-
Multiple Soil Health Assessment Methods for Evaluating Effects of Organic Fertilization in Farmland Soil of Agro-Pastoral Ecotone
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The Nutritional Year-Cycle of Italian Honey Bees (Apis mellifera ligustica) in a Southern Temperate Climate
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Adaptation Mechanisms of Olive Tree under Drought Stress: The Potential of Modern Omics Approaches
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Precision Livestock Farming Technology: Applications and Challenges of Animal Welfare and Climate Change
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Challenges in Sustainable Agriculture—The Role of Organic Amendments
Journal Description
Agriculture
Agriculture
is an international, scientific peer-reviewed open access journal published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubAg, AGRIS, RePEc, and other databases.
- Journal Rank: JCR - Q1 (Agronomy) / CiteScore - Q1 (Plant Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.2 days after submission; acceptance to publication is undertaken in 2.3 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Agriculture include: Poultry, Grasses and Crops.
Impact Factor:
3.3 (2023);
5-Year Impact Factor:
3.5 (2023)
Latest Articles
Design and Experimental Study of a Cleaning Device for Edible Sunflower Harvesting
Agriculture 2024, 14(8), 1344; https://doi.org/10.3390/agriculture14081344 (registering DOI) - 11 Aug 2024
Abstract
Existing cleaning devices for edible sunflower have a low cleaning efficiency, high cleaning loss rate, and high impurity rate; therefore, a wind-sieve-type cleaning device for edible sunflower harvesting was designed. According to the characteristics of dislodged objects, a vibrating screen for the device
[...] Read more.
Existing cleaning devices for edible sunflower have a low cleaning efficiency, high cleaning loss rate, and high impurity rate; therefore, a wind-sieve-type cleaning device for edible sunflower harvesting was designed. According to the characteristics of dislodged objects, a vibrating screen for the device was designed, and the dislodged edible sunflower objects in the device were used for a mechanical analysis of the force conditions to determine the displacement of the different edible sunflower objects dislodged by the action of airflow. Using FLUENT-DEM gas–solid coupling simulation technology, the velocity of the flow field, the velocity vector, and the trajectory of the dislodged objects inside the cleaning device were analyzed, and the law of motion applied to the airflow and the dislodged objects inside the device was clarified. According to the results of the coupled simulation analysis, the key factors affecting the operation of the cleaning device were wind speed, vibration frequency, and amplitude. Based on the key factors of wind speed, vibration frequency, and amplitude, an orthogonal rotary combination test was carried out with the loss rate and impurity rate of cleaned grains as the evaluation indexes, and the test parameters were optimized to obtain the optimal combination of operating parameters of the device, which were as follows: wind speed: 30 m·s−1; vibration frequency: 8.44 Hz; and amplitude: 41.35 mm. With this combination of parameters, the seed loss rate and impurity rate reached 3.47% and 6.17%, respectively. Based on the optimal combination of operating parameters, a validation test was performed, and the results of this test were compared with the results of the test bench using this combination of parameters. The results show that the relative errors of the loss rate and impurity rate between the bench test and the simulation test were 3.45% and 3.07%, respectively, which are less than 5%, proving the reliability of the simulation analysis and the reasonableness of the design of the test bench.
Full article
(This article belongs to the Special Issue From Planting to Harvesting: The Role of Agricultural Machinery in Crop Cultivation)
Open AccessArticle
The Impact of Pig Futures on the Price Transmission in the Pig Industry Chain during Market Shocks
by
Yingman Wang and Yubin Huangfu
Agriculture 2024, 14(8), 1343; https://doi.org/10.3390/agriculture14081343 (registering DOI) - 11 Aug 2024
Abstract
In recent years, frequent external emergencies have continuously impacted China’s pig industry chain. As the scale and standardization of pig farming in China have increasingly improved, pig futures have met the conditions for good operation and were listed for trading on the Dalian
[...] Read more.
In recent years, frequent external emergencies have continuously impacted China’s pig industry chain. As the scale and standardization of pig farming in China have increasingly improved, pig futures have met the conditions for good operation and were listed for trading on the Dalian Commodity Exchange on 8 January 2021. To study the impact and influence of African swine fever, COVID-19, and the listing of pig futures on the price transmission mechanism at various stages of China’s pig industry, weekly price data from the pig industry from January 2015 to June 2023 were selected to construct an SV-TVP-VAR model for analysis. The empirical results showed that the shocks of African swine fever and COVID-19 caused price fluctuations at various stages of the pig industry chain, while price fluctuations significantly decreased after the listing of pig futures. Therefore, the introduction of pig futures effectively alleviated the price fluctuations at various stages of the pig industry chain following the shocks of African swine fever and COVID-19, and relevant policy recommendations are proposed accordingly.
Full article
(This article belongs to the Topic Novel Studies in Agricultural Economics and Sustainable Farm Management)
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<p>Diagram illustrating the transmission path of prices from the upstream to downstream in the Chinese pig industry chain.</p> Full article ">Figure 2
<p>Trend Chart for Piglet Prices, Pork Wholesale Prices, and Feed Prices (Unit: The log of the prices). Source: Wind, Ministry of Agriculture and Rural Affairs of the People’s Republic of China.</p> Full article ">Figure 3
<p>Time-Varying Characteristics of Random Volatility in Variable Structural Shocks. It reflects the changes in the volatility of variables over different periods due to external shocks (structural changes), helping us understand whether the impacts of the shocks on the variables exhibit periodic or irregular fluctuation patterns.</p> Full article ">Figure 4
<p>Equidistant Pulse Response Function Plot. It is used to describe the dynamic responses of variables within a model over different years and lag periods when subjected to an impulse.</p> Full article ">Figure 5
<p>Impulse Response Function Plot. It is used to describe the immediate dynamic responses of variables within a model when subjected to an external shock at a specific point in time.</p> Full article ">Figure 6
<p>Impulse Response Function Plot.</p> Full article ">
<p>Diagram illustrating the transmission path of prices from the upstream to downstream in the Chinese pig industry chain.</p> Full article ">Figure 2
<p>Trend Chart for Piglet Prices, Pork Wholesale Prices, and Feed Prices (Unit: The log of the prices). Source: Wind, Ministry of Agriculture and Rural Affairs of the People’s Republic of China.</p> Full article ">Figure 3
<p>Time-Varying Characteristics of Random Volatility in Variable Structural Shocks. It reflects the changes in the volatility of variables over different periods due to external shocks (structural changes), helping us understand whether the impacts of the shocks on the variables exhibit periodic or irregular fluctuation patterns.</p> Full article ">Figure 4
<p>Equidistant Pulse Response Function Plot. It is used to describe the dynamic responses of variables within a model over different years and lag periods when subjected to an impulse.</p> Full article ">Figure 5
<p>Impulse Response Function Plot. It is used to describe the immediate dynamic responses of variables within a model when subjected to an external shock at a specific point in time.</p> Full article ">Figure 6
<p>Impulse Response Function Plot.</p> Full article ">
Open AccessArticle
Solid-State Fermentation for Phenolic Compounds Recovery from Mexican Oregano (Lippia graveolens Kunth) Residual Leaves Applying a Lactic Acid Bacteria (Leuconostoc mesenteroides)
by
Israel Bautista-Hernández, Ricardo Gómez-García, Cristóbal N. Aguilar, Guillermo C. G. Martínez-Ávila, Cristian Torres-León and Mónica L. Chávez-González
Agriculture 2024, 14(8), 1342; https://doi.org/10.3390/agriculture14081342 (registering DOI) - 11 Aug 2024
Abstract
The Mexican oregano by-products are a source of bioactive molecules (polyphenols) that could be extracted using solid-state fermentation (SSF). This study fermented the by-products via SSF (120 h) with a lactic acid bacteria (LAB) Leuconostoc mesenteroides. Sequentially, a bioactive and chemical determination
[...] Read more.
The Mexican oregano by-products are a source of bioactive molecules (polyphenols) that could be extracted using solid-state fermentation (SSF). This study fermented the by-products via SSF (120 h) with a lactic acid bacteria (LAB) Leuconostoc mesenteroides. Sequentially, a bioactive and chemical determination was made according to the phenolic content, antioxidant activity (DPPH●/FRAP), bioactive properties (α-amylase inhibition and antimicrobial activity against Escherichia coli), and chemical composition (HPLC-MS). The results showed that the total phenolics and flavonoid content, as well as the antioxidant activity, increased (0.60, 2.55, and 3.01 times, respectively) during the SSF process compared with unfermented material. Also, the extracts showed antimicrobial activity against E. coli and α-amylase inhibition. These inhibitory results could be attributed to bioactive compounds identified via HPLC, such as gardenin B, trachelogenin, ferulic acid, and resveratrol 3-O-glucoside. Therefore, the application of L. mesenteroides under SSF on oregano by-products comprises an eco-friendly strategy for their valorization as raw materials for the recovery of phenolic compounds that could be natural alternatives against synthetic antioxidant and antimicrobial agents, promoting a more circular and sustainable supply system within the oregano industry.
Full article
(This article belongs to the Special Issue Innovative and Sustainable Biorefinery Processes for Food Waste Valorization towards Circular Bioeconomy in Modern Agriculture and Agro-Industry)
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<p>General process diagram of bioactive activity and chemical evaluation of <span class="html-italic">Lippia graveolens</span> by-product valorization through SSF process.</p> Full article ">Figure 2
<p>Polyphenolic compounds concentration in fermentative extracts obtained from SSF process using <span class="html-italic">L. mesenteroides</span>. (<b>A</b>) Total polyphenolic content (TPC) and (<b>B</b>) total flavonoid content (TFC). Different letters show significant differences (α = 0.05).</p> Full article ">Figure 3
<p>Antioxidant activity of fermentative extracts via the SSF process using <span class="html-italic">L. mesenteroides</span>; (<b>A</b>) FRAP assay and (<b>B</b>) DPPH<sup>●</sup> assay. The different letters show significant differences (α = 0.05).</p> Full article ">
<p>General process diagram of bioactive activity and chemical evaluation of <span class="html-italic">Lippia graveolens</span> by-product valorization through SSF process.</p> Full article ">Figure 2
<p>Polyphenolic compounds concentration in fermentative extracts obtained from SSF process using <span class="html-italic">L. mesenteroides</span>. (<b>A</b>) Total polyphenolic content (TPC) and (<b>B</b>) total flavonoid content (TFC). Different letters show significant differences (α = 0.05).</p> Full article ">Figure 3
<p>Antioxidant activity of fermentative extracts via the SSF process using <span class="html-italic">L. mesenteroides</span>; (<b>A</b>) FRAP assay and (<b>B</b>) DPPH<sup>●</sup> assay. The different letters show significant differences (α = 0.05).</p> Full article ">
Open AccessArticle
Design of a Leaf-Bottom Pest Control Robot with Adaptive Chassis and Adjustable Selective Nozzle
by
Dongshen Li, Fei Gao, Zemin Li, Yutong Zhang, Chuang Gao and Hongbo Li
Agriculture 2024, 14(8), 1341; https://doi.org/10.3390/agriculture14081341 (registering DOI) - 11 Aug 2024
Abstract
Pest control is an important guarantee for agricultural production. Pests are mostly light-avoiding and often gather on the bottom of crop leaves. However, spraying agricultural machinery mostly adopts top-down spraying, which suffers from low pesticide utilization and poor insect removal effect. Therefore, the
[...] Read more.
