Pub Date : 2025-03-06DOI: 10.1016/j.agwat.2025.109415
Lingling Cui , Jilian Lu , Shihao Ding , Xiaosa Song , Pengliang Chen , Baili Feng , Lixin Tian
Ridge-furrow mulching patterns and nitrogen application boosted crop yields in arid and semi-arid regions. Nevertheless, their combined impacts on broomcorn millet growth were unclear. A two-year field experiment was conducted to investigate the impacts of three ridge-furrow mulching configurations [traditional planting without mulch(TP), and two ridge-furrow mulching ratios, namely 40 cm: 40 cm(RF40), and 40 cm: 80 cm(RF80)] and four urea type ratios [100 % conventional urea application(U), 30 % conventional urea combined with 70 % controlled release urea(U3C7), 70 % conventional urea combined with 30 % controlled release urea(U7C3), 100 % controlled release urea(C), and no nitrogen fertilizer treatment(N0)] on water/nitrogen use efficiency and yield of broomcorn millet on the Loess Plateau. Results showed that in 2021, compared to TP, RF40 had higher soil moisture content, improved WUE, and increased the dry matter accumulation, thereby boosting the yield of broomcorn millet by 13.42 % and 17.15 % under U7C3 and U3C7 treatments, respectively. Meanwhile, U3C7 and U7C3 treatments significantly increased N partial factor productivity, nitrogen use efficiency, nitrogen recovery efficiency, and improved agronomic traits of broomcorn millet by coordinating fertilizer release with crop growth. Notably, the combination of RF40 and U7C3 maximized resource utilization efficiency and grain yield, with yield and water use efficiency increase of 42.79 % and 35.46 %, respectively. Partial least squares path modeling analysis indicated that fertilizer regimes were the key factor affecting the yield of broomcorn millet. This study offers a scientific foundation for enhancing resource utilization efficiency in arid and semi-arid regions.
{"title":"Optimizing the ratios of ridge-furrow mulching patterns and urea types improve the resource use efficiency and yield of broomcorn millet on the Loess Plateau of China","authors":"Lingling Cui , Jilian Lu , Shihao Ding , Xiaosa Song , Pengliang Chen , Baili Feng , Lixin Tian","doi":"10.1016/j.agwat.2025.109415","DOIUrl":"10.1016/j.agwat.2025.109415","url":null,"abstract":"<div><div>Ridge-furrow mulching patterns and nitrogen application boosted crop yields in arid and semi-arid regions. Nevertheless, their combined impacts on broomcorn millet growth were unclear. A two-year field experiment was conducted to investigate the impacts of three ridge-furrow mulching configurations [traditional planting without mulch(TP), and two ridge-furrow mulching ratios, namely 40 cm: 40 cm(RF40), and 40 cm: 80 cm(RF80)] and four urea type ratios [100 % conventional urea application(U), 30 % conventional urea combined with 70 % controlled release urea(U3C7), 70 % conventional urea combined with 30 % controlled release urea(U7C3), 100 % controlled release urea(C), and no nitrogen fertilizer treatment(N0)] on water/nitrogen use efficiency and yield of broomcorn millet on the Loess Plateau. Results showed that in 2021, compared to TP, RF40 had higher soil moisture content, improved WUE, and increased the dry matter accumulation, thereby boosting the yield of broomcorn millet by 13.42 % and 17.15 % under U7C3 and U3C7 treatments, respectively. Meanwhile, U3C7 and U7C3 treatments significantly increased N partial factor productivity, nitrogen use efficiency, nitrogen recovery efficiency, and improved agronomic traits of broomcorn millet by coordinating fertilizer release with crop growth. Notably, the combination of RF40 and U7C3 maximized resource utilization efficiency and grain yield, with yield and water use efficiency increase of 42.79 % and 35.46 %, respectively. Partial least squares path modeling analysis indicated that fertilizer regimes were the key factor affecting the yield of broomcorn millet. This study offers a scientific foundation for enhancing resource utilization efficiency in arid and semi-arid regions.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"312 ","pages":"Article 109415"},"PeriodicalIF":5.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550904","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}
Pub Date : 2025-03-06DOI: 10.1016/j.indcrop.2025.120782
Lin Chen , Heyun Song , Jia Xin , Wei Cheng , Mei Yang , Heng Sun
Lotus (Nelumbo nucifera) is a prominent ornamental plant renowned for its delightful floral aroma. We conducted an integrated analysis of volatile metabolomics and transcriptomics to investigate the composition and emission patterns of lotus floral aromas. A total of 1430 volatile organic compounds (VOCs) were identified across different flower organs, with stamens identified as the primary source. Notably, 1072 VOCs were detected in the stamens during various flowering stages, particularly on the day of flowering (S2) and the day after (S3), which were crucial for scent emission. The major contributors to the aroma included terpenoids, esters, and ketones. Transcriptome analysis identified 13,034 differentially expressed genes (DEGs) across various stages of stamen development. These DEGs were predominantly enriched in metabolic pathways and the biosynthesis of secondary metabolites. The expression of DEGs closely correlated with the accumulation profiles of VOCs, which increased during stages S2 and S3, before decreasing at stage S4, two days after flowering. Integrated analysis revealed that specific structural genes from the mevalonate (MVA) and 2-C-methyl-D-erythritol-4-phosphate (MEP) pathways significantly influence terpenoid biosynthesis and floral aroma formation. These findings deepen our understanding of lotus floral aroma and provide a foundation for the genetic improvement of floral aroma traits in lotus.
