM_SIE module captures the similarities and differences between upper and lower features through spatial information exchange and enhances feature representation through inter-feature information exchange. In addition, an improved feature fusion operation, Concat_Weighting, is proposed on the basis of Concat. The module uses the learned weights to carry out channel weighting and feature graph weighting for input features, which realizes more flexible and effective feature fusion. The results of experiments conducted on the publicly available large-scale crop pest and disease dataset IP102 show that the performance of the AEC-YOLOv8n model is significantly improved compared with the original YOLOv8n model, with mAP50 increased by 8.9%, accuracy increased by 6.8%, and recall rate increased by 6.3%. The AEC-YOLOv8n model proposed in this study can effectively identify and deal with a variety of crop pests and has achieved the best detection accuracy on the IP102 dataset, which has high application value. [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]

Wheat is one of the most crucial food crops globally, with its yield directly impacting global food security. The accurate detection and counting of wheat spikes is essential for monitoring wheat growth, predicting yield, and managing fields. However, the current methods face challenges, such as spike size variation, shading, weed interference, and dense distribution. Conventional machine learning approaches have partially addressed these challenges, yet they are hampered by limited detection accuracy, complexities in feature extraction, and poor robustness under complex field conditions. In this paper, we propose an improved YOLOv10 algorithm that significantly enhances the model's feature extraction and detection capabilities. This is achieved by introducing a bidirectional feature pyramid network (BiFPN), a separated and enhancement attention module (SEAM), and a global context network (GCNet). BiFPN leverages both top-down and bottom-up bidirectional paths to achieve multi-scale feature fusion, improving performance in detecting targets of various scales. SEAM enhances feature representation quality and model performance in complex environments by separately augmenting the attention mechanism for channel and spatial features. GCNet captures long-range dependencies in the image through the global context block, enabling the model to process complex information more accurately. The experimental results demonstrate that our method achieved a precision of 93.69%, a recall of 91.70%, and a mean average precision (mAP) of 95.10% in wheat spike detection, outperforming the benchmark YOLOv10 model by 2.02% in precision, 2.92% in recall, and 1.56% in mAP. Additionally, the coefficient of determination (R2) between the detected and manually counted wheat spikes was 0.96, with a mean absolute error (MAE) of 3.57 and a root-mean-square error (RMSE) of 4.09, indicating strong correlation and high accuracy. The improved YOLOv10 algorithm effectively solves the difficult problem of wheat spike detection under complex field conditions, providing strong support for agricultural production and research. [ABSTRACT FROM AUTHOR]

Sustainable management of potassium nutrition in alfalfa crop production is one of the major key factors for achieving optimum seed and biomass yields. An inappropriate supply of mineral potassium nutrition in alfalfa production could result in a decrease in biomass and grain yield production, leading to luxury consumption with cost implications. Alfalfa (Medicago sativa L.) is a perennial leguminous forage crop known for its high protein content, nutritive value, biomass yield production, soil-improving abilities, and livestock feed. Potassium nutrition plays a crucial role in alfalfa production by influencing several physiological processes essential for biomass yield, growth, development, photosynthesis, nutrient uptake, and stress tolerance. Although several studies have been conducted regarding the role of potassium nutrition in agriculture productivity, only limited research has focused on crop-specific impacts. Therefore, this paper reviews (i) the significant role potassium nutrition plays in alfalfa production along with its implications for quality, yield, growth, and resistance to abiotic stress; (ii) the factors affecting the availability, absorption, and transport of potassium; (iii) the source of potassium and the consequences of inadequate availability; and (iv) highlights some strategies for mitigating potassium nutrient deficiency to optimize alfalfa productivity and sustainability in agricultural systems. [ABSTRACT FROM AUTHOR]

The non-judicious use of water at the farm level in traditional irrigation application methods is a present-day concern across the world that can be resolved by enhancing application efficiency through the adoption of advanced irrigation techniques. Sprinkler irrigation is a method that has high application efficiency, which can be further increased when coupled with automation toward precision irrigation. The objectives of this review are to summarize the main aspects of sprinkler and precision irrigation and their development, scope, and future prospects specifically in Asian countries. In this paper, a modified methodology, inspired by PRISMA guidelines, was used to explore the available literature to summarize the existing knowledge in the field. Regarding the technological aspects of the analyzed works, it became evident that sprinkler systems are an efficient method to not only irrigate crops (with 39% water saving) but also for the application of fertilizers with higher efficiency (>35%) and water productivity (>14.1%) compared with gravity irrigation systems. Moreover, this paper highlights the prominent features of precision irrigation for maximizing agricultural productivity. The use of sprinkler irrigation with precision applications using automation with a sensor-based mechanism for field data collection, data transformation, data analysis, and operation of IoT-based automatic solenoid valves can save 20–30% more irrigation water and increase crop yield by 20–27%. An analytical understanding and knowledge of the field were used to draw conclusions that are thought-provoking for scientists, researchers, and other stakeholders. [ABSTRACT FROM AUTHOR]

Global warming is one of the greatest threats to the social development of human beings. It is a typical example of global climate change, and has profoundly affected human production and life in various aspects. As the foundation of human existence, agricultural production is particularly vulnerable to climate change, which has altered environmental factors such as temperature, precipitation, and wind speed, and affected crop growth cycles, the frequency of extreme weather events, and the occurrence patterns of pests and diseases directly or indirectly, ultimately influencing crop yield and quality. This article reviews the latest research progress in this field, summarizes the impact of global climate change on agricultural production as well as the feedback mechanisms of agricultural activities on climate change, and proposes strategies for agricultural production to cope with global climate change. This paper aims to provide a scientific basis and suggestions for ensuring the sustainable development of agricultural production. [ABSTRACT FROM AUTHOR]

Precision irrigation and fertilization in agriculture are vital for sustainable crop production, relying on accurate determination of the crop's nutritional status. However, there are challenges in optimizing traditional neural networks to achieve this accurately. This paper aims to propose a rapid identification method for crop water and nitrogen content using optimized neural networks. This method addresses the difficulty in optimizing the traditional backpropagation neural network (BPNN) structure. It uses 179 multi−spectral images of crops (such as maize) as samples for the neural network model. Particle swarm optimization (PSO) is applied to optimize the hidden layer nodes. Additionally, this paper proposes a double−hidden−layer network structure to improve the model's prediction accuracy. The proposed double−hidden−layer PSO−BPNN model showed a 9.87% improvement in prediction accuracy compared with the traditional BPNN model. The correlation coefficient R2 for predicted crop nitrogen and water content was 0.9045 and 0.8734, respectively. The experimental results demonstrate high training efficiency and accuracy. This method lays a strong foundation for developing precision irrigation and fertilization plans for modern agriculture and holds promising prospects. [ABSTRACT FROM AUTHOR]

Due to the changes in the agricultural sector, there is a demand among policymakers, administration, advisors and farmers for comparisons of the economic efficiency of organic versus conventional farms and their environmental impact. The authors of the paper hypothesised that in some conditions, organic farms can reach similar productive and economic results as conventional farms and, at the same time, achieve better environmental effects. The aim of the research was to compare the production, environmental and economic effects of selected organic and conventional farms from eastern Poland (mixed, crop production, animal production). The basis for the comparative analysis was the data from 12 farms obtained using the questionnaire method (direct interview) from the years 2020 to 2021. The yields of cereals in the tested organic farms were about one-third lower than the average obtained on conventional farms. Total organic crop production in cereal units per ha was 43% of conventional production. Balances of NPK indicated surpluses or deficiency, which suggested that in both systems nutrient management should be improved and optimised. The compared groups of organic farms generally had higher economic efficiency—both with and without subsidies—than conventional farms, despite the fact that the latter obtained significantly higher incomes. Gross farm income on conventional farms was higher than on organic farms by 28%, but conventional farms had higher direct costs by 332% than organic ones. As a result, the economic efficiency of agricultural production of organic farms was higher by 59% than conventional farms. The lower level of inputs incurred on organic farms was the main factor determining their high economic efficiency. Organic farms pursuing an intensive model of agricultural production (milk production), but also with specialisation in crop production, proved to be the most economically efficient. Despite diversified production, reducing the income risk, the mixed production, both organic and conventional, in the studied set of farms turned out to be the least profitable, indicating the need to support it. It is suggested that moderate specialisation increases the efficiency of management in organic farming. [ABSTRACT FROM AUTHOR]