Pest control is an important guarantee for agricultural production. Pests are mostly light-avoiding and often gather on the bottom of crop leaves. However, spraying agricultural machinery mostly adopts top-down spraying, which suffers from low pesticide utilization and poor insect removal effect. Therefore, the upward spraying mode and intelligent nozzle have gradually become the research hotspot of precision agriculture. This paper designs a leaf-bottom pest control robot with adaptive chassis and adjustable selective nozzle. Firstly, the adaptive chassis is designed based on the MacPherson suspension, which uses shock absorption to drive the track to swing within a 30° angle. Secondly, a new type of cone angle adjustable selective nozzle was developed, which achieves adaptive selective precision spraying under visual guidance. Then, based on a convolutional block attention module (CBAM), the multi-CBAM-YOLOv5s network model was improved to achieve a 70% recognition rate of leaf-bottom spotted bad point in video streams. Finally, functional tests of the adaptive chassis and the adjustable selective spraying system were conducted. The data indicate that the adaptive chassis can adapt to diverse single-ridge requirements of soybeans and corn while protecting the ridge slopes. The selective spraying system achieves 70% precision in pesticide application, greatly reducing the use of pesticides. The scheme explores a ridge-friendly leaf-bottom pest control plan, providing a technical reference for improving spraying effect, reducing pesticide usage, and mitigating environmental pollution.
Full article
(This article belongs to the Section Agricultural Technology)
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<p>Leaf-bottom pest control robot. The red arrow points to the direction of travel.</p> Full article ">Figure 2
<p>Circuit diagram and information flow of the robot.</p> Full article ">Figure 3
<p>The schematic of the chassis deformation and static analysis.</p> Full article ">Figure 4
<p>Schematic diagram of the adaptive module with reference lines marked. <b>A</b>, <b>B</b> is the rotation axis; <b>C</b>, <b>D</b> is the lower and upper fulcrum.</p> Full article ">Figure 5
<p>(<b>a</b>) Curve of shock absorption length changing with swing angle at different installation angles; (<b>b</b>) curves of the slope of the shock absorption length change in three cases.</p> Full article ">Figure 6
<p>The modelling and physical model of the adjustable selective spraying system. (<b>a-i</b>) and (<b>a-ii</b>) are 3D modeling diagrams; (<b>b</b>) is a physical connection diagram of the circulation loop and the water pump; (<b>c</b>) is a physical diagram of the selective adjustable nozzle.</p> Full article ">Figure 7
<p>The pesticide application flowchart.</p> Full article ">Figure 8
<p>The parameter adjustment function schematic.</p> Full article ">Figure 9
<p>The multi-CBAM-YOLOv5s structure.</p> Full article ">Figure 10
<p>CBAM structure: channel attention mechanism and spatial attention mechanism.</p> Full article ">Figure 11
<p>Image preprocessing. The red box is the recognition box.</p> Full article ">Figure 12
<p>Comparison between multi-CBAM-YOLOv5s and basic network model. Model a is Basic-YOLOv5s trained for 40 rounds; Model b is multi-CBAM-YOLOv5s trained for 40 rounds; Model c is multi-CBAM-YOLOv5s trained for 100 rounds. <b>1</b> is the Precision-Confidence curve; <b>2</b> is the Precision-Recall curve; <b>3</b> is the Recall-Confidence curve; the curve of <b>F1</b> and confidence represents the comprehensive score.</p> Full article ">Figure 13
<p>The Adams motion simulation of the adaptive chassis. (<b>a</b>) is a traveling simulation test in Adams; (<b>b</b>) is a real machine simulation test in the laboratory; (<b>c</b>) is a static schematic diagram of the machine on the ridge slope.</p> Full article ">Figure 14
<p>Adaptive chassis swing angle and slope angle change curve diagram.</p> Full article ">Figure 15
<p>Initial angle of shock absorbers and response curves during deformation. Sub-figures (<b>a</b>–<b>c</b>) are from the Adams simulation test. Different initial installation angles correspond to different response speeds. The curves correspond to the Adams screenshots.</p> Full article ">Figure 16
<p>The physical test of the adaptive chassis. (<b>a</b>–<b>c</b>) are the chassis conditions of the actual machine under different working conditions. (<b>d</b>,<b>e</b>) are the chassis working conditions of the actual machine in the test field.</p> Full article ">Figure 17
<p>The recognition results of the leaf database defect points and field test. (<b>a</b>) is the recognition situation of the image stream; (<b>b</b>) is a video screenshot of the real-time recognition of the field video stream.</p> Full article ">Figure 17 Cont.
<p>The recognition results of the leaf database defect points and field test. (<b>a</b>) is the recognition situation of the image stream; (<b>b</b>) is a video screenshot of the real-time recognition of the field video stream.</p> Full article ">Figure 18
<p>The recognition results of the simulated complex environment. (<b>a</b>) is the original picture; (<b>b</b>) is the image binarization; (<b>c</b>) is the recognition situation of multi-CBAM-YOLOv5s; (<b>d</b>) is the recognition situation of the basic network.</p> Full article ">Figure 19
<p>The real-life spraying tests. Sub-image (<b>a</b>) shows the nozzle’s adjustment of spray parameters; (<b>b</b>) is a screenshot of the actual spraying.</p> Full article ">
<p>Leaf-bottom pest control robot. The red arrow points to the direction of travel.</p> Full article ">Figure 2
<p>Circuit diagram and information flow of the robot.</p> Full article ">Figure 3
<p>The schematic of the chassis deformation and static analysis.</p> Full article ">Figure 4
<p>Schematic diagram of the adaptive module with reference lines marked. <b>A</b>, <b>B</b> is the rotation axis; <b>C</b>, <b>D</b> is the lower and upper fulcrum.</p> Full article ">Figure 5
<p>(<b>a</b>) Curve of shock absorption length changing with swing angle at different installation angles; (<b>b</b>) curves of the slope of the shock absorption length change in three cases.</p> Full article ">Figure 6
<p>The modelling and physical model of the adjustable selective spraying system. (<b>a-i</b>) and (<b>a-ii</b>) are 3D modeling diagrams; (<b>b</b>) is a physical connection diagram of the circulation loop and the water pump; (<b>c</b>) is a physical diagram of the selective adjustable nozzle.</p> Full article ">Figure 7
<p>The pesticide application flowchart.</p> Full article ">Figure 8
<p>The parameter adjustment function schematic.</p> Full article ">Figure 9
<p>The multi-CBAM-YOLOv5s structure.</p> Full article ">Figure 10
<p>CBAM structure: channel attention mechanism and spatial attention mechanism.</p> Full article ">Figure 11
<p>Image preprocessing. The red box is the recognition box.</p> Full article ">Figure 12
<p>Comparison between multi-CBAM-YOLOv5s and basic network model. Model a is Basic-YOLOv5s trained for 40 rounds; Model b is multi-CBAM-YOLOv5s trained for 40 rounds; Model c is multi-CBAM-YOLOv5s trained for 100 rounds. <b>1</b> is the Precision-Confidence curve; <b>2</b> is the Precision-Recall curve; <b>3</b> is the Recall-Confidence curve; the curve of <b>F1</b> and confidence represents the comprehensive score.</p> Full article ">Figure 13
<p>The Adams motion simulation of the adaptive chassis. (<b>a</b>) is a traveling simulation test in Adams; (<b>b</b>) is a real machine simulation test in the laboratory; (<b>c</b>) is a static schematic diagram of the machine on the ridge slope.</p> Full article ">Figure 14
<p>Adaptive chassis swing angle and slope angle change curve diagram.</p> Full article ">Figure 15
<p>Initial angle of shock absorbers and response curves during deformation. Sub-figures (<b>a</b>–<b>c</b>) are from the Adams simulation test. Different initial installation angles correspond to different response speeds. The curves correspond to the Adams screenshots.</p> Full article ">Figure 16
<p>The physical test of the adaptive chassis. (<b>a</b>–<b>c</b>) are the chassis conditions of the actual machine under different working conditions. (<b>d</b>,<b>e</b>) are the chassis working conditions of the actual machine in the test field.</p> Full article ">Figure 17
<p>The recognition results of the leaf database defect points and field test. (<b>a</b>) is the recognition situation of the image stream; (<b>b</b>) is a video screenshot of the real-time recognition of the field video stream.</p> Full article ">Figure 17 Cont.
<p>The recognition results of the leaf database defect points and field test. (<b>a</b>) is the recognition situation of the image stream; (<b>b</b>) is a video screenshot of the real-time recognition of the field video stream.</p> Full article ">Figure 18
<p>The recognition results of the simulated complex environment. (<b>a</b>) is the original picture; (<b>b</b>) is the image binarization; (<b>c</b>) is the recognition situation of multi-CBAM-YOLOv5s; (<b>d</b>) is the recognition situation of the basic network.</p> Full article ">Figure 19
<p>The real-life spraying tests. Sub-image (<b>a</b>) shows the nozzle’s adjustment of spray parameters; (<b>b</b>) is a screenshot of the actual spraying.</p> Full article ">
Open AccessArticle
Modelling Soil Moisture Content With Hydrus 2D in a Conti-Nental Climate for Effective Maize Irrigation Planning
by
Nxumalo Gift Siphiwe, Tamás Magyar, János Tamás and Attila Nagy
Agriculture 2024, 14(8), 1340; https://doi.org/10.3390/agriculture14081340 (registering DOI) - 10 Aug 2024
Abstract
In light of climate change and limited water resources, optimizing water usage in agriculture is crucial. This study models water productivity to help regional planners address these challenges. We integrate CROPWAT-based reference evapotranspiration (ETo) with Sentinel 2 data to calculate daily
[...] Read more.
