Pub Date : 2024-07-19DOI: 10.3390/agriculture14071188
Mingsheng Li, Yulin Yan, Lin Tian, Xingzheng Chen, Fanyi Liu
Drawing upon advancements in profiling technology, this paper presents an innovative lateral profiling mechanism for the header to improve mowing efficiency and the ability to adapt to terrain for river dike mowers. It delves into the imitation principle and forced situations. Furthermore, a novel lawn protection boot design has been introduced, capable of adjusting mowing heights with swift transitions. The structural integrity of this boot has been optimized through rigorous finite element analysis. Meanwhile, the rolling shaft and cutter have been carefully selected and designed, with a mechanical model of the cutter established to examine its motion and force characteristics. In addition, hydraulic circuits tailored to fulfill the required functions of the header have been devised, and key hydraulic components have been appropriately selected. Key components are subjected to finite element analysis by using ANSYS to verify and optimize their structural strength. Prototype testing and field trials are subsequently conducted, revealing that the mower can achieve a mowing speed of 0.85 m/s on flat ground and a 25-degree slope, thereby fulfilling the design requirements for mowing speed. The imitation mechanism adapts to different embankment terrains. Notably, the lawn protection boots offer adjustable mowing heights of 10.4 cm, 12 cm, and 14 cm, respectively, with a height adjustment range of approximately 2 cm for each position, meeting the requirement for adjusting mowing heights. In addition, the transition time between different positions of the lawn protection boots is less than 5 min, achieving rapid switching and operational efficiency. Furthermore, a mowing uniformity test is conducted by using a header equipped with profiling functionality. The results reveal that the mowing effect of the profiling header meets design requirements, demonstrating its effectiveness and reliability in agricultural applications.
{"title":"Design and Experiment of the Profiling Header of River Dike Mower","authors":"Mingsheng Li, Yulin Yan, Lin Tian, Xingzheng Chen, Fanyi Liu","doi":"10.3390/agriculture14071188","DOIUrl":"https://doi.org/10.3390/agriculture14071188","url":null,"abstract":"Drawing upon advancements in profiling technology, this paper presents an innovative lateral profiling mechanism for the header to improve mowing efficiency and the ability to adapt to terrain for river dike mowers. It delves into the imitation principle and forced situations. Furthermore, a novel lawn protection boot design has been introduced, capable of adjusting mowing heights with swift transitions. The structural integrity of this boot has been optimized through rigorous finite element analysis. Meanwhile, the rolling shaft and cutter have been carefully selected and designed, with a mechanical model of the cutter established to examine its motion and force characteristics. In addition, hydraulic circuits tailored to fulfill the required functions of the header have been devised, and key hydraulic components have been appropriately selected. Key components are subjected to finite element analysis by using ANSYS to verify and optimize their structural strength. Prototype testing and field trials are subsequently conducted, revealing that the mower can achieve a mowing speed of 0.85 m/s on flat ground and a 25-degree slope, thereby fulfilling the design requirements for mowing speed. The imitation mechanism adapts to different embankment terrains. Notably, the lawn protection boots offer adjustable mowing heights of 10.4 cm, 12 cm, and 14 cm, respectively, with a height adjustment range of approximately 2 cm for each position, meeting the requirement for adjusting mowing heights. In addition, the transition time between different positions of the lawn protection boots is less than 5 min, achieving rapid switching and operational efficiency. Furthermore, a mowing uniformity test is conducted by using a header equipped with profiling functionality. The results reveal that the mowing effect of the profiling header meets design requirements, demonstrating its effectiveness and reliability in agricultural applications.","PeriodicalId":7447,"journal":{"name":"Agriculture","volume":"117 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822176","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 : 2024-07-18DOI: 10.3390/agriculture14071185
Min Fu, Zhong Cao, Mingyu Zhan, Yulong Wang, Lei Chen
The rotor cage is a key component of the classifying device, and its structural parameters directly affect classification performance. To improve the classification performance of the straw micro-crusher classifying device, this paper proposes a CFD-ML-GA (Computational Fluid Dynamics-Machine Learning-Genetic Algorithm) method to quantitatively analyze the coupled effects of rotor cage structural parameters on classification performance. Firstly, CFD and orthogonal experimental methods are used to qualitatively investigate the effects of the number of blades, length of rotor blades, and blade installation angle on the classification performance. The conclusion obtained is that the blade installation angle exerts the greatest effect on classification performance, while the number of blades has the least effect. Subsequently, four machine learning algorithms are used to build a cut size prediction model, and, after comparison, the Random Forest Regression (RFR) model is selected. Finally, RFR is integrated with a Genetic Algorithm (GA) for quantitative parameter optimization. The quantitative analysis results of GA indicate that with 29 blades, a blade length of 232.8 mm, and a blade installation angle of 36.8°, the cut size decreases to 47.6 μm and the classifying sharpness index improves to 0.62. Compared with the optimal solution from the orthogonal experiment, the GA solution reduces the cut size by 9.33% and improves the classifying sharpness index by 9.68%. This validates the feasibility of the proposed method.
{"title":"Influence of Rotor Cage Structural Parameters on the Classification Performance of a Straw Micro-Crusher Classifying Device: CFD and Machine Learning Approach","authors":"Min Fu, Zhong Cao, Mingyu Zhan, Yulong Wang, Lei Chen","doi":"10.3390/agriculture14071185","DOIUrl":"https://doi.org/10.3390/agriculture14071185","url":null,"abstract":"The rotor cage is a key component of the classifying device, and its structural parameters directly affect classification performance. To improve the classification performance of the straw micro-crusher classifying device, this paper proposes a CFD-ML-GA (Computational Fluid Dynamics-Machine Learning-Genetic Algorithm) method to quantitatively analyze the coupled effects of rotor cage structural parameters on classification performance. Firstly, CFD and orthogonal experimental methods are used to qualitatively investigate the effects of the number of blades, length of rotor blades, and blade installation angle on the classification performance. The conclusion obtained is that the blade installation angle exerts the greatest effect on classification performance, while the number of blades has the least effect. Subsequently, four machine learning algorithms are used to build a cut size prediction model, and, after comparison, the Random Forest Regression (RFR) model is selected. Finally, RFR is integrated with a Genetic Algorithm (GA) for quantitative parameter optimization. The quantitative analysis results of GA indicate that with 29 blades, a blade length of 232.8 mm, and a blade installation angle of 36.8°, the cut size decreases to 47.6 μm and the classifying sharpness index improves to 0.62. Compared with the optimal solution from the orthogonal experiment, the GA solution reduces the cut size by 9.33% and improves the classifying sharpness index by 9.68%. This validates the feasibility of the proposed method.","PeriodicalId":7447,"journal":{"name":"Agriculture","volume":" June","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141824108","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 : 2024-07-18DOI: 10.3390/agriculture14071178
Xuan Liao, Huanxiong Xie, Zhichao Hu, Jiannan Wang, Minji Liu, Jiyou An, Hai Wei, Huijuan Zhang
Peanut is an important oil crop and cash crop, with a wide range of applications in many fields such as the food industry, light industry, and chemical industry. Mechanized shelling is a necessary part of the post-production processing of peanuts, and it is also the key to determining the quality of peanut products. Reducing shelling damage is an effective way to improve the quality and comprehensive benefits of peanut products. Consequently, it is of great significance to strengthen the research on damage reduction in mechanized peanut-shelling. China is a large peanut producer, but the research on mechanized shelling started relatively late, and the existing technology is not compatible with the high-quality shelling requirements of farmers. This paper reviews the status of mechanized peanut-shelling technology, compares the technical characteristics and equipment development of the world’s important peanut producing countries, it summarizes and proposes the suggestions to reduce loss from the aspects of varieties, agronomy, technology, and technical equipment; further deepen innovative research; and strengthen the construction of peanut-shelling socialized service systems. It is expected to provide reference for effectively reducing damage and improving quality of China’s mechanized shelling, and promoting the sustainable development of peanut shelling industry.
{"title":"Peanut-Shelling Technologies and Equipment: A Review of Recent Developments","authors":"Xuan Liao, Huanxiong Xie, Zhichao Hu, Jiannan Wang, Minji Liu, Jiyou An, Hai Wei, Huijuan Zhang","doi":"10.3390/agriculture14071178","DOIUrl":"https://doi.org/10.3390/agriculture14071178","url":null,"abstract":"Peanut is an important oil crop and cash crop, with a wide range of applications in many fields such as the food industry, light industry, and chemical industry. Mechanized shelling is a necessary part of the post-production processing of peanuts, and it is also the key to determining the quality of peanut products. Reducing shelling damage is an effective way to improve the quality and comprehensive benefits of peanut products. Consequently, it is of great significance to strengthen the research on damage reduction in mechanized peanut-shelling. China is a large peanut producer, but the research on mechanized shelling started relatively late, and the existing technology is not compatible with the high-quality shelling requirements of farmers. This paper reviews the status of mechanized peanut-shelling technology, compares the technical characteristics and equipment development of the world’s important peanut producing countries, it summarizes and proposes the suggestions to reduce loss from the aspects of varieties, agronomy, technology, and technical equipment; further deepen innovative research; and strengthen the construction of peanut-shelling socialized service systems. It is expected to provide reference for effectively reducing damage and improving quality of China’s mechanized shelling, and promoting the sustainable development of peanut shelling industry.","PeriodicalId":7447,"journal":{"name":"Agriculture","volume":" 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825608","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 : 2024-07-18DOI: 10.3390/agriculture14071177
Mariusz Pożoga, D. Olewnicki, Piotr Latocha
The aim of the study was to compare the variable costs of planting material production using the example of vitro cultures of Pennisetum × advena ‘Rubrum’. In the study, temporary immersion system (TIS)- and agar-based methods were used in innovative workday organisation. The workday structure involved a six-hour passaging period followed by a two-hour break for medium preparation, autoclaving, and maintenance tasks. The TIS was found to be more cost-effective than the agar cultures, with lower labour costs and comparable growing expenses. The most expensive element of agar production was labour which was 43% of the costs. The second biggest cost was materials and reagents which represented 25%. In a TIS, production materials and reagents are the most expensive part of production (44%), while labour represents 24% of costs. A TIS offers a much faster multiplication of plants than agar cultures. Plants obtained in the multiplication phase are two times cheaper using a TIS. Rooting accounted for a significant portion of production costs in both methods. Overall, the TIS demonstrated superior efficiency and cost-effectiveness compared to agar cultures in producing Pennisetum × advena ‘Rubrum’ plants.
{"title":"A Temporary Immersion System as a Tool for Lowering Planting Material Production Costs Using the Example of Pennisetum × advena ‘Rubrum’","authors":"Mariusz Pożoga, D. Olewnicki, Piotr Latocha","doi":"10.3390/agriculture14071177","DOIUrl":"https://doi.org/10.3390/agriculture14071177","url":null,"abstract":"The aim of the study was to compare the variable costs of planting material production using the example of vitro cultures of Pennisetum × advena ‘Rubrum’. In the study, temporary immersion system (TIS)- and agar-based methods were used in innovative workday organisation. The workday structure involved a six-hour passaging period followed by a two-hour break for medium preparation, autoclaving, and maintenance tasks. The TIS was found to be more cost-effective than the agar cultures, with lower labour costs and comparable growing expenses. The most expensive element of agar production was labour which was 43% of the costs. The second biggest cost was materials and reagents which represented 25%. In a TIS, production materials and reagents are the most expensive part of production (44%), while labour represents 24% of costs. A TIS offers a much faster multiplication of plants than agar cultures. Plants obtained in the multiplication phase are two times cheaper using a TIS. Rooting accounted for a significant portion of production costs in both methods. Overall, the TIS demonstrated superior efficiency and cost-effectiveness compared to agar cultures in producing Pennisetum × advena ‘Rubrum’ plants.","PeriodicalId":7447,"journal":{"name":"Agriculture","volume":" 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825729","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 : 2024-07-18DOI: 10.3390/agriculture14071173
Sushil Paudyal
This study explored smallholder dairy production and cattle welfare in Eastern Uganda using mixed methods evaluation approaches. A focus group of 17 members performed a SWOT analysis of local farms, identifying strengths like available land, forage, and community support but weaknesses including disease, parasites, and lack of technologies. Field assessments of 12 farms using a modified Welfare Quality® protocol revealed 25% had inadequate body condition; 33% lacked adequate water access; 50% provided comfortable housing; and 42% had animals with health issues. Six recommendations were formulated to address needs via training, veterinary services access, data systems, finance, milk market development, and pasture improvements. Though struggling with resource constraints and animal health, eastern Uganda’s favorable climate and community present opportunities to enhance productivity and welfare with targeted actions like skills development and access to technologies. However, external inputs require alignment with smallholder realities. Findings detail current conditions while highlighting local perspectives to guide appropriate innovations sensitive to economic limitations and values-based motives. Collaborating with producers to incrementally elevate management can improve livelihoods and animal well-being.
{"title":"Realizing the Potential of Eastern Uganda’s Smallholder Dairy Sector through Participatory Evaluation","authors":"Sushil Paudyal","doi":"10.3390/agriculture14071173","DOIUrl":"https://doi.org/10.3390/agriculture14071173","url":null,"abstract":"This study explored smallholder dairy production and cattle welfare in Eastern Uganda using mixed methods evaluation approaches. A focus group of 17 members performed a SWOT analysis of local farms, identifying strengths like available land, forage, and community support but weaknesses including disease, parasites, and lack of technologies. Field assessments of 12 farms using a modified Welfare Quality® protocol revealed 25% had inadequate body condition; 33% lacked adequate water access; 50% provided comfortable housing; and 42% had animals with health issues. Six recommendations were formulated to address needs via training, veterinary services access, data systems, finance, milk market development, and pasture improvements. Though struggling with resource constraints and animal health, eastern Uganda’s favorable climate and community present opportunities to enhance productivity and welfare with targeted actions like skills development and access to technologies. However, external inputs require alignment with smallholder realities. Findings detail current conditions while highlighting local perspectives to guide appropriate innovations sensitive to economic limitations and values-based motives. Collaborating with producers to incrementally elevate management can improve livelihoods and animal well-being.","PeriodicalId":7447,"journal":{"name":"Agriculture","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141824054","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 : 2024-07-18DOI: 10.3390/agriculture14071186
Lei Du, Shanjun Luo
As a vital pigment for photosynthesis in rice, chlorophyll content is closely correlated with growth status and photosynthetic capacity. The estimation of chlorophyll content allows for the monitoring of rice growth and facilitates precise management in the field, such as the application of fertilizers and irrigation. The advancement of hyperspectral remote sensing technology has made it possible to estimate chlorophyll content non-destructively, quickly, and effectively, offering technical support for managing and monitoring rice growth across wide areas. Although hyperspectral data have a fine spectral resolution, they also cause a large amount of information redundancy and noise. This study focuses on the issues of unstable input variables and the estimation model’s poor applicability to various periods when predicting rice chlorophyll content. By introducing the theory of harmonic analysis and the time-frequency conversion method, a deep neural network (DNN) model framework based on wavelet packet transform-first order differential-harmonic analysis (WPT-FD-HA) was proposed, which avoids the uncertainty in the calculation of spectral parameters. The accuracy of estimating rice chlorophyll content based on WPT-FD and WPT-FD-HA variables was compared at seedling, tillering, jointing, heading, grain filling, milk, and complete periods to evaluate the validity and generalizability of the suggested framework. The results demonstrated that all of the WPT-FD-HA models’ single-period validation accuracy had coefficients of determination (R2) values greater than 0.9 and RMSE values less than 1. The multi-period validation model had a root mean square error (RMSE) of 1.664 and an R2 of 0.971. Even with independent data splitting validation, the multi-period model accuracy can still achieve R2 = 0.95 and RMSE = 1.4. The WPT-FD-HA-based deep learning framework exhibited strong stability. The outcome of this study deserves to be used to monitor rice growth on a broad scale using hyperspectral data.
{"title":"Spectral-Frequency Conversion Derived from Hyperspectral Data Combined with Deep Learning for Estimating Chlorophyll Content in Rice","authors":"Lei Du, Shanjun Luo","doi":"10.3390/agriculture14071186","DOIUrl":"https://doi.org/10.3390/agriculture14071186","url":null,"abstract":"As a vital pigment for photosynthesis in rice, chlorophyll content is closely correlated with growth status and photosynthetic capacity. The estimation of chlorophyll content allows for the monitoring of rice growth and facilitates precise management in the field, such as the application of fertilizers and irrigation. The advancement of hyperspectral remote sensing technology has made it possible to estimate chlorophyll content non-destructively, quickly, and effectively, offering technical support for managing and monitoring rice growth across wide areas. Although hyperspectral data have a fine spectral resolution, they also cause a large amount of information redundancy and noise. This study focuses on the issues of unstable input variables and the estimation model’s poor applicability to various periods when predicting rice chlorophyll content. By introducing the theory of harmonic analysis and the time-frequency conversion method, a deep neural network (DNN) model framework based on wavelet packet transform-first order differential-harmonic analysis (WPT-FD-HA) was proposed, which avoids the uncertainty in the calculation of spectral parameters. The accuracy of estimating rice chlorophyll content based on WPT-FD and WPT-FD-HA variables was compared at seedling, tillering, jointing, heading, grain filling, milk, and complete periods to evaluate the validity and generalizability of the suggested framework. The results demonstrated that all of the WPT-FD-HA models’ single-period validation accuracy had coefficients of determination (R2) values greater than 0.9 and RMSE values less than 1. The multi-period validation model had a root mean square error (RMSE) of 1.664 and an R2 of 0.971. Even with independent data splitting validation, the multi-period model accuracy can still achieve R2 = 0.95 and RMSE = 1.4. The WPT-FD-HA-based deep learning framework exhibited strong stability. The outcome of this study deserves to be used to monitor rice growth on a broad scale using hyperspectral data.","PeriodicalId":7447,"journal":{"name":"Agriculture","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141826337","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 : 2024-07-18DOI: 10.3390/agriculture14071172
Aichun Liu, Wenfei Xiao, Wenguo Lai, Jianrong Wang, Xiaoyuan Li, Hong Yu, Yan Zha