{"title":"Unraveling the floral aroma of lotus (Nelumbo nucifera): Insights from volatile metabolomics and transcriptomics","authors":"Lin Chen , Heyun Song , Jia Xin , Wei Cheng , Mei Yang , Heng Sun","doi":"10.1016/j.indcrop.2025.120782","DOIUrl":"10.1016/j.indcrop.2025.120782","url":null,"abstract":"<div><div>Lotus (<em>Nelumbo nucifera</em>) is a prominent ornamental plant renowned for its delightful floral aroma. We conducted an integrated analysis of volatile metabolomics and transcriptomics to investigate the composition and emission patterns of lotus floral aromas. A total of 1430 volatile organic compounds (VOCs) were identified across different flower organs, with stamens identified as the primary source. Notably, 1072 VOCs were detected in the stamens during various flowering stages, particularly on the day of flowering (S2) and the day after (S3), which were crucial for scent emission. The major contributors to the aroma included terpenoids, esters, and ketones. Transcriptome analysis identified 13,034 differentially expressed genes (DEGs) across various stages of stamen development. These DEGs were predominantly enriched in metabolic pathways and the biosynthesis of secondary metabolites. The expression of DEGs closely correlated with the accumulation profiles of VOCs, which increased during stages S2 and S3, before decreasing at stage S4, two days after flowering. Integrated analysis revealed that specific structural genes from the mevalonate (MVA) and 2-<em>C</em>-methyl-D-erythritol-4-phosphate (MEP) pathways significantly influence terpenoid biosynthesis and floral aroma formation. These findings deepen our understanding of lotus floral aroma and provide a foundation for the genetic improvement of floral aroma traits in lotus.</div></div>","PeriodicalId":13581,"journal":{"name":"Industrial Crops and Products","volume":"227 ","pages":"Article 120782"},"PeriodicalIF":5.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549036","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}
Huailin Zhou, Qijin He, Guangsheng Zhou, Xingyang Song
Photochemical reflectance index (PRI) has been a promising indicator for estimating vegetation photosynthetic efficiency. However, its capability to track drought stress-induced changes in canopy radiation use efficiency (RUE) and the underlying mechanisms remains insufficiently explored, largely due to the confounding effects of soil background and canopy characteristics. This study aimed to explain how the canopy PRI responds to drought stress and quantify the relative contributions of soil moisture and canopy characteristics to its variability. Using maize field experimental data across varying drought treatments, we found that drought significantly altered the PRI-RUE relationship, with canopy PRI exhibiting a stronger correlation with RUE under increasing soil drying. This enhancement in the PRI-RUE relationship was primarily attributed to changes in canopy structure and physiological characteristics. Specifically, the fraction of absorbed photosynthetic available radiation (fAPAR), canopy water content (CWC) and canopy chlorophyll content (CCC) were more related to PRI than leaf area index (LAI). While available soil water content (ASWC) was not directly linked to PRI, a positive linear relationship emerged after accounting for the effects of canopy characteristics, particularly fAPAR. Furthermore, fAPAR and LAI were identified as the most important direct and indirect factors influencing canopy PRI, respectively. These findings underscore the importance of considering fAPAR's contribution to accurately estimate photosynthetic efficiency and monitor crop stress under soil drying scenarios. By demonstrating how drought strengthens the PRI-RUE relationship and elucidating its underlying mechanisms, this study provides insights for improving crop stress monitoring and photosynthetic capacity assessment.
{"title":"Capability of Photochemical Reflectance Index to Track Maize Canopy Radiation Use Efficiency and Its Drivers Under Soil Drying","authors":"Huailin Zhou, Qijin He, Guangsheng Zhou, Xingyang Song","doi":"10.1111/jac.70044","DOIUrl":"https://doi.org/10.1111/jac.70044","url":null,"abstract":"<div>\u0000 \u0000 <p>Photochemical reflectance index (PRI) has been a promising indicator for estimating vegetation photosynthetic efficiency. However, its capability to track drought stress-induced changes in canopy radiation use efficiency (RUE) and the underlying mechanisms remains insufficiently explored, largely due to the confounding effects of soil background and canopy characteristics. This study aimed to explain how the canopy PRI responds to drought stress and quantify the relative contributions of soil moisture and canopy characteristics to its variability. Using maize field experimental data across varying drought treatments, we found that drought significantly altered the PRI-RUE relationship, with canopy PRI exhibiting a stronger correlation with RUE under increasing soil drying. This enhancement in the PRI-RUE relationship was primarily attributed to changes in canopy structure and physiological characteristics. Specifically, the fraction of absorbed photosynthetic available radiation (fAPAR), canopy water content (CWC) and canopy chlorophyll content (CCC) were more related to PRI than leaf area index (LAI). While available soil water content (ASWC) was not directly linked to PRI, a positive linear relationship emerged after accounting for the effects of canopy characteristics, particularly fAPAR. Furthermore, fAPAR and LAI were identified as the most important direct and indirect factors influencing canopy PRI, respectively. These findings underscore the importance of considering fAPAR's contribution to accurately estimate photosynthetic efficiency and monitor crop stress under soil drying scenarios. By demonstrating how drought strengthens the PRI-RUE relationship and elucidating its underlying mechanisms, this study provides insights for improving crop stress monitoring and photosynthetic capacity assessment.</p>\u0000 </div>","PeriodicalId":14864,"journal":{"name":"Journal of Agronomy and Crop Science","volume":"211 2","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays, soy protein-based food is consumed globally as an eco-friendly and healthy plant-based alterative. Nevertheless, more effort is still needed to improve the functionalities of soy protein for a wider application and addressing its existing challenges. Heat treatment is a fundamental approach in industrial food processing due to its simplicity, cost-effectiveness, and versatility. This review gave an emphasis on the recent advance in improving the functionalities of soy protein by heat treatment, including traditional and innovative heating, as well as a combination of heating and other techniques. Traditional thermal treatment has been proven to effectively improve the techno-functional properties of soy protein (e.g., heat stability; emulsifying, foaming, and gelation properties; and fibrillation), or to overcome its drawbacks (e.g., nutritional issues and antigenicity), or to promote its interactions with other compounds for novel functionalities via complicated protein changes (including conformational changes (e.g., unfolding, secondary and tertiary structures, surface hydrophobicity/charge) and covalent and/or non-covalent aggregation, as well as binding with other compounds). Recently, researchers have also proposed innovative heating and combination of heating and other techniques for a more efficient and effective soy protein modification. This review gave hints for a more precise and tailored modulation of soy protein functionalities via heat treatment in the commercial application.
{"title":"Current insights into heat treatment for improving functionalities of soy protein: A review","authors":"Simin Chen, Wenjuan Jiao, Jianfeng Wu","doi":"10.1111/1541-4337.70141","DOIUrl":"https://doi.org/10.1111/1541-4337.70141","url":null,"abstract":"<p>Nowadays, soy protein-based food is consumed globally as an eco-friendly and healthy plant-based alterative. Nevertheless, more effort is still needed to improve the functionalities of soy protein for a wider application and addressing its existing challenges. Heat treatment is a fundamental approach in industrial food processing due to its simplicity, cost-effectiveness, and versatility. This review gave an emphasis on the recent advance in improving the functionalities of soy protein by heat treatment, including traditional and innovative heating, as well as a combination of heating and other techniques. Traditional thermal treatment has been proven to effectively improve the techno-functional properties of soy protein (e.g., heat stability; emulsifying, foaming, and gelation properties; and fibrillation), or to overcome its drawbacks (e.g., nutritional issues and antigenicity), or to promote its interactions with other compounds for novel functionalities via complicated protein changes (including conformational changes (e.g., unfolding, secondary and tertiary structures, surface hydrophobicity/charge) and covalent and/or non-covalent aggregation, as well as binding with other compounds). Recently, researchers have also proposed innovative heating and combination of heating and other techniques for a more efficient and effective soy protein modification. This review gave hints for a more precise and tailored modulation of soy protein functionalities via heat treatment in the commercial application.</p>","PeriodicalId":155,"journal":{"name":"Comprehensive Reviews in Food Science and Food Safety","volume":"24 2","pages":""},"PeriodicalIF":12.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554832","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}
Pub Date : 2025-03-06DOI: 10.1016/j.agee.2025.109559
José Pinzón , Leydy Aceros , Björn Reu , Martha Patricia Ramírez-Pinilla , Raffael Ernst
Land use change and intensification are among the major threats to amphibian diversity. Nonetheless, human-modified agroforestry systems have been shown to preserve significant amphibian species richness, presumably because they provide alternative microhabitat structures that can be used by several species. Few studies have systematically analyzed the response of amphibians to different management types within these agroforestry systems considering multiple components of diversity. We assessed the composition of amphibians and their taxonomic, functional, and phylogenetic diversity across an agroforestry management gradient in the northern Andes, Colombia. This nonlinear gradient included montane and riparian forests and 6 different land-use systems. We evaluated amphibian assemblages along 34 independent transects covering the entire gradient and applying standardized visual and acoustic encounter techniques. We recorded 18 ecosystem structure variables to characterize the different management systems. We recorded a total of 3796 individuals belonging to 14 species and 7 families during 320 hours of transect sampling. Agroforestry systems with shaded plantations showed the highest overall amphibian species richness, while functional and phylogenetic diversity was highest in wetlands and a riparian forest fragment. Cattle pastures, the most intensive management type showed functional and phylogenetic homogenization and the lowest species richness. Structural habitat parameters that best explained the change in species composition were related to vegetation coverage and density. Our results highlight that total amphibian diversity cannot be preserved within a single management type. While less intensive agroforestry systems could be a helpful alternative for amphibian conservation in managed landscapes, linking natural forest fragments and sustainable agroforestry systems in a mosaic matrix design are key to maintaining diversity outside and inside of protected areas.
{"title":"Matrix-protection rather than protected area conservation can safeguard multilevel amphibian diversity in Colombian agroforestry systems","authors":"José Pinzón , Leydy Aceros , Björn Reu , Martha Patricia Ramírez-Pinilla , Raffael Ernst","doi":"10.1016/j.agee.2025.109559","DOIUrl":"10.1016/j.agee.2025.109559","url":null,"abstract":"<div><div>Land use change and intensification are among the major threats to amphibian diversity. Nonetheless, human-modified agroforestry systems have been shown to preserve significant amphibian species richness, presumably because they provide alternative microhabitat structures that can be used by several species. Few studies have systematically analyzed the response of amphibians to different management types within these agroforestry systems considering multiple components of diversity. We assessed the composition of amphibians and their taxonomic, functional, and phylogenetic diversity across an agroforestry management gradient in the northern Andes, Colombia. This nonlinear gradient included montane and riparian forests and 6 different land-use systems. We evaluated amphibian assemblages along 34 independent transects covering the entire gradient and applying standardized visual and acoustic encounter techniques. We recorded 18 ecosystem structure variables to characterize the different management systems. We recorded a total of 3796 individuals belonging to 14 species and 7 families during 320 hours of transect sampling. Agroforestry systems with shaded plantations showed the highest overall amphibian species richness, while functional and phylogenetic diversity was highest in wetlands and a riparian forest fragment. Cattle pastures, the most intensive management type showed functional and phylogenetic homogenization and the lowest species richness. Structural habitat parameters that best explained the change in species composition were related to vegetation coverage and density. Our results highlight that total amphibian diversity cannot be preserved within a single management type. While less intensive agroforestry systems could be a helpful alternative for amphibian conservation in managed landscapes, linking natural forest fragments and sustainable agroforestry systems in a mosaic matrix design are key to maintaining diversity outside and inside of protected areas.</div></div>","PeriodicalId":7512,"journal":{"name":"Agriculture, Ecosystems & Environment","volume":"386 ","pages":"Article 109559"},"PeriodicalIF":6.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548085","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}
Pub Date : 2025-03-06DOI: 10.1016/j.compag.2025.110165
Feifei Lu , Haonan Shangguan , Yizhe Yuan , Zheng Yan , Tianshuo Yuan , Yang Yang , Hongyu Wang , Weiming Xie , Guoxu Zhang , Zhiguo Wang , Zhaomin Yao
With the burgeoning global population, the necessity for sustainable and efficient agricultural practices has become paramount. The primary objective of this paper is to develop an accurate and efficient deep learning model for the timely detection of plant diseases, with a focus on improving crop yield and reducing economic loss. Specifically, this study addresses diseases affecting plant leaves, which present a significant challenge to agricultural productivity. To meet this objective, we introduce LeafConvNeXt, a novel deep learning model tailored to identify plant diseases by meticulously analyzing distinctive features of infected leaves. The secondary objectives include enhancing the interpretability of the model and ensuring its adaptability in resource-constrained environments. LeafConvNeXt integrates convolutional and attention mechanisms, achieving outstanding performance with an accuracy rate exceeding 99% across 52 distinct leaf diseases, outperforming existing contemporary methods. The model’s interpretability is further improved by utilizing LayerCAM, allowing for user-friendly visualization of the diagnostic process. Additionally, its low computational demands and high adaptability make it a practical solution for diverse applications, particularly in its potential integration into intelligent agricultural systems for real-time plant disease monitoring. By emphasizing green energy utilization and regulatory compliance in the era of Artificial Intelligence, LeafConvNeXt lays the groundwork for unmanned farming and a sustainable future in agriculture.
{"title":"LeafConvNeXt: Enhancing plant disease classification for the future of unmanned farming","authors":"Feifei Lu , Haonan Shangguan , Yizhe Yuan , Zheng Yan , Tianshuo Yuan , Yang Yang , Hongyu Wang , Weiming Xie , Guoxu Zhang , Zhiguo Wang , Zhaomin Yao","doi":"10.1016/j.compag.2025.110165","DOIUrl":"10.1016/j.compag.2025.110165","url":null,"abstract":"<div><div>With the burgeoning global population, the necessity for sustainable and efficient agricultural practices has become paramount. The primary objective of this paper is to develop an accurate and efficient deep learning model for the timely detection of plant diseases, with a focus on improving crop yield and reducing economic loss. Specifically, this study addresses diseases affecting plant leaves, which present a significant challenge to agricultural productivity. To meet this objective, we introduce LeafConvNeXt, a novel deep learning model tailored to identify plant diseases by meticulously analyzing distinctive features of infected leaves. The secondary objectives include enhancing the interpretability of the model and ensuring its adaptability in resource-constrained environments. LeafConvNeXt integrates convolutional and attention mechanisms, achieving outstanding performance with an accuracy rate exceeding 99% across 52 distinct leaf diseases, outperforming existing contemporary methods. The model’s interpretability is further improved by utilizing LayerCAM, allowing for user-friendly visualization of the diagnostic process. Additionally, its low computational demands and high adaptability make it a practical solution for diverse applications, particularly in its potential integration into intelligent agricultural systems for real-time plant disease monitoring. By emphasizing green energy utilization and regulatory compliance in the era of Artificial Intelligence, LeafConvNeXt lays the groundwork for unmanned farming and a sustainable future in agriculture.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"233 ","pages":"Article 110165"},"PeriodicalIF":7.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548415","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}
Pub Date : 2025-03-06DOI: 10.1016/j.catena.2025.108879
H.G. Momm , R.R. Wells , R. ElKadiri , T. Seever , D. Yoder , R.P. McGehee , R.L. Bingner , C.J.G. Darnault
Soil erosion computation technology plays an important role in planning to prevent and mitigate soil loss and non-point source pollution from agricultural fields. In the US, the RUSLE2 erosion model is extensively used by conservationists to support efforts for adoption of new farm management practices and implementation of conservation alternatives. Within RUSLE2, the impact of precipitation is described by average annual rainfall erosivity () which is represented by a smoothly and spatially varying surface that covers the entire US, assuring consistency in erosion predictions for conservation planning. In the current RUSLE2 erosivity database, these surfaces were developed by a laborious process of analyzing and processing data by hand, so this had not been updated since 2001. In this study, a protocol to generate isoerodent surfaces for the continental US is proposed and evaluated. The methodology describes steps that integrate the official RUSLE2 calculations with proposed new methods. The newly generated surfaces were compared to official RUSLE2 erosivity surfaces and evaluated for smoothness. Results indicate agreement with RUSLE2 surfaces for absolute values but with slightly higher spatial and temporal smoothness. Further refinements include the inclusion of small events, determination of spatially varying recurrence intervals, and consideration of two-axis trend interpolation enhanced with additional weighting accounting for data gaps, which gives more weight to weather stations that have more complete datasets. The protocol provides the means for capturing long-term climatic variations impacting soil erosion in a consistent way. This protocol supports forthcoming updates to the RUSLE2 climate database and serves as a baseline for future enhancements in the characterization of changing climatological drivers impacting soil erosion.
{"title":"Isoerodent surfaces of the continental US for conservation planning with the RUSLE2 water erosion model","authors":"H.G. Momm , R.R. Wells , R. ElKadiri , T. Seever , D. Yoder , R.P. McGehee , R.L. Bingner , C.J.G. Darnault","doi":"10.1016/j.catena.2025.108879","DOIUrl":"10.1016/j.catena.2025.108879","url":null,"abstract":"<div><div>Soil erosion computation technology plays an important role in planning to prevent and mitigate soil loss and non-point source pollution from agricultural fields. In the US, the RUSLE2 erosion model is extensively used by conservationists to support efforts for adoption of new farm management practices and implementation of conservation alternatives. Within RUSLE2, the impact of precipitation is described by average annual rainfall erosivity (<span><math><mi>R</mi></math></span>) which is represented by a smoothly and spatially varying surface that covers the entire US, assuring consistency in erosion predictions for conservation planning. In the current RUSLE2 erosivity database, these surfaces were developed by a laborious process of analyzing and processing data by hand, so this had not been updated since 2001. In this study, a protocol to generate isoerodent surfaces for the continental US is proposed and evaluated. The methodology describes steps that integrate the official RUSLE2 calculations with proposed new methods. The newly generated surfaces were compared to official RUSLE2 erosivity surfaces and evaluated for smoothness. Results indicate agreement with RUSLE2 surfaces for absolute values but with slightly higher spatial and temporal smoothness. Further refinements include the inclusion of small events, determination of spatially varying recurrence intervals, and consideration of two-axis trend interpolation enhanced with additional weighting accounting for data gaps, which gives more weight to weather stations that have more complete datasets. The protocol provides the means for capturing long-term climatic variations impacting soil erosion in a consistent way. This protocol supports forthcoming updates to the RUSLE2 climate database and serves as a baseline for future enhancements in the characterization of changing climatological drivers impacting soil erosion.</div></div>","PeriodicalId":9801,"journal":{"name":"Catena","volume":"253 ","pages":"Article 108879"},"PeriodicalIF":5.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552895","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}
Pub Date : 2025-03-06DOI: 10.1016/j.soilbio.2025.109769
Anna Favaro, Balwant Singh, Charles Warren, Feike A. Dijkstra
The addition of fresh carbon (C) substrates to the soil can result either in the acceleration or retardation of native soil organic matter (SOM) decomposition, referred to as the priming effect (PE). Related to PE is the rhizosphere priming effect (RPE), which involves the activity of plant roots. It remains unclear whether PE and RPE differ in their impacts on soil C dynamics and their response to soil nitrogen (N) availability. Here, we highlight drivers and mechanisms that distinguish RPE from PE and how they change with N addition. We develop different scenarios for PE and RPE separately and their responses to N addition. We describe temporal differences between PE and RPE, where microbes become more C limited with time in PE studies and more N limited in RPE studies. We then analysed published data to assess support for our scenarios. Without N addition PE and RPE were mostly positive and of similar magnitude, indicating that stoichiometric decomposition under microbial C limitation and N mining under microbial N limitation are major drivers of PE and RPE. Root effects on physical and chemical destabilization of native SOM and plant-microbe competition for N may also cause positive RPEs. With N addition, more than half of all observations showed reductions in PE (56%) and RPE (51%), possibly because of N-induced soil acidification. The RPEs and their responses to N could potentially last longer than for PE, due to ongoing root-induced destabilization of SOM and competition for N between plants and microbes thereby maintaining microbial N limitation and N mining. The distinctly different mechanisms and temporal dynamics between PE and RPE will have important implications for short- and long-term SOM dynamics and their responses to N addition.
{"title":"Differences between priming and rhizosphere priming effects: concepts and mechanisms","authors":"Anna Favaro, Balwant Singh, Charles Warren, Feike A. Dijkstra","doi":"10.1016/j.soilbio.2025.109769","DOIUrl":"https://doi.org/10.1016/j.soilbio.2025.109769","url":null,"abstract":"The addition of fresh carbon (C) substrates to the soil can result either in the acceleration or retardation of native soil organic matter (SOM) decomposition, referred to as the priming effect (PE). Related to PE is the rhizosphere priming effect (RPE), which involves the activity of plant roots. It remains unclear whether PE and RPE differ in their impacts on soil C dynamics and their response to soil nitrogen (N) availability. Here, we highlight drivers and mechanisms that distinguish RPE from PE and how they change with N addition. We develop different scenarios for PE and RPE separately and their responses to N addition. We describe temporal differences between PE and RPE, where microbes become more C limited with time in PE studies and more N limited in RPE studies. We then analysed published data to assess support for our scenarios. Without N addition PE and RPE were mostly positive and of similar magnitude, indicating that stoichiometric decomposition under microbial C limitation and N mining under microbial N limitation are major drivers of PE and RPE. Root effects on physical and chemical destabilization of native SOM and plant-microbe competition for N may also cause positive RPEs. With N addition, more than half of all observations showed reductions in PE (56%) and RPE (51%), possibly because of N-induced soil acidification. The RPEs and their responses to N could potentially last longer than for PE, due to ongoing root-induced destabilization of SOM and competition for N between plants and microbes thereby maintaining microbial N limitation and N mining. The distinctly different mechanisms and temporal dynamics between PE and RPE will have important implications for short- and long-term SOM dynamics and their responses to N addition.","PeriodicalId":21888,"journal":{"name":"Soil Biology & Biochemistry","volume":"30 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560639","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}
Pub Date : 2025-03-06DOI: 10.1016/j.biosystemseng.2025.02.013
Mengyuan Chu , Yongsheng Si , Qian Li , Xiaowen Liu , Gang Liu
The occurrence and prevalence of dairy cow mastitis has brought significant challenges to animal welfare and economy. To overcome the complexities and accumulated errors present in previous detection methods, a rapid and accurate mastitis detection approach is developed based on image processing and deep learning, leveraging thermal infrared imaging. Image processing techniques, including the Hough transform and morphological operations, are used to classify affected cows from thermal images. An image pyramid is constructed based on upsampling to tackle motion blur induced by the cows' rapid movement. The multi-scale convolution and the spatial and channel Squeeze & Excitation (scSE) block were integrated into the DenseNet-201 architecture to enhance the feature extraction process. This enabled the network to adaptively recalibrate channel-wise feature responses and strengthening the discriminative power of the learned representations. For mastitis detection, a deep learning model, the multi-scale scSE-DenseNet-201 (MS-scSE-DenseNet-201) architecture, is refined to predict the severity of mastitis. The framework takes images of both sides of the cow's udder as input, and outputs one of three mastitis severity levels: negative (N), subclinical mastitis (SCM), or clinical mastitis (CM). To assess the model's performance in detecting mastitis, a dataset comprising 5000 thermal images from 802 cows, was used. The model achieved accuracy, precision, and recall of 90.18%, 92.16%, and 88.38%, respectively, showing notable improvement over previous methods. This work integrated object segmentation and blind deblurring to strengthen the MS-scSE-DenseNet-201 in the automatic detection of cow mastitis, which will open a promising application horizon for other animal disease diagnostics.
{"title":"Deep learning-based model to classify mastitis in Holstein dairy cows","authors":"Mengyuan Chu , Yongsheng Si , Qian Li , Xiaowen Liu , Gang Liu","doi":"10.1016/j.biosystemseng.2025.02.013","DOIUrl":"10.1016/j.biosystemseng.2025.02.013","url":null,"abstract":"<div><div>The occurrence and prevalence of dairy cow mastitis has brought significant challenges to animal welfare and economy. To overcome the complexities and accumulated errors present in previous detection methods, a rapid and accurate mastitis detection approach is developed based on image processing and deep learning, leveraging thermal infrared imaging. Image processing techniques, including the Hough transform and morphological operations, are used to classify affected cows from thermal images. An image pyramid is constructed based on upsampling to tackle motion blur induced by the cows' rapid movement. The multi-scale convolution and the spatial and channel Squeeze & Excitation (scSE) block were integrated into the DenseNet-201 architecture to enhance the feature extraction process. This enabled the network to adaptively recalibrate channel-wise feature responses and strengthening the discriminative power of the learned representations. For mastitis detection, a deep learning model, the multi-scale scSE-DenseNet-201 (MS-scSE-DenseNet-201) architecture, is refined to predict the severity of mastitis. The framework takes images of both sides of the cow's udder as input, and outputs one of three mastitis severity levels: negative (N), subclinical mastitis (SCM), or clinical mastitis (CM). To assess the model's performance in detecting mastitis, a dataset comprising 5000 thermal images from 802 cows, was used. The model achieved accuracy, precision, and recall of 90.18%, 92.16%, and 88.38%, respectively, showing notable improvement over previous methods. This work integrated object segmentation and blind deblurring to strengthen the MS-scSE-DenseNet-201 in the automatic detection of cow mastitis, which will open a promising application horizon for other animal disease diagnostics.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"252 ","pages":"Pages 92-104"},"PeriodicalIF":4.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551919","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}
Nandan Kumar, Shan Hong, Yi Zhu, Antonio Garay, Jun Yang, Douglas Henderson, Xin Zhang, Yixiang Xu, Yonghui Li
Chickpeas (Cicer arietinum L.) are globally valued legume known for their affordability, nutritional significance, and health benefits. They are rich in protein, fiber, vitamins, and minerals such as iron, zinc, folate, and magnesium. This review comprehensively explores the chemical composition of chickpeas and their functional properties, focusing on macronutrients, micronutrients, phytochemicals, and antinutritional factors. It also delves into the potential health benefits of bioactive compounds and peptides derived from chickpeas, highlighting their roles in various physiological functions and applications. The exceptional technofunctional properties of chickpea proteins, including gel formation, texture enhancement, emulsification, and fat/water binding, make them ideal ingredients for diverse food products. Their versatility allows for use in various forms (isolates, concentrates, textured proteins), contributing to the development of a wide range of plant-based foods, nutritional supplements, and gluten-free options. While chickpeas contain some antinutrients like phytates, lectins, and enzyme inhibitors, effective processing methods can significantly reduce their potential negative effects. This review provides valuable insights, offering the novel contributions and an enhanced understanding it brings to the scientific community and food industry. By bridging compositional data with physiological implications, the review reinforces the pivotal role of chickpeas as a dietary component and enriches the existing scientific literature on this essential legume.
{"title":"Comprehensive review of chickpea (Cicer arietinum): Nutritional significance, health benefits, techno-functionalities, and food applications","authors":"Nandan Kumar, Shan Hong, Yi Zhu, Antonio Garay, Jun Yang, Douglas Henderson, Xin Zhang, Yixiang Xu, Yonghui Li","doi":"10.1111/1541-4337.70152","DOIUrl":"https://doi.org/10.1111/1541-4337.70152","url":null,"abstract":"<p>Chickpeas (<i>Cicer arietinum</i> L.) are globally valued legume known for their affordability, nutritional significance, and health benefits. They are rich in protein, fiber, vitamins, and minerals such as iron, zinc, folate, and magnesium. This review comprehensively explores the chemical composition of chickpeas and their functional properties, focusing on macronutrients, micronutrients, phytochemicals, and antinutritional factors. It also delves into the potential health benefits of bioactive compounds and peptides derived from chickpeas, highlighting their roles in various physiological functions and applications. The exceptional technofunctional properties of chickpea proteins, including gel formation, texture enhancement, emulsification, and fat/water binding, make them ideal ingredients for diverse food products. Their versatility allows for use in various forms (isolates, concentrates, textured proteins), contributing to the development of a wide range of plant-based foods, nutritional supplements, and gluten-free options. While chickpeas contain some antinutrients like phytates, lectins, and enzyme inhibitors, effective processing methods can significantly reduce their potential negative effects. This review provides valuable insights, offering the novel contributions and an enhanced understanding it brings to the scientific community and food industry. By bridging compositional data with physiological implications, the review reinforces the pivotal role of chickpeas as a dietary component and enriches the existing scientific literature on this essential legume.</p>","PeriodicalId":155,"journal":{"name":"Comprehensive Reviews in Food Science and Food Safety","volume":"24 2","pages":""},"PeriodicalIF":12.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554888","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}
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