In light of climate change and limited water resources, optimizing water usage in agriculture is crucial. This study models water productivity to help regional planners address these challenges. We integrate CROPWAT-based reference evapotranspiration (ETo) with Sentinel 2 data to calculate daily evapotranspiration and water needs for maize using soil and climate data from 2021 to 2023. The HYDRUS model predicted volumetric soil moisture content, validated against observed data. A 2D hydrodynamic model within HYDRUS simulated temporal and spatial variations in soil water distribution for maize at a non-irrigated site in Hungary. The model used soil physical properties and crop evapotranspiration rates as inputs, covering crop development stages from planting to harvest. The model showed good performance, with R² values of 0.65 (10 cm) and 0.81 (60 cm) in 2021, 0.51 (10 cm) and 0.50 (60 cm) in 2022, and 0.38 (10 cm) and 0.72 (60 cm) in 2023. RMSE and NRMSE values indicated reliability. The model revealed water deficits and proposed optimal irrigation schedules to maintain soil moisture between 32.2 and 17.51 V/V%. This integrated approach offers a reliable tool for monitoring soil moisture and developing efficient irrigation systems, aiding maize production’s adaptation to climate change.
Full article
(This article belongs to the Section Agricultural Water Management)
Open AccessArticle
Adoption of Fertilizer-Reduction and Efficiency-Increasing Technologies in China: The Role of Information Acquisition Ability
by
Caiyan Yang, Weihong Huang, Yu Xiao, Zhenhong Qi, Yan Li and Kun Zhang
Agriculture 2024, 14(8), 1339; https://doi.org/10.3390/agriculture14081339 (registering DOI) - 10 Aug 2024
Abstract
Reducing fertilizer use and increasing its efficiency will improve the quality of farmland and resource conservation. These are necessary steps to achieving green development in agriculture. Nevertheless, fertilizer-reduction and efficiency-increasing technologies (FREITs) remain limited. To improve the situation, 538 farmers in Jiangsu and
[...] Read more.
Reducing fertilizer use and increasing its efficiency will improve the quality of farmland and resource conservation. These are necessary steps to achieving green development in agriculture. Nevertheless, fertilizer-reduction and efficiency-increasing technologies (FREITs) remain limited. To improve the situation, 538 farmers in Jiangsu and Hubei Provinces were surveyed with the goal of measuring the information acquisition ability (IAA) of farmers using an Item Response Theory (IRT) model. A model of improved technology selection was employed in conjunction with an IV-probit model to examine the impacts of IAA on farmers’ adoption of FREITs. The results showed that 34.76% of the surveyed farmers had adopted FREITs, with 12.45% and 26.02% having adopted Soil Testing and Formula Fertilization Technology (STFFT) and Organic Fertilizer Replacement Technology (OFRT), respectively. Second, farmers who used more information access channels had greater IAA, which significantly improved their adoption of FREITs. Third, participation in technical training and an increased degree of technical understanding increased the probability of farmers adopting FREITs. The results remained robust after accounting for endogeneity and correlation. Consequently, enhancing farmers’ IAA, organizing technical training, and improving technical publicity will promote the adoption of FREITs.
Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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<p>Theoretical framework diagram. Note: The dashed arrows denote the reasoning process, whereas the solid arrows denote empirical testing.</p> Full article ">Figure 2
<p>Research area and sample distribution.</p> Full article ">Figure 3
<p>Adoption of FREITs by sampled farmers.</p> Full article ">Figure 4
<p>Distribution of the IAA of farmers based on the number of channels used.</p> Full article ">
<p>Theoretical framework diagram. Note: The dashed arrows denote the reasoning process, whereas the solid arrows denote empirical testing.</p> Full article ">Figure 2
<p>Research area and sample distribution.</p> Full article ">Figure 3
<p>Adoption of FREITs by sampled farmers.</p> Full article ">Figure 4
<p>Distribution of the IAA of farmers based on the number of channels used.</p> Full article ">
Open AccessArticle
Evaluating Regional Potentials of Agricultural E-Commerce Development Using a Novel MEREC Heronian-CoCoSo Approach
by
Shupeng Huang, Hong Cheng, Manyi Tan, Zhiqing Tang and Chuyi Teng
Agriculture 2024, 14(8), 1338; https://doi.org/10.3390/agriculture14081338 (registering DOI) - 10 Aug 2024
Abstract
In terms of both economy and sustainability, rural areas can greatly benefit from adopting E-commerce. The Chinese government is currently devoting significant efforts to developing agricultural E-commerce. However, one of the most significant problems is the lack of effective tools for evaluating regional
[...] Read more.
In terms of both economy and sustainability, rural areas can greatly benefit from adopting E-commerce. The Chinese government is currently devoting significant efforts to developing agricultural E-commerce. However, one of the most significant problems is the lack of effective tools for evaluating regional potentials in this regard, possibly leading to inappropriate policymaking, investment allocation, and regional planning. To address this issue, this study proposes a novel and effective method for evaluating regional potentials for agricultural E-commerce development, integrating the method based on the removal effects of criteria (MEREC), Heronian mean operator, and combined compromise solution (CoCoSo) method. The method’s effectiveness is then tested and confirmed in the Chinese city of Yibin through an evaluation of its ten regions. The results suggest that such a method is robust, objective, and able to consider indicator interactions effectively. By applying this method, regional agricultural E-commerce development potentials can be thoroughly evaluated and ranked. This study contributes to the literature by providing new analytical techniques for agricultural studies, as well as by supporting political and investment decision-making for governments and E-commerce practitioners in the agriculture sector.
Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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<p>The map of Yibin for agricultural E-commerce development potential evaluation.</p> Full article ">Figure 2
<p>Methodological flow chart of agricultural E-commerce development potential evaluation.</p> Full article ">Figure 3
<p>Sensitivity analysis for the CoCoSo parameter.</p> Full article ">
<p>The map of Yibin for agricultural E-commerce development potential evaluation.</p> Full article ">Figure 2
<p>Methodological flow chart of agricultural E-commerce development potential evaluation.</p> Full article ">Figure 3
<p>Sensitivity analysis for the CoCoSo parameter.</p> Full article ">
Open AccessArticle
Physiological Phenotyping and Biochemical Characterization of Mung Bean (Vigna radiata L.) Genotypes for Salt and Drought Stress
by
Mayur Patel, Divya Gupta, Amita Saini, Asha Kumari, Rishi Priya and Sanjib Kumar Panda
Agriculture 2024, 14(8), 1337; https://doi.org/10.3390/agriculture14081337 (registering DOI) - 10 Aug 2024
Abstract
Vigna radiata (L.) R. Wilczek, generally known as mung bean, is a crucial pulse crop in Southeast Asia that is renowned for its high nutritional value. However, its cultivation faces substantial challenges due to numerous abiotic stresses. Here, we investigate the influence
[...] Read more.
Vigna radiata (L.) R. Wilczek, generally known as mung bean, is a crucial pulse crop in Southeast Asia that is renowned for its high nutritional value. However, its cultivation faces substantial challenges due to numerous abiotic stresses. Here, we investigate the influence of salt and drought stress on mung bean genotypes by evaluating its morpho-physiological traits and biochemical characteristics. This phenotypic analysis revealed that both salt and drought stress adversely affected mung bean, which led to reduced plant height, leaf senescence, loss of plant biomass, and premature plant death. Reactive oxygen species (ROS) production increased under these abiotic stresses. In response, to prevent damage by ROS, the plant activates defense mechanisms to scavenge ROS by producing antioxidants. This response was validated through morpho-physiological, histological, and biochemical assays that characterized KVK Puri-3 and KVK Jharsuguda-1 as salt and drought sensitive genotypes, respectively, and Pusa ratna was identified as a drought and salt tolerant genotype.
Full article
(This article belongs to the Section Crop Production)
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<p>This figure shows the physical changes that occur in response to salt and drought stress. (<b>A</b>) Salt tolerant germplasm: Pusa Ratna; (<b>B</b>) salt sensitive germplasm: KVK Puri-3; (<b>C</b>) drought tolerant germplasm: Pusa Ratna; (<b>D</b>) drought sensitive germplasm: KVK Jharsuguda.</p> Full article ">Figure 2
<p>Comparing sensitive and tolerant plant genotypes under salt and drought stress reveals distinct differences in their RWC, PHTI, and Fv/Fm values. (<b>A</b>) Graphs between the salt sensitive vs. salt tolerant; (<b>B</b>) graphs between drought sensitive and drought tolerant genotypes. Here, ***—<span class="html-italic">p</span>-value-0.001, ****—<span class="html-italic">p</span>-value-0.0002.</p> Full article ">Figure 3
<p>Heat map data analysis of 61 mung bean genotypes for sensitive and tolerant genotypes. This heat map was made by measuring RWC, PHTI, and Fv/Fm parameters for (<b>A</b>) salt and (<b>B</b>) drought stress to identify the most sensitive and most tolerant genotype among all 61 genotypes.</p> Full article ">Figure 4
<p>Principal component analysis of 61 mung bean genotypes. This PCA plot depicts the characterization of genotypes under salt stress. Here red dot corresponds to a named genotype.</p> Full article ">Figure 5
<p>Principal component analysis of 61 mung bean genotypes. This PCA plot depicts the characterization of genotypes under drought stress. Here, red dot corresponds to a named genotype.</p> Full article ">Figure 6
<p>Biochemical and antioxidative analysis for salt stress between sensitive and tolerant genotypes. The comparison graphs of MDA, H<sub>2</sub>O<sub>2,</sub> Proline, CAT, GPX, GR, APX, SOD, MDHAR, and DHAR, respectively. Here, *—<span class="html-italic">p</span>-value-0.01, **—<span class="html-italic">p</span>-value-0.001, ***—<span class="html-italic">p</span>-value-0.0002, ****—<span class="html-italic">p</span>-value < 0.00001.</p> Full article ">Figure 7
<p>Biochemical and antioxidative analysis for drought stress between sensitive and tolerant genotypes. The comparison graphs of MDA, H<sub>2</sub>O<sub>2,</sub> Proline, CAT, GPX, GR, APX, SOD, and MDHAR, respectively. Here, **—<span class="html-italic">p</span>-value-0.001, ***—<span class="html-italic">p</span>-value-0.0002, ****—<span class="html-italic">p</span>-value < 0.00001.</p> Full article ">Figure 8
<p>The loss of plasma membrane during stress was depicted by Evan’s blue staining.</p> Full article ">Figure 9
<p>A scenario of a correlation study conducted in salt conditions using different biochemical assays. (<b>A</b>) In this instance, the circle’s size and color proportionately correspond to correlation coefficients; (<b>B</b>) we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p> Full article ">Figure 9 Cont.
<p>A scenario of a correlation study conducted in salt conditions using different biochemical assays. (<b>A</b>) In this instance, the circle’s size and color proportionately correspond to correlation coefficients; (<b>B</b>) we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p> Full article ">Figure 10
<p>A scenario of a correlation study conducted in drought conditions using different biochemical assays. (<b>A</b>) Drought; (<b>B</b>) salt. In this instance, the circle’s size and color proportionately correspond to correlation coefficients, and we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p> Full article ">Figure 10 Cont.
