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BACKGROUND: Different thermoplastic starch (TPS) films were prepared with or without the addition of microcrystalline cellulose (MCC) obtained via the melt‐extrusion method, and then the hot‐press method was used to produce environmentally friendly TPS‐based film/paper composites to replace petroleum‐based materials. RESULTS: The paper–plastic composites exhibited good interfacial adhesion from the scannign elctron microscopy images. It was seen that 5 wt.% MCC was added to reinforce the mechanical properties of TPS films, such that it also improved the barrier properties of MCC@TPS/paper composites and extended the path of water vapor through TPS films, which decreased the water vapor transmission rate of MCC@TPS/paper composites. TPS/paper composites and MCC@TPS/paper composites have better physical properties (i.e. smoothness, flexibility and folding resistance) than only paper. In particular, it was found that the water contact angle of MCC@TPS/paper composites and TPS/paper composites were higher than single‐layer paper. Furthermore, MCC reinforced paper–plastic composites demonstrated good barrier properties which can meet the requirement of the need for lower water sensitive materials in the food packaging industry. CONCLUSION: Thermoplastic corn starch‐based film/paper composites have good application properties as a potential source of bioplastic materials. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

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]

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher 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 stem sculptures, produced during the 16th and 17th centuries in New Spain (today, Mexico) are a clear example of the convergence of the artistic traditions from the American indigenous populations and European influence. This typology of sculptures is not limited to the Americas, as the examples found in European countries have shown. Therefore, a detailed technological investigation is required to correctly classify them. This work presents the interdisciplinary and multianalytical investigation of a 16th-century sculpture made with a maize stem preserved in Guadalajara city, Mexico. We used a set of techniques, such as CT, SEM-EDX, µ-FTIR, and µ-Raman, to study, from a macro to a micro level, the structure, the polychromy, and the modification of the sculpture. The results showed the use of maize stems, paper, and wood in the construction of the sculpture and the use of the traditional polychromy, as well as the numerous modifications that changed its appearance considerably resulting in its misclassification. We were able to associate the statue with the Cortés workshop (Mexico City region), probably produced in the decade of 1580, and track its liturgical use and historical development through the centuries. [ABSTRACT FROM AUTHOR]

BACKGROUND: Maize varieties that are rich in carotenoids have been developed to combat vitamin A deficiency in Sub‐Saharan Africa. Unfortunately, after harvest, carotenoids degrade and off‐flavor volatiles develop, which affect nutrient intake and consumer acceptance. This study evaluated carotenoid retention and aroma compound stability in provitamin A biofortified maize, variety Pool 8A, as influenced by dry milling and storage in different packaging and temperature conditions. RESULTS: The lowest amount of total carotenoids was found in flour stored in laminated paper bags at 37 °C (only 16% retention after 180 days), attributable to the high storage temperature and oxygen permeability of the packaging material. No significant effect on carotenoid degradation was found for dry milling, either by rotor mill or freezer mill, but the formation of volatile compounds was significantly (P < 0.05) affected. Volatile compounds such as hexanal, 2‐pentylfuran, 1‐propanol, 2‐heptanone, butyrolactone, limonene, and hexanoic acid were found in different proportions after milling. The highest concentration of hexanal was in flour milled by rotor mill or freezer mill, and stored in laminated paper bags at 37 °C after 180 days, and the lowest concentrations were for flour in aluminium bags and double‐layered polyethylene bags stored at 4 °C. CONCLUSION: Maize flour stored in double‐layered polyethylene bags had the highest carotenoid retention and aroma stability. Importantly, the use of these bags is economically feasible in low‐income countries. Overall, our results show that effective control of storage conditions is crucial to prevent carotenoid loss and decrease off‐odor formation. © 2020 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [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]

Copyright of Tropical Grasslands / Forrajes Tropicales is the property of International Centre for Tropical Agriculture - CIAT 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.)

