Pub Date : 2023-09-02DOI: 10.3390/agriculture13091746
M. Savoia, V. Fanelli, M. Miazzi, F. Taranto, Silvia Procino, L. Susca, Vito Montilon, Oriana Potere, Franco Nigro, C. Montemurro
The olive tree (Olea europaea subsp. europaea var. europaea) represents the cornerstone crop of Apulian agriculture, which is based on the production of oil and table olives. The high genetic variability of the Apulian olive germplasm is at risk of genetic erosion due to social, economic, and climatic changes. Furthermore, since 2013, the spread of the Gram-negative bacterium Xylella fastidiosa subsp. pauca responsible for the olive quick decline syndrome (OQDS) has been threatening olive biodiversity in Apulia, damaging the regional economy and landscape heritage. The aim of this study was to investigate the differential response to X. fastidiosa infection in a collection of 100 autochthonous Apulian olive genotypes, including minor varieties, F1 genotypes, and reference cultivars. They were genotyped using 10 SSR markers and grown for 5 years in an experimental field; then, they were inoculated with the bacterium. Symptom assessments and the quantification of bacterium using a qPCR assay and colony forming units (CFUs) were carried out three and five years after inoculation. The study allowed the identification of nine putatively resistant genotypes that represent a first panel of olive germplasm resources that are useful both for studying the mechanisms of response to the pathogen and as a reserve for replanting in infected areas.
{"title":"Apulian Autochthonous Olive Germplasm: A Promising Resource to Restore Cultivation in Xylella fastidiosa-Infected Areas","authors":"M. Savoia, V. Fanelli, M. Miazzi, F. Taranto, Silvia Procino, L. Susca, Vito Montilon, Oriana Potere, Franco Nigro, C. Montemurro","doi":"10.3390/agriculture13091746","DOIUrl":"https://doi.org/10.3390/agriculture13091746","url":null,"abstract":"The olive tree (Olea europaea subsp. europaea var. europaea) represents the cornerstone crop of Apulian agriculture, which is based on the production of oil and table olives. The high genetic variability of the Apulian olive germplasm is at risk of genetic erosion due to social, economic, and climatic changes. Furthermore, since 2013, the spread of the Gram-negative bacterium Xylella fastidiosa subsp. pauca responsible for the olive quick decline syndrome (OQDS) has been threatening olive biodiversity in Apulia, damaging the regional economy and landscape heritage. The aim of this study was to investigate the differential response to X. fastidiosa infection in a collection of 100 autochthonous Apulian olive genotypes, including minor varieties, F1 genotypes, and reference cultivars. They were genotyped using 10 SSR markers and grown for 5 years in an experimental field; then, they were inoculated with the bacterium. Symptom assessments and the quantification of bacterium using a qPCR assay and colony forming units (CFUs) were carried out three and five years after inoculation. The study allowed the identification of nine putatively resistant genotypes that represent a first panel of olive germplasm resources that are useful both for studying the mechanisms of response to the pathogen and as a reserve for replanting in infected areas.","PeriodicalId":48587,"journal":{"name":"Agriculture-Basel","volume":"59 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85981875","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}
Pub Date : 2023-09-02DOI: 10.3390/agriculture13091748
Yan Liu, Ya Deng, Binyao Peng
Under the “two-carbon” goal, the green and low-carbon development of agriculture is a critical way to consummate agricultural modernization and high-quality economic establishment. Digital inclusive finance eases credit restrictions. It enhances the availability of funds for farmers. It promotes the integration of agricultural industries and talent gathering through digitalization, improves the standard of agricultural production and promotes the development of green and low-carbon agricultural modernization in China. This paper uses panel data for 2011–2021, which includes 31 provinces in China. Green and low-carbon development indicators of agriculture were constructed and calculated, and the comprehensive horizontal spatial differentiation map of GIS technology was used for analysis. A spatial panel model was set up at the same time, to explore the impact and mechanism test of digital financial inclusion on the green and low-carbon development of agriculture, and regional heterogeneity was analyzed. (1) Digital financial inclusion can promote the green and low-carbon development of agriculture, and its influence has a positive spatial spillover effect. (2) The education level of the labor force plays an intermediary role and is the transmission mechanism of digital financial inclusion and the green and low-carbon development of agriculture. (3) The impact of digital financial inclusion on green and low-carbon agricultural development has regional heterogeneity.
