European Journal of Agronomy最新文献

{"title":"Simulating phosphorus dynamics between the soil and the crop with the STICS model: Formalization and multi-site evaluation on maize in temperate area","authors":"Mounir Seghouani ,&nbsp;Matthieu N. Bravin ,&nbsp;Patrice Lecharpentier ,&nbsp;Alain Mollier","doi":"10.1016/j.eja.2024.127475","DOIUrl":"10.1016/j.eja.2024.127475","url":null,"abstract":"<div><div>Soil-crop models are pertinent tools to study and manage phosphorus (P) in agroecosystems. However, P modeling is suffering a delay as compared to nitrogen and carbon. In this study, we extended the STICS model to simulate the P uptake and P feed-back by coupling it with a soil-plant P model. The paper aims at describing the P model and present the results showing the ability of the model to simulate contrasting P uptake and growth response pattern of maize submitted to different level of P inputs. in temperate area. The model simulates the soil P availability and the crop P demand, uptake, and partitioning. A major originality of this work is that it relies on soil solution P concentration and P sorption curves to simulate soil P availability, and critical P dilution curves to simulate crop P demand. We evaluated the model against a dataset coming from four field fertilization trials located at different site in mainland France. The trials consisted of fertilizing maize with a mineral fertilizer at three application rates (P0, P1, P2) which induced contrasted crop responses to P shortage. The model has shown great ability in predicting P uptake both dynamically and at the end of the cropping season for the entire dataset (EF &gt;0.75). The model has satisfactory predictions of crop biomass accumulation (EF &gt;0.5) and leaf area index. Considering each fertilization level separately, the evaluation has shown that the model had predicted the final P uptake of P1 and P2 treatments better than that of P0 treatment (EF of 0.74, 0.73 and 0.62 for P2, P1, and P0, respectively). The predictions made for the P0 treatment remained nonetheless satisfactory for both P uptake and plant growth. The good performance of the model is promising as it shows that the model is sufficiently robust to simulate maize P uptake across a range of soil P availability and P fertilization under contrasting temperate climatic conditions. Further validation on other crop species and soil and climatic conditions is discussed.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127475"},"PeriodicalIF":4.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating high-frequency detail information for enhanced corn leaf disease recognition: A model utilizing fusion imagery","authors":"Haidong Li ,&nbsp;Chao Ruan ,&nbsp;Jinling Zhao ,&nbsp;Linsheng Huang ,&nbsp;Yingying Dong ,&nbsp;Wenjiang Huang ,&nbsp;Dong Liang","doi":"10.1016/j.eja.2024.127489","DOIUrl":"10.1016/j.eja.2024.127489","url":null,"abstract":"<div><div>There are various types of corn diseases, many of which affect the leaves. However, the specific details such as shape, size, color, and texture of these diseases in images can present challenges for accurate recognition by deep neural networks (DNNs). Furthermore, images of corn leaf diseases captured in the field often contain noise, which can reduce the robustness and effectiveness of the trained model. Addressing these challenges and acknowledging the limitations of current DNNs models in capturing intricate high-frequency details when identifying corn leaf disease images in complex backgrounds, this study proposes a novel corn leaf disease recognition model that incorporates high-frequency information from images. The proposed model enhances the network's fitting capability by integrating high-frequency detailed features from images into the final three layers of the lightweight MobileNetV3-Large architecture. To effectively represent high-frequency information, a high-frequency feature extraction block (HFFE) is devised, and the adaptive ACON-C activation function is employed to enhance the nonlinear expression capacity of high-frequency details. The end-to-end recognition approach yields a 2.1 % increase in average recognition accuracy compared to the baseline MobileNetV3-Large model, indicating that the inclusion of high-frequency information features enhances model performance. Furthermore, experiments introducing varying levels of noise to the test data illustrate the model's superior anti-interference capabilities and robustness. Consequently, our model exhibits significant value and practical utility for real-world applications.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127489"},"PeriodicalIF":4.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accounting for soil water improves prediction of lentil phenology for improved frost and heat stress management","authors":"Yashvir Singh Chauhan ,&nbsp;Muhuddin Rajin Anwar ,&nbsp;Mark F. Richards ,&nbsp;Ryan H.L. Ip ,&nbsp;David J. Luckett ,&nbsp;Lachlan Lake ,&nbsp;Victor O. Sadras ,&nbsp;Kadambot H.M. Siddique","doi":"10.1016/j.eja.2024.127486","DOIUrl":"10.1016/j.eja.2024.127486","url":null,"abstract":"<div><div>Lentils in Australia are primarily grown in temperate and Mediterranean climates, especially in the southern and western regions of the country. As in other parts of the world, lentil yields in these areas are significantly influenced by factors such as frost, heat, and drought, contributing to variable production. Therefore, selecting appropriate lentil varieties and determining optimal sowing times that align with favourable growing conditions is crucial. Accurate predictions of crop development are essential in this context. Current models mainly rely on photoperiod and temperature to predict lentil phenology; however, they often neglect the impact of soil water on flowering and pod set. This study investigated whether incorporating soil water as an additional factor could improve predictions for these critical growth stages. The modified model was tested using 281 data points from various lentil experiments that examined the timing of flowering (61–147 days) and pod set (77–163 days) across different combinations of location, variety, sowing time, and season. The results indicated that including soil water in the prediction model achieved an R² value of 0.84 for flowering and 0.83 for pod set. The normalised root mean square error (NRMSE) was 0.07, and Lin's concordance correlation coefficient (LinCCC) was 0.91. The model produced an R² of 0.88, an NRMSE of 0.05, and a LinCCC of 0.93 flowering compared to the default model, which yielded an R² of 0.24, an NRMSE of 0.17, and a LinCCC of 0.36 for flowering. A limited sensitivity analysis of the modified model showed that variations in initial soil water and in-season rainfall significantly affected the timing of flowering and pod set. Additionally, we employed a probability framework to assess the crop's vulnerability to the last frost day and early heat stress events during the reproductive stage. This approach provided valuable insights for decision-making to mitigate risks associated with frost and heat stress. Our study suggests that integrating soil water dynamics into lentil phenology models improves the accuracy and precision of predictions regarding the timing of flowering and pod set. These improvements lead to better forecasts, ultimately helping to minimise damage from frost and heat stress during lentil cultivation and can better explain the effect of climate variability.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127486"},"PeriodicalIF":4.5,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Field-based hyperspectral imaging for detection and spatial mapping of fusarium head blight in wheat","authors":"Muhammad Baraa Almoujahed ,&nbsp;Orly Enrique Apolo-Apolo ,&nbsp;Rebecca L. Whetton ,&nbsp;Marius Kazlauskas ,&nbsp;Zita Kriaučiūnienė ,&nbsp;Egidijus Šarauskis ,&nbsp;Abdul Mounem Mouazen","doi":"10.1016/j.eja.2024.127485","DOIUrl":"10.1016/j.eja.2024.127485","url":null,"abstract":"<div><div>Fusarium head blight (FHB) poses a substantial threat to cereal crop production, significantly affecting both grain yield and quality by producing harmful mycotoxins such as deoxynivalenol (DON), which is detrimental to human and animal health. To manage this threat effectively, precise detection and mapping of FHB spatial distribution at the field level are crucial. This study aimed to detect and map FHB in four commercial winter wheat fields in Belgium and Lithuania using a push-broom hyperspectral camera (400–1000 nm), mounted on a tractor. The on-line collected hyperspectral data were first subjected to a linear regression model to segment wheat ears from the background using a linear regression model, achieving a precision of 0.99. The segmented hyperspectral data were then correlated with FHB severity, assessed by means of groundtruth captured RGB images using two dataset. The first dataset (M1) combined data from both countries, whereas the second dataset (M2) used data from the three fields in Lithuania only. The two datasets were then subjected to four machine learning (ML) modelling techniques, namely, extra trees regression (ETR), random forest regression (RFR), support vector regression (SVR), and one-dimensional convolutional neural network (1DCNN). Once validated using an independent validation set, these models were used to predict and map FHB using the on-line collected spectra in the four fields. Additionally, recursive feature elimination (RFE) and mutual information (MI) approaches to select the optimal wavebands for FHB detection were employed. Results demonstrated the capability of ETR to predict FHB severity successfully, surpassing the other ML models, achieving coefficients of determination (R²) values of 0.68 and 0.79 for M1 and M2, respectively. The residual prediction deviation (RPD) values recorded were 1.77 for M1 and 2.18 for M2, and the ration of performance to inter-quartile range (RPIQ) values were 2.89 and 3.51, respectively. Moreover, M2 showed enhanced model accuracy for the used ML models, except for SVM. The application of MI on ETR significantly improved the predictive accuracy, with R² values of 0.75 for M1 and 0.82 for M2, In contrast, the application of RFE did not result in any improvement in the models effectiveness, as evidenced by R² values of 0.65 and 0.75 for M1 and M2, respectively. A comparison between the predicted points from the on-line scanning and ground truth maps shows varying levels of spatial similarity with a kappa value reaching 0.58. These results confirm the potential of integrating hyperspectral imaging with ML models for effective detection and spatial mapping of FHB in wheat fields.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127485"},"PeriodicalIF":4.5,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep placement of controlled-release and common urea achieves the win–win of enhancing maize productivity and decreasing environmental pollution","authors":"Peng Wu ,&nbsp;Jinyu Yu ,&nbsp;Qinhe Wang ,&nbsp;Zeyu Liu ,&nbsp;Hua Huang ,&nbsp;Qi Wu ,&nbsp;Liangqi Ren ,&nbsp;Guangxin Zhang ,&nbsp;Enke Liu ,&nbsp;Kemoh Bangura ,&nbsp;Min Sun ,&nbsp;Kejun Yang ,&nbsp;Zhiqiang Gao ,&nbsp;Peng Zhang ,&nbsp;Zhikuan Jia ,&nbsp;Jianfu Xue","doi":"10.1016/j.eja.2024.127484","DOIUrl":"10.1016/j.eja.2024.127484","url":null,"abstract":"<div><div>The application of controlled-release urea or deep fertilization is effective for increasing crop yields. However, more research is needed to determine whether the deep placement of controlled-release and common urea can increase crop productivity and reduce environmental pollution. Between 2019 and 2021, we conducted field experiments in semi-humid and drought prone areas of the Loess Plateau region in China to study the effects of different controlled-release and common urea fertilization methods on maize productivity. Further experiments were conducted in semi-arid areas in 2022 and 2023 to verify the research results. We used the traditional fertilization strategy with common urea (TFC) as the control, deep placement of common urea (DFC), traditional fertilization with controlled-release and common urea (TFB), and deep placement with controlled-release and common urea (DFB) as optimized fertilizer management strategies. The results showed that the deep placement of controlled release and common urea changed the temporal and spatial distributions of the soil NO₃^–-N and NH₄⁺-N, which affected the N₂O and NH₃ emissions. The NH₃ emissions under DFC, TFB, and DFB were lower by 29.78 %, 32.77 %, and 59.08 % than TFC, and N₂O emissions were lower by 38.21 %, 40.96 %, and 72.89 %, respectively. Compared with TFC, the maize yields under DFC, TFB, and DFB were 7.91 %, 8.41 %, and 15.11 % higher, respectively, and the nitrogen use efficiencies were 14.23 %, 15.60 %, and 27.83 % higher, whereas the yield-scaled N₂O emissions were 38.21 %, 40.96 %, and 72.89 % lower, and the yield-scaled NH₃ emissions were 29.78 %, 32.77 %, and 59.08 % lower. Overall, DFB obtained the highest maize yield (12013.35 kg ha^–1) and nitrogen use efficiency (47.15 %), as well as the lowest gaseous nitrogen loss intensity (1.13 g N kg^–1 grain), global warming potential (323.08 kg CO₂-eq ha^–1), and greenhouse gas emission intensity (GHGI, 27.13 g CO₂-eq kg^–1 grain). Therefore, deep placement of controlled-release and common urea is an effective fertilizer management strategy that can balance maize productivity and environmental pollution in the Loess Plateau region of China.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127484"},"PeriodicalIF":4.5,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deriving fertiliser recommendations for cocoa: An offtake model approach","authors":"Ekatherina Vasquez-Zambrano ,&nbsp;Lotte Suzanne Woittiez ,&nbsp;Joost van Heerwaarden ,&nbsp;Leonard Rusinamhodzi ,&nbsp;Stefan Hauser ,&nbsp;Ken E. Giller","doi":"10.1016/j.eja.2024.127463","DOIUrl":"10.1016/j.eja.2024.127463","url":null,"abstract":"<div><div>Cocoa production in West Africa has increased over the years, yet yields are stagnant due to factors such as limited fertiliser use, poor maintenance, and inadequate pest control. The existing knowledge on cocoa mineral nutrition is limited, with outdated and inconsistent fertiliser recommendations across countries and regions. This study aimed to develop and describe a cocoa N, P, K offtake model based on nutrient export (pods and beans) and immobilisation in the tree. The model was used to calculate fertiliser rates for a series of 195 on-farm trials in Côte d’Ivoire, Ghana, Nigeria, and Cameroon. We compare the cocoa yields in response to fertiliser rates derived using the offtake model with the response to national recommendations in each country. On each farm, four treatment plots were delineated. The treatments were: T1 = current farmer practice, T2 = weeding + pruning + insecticide application + fungicide application (no fertiliser application), T3 = weeding + pruning + insecticide application + fungicide application + current national fertiliser recommendation, and T4 = weeding + pruning + insecticide application + fungicide application + offtake model-based fertiliser recommendation. Yields were recorded from September 2021 to August 2022 and an economic assessment was conducted using two different scenario prices for the years 2020/2021 and 2022/2023. Our results showed a positive effect of fertiliser on cocoa yield wherein T3 (1109 kg ha⁻¹) and T4 (1227 kg ha⁻¹) had significantly higher yields than T1 (912 kg ha⁻¹) and T2 (917 kg ha⁻¹). A positive overall yield effect of T4 over T3 was also observed; however, the difference was significant only in Côte d’Ivoire. The economic assessment showed that the treatment based on the offtake model (T4) gave a higher gross return than the national recommendations (T3) in all countries. However, the benefits decreased from 20/21–22/23 due to an increase in fertiliser prices. Our findings show that using an offtake model approach could provide a more accurate approximation of cocoa’s nutrient needs. Nonetheless, while the cocoa farm-gate price remains low, the investment capacity of the farmers to purchase fertiliser will remain limited.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127463"},"PeriodicalIF":4.5,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of remote sensing data fusion on agriculture applications: A review","authors":"Ayyappa Reddy Allu,&nbsp;Shashi Mesapam","doi":"10.1016/j.eja.2024.127478","DOIUrl":"10.1016/j.eja.2024.127478","url":null,"abstract":"<div><div>Remote sensing data fusion has emerged as a pivotal tool in agricultural monitoring and management. This review delves into the profound impact of remote sensing data fusion techniques on agricultural practices. Through a comprehensive examination of existing literature, the review elucidates the diverse fusion methodologies employed and their implications for agricultural applications. Various fusion techniques are explored in terms of their efficacy in enhancing agricultural monitoring capabilities. The review assesses the advantages and limitations of different fusion approaches and highlights their role in improving crop monitoring, pest and disease identification, weed detection, crop classification, and yield estimation. Furthermore, the review addresses the challenges, limitations and future prospects associated with remote sensing data fusion in agriculture mapping and monitoring. By synthesizing existing knowledge and insights, this review provides valuable guidance for researchers and practitioners seeking to leverage remote sensing data fusion for agricultural sustainability and productivity.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127478"},"PeriodicalIF":4.5,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Companion legume species for chicory in a phased farming system","authors":"Guangdi D. Li ,&nbsp;Richard C. Hayes ,&nbsp;Matthew J. Gardner ,&nbsp;Jeff I. McCormick ,&nbsp;Matthew T. Newell","doi":"10.1016/j.eja.2024.127488","DOIUrl":"10.1016/j.eja.2024.127488","url":null,"abstract":"<div><div>Chicory (<em>Cichorium intybus</em> L.) is a productive perennial pasture species that is well adapted to a range of environments and could be a viable ‘new’ alternative perennial species suited to phased farming systems in south-eastern Australia. However, chicory needs suitable legume companion species to meet its high nitrogen demand. The objectives of this study were to assess <em>a</em>) the compatibility of three commonly used self-regenerating annual legume species, arrowleaf clover (<em>Trifolium vesiculosum</em> Savi), balansa clover (<em>T. michelianum</em> Savi) and subterranean clover (<em>T. subterraneum</em> L.), and a perennial legume species, lucerne (<em>Medicago sativa</em> L.), as companion species for chicory; <em>b</em>) the productivity and persistence of different chicory-legume mixtures over 3 years; and <em>c</em>) the performance of subsequent crops after pastures were terminated. Four field experiments were conducted in two contrasting environments over 5 years. Pasture establishment, productivity and persistence were monitored over 3 years for each experiment in the pasture phase. Crop performance was assessed at one field site for two cropping seasons following removal of pastures either in spring or autumn. Results showed that the chicory-legume mixtures produced more herbage dry matter compared to the chicory only treatment. Subterranean clover was the most compatible legume species with chicory due to its greater persistence and reliable regeneration at both sites. By contrast, lucerne was highly competitive with chicory at the high fertility site, but failed to persist at the low fertility site making it a poor companion legume species for chicory in both instances. There was no grain yield penalty in the first wheat (<em>Triticum aestivum</em> L.) crop after late pasture removal, e.g. autumn removal, for most chicory-legume mixtures, providing up to 6 months additional feed for livestock before commencing a cropping phase.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127488"},"PeriodicalIF":4.5,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey of unmanned aerial vehicles and deep learning in precision agriculture","authors":"Dashuai Wang ,&nbsp;Minghu Zhao ,&nbsp;Zhuolin Li ,&nbsp;Sheng Xu ,&nbsp;Xiaohu Wu ,&nbsp;Xuan Ma ,&nbsp;Xiaoguang Liu","doi":"10.1016/j.eja.2024.127477","DOIUrl":"10.1016/j.eja.2024.127477","url":null,"abstract":"<div><div>In the wake of significant advances in agronomy, biology, informatics, agricultural robots (Agri-robots), and artificial intelligence, modern agriculture is transforming from labor-intensive to data-driven mode. Precision agriculture (PA) is one of the most practical solutions for bridging the crop yield gap by performing the right treatments in the right place and at the right time. As a rising star among Agri-robots, unmanned aerial vehicles (UAVs) equipped with high-resolution onboard sensors and dedicated application systems are playing an increasingly vital role in collecting multi-scale agricultural information and implementing site-specific treatment. In this process, a large number of images are produced. However, considerable effort is required to extract high-value information from the explosively growing number of images. Over the past decade, deep learning (DL) has demonstrated unparalleled advantages in agricultural analytics, such as crop/weed classification, biotic/abiotic stress detection, crop growth monitoring, yield prediction, natural disaster assessment, etc. The combination of UAVs and DL is of great significance for agricultural information acquisition, processing, analysis, decision-making, and deployment. With the rapid development of UAVs, DL, and PA, this work firstly introduces the key components of PA, UAVs, and DL, respectively, and summarizes their major research progress. Subsequently, we focus on the successful applications of UAVs and DL in PA. Furthermore, based on our extensive literature survey, their current challenges and future development trends are sorted out. Ultimately, we hope this survey can draw more attention to the novel applications of UAVs and DL in PA among multidisciplinary scientists around the world and inspire more exciting and practical studies.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127477"},"PeriodicalIF":4.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The biosynthesis of 2-acetyl-1-pyrroline is physiologically driven by carbon-nitrogen metabolism in fragrant rice","authors":"Zhe Jiang ,&nbsp;Xiangbin Yao ,&nbsp;Bin Du ,&nbsp;Xinyi Wang ,&nbsp;Xiangru Tang ,&nbsp;Shenggang Pan ,&nbsp;Zhaowen Mo","doi":"10.1016/j.eja.2024.127476","DOIUrl":"10.1016/j.eja.2024.127476","url":null,"abstract":"<div><div>Carbon and nitrogen are important energy sources and nutrients, and carbon metabolism and nitrogen metabolism play key roles in the growth and development of fragrant rice. However, little is known about how carbon-nitrogen treatments regulate the biosynthesis of 2-acetyl-1-pyrroline (2-AP), which imparts a special flavor and makes fragrant rice a preferred rice variety, in fragrant rice. The aim of this study was to explore the effects of carbon-nitrogen treatments on 2-AP biosynthesis, carbon metabolism, and nitrogen metabolism and their relationships in fragrant rice. A two-year field experiment was conducted at the experimental farm of South China Agricultural University. Two fragrant rice varieties, Meixiangzhan2 and Xiangyaxiangzhan, were used as materials, three nitrogen levels (urea sources at concentrations of 0, 0.5 %, and 1.0 %) and three carbon levels (glucose sources at concentrations of 0, 1.5 %, and 3 %) were used at the booting stage, and treatment without the application of glucose and urea was used as the control treatment. The 2-AP accumulation, carbon metabolism, and nitrogen metabolism parameters were investigated. The results showed that the carbon-nitrogen treatments differed from the control treatment. The 2-AP content in grains was significantly influenced by carbon, nitrogen, and their interactions, yet a significant impact of nitrogen on head rice yield was detected only. Treatment with 0.5 % urea + 1.5 % glucose resulted in the highest 2-AP content in the grains, which was 39.83–138.27 % and 56.45–123.77 % greater than that in the control treatment in Meixiangzhan2 and Xiangyaxiangzhan, respectively. Furthermore, a structural equation model indicated that nitrogen metabolism and sugar metabolism benefit 2-AP formation-related attributes in grain and ultimately lead to 2-AP accumulation. Sugar metabolism can directly lead to the accumulation of sugar and starch in stems and sheaths and 2-AP accumulation in grains. Overall, our results indicate that optimized carbon-nitrogen levels and/or carbon-nitrogen metabolism in fragrant rice plants benefit head rice yield and 2-AP accumulation in fragrant rice. Treatment with 0.5 % urea + 1.5 % glucose effectively increased the 2-AP content while maintaining a relatively high head rice yield in fragrant rice.</div></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":"164 ","pages":"Article 127476"},"PeriodicalIF":4.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}