[Display omitted] • A new method for detection of Fumonisin B1 mycotoxin in real maize samples. • The method consisted combine MIP membrane and PSI-MS for mycotoxin analysis. • The method exhibits a low detection limit (22.0 μg L-1) and good recoveries (98–105.5%). • A fast and straightforward way determined fumonisin B1 in maize-based products. Fumonisin B 1 (FB 1) is the most abundant and important mycotoxin for agriculture, food, and health among all fumonisins due to its widespread presence and toxicity. This compound is a concern for industries involving maize and maize-based products. This study proposes a new approach using molecularly imprinted polymer (MIP) combined with paper spray ionization mass spectrometry (PSI-MS) to enhance the sensitivity of analysis of FB 1 in maize samples. MIP was synthesized directly on a cellulose membrane using 1,2,3-propanetricarboxylic acid as a template molecule. The analytical performance of MIP-PSI-MS provided a linear calibration curve with R2>0.99. Precision and accuracy were less than 5.5 % for all analyses. Finally, the MIP-PSI-MS method was applied to quantify the contents of FB 1 in commercial maize samples, and 0.439 ± 0.060 and 0.486 ± 0.061 μg mL−1 were found in two samples. [ABSTRACT FROM AUTHOR]

Copyright of Revista Mexicana de Ciencias Agrícolas is the property of Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias 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.)

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]

Canavalia ensiformis is a Fabaceae used for soil conservation and control of weeds. The objective of this study was to identify gall wasps of C. ensiformis at Guerrero, Mexico. A survey was done in June 2022 in a crop of C. ensiformis planted to provide cover for Zea mays L. plots at Anahuac, San Marcos, Guerrero, México. Abnormal galls were found on several organs of the plants, especially the branches. Three branches from 10 plants were gathered, put into paper bags, labelled, and transported in polystyrene containers to Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP) for rearing wasps. Adults were identified based on morphological characteristics. C. ensiformis was reported for the first time as an alternate host of wasps of the genus Eurytoma. Further studies of the genus and species identification using molecular techniques are needed. [ABSTRACT FROM AUTHOR]

Potato (Solanum tuberosum L.) has been recognized as the fourth largest crop after maize, wheat and rice and is a crop of importance in developing countries for ensuring human nutrition and food security. Based on the Web of Science, this study analysed 3943 article- and review-type papers of potato research from agronomy category during 2000 to 2021 and included 6 highly cited papers. The papers mainly written in English (96.88%) were from 11,479 authors, 120 countries/territories and 2719 organizations and published in 97 journals and one book series. The top 5 core journals were American Journal of Potato Research (949, 24.07%), Potato Research (437, 11.08%), European Journal of Plant Pathology (172, 4.36%), Plant Pathology (171, 4.34%) and Theoretical and Applied Genetics (142, 3.60%). The top 5 countries and regions were the USA, Canada, China, the Netherlands and Brazil. The top five organizations were USDA ARS, Agriculture and Agri-Food Canada, University of Wisconsin, Wageningen University and Research Centre and University of Idaho, each with more than 157 papers. The top 6 authors were C. R. Brown, R. G. Novy, J. B. Bamberg, R. G. F. Visser, K. G. Haynes and P. C. Struik, each publishing more than 43 papers. All keywords of the potato research from agronomy category were separated into seven clusters for different research topics. Visualizations offered exploratory information on the current state in a scientific field or discipline as well as indicating possible developments in the future. This work is useful for students identifying graduate schools and for researchers selecting journals. [ABSTRACT FROM AUTHOR]

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 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]

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]