{"title":"The Impact of Digital Financial Inclusion on Green and Low-Carbon Agricultural Development","authors":"Yan Liu, Ya Deng, Binyao Peng","doi":"10.3390/agriculture13091748","DOIUrl":"https://doi.org/10.3390/agriculture13091748","url":null,"abstract":"Under the “two-carbon” goal, the green and low-carbon development of agriculture is a critical way to consummate agricultural modernization and high-quality economic establishment. Digital inclusive finance eases credit restrictions. It enhances the availability of funds for farmers. It promotes the integration of agricultural industries and talent gathering through digitalization, improves the standard of agricultural production and promotes the development of green and low-carbon agricultural modernization in China. This paper uses panel data for 2011–2021, which includes 31 provinces in China. Green and low-carbon development indicators of agriculture were constructed and calculated, and the comprehensive horizontal spatial differentiation map of GIS technology was used for analysis. A spatial panel model was set up at the same time, to explore the impact and mechanism test of digital financial inclusion on the green and low-carbon development of agriculture, and regional heterogeneity was analyzed. (1) Digital financial inclusion can promote the green and low-carbon development of agriculture, and its influence has a positive spatial spillover effect. (2) The education level of the labor force plays an intermediary role and is the transmission mechanism of digital financial inclusion and the green and low-carbon development of agriculture. (3) The impact of digital financial inclusion on green and low-carbon agricultural development has regional heterogeneity.","PeriodicalId":48587,"journal":{"name":"Agriculture-Basel","volume":"51 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80991889","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}
Pub Date : 2023-09-02DOI: 10.3390/agriculture13091745
Jingyi Li, Jiaxin He, Lun Yang, Qingwen Min
The protection and management of important agricultural heritage systems (IAHS) are essential to the sustainable economic and social development of heritage sites. Using the time-varying difference-in-differences (DID) model, this paper analyzes the influence of the identification of IAHS on economic growth and compares the difference between Globally Important Agricultural Heritage Systems (GIAHS) and China’s Nationally Important Agricultural Heritage Systems (China-NIAHS). The results show that the identification of IAHS can significantly promote the economic growth of heritage sites, and the identification of GIAHS has a stronger role. Heterogeneity analysis shows that the economic driving effect of IAHS identification on heritage sites is affected by geographical location and poverty. The economic driving effect is stronger in Western China and in relatively poor areas. In addition, the influencing mechanism of regional economic growth after IAHS identification is discussed. The results show that IAHS identification can promote the development of the grain processing industry and the improvement of infrastructure construction, so as to increase the added value of secondary industries at heritage sites. Moreover, the level of heritage recognition leads to different policy tendencies. Among these, GIAHS identification significantly promotes investment growth, while China-NIAHS identification significantly promotes the population agglomeration of heritage sites.
{"title":"Does the Identification of Important Agricultural Heritage Systems Promote Economic Growth? Empirical Analysis Based on County Data from China","authors":"Jingyi Li, Jiaxin He, Lun Yang, Qingwen Min","doi":"10.3390/agriculture13091745","DOIUrl":"https://doi.org/10.3390/agriculture13091745","url":null,"abstract":"The protection and management of important agricultural heritage systems (IAHS) are essential to the sustainable economic and social development of heritage sites. Using the time-varying difference-in-differences (DID) model, this paper analyzes the influence of the identification of IAHS on economic growth and compares the difference between Globally Important Agricultural Heritage Systems (GIAHS) and China’s Nationally Important Agricultural Heritage Systems (China-NIAHS). The results show that the identification of IAHS can significantly promote the economic growth of heritage sites, and the identification of GIAHS has a stronger role. Heterogeneity analysis shows that the economic driving effect of IAHS identification on heritage sites is affected by geographical location and poverty. The economic driving effect is stronger in Western China and in relatively poor areas. In addition, the influencing mechanism of regional economic growth after IAHS identification is discussed. The results show that IAHS identification can promote the development of the grain processing industry and the improvement of infrastructure construction, so as to increase the added value of secondary industries at heritage sites. Moreover, the level of heritage recognition leads to different policy tendencies. Among these, GIAHS identification significantly promotes investment growth, while China-NIAHS identification significantly promotes the population agglomeration of heritage sites.","PeriodicalId":48587,"journal":{"name":"Agriculture-Basel","volume":"8 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81766452","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}
The accurate identification of the origin of Chinese medicinal materials is crucial for the orderly management of the market and clinical drug usage. In this study, a deep learning-based algorithm combined with machine vision was developed to automatically identify the origin of Angelica sinensis (A. sinensis) from eight areas including 1859 samples. The effects of different datasets, learning rates, solver algorithms, training epochs and batch sizes on the performance of the deep learning model were evaluated. The optimized hyperparameters of the model were the dataset 4, learning rate of 0.001, solver algorithm of rmsprop, training epochs of 6, and batch sizes of 20, which showed the highest accuracy in the training process. Compared to support vector machine (SVM), K-nearest neighbors (KNN) and decision tree, the deep learning-based algorithm could significantly improve the prediction performance and show better robustness and generalization performance. The deep learning-based model achieved the highest accuracy, precision, recall rate and F1_Score values, which were 99.55%, 99.41%, 99.49% and 99.44%, respectively. These results showed that deep learning combined with machine vision can effectively identify the origin of A. sinensis.
{"title":"Origin Intelligent Identification of Angelica sinensis Using Machine Vision and Deep Learning","authors":"Zimei Zhang, Jianwei Xiao, Shanyu Wang, Min Wu, Wenjie Wang, Ziliang Liu, Zhian Zheng","doi":"10.3390/agriculture13091744","DOIUrl":"https://doi.org/10.3390/agriculture13091744","url":null,"abstract":"The accurate identification of the origin of Chinese medicinal materials is crucial for the orderly management of the market and clinical drug usage. In this study, a deep learning-based algorithm combined with machine vision was developed to automatically identify the origin of Angelica sinensis (A. sinensis) from eight areas including 1859 samples. The effects of different datasets, learning rates, solver algorithms, training epochs and batch sizes on the performance of the deep learning model were evaluated. The optimized hyperparameters of the model were the dataset 4, learning rate of 0.001, solver algorithm of rmsprop, training epochs of 6, and batch sizes of 20, which showed the highest accuracy in the training process. Compared to support vector machine (SVM), K-nearest neighbors (KNN) and decision tree, the deep learning-based algorithm could significantly improve the prediction performance and show better robustness and generalization performance. The deep learning-based model achieved the highest accuracy, precision, recall rate and F1_Score values, which were 99.55%, 99.41%, 99.49% and 99.44%, respectively. These results showed that deep learning combined with machine vision can effectively identify the origin of A. sinensis.","PeriodicalId":48587,"journal":{"name":"Agriculture-Basel","volume":"128 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87638891","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}
Drivable area detection is crucial for the autonomous navigation of agricultural robots. However, semi-structured agricultural roads are generally not marked with lanes and their boundaries are ambiguous, which impedes the accurate segmentation of drivable areas and consequently paralyzes the robots. This paper proposes a deep learning network model for realizing high-resolution segmentation of agricultural roads by leveraging contextual representations to augment road objectness. The backbone adopts HRNet to extract high-resolution road features in parallel at multiple scales. To strengthen the relationship between pixels and corresponding object regions, we use object-contextual representations (OCR) to augment the feature representations of pixels. Finally, a differentiable binarization (DB) decision head is used to perform threshold-adaptive segmentation for road boundaries. To quantify the performance of our method, we used an agricultural semi-structured road dataset and conducted experiments. The experimental results show that the mIoU reaches 97.85%, and the Boundary IoU achieves 90.88%. Both the segmentation accuracy and the boundary quality outperform the existing methods, which shows the tailored segmentation networks with contextual representations are beneficial to improving the detection accuracy of the semi-structured drivable areas in agricultural scene.
{"title":"Drivable Agricultural Road Region Detection Based on Pixel-Level Segmentation with Contextual Representation Augmentation","authors":"Yefeng Sun, Liang Gong, Wei Zhang, Bishu Gao, Yanming Li, Chengliang Liu","doi":"10.3390/agriculture13091736","DOIUrl":"https://doi.org/10.3390/agriculture13091736","url":null,"abstract":"Drivable area detection is crucial for the autonomous navigation of agricultural robots. However, semi-structured agricultural roads are generally not marked with lanes and their boundaries are ambiguous, which impedes the accurate segmentation of drivable areas and consequently paralyzes the robots. This paper proposes a deep learning network model for realizing high-resolution segmentation of agricultural roads by leveraging contextual representations to augment road objectness. The backbone adopts HRNet to extract high-resolution road features in parallel at multiple scales. To strengthen the relationship between pixels and corresponding object regions, we use object-contextual representations (OCR) to augment the feature representations of pixels. Finally, a differentiable binarization (DB) decision head is used to perform threshold-adaptive segmentation for road boundaries. To quantify the performance of our method, we used an agricultural semi-structured road dataset and conducted experiments. The experimental results show that the mIoU reaches 97.85%, and the Boundary IoU achieves 90.88%. Both the segmentation accuracy and the boundary quality outperform the existing methods, which shows the tailored segmentation networks with contextual representations are beneficial to improving the detection accuracy of the semi-structured drivable areas in agricultural scene.","PeriodicalId":48587,"journal":{"name":"Agriculture-Basel","volume":"11 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78406012","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}
Pub Date : 2023-09-01DOI: 10.3390/agriculture13091739
Yuhua Wang, Qi Zhang, Jianjuan Li, S. Lin, X. Jia, Qingxu Zhang, J. Ye, Haibin Wang, Zeyan Wu
In order to fully comprehend the impact of soil acidification on the quality of tea, further analyses are essential and are of the utmost importance to the cultivation of tea trees and the simultaneous enhancement of tea quality. In May 2022, Tieguanyin tea trees planted in soils with different pH levels were selected as the research object of this study to analyze the effect of soil pH on the soil chemical index, soil fertility and the aroma quality of tea leaves. The results showed that the organic matter content, cation exchange capacity and the available nitrogen, available phosphorus and available potassium contents in the rhizosphere soil of the tea trees decreased significantly with decreasing soil pH levels (5.32–3.29), while the total nitrogen, total phosphorus and total potassium contents did not change significantly. The results of an aroma quality analysis showed that the aroma of the Tieguanyin tea was mainly floral, and the formation of floral odor characteristics was mainly derived from geraniol. The results of an interaction network analysis showed that the soil chemical indexes were significantly positively correlated with geraniol and floral aromas except for the total phosphorus and total potassium contents. In conclusion, with a decrease in the pH of soil, the soil’s cation exchange capacity, organic matter content and available nutrient content showed decreasing trends which, in turn, hindered the synthesis of geraniol and reduced the floral odor characteristics of tea leaves.
{"title":"Study on the Effect of pH on Rhizosphere Soil Fertility and the Aroma Quality of Tea Trees and Their Interactions","authors":"Yuhua Wang, Qi Zhang, Jianjuan Li, S. Lin, X. Jia, Qingxu Zhang, J. Ye, Haibin Wang, Zeyan Wu","doi":"10.3390/agriculture13091739","DOIUrl":"https://doi.org/10.3390/agriculture13091739","url":null,"abstract":"In order to fully comprehend the impact of soil acidification on the quality of tea, further analyses are essential and are of the utmost importance to the cultivation of tea trees and the simultaneous enhancement of tea quality. In May 2022, Tieguanyin tea trees planted in soils with different pH levels were selected as the research object of this study to analyze the effect of soil pH on the soil chemical index, soil fertility and the aroma quality of tea leaves. The results showed that the organic matter content, cation exchange capacity and the available nitrogen, available phosphorus and available potassium contents in the rhizosphere soil of the tea trees decreased significantly with decreasing soil pH levels (5.32–3.29), while the total nitrogen, total phosphorus and total potassium contents did not change significantly. The results of an aroma quality analysis showed that the aroma of the Tieguanyin tea was mainly floral, and the formation of floral odor characteristics was mainly derived from geraniol. The results of an interaction network analysis showed that the soil chemical indexes were significantly positively correlated with geraniol and floral aromas except for the total phosphorus and total potassium contents. In conclusion, with a decrease in the pH of soil, the soil’s cation exchange capacity, organic matter content and available nutrient content showed decreasing trends which, in turn, hindered the synthesis of geraniol and reduced the floral odor characteristics of tea leaves.","PeriodicalId":48587,"journal":{"name":"Agriculture-Basel","volume":"49 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79774033","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}
Pub Date : 2023-09-01DOI: 10.3390/agriculture13091740
Hannah Jona von Czettritz, S. Hosseini-Yekani, Johannes Schuler, K. Kersebaum, Peter Zander
Climate-induced production risk is expected to increase in the future. This study assesses the effectiveness of adapting crop rotations on arable farms in Brandenburg as a tool to enhance climate resilience. Two risk-minimizing measures are investigated: crop diversification and the inclusion of irrigated crops. Based on state-wide simulated yield data, the study compares two different scenarios. In the first scenario, the most profitable crop rotations based on predicted future weather conditions are chosen for each agro-ecological zone. In the second scenario, cropping plans are derived based on an adaption of the Target MOTAD (Minimization of Total Absolute Deviation) model taking climate-induced risks into account. A comparison of the scenarios shows a high risk reduction effect of diversification, while the economic risk reduction effect of irrigation only increases slightly. The trade-off between the highest possible gross margins and lower possible losses varies depending on the soil and climate conditions. Diversification contributed most to economic resilience in areas with moderate to low agricultural productivity. Subsidies focusing on diversification in less productive areas might be a tool to increase economic resilience with low risk-avoidance costs.
{"title":"Adapting Cropping Patterns to Climate Change: Risk Management Effectiveness of Diversification and Irrigation in Brandenburg (Germany)","authors":"Hannah Jona von Czettritz, S. Hosseini-Yekani, Johannes Schuler, K. Kersebaum, Peter Zander","doi":"10.3390/agriculture13091740","DOIUrl":"https://doi.org/10.3390/agriculture13091740","url":null,"abstract":"Climate-induced production risk is expected to increase in the future. This study assesses the effectiveness of adapting crop rotations on arable farms in Brandenburg as a tool to enhance climate resilience. Two risk-minimizing measures are investigated: crop diversification and the inclusion of irrigated crops. Based on state-wide simulated yield data, the study compares two different scenarios. In the first scenario, the most profitable crop rotations based on predicted future weather conditions are chosen for each agro-ecological zone. In the second scenario, cropping plans are derived based on an adaption of the Target MOTAD (Minimization of Total Absolute Deviation) model taking climate-induced risks into account. A comparison of the scenarios shows a high risk reduction effect of diversification, while the economic risk reduction effect of irrigation only increases slightly. The trade-off between the highest possible gross margins and lower possible losses varies depending on the soil and climate conditions. Diversification contributed most to economic resilience in areas with moderate to low agricultural productivity. Subsidies focusing on diversification in less productive areas might be a tool to increase economic resilience with low risk-avoidance costs.","PeriodicalId":48587,"journal":{"name":"Agriculture-Basel","volume":"75 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91237219","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}
Pub Date : 2023-09-01DOI: 10.3390/agriculture13091738
Da-Hee An, Dong-Chil Chang, Kwang-Soo Kim, Ji-Eun Lee, Young-Lok Cha, Jae-Hee Jeong, Ji-Bong Choi, Soo-Yeon Kim
As biochar improves soil fertility and crop productivity, there is a growing interest in it as a resource for sustainable agriculture. Miscanthus sacchariflorus has promising applications in various industries because it has a large amount of biomass. However, research on the agricultural utilization of Miscanthus-derived biochar is insufficient. The aim of this study was to demonstrate the effects of Miscanthus biochar on the soil environment and soybean growth. First, Miscanthus biochar was amended at different levels (3 or 10 tons/ha) in upland soil, after which the soil properties, root development, and yield of soybeans were compared with the control (without biochar). In the soil amended with 10 tons/ha of biochar (BC10), organic matter (OM) and available phosphate increased 1.6 and 2.0 times, respectively, compared with that in the control soil (CON). In addition, the soil dehydrogenase activity increased by 70% in BC10, and 16S rRNA gene sequence analysis revealed that the structure of the microbial community changed after amendment with biochar. The bacterial phyla that differed between CON and BC10 were Acidobacteria and Chloroflexi, which are known to be involved in carbon cycling. Owing to these changes in soil properties, the root dry weight and number of nodules in soybeans increased by 23% and 27%, respectively, and the seed yield increased 1.5-fold in BC10. In conclusion, Miscanthus biochar increased the fertility of soybean-growing soil and consequently increased seed yield. This study is valuable for the practical application of biochar for sustainable agriculture.
由于生物炭可以提高土壤肥力和作物生产力，人们对它作为可持续农业资源的兴趣日益浓厚。芒草生物量大，在工业生产中具有广阔的应用前景。然而，对芒草生物炭的农业利用研究还很不足。本研究旨在探讨芒草生物炭对土壤环境和大豆生长的影响。首先，在旱地土壤中添加不同水平(3或10吨/公顷)的芒草生物炭，然后与对照(不添加生物炭)比较土壤性质、根系发育和大豆产量。施用10 t / hm2生物炭(BC10)的土壤有机质(OM)和有效磷(速效磷)分别比对照土壤(CON)增加1.6倍和2.0倍。此外，土壤脱氢酶活性增加了70%，16S rRNA基因序列分析显示，生物炭改性后土壤微生物群落结构发生了变化。CON和BC10的细菌门类分别是参与碳循环的酸杆菌和氯氟菌。由于这些土壤性质的变化，大豆根系干重和根瘤数分别增加了23%和27%，种子产量增加了1.5倍。综上所述，芒草生物炭提高了大豆生长土壤的肥力，从而提高了种子产量。本研究对生物炭在可持续农业中的实际应用具有一定的参考价值。
{"title":"Miscanthus-Derived Biochar Enhanced Soil Fertility and Soybean Growth in Upland Soil","authors":"Da-Hee An, Dong-Chil Chang, Kwang-Soo Kim, Ji-Eun Lee, Young-Lok Cha, Jae-Hee Jeong, Ji-Bong Choi, Soo-Yeon Kim","doi":"10.3390/agriculture13091738","DOIUrl":"https://doi.org/10.3390/agriculture13091738","url":null,"abstract":"As biochar improves soil fertility and crop productivity, there is a growing interest in it as a resource for sustainable agriculture. Miscanthus sacchariflorus has promising applications in various industries because it has a large amount of biomass. However, research on the agricultural utilization of Miscanthus-derived biochar is insufficient. The aim of this study was to demonstrate the effects of Miscanthus biochar on the soil environment and soybean growth. First, Miscanthus biochar was amended at different levels (3 or 10 tons/ha) in upland soil, after which the soil properties, root development, and yield of soybeans were compared with the control (without biochar). In the soil amended with 10 tons/ha of biochar (BC10), organic matter (OM) and available phosphate increased 1.6 and 2.0 times, respectively, compared with that in the control soil (CON). In addition, the soil dehydrogenase activity increased by 70% in BC10, and 16S rRNA gene sequence analysis revealed that the structure of the microbial community changed after amendment with biochar. The bacterial phyla that differed between CON and BC10 were Acidobacteria and Chloroflexi, which are known to be involved in carbon cycling. Owing to these changes in soil properties, the root dry weight and number of nodules in soybeans increased by 23% and 27%, respectively, and the seed yield increased 1.5-fold in BC10. In conclusion, Miscanthus biochar increased the fertility of soybean-growing soil and consequently increased seed yield. This study is valuable for the practical application of biochar for sustainable agriculture.","PeriodicalId":48587,"journal":{"name":"Agriculture-Basel","volume":"41 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76156691","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}
Pub Date : 2023-09-01DOI: 10.3390/agriculture13091743
Yan-Shiang Chiou, Pei-Ing Wu, J. Liou, Ta-Ken Huang, Chu-Wei Chen
The purpose of this study is to construct a model by combining the theory of planned behavior (TPB) with conjoint analysis to evaluate baskets of agricultural goods. Each basket of agricultural goods contains various different products, including white rice and leaf vegetables are either organic or non-organic, hens’ eggs and chicken drumsticks obtained from chickens bred with and without due consideration for animal welfare, and soy sauce and jam with or without additives. The evaluation of these various features is innovative and in accordance with the shopping behavior of most consumers who, most of the time, concurrently evaluate these multi-features and multi-products. The price premium for each feature and the willingness to pay, the highest amount that a consumer is willing to pay, for a specific basket of agricultural goods is evaluated by using the multinomial logit model and the linear regression model. The relationship between essential factors in the TPB and the sociodemographic characteristics of consumers is examined. In general, the ranking of the price premium paid for products from the highest to the lowest is soy sauce, jam, chicken drumsticks, white rice, hens’ eggs, and leaf vegetables, respectively. The price premium for natural products with no additives is higher than that for organic and animal welfare-based products. The evaluation of these multi-features of agricultural goods allows us to observe the relative importance of an agricultural product through the price premium, with different combinations of other products. This indicates that the evaluation of the price premium for only a single product or for multiple products with a single feature might be either over-estimated or under-estimated.
{"title":"What Is the Willingness to Pay for a Basket of Agricultural Goods? Multi-Features of Organic, Animal Welfare-Based and Natural Products with No Additives","authors":"Yan-Shiang Chiou, Pei-Ing Wu, J. Liou, Ta-Ken Huang, Chu-Wei Chen","doi":"10.3390/agriculture13091743","DOIUrl":"https://doi.org/10.3390/agriculture13091743","url":null,"abstract":"The purpose of this study is to construct a model by combining the theory of planned behavior (TPB) with conjoint analysis to evaluate baskets of agricultural goods. Each basket of agricultural goods contains various different products, including white rice and leaf vegetables are either organic or non-organic, hens’ eggs and chicken drumsticks obtained from chickens bred with and without due consideration for animal welfare, and soy sauce and jam with or without additives. The evaluation of these various features is innovative and in accordance with the shopping behavior of most consumers who, most of the time, concurrently evaluate these multi-features and multi-products. The price premium for each feature and the willingness to pay, the highest amount that a consumer is willing to pay, for a specific basket of agricultural goods is evaluated by using the multinomial logit model and the linear regression model. The relationship between essential factors in the TPB and the sociodemographic characteristics of consumers is examined. In general, the ranking of the price premium paid for products from the highest to the lowest is soy sauce, jam, chicken drumsticks, white rice, hens’ eggs, and leaf vegetables, respectively. The price premium for natural products with no additives is higher than that for organic and animal welfare-based products. The evaluation of these multi-features of agricultural goods allows us to observe the relative importance of an agricultural product through the price premium, with different combinations of other products. This indicates that the evaluation of the price premium for only a single product or for multiple products with a single feature might be either over-estimated or under-estimated.","PeriodicalId":48587,"journal":{"name":"Agriculture-Basel","volume":"9 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89249900","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}
The Pitaya industry is a specialty fruit industry in the mountainous region of Guizhou, China. The planted area in Guizhou reaches 7200 ha, ranking first in the country. At present, Pitaya planting lacks efficient yield estimation methods, which has a negative impact on the Pitaya downstream industry chain, stymying the constant growing market. The fragmented and complex terrain in karst mountainous areas and the capricious local weather have hindered accurate crop identification using traditional satellite remote sensing methods, and there is currently little attempt made to tackle the mountainous specialty crops’ yield estimation. In this paper, based on UAV (unmanned aerial vehicle) remote sensing images, the complexity of Pitaya planting sites in the karst background has been divided into three different scenes as complex scenes with similar colors, with topographic variations, and with the coexistence of multiple crops. In scenes with similar colors, using the Close Color Vegetation Index (CCVI) to extract Pitaya plants, the accuracy reached 92.37% on average in the sample sites; in scenes with complex topographic variations, using point clouds data based on the Canopy Height Model (CHM) to extract Pitaya plants, the accuracy reached 89.09%; and in scenes with the coexistence of multiple crops, using the U-Net Deep Learning Model (DLM) to identify Pitaya plants, the accuracy reached 92.76%. Thereafter, the Pitaya yield estimation model was constructed based on the fruit yield data measured in the field for several periods, and the fast yield estimations were carried out and examined for three application scenes. The results showed that the average accuracy of yield estimation in complex scenes with similar colors was 91.25%, the average accuracy of yield estimation in scenes with topographic variations was 93.40%, and the accuracy of yield estimation in scenes with the coexistence of multiple crops was 95.18%. The overall yield estimation results show a high accuracy. The experimental results show that it is feasible to use UAV remote sensing images to identify and rapidly estimate the characteristic crops in the complex karst habitat, which can also provide scientific reference for the rapid yield estimation of other crops in mountainous regions.
{"title":"Remote Sensing Identification and Rapid Yield Estimation of Pitaya Plants in Different Karst Mountainous Complex Habitats","authors":"Zhongfa Zhou, Ruiwen Peng, Ruoshuang Li, Yiqiu Li, Denghong Huang, Meng Zhu","doi":"10.3390/agriculture13091742","DOIUrl":"https://doi.org/10.3390/agriculture13091742","url":null,"abstract":"The Pitaya industry is a specialty fruit industry in the mountainous region of Guizhou, China. The planted area in Guizhou reaches 7200 ha, ranking first in the country. At present, Pitaya planting lacks efficient yield estimation methods, which has a negative impact on the Pitaya downstream industry chain, stymying the constant growing market. The fragmented and complex terrain in karst mountainous areas and the capricious local weather have hindered accurate crop identification using traditional satellite remote sensing methods, and there is currently little attempt made to tackle the mountainous specialty crops’ yield estimation. In this paper, based on UAV (unmanned aerial vehicle) remote sensing images, the complexity of Pitaya planting sites in the karst background has been divided into three different scenes as complex scenes with similar colors, with topographic variations, and with the coexistence of multiple crops. In scenes with similar colors, using the Close Color Vegetation Index (CCVI) to extract Pitaya plants, the accuracy reached 92.37% on average in the sample sites; in scenes with complex topographic variations, using point clouds data based on the Canopy Height Model (CHM) to extract Pitaya plants, the accuracy reached 89.09%; and in scenes with the coexistence of multiple crops, using the U-Net Deep Learning Model (DLM) to identify Pitaya plants, the accuracy reached 92.76%. Thereafter, the Pitaya yield estimation model was constructed based on the fruit yield data measured in the field for several periods, and the fast yield estimations were carried out and examined for three application scenes. The results showed that the average accuracy of yield estimation in complex scenes with similar colors was 91.25%, the average accuracy of yield estimation in scenes with topographic variations was 93.40%, and the accuracy of yield estimation in scenes with the coexistence of multiple crops was 95.18%. The overall yield estimation results show a high accuracy. The experimental results show that it is feasible to use UAV remote sensing images to identify and rapidly estimate the characteristic crops in the complex karst habitat, which can also provide scientific reference for the rapid yield estimation of other crops in mountainous regions.","PeriodicalId":48587,"journal":{"name":"Agriculture-Basel","volume":"28 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82082358","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：