<p>A scenario of a correlation study conducted in drought conditions using different biochemical assays. (<b>A</b>) Drought; (<b>B</b>) salt. In this instance, the circle’s size and color proportionately correspond to correlation coefficients, and we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p> Full article ">
<p>This figure shows the physical changes that occur in response to salt and drought stress. (<b>A</b>) Salt tolerant germplasm: Pusa Ratna; (<b>B</b>) salt sensitive germplasm: KVK Puri-3; (<b>C</b>) drought tolerant germplasm: Pusa Ratna; (<b>D</b>) drought sensitive germplasm: KVK Jharsuguda.</p> Full article ">Figure 2
<p>Comparing sensitive and tolerant plant genotypes under salt and drought stress reveals distinct differences in their RWC, PHTI, and Fv/Fm values. (<b>A</b>) Graphs between the salt sensitive vs. salt tolerant; (<b>B</b>) graphs between drought sensitive and drought tolerant genotypes. Here, ***—<span class="html-italic">p</span>-value-0.001, ****—<span class="html-italic">p</span>-value-0.0002.</p> Full article ">Figure 3
<p>Heat map data analysis of 61 mung bean genotypes for sensitive and tolerant genotypes. This heat map was made by measuring RWC, PHTI, and Fv/Fm parameters for (<b>A</b>) salt and (<b>B</b>) drought stress to identify the most sensitive and most tolerant genotype among all 61 genotypes.</p> Full article ">Figure 4
<p>Principal component analysis of 61 mung bean genotypes. This PCA plot depicts the characterization of genotypes under salt stress. Here red dot corresponds to a named genotype.</p> Full article ">Figure 5
<p>Principal component analysis of 61 mung bean genotypes. This PCA plot depicts the characterization of genotypes under drought stress. Here, red dot corresponds to a named genotype.</p> Full article ">Figure 6
<p>Biochemical and antioxidative analysis for salt stress between sensitive and tolerant genotypes. The comparison graphs of MDA, H<sub>2</sub>O<sub>2,</sub> Proline, CAT, GPX, GR, APX, SOD, MDHAR, and DHAR, respectively. Here, *—<span class="html-italic">p</span>-value-0.01, **—<span class="html-italic">p</span>-value-0.001, ***—<span class="html-italic">p</span>-value-0.0002, ****—<span class="html-italic">p</span>-value < 0.00001.</p> Full article ">Figure 7
<p>Biochemical and antioxidative analysis for drought stress between sensitive and tolerant genotypes. The comparison graphs of MDA, H<sub>2</sub>O<sub>2,</sub> Proline, CAT, GPX, GR, APX, SOD, and MDHAR, respectively. Here, **—<span class="html-italic">p</span>-value-0.001, ***—<span class="html-italic">p</span>-value-0.0002, ****—<span class="html-italic">p</span>-value < 0.00001.</p> Full article ">Figure 8
<p>The loss of plasma membrane during stress was depicted by Evan’s blue staining.</p> Full article ">Figure 9
<p>A scenario of a correlation study conducted in salt conditions using different biochemical assays. (<b>A</b>) In this instance, the circle’s size and color proportionately correspond to correlation coefficients; (<b>B</b>) we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p> Full article ">Figure 9 Cont.
<p>A scenario of a correlation study conducted in salt conditions using different biochemical assays. (<b>A</b>) In this instance, the circle’s size and color proportionately correspond to correlation coefficients; (<b>B</b>) we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p> Full article ">Figure 10
<p>A scenario of a correlation study conducted in drought conditions using different biochemical assays. (<b>A</b>) Drought; (<b>B</b>) salt. In this instance, the circle’s size and color proportionately correspond to correlation coefficients, and we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p> Full article ">Figure 10 Cont.
<p>A scenario of a correlation study conducted in drought conditions using different biochemical assays. (<b>A</b>) Drought; (<b>B</b>) salt. In this instance, the circle’s size and color proportionately correspond to correlation coefficients, and we can analyze the correlation between each of the pairwise combinations of distinct variables in this image.</p> Full article ">
Open AccessArticle
Three-Dimensional Obstacle Avoidance Harvesting Path Planning Method for Apple-Harvesting Robot Based on Improved Ant Colony Algorithm
by
Bin Yan, Jianglin Quan and Wenhui Yan
Agriculture 2024, 14(8), 1336; https://doi.org/10.3390/agriculture14081336 (registering DOI) - 10 Aug 2024
Abstract
The cultivation model for spindle-shaped apple trees is widely used in modern standard apple orchards worldwide and represents the direction of modern apple industry development. However, without an effective obstacle avoidance path, the robotic arm is prone to collision with obstacles such as
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The cultivation model for spindle-shaped apple trees is widely used in modern standard apple orchards worldwide and represents the direction of modern apple industry development. However, without an effective obstacle avoidance path, the robotic arm is prone to collision with obstacles such as fruit tree branches during the picking process, which may damage fruits and branches and even affect the healthy growth of fruit trees. To address the above issues, a three-dimensional path -planning algorithm for full-field fruit obstacle avoidance harvesting for spindle-shaped fruit trees, which are widely planted in modern apple orchards, is proposed in this study. Firstly, based on three typical tree structures of spindle-shaped apple trees (free spindle, high spindle, and slender spindle), a three-dimensional spatial model of fruit tree branches was established. Secondly, based on the grid environment representation method, an obstacle map of the apple tree model was established. Then, the initial pheromones were improved by non-uniform distribution on the basis of the original ant colony algorithm. Furthermore, the updating rules of pheromones were improved, and a biomimetic optimization mechanism was integrated with the beetle antenna algorithm to improve the speed and stability of path searching. Finally, the planned path was smoothed using a cubic B-spline curve to make the path smoother and avoid unnecessary pauses or turns during the harvesting process of the robotic arm. Based on the proposed improved ACO algorithm (ant colony optimization algorithm), obstacle avoidance 3D path planning simulation experiments were conducted for three types of spindle-shaped apple trees. The results showed that the success rates of obstacle avoidance path planning were higher than 96%, 86%, and 92% for free-spindle-shaped, high-spindle-shaped, and slender-spindle-shaped trees, respectively. Compared with traditional ant colony algorithms, the average planning time was decreased by 49.38%, 46.33%, and 51.03%, respectively. The proposed improved algorithm can effectively achieve three-dimensional path planning for obstacle avoidance picking, thereby providing technical support for the development of intelligent apple picking robots.
Full article
(This article belongs to the Special Issue From Planting to Harvesting: The Role of Agricultural Machinery in Crop Cultivation)
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Figure 1
Figure 1
<p>Pictures of traditional closed apple orchard fruit trees (<b>a</b>) and cultivation model for modern spindle-shaped apple trees in orchards (<b>b</b>).</p> Full article ">Figure 2
<p>The process of establishing a high-spindle-shaped model: determining the central stem (<b>a</b>), determining the number of lateral branches (<b>b</b>), adjusting the angle between lateral branches and the central stem (<b>c</b>), determining the relative length of lateral branches (<b>d</b>), adjusting the stem height (<b>e</b>), and adjusting the crown size (<b>f</b>).</p> Full article ">Figure 3
<p>Modeling results of three typical spindle-shaped tree: free-spindle (<b>a</b>), high-spindle (<b>b</b>), and slender-spindle (<b>c</b>) trees.</p> Full article ">Figure 4
<p>Visualization results of three typical spindle-shaped trees: free-spindle (<b>a</b>), high-spindle (<b>b</b>), and slender-spindle (<b>c</b>) trees.</p> Full article ">Figure 5
<p>The expansion effect of feature points in three typical tree shapes: free spindle (<b>a</b>), high spindle (<b>b</b>), and slender spindle (<b>c</b>).</p> Full article ">Figure 6
<p>Comparison of path smoothing before smoothing (<b>a</b>) and after smoothing (<b>b</b>).</p> Full article ">Figure 7
<p>Flowchart of the improved ant colony algorithm (<b>a</b>) and flowchart for the whole study (<b>b</b>).</p> Full article ">Figure 8
<p>Visualization experiment results of three-dimensional path planning for free-spindle apple tree: group 1 (<b>a</b>), group 2 (<b>b</b>), and group 3 (<b>c</b>).</p> Full article ">Figure 9
<p>Visualization experiment results of three-dimensional path planning for high-spindle apple tree: group 1 (<b>a</b>), group 2 (<b>b</b>), and group 3 (<b>c</b>).</p> Full article ">Figure 10
<p>Visualization experiment results of three-dimensional path planning for slender-spindle apple tree: group 1 (<b>a</b>), group 2 (<b>b</b>), and group 3 (<b>c</b>).</p> Full article ">Figure 11
<p>Visualization experiment results of three-dimensional path planning for free-spindle-shaped apple tree: A* (<b>a</b>), RRT (<b>b</b>), traditional ACO (<b>c</b>), and improved ACO (<b>d</b>).</p> Full article ">Figure 12
<p>Visualization experiment results of three-dimensional path planning for high-spindle-shaped apple tree: A* (<b>a</b>), RRT (<b>b</b>), traditional ACO (<b>c</b>), and improved ACO (<b>d</b>).</p> Full article ">Figure 13
<p>Visualization experiment results of three-dimensional path planning for slender-spindle-shaped apple tree: A* (<b>a</b>), RRT (<b>b</b>), traditional ACO (<b>c</b>), and improved ACO (<b>d</b>).</p> Full article ">
<p>Pictures of traditional closed apple orchard fruit trees (<b>a</b>) and cultivation model for modern spindle-shaped apple trees in orchards (<b>b</b>).</p> Full article ">Figure 2
<p>The process of establishing a high-spindle-shaped model: determining the central stem (<b>a</b>), determining the number of lateral branches (<b>b</b>), adjusting the angle between lateral branches and the central stem (<b>c</b>), determining the relative length of lateral branches (<b>d</b>), adjusting the stem height (<b>e</b>), and adjusting the crown size (<b>f</b>).</p> Full article ">Figure 3
<p>Modeling results of three typical spindle-shaped tree: free-spindle (<b>a</b>), high-spindle (<b>b</b>), and slender-spindle (<b>c</b>) trees.</p> Full article ">Figure 4
<p>Visualization results of three typical spindle-shaped trees: free-spindle (<b>a</b>), high-spindle (<b>b</b>), and slender-spindle (<b>c</b>) trees.</p> Full article ">Figure 5
<p>The expansion effect of feature points in three typical tree shapes: free spindle (<b>a</b>), high spindle (<b>b</b>), and slender spindle (<b>c</b>).</p> Full article ">Figure 6