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 paper aimed to study the effectiveness of corn cultivation under different irrigation methods on the experimental field of the Askanian State Agricultural Research Station, which is located in the semi-arid, steppe, arid climate zone of the Southern Steppe of Ukraine, in the Kakhovsky irrigated massif. The investigation pointed out the effectiveness and provided an economic assessment of the use of different irrigation methods in corn cultivation. Two methods of irrigation (sprinkling and subsoil drip irrigation) were studied, with the maintenance of the humidity of the soil layer 0-50 cm at the level of 80% LMC and corn hybrids of different maturity groups. Under subsoil drip irrigation, the irrigation rate was lower on average by 24% than when sprinkled, on the other hand, the yield was higher on average by 40.9%, which significantly affected the cost price of the obtained corn grain. Subsoil drip irrigation yielded a net profit of 62% higher than sprinkler irrigation. and the profitability accordingly increased by 78.8%. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Agricultural Sciences / Revista Brasileira de Ciências Agrárias is the property of Universidade Federal Rural de Pernambuco 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 grasses, the apical part of the root is covered by a two-layered deposit of extracellular material, the pellicle, which together with the outer periclinal wall of protodermal cells forms the three-layered epidermal surface. In this study, the effect of mechanical stress on the pellicle was examined. An experiment was performed, in which maize roots were grown in narrow diameter plastic tubes with conical endings for 24 h. Two groups of experimental roots were included in the analysis: stressed (S) roots, whose tips did not grow out of the tubes, and recovering (R) roots, whose apices grew out of the tube. Control (C) roots grew freely between the layers of moist filter paper. Scanning electron microscopy and confocal microscopy analysis revealed microdamage in all the layers of the epidermal surface of S roots, however, protodermal cells in the meristematic zone remained viable. The outermost pellicle layer was twice as thick as in C roots. In R roots, large areas of dead cells were observed between the meristematic zone and the transition zone. The pellicle was defective with a discontinuous and irregular outermost layer. In the meristematic zone the pellicle was undamaged and the protodermal cells were intact. The results lead to the conclusion that the pellicle may prevent damage to protodermal cells, thus protecting the root apical meristem from the negative effects of mechano-stress. [ABSTRACT FROM AUTHOR]

Maize diseases are reported to occur often, and are complicated and difficult to control, which seriously affects the yield and quality of maize. This paper proposes an improved YOLOv5n model incorporating a CA (Coordinate Attention) mechanism and STR (Swin Transformer) detection head, CTR_YOLOv5n, to identify common maize leaf spot, gray spot, and rust diseases in mobile applications. Based on the lightweight model YOLOv5n, the accuracy of the model is improved by adding a CA attention module, and the global information acquisition capability is enhanced by using TR2 as the detection head. The average recognition accuracy of the algorithm model can reach 95.2%, which is 2.8 percent higher than the original model, and the memory size is reduced to 5.1MB compared to 92.9MB of YOLOv5l, which is 94.5% smaller and meets the requirement of being light weight. Compared with SE, CBAM, and ECA, which are the mainstream attention mechanisms, the recognition effect we used is better and the accuracy is higher, achieving fast and accurate recognition of maize leaf diseases with fewer computational resources, providing new ideas and methods for real-time recognition of maize and other crop spots in mobile applications. [ABSTRACT FROM AUTHOR]

The paper purpose was to assess the impact of climate change on wheat, maize and sunflower yield in the period 2017-2021, using statistical data from National Institute of Statistics (NIS) and National Administration of Meteorology (NAM) and other sources. Comparison method was used to evaluate the deviations between the registered air temperatures and precipitations and the 1981-2010 climatological norm. Graphic method reflected the dynamics of monthly air temperatures and rainfalls in each year. Descriptive statistics for mean, standard deviation and coefficient of variation reflected an more comprehensive image upon air temperature, precipitations and yield. Correlations and regression equations were used to establish the intensity of the kinks between climate factors and yield. The highest average air temperature In Romania was 12.130C in 2019. In the period 2017-2020, the average annual temperatures exceeded the climatological norm 1981-2010 accounting for 9.10C. The lowest rainfalls, 614.2 mm, were recorded in 2019, being below of 633.1 mm the norm for the period 1981-2010. The high temperatures associated with drought and low precipitations have deeply affected yield of many agricultural crops, including maize, wheat and sunflower. The highest wheat yield 4,888 kg/ha was achieved in 2017, and the lowest one, 2,966 kg/ha in 2020. Maize registered the highest performance of 7,644 kg/ha in 2018, and the lowest one, 3,977 kg/ha in 2020. Sunflower carried out the high performance 3,041 kg/ha in 2018, and the lowest one, 1,858 kg/ha in 2020. The worst agricultural years for these three crops was 2020, but 2017 favored wheat, while 2018 favored maize and sunflower. The correlation coefficient had in general small values between average temperature and yield, but a higher link with precipitations level. The multiple correlation between yield, air temperatures and precipitations was: r= 0.817 for wheat, r = 0.116 for maize and r = 0.504 for sunflower. In the South Eastern Dobrogea, the driest area in Romania, the rainfalls declined by 65% in the period 2018-2020 having a deep negative impact on sunflower seeds yield, which decreased from 4,282 kg/ha in 2018, to 1,503 kg/ha in 2020. The correlation between precipitations and sunflower yield positive and very strong (r = 0.737). As a conclusion, the climate conditions should be analyzed in each farm and farmers have to take measure to adapt the technologies for sustaining production. [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]

Purpose: The purpose of this paper is to measure technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from actual inefficiency using comprehensive household-level panel data. Design/methodology/approach: This paper estimates technical efficiency based on the true random-effects stochastic production frontier estimator with a Mundlak adjustment. By utilising comprehensive panel data with 4,694 observations from 39 districts of four major maize-producing regions in Ethiopia, the author measures technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from technical inefficiency. By using competing stochastic production frontier estimators, the author provides insights into the influence of farm heterogeneity on measuring farm efficiency and the subsequent impact on the ranking of farmers based on their efficiency scores. Findings: The study results indicate that ignoring unobservable farmer heterogeneity leads to a downwards bias of technical efficiency estimates with a consequent effect on the ranking of farmers based on their efficiency scores. The mean technical efficiency score implied that about a 34% increase in maize productivity can be achieved with the current input use and technology in Ethiopia. The key determinants of the technical inefficiency of maize farmers are the age, gender and formal education level of the household head, household size, income, livestock ownership, and participation in off-farm activities. Research limitations/implications: While the findings of this study are critical for informing policy on improving agricultural production and productivity, a few important things are worth considering in terms of the generalisability of the findings. First, the study relied on secondary data, so only a snapshot of environmental factors was accounted for in the empirical estimations. Second, there could be other sources of unmeasured potential sources of heterogeneity caused by persistent technical inefficiency and endogeneity of inputs. Third, the study is limited to one country. Therefore, future research should extend the analysis to ensure the generalisability of the empirical findings regarding the extent to which unmeasured potential sources of heterogeneity caused by persistent technical inefficiency, endogeneity of inputs and other unobservable country-specific features – such as geographical differences. Originality/value: This paper contributes to the literature on agricultural productivity and efficiency by providing new evidence on the influence of unobservable heterogeneity in a farm efficiency analysis. While agricultural production is characterised by heterogeneous production conditions, the influence of unobservable farm heterogeneity has generally been ignored in technical efficiency estimations, particularly in the context of smallholder farming. The value of this paper comes from disentailing producer-specific random heterogeneity from the actual inefficiency. [ABSTRACT FROM AUTHOR]

Situated at middle-to-high latitudes with limited thermal resources, Northeast China is the primary maize-producing region in China. It is also one of the regions most significantly impacted by climate change. Given the persistent impact of climate change, it is crucial to elucidate the effects of the varying thermal conditions and low temperatures for different sowing dates on the growth, development, and grain maturity of spring maize. To ensure secure maize production and disaster prevention, choosing the optimal sowing time for spring maize holds significant implications for the judicious utilization of climatic resources, risk mitigation, and the provision of meteorological guidance. Moreover, it can serve as a technical reference for relevant departments to conduct climate evaluation, disaster monitoring, prediction, and assessment, as well as impact analysis of corn production safety. Additionally, it can provide meteorological evidence to ensure food security and promote the sustainable development of modern agriculture. An interval sowing experiment of spring maize was conducted in Harbin in the north of Northeast China. Two varieties were used in the experiment. Four sowing dates were set, and the interval between adjacent sowing dates was 10 days. The local perennial sowing time, 5 May, was set as the second sowing date, with one date set later and two dates set earlier. During the experiment, the growth process, grain dry matter, seed moisture content, yield components, and temperature of spring maize were observed. The impact of temperature conditions on maize growth and yield formation was analyzed in this paper through mathematical statistics, which further led to the establishment of a monitoring and evaluation model for assessing the effect of thermal conditions and temperature on maize. The results showed that the growth rate of spring maize was closely related to temperature. When the average temperature, minimum temperature, and maximum temperature increased by 1 °C, the average emergence rate increased by 1.05%, 0.99%, and 1.07%, respectively, and the average vegetative growth rate increased by 0.16%, 0.16%, and 0.09%, respectively. The change rate of ≥10 °C active accumulated temperature was significantly correlated with the change rate of the dry weight of the grain kernel, which conformed to the quadratic equation of one variable. The temperature influence coefficients of different sowing dates varied from 1.0% to 1.7%. The relationship between the accumulated values of 10 ℃ active accumulated temperature and the grain moisture content of spring maize was a logarithmic function. From 10 to 50 days after anthesis, the effect of temperature can explain about 95% of the change in grain moisture content. After physiological maturity, the effect of thermal conditions can only explain 56–83%. The temperature influence coefficient ranges from 1.3% to 13.8%. Comparatively speaking, the second sowing date is the most suitable sowing date. Early sowing is prone to encounter low temperatures, resulting in underutilization of the early heat, while late sowing is prone to less heat. Both conditions are not conducive to better improve the yield of spring maize. [ABSTRACT FROM AUTHOR]

This paper determines the socioeconomic and physical characteristics that influence maize farmer's choice of land ownership systems in Tanzania, i.e., owned, sharecropped, and rented title land. The paper uses the Tanzania National Panel Survey (TZNPS) data basing on 2,073 observations comprising of a sample size of 691 households in three consecutive waves 2008/2009, 2010/2011, and 2012/2013. Using a t-test for mean comparison, the paper found that there is a significant difference between male and female-headed households across socioeconomic and physical factors determining the choice of land ownership systems among maize smallholder farmers. Moreover, the paper findings revealed that 90 percent, 7 percent, and 3 percent of the sampled maize smallholder farmers chose to farm under-owned, shared, and rented title land system respectively. Furthermore, the econometric analysis which is grounded from the multinomial logit regression model showed that accessibility, farm size, quantity harvested, accessibility of extension services, gender, household size, and age are all pertinent in influencing the smallholder maize farmer's choice of land ownership. The paper recommends that these characteristics of the smallholder maize producers should be critically considered when formulating any land and agricultural policies to enhance proper decision making by smallholder farmers on the choice of land ownership system that may led to sustainable agricultural production, productivity, and food security in developing countries particularly in Tanzania. [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]

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]

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]

The main concern of today's scientists is the sustainable development of agriculture to achieve global food and nutrition security. Genome editing technology is recognized worldwide for its potential for sustainable agricultural intensification. In this paper, applications of genome editing in maize were analyzed and exemplified, based on statistical data collected from the EU-SAGE Database from January 1996 to July 2023. In addition, the existing regulations for this new technology were also discussed. The results showed that, so far, 51 applications of maize genome editing have been reported in the EU-SAGE database, and the CRISPR/Cas system was the most used genome editing tool with the potential to rapidly generate new genotypes with high yield, improved quality, and stresses resistance. Most applications were developed in China and the USA. Due to current GMO legislation and political indecision regarding the authorization of genome-edited products, the European Union is not yet able to use and cultivate these new improved genotypes. [ABSTRACT FROM AUTHOR]

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Many factors influence the performance of the agricultural activity within a farm. From this multitude of factors, one of the most important and taken into consideration are the climatic condition and the fertilization plan projected and implemented by the managerial team of the farm. In this matter, this paper presents the experimental results of these factors on the performance of the agricultural output on a specific geographic area in Boureni-Bailesti area in Southern Romania. The experiment has researched the influence of the climatic condition and the fertilizers on the level and the quality of the yields. In this manner, the grain yields have ranged between 8,040 and 10,260 kg/ha, the grain output for 1 kg of fertilizer being between 10.7 and 16.8 kg and the protein content in function of the fertilization was 12.4-12.7%. [ABSTRACT FROM AUTHOR]