Path-tracking control algorithms in agriculture typically focus on how to improve the trajectory-tracking performance of autonomous agricultural machinery, and the agricultural productivity is optimized in a two-layer way. The upper operational layer optimizes an optimal tracking trajectory with the best agricultural productivity, and the lower control layer—such as Nonlinear Model Predictive Control (NMPC)—receives this optimized tracking trajectory first, and then steers the vehicle to track this trajectory with high accuracy. However, this two-layer structure cannot improve the agricultural productivity at the control layer online, which makes the agricultural operation sub-optimal. In this paper, we focus on agricultural machinery operational efficiency, to represent agricultural productivity; in order to realize optimizing control to further improve agricultural machinery operational efficiency, a new path-tracking control algorithm, named Efficiency-oriented Model Predictive Control (EfiMPC), is proposed. EfiMPC is intrinsically a nested structure, which can consider the global performance of the whole system defined in the operational layer—like the agricultural machinery operational efficiency considered in this paper—in the control layer online; thus, the agricultural machinery operational efficiency can be improved during the farming operation. An unreachable tracking point, denoted as the pseudo-point, has been proposed, to indicate the agricultural machinery operational efficiency objective in a receding horizon fashion; EfiMPC can utilize this pseudo-point to realize the optimizing control online. A simulation case study was used to test the superiority of the proposed EfiMPC algorithm, and the results show that, compared with the traditional NMPC algorithm, the agricultural machinery operational efficiency realized by EfiMPC was improved by 8.56%; thus, the effectiveness of the EfiMPC has been demonstrated. [ABSTRACT FROM AUTHOR]

A tomato automatic detection method based on an improved YOLOv8s model is proposed to address the low automation level in tomato harvesting in agriculture. The proposed method provides technical support for the automatic harvesting and classification of tomatoes in agricultural production activities. The proposed method has three key components. Firstly, the depthwise separable convolution (DSConv) technique replaces the ordinary convolution, which reduces the computational complexity by generating a large number of feature maps with a small amount of calculation. Secondly, the dual-path attention gate module (DPAG) is designed to improve the model's detection precision in complex environments by enhancing the network's ability to distinguish between tomatoes and the background. Thirdly, the feature enhancement module (FEM) is added to highlight the target details, prevent the loss of effective features, and improve detection precision. We built, trained, and tested the tomato dataset, which included 3098 images and 3 classes. The proposed algorithm's performance was evaluated by comparison with the SSD, faster R-CNN, YOLOv4, YOLOv5, and YOLOv7 algorithms. Precision, recall rate, and mAP (mean average precision) were used for evaluation. The test results show that the improved YOLOv8s network has a lower loss and 93.4% mAP on this dataset. This improvement is a 1.5% increase compared to before the improvement. The precision increased by 2%, and the recall rate increased by 0.8%. Moreover, the proposed algorithm significantly reduced the model size from 22 M to 16 M, while achieving a detection speed of 138.8 FPS, which satisfies the real-time detection requirement. The proposed method strikes a balance between model size and detection precision, enabling it to meet agriculture's tomato detection requirements. The research model in this paper will provide technical support for a tomato picking robot to ensure the fast and accurate operation of the picking robot. [ABSTRACT FROM AUTHOR]

Due to the massive surge in the world population, the agriculture cycle expansion is necessary to accommodate the anticipated demand. However, this expansion is challenged by weed invasion, a detrimental factor for agricultural production and quality. Therefore, an accurate, automatic, low-cost, environment-friendly, and real-time weed detection technique is required to control weeds on fields. Furthermore, automating the weed classification process according to growth stages is crucial for using appropriate weed controlling techniques, which represents a gap of research. The main focus of the undertaken research described in this paper is on providing a feasibility study for the agriculture community using recent deep-learning models to address this gap of research on classification of weed growth stages. For this paper we used a drone to collect a dataset of four weed (Consolida regalis) growth stages. In addition, we developed and trained one-stage and two-stage models YOLOv5, RetinaNet (with Resnet-101-FPN, Resnet-50-FPN backbones) and Faster R-CNN (with Resnet-101-DC5, Resnet-101-FPN, Resnet-50-FPN backbones), respectively. The results show that the generated Yolov5-small model succeeds in detecting weeds and classifying weed growth stages in real time with the highest recall of 0.794. RetinaNet with ResNet-101-FPN backbone shows accurate results in the testing phase (average precision of 87.457). Although Yolov5-large showed the highest precision in classifying almost all weed growth stages, Yolov5-large could not detect all objects in tested images. Overall, RetinaNet with ResNet-101-FPN backbones shows accurate and high precision, whereas Yolov5-small shows the shortest inference time in real time for detecting a weed and classifying its growth stages. [ABSTRACT FROM AUTHOR]

As essential environmental parameters in the greenhouse, appropriate light and CO2 will improve agricultural productivity and quality. Although many related studies have been carried out on the intelligent regulation of these environmental factors, the regulation of light and CO2 is usually controlled separately, and energy consumption is rarely considered. This paper proposed a coordinated control strategy for greenhouse light and CO2 based on the multi-objective optimization model. Firstly, the experiments on the net photosynthetic rate of blueberry under different temperatures, photon flux density, and CO2 concentration nesting were carried out to establish a blueberry net photosynthetic rate prediction model based on Support Vector Regression (SVR). Secondly, a model for calculating the energy cost of both light and CO2 was constructed. Thirdly, taking the maximum net photosynthetic rate and the minimum energy cost as the objective functions, the Non-dominated Sorting Genetic Algorithm (NSGA-II) was leveraged to obtain the Pareto optimal solutions of the target regulation values of light and CO2 concentration in different temperature ranges. Then, the optimal values were selected based on two different strategies. Finally, the multi-objective optimal control strategy proposed in this paper was compared with both the classical threshold control strategy and the Gaussian curvature maximization control strategy. The results indicated that the strategy which prioritized energy saving could reduce the energy cost by about 22.33% and 19.08%, respectively, under the premise that the net photosynthetic rate was consistent. Meanwhile, the strategy that prioritized production efficiency could increase the net photosynthetic rate by about 8.40% and 4.42%, respectively, with the same energy cost. In conclusion, the proposed multi-objective optimization control can improve the greenhouse climate control performance and reduce cost compared with other mentioned strategies. [ABSTRACT FROM AUTHOR]