<p>Comparison of path smoothing before smoothing (<b>a</b>) and after smoothing (<b>b</b>).</p> Full article ">Figure 7
<p>Flowchart of the improved ant colony algorithm (<b>a</b>) and flowchart for the whole study (<b>b</b>).</p> Full article ">Figure 8
<p>Visualization experiment results of three-dimensional path planning for free-spindle apple tree: group 1 (<b>a</b>), group 2 (<b>b</b>), and group 3 (<b>c</b>).</p> Full article ">Figure 9
<p>Visualization experiment results of three-dimensional path planning for high-spindle apple tree: group 1 (<b>a</b>), group 2 (<b>b</b>), and group 3 (<b>c</b>).</p> Full article ">Figure 10
<p>Visualization experiment results of three-dimensional path planning for slender-spindle apple tree: group 1 (<b>a</b>), group 2 (<b>b</b>), and group 3 (<b>c</b>).</p> Full article ">Figure 11
<p>Visualization experiment results of three-dimensional path planning for free-spindle-shaped apple tree: A* (<b>a</b>), RRT (<b>b</b>), traditional ACO (<b>c</b>), and improved ACO (<b>d</b>).</p> Full article ">Figure 12
<p>Visualization experiment results of three-dimensional path planning for high-spindle-shaped apple tree: A* (<b>a</b>), RRT (<b>b</b>), traditional ACO (<b>c</b>), and improved ACO (<b>d</b>).</p> Full article ">Figure 13
<p>Visualization experiment results of three-dimensional path planning for slender-spindle-shaped apple tree: A* (<b>a</b>), RRT (<b>b</b>), traditional ACO (<b>c</b>), and improved ACO (<b>d</b>).</p> Full article ">
Open AccessArticle
Fruit Variation in Yellow-Fleshed Actinidia (Actinidia chinensis Planch) Plants Grown in Southern Italy as a Function of Shoot Type
by
Antonio Dattola, Antonella Accardo, Rocco Zappia and Gregorio Antonio Maria Gullo
Agriculture 2024, 14(8), 1335; https://doi.org/10.3390/agriculture14081335 (registering DOI) - 10 Aug 2024
Abstract
One of the goals of modern orcharding is to produce a high volume of fruits with uniform size, organoleptic parameters, and health characteristics. The aim of this work was to study various shoot types and their prevailing positions along the cane and to
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One of the goals of modern orcharding is to produce a high volume of fruits with uniform size, organoleptic parameters, and health characteristics. The aim of this work was to study various shoot types and their prevailing positions along the cane and to identify how shoot type can influence the quality of fruit from the Actinidia tree. The experiment was conducted over a two-year period in a commercial orchard of Actinidia chinensis, cv. Gold 3. The shoots along the cane were classified as follows: spur shoots (SPs), terminated shoots (TEs), non-terminated shoots (NTs), and cut non-terminated shoots (CNTs). The data were statistically processed using ANOVA and Principal Component Analysis (ACP). Four different categories of fruit were obtained from the four shoot types, and their various attributes were compared. The prevailing category (comprising 55% of the studied fruits) was TEs, which are characterised by a higher soluble solid content, sweetness, and excellent health characteristics, as well as the reduced hardness of their pulp, which would support the hypothesis that harvesting could be brought forward. The second most common category (comprising 19% of total fruit) with the lowest soluble solid content, but a high antioxidant capacity, was that which was detached from the CNTs, while 13% of the fruit was produced from NTs, which had the lowest health value but good sweetness perception. Finally, the category with the lowest fruit percentage over the total fruit harvested (10%) was SPs, which are characterised by their smaller size. It has yet to be determined what the performance of each category will be post-harvest; whether it is possible to assign the quality categories while harvesting the fruit or to differentiate the harvest time accordingly remains subject to debate.
Full article
(This article belongs to the Section Crop Production)
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<p>Biplot with centroid and variable vectors on F1 and F2 planes.</p> Full article ">Figure 2
<p>Three-dimensional representation of latent variables discriminated according to shoot type.</p> Full article ">Scheme 1
<p>Thermo-pluviometric regime of the 2022–2023 biennium of the area of interest.</p> Full article ">
<p>Biplot with centroid and variable vectors on F1 and F2 planes.</p> Full article ">Figure 2
<p>Three-dimensional representation of latent variables discriminated according to shoot type.</p> Full article ">Scheme 1
<p>Thermo-pluviometric regime of the 2022–2023 biennium of the area of interest.</p> Full article ">
Open AccessArticle
Analysis of BnGPAT9 Gene Expression Patterns in Brassica napus and Its Impact on Seed Oil Content
by
Man Xing, Bo Hong, Mengjie Lv, Xueyi Lan, Danhui Zhang, Chunlei Shu, Shucheng Qi, Zechuan Peng, Chunyun Guan, Xinghua Xiong and Luyao Huang
Agriculture 2024, 14(8), 1334; https://doi.org/10.3390/agriculture14081334 (registering DOI) - 10 Aug 2024
Abstract
Glycerol-3-phosphate acyltransferase (GPAT) genes encode enzymes involved in the biosynthesis of plant oils. Rapeseed has four BnGPAT9 genes, but the expression patterns and functions of these four homologous copies in rapeseed for seed oil accumulation are not well understood. In this
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Glycerol-3-phosphate acyltransferase (GPAT) genes encode enzymes involved in the biosynthesis of plant oils. Rapeseed has four BnGPAT9 genes, but the expression patterns and functions of these four homologous copies in rapeseed for seed oil accumulation are not well understood. In this study, we cloned the four BnGPAT9 genes and their promoters from Brassica napus and found significant differences in the expression of BnGPAT9 genes among different rapeseed varieties. We confirmed that BnGPAT9-A01/C01 are highly conserved in rapeseed, with high expression levels in various tissues, especially during the late stages of silique development and seed maturation. All four BnGPAT9 genes (BnGPAT9-A01/C01/A10/C09) can promote seed oil accumulation, but BnGPAT9-A01/C01 have a greater effect. Overexpression in Arabidopsis and rapeseed increased seed oil content and altered fatty acid composition, significantly increasing linolenic acid content. Transcriptome analysis revealed that BnGPAT9 genes promote the upregulation of genes related to oil synthesis, particularly those in the Plant–pathogen interaction, alpha-Linolenic acid metabolism, MAPK signaling pathway—plant, and Glutathione metabolism pathways. In summary, these results indicate that the four BnGPAT9 genes in rapeseed have different expression patterns and roles in regulating seed oil accumulation, with BnGPAT9-A01/C01 contributing the most to promoting oil accumulation.
Full article
(This article belongs to the Special Issue Molecular Breeding and Genetic Improvement of Oilseed Crops)
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Figure 1
Figure 1
<p>Characterization of the BnGPAT9 protein sequences. (<b>A</b>) The analysis of conserved motifs in BnGPAT9 and AtGPAT9 proteins; (<b>B</b>) the multiple sequence alignment of plant BnGPAT9 proteins.</p> Full article ">Figure 2
<p>Cis elements detected in the promoter of the <span class="html-italic">BnGPAT9</span> genes. (<b>A</b>) Promoter element distribution, where different colors correspond to different elements in the figure below; (<b>B</b>) the heat map shows the number of promoter elements, and the gray square indicates that the elements could not be detected.</p> Full article ">Figure 3
<p>Tissue expression patterns of <span class="html-italic">BnGPAT9</span> genes. (<b>A</b>) The GUS staining results of <span class="html-italic">Arabidopsis thaliana</span>. (<b>A1</b>,<b>B1</b>,<b>C1</b>,<b>D1</b>,<b>E1</b>,<b>F1</b>): GUS staining in stems; (<b>A2</b>,<b>B2</b>,<b>C2</b>,<b>D2</b>,<b>E2</b>,<b>F2</b>): GUS staining in leaves; (<b>A3</b>,<b>B3</b>,<b>C3</b>,<b>D3</b>,<b>E3</b>,<b>F3</b>): GUS staining in inflorescences; (<b>A4</b>,<b>B4</b>,<b>C4</b>,<b>D4</b>,<b>E4</b>,<b>F4</b>): GUS staining in flowers; (<b>A5</b>,<b>B5</b>,<b>C5</b>,<b>D5</b>,<b>E5</b>,<b>F5</b>): GUS staining in siliques. Bar = 1 mm. (<b>B</b>) Expression profiles of <span class="html-italic">BnGPAT9</span> genes in ZS11. Data sourced from BnTIR (<a href="https://yanglab.hzau.edu.cn/BnTIR" target="_blank">https://yanglab.hzau.edu.cn/BnTIR</a> (accessed on 15 October 2019)).</p> Full article ">Figure 4
<p>Analysis of tissue-specific expression patterns of <span class="html-italic">BnGPAT9</span> genes and seed oil accumulation. (<b>A</b>) qRT-PCR detection of <span class="html-italic">BnGPAT9</span> expression in roots, stems, leaves, flowers, and seeds at 1–7 weeks of development, as well as siliques of XY15. (<b>B</b>–<b>D</b>) qRT-PCR detection of <span class="html-italic">BnGPAT9</span> expression in seeds and siliques at 1–7 weeks of development in transgenic lines overexpressing the <span class="html-italic">BnGPAT9-C01</span> gene. (<b>E</b>) Oil content in the seeds of XY15 and the three transgenic lines.</p> Full article ">Figure 5
<p>Analysis of the thousand-seed weight and oil content in <span class="html-italic">BnGPAT9</span> transgenic Arabidopsis seeds. (<b>A</b>) Thousand-seed weight of seeds from four <span class="html-italic">BnGPAT9</span> transgenic lines. (<b>B</b>) Oil content of seeds from four <span class="html-italic">BnGPAT9</span> transgenic lines. “*” indicates highly significant differences, <span class="html-italic">p</span> < 0.05. “**” indicates highly significant differences, <span class="html-italic">p</span> < 0.01.</p> Full article ">Figure 6
<p>An analysis of differential gene expression in siliques of <span class="html-italic">BnGPAT9</span> transgenic Arabidopsis. (<b>A</b>) A statistical analysis of the number of DEGs. The red bars represent upregulated genes, while the blue bars represent downregulated genes. (<b>B</b>) A Venn diagram analysis of upregulated DEGs. This diagram illustrates the common and unique upregulated genes across the different <span class="html-italic">BnGPAT9</span> transgenic lines. (<b>C</b>) A Venn diagram analysis of downregulated DEGs. This diagram shows the common and unique downregulated genes among the various <span class="html-italic">BnGPAT9</span> transgenic lines. (<b>D</b>) A clustering heatmap of DEGs. Each column represents a sample, and each row represents a gene. The color in the heatmap indicates the normalized expression level of the gene in each sample, with red representing higher expression levels and blue representing lower expression levels.</p> Full article ">Figure 7
<p>KEGG enrichment analysis of DEGs in four <span class="html-italic">BnGPAT9</span> transgenic <span class="html-italic">Arabidopsis thaliana.</span> (<b>A</b>) KEGG enrichment analysis of DEGs between <span class="html-italic">BnGPAT9-A01</span> transgenic <span class="html-italic">Arabidopsis thaliana</span> and wild type. (<b>B</b>) KEGG enrichment analysis of DEGs between <span class="html-italic">BnGPAT9-A10</span> transgenic <span class="html-italic">Arabidopsis thaliana</span> and wild type. (<b>C</b>) KEGG enrichment analysis of DEGs between <span class="html-italic">BnGPAT9-C01</span> transgenic <span class="html-italic">Arabidopsis thaliana</span> and wild type. (<b>D</b>) KEGG enrichment analysis of DEGs between <span class="html-italic">BnGPAT9-C09</span> transgenic <span class="html-italic">Arabidopsis thaliana</span> and wild type. (<b>E</b>) Heatmap of expression of DEGs enriched in alpha-Linolenic acid metabolism pathway. “**” indicates highly significant differences, <span class="html-italic">p</span> < 0.01.</p> Full article ">
<p>Characterization of the BnGPAT9 protein sequences. (<b>A</b>) The analysis of conserved motifs in BnGPAT9 and AtGPAT9 proteins; (<b>B</b>) the multiple sequence alignment of plant BnGPAT9 proteins.</p> Full article ">Figure 2
<p>Cis elements detected in the promoter of the <span class="html-italic">BnGPAT9</span> genes. (<b>A</b>) Promoter element distribution, where different colors correspond to different elements in the figure below; (<b>B</b>) the heat map shows the number of promoter elements, and the gray square indicates that the elements could not be detected.</p> Full article ">Figure 3
<p>Tissue expression patterns of <span class="html-italic">BnGPAT9</span> genes. (<b>A</b>) The GUS staining results of <span class="html-italic">Arabidopsis thaliana</span>. (<b>A1</b>,<b>B1</b>,<b>C1</b>,<b>D1</b>,<b>E1</b>,<b>F1</b>): GUS staining in stems; (<b>A2</b>,<b>B2</b>,<b>C2</b>,<b>D2</b>,<b>E2</b>,<b>F2</b>): GUS staining in leaves; (<b>A3</b>,<b>B3</b>,<b>C3</b>,<b>D3</b>,<b>E3</b>,<b>F3</b>): GUS staining in inflorescences; (<b>A4</b>,<b>B4</b>,<b>C4</b>,<b>D4</b>,<b>E4</b>,<b>F4</b>): GUS staining in flowers; (<b>A5</b>,<b>B5</b>,<b>C5</b>,<b>D5</b>,<b>E5</b>,<b>F5</b>): GUS staining in siliques. Bar = 1 mm. (<b>B</b>) Expression profiles of <span class="html-italic">BnGPAT9</span> genes in ZS11. Data sourced from BnTIR (<a href="https://yanglab.hzau.edu.cn/BnTIR" target="_blank">https://yanglab.hzau.edu.cn/BnTIR</a> (accessed on 15 October 2019)).</p> Full article ">Figure 4
<p>Analysis of tissue-specific expression patterns of <span class="html-italic">BnGPAT9</span> genes and seed oil accumulation. (<b>A</b>) qRT-PCR detection of <span class="html-italic">BnGPAT9</span> expression in roots, stems, leaves, flowers, and seeds at 1–7 weeks of development, as well as siliques of XY15. (<b>B</b>–<b>D</b>) qRT-PCR detection of <span class="html-italic">BnGPAT9</span> expression in seeds and siliques at 1–7 weeks of development in transgenic lines overexpressing the <span class="html-italic">BnGPAT9-C01</span> gene. (<b>E</b>) Oil content in the seeds of XY15 and the three transgenic lines.</p> Full article ">Figure 5
<p>Analysis of the thousand-seed weight and oil content in <span class="html-italic">BnGPAT9</span> transgenic Arabidopsis seeds. (<b>A</b>) Thousand-seed weight of seeds from four <span class="html-italic">BnGPAT9</span> transgenic lines. (<b>B</b>) Oil content of seeds from four <span class="html-italic">BnGPAT9</span> transgenic lines. “*” indicates highly significant differences, <span class="html-italic">p</span> < 0.05. “**” indicates highly significant differences, <span class="html-italic">p</span> < 0.01.</p> Full article ">Figure 6
<p>An analysis of differential gene expression in siliques of <span class="html-italic">BnGPAT9</span> transgenic Arabidopsis. (<b>A</b>) A statistical analysis of the number of DEGs. The red bars represent upregulated genes, while the blue bars represent downregulated genes. (<b>B</b>) A Venn diagram analysis of upregulated DEGs. This diagram illustrates the common and unique upregulated genes across the different <span class="html-italic">BnGPAT9</span> transgenic lines. (<b>C</b>) A Venn diagram analysis of downregulated DEGs. This diagram shows the common and unique downregulated genes among the various <span class="html-italic">BnGPAT9</span> transgenic lines. (<b>D</b>) A clustering heatmap of DEGs. Each column represents a sample, and each row represents a gene. The color in the heatmap indicates the normalized expression level of the gene in each sample, with red representing higher expression levels and blue representing lower expression levels.</p> Full article ">Figure 7
<p>KEGG enrichment analysis of DEGs in four <span class="html-italic">BnGPAT9</span> transgenic <span class="html-italic">Arabidopsis thaliana.</span> (<b>A</b>) KEGG enrichment analysis of DEGs between <span class="html-italic">BnGPAT9-A01</span> transgenic <span class="html-italic">Arabidopsis thaliana</span> and wild type. (<b>B</b>) KEGG enrichment analysis of DEGs between <span class="html-italic">BnGPAT9-A10</span> transgenic <span class="html-italic">Arabidopsis thaliana</span> and wild type. (<b>C</b>) KEGG enrichment analysis of DEGs between <span class="html-italic">BnGPAT9-C01</span> transgenic <span class="html-italic">Arabidopsis thaliana</span> and wild type. (<b>D</b>) KEGG enrichment analysis of DEGs between <span class="html-italic">BnGPAT9-C09</span> transgenic <span class="html-italic">Arabidopsis thaliana</span> and wild type. (<b>E</b>) Heatmap of expression of DEGs enriched in alpha-Linolenic acid metabolism pathway. “**” indicates highly significant differences, <span class="html-italic">p</span> < 0.01.</p> Full article ">
Open AccessArticle
Rolling Bearing Fault Diagnosis in Agricultural Machinery Based on Multi-Source Locally Adaptive Graph Convolution
by
Fengyun Xie, Enguang Sun, Linglan Wang, Gan Wang and Qian Xiao
Agriculture 2024, 14(8), 1333; https://doi.org/10.3390/agriculture14081333 (registering DOI) - 9 Aug 2024
Abstract
Maintaining agricultural machinery is crucial for efficient mechanized farming. Specifically, diagnosing faults in rolling bearings, which are essential rotating components, is of significant importance. Domain-adaptive technology often addresses the challenge of limited labeled data from a single source domain. However, information transfer can
[...] Read more.
Maintaining agricultural machinery is crucial for efficient mechanized farming. Specifically, diagnosing faults in rolling bearings, which are essential rotating components, is of significant importance. Domain-adaptive technology often addresses the challenge of limited labeled data from a single source domain. However, information transfer can sometimes fall short in providing adequate relevant details for supporting target diagnosis tasks, leading to poor recognition performance. This paper introduces a novel fault diagnosis model based on a multi-source locally adaptive graph convolution network to diagnose rolling bearing faults in agricultural machinery. The model initially employs an overlapping sampling method to enhance sample data. Recognizing that two-dimensional time–frequency signals possess richer spatial characteristics in neural networks, wavelet transform is used to convert time series samples into time–frequency graph samples before feeding them into the feature network. This approach constructs a sample data pair from both source and target domains. Furthermore, a feature extraction network is developed by integrating the strengths of deep residual networks and graph convolutional networks, enabling the model to better learn invariant features across domains. The locally adaptive method aids the model in more effectively aligning features from the source and target domains. The model incorporates a Softmax layer as the bearing state classifier, which is set up after the graph convolutional network layer, and outputs bearing state recognition results upon reaching a set number of iterations. The proposed method’s effectiveness was validated using a bearing dataset from Jiangnan University. For three different groups of bearing fault diagnosis tasks under varying working conditions, the proposed method achieved recognition accuracies above 99%, with an improvement of 0.30%-4.33% compared to single-source domain diagnosis models. Comparative results indicate that the proposed method can effectively identify bearing states even without target domain labels, showcasing its practical engineering application value.
Full article
(This article belongs to the Special Issue Autonomous and Automated Agricultural Machinery: Safety Issues, Focused Applications, and Regulatory Aspects)
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<p>Conversion relationship diagram.</p> Full article ">Figure 2
<p>The diagnostic flow chart.</p> Full article ">Figure 3
<p>Multi-source rolling bearing fault diagnosis under variable working conditions.</p> Full article ">Figure 4
<p>Experimental platform: (<b>a</b>) experimental platform structure diagram and (<b>b</b>) data acquisition diagram.</p> Full article ">Figure 5
<p>Recognition accuracy confusion matrix for fault diagnosis in task A + C→B by different models is as follows: (<b>a</b>) accuracy of the JMMD method for the A + C→B task; (<b>b</b>) accuracy of the CORAL method for the C→A task; (<b>c</b>) accuracy of the MK-MMD method for the B + C→A task; and (<b>d</b>) accuracy of the MSLA method for the B, C→A tasks.</p> Full article ">Figure 5 Cont.
<p>Recognition accuracy confusion matrix for fault diagnosis in task A + C→B by different models is as follows: (<b>a</b>) accuracy of the JMMD method for the A + C→B task; (<b>b</b>) accuracy of the CORAL method for the C→A task; (<b>c</b>) accuracy of the MK-MMD method for the B + C→A task; and (<b>d</b>) accuracy of the MSLA method for the B, C→A tasks.</p> Full article ">Figure 6
<p>The feature distribution maps extracted from the 4 models: (<b>a</b>) feature distribution of SSLA methods in the B→A task; (<b>b</b>) feature distribution of SSLA methods in the C→A task; (<b>c</b>) feature distribution of SSLA methods in the B + C→A task; and (<b>d</b>) feature distribution of MSLA methods in the B + C→A tasks.</p> Full article ">
<p>Conversion relationship diagram.</p> Full article ">Figure 2
<p>The diagnostic flow chart.</p> Full article ">Figure 3
<p>Multi-source rolling bearing fault diagnosis under variable working conditions.</p> Full article ">Figure 4
<p>Experimental platform: (<b>a</b>) experimental platform structure diagram and (<b>b</b>) data acquisition diagram.</p> Full article ">Figure 5
<p>Recognition accuracy confusion matrix for fault diagnosis in task A + C→B by different models is as follows: (<b>a</b>) accuracy of the JMMD method for the A + C→B task; (<b>b</b>) accuracy of the CORAL method for the C→A task; (<b>c</b>) accuracy of the MK-MMD method for the B + C→A task; and (<b>d</b>) accuracy of the MSLA method for the B, C→A tasks.</p> Full article ">Figure 5 Cont.
<p>Recognition accuracy confusion matrix for fault diagnosis in task A + C→B by different models is as follows: (<b>a</b>) accuracy of the JMMD method for the A + C→B task; (<b>b</b>) accuracy of the CORAL method for the C→A task; (<b>c</b>) accuracy of the MK-MMD method for the B + C→A task; and (<b>d</b>) accuracy of the MSLA method for the B, C→A tasks.</p> Full article ">Figure 6
<p>The feature distribution maps extracted from the 4 models: (<b>a</b>) feature distribution of SSLA methods in the B→A task; (<b>b</b>) feature distribution of SSLA methods in the C→A task; (<b>c</b>) feature distribution of SSLA methods in the B + C→A task; and (<b>d</b>) feature distribution of MSLA methods in the B + C→A tasks.</p> Full article ">
Open AccessArticle
The Design and Optimization of a Peanut-Picking System for a Fresh-Peanut-Picking Crawler Combine Harvester
by
Jie Ling, Haiyang Shen, Man Gu, Zhichao Hu, Sheng Zhao, Feng Wu, Hongbo Xu, Fengwei Gu and Peng Zhang
Agriculture 2024, 14(8), 1332; https://doi.org/10.3390/agriculture14081332 (registering DOI) - 9 Aug 2024
Abstract
In view of the problem that peanut harvesting in hilly areas mainly involves fresh food, and that the peanut-picking purity rate is low and there is high breakage in the peanut-harvesting process, key components such as the picking system of a fresh-peanut-picking crawler
[...] Read more.
In view of the problem that peanut harvesting in hilly areas mainly involves fresh food, and that the peanut-picking purity rate is low and there is high breakage in the peanut-harvesting process, key components such as the picking system of a fresh-peanut-picking crawler combine harvester, the picking tooth, and the concave screen were designed, and ANSYS Workbench 2020 software was used to check the reliability of the picking roller under working conditions in hilly areas. In the process of equipment operation, the picking purity rate and breakage rate were the main evaluation indexes, and the Box–Behnken test method was used to study the speed of the peanut-picking roller, the feeding amount, and the picking gap as the test factors. The results showed that the picking purity rate is 98.95%, with an error margin of 0.98%, compared to the predicted value under the conditions of 342 r/min speed, 0.75 kg/s feeding amount, and 32 mm picking gap. The breakage rate is 4.23% and the error is 0.4% compared with the predicted value, indicating that the optimized model is reliable and predictive. This study provides a theoretical basis for the optimal design of the peanut-picking system of peanut-picking combine harvesters in hilly areas.
Full article
(This article belongs to the Section Agricultural Technology)
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![](https://pub.mdpi-res.com/agriculture/agriculture-14-01332/article_deploy/html/images/agriculture-14-01332-g001-550.jpg?1723211821)
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<p>Fresh-peanut-picking crawler combine harvester: 1: chassis walking system; 2: dynamical system; 3: peanut collection device; 4: cleaning device; 5: inclined scraper conveyor; 6: transmission system; 7: control console; 8: peanut-picking device; 9: picking platform.</p> Full article ">Figure 2
<p>Peanut-picking system. (<b>a</b>) The overall structure of the peanut-picking system and (<b>b</b>) (1) the first-stage concave screen, (2) the upper shell, (3) the deflector, (4) the peanut-picking roller, (5) the secondary concave screen, (6) the three-stage concave screen, (7) the bottom auger, and (8) the lower shell of the peanut-picking chamber.</p> Full article ">Figure 3
<p>Peanut-picking roller. 1: Peanut-picking nail tooth. 2: Tooth bar. 3: Tooth holder. 4: Flange disc.</p> Full article ">Figure 4
<p>Length of the picking roller.</p> Full article ">Figure 5
<p>Peanut-picking tooth—partial enlargement.</p> Full article ">Figure 6
<p>Analysis of the force of peanut plants in the picking roller.</p> Full article ">Figure 7
<p>Peanut miscellaneous conveying auger. 1: Spindle. 2: Spiral blade. 3: Scraper.</p> Full article ">Figure 8
<p>Concave plate screen: (<b>a</b>) first-stage concave plate screen, (<b>b</b>) secondary concave plate screen, and (<b>c</b>) third-stage concave plate screen.</p> Full article ">Figure 9
<p>Contour diagram of (<b>a</b>) total deformation, (<b>b</b>) strain, and (<b>c</b>) stress of peanut−picking roller.</p> Full article ">Figure 10
<p>The modal analysis of the first six orders for picking rollers: (<b>a</b>) Model 1, (<b>b</b>) Model 2, (<b>c</b>) Model 3, (<b>d</b>) Model 4, (<b>e</b>) Model 5, and (<b>f</b>) Model 6.</p> Full article ">Figure 10 Cont.
<p>The modal analysis of the first six orders for picking rollers: (<b>a</b>) Model 1, (<b>b</b>) Model 2, (<b>c</b>) Model 3, (<b>d</b>) Model 4, (<b>e</b>) Model 5, and (<b>f</b>) Model 6.</p> Full article ">Figure 11
<p>The influence of interaction factors on picking purity rate. (<b>a</b>) The effect of peanut-picking roller speed and feeding amount on picking purity rate. (<b>b</b>) The effect of peanut-picking roller speed and peanut-picking gap on picking purity rate. (<b>c</b>) The effects of feeding amount and peanut-picking gap on picking purity rate.</p> Full article ">Figure 12
<p>The influence of interaction factors on breakage rate. (<b>a</b>) The effect of peanut-picking roller speed and feeding amount on breakage rate. (<b>b</b>) The effect of peanut-picking roller speed and peanut-picking gap on breakage rate. (<b>c</b>) The effects of feeding amount and peanut-picking gap on breakage rate.</p> Full article ">Figure 13
<p>Field test of fresh-peanut-picking crawler combine harvester.</p> Full article ">
<p>Fresh-peanut-picking crawler combine harvester: 1: chassis walking system; 2: dynamical system; 3: peanut collection device; 4: cleaning device; 5: inclined scraper conveyor; 6: transmission system; 7: control console; 8: peanut-picking device; 9: picking platform.</p> Full article ">Figure 2
<p>Peanut-picking system. (<b>a</b>) The overall structure of the peanut-picking system and (<b>b</b>) (1) the first-stage concave screen, (2) the upper shell, (3) the deflector, (4) the peanut-picking roller, (5) the secondary concave screen, (6) the three-stage concave screen, (7) the bottom auger, and (8) the lower shell of the peanut-picking chamber.</p> Full article ">Figure 3
<p>Peanut-picking roller. 1: Peanut-picking nail tooth. 2: Tooth bar. 3: Tooth holder. 4: Flange disc.</p> Full article ">Figure 4
<p>Length of the picking roller.</p> Full article ">Figure 5
<p>Peanut-picking tooth—partial enlargement.</p> Full article ">Figure 6
<p>Analysis of the force of peanut plants in the picking roller.</p> Full article ">Figure 7
<p>Peanut miscellaneous conveying auger. 1: Spindle. 2: Spiral blade. 3: Scraper.</p> Full article ">Figure 8
<p>Concave plate screen: (<b>a</b>) first-stage concave plate screen, (<b>b</b>) secondary concave plate screen, and (<b>c</b>) third-stage concave plate screen.</p> Full article ">Figure 9
<p>Contour diagram of (<b>a</b>) total deformation, (<b>b</b>) strain, and (<b>c</b>) stress of peanut−picking roller.</p> Full article ">Figure 10
<p>The modal analysis of the first six orders for picking rollers: (<b>a</b>) Model 1, (<b>b</b>) Model 2, (<b>c</b>) Model 3, (<b>d</b>) Model 4, (<b>e</b>) Model 5, and (<b>f</b>) Model 6.</p> Full article ">Figure 10 Cont.
<p>The modal analysis of the first six orders for picking rollers: (<b>a</b>) Model 1, (<b>b</b>) Model 2, (<b>c</b>) Model 3, (<b>d</b>) Model 4, (<b>e</b>) Model 5, and (<b>f</b>) Model 6.</p> Full article ">Figure 11
<p>The influence of interaction factors on picking purity rate. (<b>a</b>) The effect of peanut-picking roller speed and feeding amount on picking purity rate. (<b>b</b>) The effect of peanut-picking roller speed and peanut-picking gap on picking purity rate. (<b>c</b>) The effects of feeding amount and peanut-picking gap on picking purity rate.</p> Full article ">Figure 12
<p>The influence of interaction factors on breakage rate. (<b>a</b>) The effect of peanut-picking roller speed and feeding amount on breakage rate. (<b>b</b>) The effect of peanut-picking roller speed and peanut-picking gap on breakage rate. (<b>c</b>) The effects of feeding amount and peanut-picking gap on breakage rate.</p> Full article ">Figure 13
<p>Field test of fresh-peanut-picking crawler combine harvester.</p> Full article ">
Open AccessEditorial
Abiotic Stresses, Biostimulants and Plant Activity—Series II
by
Luca Regni, Daniele Del Buono and Primo Proietti
Agriculture 2024, 14(8), 1331; https://doi.org/10.3390/agriculture14081331 (registering DOI) - 9 Aug 2024
Abstract
Agricultural practices often mainly focus on maximizing productivity [...]
Full article
(This article belongs to the Special Issue Abiotic Stresses, Biostimulant and Plant Activity—Series II)
Open AccessReview
Integrated Nutrient Management of Fruits, Vegetables, and Crops through the Use of Biostimulants, Soilless Cultivation, and Traditional and Modern Approaches—A Mini Review
by
Awais Ali, Genhua Niu, Joseph Masabni, Antonio Ferrante and Giacomo Cocetta
Agriculture 2024, 14(8), 1330; https://doi.org/10.3390/agriculture14081330 (registering DOI) - 9 Aug 2024
Abstract
The increasing population, its requirements for food, and the environmental impact of the excessive use of inputs make crop production a pressing challenge. Integrated nutrient management (INM) has emerged as a critical solution by maximizing nutrient availability and utilization for crops and vegetables.