Drought stress can reduce strawberry yield and quality and is one of the main abiotic factors restricting strawberry production in China. Nano-agricultural technology has significant regulatory effects in improving crop yield and quality and reducing agricultural environmental pollution. We performed a pot experiment using FenYu No. 1 strawberry and applied copper nanoparticles (CuNPs) and selenium NPs (SeNPs) to study their effects on the growth, quality, photosynthetic parameter indexes, and physiological characteristics of strawberry plants under drought stress. The growth and photosynthesis of strawberry plants were significant adversely affected by moderate drought stress (DS, 60% field capacity (FC)) and severe drought stress (SS, 25% FC). Compared with normal water-holding conditions, the application of CuNPs, SeNPs, and their combination effectively increased the agronomic traits of strawberry plants; improved fruit quality; and enhanced the content of photosynthetic pigments (chlorophyll a, chlorophyll b, and total chlorophyll), photosynthetic characteristic parameters, chlorophyll fluorescence parameters, and water-use efficiency. In addition, the exogenous application of CuNPs and SeNPs improved the drought tolerance of plants by increasing the activities of antioxidant enzymes catalase, peroxidase, and superoxide dismutase, and decreasing the malondialdehyde content, with the following overall trend among the treatments: control < CuNPs < SeNPs < CuNPs + SeNPs. The results of the principal component analysis showed that the two extracted principal components could reflect 85.54% of the information of the original data, leaf photosynthetic pigments, photosynthetic characteristic parameters, chlorophyll fluorescence parameters, and strawberry agronomic traits indexes and could be used as the primary indexes for evaluating the improvement of strawberry growth by nanofertilizers under drought-stress conditions. Taken together, our results indicate that nanofertilizers have potential for improving the growth, quality, and physiological characteristics of strawberries under drought stress.
{"title":"Potential Application of Selenium and Copper Nanoparticles in Improving Growth, Quality, and Physiological Characteristics of Strawberry under Drought Stress","authors":"Aichun Liu, Wenfei Xiao, Wenguo Lai, Jianrong Wang, Xiaoyuan Li, Hong Yu, Yan Zha","doi":"10.3390/agriculture14071172","DOIUrl":"https://doi.org/10.3390/agriculture14071172","url":null,"abstract":"Drought stress can reduce strawberry yield and quality and is one of the main abiotic factors restricting strawberry production in China. Nano-agricultural technology has significant regulatory effects in improving crop yield and quality and reducing agricultural environmental pollution. We performed a pot experiment using FenYu No. 1 strawberry and applied copper nanoparticles (CuNPs) and selenium NPs (SeNPs) to study their effects on the growth, quality, photosynthetic parameter indexes, and physiological characteristics of strawberry plants under drought stress. The growth and photosynthesis of strawberry plants were significant adversely affected by moderate drought stress (DS, 60% field capacity (FC)) and severe drought stress (SS, 25% FC). Compared with normal water-holding conditions, the application of CuNPs, SeNPs, and their combination effectively increased the agronomic traits of strawberry plants; improved fruit quality; and enhanced the content of photosynthetic pigments (chlorophyll a, chlorophyll b, and total chlorophyll), photosynthetic characteristic parameters, chlorophyll fluorescence parameters, and water-use efficiency. In addition, the exogenous application of CuNPs and SeNPs improved the drought tolerance of plants by increasing the activities of antioxidant enzymes catalase, peroxidase, and superoxide dismutase, and decreasing the malondialdehyde content, with the following overall trend among the treatments: control < CuNPs < SeNPs < CuNPs + SeNPs. The results of the principal component analysis showed that the two extracted principal components could reflect 85.54% of the information of the original data, leaf photosynthetic pigments, photosynthetic characteristic parameters, chlorophyll fluorescence parameters, and strawberry agronomic traits indexes and could be used as the primary indexes for evaluating the improvement of strawberry growth by nanofertilizers under drought-stress conditions. Taken together, our results indicate that nanofertilizers have potential for improving the growth, quality, and physiological characteristics of strawberries under drought stress.","PeriodicalId":7447,"journal":{"name":"Agriculture","volume":" 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141827937","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 : 2024-07-18DOI: 10.3390/agriculture14071176
Jiaorong Qian, Mao Ye, Xi Zhang, Miaomiao Li, Weilong Chen, Guoyan Zeng, Jing Che, Yexin Lv
In order to explore the changes and interrelationships of grassland plant community species diversity and soil physicochemical properties with elevation gradient, this study takes the grassland in the Burzin forest area of Xinjiang as the research object and analyzes the responses of grassland species diversity, aboveground biomass, and soil physicochemical properties to the changes of elevation gradient within the altitude range of 1000~2200 m in this area. The results of the study show that: (1) The number of species and aboveground biomass reached the highest levels at elevation gradient III and showed a tendency of increasing and then decreasing with elevation. The Margalef and Shannon–Wiener indices were the largest at elevation III, while the Simpson and Alatalo indices were the largest at elevation I. (2) With the change of elevation, the available nitrogen (AN), available phosphorus (AP), soil electric conductivity (SEC), and soil pH showed a trend of increasing and then decreasing, while soil temperature decreased with elevation. Available potassium and soil water content reached their maximum values at elevation I and elevation IV, respectively. (3) The soil conductivity and diversity index were negatively correlated in elevation gradients I to III. In elevation gradient I~III, soil conductivity was positively correlated with the diversity index and aboveground biomass. Available nitrogen had a significant effect on plant diversity and biomass in elevation gradients IV to VI. (4) Aboveground biomass was significantly positively correlated with the Simpson’s index, while the relationship with the Shannon–Wiener index was less significant, and Margalef’s and Alatalo’s indices were not significant. Soil conductivity and pH significantly affected the Margalef and Simpson indices. Available nitrogen was closely related to the aboveground biomass and Margalef and Alatalo indices. Soil moisture content significantly affected Simpson’s index and the aboveground biomass. This study provides a solid theoretical foundation for the conservation and management of grassland plant community ecosystems along the elevation gradient, and has important reference value for study of the impact of environmental change on species diversity and biodiversity conservation.
{"title":"Characteristics of Grassland Species Diversity and Soil Physicochemical Properties with Elevation Gradient in Burzin Forest Area","authors":"Jiaorong Qian, Mao Ye, Xi Zhang, Miaomiao Li, Weilong Chen, Guoyan Zeng, Jing Che, Yexin Lv","doi":"10.3390/agriculture14071176","DOIUrl":"https://doi.org/10.3390/agriculture14071176","url":null,"abstract":"In order to explore the changes and interrelationships of grassland plant community species diversity and soil physicochemical properties with elevation gradient, this study takes the grassland in the Burzin forest area of Xinjiang as the research object and analyzes the responses of grassland species diversity, aboveground biomass, and soil physicochemical properties to the changes of elevation gradient within the altitude range of 1000~2200 m in this area. The results of the study show that: (1) The number of species and aboveground biomass reached the highest levels at elevation gradient III and showed a tendency of increasing and then decreasing with elevation. The Margalef and Shannon–Wiener indices were the largest at elevation III, while the Simpson and Alatalo indices were the largest at elevation I. (2) With the change of elevation, the available nitrogen (AN), available phosphorus (AP), soil electric conductivity (SEC), and soil pH showed a trend of increasing and then decreasing, while soil temperature decreased with elevation. Available potassium and soil water content reached their maximum values at elevation I and elevation IV, respectively. (3) The soil conductivity and diversity index were negatively correlated in elevation gradients I to III. In elevation gradient I~III, soil conductivity was positively correlated with the diversity index and aboveground biomass. Available nitrogen had a significant effect on plant diversity and biomass in elevation gradients IV to VI. (4) Aboveground biomass was significantly positively correlated with the Simpson’s index, while the relationship with the Shannon–Wiener index was less significant, and Margalef’s and Alatalo’s indices were not significant. Soil conductivity and pH significantly affected the Margalef and Simpson indices. Available nitrogen was closely related to the aboveground biomass and Margalef and Alatalo indices. Soil moisture content significantly affected Simpson’s index and the aboveground biomass. This study provides a solid theoretical foundation for the conservation and management of grassland plant community ecosystems along the elevation gradient, and has important reference value for study of the impact of environmental change on species diversity and biodiversity conservation.","PeriodicalId":7447,"journal":{"name":"Agriculture","volume":" 81","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825595","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 : 2024-07-18DOI: 10.3390/agriculture14071183
Jiahao Li, Liqi Chu
Whether fiscal decentralization will lead to agricultural land “non-grainization” has been widely debated in academic circles. How to improve the efficiency of financial support to agriculture and optimize the grain planting structure by clarifying the relationship between central and local powers and responsibilities is the key to ensuring food security. Based on the panel data of 170 cities in China from 2004 to 2017, this paper uses system moment estimation and a threshold effect model to explore the impact of fiscal decentralization on grain planting structure. The results show that (1) fiscal decentralization has a significant negative effect on the share of food crop cultivation in the major grain-producing areas. (2) Taking the wage level, financial support for agriculture, and land finance as the threshold variables, the test finds that there is a threshold effect of fiscal decentralization on the proportion of food crop cultivation, in which land finance dependence and rises in the wage level are conducive to mitigating the negative effect of fiscal decentralization on the proportion of food crop cultivation. (3) For the three major types of food crop varieties, the negative impact of fiscal decentralization on the share of wheat and corn crop cultivation is subject to the threshold effect of wage level, financial support for agriculture, and land finance, while the impact of fiscal decentralization on the share of rice crop cultivation is not significant. The results of the study have an important guiding role for the government to deepen the reform of the tax-sharing system, improve the long-term mechanism of stable growth of financial support for grain, and optimize the layout of the grain industry.
{"title":"Decentralization versus Centralization: What Ensures Food Security? Empirical Evidence from 170 Prefecture-Level Cities in China’s Major Grain-Producing Areas","authors":"Jiahao Li, Liqi Chu","doi":"10.3390/agriculture14071183","DOIUrl":"https://doi.org/10.3390/agriculture14071183","url":null,"abstract":"Whether fiscal decentralization will lead to agricultural land “non-grainization” has been widely debated in academic circles. How to improve the efficiency of financial support to agriculture and optimize the grain planting structure by clarifying the relationship between central and local powers and responsibilities is the key to ensuring food security. Based on the panel data of 170 cities in China from 2004 to 2017, this paper uses system moment estimation and a threshold effect model to explore the impact of fiscal decentralization on grain planting structure. The results show that (1) fiscal decentralization has a significant negative effect on the share of food crop cultivation in the major grain-producing areas. (2) Taking the wage level, financial support for agriculture, and land finance as the threshold variables, the test finds that there is a threshold effect of fiscal decentralization on the proportion of food crop cultivation, in which land finance dependence and rises in the wage level are conducive to mitigating the negative effect of fiscal decentralization on the proportion of food crop cultivation. (3) For the three major types of food crop varieties, the negative impact of fiscal decentralization on the share of wheat and corn crop cultivation is subject to the threshold effect of wage level, financial support for agriculture, and land finance, while the impact of fiscal decentralization on the share of rice crop cultivation is not significant. The results of the study have an important guiding role for the government to deepen the reform of the tax-sharing system, improve the long-term mechanism of stable growth of financial support for grain, and optimize the layout of the grain industry.","PeriodicalId":7447,"journal":{"name":"Agriculture","volume":" 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141826709","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 : 2024-07-18DOI: 10.3390/agriculture14071175
Yihan Yao, Jibo Yue, Yang Liu, Hao Yang, Haikuan Feng, Jianing Shen, Jingyu Hu, Qian Liu
Maize, an important cereal crop and crucial industrial material, is widely used in various fields, including food, feed, and industry. Maize is also a highly adaptable crop, capable of thriving under various climatic and soil conditions. Against the backdrop of intensified climate change, studying the classification of maize growth stages can aid in adjusting planting strategies to enhance yield and quality. Accurate classification of the growth stages of maize breeding materials is important for enhancing yield and quality in breeding endeavors. Traditional remote sensing-based crop growth stage classifications mainly rely on time series vegetation index (VI) analyses; however, VIs are prone to saturation under high-coverage conditions. Maize phenotypic traits at different growth stages may improve the accuracy of crop growth stage classifications. Therefore, we developed a method for classifying maize growth stages during the vegetative growth phase by combining maize phenotypic traits with different classification algorithms. First, we tested various VIs, texture features (TFs), and combinations of VI and TF as input features to estimate the leaf chlorophyll content (LCC), leaf area index (LAI), and fractional vegetation cover (FVC). We determined the optimal feature inputs and estimation methods and completed crop height (CH) extraction. Then, we tested different combinations of maize phenotypic traits as input variables to determine their accuracy in classifying growth stages and to identify the optimal combination and classification method. Finally, we compared the proposed method with traditional growth stage classification methods based on remote sensing VIs and machine learning models. The results indicate that (1) when the VI+TFs are used as input features, random forest regression (RFR) shows a good estimation performance for the LCC (R2: 0.920, RMSE: 3.655 SPAD units, MAE: 2.698 SPAD units), Gaussian process regression (GPR) performs well for the LAI (R2: 0.621, RMSE: 0.494, MAE: 0.397), and linear regression (LR) exhibits a good estimation performance for the FVC (R2: 0.777, RMSE: 0.051, MAE: 0.040); (2) when using the maize LCC, LAI, FVC, and CH phenotypic traits to classify maize growth stages, the random forest (RF) classification method achieved the highest accuracy (accuracy: 0.951, precision: 0.951, recall: 0.951, F1: 0.951); and (3) the effectiveness of the growth stage classification based on maize phenotypic traits outperforms that of traditional remote sensing-based crop growth stage classifications.
玉米是一种重要的谷类作物和重要的工业原料，被广泛应用于食品、饲料和工业等各个领域。玉米也是一种适应性很强的作物，能够在各种气候和土壤条件下茁壮成长。在气候变化加剧的背景下，研究玉米生长阶段的分类有助于调整种植策略，提高产量和质量。准确划分玉米育种材料的生长阶段对提高育种工作的产量和质量非常重要。传统的基于遥感的作物生长阶段分类主要依靠时间序列植被指数（VI）分析，但在高覆盖条件下，植被指数容易饱和。不同生长阶段的玉米表型特征可提高作物生长阶段分类的准确性。因此，我们通过将玉米表型特征与不同的分类算法相结合，开发了一种对玉米无性生长阶段进行分类的方法。首先，我们测试了各种VI、纹理特征（TF）以及VI和TF的组合作为输入特征来估计叶片叶绿素含量（LCC）、叶面积指数（LAI）和植被覆盖度（FVC）。我们确定了最佳特征输入和估算方法，并完成了作物高度（CH）提取。然后，我们测试了作为输入变量的玉米表型性状的不同组合，以确定它们在生长阶段分类中的准确性，并确定最佳组合和分类方法。最后，我们将所提出的方法与基于遥感 VI 和机器学习模型的传统生长阶段分类方法进行了比较。结果表明：(1) 当使用 VI+TF 作为输入特征时，随机森林回归（RFR）对 LCC 的估计性能良好（R2：0.920，RMSE：3.655 SPAD 单位，MAE：2.698 SPAD 单位），高斯过程回归（GPR）对 LAI 的估计性能良好（R2：0.621，RMSE：0.494，MAE：0.397），线性回归（LR）对 FVC 的估计性能良好（R2：0.777，RMSE：0.051，MAE：0.040）；（2）利用玉米 LCC、LAI、FVC 和 CH 表型性状对玉米生长阶段进行分类时，随机森林（RF）分类方法的准确度最高（准确度：0.951，精确度：0.951，召回率：0.951，F1：0.951）；（3）基于玉米表型性状的生长阶段分类的有效性优于传统的基于遥感的作物生长阶段分类。
{"title":"Classification of Maize Growth Stages Based on Phenotypic Traits and UAV Remote Sensing","authors":"Yihan Yao, Jibo Yue, Yang Liu, Hao Yang, Haikuan Feng, Jianing Shen, Jingyu Hu, Qian Liu","doi":"10.3390/agriculture14071175","DOIUrl":"https://doi.org/10.3390/agriculture14071175","url":null,"abstract":"Maize, an important cereal crop and crucial industrial material, is widely used in various fields, including food, feed, and industry. Maize is also a highly adaptable crop, capable of thriving under various climatic and soil conditions. Against the backdrop of intensified climate change, studying the classification of maize growth stages can aid in adjusting planting strategies to enhance yield and quality. Accurate classification of the growth stages of maize breeding materials is important for enhancing yield and quality in breeding endeavors. Traditional remote sensing-based crop growth stage classifications mainly rely on time series vegetation index (VI) analyses; however, VIs are prone to saturation under high-coverage conditions. Maize phenotypic traits at different growth stages may improve the accuracy of crop growth stage classifications. Therefore, we developed a method for classifying maize growth stages during the vegetative growth phase by combining maize phenotypic traits with different classification algorithms. First, we tested various VIs, texture features (TFs), and combinations of VI and TF as input features to estimate the leaf chlorophyll content (LCC), leaf area index (LAI), and fractional vegetation cover (FVC). We determined the optimal feature inputs and estimation methods and completed crop height (CH) extraction. Then, we tested different combinations of maize phenotypic traits as input variables to determine their accuracy in classifying growth stages and to identify the optimal combination and classification method. Finally, we compared the proposed method with traditional growth stage classification methods based on remote sensing VIs and machine learning models. The results indicate that (1) when the VI+TFs are used as input features, random forest regression (RFR) shows a good estimation performance for the LCC (R2: 0.920, RMSE: 3.655 SPAD units, MAE: 2.698 SPAD units), Gaussian process regression (GPR) performs well for the LAI (R2: 0.621, RMSE: 0.494, MAE: 0.397), and linear regression (LR) exhibits a good estimation performance for the FVC (R2: 0.777, RMSE: 0.051, MAE: 0.040); (2) when using the maize LCC, LAI, FVC, and CH phenotypic traits to classify maize growth stages, the random forest (RF) classification method achieved the highest accuracy (accuracy: 0.951, precision: 0.951, recall: 0.951, F1: 0.951); and (3) the effectiveness of the growth stage classification based on maize phenotypic traits outperforms that of traditional remote sensing-based crop growth stage classifications.","PeriodicalId":7447,"journal":{"name":"Agriculture","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141824437","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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