The potential benefits of applying information and communication technology (ICT) in precision agriculture to enhance sustainable agricultural growth were discussed in this review article. The current technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), as well as their applications, must be integrated into the agricultural sector to ensure long-term agricultural productivity. These technologies have the potential to improve global food security by reducing crop output gaps, decreasing food waste, and minimizing resource use inefficiencies. The importance of collecting and analyzing big data from multiple sources, particularly in situ and on-the-go sensors, is also highlighted as an important component of achieving predictive decision making capabilities in precision agriculture and forecasting yields using advanced yield prediction models developed through machine learning. Finally, we cover the replacement of wired-based, complicated systems in infield monitoring with wireless sensor networks (WSN), particularly in the agricultural sector, and emphasize the necessity of knowing the radio frequency (RF) contributing aspects that influence signal intensity, interference, system model, bandwidth, and transmission range when creating a successful Agricultural Internet of Thing Ag-IoT system. The relevance of communication protocols and interfaces for presenting agricultural data acquired from sensors in various formats is also emphasized in the paper, as is the function of 4G, 3G, and 5G technologies in IoT-based smart farming. Overall, these research sheds light on the significance of wireless sensor networks and big data in the future of precision crop production [ABSTRACT FROM AUTHOR]

In order to clarify the temporal and spatial changes in the internal photothermal environment in an open-field agrivoltaic system (OAVS), this paper took the OAVS in eastern China as the research object and divided the internal area into the southern area, middle area and northern area, according to the spatial structure. Further, a photothermal environment test was conducted in the above three areas in the summer and winter. The results showed that the summer average daylight rate (Rm-avg) in the middle area was 66.6%, while the Rm-avg in the other two areas was about 20%, with no significant difference. In the winter, the light environment in the southern area was slightly better, and the Rm-avg in the above three areas was 26.4%, 24.7% and 19.7%, respectively. On the whole, the relationship between the thermal environmental factors and the solar radiation intensity was consistent. Further, a 3D model of an OAVS was established using Autodesk Ecotect Analysis 2011, and the internal light environment was simulated. Compared with the measured values, the relative error was less than 10%, which verified the reliability of the OAVS model. Then, the model was used to reveal the temporal and spatial changes in the light environment of the OAVS. The simulation results showed that the daylighting rate in the summer from the ground to the height of the fig canopy inside the system was 20.7% to 61.5%. In the winter, the daylighting rate from the ground to the height of the fig canopy inside the system was 17.7% to 36.4%. The effectiveness of the OAVS in reducing the level of solar radiation intensity depended on the time of day and the angle of the sun. At the spatial scale, due to the strong consumption of light by photovoltaic panels, there was a strong horizontal and vertical light environment gradient inside the system. In conclusion, the photothermal environment research of an OAVS based on Autodesk Ecotect Analysis 2011 can not only provide a basis for agricultural production and structural design such as span, height and the laying density of PV panels, but also expand its application to regions with different latitudes and longitudes and specific climates. [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]

Climate change is a very serious threat to the agricultural sector and potentially brings new problems to the sustainability of agricultural production systems. This paper aims to know how to improve crop yield by changing the nitrogen application ratio and sowing time under future climate change conditions based on the CERES-Rice model. The CERES-Rice model was calibrated and validated with a three-year field experiment (2018–2020), which was coupled with four N rates (50, 100, 150, and 200 kg/ha) and three different N ratios (B:T:S = 3:1:0; B:T:S = 5:3:2; B:T:S = 6:3:1). The results showed that the CERES-Rice model had better simulation effect on the phenophase (n-RMSE < 15%, d > 0.9 and R2 = 0.978) and yield (n-RMSE < 10%, d > 0.9 and R2 = 0.910) of double-harvest rice. The calibrated model was used to evaluate the growth period and yield of double-harvest rice under the RCP4.5 climate scenario and the results revealed that future yields of double-harvest rice in Nanchang are lower than those in experimental years, especially for early rice. Adjusting the nitrogen application ratio and sowing time can improve the yield of double-harvest rice to a certain extent, and the nitrogen application ratio of 5:3:2 has the best effect. In 2021–2035, the best yield of double-harvest rice can be obtained when the sowing date of early rice is about 15 days earlier and the sowing date of late rice is about 10 days earlier than the experiment year. From 2035 to 2050, the sowing date of early rice and late rice will be advanced by about 10 days, and the total yield of double-harvest rice will be higher. In 2050–2070, the total yield of double-harvest rice may reach the best when the sowing date is delayed by 10–15 days. Therefore, reasonably changing the sowing date of double-harvest rice and the nitrogen application regime of early rice can be used as a possible adaptive strategy to cope with the yield reduction in double-harvest rice in future climate scenarios. [ABSTRACT FROM AUTHOR]

To date, effectively controlling resistant weeds has been a great challenge in modern agricultural production. Developing new modes of action of herbicides would be an efficient, convenient, and timely means of controlling resistant weeds. In particular, new modes of herbicide action do not appear to have evolutionary resistance or cross-resistance with existing herbicides. However, a few successful herbicides with new modes of action (MoAs) have been marketed in the past 20 years. In this paper, we analyzed limiting factors for the slow development of novel herbicide MoAs. We then summarized the positive herbicide targets for the herbicides that have been discovered in recent years, such as Solanyl Diphosphate Synthase (SPS), Fatty Acid Thioesterase (FAT), Plastid Peptide Deformylase (PDEF), and Dihydroxy-Acid Dehydratase (DHAD). Some commercial herbicide varieties have been obtained based on novel herbicide targets, such as Homogentisate Solanesyltransferase (HST) and Dihydroorotate Dehydrogenase (DHODH). This provides a new reference and idea for herbicide molecular design in the future. In addition, some practical and efficient methods were mentioned for the rational design, discovery, and development of targeted herbicides development. In order to overcome the adverse conditions of compound druggability, prodrug strategies are also used in herbicide development, which can optimize the administration, permeability, absorption, and distribution of the original drug molecule or a candidate compound and may provide more possibilities for the development of new herbicides. The development of new herbicides is fascinating, the challenges and rewards are great, and the path to success is becoming more apparent. [ABSTRACT FROM AUTHOR]

Estimating foliar damage is essential in agricultural processes to provide proper crop management, such as monitoring the defoliation level to take preventive actions. Furthermore, it is helpful to avoid the reduction of plant energy production, nutrition decrement, and consequently, the reduction of the final production of the crop and economic losses. In this sense, numerous proposals support the defoliation estimate task, ranging from traditional methodologies to computational solutions. However, subjectivity characteristics, reproducibility limitations, and imprecise results persist. Then, these circumstances justify the search for new solutions, especially in defoliation assessments. The main goal of this paper consists of developing an automatic method to estimate the percentage of damaged leaf areas consumed by insects. As a novelty, our method provides high precision in calculating defoliation severity caused by insect predation on the leaves of various plant species and works effectively to estimate leaf loss in leaves with border damage. We describe our method and evaluate its performance concerning 12 different plant species. Our experimental results demonstrate high accuracy in the determination of leaf area loss with a correlation coefficient superior to 0.84 for apple, blueberry, cherry, corn, grape, bell pepper, potato, raspberry, soybean, and strawberry leaves, and mean absolute error (MAE) less than 4% in defoliation levels up to 54% in soybean, strawberry, potato, and corn leaves. In addition, the method maintains a mean error of less than 50%, even for severe defoliation levels up to 99%. [ABSTRACT FROM AUTHOR]

Wheat is one of the most important crops in the world and was considered the major grain crop grown in Egypt. Nowadays, Egypt is the largest wheat importer in the world and consumes an extensive amount of it. To decrease the gap between production and consumption and increase the yield, we need to control wheat diseases, especially stripe rust, due to its major damage to wheat. Further, we need to advise farmers as early as we can to control and treat them. The paper proposed an interactive intelligent system to monitor, predict and give the correct advice at the right time to farmers. This system is called MISSR (Mentoring Interactive System for Stripe Rust). The system is considered an important means to effectively prevent risks in agricultural production. It also plays an important role in guiding farmers and decision-makers to plan and implement suitable practices to increase yield and mitigate stripe-rust disease. On the other hand, it can acquire relevant and timely information in the areas where this information or data is unavailable. To build this model for the wheat crop in Egypt, we used wheat experts' knowledge and climate data API. MISSR is available as a mobile application to provide access for more farmers and increase its availability. [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]