[...] Read more.
The increasing population, its requirements for food, and the environmental impact of the excessive use of inputs make crop production a pressing challenge. Integrated nutrient management (INM) has emerged as a critical solution by maximizing nutrient availability and utilization for crops and vegetables. This review paper highlights the potential benefits of INM for various vegetables and field crops and explores the conceptual strategies, components, and principles underlying this approach. Studies have shown that a wide range of vegetables and field crops benefit from INM, in terms of increased yield and improvements in yield attributes, nutrient contents and uptake, growth parameters, and various physiological and biochemical characteristics. This paper discusses biostimulants, their categories, and their impact on plant propagation, growth, photosynthesis, seed germination, fruit set, and quality. Additionally, this review explores modern sustainable soilless production techniques such as hydroponics, aeroponics, and aquaponics. These cultivation methods highlight the advancements of controlled-environment agriculture (CEA) and its contribution to nutrient management, food security and minimizing the environmental footprint. The review concludes by proposing methods and fostering discussions on INM’s future development, while acknowledging the challenges associated with its adoption. Finally, this review emphasizes the substantial evidence supporting INM as a novel and ecologically sound strategy for achieving sustainable agricultural production worldwide.
Full article
(This article belongs to the Special Issue Fertilizer Management Strategies for Enhancing the Growth, Yield and Quality in Crops)
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<p>Biostimulants and their different categories employed in agriculture.</p> Full article ">Figure 2
<p>Components of INM.</p> Full article ">Figure 3
<p>Flow sheet diagram highlighting the advantages offered by integrated nutrient management.</p> Full article ">Figure 4
<p>Biostimulants and their applications and impacts on different stages of ornamentals, fruits, vegetables, and field crops.</p> Full article ">Figure 5
<p>A sketch of soilless cultivation systems.</p> Full article ">
<p>Biostimulants and their different categories employed in agriculture.</p> Full article ">Figure 2
<p>Components of INM.</p> Full article ">Figure 3
<p>Flow sheet diagram highlighting the advantages offered by integrated nutrient management.</p> Full article ">Figure 4
<p>Biostimulants and their applications and impacts on different stages of ornamentals, fruits, vegetables, and field crops.</p> Full article ">Figure 5
<p>A sketch of soilless cultivation systems.</p> Full article ">
Open AccessArticle
Rice Bund Management by Filipino Farmers and Willingness to Adopt Ecological Engineering for Pest Suppression
by
Finbarr G. Horgan, Angelee F. Ramal, James M. Villegas, Alexandra Jamoralin, John Michael V. Pasang, Buyung A. R. Hadi, Enrique A. Mundaca and Eduardo Crisol-Martínez
Agriculture 2024, 14(8), 1329; https://doi.org/10.3390/agriculture14081329 (registering DOI) - 9 Aug 2024
Abstract
Ecological engineering is defined as the design of ecosystems for the benefit of human society and the environment. In Asia, the ecological engineering of rice fields by establishing vegetation on bunds/levees for natural enemies has recently gained traction; however, its success depends on
[...] Read more.
Ecological engineering is defined as the design of ecosystems for the benefit of human society and the environment. In Asia, the ecological engineering of rice fields by establishing vegetation on bunds/levees for natural enemies has recently gained traction; however, its success depends on farmers’ willingness to implement changes. We surveyed 291 rice farmers in four regions of the Philippines to assess their bund management practices and willingness to establish bund vegetation that restores rice regulatory ecosystem services for pest management. During pre- and post-open field day (OFD) interviews, we assessed farmers’ perceptions of ecological engineering practices and sought their advice concerning bund vegetation. Over 60% of the farmers grew crops or allowed weeds on their bunds. Vegetables were grown as a source of extra food or income, and flowers were grown for pest management. Among the remaining farmers, their willingness to try ecological engineering increased from 36 to 74% after the OFDs. Furthermore, after the OFDs, willing farmers increasingly (from 2.6 to 15%) cited pest management as a reason to grow vegetables on bunds, and farmers almost exclusively focused on growing vegetables rather than flowers to adapt the system. While 46.5% of farmers who grew vegetables on their bunds applied insecticides, only ca 20% indicated that they would do so after the OFDs, if needed. Farmers had differing opinions on how vegetables would be incorporated into their rice farms. This range of options could be encouraged wherever farmers recognize the potential harm from pesticides for biodiversity and the restoration value of a diversified farm habitat.
Full article
(This article belongs to the Special Issue Current Prospects of Social-Ecologically More Sustainable Agriculture and Urban Agriculture)
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<p>Farmers’ preferences for vegetables grown on bunds. The farmers were interviewed in (<b>A</b>) Laguna, (<b>B</b>) Rizal, (<b>C</b>) Iloilo, and (<b>D</b>) Bukidnon. Gray bars indicate the plant species that farmers currently grow on their bunds (i.e., practicing), while the blue outlines indicate farmer preferences after the OFDs (i.e., willing). Full details of the vegetables mentioned by the farmers are presented in <a href="#app1-agriculture-14-01329" class="html-app">Table S3</a>.</p> Full article ">Figure 2
<p>Flowers grown by farmers on rice bunds. The farmers were interviewed in (<b>A</b>) Laguna, (<b>B</b>) Rizal, (<b>C</b>) Iloilo, and (<b>D</b>) Bukidnon. The bars indicate the plant species that the farmers currently grow. Further details on the flowers mentioned by the farmers are presented in <a href="#app1-agriculture-14-01329" class="html-app">Table S3</a>.</p> Full article ">Figure 3
<p>Summary of current practices and willingness to plant flowers and vegetables on rice bunds as indicated during pre- and post-event interviews. Gray areas indicate farmers who are not practicing or willing to grow crops on bunds, yellow areas indicate farmers with knowledge of bund crops and currently growing plants on bunds or allowing wild flowers to grow, pink areas indicate farmers willing to grow plants on bunds before the open field days (OFDs), and blue indicates farmers practicing or willing to grow plants on bunds after the OFDs. Note that, while 90% of the farmers indicated willingness after the OFDs to grow vegetables on bunds, only 74% expressed no reservations or obstacles for doing so. Pie charts indicate relevant reasons given by farmers to grow vegetables or flowers on the bunds, as indicated in the legend. See <a href="#agriculture-14-01329-t001" class="html-table">Table 1</a>, <a href="#agriculture-14-01329-t002" class="html-table">Table 2</a>, and <a href="#agriculture-14-01329-t004" class="html-table">Table 4</a> for further details.</p> Full article ">
<p>Farmers’ preferences for vegetables grown on bunds. The farmers were interviewed in (<b>A</b>) Laguna, (<b>B</b>) Rizal, (<b>C</b>) Iloilo, and (<b>D</b>) Bukidnon. Gray bars indicate the plant species that farmers currently grow on their bunds (i.e., practicing), while the blue outlines indicate farmer preferences after the OFDs (i.e., willing). Full details of the vegetables mentioned by the farmers are presented in <a href="#app1-agriculture-14-01329" class="html-app">Table S3</a>.</p> Full article ">Figure 2
<p>Flowers grown by farmers on rice bunds. The farmers were interviewed in (<b>A</b>) Laguna, (<b>B</b>) Rizal, (<b>C</b>) Iloilo, and (<b>D</b>) Bukidnon. The bars indicate the plant species that the farmers currently grow. Further details on the flowers mentioned by the farmers are presented in <a href="#app1-agriculture-14-01329" class="html-app">Table S3</a>.</p> Full article ">Figure 3
<p>Summary of current practices and willingness to plant flowers and vegetables on rice bunds as indicated during pre- and post-event interviews. Gray areas indicate farmers who are not practicing or willing to grow crops on bunds, yellow areas indicate farmers with knowledge of bund crops and currently growing plants on bunds or allowing wild flowers to grow, pink areas indicate farmers willing to grow plants on bunds before the open field days (OFDs), and blue indicates farmers practicing or willing to grow plants on bunds after the OFDs. Note that, while 90% of the farmers indicated willingness after the OFDs to grow vegetables on bunds, only 74% expressed no reservations or obstacles for doing so. Pie charts indicate relevant reasons given by farmers to grow vegetables or flowers on the bunds, as indicated in the legend. See <a href="#agriculture-14-01329-t001" class="html-table">Table 1</a>, <a href="#agriculture-14-01329-t002" class="html-table">Table 2</a>, and <a href="#agriculture-14-01329-t004" class="html-table">Table 4</a> for further details.</p> Full article ">
Open AccessArticle
Screening New Mungbean Varieties for Terminal Drought Tolerance
by
Sobia Ikram, Surya Bhattarai and Kerry B. Walsh
Agriculture 2024, 14(8), 1328; https://doi.org/10.3390/agriculture14081328 (registering DOI) - 9 Aug 2024
Abstract
Rainfed mungbean crops in Queensland Australia frequently experience terminal drought (drought stress in the final stages of reproductive development), highlighting the importance of drought-tolerant varieties for sustainable mungbean production. Given there is limited information on the relative drought tolerance of current mungbean varieties
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Rainfed mungbean crops in Queensland Australia frequently experience terminal drought (drought stress in the final stages of reproductive development), highlighting the importance of drought-tolerant varieties for sustainable mungbean production. Given there is limited information on the relative drought tolerance of current mungbean varieties in Australia, the study of genetic variations and mechanisms of drought tolerance in summer mungbean can provide a basis for developing drought-tolerant mungbean varieties. This study evaluated the physiological, biochemical, and phenological traits underpinning yield attributes associated with drought tolerance in selected mungbean varieties. Four new mungbean varieties (AVTMB#1 to 4) and the Australian commercial line (Jade-AU) were grown in tall (75 cm) polyvinyl chloride (PVC) lysimeters where drought stress was imposed at the early flowering stage (R1) and maintained until maturity. Drought stress significantly impacted all the varieties. Averaged across all the varieties, drought stress was associated with a reduction in stomatal conductance (gs) and photosynthetic rate (Asat) by 78% and 86%, respectively, compared to well-watered plants. Internal carbon dioxide concentration (Ci), the effective quantum yield of photosystem II (ΦPSII) and maximum light-use efficiency of light-acclimated photosystem II (PSII) centres (Fv’/Fm’) were also decreased, while excitation pressure (1-qP) increased with drought treatment. A positive correlation (r = 0.60) existed between seed yield and ΦPSII assessed at R1, while a weak correlation with Fv’/Fm’ (r = 0.24) was observed. Excitation pressure (1-qP) at the R1 stage was negatively correlated with seed yield (r = −0.66). Therefore, leaf fluorescence measures, viz., 1-qP and ