Pub Date : 2025-12-01DOI: 10.1016/j.agrcom.2025.100114
Lingling Wu , Zhaolu Wang , Xiaoting Wu, Yidi Zhang, Gongshe Yang, Jianjun Jin, Xin'e Shi
MicroRNAs (miRNAs) are key regulators of porcine myogenesis. In this study, we elucidated the regulatory role of miR-493-3p in skeletal muscle development. miR-493-3p showed a preferential expression pattern in skeletal muscle, with significantly higher expression in slow-twitch muscle than in fast-twitch muscle. Functional analyses revealed that overexpression of miR-493-3p inhibited the proliferation of porcine primary muscle satellite cells (MSCs) while promoting their differentiation and slow-twitch myofiber formation. In mouse, adeno-associated virus (AAV)-mediated overexpression of miR-493-3p significantly increased the cross-sectional area (CSA) of tibialis anterior muscle and promoted the proportion of slow-twitch myofibers. Furthermore, bioinformatic analysis and Dual-Luciferase Reporter Assay identified MAP kinase kinase 7 (MKK7) as a direct target of miR-493-3p. The miR-493-3p inhibited MKK7 expression, consequently reducing phosphorylated c-Jun N-terminal kinase (p-JNK) levels. Overall, our data demonstrate that miR-493-3p promotes porcine MSC differentiation and slow-twitch myofiber formation via inhibiting the MKK7/JNK axis. This finding enhances our understanding of miRNA-regulated skeletal muscle developmental networks and provides a potential strategy for improving pork production and meat quality.
MicroRNAs （miRNAs）是猪肌肉发生的关键调控因子。在这项研究中，我们阐明了miR-493-3p在骨骼肌发育中的调节作用。miR-493-3p在骨骼肌中表现出优先表达模式，在慢肌肌中的表达明显高于在快肌肌中的表达。功能分析显示，miR-493-3p的过表达抑制了猪原代肌卫星细胞（MSCs）的增殖，同时促进其分化和慢肌纤维的形成。在小鼠中，腺相关病毒（AAV）介导的miR-493-3p过表达显著增加了胫骨前肌的横截面积（CSA），促进了慢肌纤维的比例。此外，生物信息学分析和双荧光素酶报告试验鉴定MAP激酶激酶7 （MKK7）是miR-493-3p的直接靶点。miR-493-3p抑制MKK7的表达，从而降低磷酸化的c-Jun n -末端激酶（p-JNK）水平。总体而言，我们的数据表明，miR-493-3p通过抑制MKK7/JNK轴促进猪间充质干细胞分化和慢肌纤维形成。这一发现增强了我们对mirna调控的骨骼肌发育网络的理解，并为提高猪肉产量和肉质提供了潜在的策略。
{"title":"miR-493-3p promotes porcine muscle satellite cells differentiation and the formation of slow muscle fibers through MKK7/JNK axis","authors":"Lingling Wu , Zhaolu Wang , Xiaoting Wu, Yidi Zhang, Gongshe Yang, Jianjun Jin, Xin'e Shi","doi":"10.1016/j.agrcom.2025.100114","DOIUrl":"10.1016/j.agrcom.2025.100114","url":null,"abstract":"<div><div>MicroRNAs (miRNAs) are key regulators of porcine myogenesis. In this study, we elucidated the regulatory role of miR-493-3p in skeletal muscle development. miR-493-3p showed a preferential expression pattern in skeletal muscle, with significantly higher expression in slow-twitch muscle than in fast-twitch muscle. Functional analyses revealed that overexpression of miR-493-3p inhibited the proliferation of porcine primary muscle satellite cells (MSCs) while promoting their differentiation and slow-twitch myofiber formation. In mouse, adeno-associated virus (AAV)-mediated overexpression of miR-493-3p significantly increased the cross-sectional area (CSA) of tibialis anterior muscle and promoted the proportion of slow-twitch myofibers. Furthermore, bioinformatic analysis and Dual-Luciferase Reporter Assay identified <em>MAP kinase kinase 7</em> (<em>MKK7</em>) as a direct target of miR-493-3p. The miR-493-3p inhibited <em>MKK7</em> expression, consequently reducing phosphorylated c-Jun N-terminal kinase (p-JNK) levels. Overall, our data demonstrate that miR-493-3p promotes porcine MSC differentiation and slow-twitch myofiber formation <em>via</em> inhibiting the MKK7/JNK axis. This finding enhances our understanding of miRNA-regulated skeletal muscle developmental networks and provides a potential strategy for improving pork production and meat quality.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"3 4","pages":"Article 100114"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Postharvest black spot of tomato causes considerable economic losses to the tomato industry. Biological control presents a sustainable and efficient alternative. This study investigated the biocontrol mechanism of Wickhamomyces anomalus and optimized its lyoprotectant formulation using response surface methodology. Results revealed that W. anomalus effectively antagonizes Alternaria alternata by colonizing tomato tissues and inhibiting spore germination and hyphal growth in vitro. Response surface analysis identified the optimal lyoprotectant formulation as follows: sorbitol (6.44 g/100 mL), skimmed milk powder (10.49 g/100 mL), L-glutamine sodium (3.18 g/100 mL), and trehalose (4.94 g/100 mL). Under this formulation, the survival rate of lyophilized W. anomalus was 85.9 %. Notably, the formulation maintained high storage stability, with a survival rate of 67.0 % after 90 days. During storage, the biocontrol efficacy against black spot remained robust – the rot rate of W. anomalus-treated tomatoes only increased from 14.1 % to 24.0 %, demonstrating its persistent biocontrol activity. In conclusion, an efficient and stable biocontrol agent based on W. anomalus has been developed, offering a promising solution for controlling postharvest diseases in tomatoes.
{"title":"The mechanisms of Wickhamomyces anomalus in control of postharvest black spot disease in tomatoes and the preparation of its biocontrol solid products","authors":"Xi Zhang, Marui Zhu, Dhanasekaran Solairaj, Kaili Wang, Qiya Yang, Hongyin Zhang","doi":"10.1016/j.agrcom.2025.100112","DOIUrl":"10.1016/j.agrcom.2025.100112","url":null,"abstract":"<div><div>Postharvest black spot of tomato causes considerable economic losses to the tomato industry. Biological control presents a sustainable and efficient alternative. This study investigated the biocontrol mechanism of <em>Wickhamomyces anomalus</em> and optimized its lyoprotectant formulation using response surface methodology. Results revealed that <em>W. anomalus</em> effectively antagonizes <em>Alternaria alternata</em> by colonizing tomato tissues and inhibiting spore germination and hyphal growth <em>in vitro</em>. Response surface analysis identified the optimal lyoprotectant formulation as follows: sorbitol (6.44 g/100 mL), skimmed milk powder (10.49 g/100 mL), L-glutamine sodium (3.18 g/100 mL), and trehalose (4.94 g/100 mL). Under this formulation, the survival rate of lyophilized <em>W. anomalus</em> was 85.9 %. Notably, the formulation maintained high storage stability, with a survival rate of 67.0 % after 90 days. During storage, the biocontrol efficacy against black spot remained robust – the rot rate of <em>W. anomalus</em>-treated tomatoes only increased from 14.1 % to 24.0 %, demonstrating its persistent biocontrol activity. In conclusion, an efficient and stable biocontrol agent based on <em>W. anomalus</em> has been developed, offering a promising solution for controlling postharvest diseases in tomatoes.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"3 4","pages":"Article 100112"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.agrcom.2025.100113
Farman Ullah , Guru-Pirasanna-Pandi Govindharaj , Moazam Hyder , Satyabrata Sarangi , Hina Gul , Xiaowei Li , Raul Narciso C. Guedes , Nicolas Desneux , Yaobin Lu
Botanical insecticides, derived from plant sources, have been used for millennia, long before the advent of synthetic chemicals. Though marginalized since the Green Revolution, growing concerns about the environmental and health impacts of synthetic insecticides have revived interest in these natural alternatives. Lepidopteran pests, particularly caterpillars, remain among the most damaging agricultural threats and are still predominantly managed with synthetic insecticides. Botanical insecticides offer a promising alternative due to their biodegradability, reduced environmental persistence, and diverse bioactivities—including insecticidal, antifeedant, and repellent effects—linked to compounds from neem, jatropha, rotenone-containing plants, and other sources. Recent breakthroughs in nanoformulations, such as nanoemulsions and metallic or polymeric nanoparticles, have significantly enhanced the efficacy, delivery efficiency, and stability of botanical insecticides. Nano-encapsulated extracts—like neem or rosemary extracts combined with silver nanoparticles—have shown superior pest control at lower dosages and reduced phytotoxicity. Yet, these technological advances have outpaced our understanding of their ecological implications. Key knowledge gaps remain regarding long-term environmental impacts, resistance evolution in target pests, and non-target organism effects. Most research continues to focus on a narrow range of plant species and active ingredients, while broader issues like large-scale production, and field-scale efficacy are underexplored. To fully exploit the potential of botanical insecticides, future efforts must prioritize ecological risk assessment, broaden the spectrum of studied plants, and integrate molecular tools such as CRISPR-Cas9, RNA interference (RNAi), transcriptomics, and machine learning. These tools provide deeper insights into pest physiology and resistance mechanisms, promoting precision, resilience, and environmental safety. Realizing this vision will require interdisciplinary collaboration to develop greener extraction methods, establish harmonized regulatory pathways, and conduct rigorous ecological risk assessments.
{"title":"From plants to pest targets: Revisiting botanical insecticides for lepidopteran pest management","authors":"Farman Ullah , Guru-Pirasanna-Pandi Govindharaj , Moazam Hyder , Satyabrata Sarangi , Hina Gul , Xiaowei Li , Raul Narciso C. Guedes , Nicolas Desneux , Yaobin Lu","doi":"10.1016/j.agrcom.2025.100113","DOIUrl":"10.1016/j.agrcom.2025.100113","url":null,"abstract":"<div><div>Botanical insecticides, derived from plant sources, have been used for millennia, long before the advent of synthetic chemicals. Though marginalized since the Green Revolution, growing concerns about the environmental and health impacts of synthetic insecticides have revived interest in these natural alternatives. Lepidopteran pests, particularly caterpillars, remain among the most damaging agricultural threats and are still predominantly managed with synthetic insecticides. Botanical insecticides offer a promising alternative due to their biodegradability, reduced environmental persistence, and diverse bioactivities—including insecticidal, antifeedant, and repellent effects—linked to compounds from neem, jatropha, rotenone-containing plants, and other sources. Recent breakthroughs in nanoformulations, such as nanoemulsions and metallic or polymeric nanoparticles, have significantly enhanced the efficacy, delivery efficiency, and stability of botanical insecticides. Nano-encapsulated extracts—like neem or rosemary extracts combined with silver nanoparticles—have shown superior pest control at lower dosages and reduced phytotoxicity. Yet, these technological advances have outpaced our understanding of their ecological implications. Key knowledge gaps remain regarding long-term environmental impacts, resistance evolution in target pests, and non-target organism effects. Most research continues to focus on a narrow range of plant species and active ingredients, while broader issues like large-scale production, and field-scale efficacy are underexplored. To fully exploit the potential of botanical insecticides, future efforts must prioritize ecological risk assessment, broaden the spectrum of studied plants, and integrate molecular tools such as CRISPR-Cas9, RNA interference (RNAi), transcriptomics, and machine learning. These tools provide deeper insights into pest physiology and resistance mechanisms, promoting precision, resilience, and environmental safety. Realizing this vision will require interdisciplinary collaboration to develop greener extraction methods, establish harmonized regulatory pathways, and conduct rigorous ecological risk assessments.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"3 4","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.agrcom.2025.100116
Robert J. Henry
Reduction in carbon emissions from the use of fossil fuels can be addressed by engineering plants to become a renewable resource to replace fossil carbon. Plant-based production of fuels and chemicals needs to be sustainable and cost competitive. The most abundant source of renewable carbon is lignocellulosic plant biomass. Conversion of this biomass to end products currently generates low yields due to the recalcitrance of lignified biomass. Improved processing technologies have contributed to making this economically feasible but widespread adoption of lignocellulose as a replacement for fossil carbon will require genetically improved plants with a biomass composition that facilitates processing. Large-scale production requires concentrated efforts on species delivering the highest biomass yields. Recent advances in biomass pre-treatment have delivered more cost-effective processing. Developments in the genomics of key biomass species and the availability of advanced spatial omics and gene editing now promise to provide pathways to engineer plant biomass to become a better raw material for these processes and drive rapid adoption.
{"title":"Engineering plants to replace fossil carbon","authors":"Robert J. Henry","doi":"10.1016/j.agrcom.2025.100116","DOIUrl":"10.1016/j.agrcom.2025.100116","url":null,"abstract":"<div><div>Reduction in carbon emissions from the use of fossil fuels can be addressed by engineering plants to become a renewable resource to replace fossil carbon. Plant-based production of fuels and chemicals needs to be sustainable and cost competitive. The most abundant source of renewable carbon is lignocellulosic plant biomass. Conversion of this biomass to end products currently generates low yields due to the recalcitrance of lignified biomass. Improved processing technologies have contributed to making this economically feasible but widespread adoption of lignocellulose as a replacement for fossil carbon will require genetically improved plants with a biomass composition that facilitates processing. Large-scale production requires concentrated efforts on species delivering the highest biomass yields. Recent advances in biomass pre-treatment have delivered more cost-effective processing. Developments in the genomics of key biomass species and the availability of advanced spatial omics and gene editing now promise to provide pathways to engineer plant biomass to become a better raw material for these processes and drive rapid adoption.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"3 4","pages":"Article 100116"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.agrcom.2025.100115
Qile Hu , Yingying Li , Xiangshi Luo , Shuya Zhang , Zhe Li , Xue Bao , Li Wang , Wenxuan Dong , Enkai Li , Lu Wang , Changhua Lai , Shuai Zhang
The pig industry is entering a new era characterized by artificial intelligence (AI) and interdisciplinary integration, with numerous innovative techniques facilitating the achievement of precision nutrition. This review summarizes relevant research on precision pig nutrition based on mathematical modeling conducted at the Ministry of Agriculture and Rural Affairs Feed Industry Centre (MAFIC), China Agricultural University, and compares these findings with studies from other research groups. Previous studies have shown that mathematical modeling is an effective tool for data integration and prediction. However, models in animal nutrition face challenges such as insufficient data and outdated algorithms. Therefore, developing and applying new data-collection methodologies, advanced algorithms, and platforms are necessary to achieve precision nutrition. Two novel, non-invasive, cost-effective, portable, and reproducible techniques – heart rate monitoring and bioelectrical impedance analysis – facilitate real-time predictions of heat production and enable analysis of body composition in pigs. The newly developed algorithms, including classification algorithms, artificial neural networks, data augmentation algorithms, interpretable machine learning algorithms, and multi-objective formulation algorithms, are utilized to forecast the net energy values of feedstuffs, construct nutrient requirement tables, and predict the growth performance of pigs in the context of big data and numerous parameters. Additionally, introducing new software and hardware, such as establishing big data analysis platforms and AI feed formulation software based on large language model architecture, is also significant. Moreover, future advancements in precision feeding equipment are of great interest. Integrating mathematical models with these new methods, algorithms, and software will enable the precise formulation of personalized nutrition plans and optimal adjustment of dietary structures for pigs, providing robust theoretical and practical guidance for the successful implementation of precision nutrition in pig production.
{"title":"Achieving precision nutrition in pigs through the utilization of mathematical modeling as a fundamental tool: A review of recent work","authors":"Qile Hu , Yingying Li , Xiangshi Luo , Shuya Zhang , Zhe Li , Xue Bao , Li Wang , Wenxuan Dong , Enkai Li , Lu Wang , Changhua Lai , Shuai Zhang","doi":"10.1016/j.agrcom.2025.100115","DOIUrl":"10.1016/j.agrcom.2025.100115","url":null,"abstract":"<div><div>The pig industry is entering a new era characterized by artificial intelligence (AI) and interdisciplinary integration, with numerous innovative techniques facilitating the achievement of precision nutrition. This review summarizes relevant research on precision pig nutrition based on mathematical modeling conducted at the Ministry of Agriculture and Rural Affairs Feed Industry Centre (MAFIC), China Agricultural University, and compares these findings with studies from other research groups. Previous studies have shown that mathematical modeling is an effective tool for data integration and prediction. However, models in animal nutrition face challenges such as insufficient data and outdated algorithms. Therefore, developing and applying new data-collection methodologies, advanced algorithms, and platforms are necessary to achieve precision nutrition. Two novel, non-invasive, cost-effective, portable, and reproducible techniques – heart rate monitoring and bioelectrical impedance analysis – facilitate real-time predictions of heat production and enable analysis of body composition in pigs. The newly developed algorithms, including classification algorithms, artificial neural networks, data augmentation algorithms, interpretable machine learning algorithms, and multi-objective formulation algorithms, are utilized to forecast the net energy values of feedstuffs, construct nutrient requirement tables, and predict the growth performance of pigs in the context of big data and numerous parameters. Additionally, introducing new software and hardware, such as establishing big data analysis platforms and AI feed formulation software based on large language model architecture, is also significant. Moreover, future advancements in precision feeding equipment are of great interest. Integrating mathematical models with these new methods, algorithms, and software will enable the precise formulation of personalized nutrition plans and optimal adjustment of dietary structures for pigs, providing robust theoretical and practical guidance for the successful implementation of precision nutrition in pig production.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"3 4","pages":"Article 100115"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1016/j.agrcom.2025.100111
Bin Zhang , Tongbing Su , Xiaoyun Xin , Weihong Wang , Xiuyun Zhao , Deshuang Zhang , Yangjun Yu , Fenglan Zhang , Peirong Li , Shuancang Yu
Brassica rapa is one of the most important vegetable crops with the largest cultivation area in China. Compared with other genotyping technologies, the Affymetrix Axiom genotyping with single nucleotide polymorphism (SNP) array has become popular due to its high-throughput, flexibility, and efficiency. In this study, we successfully developed the first SNP14K array for B. rapa based on resequencing data from 189 accessions. The array contains 148,399 high-quality SNPs evenly distributed across the genome. Minor Allele Frequency (MAF) analysis indicated that these SNPs are highly polymorphic. Principal component analysis (PCA) clearly distinguished different subspecies among the 189 B. rapa accessions. Population structure and phylogenetic analyses demonstrated that the 148,399 high-quality SNPs are representative. Next, we assessed the genetic relationships of 97 B. rapa varieties using the SNP14K array on the Axiom genotyping system. Phylogenetic analysis showed that these 97 cultivars were divided into seven distinct subpopulations: Chinese cabbage, Pak choi, Wawacai, Baibangkuaicai, Qingbangkuaicai, Caixin, and one mixed type. In addition, major quantitative trait loci (QTLs) related to leaf trichome and flowering time were accurately identified using the SNP14K array. This newly developed SNP14K array provides a valuable tool for genetic diversity analysis, gene/QTL mapping, and molecular breeding in B. rapa.
{"title":"Development and application of the SNP14K array to accelerate Brassica rapa genetic research","authors":"Bin Zhang , Tongbing Su , Xiaoyun Xin , Weihong Wang , Xiuyun Zhao , Deshuang Zhang , Yangjun Yu , Fenglan Zhang , Peirong Li , Shuancang Yu","doi":"10.1016/j.agrcom.2025.100111","DOIUrl":"10.1016/j.agrcom.2025.100111","url":null,"abstract":"<div><div><em>Brassica rapa</em> is one of the most important vegetable crops with the largest cultivation area in China. Compared with other genotyping technologies, the Affymetrix Axiom genotyping with single nucleotide polymorphism (SNP) array has become popular due to its high-throughput, flexibility, and efficiency. In this study, we successfully developed the first SNP14K array for <em>B. rapa</em> based on resequencing data from 189 accessions. The array contains 148,399 high-quality SNPs evenly distributed across the genome. Minor Allele Frequency (MAF) analysis indicated that these SNPs are highly polymorphic. Principal component analysis (PCA) clearly distinguished different subspecies among the 189 <em>B. rapa</em> accessions. Population structure and phylogenetic analyses demonstrated that the 148,399 high-quality SNPs are representative. Next, we assessed the genetic relationships of 97 <em>B. rapa</em> varieties using the SNP14K array on the Axiom genotyping system. Phylogenetic analysis showed that these 97 cultivars were divided into seven distinct subpopulations: Chinese cabbage, Pak choi, Wawacai, Baibangkuaicai, Qingbangkuaicai, Caixin, and one mixed type. In addition, major quantitative trait loci (QTLs) related to leaf trichome and flowering time were accurately identified using the SNP14K array. This newly developed SNP14K array provides a valuable tool for genetic diversity analysis, gene/QTL mapping, and molecular breeding in <em>B</em>. <em>rapa</em>.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"3 4","pages":"Article 100111"},"PeriodicalIF":0.0,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.agrcom.2025.100110
Yiftah Szoke , Guy Shani
Automated plant phenotyping can help to monitor the growth process of crops, eliminating the high costs associated with traditional manual approaches. Using low-cost devices (e.g., digital cameras), RGB images can be captured under field or greenhouse conditions to track various phenotypes. In this paper, we focused on a particular task – tracking plant growth by identifying and monitoring plant nodes in greenhouse-grown crops. We used a setup where a digital camera captured images at 1-h intervals, with object detection algorithms employed to facilitate rapid and cost-effective tracking of nodes. The main challenge addressed in this paper involved tracking nodes that were hidden temporarily caused by diurnal leaf movements – leaves obscure some nodes at different times throughout the day. Because a node may be hidden for a few hours but visible at other times during the day, one can predict its location while it is hidden. We proposed two approaches, clustering and linear interpolation, for estimating hidden node locations. We collected a set of greenhouse datasets for different crops and conducted empirical comparisons of our methods. Results showed that our approach predicted the node location with an average error of less than 4 cm.
{"title":"Tracking plant growth using image sequence analysis","authors":"Yiftah Szoke , Guy Shani","doi":"10.1016/j.agrcom.2025.100110","DOIUrl":"10.1016/j.agrcom.2025.100110","url":null,"abstract":"<div><div>Automated plant phenotyping can help to monitor the growth process of crops, eliminating the high costs associated with traditional manual approaches. Using low-cost devices (e.g., digital cameras), RGB images can be captured under field or greenhouse conditions to track various phenotypes. In this paper, we focused on a particular task – tracking plant growth by identifying and monitoring plant nodes in greenhouse-grown crops. We used a setup where a digital camera captured images at 1-h intervals, with object detection algorithms employed to facilitate rapid and cost-effective tracking of nodes. The main challenge addressed in this paper involved tracking nodes that were hidden temporarily caused by diurnal leaf movements – leaves obscure some nodes at different times throughout the day. Because a node may be hidden for a few hours but visible at other times during the day, one can predict its location while it is hidden. We proposed two approaches, clustering and linear interpolation, for estimating hidden node locations. We collected a set of greenhouse datasets for different crops and conducted empirical comparisons of our methods. Results showed that our approach predicted the node location with an average error of less than 4 cm.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"3 4","pages":"Article 100110"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1016/j.agrcom.2025.100104
Zhipeng Nan , Xinting Yang , Luis Ruiz-Garcia , Jingna Qiu , Yimeng Feng , Jiawei Han
This study presents an advanced multi-objective optimization model for cold chain distribution (CCD) that explicitly accounts for the impact of traffic congestion on key cost components. By integrating fixed costs, transportation costs, refrigeration costs, and carbon emission costs, along with customer satisfaction, the model aims to minimize total distribution costs while satisfying both environmental and operational constraints. An improved genetic algorithm (I-GA) is applied to optimize CCD routes under these constraints. Simulation results demonstrate that the I-GA significantly outperforms the traditional genetic algorithm (T-GA) in terms of the number of vehicles used and total travel distance. Notably, although incorporating traffic congestion into the model increases the overall CCD cost by 7.06 %, it concurrently reduces carbon emission costs by 3.72 %. Furthermore, the study identifies a synergistic effect when optimizing refrigeration costs and carbon emission costs jointly: this dual optimization results in only minimal increases in overall cost. This research provides a valuable decision-support tool for enterprises to develop more efficient, sustainable, and profitable CCD strategies.
{"title":"Multi-objective optimization of cold chain distribution routes considering traffic congestion","authors":"Zhipeng Nan , Xinting Yang , Luis Ruiz-Garcia , Jingna Qiu , Yimeng Feng , Jiawei Han","doi":"10.1016/j.agrcom.2025.100104","DOIUrl":"10.1016/j.agrcom.2025.100104","url":null,"abstract":"<div><div>This study presents an advanced multi-objective optimization model for cold chain distribution (CCD) that explicitly accounts for the impact of traffic congestion on key cost components. By integrating fixed costs, transportation costs, refrigeration costs, and carbon emission costs, along with customer satisfaction, the model aims to minimize total distribution costs while satisfying both environmental and operational constraints. An improved genetic algorithm (I-GA) is applied to optimize CCD routes under these constraints. Simulation results demonstrate that the I-GA significantly outperforms the traditional genetic algorithm (T-GA) in terms of the number of vehicles used and total travel distance. Notably, although incorporating traffic congestion into the model increases the overall CCD cost by 7.06 %, it concurrently reduces carbon emission costs by 3.72 %. Furthermore, the study identifies a synergistic effect when optimizing refrigeration costs and carbon emission costs jointly: this dual optimization results in only minimal increases in overall cost. This research provides a valuable decision-support tool for enterprises to develop more efficient, sustainable, and profitable CCD strategies.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"3 4","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1016/j.agrcom.2025.100105
Devi Balakrishnan, Jessica Ayala, Alejandro Vasquez, Nick Bateman, Rupesh Kariyat
Rice (Oryza sativa L.) is considered as the most important staple food crop in the world, feeding half of the global population. One of the emerging concerns in rice is the fall armyworm (Spodoptera frugiperda, FAW), a highly polyphagous insect herbivore. Although FAW has been well studied across many host systems, rice-FAW interactions—including the effects of phenology and seed treatment—are less understood. In this study, we examined the response of FAW to rice under a thiamethoxam seed treatment across three different phenological stages (V5: tillering; V11: maximum tillering; R3: panicle exsertion). We used four commonly grown cultivars in USA, exposed them to FAW for a short period of 48 h, and conducted a separate long-term feeding experiment using an artificial diet fortified with leaf material from treated and untreated plants. Data on FAW growth and development traits, rice surface defense levels, and FAW larval feeding behavior were collected. Our results showed that FAW response was significantly affected by seed treatment and rice phenological stages. Interestingly, maximum tillering, the most resistant stage against FAW was also found to have the highest levels of epicuticular wax and trichomes (after the V5 stage)—the two major surface defenses compared to the reproductive stage. We also found that seed treatment negatively affected wax content and increased FAW mortality. In light of the consequent changes in pest biology and emerging insecticide resistance, our data highlight the importance of rice phenology and seed treatment effects on innate rice defenses, as well as their consequences for herbivore feeding.
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Pub Date : 2025-09-03DOI: 10.1016/j.agrcom.2025.100103
Chen Feng , Qingqing Guo , Chuanbao Wu , Xiaoming Zhang , Jing Wang , Guoqing Song , Guohua Yan , Yu Zhou , Wei Wang , Kaichun Zhang , Xuwei Duan
Bagging is a generic technique to improve fruit quality in fruit trees, but its specific impact on the coloration of sweet cherries remains unclear. In this study, we integrated transcriptomic and metabolomic analyses to dissect the regulatory networks of metabolites and genes affected by yellow-black double-layered bagging. Our results demonstrated that bagging enhanced the coloration of ‘Lapins’ cherries, accompanied by significant increases in the concentrations of key anthocyanins (ACNs), including cyanidin-3-O-glucoside, pelargonidin-3-O-rutinoside, peonidin-3-O-rutinoside, and cyanidin-3-O-rutinoside. Bagged cherries also showed marked accumulation of glucose, D-sorbitol, and D-fructose, which may contribute to ACN synthesis. In terms of hormones, the concentrations of four compounds—one abscisic acid derivative, two auxins, and one jasmonic acid derivative—exhibited positive correlations with ACN accumulation, while two cytokinins exhibited negative correlations, suggesting hormone-specific regulatory roles in coloration. Transcriptomic analysis revealed that changes in ACN synthesis-related genes drove the observed coloration differences. Specifically, PavMYB10.1 and PavNAC02 acted as positive regulators, promoting ACN biosynthesis, while PavBBX24 functioned as a negative regulator, preventing excessive ACN accumulation under normal light conditions. These findings suggest that bagging affects sweet cherry fruit coloration by regulating ACN accumulation, thereby enhancing our understanding of the underlying molecular mechanisms.