The increasing global demand for food has forced farmers to produce higher crop yields in order to keep up with population growth, while maintaining sustainable production for the environment. As knowledge about natural cropland suitability is mandatory to achieve this, the aim of this paper is to provide a review of methods for suitability prediction according to abiotic environmental criteria. The conventional method for calculating cropland suitability in previous studies was a geographic information system (GIS)-based multicriteria analysis, dominantly in combination with the analytic hierarchy process (AHP). Although this is a flexible and widely accepted method, it has significant fundamental drawbacks, such as a lack of accuracy assessment, high subjectivity, computational inefficiency, and an unsystematic approach to selecting environmental criteria. To improve these drawbacks, methods for determining cropland suitability based on machine learning have been developed in recent studies. These novel methods contribute to an important paradigm shift when determining cropland suitability, being objective, automated, computationally efficient, and viable for widespread global use due to the availability of open data sources on a global scale. Nevertheless, both approaches produce invaluable complimentary benefits to cropland management planning, with novel methods being more appropriate for major crops and conventional methods more appropriate for less frequent crops. [ABSTRACT FROM AUTHOR]

Pathogen infection has greatly reduced crop production. As the symptoms of diseases usually appear when the plants are infected severely, rapid identification approaches are required to monitor plant diseases at early the infection stage and optimize control strategies. Hyperspectral imaging, as a fast and nondestructive sensing technology, has achieved remarkable results in plant disease identification. Various models have been developed for disease identification in different plants such as arable crops, vegetables, fruit trees, etc. In these models, important algorithms, such as the vegetation index and machine learning classification and methods have played significant roles in the detection and early warning of disease. In this paper, the principle of hyperspectral imaging technology and common spectral characteristics of plant disease symptoms are discussed. We reviewed the impact mechanism of pathogen infection on the photo response and spectrum features of the plants, the data processing tools and algorithms of the hyperspectral information of pathogen-infected plants, and the application prospect of hyperspectral imaging technology for the identification of plant diseases. [ABSTRACT FROM AUTHOR]

This paper reviews changes to lifecycle temporality in Southwest Asian plant and animal domestication, exploring their relationship to long-term processes associated with ancient and contemporary globalization. We survey changes under domestication to the timing of seed dispersal, germination, vegetative growth, flowering and maturation in wheat and barley and to birth, reproduction, lactation, wool production and death in sheep, goats and cattle. Changes in biological temporality among domesticates are ultimately related to globally increasing production intensity, geographic diffusion, and agricultural diversity associated with cultivar/breed evolution. Recently, however, increasing crop production intensity and geographic diffusion are accompanied by declining agricultural diversity worldwide. Similar processes are apparent in contemporary economic and cultural globalization, suggesting that long-term agricultural developments might be viewed as a subset of globalization. Moreover, the origin of certain features of contemporary globalization may be traced back to the beginnings of plant and animal domestication. Thus, while biologists since Darwin considered domestication as a model for the study of evolution, we suggest that domestication may also offer a model for the study of globalization. [ABSTRACT FROM AUTHOR]

Drought has a significant impact on agricultural production, but the evolution of drought in the North China Plain (NCP) and its impact on winter wheat yield remain unclear. In this paper, we used the Standardized Precipitation Evapotranspiration Index (SPEI) and combined with the Standardized Yield Residual Series (SYRS) and the Vegetation Condition Index (VCI) to study the spatiotemporal distribution of drought at different time scales and its impact on winter wheat yield. We found that: (1) The southern NCP was dominated by mild drought at the 1-month scale. In the northern NCP, mild drought was observed at the short-time scale and moderate drought at the medium- and long-time scales; (2) the frequency of mild drought was high and the frequency of moderate drought was a step lower. Moreover, drought risks were increasing in the north-central, eastern, and northeastern NCP, while the southern, west-central, southeastern, and northwestern NCP were becoming wetter (at the 6- and 9-month scales); (3) the maximum correlation coefficients were concentrated in the seedling to the greening stages. (4) Drought affected winter wheat with a lag of approximately 3 months, and the VCI was the most sensitive to the short-term SPEI. [ABSTRACT FROM AUTHOR]

Hoppers for unloading bulk materials are an indispensable feature of many technological machines and not only those employed in agricultural production. One of the problems in the operation of hoppers is the appearance of dynamic arches which make the outflow of grain uneven. Experimental studies have previously shown that the formation of dynamic arches creates an uneven outflow of grain along the vertical zones of outlet hoppers. This can lead to the processing mode violation of bulk material and loss of machine productivity. This paper theoretically shows that the use of arch-breaking devices with an element oscillating in the grain layer requires systems for automatic adjustment of oscillation frequency. This complicates the design and makes it more expensive. It is proposed to amend the type of material outflow as it moves along the hopper's height to solve this problem. The possibility of employing this approach has been tested on the use of a bulk material flow separator in the outlet hopper. The bulk material flow separator is made in a plate form and is installed rigidly between two opposite walls of the outlet hopper. Thus, the volume of the hopper is divided into two vertical equal parts. With the help of experimental studies, it was determined that the lower side of the bulk material flow separator should be at a distance of 0.3 of the total hopper height from the discharge opening, while the upper side of the separator should be at a distance of 0.25 of the hopper's height from the top edge of the hopper. The experimental verification of flow separator use confirmed its effectiveness. [ABSTRACT FROM AUTHOR]

Recently, large-scale agriculture has led to increasing crop production. To increase crop productivity in large-scale cropping systems, attempts have been made to make nano-fertilizers and deliver them to the crops by extension of nanotechnology. Hence, nano-fertilizers might be defined as nanoparticles that may directly assist in supplying essential nutrients for crop productivity. Seed germination is the first and the most susceptible stage in the plant's growing phases, so could be considered as an index to evaluate the effect of newly developed materials such as nanoparticles (NPs), providing useful information for researchers. In our experiments, germination tests have been carried out in Petri dishes containing wet filter paper and nano-primed seeds. We had biosynthesized seven nanoparticles in our previous studies including calcinated and non-calcinated zinc oxide, zinc, magnesium oxide, silver, copper, and iron nanoparticles. The effect of these biogenic nanoparticles and their counterpart metallic salts including zinc acetate, magnesium sulfate, silver nitrate, copper sulfate, and iron (III) chloride was studied on two popularly grown plants, wheat and flax, in laboratory conditions to obtain preliminary information for future field experiments. Germination percentage, shoot length, root length, seedlings length, root–shoot ratio, seedling vigor index (SVI), shoot length stress tolerance index (SLSI), and root length stress tolerance index (RLSI) were calculated on the second and seventh days of the experiment. According to the results, the response of the plants to metal containing nanoparticles and metal salts mainly depend on the type of the metal, plant species, concentration of the NP suspension or salt solution, condition of the exposure, and the stage of growth. [ABSTRACT FROM AUTHOR]

Sustainable agriculture faces challenges balancing the need for increasing crop production with environmental protection. Straw mulching is widely used to improve crop productivity, while reducing nitrogen application is an effective strategy to reduce greenhouse gas (GHG) emissions. However, the comprehensive evaluation of soil properties, GHG emissions, and soil quality under straw mulching conditions with different N application rates has not been extensively studied, especially in the Loess Plateau region of Central Gansu. Therefore, a two-year field experiment was conducted, involving two tillage practices (CT: conventional tillage, CTS: conventional tillage + straw mulching) and three fertilization levels (LN: low nitrogen fertilizer, 55 kg N ha−1; MN: medium nitrogen fertilizer, 110 kg N ha−1 HN: high nitrogen fertilizer, 220 kg N ha−1). Based on the greenhouse gas emission intensity (GHGI), the minimum data set (MDS) was constructed by principal component analysis, correlation analysis, and Norm value, and the weighted index method was applied to comprehensively evaluate the soil quality. Our results showed that compared with CT, CTS significantly increased crop yields and improved the content of soil nutrients, and it was more effective when applying moderate amounts of nitrogen fertilizer. Wheat field soil behaves as a nitrous oxide (N2O) emission source and a methane (CH4) absorption sink. CTS–MN and CTS–LN reduced N2O emission by 52.95–87.76%, increased total CH4 uptake by 16.69–43.60%, and slowed down global warming potential (GWP) by 35.70–48.17% compared with CTS–HN in 2021–2022. They also reduced GHGI. Furthermore, CTS enhanced soil quality at different nitrogen application levels compared with CT, with CTS–MN exhibiting the highest Soil Quality Index (SQI) over the two years. Consequently, considering both economic and environmental benefits, CTS–MN can be regarded as a key practice for the sustainable development of agroecosystems in the Loess Plateau region of Central Gansu. [ABSTRACT FROM AUTHOR]

In agriculture, there are various types of progress, and trends in agricultural production include the minimized use of chemicals and the production of food with favorable taste and increased nutritional value. In this study, during a three-year field experiment on potato cultivar Agria, ozone was applied in a fumigation process with an invented device, and hydrogen peroxide was applied as a foliar spray. The yield of ozone-treated plants was lower than of the control plants. Hydrogen peroxide treatment did not affect the yield of potatoes. Ozone fumigation had a positive effect on the chemical composition of tubers, while in the case of hydrogen peroxide, this effect occurred only in one year of research and only for selected parameters. The antioxidant response of plants, which is a defense response to artificially induced oxidative stress, contributes to the production of antioxidant-like metabolites by increasing the antiradical activity of crops by 3–23%, increasing the total content of polyphenolic compounds by 12–108%, and increasing the total antioxidant capacity by 23–42%. The obtained results confirmed that appropriate doses of ROS can be used in the field cultivation of potatoes to increase the nutritional value of tubers. [ABSTRACT FROM AUTHOR]

Struvite (MgNH4PO4·6H2O), a mineral with low water solubility that can be recovered from industrial wastewater, has the potential to be used as a slow-release phosphorus (P) fertilizer. However, the effect of struvite on the yield and P uptake efficiency of different crops remains unclear. In this study, the effects of struvite, diammonium phosphate (DAP), and a mixed fertilizer consisting of struvite + DAP (MIX) on biomass, P uptake, and soil P fractions of Chinese cabbage, cowpea, and maize were investigated in pot experiments. The results showed that compared to DAP, the mixed fertilizer reduced the biomass of Chinese cabbage by 47%, while there was no difference in the biomass of cowpea and maize under P fertilizer application. There was no difference in total P concentration in Chinese cabbage and cowpea plants between DAP and MIX, while total P concentration in maize under mixed fertilizer treatment decreased by 16.73% compared to DAP treatment. Compared to DAP, the MIX treatment reduced total P uptake in Chinese cabbage and maize by 45.82% and 33.41%, respectively, with no direct difference in cowpea. Soil Olsen-P and CaCl2-P concentrations were highest in DAP among the different treatments. The MIX treatment significantly increased the water-soluble P in Chinese cabbage and cowpea by 5.87% and 5.23%, respectively, while the water-soluble P in maize was lower in the mixed fertilizer treatment than in the DAP treatment. In addition, mixed fertilizer significantly increased soil pH and soil phosphatase activity compared to DAP. This result suggested that among the three treatments of struvite, DAP, and MIX, struvite had the weakest effect on crop growth. In addition, among the three crops, Chinese cabbage, cowpea, and maize, the compatibility between struvite and maize was the highest. These results provide valuable insights for the future application of struvite in agricultural production for achieving stable yields while mitigating environmental risks. [ABSTRACT FROM AUTHOR]

Solanum torvum, a wild relative of eggplant in the Solanaceae family, is widely used as a rootstock to prevent various soil-borne diseases. Despite this valued trait, S. torvum seeds exhibit strong seed dormancy, resulting in low-frequency germination and uneven emergence rates during agricultural production. In this study, exogenous gibberellin (GA) was applied to induce the germination of seeds, thereby enabling the application of transcriptome analysis to explore the effects of exogenous GA on germination. A total of 9723 genes were identified to be involved in GA-induced germination of S. torvum seeds. These GA-associated genes were enriched in amino sugar and nucleotide sugar metabolism, glycolysis, the citrate cycle, pyruvate metabolism, hormone biosynthesis, and signaling transduction. Further analysis of these genes reveals that exogenous GA impaired the endogenous biosynthesis of GA. Analysis of the 285 key genes influenced by GA, auxin, abscisic acid (ABA), and other hormones suggests mutual crosstalk in the signaling of various hormones, enabling the joint regulation of the germination of S. torvum seeds. Additionally, a total of 440 genes encoding transcription factors were identified to be responsible for transcriptional reprogramming during GA-induced seed germination, and putative candidate genes responsible for S. torvum seed dormancy and germination, including ABI5, DOG1, DRM1, LEC1, and PIF, were significantly down-regulated in germinated S. torvum seeds. These findings provide the first insights into transcriptome regulation during GA-induced germination of S. torvum seeds. [ABSTRACT FROM AUTHOR]

The North China Plain (NCP) serves as a critical grain-producing region in China, playing a pivotal role in ensuring the nation's food security. A comprehensive analysis of the carbon footprint (CF) related to the cultivation of major grain crops within this region and the proposal of strategies to reduce emissions through low-carbon production methods are crucial for advancing sustainable agricultural practices in China. This study employed the lifecycle assessment (LCA) method to estimate the CF of wheat, maize, and rice crops over a period from 2013 to 2022, based on statistical data collected from five key provinces and cities in the NCP: Beijing, Tianjin, Hebei, Shandong, and Henan. Additionally, the Logarithmic Mean Divisia Index (LMDI) model was utilized to analyze the influencing factors. The results indicated that the carbon footprints per unit area (CFA) of maize, wheat, and rice increased between 2013 and 2022. Rice had the highest carbon footprint per unit yield (CFY), averaging 1.1 kg CO2-eq kg−1, with significant fluctuations over time. In contrast, the CFY of wheat and maize remained relatively stable from 2013 to 2022. Fertilizers contributed the most to CF composition, accounting for 48.8%, 48.0%, and 25.9% of the total carbon inputs for wheat, maize, and rice, respectively. The electricity used for irrigation in rice production was 31.8%, which was much higher than that of wheat (6.8%) and maize (7.1%). The LMDI model showed that the labor effect was a common suppressing factor for the carbon emissions of maize, wheat, and rice in the NCP, while the agricultural structure effect and the economic development effect were common driving factors. By improving the efficiency of fertilizer and pesticide utilization, cultivating new varieties, increasing the mechanical operation efficiency, the irrigation efficiency, and policy support, the CF of grain crop production in the NCP can be effectively reduced. These efforts will contribute to the sustainable development of agricultural practices in the NCP and support China's efforts to achieve its "double carbon" target. [ABSTRACT FROM AUTHOR]

In agricultural production, it is crucial to increase the availability of phosphorus (P) in cultivated soil to solve the P limitation. Arbuscular mycorrhizal fungi (AMF) have been proven to promote crop nutrient absorption effectively, while biochar can lead to improvements in soil properties. However, the possible synergistic effect of AMF and biochar on P uptake by crops as well as its underlying mechanisms are unclear. In this study, we conducted a pot experiment to explore the effects of biochar and AMF (Glomus etunicatum) on the community of rhizospheric phosphate-solubilizing microorganisms (PSMs) of maize (Zea mays L. Xianyu-335) using metagenomic methods. The experiment used 0 mg P2O5 g·kg−1 soil (P0) and 30 mg P2O5 g·kg−1 soil (P30) application rates. Each P application rate included 0 (NC), 20 g·kg−1 biochar (BC) addition, inoculation AMF, and without AMF treatments (NM) for a total of eight treatments. During the experiment, both the P uptake and the biomass of maize were measured. The study found that the combination of AMF and biochar significantly increased the mycorrhizal colonization rate of maize roots, regardless of P application level. It was observed that the P uptake by maize was significantly increased when exposed to a combination of AMF and biochar. The increase in P uptake in P0 treatments was 67% higher than the sum of the effects of biochar and AMF inoculation alone. The increase was only 35% higher in P30 treatments, demonstrating a substantially higher interactive effect under P0 than under P30 conditions. The AM-BC treatments significantly increased the abundance of Streptomyces, Bacillus, and Pseudomonas, genera that are known to contain PSMs. In addition, the abundance of genes related to P-cycling (gcd, phoD, and ugpQ) in PSMs increased significantly by 1.5–1.8 times in AM-BC treatments compared with NM-BC and AM-NC treatments under P0 conditions. This increase was significantly and positively correlated with the P uptake. Overall, the results suggest that biochar can help AMF colonize the roots, increasing the functional roles of PSMs in the rhizosphere, which in turn promotes P uptake and biomass in maize. This study provides a new way to improve P-use efficiency and reduce the need for P-fertilizer application in agricultural production. [ABSTRACT FROM AUTHOR]

Salinity stress can significantly impact productivity in agricultural area with limited water re-sources. Our study focused on how plants under salt stress respond to phosphorus availability in terms of growth and biochemical reactions in cotton genotypes. Two cotton genotypes with different P efficiencies (SK39 and JM21) were used in a hydroponic experiment with 300 mM NaCl and three P treatments (10, 20, and 30 mM). Salinity stress decreases root growth, shoot growth, biomass production, and chlorophyll content, according to the experimental findings. In treated plants, it also increased the levels of oxidative stress. However, this effect was alleviated by phosphorus therapy, which controlled the production of proline, total soluble sugars, and hydrogen peroxide (H2O2). Interestingly, salt-sensitive JM21 responded to phosphorus supplementation more favorably than salt-tolerant SK39. Our research emphasizes the critical role that phosphorus especially P20 plays increasing the salinity stress sensitivity of cotton plants and offers insightful in-formation on the mechanisms underlying the role of phosphorus in reducing salinity stress effects. This study also revealed interspecific variability in cotton genotypes and characteristics, primarily represented by attributes related to cotton growth and morphological indicators such as dry matter biomass. [ABSTRACT FROM AUTHOR]

Accurately estimating rice yield is essential for ensuring global food security, enhancing agricultural productivity, and promoting agricultural economic growth. This study constructed a dataset of rice panicles at different growth stages and combined it with an attention mechanism and the YOLOv8 network to propose the YOLOv8s+LSKA+HorNet rice panicle detection and counting model, based on a drone remote sensing platform. Using the panicle count data collected by this model, along with the thousand-grain weight, number of grains per panicle, and actual yield data from a rice nitrogen gradient experimental field, various machine learning models were trained to ultimately propose a field-level rapid rice yield estimation model, RFYOLO. The experimental results show that the rice panicle detection and counting model can achieve an average precision (AP) of 98.0% and a detection speed of 20.3 milliseconds. The final yield estimation model achieved a prediction R2 value of 0.84. The detection and counting model significantly reduced missed and duplicate detections of rice panicles. Additionally, this study not only enhanced the model's generalization ability and practicality through algorithmic innovation but also verified the impact of yield data range on the stability of the estimation model through the rice nitrogen gradient experiment. This is significant for early rice yield estimation and helping agricultural producers make more informed planting decisions. [ABSTRACT FROM AUTHOR]

In this work, an exhaustive revision is given of the literature associated with advanced information and communication technologies in agriculture within a window of 25 years using bibliometric tools enabled to detect of the main actors, structure, and dynamics in the scientific papers. The main findings are a trend of growth in the dynamics of publications associated with advanced information and communication technologies in agriculture productivity. Another assertion is that countries, like the USA, China, and Brazil, stand out in many publications due to allocating more resources to research, development, and agricultural productivity. In addition, the collaboration networks between countries are frequently in regions with closer cultural and idiomatic ties; additionally, terms' occurrence are obtained with Louvain algorithm predominating four clusters: precision agriculture, smart agriculture, remote sensing, and climate smart agriculture. Finally, the thematic-map characterization with Callon's density and centrality is applied in three periods. The first period of thematic analysis shows a transition in detecting the variability of a nutrient, such as nitrogen, through the help of immature georeferenced techniques, towards greater remote sensing involvement. In the transition from the second to the third stage, the maturation of technologies, such as unmanned aerial vehicles, wireless sensor networks, and the machine learning area, is observed. [ABSTRACT FROM AUTHOR]

Reduced reliance on synthetic chemical fertilizers necessarily requires using renewable biomaterial-derived soil organic amendments (SOAs) in agriculture for sustained retention of nutrients through improvement in the soil organic matter (SOM). SOM replenishment through SOAs derived from wasted materials could help in its valorization by furthering the sustainability prospects of agronomic crop production systems. In the current study, compost (CP) and biochar (BC) were derived as SOAs from combustible and compostable fractions of municipal solid waste (MSW) for their potential valorization by adding SOAs as potential sustainable sources of nutrients as a replacement of chemical fertilizers (CF) for sunflower crops cultivated in potted soils. The experimental design included quadruplicated soil application of MSW-derived BC and CP in discrete and combined forms, each in three doses (% w:w), viz., low (L), medium (M), and high (H), i.e., BC-L, BC-M, BC-H; CP-L, CP-M, CP-H; and BC + CP-L, BC + CP-M, BC-CP-H. The results showed that, compared to the control (soil only), the sunflower growth and harvestable yield were significantly greater in BC + CF with a medium dose and were comparable to the growth and yield obtained in soils with CF. Sunflower growth in the discrete SOAs remained less than in the combined SOAs (BC + CP) and was attributed to the comprehensive soil health improvement rendered by the applied SOAs. The soil health improvement factors included SOM, CEC, and concentrations of total and available NPK. The dose-effect comparison of the SOAs showed highly variable trends, i.e., the sunflower growth did not correspond with the increase in dose of the SOAs. It is concluded that the combined application of BC + CP derived from MSW components at a medium dose could act as a potential alternative to CF. The developed approach resulted in MSW valorization, which improved soil health and yielded a better sunflower crop. [ABSTRACT FROM AUTHOR]

The global human population is projected to reach 9.7 billion by 2050, increasing the demand for food and fiber, but also raising concerns about the environmental impact of agricultural production scaled to meet their needs. Silvopastures—integrated tree–forage–livestock systems—have emerged as a viable practice to meet the required productivity and environmental stewardship outcomes. This review consolidates the extensive research on silvopasture practices in the United States and highlights the benefits of these systems. A comprehensive literature search across databases such as ScienceDirect and Google Scholar revealed 152 publications on silvopastures in the United States since 2000, indicating growing interest. These studies have primarily focused on the impacts of silvopastures on livestock welfare and productivity, forage production and composition, soil health and nutrient dynamics, and socio-economic factors. Geographical distribution analysis indicated that the research is more focused in the Southeastern United States, with Florida, Virginia, Alabama, Missouri, and Arkansas being the top five contributing states. The review also offers insights into the tree and forage species used across these states and discusses the challenges to silvopasture adoption among producers and land managers while exploring future prospects. This review may be used as a resource for understanding the multifaceted dimensions of silvopasture adoption, providing insights for researchers, policymakers, and practitioners alike. [ABSTRACT FROM AUTHOR]

Flood submergence has devastating effects on agricultural production in China, with rice being particularly vulnerable to its impacts. Previous studies on rice submergence stress have primarily focused on immediate yield reduction and short-term growth. In this study, a submergence stress experiment was carried out by using the method of potted rice flooding. The growth recovery characteristics of rice under different submergence stress were analyzed through the continuous observation of rice growth after the disaster. The results showed that submergence stress had a persistent effect on rice growth, which persisted until the recovery period after the disaster. The recovery ability of rice plants decreased with the aggravation of stress, leading to increased damage to the plant. The average yield decreased by 17.07% and 15.56% due to submergence stress during the jointing and booting stage, respectively. The current study pointed out that the growth traits of and, furthermore, the mechanism of physiological changes in rice during the recovery period need to be explored in order to understand the effects of flooding stress on rice. [ABSTRACT FROM AUTHOR]

The low solubility and enhanced fixation of zinc (Zn) in semi-arid and dry climates limits Zn uptake in plants. Zn deficiency in soil impairs crop production and human health, necessitating agricultural biofortification. A pot experiment was conducted to evaluate the effect of Zn and various types of manure on the Zn biofortification of lentils. The treatments, consisting of a control (Con), normal manure (NM), composted manure (CM), and acidified manure (AM), were applied under saline soil (EC 8.00 dS m−1) and non-saline soil (EC 1.48 dS m−1) conditions along with two levels of Zn, including Zn at 0 kg ha−1 (native soil Zn = 2.2 mg kg−1) and Zn at 25 kg ha−1 (62.2 mg Zn kg−1 soil was achieved). The AM was prepared by adding sulfur and sulfur-oxidizing bacteria to the composted manure. All the manures were applied at 1% (w/w), and ZnSO4 (33% Zn) was used as a Zn source. Lentil variety Masoor 2021 was cultivated as a test crop in five replications of each treatment arranged in a completely randomized design. Applying AM with Zn considerably increased the lentils' growth, yield, and Zn content under saline and non-saline conditions. Under non-saline soils, the treatment of AM + Zn significantly promoted the Zn content in the root (132.5%), shoot (91.7%), grain (49.1%), root length (79.7%), plant height (33.7%), and SPAD value (29.9%). Under saline conditions, application of AM + Zn promoted uptake of Zn in the root (218.5%), Zn content in the shoot (175.7%), Zn accumulation in the grain (107.7%), root length (109.7%), plant height (37.8%), and SPAD value (52.8%) compared to the control. According to the results, lentils should be grown with AM and Zn to increase their growth, yield, and Zn content significantly. This is a cost-effective and sustainable way to combat Zn deficiency in lentils. [ABSTRACT FROM AUTHOR]

Drought poses a significant threat to agricultural productivity, particularly affecting economic crops like tomatoes. To address this challenge, various alternatives have been explored, including the use of elicitors or biostimulants such as salicylic acid (SA). This study aims to assess the stimulatory action of SA in alleviating drought stress in tomato plants under greenhouse conditions. The experiment was designed with two main factors: water availability (controlled versus drought) and the foliar application of SA at four different concentrations ranging from 100 to 250 mg L−1. The application of SA, particularly at a concentration of 250 mg L−1, showed promising results in mitigating the adverse effects of drought stress followed by 200 mg L−1. This was evidenced by the increased activity of antioxidant enzymes such as superoxide dismutase (SOD), ascorbate peroxidase (APX), and catalase (CAT). Gene expression analysis revealed optimal APX expression with SA application at concentrations of 200 mg L−1 or 250 mg L−1. Additionally, the application of SA at 250 mg L−1 led to a high accumulation of bioactive compounds without compromising yield. Furthermore, SA application positively influenced both shoot and root weights, with the highest values observed at a concentration of 250 mg L−1. While SA is known to enhance plant tolerance to abiotic stress, further research is needed to fully elucidate its biochemical, physiological, and molecular mechanisms in supporting plant tolerance to drought stress. Utilizing salicylic acid can help growers mitigate environmental stresses, enhancing tomato crop yield and quality. Integrating SA treatments into agriculture offers a sustainable alternative elicitor for ensuring food security under challenging climate conditions. [ABSTRACT FROM AUTHOR]

Rapid and accurate grape leaf disease diagnosis is of great significance to its yield and quality of grape. In this paper, aiming at the identification of grape leaf diseases, a fast and accurate detection method based on fused deep features, extracted from a convolutional neural network (CNN), plus a support vector machine (SVM) is proposed. In the research, based on an open dataset, three types of state-of-the-art CNN networks, three kinds of deep feature fusion methods, seven species of deep feature layers, and a multi-class SVM classifier were studied. Firstly, images were resized to meet the input requirements of the CNN network; then, the deep features of the input images were extracted via the specific deep feature layer of the CNN network. Two kinds of deep features from different networks were then fused using different fusion methods to increase the effective classification feature information. Finally, a multi-class SVM classifier was trained with the fused deep features. The experimental results on the open dataset show that the fused deep features with any kind of fusion method can obtain a better classification performance than using a single type of deep feature. The direct concatenation of the Fc1000 deep feature extracted from ResNet50 and ResNet101 can achieve the best classification result compared with the other two fusion methods, and its F1 score is 99.81%. Furthermore, the SVM classifier trained using the proposed method can achieve a classification performance comparable to that of using the CNN model directly, but the training time is less than 1 s, which has an advantage over spending tens of minutes training a CNN model. The experimental results indicate that the method proposed in this paper can achieve fast and accurate identification of grape leaf diseases and meet the needs of actual agricultural production. [ABSTRACT FROM AUTHOR]

Considering an expanding research and development pipeline of and growing practical experiences with genetically modified (GM) crops, governments in Sub-Sahara Africa have in recent years authorized various GM crop events for general, unconfined environmental release, and in a few cases, subsequent commercial variety registration and cultivation. These decisions are consistent with more business-oriented agricultural development policies aimed at turning agriculture into a leading driver of economic growth and wealth creation. Such policy reforms are also motivated by continent-wide initiatives described in this review paper, which emphasize seed sector reform, agricultural productivity growth, intra-regional trade, and agri-business development. While such policy developments spur a more proactive approach to introducing agricultural biotechnology, it becomes apparent that authorizing GM crops for general environmental release and commercial seed registration requires the harmonization of government policies in various sectors relevant to seed systems, most notably those affecting plant breeding research, testing, and variety release. Critical areas for such policy reforms and harmonization were analyzed in this paper, with associated recommendations. [ABSTRACT FROM AUTHOR]

Agricultural robotics has been a popular subject in recent years from an academic as well as a commercial point of view. This is because agricultural robotics addresses critical issues such as seasonal shortages in manual labor, e.g., during harvest, as well as the increasing concern regarding environmentally friendly practices. On one hand, several individual agricultural robots have already been developed for specific tasks (e.g., for monitoring, spraying, harvesting, transport, etc.) with varying degrees of effectiveness. On the other hand, the use of cooperative teams of agricultural robots in farming tasks is not as widespread; yet, it is an emerging trend. This paper presents a comprehensive overview of the work carried out so far in the area of cooperative agricultural robotics and identifies the state-of-the-art. This paper also outlines challenges to be addressed in fully automating agricultural production; the latter is promising for sustaining an increasingly vast human population, especially in cases of pandemics such as the recent COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Sustainable development has become a central and key goal for humanity (1 UN: Millenium Development Goals, 2 SDGs). There is no scientific or political consensus, however, about the root cause of unsustainability, so an effective cure is impossible. Nevertheless, unlimited inequality among nations and individuals, extreme poverty, and hunger are always among the most significant problems. It is also often claimed that the world's current food production would be sufficient to feed all 7.7 billion people on Earth (see Appendix A 1) with a better (more even, 'just') distribution. Others use food hunger and poverty data to support claims for increased food production and agricultural output. In this paper we examine the first claim with real data, but from a theoretical perspective: is it true, theoretically, that if all of the produced and wasted food were made available to feed the extremely poor and hungry, we could reach zero hunger-related deaths? We will use food waste, hunger-related (+thirst) death, obesity, and calorie consumption data from the "happy" and "unhappy" parts of the Earth. The cause of food waste is dissonant in different economic systems, so its macro- and microeconomic approach is also completely different in different countries. The relationship between obesity, hunger, and food waste has not yet been explored in a structured framework; data are available but scattered. In this paper we propose a unified framework of comparable data as a first step in mapping the biggest missing, or at least wobbling circle of modern bio-economy: global food supply. Thinking globally is a first step toward progress development in food justice and a solution to UN sustainable development goals (SDG 2, 1, 3, and 10). [ABSTRACT FROM AUTHOR]

The scientific aim of this article is to elucidate the effects of various tillage practices on soil properties and crop yields; additionally, it seeks to highlight the significant potential of specific farming systems in enhancing soil organic carbon, thereby positively influencing CO2 emissions from soil. In the experimental station of Vytautas Magnus University, Kaunas District, Lithuania (54°52′50″ N and 23°49′41″ E), a long-term field experiment has been established since 1999, and studies have been conducted since 2003. The soil of the experimental site is classified as Epieutric Endocalcaric Planosol (Endoclayic, Episiltic, Aric, Drainic, Endoraptic, Uterquic), according to the World Reference Base (WRB, 2022). Two primary factors were assessed. Factor A incorporated practices of straw removal versus straw chopping and spreading, while Factor B evaluated a spectrum of tillage techniques: conventional deep plowing and two no-tillage practices, one of which involved cover crops. The findings from this long-term study highlight a significant increase in SOC stocks across all treatments over the 20-year period. Notably, the no-tillage practices, coupled with the spreading of chopped straw, demonstrated the most substantial growth in SOC levels, particularly in the top 0–10 cm soil layer. This trend underscores the effectiveness of minimizing soil disturbance and incorporating organic matter in boosting SOC stocks. The different tillage systems influence CO2 emissions from soil. Initially, direct sowing into uncultivated land, both with and without cover crops, led to a notable reduction in CO2 emissions compared to conventional plowing. However, this effect was found to vary over the growth cycle of the plant, highlighting the dynamic interaction between tillage practices, soil properties, and environmental conditions. Collaborative research efforts that involve farmers, scientists, policymakers, and other stakeholders are crucial for the development of holistic, practical, scalable solutions that enhance the sustainability and productivity of agricultural systems. This study contributes to the growing body of knowledge on sustainable agriculture, providing insights for farmers, agronomists, and policymakers in their quest to promote environmentally sound and productive agricultural systems. [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]

Increase in the concentration of atmospheric greenhouse gases significantly contributes to global warming, representing a substantial challenge for crop production. The study was conducted to determine the growth competition between rice (Oryza sativa) and barnyardgrass (Echinochloa oryzicola) under (i) different cropping patterns and (ii) elevated air temperatures in phytotrons under field condition, at two plant densities (4 and 16 plants per pot). Rice and barnyardgrass were planted with varying cropping patterns (rice: barnyardgrass mixture proportions); 100:0, 75:25, 50:50, 25:75 and 0:100. Air temperatures were maintained in phytotrons as follows: Ambient–A (Control), A +0.8 °C, A +1.9 °C and A +3.4 °C. Plant attributes such as plant height, number of effective tillers, shoot dry weight and grain yield of rice were recorded in this study in the rice/barnyardgrass mixture proportions in the order of 100:0 > 75:25 > 50:50 > 25:75. The highest rice grain yield (37.7 g/pot) was recorded in the monoculture (100:0 rice/barnyardgrass) under ambient temperature, whereas the lowest rice grain yield (0.3 g/pot) was recorded at the 25:75 rice/barnyardgrass mixture proportion under ambient +3.4 °C. The increase in temperature had a significant impact on growth, number of tillers and shoot dry weight of both rice and barnyardgrass plants and followed the order of ambient +3.4 °C > ambient +1.9 °C > ambient +0.8 °C > ambient. However, higher temperature negatively affected the yield of rice and resulted in a substantial decrease in the grain yield. Barnyardgrass showed the highest plant characteristics when grown alongside rice compared to the growth in monoculture. This indicates that barnyardgrass was highly competitive when grown under interspecific competition compared to an intraspecific competition. In contrast, rice grew better in monoculture than in mixture with barnyardgrass. [ABSTRACT FROM AUTHOR]

Nitrogen (N) and sulfur (S) are essential nutrient elements, and their deficiency affects crop growth, productivity, and nutrient uptake due to their multifaceted role in plant metabolism, which has been well documented. Therefore, agricultural management strategies that can overcome these deficiencies are the need of the hour. In this context, a study was undertaken with the objective to assess the impacts of N and S applications, either basally or through split application (12.5, 25 and 50 kg ha−1), on the nutrient uptake, productivity, use efficiency, and micronutrient content status in soybean seeds, and also the change in soil nutrient zinc (Zn) and iron (Fe) content at different critical stages of soybean crop growth. The field trial was conducted utilizing a randomized complete-block design, and comprised fourteen treatments with varying N and S quantities. N and S were applied through basal and split applications in different combinations. The salient findings indicated that the highest seed, straw yield, N, and S uptake were obtained with the application of N25+25, S25+25, and did not significantly vary with N25+25, S12.5+12.5, N50, and N25+S50. The highest N use efficiency was recorded with the application of N25+S50, and S use efficiency with the application of N25+25, S25+25. The split application of N and S as N25+25, S25+25 significantly increased soil Zn and Fe content at R2 and R5 stages of soybean crop growth, as well as seed Zn and Fe uptake. It can be concluded that the basal and split application of N and S at the rate of 25 kg ha−1 can improve soybean productivity through increased mobilization and assimilation by plants. The findings indicated that applying N and S separately, with 25 kg ha−1 each basally and at the R2 stage resulted in the highest nutrient uptake, and seed and straw yields. The nutrient use efficiencies, along with Zn and Fe uptake by seeds, exhibited noticeable improvements with this split application approach compared to the control. Furthermore, the soil Zn and Fe contents also experienced enhancements due to the split application of both Nand S fertilizers. These results underscore the potential benefits of temporally adopting optimized fertilizer application strategies to maximize agricultural productivity while ensuring efficient nutrient utilization and soil health maintenance. Further research and field trials could provide deeper insights into the long-term impacts and scalability of this approach across different crop varieties and environmental conditions. [ABSTRACT FROM AUTHOR]