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Copyright of Studies in Conservation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, a paper‐based electrochemical aptamer sensor was developed for the detection of aflatoxin B1 (AFB1) using a combination of MXene–Ti3C2Tx and nucleic acid aptamers. The prepared single‐layer or few‐layer MXene suspension is suction‐filtered onto MXene paper, which is cut to prepare MXene electrodes. To accomplish AFB1 specific detection, an amino‐labeled AFB1 aptamer is mounted on the surface of the carboxy‐functionalized MXene electrode. When AFB1 is present, it particularly binds to the aptamer to form a 3D structure, reducing the efficiency of electron transmission on the sensor surface. The difference in impedance signal change at the electrode/electrolyte interface is used to quantify AFB1. The results indicated that the detection range is 0.05–100 ng/mL, the detection limit is 0.04 ng/mL, and the recovery rate of AFB1 in corn samples is 97.8%–111.52% with the optimal detection conditions. The MXene paper‐based label‐free aptasensor is versatile and can detect different targets by simply swapping out the aptamers of different targets. The sensor also has a wide range of applications in food analysis and environmental testing. Practical applications: A paper‐based electrochemical aptamer sensor was developed to detect aflatoxin B1 using a combination of MXene–Ti3C2Tx and nucleic acid aptamers.The design is based on the preparation of MXene electrodes by pumping and filtering monolayer or multilayer MXene suspensions onto MXene paper and cutting.The MXene paper‐based label‐free aptamer sensor was designed to be versatile, allowing the detection of different targets by simply replacing the aptamer with one from a different targets. [ABSTRACT FROM AUTHOR]

This article explores the applications of fast breeding technologies in crop improvement and functional genomics studies. These technologies, including molecular selection, gene mapping, haploid induction, and genome editing, have greatly expedited the process of modifying crop traits and mapping genes. Examples of these technologies being used include the use of CRISPR/Cas9 to modify crop traits and the development of new haploid inducer lines in various plants. The article also highlights specific research papers on genetic improvement in melon, potato, wheat, rice, and tomato crops using these technologies. Additionally, the document discusses various research papers in the field of plant science, covering topics such as stress tolerance in tomatoes, doubled haploid plants in maize and wheat, and gene exploration in different crops. These papers provide valuable insights into breeding materials, marker development, and the cloning of new genes, contributing to the development of new germplasms and varieties in crops. [Extracted from the article]

The occurrence of maize diseases is frequent but challenging to manage. Traditional identification methods have low accuracy and complex model structures with numerous parameters, making them difficult to implement on mobile devices. To address these challenges, this paper proposes a corn leaf disease recognition model SNMPF based on convolutional neural network ShuffleNetV2. In the down-sampling module of the ShuffleNet model, the max pooling layer replaces the deep convolutional layer to perform down-sampling. This improvement helps to extract key features from images, reduce the overfitting of the model, and improve the model's generalization ability. In addition, to enhance the model's ability to express features in complex backgrounds, the Sim AM attention mechanism was introduced. This mechanism enables the model to adaptively adjust focus and pay more attention to local discriminative features. The results on a maize disease image dataset demonstrate that the SNMPF model achieves a recognition accuracy of 98.40%, representing a 4.1 percentage point improvement over the original model, while its size is only 1.56 MB. Compared with existing convolutional neural network models such as EfficientNet, MobileViT, EfficientNetV2, RegNet, and DenseNet, this model offers higher accuracy and a more compact size. As a result, it can automatically detect and classify maize leaf diseases under natural field conditions, boasting high-precision recognition capabilities. Its accurate identification results provide scientific guidance for preventing corn leaf disease and promote the development of precision agriculture. [ABSTRACT FROM AUTHOR]

Introduction: China is the largest producer, consumer, and trader of grain. Changes in China's agricultural policies will affect global food trade and thus impact food security. In this paper, we use China's maize subsidy system reform (MSSR) as a quasi-natural experiment to investigate the impact of marketoriented reforms in price support policy on the productivity of grain. Methods: We use official Chinese government panel data on farm households and a PSM-DID model to overcome the endogeneity problem of policy change. Results and discussion: The empirical results show that MSSR can increase maize productivity. The MSSR is divided into two phases: eliminating the maize purchase price and implementing maize producer subsidies. The policy effect of eliminating the purchase price exceeds the implementation of producer subsidies. Further analysis reveals that for farmers with a larger scale of cultivation, higher level of specialization, and higher degree of part-time employment, the MSSR enhances their productivity more significantly. In the high quartile, the MSSR reduces farmers' productivity. In the low quartile, the MSSR raises farmers' productivity, suggesting that the MSSR reduces the productivity differences among farmers. The results of our study suggest that market-based reform of price subsidies is an effective institutional arrangement to mitigate resource mismatch and increase food productivity, and point to the need to continue to improve the MSSR, explore diversified maize producer subsidy policies, and take into account the impact of other subsidies on farmers' maize production behavior. [ABSTRACT FROM AUTHOR]

Bibliometric analysis explores large volume of scientific data, revealing trends and insights in a specific research field. Consistently, a bibliometric analysis of 30 years (1990–2020) was performed within the US corn (Zea mays L.) production using the Scopus database and VOSviewer. Search query was performed within the article title, abstract, and keywords indicative of management practices in corn. Exclusion criterion based on subject area and journals generated a total of 7468 publications. The data analysis revealed contributions from 7327 authors and 47 organizations documented in 69 journals. The top five organizations leading the investigation were United States Department of Agriculture – Agricultural Research Service, Iowa State University, University of Nebraska, University of Illinois, and Purdue University. The most prolific authors were Dr. Rattan Lal (Ohio State University, Columbus, OH), Dr. Douglas L. Karlen (USDA‐ARS, Ames, IA), Dr. Kenneth G. Cassman (University of Nebraska, Lincoln, NE), Dr. Lajpat Rai Ahuja (USDA‐ARS, Ft. Collins, CO), and Dr. John Walsh Doran (USDA‐ARS, Lincoln, NE). Journals with most publications were Agronomy Journal; Soil Science Society of America Journal; Soil and Tillage Research; Crop Science; and Agriculture, Ecosystems & Environment. Furthermore, author keywords differed from queried keywords, and no‐till, nitrogen, cover crop, soybean, irrigation, phosphorus, conservation tillage, yield, and water quality were most prominent. Moreover, there was an evident shift in keywords and an observed trend between 1998 and 2020. Overall, these findings allow researchers to explore network maps via the hyperlinks present in papers, identifying research gaps and advancing original studies to bridge gaps in the literature. Core Ideas: There has been a consistent increase in corn research in last 30 years.United States Department of Agriculture – Agricultural Research Service (USDA‐ARS) was the top funding sponsor and leading investigator.Queried keywords differed from author keywords, and a trend was observed in their use.The top journal with most publications on corn production was Agronomy Journal. [ABSTRACT FROM AUTHOR]

Copyright of Plants, People, Planet is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In cornfields, factors such as the similarity between corn seedlings and weeds and the blurring of plant edge details pose challenges to corn and weed segmentation. In addition, remote areas such as farmland are usually constrained by limited computational resources and limited collected data. Therefore, it becomes necessary to lighten the model to better adapt to complex cornfield scene, and make full use of the limited data information. In this paper, we propose an improved image segmentation algorithm based on unet. Firstly, the inverted residual structure is introduced into the contraction path to reduce the number of parameters in the training process and improve the feature extraction ability; secondly, the pyramid pooling module is introduced to enhance the network's ability of acquiring contextual information as well as the ability of dealing with the small target loss problem; and lastly, Finally, to further enhance the segmentation capability of the model, the squeeze and excitation mechanism is introduced in the expansion path. We used images of corn seedlings collected in the field and publicly available corn weed datasets to evaluate the improved model. The improved model has a total parameter of 3.79M and miou can achieve 87.9%. The fps on a single 3050 ti video card is about 58.9. The experimental results show that the network proposed in this paper can quickly segment corn weeds in a cornfield scenario with good segmentation accuracy. [ABSTRACT FROM AUTHOR]

The intelligent control of precision planting can detect and regulate the operation quality of the planter in real time, which plays an important role in improving the operation quality of the planter and the yield of the corn. In this paper, the control system of a corn precision planter is designed to realize the operating quality monitoring and electric driving of the seed-metering device. The planting quality is calculated by the time interval between the neighboring falling seeds, instead of the plant spacing, to improve the operational efficiency of the system. At the same time, the forward speed of the planter is obtained by radar, which is used to accurately match the speed of the seed-metering device with the forward speed of the planter. The velocity error of the radar is analyzed, and the relevant relationship of the radar output frequency and forward speed is established. Comparative test results of this system and the JPS-12 test bench show that the detection performance of the system is reliable, and the maximum detection error of the quality parameters is less than 2.88%. Field experiments were carried out to verify the operational performance of the control system. Two speed sensors, radar and GPS, were chosen to study the effect of speed measuring on the performance of the control system. We found that speed measuring has a significant effect on planting performance. The qualified parameters of radar were significantly higher than those of GPS, at a forward speed of 6–12 km/h. The qualification feeding index (QFI) of radar was 0.51%, 0.67%, and 2.05% higher than that of GPS at speeds of 6, 8, 10, and 12 km/h. The precision index (PREC) of radar was 17.60%, 5.44%, 16.81%, and 17.30% lower than that of GPS. Therefore, the control system based on the radar speed measurement developed in this paper can significantly improve the operating quality of the planter. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to discuss climate change, by modifying the timing of several agricultural operations, reduce the efficiency and yield of inputs leading to a lower production level. The reduction of the effects of climate change on production yields and on farmers' technical efficiency (TE) requires the adoption of adaptation strategies. This paper analyses the impact of climate change adaptation strategies adopted on maize farmers' TE in Benin. Design/methodology/approach: This paper uses an endogeneity-corrected stochastic production frontier approach based on data randomly collected from 354 farmers located in three different agro-ecological zones of Benin. Findings: Estimation results revealed that the adoption of adaptation strategies improve maize farmers' TE by 1.28%. Therefore, polices to improve farmers' access to climate change adaptation strategies are necessarily for the improvement of farmers' TE and yield. Research limitations/implications: The results of this study contribute to the policy debate on the enhancement of food security by increasing farmers' TE through easy access to climate change adaptation strategies. The improvement of farmers' TE will in turn improve the livelihoods of the communities and therefore contribute to the achievement of Sustainable Development Goals 1, 2 and 13. Originality/value: This study contributes to theoretical and empirical debate on the relationship between adaptation to climate change and farmers' TE. It also adapts a new methodology (endogeneity-corrected stochastic production frontier approach) to correct the endogeneity problem due to the farmers' adaptation decision. [ABSTRACT FROM AUTHOR]

The study of yield gaps has become more complex, prompting the use of varied approaches to measure yields and a wider range of factors to explain these gaps. In the Global North, the focus is on precision farming, whereas in sub-Saharan Africa (SSA), a broader perspective is necessary due to pronounced variability in farmland conditions. While biogeophysical and management factors have been traditional focal points in yield gap analyses, socio-economic and institutional factors are increasingly recognized as significant, especially in SSA. This review synthesizes research from the past decade in SSA that integrates biogeophysical, management, farm characteristics, and institutional factors in yield gap discussions. The findings indicate a slow shift in including socio-economic factors, with management, particularly nutrient supply and crop management, remaining predominant. However, there is a growing trend towards methodological diversity, such as the adoption of remote sensing and GIS in recent years. Case studies from Kenya and Ghana, utilizing field surveys, interviews, panel data, and spatial analysis, highlight how a multifaceted approach can enhance our understanding of the various elements influencing maize yield gaps in SSA. [ABSTRACT FROM AUTHOR]

Addressing the excessive input and inefficient use of agrochemicals are crucial for global food security, environmental protection, and human health. This paper offers a new idea from the perspective of outsourcing agricultural production. The impact of outsourcing on the frequency and intensity of agrochemical inputs were theoretically analyzed and empirically tested using a field survey of 1211 farmers in Heilongjiang, Henan, and Hunan, the major food-producing areas in China. A Logit regression framework was used to analyze the effect, a conditional mixture process (CMP) method was used to address potential endogeneity concerns, and a mediation effect model was used to dissect the mechanism. The results show that the effect of outsourcing on both input frequency and input intensity of agrochemicals was positive at the 1% significance level. The positive effect conclusion still holds even after addressing the potential endogeneity concerns, and in the sub-sample estimates for maize, wheat, and rice. We conclude that outsourcing can improve the utilization efficiency of agrochemicals by increasing the frequency of agrochemical inputs, but fail to solve the excessive agrochemical inputs and even leads to a further increase in the intensity of agrochemical inputs. Moreover, the mechanism for an increase in agrochemical input intensity due to outsourcing was explored, and it is more likely to be caused by inhibiting farmers' investment in soil improvement measures. [ABSTRACT FROM AUTHOR]

The potential adverse effects of maize leaf diseases on agricultural productivity highlight the significance of precise disease diagnosis using effective leaf segmentation techniques. In order to improve maize leaf segmentation, especially for maize leaf disease detection, a hybrid optimization method is proposed in this paper. The proposed method provides better segmentation accuracy and outperforms traditional approaches by combining enhanced Particle Swarm Optimisation (PSO) with Firefly algorithm (FFA). Extensive tests on images of maize leaves taken from the Plant Village dataset are used to show the algorithm's superiority. Experimental results show a considerable decrease in Hausdorff distances, indicating better segmentation accuracy than conventional methods. The proposed method also performs better than expected in terms of Jaccard and Dice coefficients, which measure the overlap and similarity between segmented sections. The proposed hybrid optimization method significantly contributes to agricultural research and indicates that the method may be helpful in real scenarios. The performance of proposed method is compared with existing techniques like K-Mean, OTSU, Canny, FuzzyOTSU, PSO and Firefly. The overall performance of the proposed method is satisfactory. [ABSTRACT FROM AUTHOR]

The number of maize seedlings is a key determinant of maize yield. Thus, timely, accurate estimation of seedlings helps optimize and adjust field management measures. Differentiating "multiple seedlings in a single hole" of maize accurately using deep learning and object detection methods presents challenges that hinder effectiveness. Multivariate regression techniques prove more suitable in such cases, yet the presence of weeds considerably affects regression estimation accuracy. Therefore, this paper proposes a maize and weed identification method that combines shape features with threshold skeleton clustering to mitigate the impact of weeds on maize counting. The threshold skeleton method (TS) ensured that the accuracy and precision values of eliminating weeds exceeded 97% and that the missed inspection rate and misunderstanding rate did not exceed 6%, which is a significant improvement compared with traditional methods. Multi-image characteristics of the maize coverage, maize seedling edge pixel percentage, maize skeleton characteristic pixel percentage, and connecting domain features gradually returned to maize seedlings. After applying the TS method to remove weeds, the estimated R2 is 0.83, RMSE is 1.43, MAE is 1.05, and the overall counting accuracy is 99.2%. The weed segmentation method proposed in this paper can adapt to various seedling conditions. Under different emergence conditions, the estimated R2 of seedling count reaches a maximum of 0.88, with an RMSE below 1.29. The proposed approach in this study shows improved weed recognition accuracy on drone images compared to conventional image processing methods. It exhibits strong adaptability and stability, enhancing maize counting accuracy even in the presence of weeds. [ABSTRACT FROM AUTHOR]

The timely and accurate prediction of maize (Zea mays L.) yields prior to harvest is critical for food security and agricultural policy development. Currently, many researchers are using machine learning and deep learning to predict maize yields in specific regions with high accuracy. However, existing methods typically have two limitations. One is that they ignore the extensive correlation in maize planting data, such as the association of maize yields between adjacent planting locations and the combined effect of meteorological features and maize traits on maize yields. The other issue is that the performance of existing models may suffer significantly when some data in maize planting records is missing, or the samples are unbalanced. Therefore, this paper proposes an end-to-end bipartite graph neural network-based model for trait data imputation and yield prediction. The maize planting data is initially converted to a bipartite graph data structure. Then, a yield prediction model based on a bipartite graph neural network is developed to impute missing trait data and predict maize yield. This model can mine correlations between different samples of data, correlations between different meteorological features and traits, and correlations between different traits. Finally, to address the issue of unbalanced sample size at each planting location, we propose a loss function based on the gradient balancing mechanism that effectively reduces the impact of data imbalance on the prediction model. When compared to other data imputation and prediction models, our method achieves the best yield prediction result even when missing data is not pre-processed. [ABSTRACT FROM AUTHOR]

To improve the crushing efficiency and crushing pass rate of high-moisture corn ears (HMCEs), a multi-stage crushing scheme is proposed in this paper. A two-stage crushing device for HMCEs is designed, and the ear crushing process is analyzed. Firstly, a simulation model for HMCEs was established in EDEM software (2018), and the accuracy of the model was verified by the shear test. Subsequently, single-factor simulation experiments were conducted, with the crushing rate serving as the evaluation index. The optimal working parameter ranges for the HMCE device were identified as a primary crushing roller speed of 1200–1600 revolutions per minute (r/min), a secondary crushing roller clearance of 1.5–2.5 mm, and a secondary crushing roller speed of 2750–3750 r/min. A Box–Behnken experiment was conducted to establish a multiple regression equation. With the objective of maximizing the qualified crushing pass rate, the optimal combination of parameters was revealed: a primary crushing roller speed of 1500 r/min, a secondary crushing roller clearance of 2.5 mm, and a secondary crushing roller speed of 3280 r/min. The pass rate of corn cob crushing in the simulation test was 98.2%. The physical tests, using the optimized parameter combination, yielded a qualified crushing rate of 97.5%, which deviates by 0.7% from the simulation results, satisfying the requirement of a qualified crushing rate exceeding 95%. The experimental outcomes validate the rationality of the proposed crushing scheme and the accuracy of the model, providing a theoretical foundation for subsequent research endeavors. [ABSTRACT FROM AUTHOR]

Purpose: This paper proposes a low-cost and low-effort solution for determining the area of corn crops damaged by the wildlife facility utilising field images collected by an unmanned aerial vehicle (UAV). The proposed solution allows for the determination of the percentage of the damaged crops and their location. Methods: The method utilises image segmentation models based on deep convolutional neural networks (e.g., UNet family) and transformers (SegFormer) trained on over 300 hectares of diverse corn fields in western Poland. A range of neural network architectures was tested to select the most accurate final solution. Results: The tests show that despite using only easily accessible RGB data available from inexpensive, consumer-grade UAVs, the method achieves sufficient accuracy to be applied in practical solutions for agriculture-related tasks, as the IoU (Intersection over Union) metric for segmentation of healthy and damaged crop reaches 0.88. Conclusion: The proposed method allows for easy calculation of the total percentage and visualisation of the corn crop damages. The processing code and trained model are shared publicly. [ABSTRACT FROM AUTHOR]

The goal of this paper is to derive closed-form approximation formulas for the spread option value and Greeks by using double integration and investigating the exercise boundary. We have found that the straight-line approximation suggested in previous research does not perform well for curved exercise boundaries. We propose a novel approach: to integrate in a sector and find a closed-form formula expressed in terms of the bivariate normal CDF. We call it the sector formula. Numerical tests show the good accuracy of our sector formula. We demonstrate applications of the formula to the market data of calendar spread options for three major commodities, WTI, Natural Gas, and Corn, listed on the CME site as of May, April, and June 2024. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Background] Dujiangyan irrigation area is an important grain producing area in Sichuan Province, and corn is the main grain crop in Sichuan Province. Dujiangyan Irrigation Project Water Conservancy Project has a large scale and reasonable layout. The area of Dujiangyan irrigation area has gradually increased, and the permanent population has reached 30.3 million. The agriculture is developed, and the water demand is large. The agricultural development in many areas is restricted by the supply capacity of water resources. [Objective] The irrigation water demand of Dujiangyan irrigation area in each growth period of corn was analyzed, and the reasonable regulation of water resources in Dujiangyan irrigation area was carried out, so as to realize the yield increase of corn in the irrigation area. [Method] Eight kinds of meteorological elements in Dujiangyan irrigation area in the past 15 years were collected to calculate water demand, effective precipitation, water profit and loss index and irrigation water demand during maize growth period in the past 15 years; Using the monthly irrigation water consumption model, the irrigation water is allocated at different growth stages of corn to maximize the corn yield in each sub irrigation area, and the irrigation water for corn at each growth stage in each sub irrigation area is determined. [Result] The average annual water demand of maize in Dujiangyan irrigation area is 376 mm, the average annual effective precipitation is 368 mm, the average annual water profit and loss index is -0.24, and the total irrigation water in the whole growth period of maize in the irrigation area is 420 million m m³, Among them, the outer river irrigation area is 30 million m m³, The irrigation area of Bihe River is 44 million m m³, The irrigation area of Tongjiyan is 19 million m m³, The Heilongtan irrigation area is 44 million m m³, The irrigation area of Renmin Canal is 96 million m m³, The second irrigation area of Renmin Canal is 112 million m³, The Dongfeng Canal irrigation area is 75 million m m³. [Conclusion] The water demand of maize in Dujiangyan irrigation area did not change significantly during its growth period, which was related to the planting varieties. During the three key growth stages of jointing, tasseling, and lactation, irrigation requires a large amount of water and requires timely regulation and replenishment of water. The water demand for corn irrigation in the Pihe irrigation area, Renmin Canal 1, and Renmin Canal 2 sub irrigation areas is relatively high, and some of them are located at the tail of the hills, which may cause problems such as seedling dehydration. Therefore, it is necessary to implement staggered scheduling in the plain irrigation area and hilly irrigation area, where water is transported from the plain irrigation area to the hilly irrigation area and stored in the hilly irrigation area. Therefore, this paper carries out precise water resources regulation and control in each growth period of maize sub irrigation areas in order to achieve the yield increase of maize in Dujiangyan irrigation area. [ABSTRACT FROM AUTHOR]

The Anthesis-Silking Interval (ASI) is a crucial indicator of the synchrony of reproductive development in maize, reflecting its sensitivity to adverse environmental conditions such as heat stress and drought. This paper presents an automated method for detecting the maize ASI index using a field high-throughput phenotyping platform. Initially, high temporal-resolution visible-light image sequences of maize plants from the tasseling to silking stage are collected using a field rail-based phenotyping platform. Then, the training results of different sizes of YOLOv8 models on this dataset are compared to select the most suitable base model for the task of detecting maize tassels and ear silks. The chosen model is enhanced by incorporating the SENetv2 and the dual-layer routing attention mechanism BiFormer, named SEBi-YOLOv8. The SEBi-YOLOv8 model, with these combined modules, shows improvements of 2.3% and 8.2% in mAP over the original model, reaching 0.989 and 0.886, respectively. Finally, SEBi-YOLOv8 is used for the dynamic detection of maize tassels and ear silks in maize populations. The experimental results demonstrate the method's high detection accuracy, with a correlation coefficient (R2) of 0.987 and an RMSE of 0.316. Based on these detection results, the ASI indices of different inbred lines are calculated and compared. [ABSTRACT FROM AUTHOR]

The integration of commodities into stock exchanges marked a pivotal moment in the analysis of price dynamics. Commodities are essential for both daily sustenance and industrial processes and are separated into hard commodities, like metals, and soft commodities, such as agricultural produce. This paper provides a review of the relevant literature concerning the implications of commodity price volatility on commercial and financial landscapes, recognizing its profound impact on global economies. Drawing from Google Scholar and Science Direct, we analyze trends in academic publications until 2022, particularly focusing on the interplay between volatility spillover and ten different commodities, providing insights into the evolution of research paradigms over time. In a nutshell, the literature suggests that relationships between hard commodities are stronger since, in addition to being raw materials, they also serve as investment products. For the same reason, relationships between agricultural products appear to be relatively weaker. [ABSTRACT FROM AUTHOR]

To effectively combat the re-infestation of boll weevils (Anthonomus grandis L.) in cotton fields, it is necessary to address the detection of volunteer cotton (VC) plants (Gossypium hirsutum L.) in rotation crops such as corn (Zea mays L.) and sorghum (Sorghum bicolor L.). The current practice involves manual field scouting at the field edges, which often leads to the oversight of VC plants growing in the middle of fields alongside corn and sorghum. As these VC plants reach the pinhead squaring stage (5–6 leaves), they can become hosts for boll weevil pests. Consequently, it becomes crucial to detect, locate, and accurately spot-spray these plants with appropriate chemicals. This paper focuses on the application of YOLOv5m to detect and locate VC plants during the tasseling (VT) growth stage of cornfields. Our results demonstrate that VC plants can be detected with a mean average precision (mAP) of 79% at an Intersection over Union (IoU) of 50% and a classification accuracy of 78% on images sized 1207 × 923 pixels. The average detection inference speed is 47 frames per second (FPS) on the NVIDIA Tesla P100 GPU-16 GB and 0.4 FPS on the NVIDIA Jetson TX2 GPU, which underscores the relevance and impact of detection speed on the feasibility of real-time applications. Additionally, we show the application of a customized unmanned aircraft system (UAS) for spot-spray applications through simulation based on the developed computer vision (CV) algorithm. This UAS-based approach enables the near-real-time detection and mitigation of VC plants in corn fields, with near-real-time defined as approximately 0.02 s per frame on the NVIDIA Tesla P100 GPU and 2.5 s per frame on the NVIDIA Jetson TX2 GPU, thereby offering an efficient management solution for controlling boll weevil pests. [ABSTRACT FROM AUTHOR]

In optimization problems, the principle of symmetry provides important guidance. This article introduces an enhanced NSGA-II algorithm, termed NDE-NSGA-II, designed for addressing multi-objective optimization problems. The approach employs Tent mapping for population initialization, thereby augmenting its search capability. During the offspring generation process, a hybrid local search strategy is implemented to augment the population's exploration capabilities. It is crucial to highlight that in elite selection, norm selection and average distance elimination strategies are adopted to strengthen the selection mechanism of the population. This not only enhances diversity but also ensures convergence, thereby improving overall performance. The effectiveness of the proposed NDE-NSGA-II is comprehensively evaluated across various benchmark functions with distinct true Pareto frontier shapes. The results consistently demonstrate that the NDE-NSGA-II method presented in this paper surpasses the performance metrics of the other five methods. Lastly, the algorithm is integrated with the DSSAT model to optimize maize irrigation and fertilization scheduling, confirming the effectiveness of the improved algorithm. [ABSTRACT FROM AUTHOR]

This paper attempts to provide insights into the current network configurations of the food-trade system and to study the short-term effects of one of the ongoing and lasting global crises, the Ukraine War, on the link intensity. Towards this end, this analysis (1) reveals the pattern of countries' network positions in two most traded subcategories of the cereal network: wheat and meslin, and maize or corn, and (2) discusses the characteristics of the global cereal networks over the 2021–2022 period. The results highlight several features of the trade networks: (1) the distribution of cereal trade is highly concentrated, with considerable dependency on a small number of exporters and a low import diversification, making the system rigid and prone to shocks; (2) a central role of several key developed countries that leave many developing countries outside the centre of the networks; (3) a high network heterogeneity which confirms the propensity to have hub nodes. Particular indicators show that the highest level of interconnectivity is specific to the cereals' export network, the densest networks are the maize or corn ones, and the greatest heterogeneity appears for the cereals export network. [ABSTRACT FROM AUTHOR]

To address the low clearance rate issue of the anti-blocking mechanism for maize no-till planters in the Huang-Huai-Hai Plain of China, experiments and simulations were conducted to analyze the individual and collective movements of straw under the action of the round roller-claw anti-blocking mechanism. A tracer-based measurement method for straw displacement was applied firstly. Experimental results showed that the straw forward displacement could be characterized by the average horizontal displacements of longitudinal and lateral tracers, while the straw side displacement could be characterized by the lateral displacement of the longitudinal tracer. The straw forward displacement was 58.95% greater than the side displacement. Forward, side, and total displacements of straw increased as the mechanism's forward speed increased from 3 km/h to 7 km/h, with corresponding rates of increase at 233.98%, 43.20%, and 162.47%, respectively. Furthermore, a model of straw–soil–mechanism interaction was constructed in EDEM 2022 software. The relative error between experimental and simulated straw clearance rates was 11.20%, confirming the applicability of the simulation model for studying straw–soil–mechanism interaction. Based on the simulation model, three straw tracers of different lengths were selected to study the motion behavior of straw. It was inferred that despite differences in straw length, the movement behaviors of the three straw tracers under the influence of the anti-blocking mechanism were similar. Additionally, longer straws exhibited greater displacements in all directions. This paper serves as a reference for studying straw motion behavior influenced by anti-blocking mechanisms. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Maize has been identified as an excellent alternative to rice as a staple food in the Philippines. The Asian corn borer (ACB; Ostrinia furnacalis Guenée), one of the most destructive insect pests of maize, damages plants throughout their vegetative and reproductive stages. Most open-pollinated varieties are threatened by pests and diseases. The CGUARD program investigated the potential of traditional Philippine maize as a source of resistance to major insect pests, including ACB. This paper evaluated the response of the selected 149 Philippine traditional maize accessions to ACB at vegetative and reproductive stages through laboratory bioefficacy procedures for potential resistance without the presence of Bt leaf and stalk-feeding resistance were determined through mean larval survival and mean tunnel length, respectively, after 5 d using laboratory-reared second-instar larvae. Improvements in screening procedures for laboratory assays are detailed in this report. Fourteen (14) accessions were identified to have promising leaf-feeding resistance, whereas 30 accessions were identified for stalk-feeding resistance. The resistance of APN 0088, a white glutinous type of maize from Palawan, to both leaf and stalk feeding of ACB was also validated. The results demonstrate the presence of natural resistance to ACB in traditional maize that has been exposed to the threat of the pest for decades. These promising accessions may be utilized to generate ACB-resistant or-tolerant maize, in addition to other breeding programs. Several pigmented varieties with ACB resistance were also identified, which may be further examined for their nutritional properties and potential as functional foods. By utilizing our traditional varieties, we are boosting local maize production that will benefit small-scale farmers and ultimately providing them with a high-performing variety that is crucial in this time of changing climate. [ABSTRACT FROM AUTHOR]

Environmental factors, such as drought stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought stress in maize plants using an existing, custom-developed, low-cost, high-throughput phenotyping platform. A pipeline is proposed for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. A classification accuracy of 85% was achieved in one of our trials, using hold-out trials for testing. Suitable regions on the plant that are more sensitive to drought stress were explored, and it was shown that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Experiments in search of an ideal window size showed that small bounding boxes surrounding the YEL and the stem area of the plant perform better in separating drought-stressed and well-watered plants than larger window sizes enclosing most of the plant. The results presented in this work show good separation between well-watered and drought-stressed categories for two out of the three imaging trials, both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance. [ABSTRACT FROM AUTHOR]

Maize Ustilago maydis is a disease that severely affects maize yield and quality. In this paper, we employed transcriptome sequencing and GWAS analysis to identify candidate genes and reveal disease-resistant germplasm resources, thereby laying the foundation for further analysis of the molecular mechanism of maize Ustilago maydis resistance and genetic improvement. The results of transcriptome sequencing revealed that a considerable number of receptor kinase genes, signal-transduction-related protein genes, redox-response-related genes, WRKYs, and P450s genes were significantly upregulated. There was a wide range of mutations of Ustilago maydis in maize inbred lines. Thirty-two high-resistance maize inbred lines were selected, and 16 SNPs were significantly associated with the disease index. By integrating the results of GWAS and RNA-seq, five genes related to disease resistance were identified, encoding the chitinase 1 protein, fatty acid elongase (FAE), IAA9, GATA TF8, and EREB94, respectively. It provides a certain reference for the cloning of maize anti-tumor smut genes and the breeding of new varieties. [ABSTRACT FROM AUTHOR]

Increasing yield is an important goal of barley breeding. In this study, 54 papers published from 2001–2022 on QTL mapping for yield and yield-related traits in barley were collected, which contained 1080 QTLs mapped to the barley high-density consensus map for QTL meta-analysis. These initial QTLs were integrated into 85 meta-QTLs (MQTL) with a mean confidence interval (CI) of 2.76 cM, which was 7.86-fold narrower than the CI of the initial QTL. Among these 85 MQTLs, 68 MQTLs were validated in GWAS studies, and 25 breeder's MQTLs were screened from them. Seventeen barley orthologs of yield-related genes in rice and maize were identified within the hcMQTL region based on comparative genomics strategy and were presumed to be reliable candidates for controlling yield-related traits. The results of this study provide useful information for molecular marker-assisted breeding and candidate gene mining of yield-related traits in barley. [ABSTRACT FROM AUTHOR]

Taxonomic identification of arbuscular mycorrhizal (AM) fungal spores extracted directly from the field is sometimes difficult because spores are often degraded or parasitized by other organisms. Single-spore inoculation of a suitable host plant allows for establishing monosporic cultures of AM fungi. This study aimed to propagate AM fungal spores isolated from maize soil using single spores for morphological characterization. First, trap cultures were established to trigger the sporulation of AM fungal species. Second, trap cultures were established with individual morphotypes by picking up only one spore under a dissecting microscope and transferring it to a small triangle of sterilized filter paper, which was then carefully inoculated below a root from germinated sorghum seeds in each pot and covered with a sterile substrate. All pots were placed in sunbags and maintained in a plant growth room for 120 days. Spores obtained from single spore trap cultures from each treatment, maize after oats (MO), maize after maize (MM), maize after peas (MP), and maize after soybean (MS), were extracted using the sieving method. Healthy spores were selected for morphological analysis. Direct PCR was conducted by crushing spores in RNAlater and applying three sets of primer pairs: ITS1 ITS4, NS31 AML2, and SSUmcf and LSUmBr. Nucleotide sequences obtained from Sanger sequencing were aligned on MEGA X. The phylogenetic tree showed that the closest neighbors of the propagated AM fungal species belonged to the genera Claroideoglomus, Funneliformis, Gigaspora, Paraglomus, and Rhizophagus. The morphological characteristics were compared to the descriptive features of described species posted on the INVAM website, and they included Acaulospora cavernata, Diversispora spurca, Funneliformis geosporus, Funneliformis mosseae, Gigaspora clarus, Gigaspora margarita, Glomus macrosporum, Paraglomus occultum, and Rhizophagus intraradices. These findings can provide a great contribution to crop productivity and sustainable management of the agricultural ecosystem. Also, the isolate analyzed could be grouped into efficient promoters of growth and mycorrhization of maize independent of their geographical location. [ABSTRACT FROM AUTHOR]

Southern leaf blight (SLB) is a foliar disease caused by the fungus Cochliobolus heterostrophus infecting maize plants in humid, warm weather conditions. SLB causes production losses to corn producers in different regions of the world such as Latin America, Europe, India, and Africa. In this paper, we demonstrate a non-destructive method to quantify the signs of fungal infection in SLB-infected corn plants using a deep UV (DUV) fluorescence spectrometer, with a 248.6 nm excitation wavelength, to acquire the emission spectra of healthy and SLB-infected corn leaves. Fluorescence emission spectra of healthy and diseased leaves were used to train an Autoencoder (AE) anomaly detection algorithm—an unsupervised machine learning model—to quantify the phenotype associated with SLB-infected leaves. For all samples, the signature of corn leaves consisted of two prominent peaks around 450 nm and 325 nm. However, SLB-infected leaves showed a higher response at 325 nm compared to healthy leaves, which was correlated to the presence of C. heterostrophus based on disease severity ratings from Visual Scores (VS). Specifically, we observed a linear inverse relationship between the AE error and the VS (R2 = 0.94 and RMSE = 0.935). With improved hardware, this method may enable improved quantification of SLB infection versus visual scoring based on e.g., fungal spore concentration per unit area and spatial localization. [ABSTRACT FROM AUTHOR]

In the last decades, the production of fuel ethanol from corn has spread as a valid renewable alternative to pursue sustainability goals. However the uncertain nature of both input (corn) and output (gasoline) prices, together with price dependent operational decisions, combine to make this difficult plant valuation require a real options approach. Moreover, this project is characterized by various sequential stages that contribute to increase its valuation difficulties. The purpose of this paper is to provide a reliable valuation methodology of a corn ethanol plant project able to consider the characteristics of the project. We apply the compound Real Options Approach to price a corn ethanol plant project considering that the corn and gasoline prices both follow a skew-geometric Brownian motion. We also propose a case study to show a real implementation of our theoretical model. The results show that the corn ethanol plant is financially attractive as renewable investment since the uncertainties inherent in the project add value, via managerial flexibility, to the real option valuation. [ABSTRACT FROM AUTHOR]

When maize is afflicted by pests and diseases, it can lead to a drastic reduction in yield, causing significant economic losses to farmers. Therefore, accurate and efficient detection of maize pest species is crucial for targeted pest control during the management process. To achieve precise detection of maize pest species, this paper proposes a deep learning detection algorithm for maize pests based on an improved YOLOv8n model: Firstly, a maize pest dataset was constructed, comprising 2,756 images of maize pests, according to the types of pests and diseases. Secondly, a deformable attention mechanism (DAttention) was introduced into the backbone network to enhance the model's capability to extract features from images of maize pests. Thirdly, spatial and channel recombination convolution (SCConv) was incorporated into the feature fusion network to reduce the miss rate of small-scale pests. Lastly, the improved model was trained and tested using the newly constructed maize pest dataset. Experimental results demonstrate that the improved model achieved a detection average precision (mAP) of 94.8% at a speed of 171 frames per second (FPS), balancing accuracy and efficiency. The improved model can be deployed in low-computing-power mobile devices to achieve real-time detection, and in the future, more types of maize pests can be detected by adding multi-category datasets and training with new models with more computational power, which is important for the healthy development of maize agriculture. [ABSTRACT FROM AUTHOR]

In the contemporary era, conventional energy sources like oil, coal, and natural gas overwhelmingly contribute 89.6% to global CO2 emissions, intensifying environmental challenges. Recognizing the urgency of addressing climate concerns, a pivotal shift towards renewable energy, encompassing solar, wind, and biofuels, is crucial for bolstering environmental sustainability. Bioethanol, a globally predominant biofuel, offers a versatile solution, replacing gasoline or integrating into gasoline–ethanol blends while serving as a fundamental building block for various valuable compounds. This review investigates the dynamic landscape of biomass generations, drawing insightful comparisons between the first, second, third, and fourth generations. Amid the drive for sustainability, the deliberate focus on the initial generation of biomass, particularly corn, in bioethanol production is grounded in the current dependence on edible crops. The established utilization of first-generation biomass, exemplified by corn, underscores the necessity for a comprehensive examination of its advantages and challenges, allowing for a nuanced exploration of existing infrastructure and practices. To produce bioethanol from corn feedstock, various milling methods can be employed. Thus, this paper delves into a comparative assessment of dry-milling and wet-milling processes scrutinizing their efficiency, environmental impact, and economic feasibility. [ABSTRACT FROM AUTHOR]

This paper demonstrates how plant metaphors in Vietnamese and English proverbs reflect cultural values through an analysis. It contends that proverbs serve as a means of transmitting cultural knowledge. The essay demonstrates the influence of cultural context on metaphorical language by contrasting proverbs that convey similar meanings ("Every rose has its thorn" and "Hồng nào mà chả có gai"). Additionally, it examines proverbs that convey the same meaning but employ distinct metaphors, such as "Cơm tẻ là mẹ đẻ" versus "Corn is the staff of life." In conclusion, the essay recognizes culture-specific proverbs and asserts that comprehension of them promotes effective intercultural communication. [ABSTRACT FROM AUTHOR]

Tocopherols are secondary metabolites synthesized through the shikimate biosynthetic pathway in the plastids of most plants. It is well known that α–Tocopherol (vitamin E) has many health benefits for humans and animals; therefore, it is highly used in human and animal diets. Tocopherols vary considerably in most crop (and plant) species and within cultivars of the same species depending on environmental and growth conditions; tocopherol content is a polygenic, complex traits, and its inheritance is poorly understood. The objective of this review paper was to summarize all identified quantitative trait loci (QTL) that control seed tocopherols and related contents identified in maize (Zea mays) during the past two decades (2002–2022). Candidate genes identified within these QTL regions are also discussed. The QTL described here, and candidate genes identified within these genomic regions could be used in breeding programs to develop maize cultivars with high, beneficial levels of seed tocopherol contents. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of participation in innovation platforms (IPs) on smallholder maize farmers' welfare in the Brong Ahafo region of Ghana using data collected from 477 rural farm households. We employed the doubly robust inverse probability weighted regression adjustment method (IPWRA) and propensity score matching (PSM) approach to examine the impact of IP participation on yield, income, and per capita consumption expenditure. The empirical results show that age, farming experience, farm size, land ownership, and credit access are the main determinants of participation in IPs in Ghana. In addition, the results show that participation in IPs increases yields and household income and reduces per capita expenditure of smallholder maize farmers in Ghana. Furthermore, the advantages of engaging in the IPs go beyond the traditional extension service delivery approach. This highlights the importance of policymakers and agricultural development agencies in developing nations to establish policies that encourage the establishment of IPs and promote their involvement among small-scale farmers, resulting in better living standards. [ABSTRACT FROM AUTHOR]

With continuous increase of population and demand for nutritional food, analyzing potential yield of world maize affected by global warming is of great significance to direct the crop production in the future. Thus, in this paper both average and top (national) yields of world maize between 2021 and 2030 are projected creatively using ARIMA-TR (Auto-regressive Integrated Moving Average and Trend Regression) model based on historic yields since 1961. The impact of global warming on the yields of world maize from 1961 to 2020 was analyzed using unary regression model. Our study concludes that between 2021 and 2030, average yield of world maize is projected to be from 5989 kg ha-1 to 6703 kg ha-1 while the top yield from 36530 kg ha-1 to 44271 kg ha-1, or the average ranging from 16.39% decreasingly to 15.14% of the top; from 1961 to 2020 global warming exerts positive effect on average yield of world maize less than on the top, which partly drives the gap between these two yields widened gradually; for world maize by 2030, the opportunities for improving global production should be mainly dependent on the advantage of high-yield countries. [ABSTRACT FROM AUTHOR]

This paper aimed to evaluate the contamination with mycotoxigenic fungi and total aflatoxins in stored corn from different sites in Puebla and Tlaxcala, Mexico. Methodology. The study was conducted at two sites in Puebla (San Salvador El Seco and Junta Auxiliar La Resurrección) and two sites in Tlaxcala (Tlaltepango and Nativitas). A total of 80 samples of stored corn were collected. Identification of Aspergillus flavus was performed by microculture techniques and specific taxonomic keys (macromorphological and micromorphological). Then, samples of contaminated corn were selected, and aflatoxin production was confirmed using a direct solid-phase ELISA kit. A total of 25 A. flavus strains were identified. Other possible mycotoxinproducing fungi were Penicillium (n=52) and Fusarium (n=19). Regarding total aflatoxin contamination, all samples were contaminated within a range of 1.589 to 11.854 µg/kg, and the average concentration was 6.3 µg/kg corn. Implications. The detection of mycotoxigenic fungi in the samples tested and of aflatoxins in corn highlights the importance of monitoring these fungi. Since food safety is at risk, it shows the need for methods to control these fungi and their metabolites. [ABSTRACT FROM AUTHOR]

Corn and soybeans play pivotal roles in the agricultural landscape of the United States, and accurately delineating their cultivation areas is indispensable for ensuring food security and addressing hunger-related challenges. Traditional methods for crop mapping are both labor-intensive and time-consuming. Fortunately, the advent of high-resolution imagery, exemplified by Sentinel-2A (S2A), has opened avenues for precise identification of these crops at a field scale, with the added advantage of cloud computing. This paper presents an innovative algorithm designed for large-scale mapping of corn and soybean planting areas on the Google Cloud Engine, drawing inspiration from symmetrical theory. The proposed methodology encompasses several sequential steps. First, S2A data undergo processing incorporating phenological information and spectral characteristics. Subsequently, texture features derived from the grayscale matrix are synergistically integrated with spectral features in the first step. To enhance algorithmic efficiency, the third step involves a feature importance analysis, facilitating the retention of influential bands while eliminating redundant features. The ensuing phase employs three base classifiers for feature training, and the final result maps are generated through a collective voting mechanism based on the classification results from the three classifiers. Validation of the proposed algorithm was conducted in two distinct research areas: Ford in Illinois and White in Indiana, showcasing its commendable classification capabilities for these crops. The experiments underscore the potential of this method for large-scale mapping of crop areas through the integration of cloud computing and high-resolution imagery. [ABSTRACT FROM AUTHOR]

Climate variability poses great challenges to food security in West Africa, a region heavily dependent on rainfall for farming. Identifying sowing strategies that minimize yield losses for farmers in the region is crucial to securing their livelihood. In this paper, we investigate three sowing strategies to assess their ability to identify safe sowing windows for smallholder farmers in the Sudanian region of West Africa (WA) in the context of a changing climate. The GIS version of the FAO crop model, AquaCrop-GIS, is used to simulate the yield response of maize (Zea mays L.) to varying sowing dates throughout the rainy season across WA. Based on an average of 38 years of data per grid cell, we identify safe sowing windows across the Sudanian region that secure at least 90% of maximal yield. We find that current sowing strategies, based on minimum thresholds for rainfall accumulated over a period that are widely applied in the region, carry a higher risk of yield failure, especially at the beginning of the rainy season. This analysis shows that delaying sowing for a month to mid-June in the central region (east of Lon 8.5°W), and to early August in the semi-arid areas is a safer strategy that ensures optimal yields. A comparison between the periods 1982–1991 and 1992–2019 shows a negative shift for LO10 mm and LO20 mm, suggesting a wetter regime compared to the dry periods of the 1970s and 1980s. On the contrary, we observe a positive shift in the safe window strategy, highlighting the need for precautions due to erratic rainfall at the beginning of the season. The precipitation-based strategies hold a high risk, while the safe sowing window strategy, easily accessible to smallholder farmers, is more fitting, given the current climate. [ABSTRACT FROM AUTHOR]

Corn silk (Zea mays L.) is the stigma of an annual gramineous plant named corn, which is distributed in many regions worldwide and has a long history of medicinal use. In recent years, with the sustainable development of traditional Chinese medicine, studies of corn silk based on modern technologies, such as GC–MS, LC–MS, and other analytical means, have offered more comprehensive analyses. Phytochemistry studies have shown that the main bioactive components in corn silk include flavonoids, polyphenols, phenolic acids, fatty acids, and terpenoids. Pharmacological studies have shown that corn silk extract has various pharmacological effects, such as reducing blood lipids, lowering blood pressure, regulating blood sugar levels, anti-inflammatory effects, and anti-oxidation effects. In this paper, the related research on corn silk from the past few years is summarized to provide a theoretical reference for the further development and utilization of corn silk. [ABSTRACT FROM AUTHOR]

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Chromosome elimination resulting in haploids is achieved by rapid loss of chromosomes from one parent during the zygote stage and is an important procedure to produce doubled haploid (DH) lines in plants. During crosses between an emasculated wheat (Triticum aestivum L.) and maize (Zea mays L.) as pollen donors, the complete loss of maize chromosomes results in wheat haploid embryos. Through embryo rescue and chromosome doubling processes, pure lines with stable traits can be quickly obtained. The technique is called the "Wheat × Maize System". Although this technology is not new, it remains a practical approach to date. In order to optimize and improve this technology and to achieve its maximum potential in the winter wheat area of China, this paper reviews the previous and ongoing research and technical procedures for the production of wheat DH lines via the maize pollen induction and presents outlooks on DH research and its application in wheat breeding. [ABSTRACT FROM AUTHOR]

Abiotic stress is a major cause of the declining crop yield worldwide, especially in tropical agricultural areas. Meeting the global food demand has become a serious challenge, especially in tropical areas, because of soil acidity, Al and Fe toxicity, drought and heat stress, and climate change. In this article, we reviewed several research and review papers from Google Scholar to list the different solutions available for the mitigation of abiotic stress, especially in tropical regions where several major crops, such as maize, sorghum, wheat, rice, soybean, and millet, are affected by abiotic stress and fertilizer input. In particular, Sub-Saharan Africa (SSA) has been affected by the low use of fertilizers owing to their high cost. Therefore, soil and plant researchers and farmers have developed many techniques to mitigate the effects of stress and improve the crop yield based on the agroecological zone and crop type. Nutrient management using chemical fertilizers alone or in combination with organic crops is a strategy recommended to cope with abiotic stress and increase the crop yield, particularly in developing countries. Notably, integrated soil fertility management has been effective in semi-arid areas under drought and heat stress and in subhumid and humid areas with high soil acidity and Fe toxicity in Africa. Recent advances in the molecular physiology of various crops considered a staple food in SSA have facilitated the breeding of transgenic tolerant plants with high yield. However, the feasibility and implementation of this technique in the African continent and most tropical developing countries are major issues that can be solved via adequate subsidies and support to farmers. This review can aid in the development of novel strategies to decrease hunger and food insecurity in SSA. [ABSTRACT FROM AUTHOR]