套袋是一种提高果树果实质量的通用技术，但其对甜樱桃颜色的具体影响尚不清楚。在这项研究中，我们整合了转录组学和代谢组学分析，剖析了受黄黑双层套袋影响的代谢物和基因的调控网络。我们的研究结果表明，套袋增强了“Lapins”樱桃的颜色，同时显著增加了关键花青素（acn）的浓度，包括花青素-3- o -葡萄糖苷、天竺葵苷-3- o -芦丁苷、芍药苷-3- o -芦丁苷和花青素-3- o -芦丁苷。袋装樱桃也显示出葡萄糖、d -山梨醇和d -果糖的显著积累，这可能有助于ACN的合成。在激素方面，四种化合物（一种脱落酸衍生物、两种生长素和一种茉莉酸衍生物）的浓度与ACN积累呈正相关，而两种细胞分裂素的浓度呈负相关，表明激素特异性调节着颜色。转录组学分析表明，ACN合成相关基因的变化驱动了观察到的颜色差异。具体来说，PavMYB10.1和PavNAC02作为正调节因子，促进ACN的生物合成，而PavBBX24作为负调节因子，在正常光照条件下防止ACN的过度积累。这些发现表明，套袋通过调节ACN积累来影响甜樱桃果实的颜色，从而增强了我们对潜在分子机制的理解。
{"title":"Effect of bagging treatment on fruit anthocyanin biosynthesis in sweet cherry","authors":"Chen Feng , Qingqing Guo , Chuanbao Wu , Xiaoming Zhang , Jing Wang , Guoqing Song , Guohua Yan , Yu Zhou , Wei Wang , Kaichun Zhang , Xuwei Duan","doi":"10.1016/j.agrcom.2025.100103","DOIUrl":"10.1016/j.agrcom.2025.100103","url":null,"abstract":"<div><div>Bagging is a generic technique to improve fruit quality in fruit trees, but its specific impact on the coloration of sweet cherries remains unclear. In this study, we integrated transcriptomic and metabolomic analyses to dissect the regulatory networks of metabolites and genes affected by yellow-black double-layered bagging. Our results demonstrated that bagging enhanced the coloration of ‘Lapins’ cherries, accompanied by significant increases in the concentrations of key anthocyanins (ACNs), including cyanidin-3-O-glucoside, pelargonidin-3-O-rutinoside, peonidin-3-O-rutinoside, and cyanidin-3-O-rutinoside. Bagged cherries also showed marked accumulation of glucose, D-sorbitol, and D-fructose, which may contribute to ACN synthesis. In terms of hormones, the concentrations of four compounds—one abscisic acid derivative, two auxins, and one jasmonic acid derivative—exhibited positive correlations with ACN accumulation, while two cytokinins exhibited negative correlations, suggesting hormone-specific regulatory roles in coloration. Transcriptomic analysis revealed that changes in ACN synthesis-related genes drove the observed coloration differences. Specifically, <em>PavMYB10.1</em> and <em>PavNAC02</em> acted as positive regulators, promoting ACN biosynthesis, while PavBBX24 functioned as a negative regulator, preventing excessive ACN accumulation under normal light conditions. These findings suggest that bagging affects sweet cherry fruit coloration by regulating ACN accumulation, thereby enhancing our understanding of the underlying molecular mechanisms.</div></div>","PeriodicalId":100065,"journal":{"name":"Agriculture Communications","volume":"3 4","pages":"Article 100103"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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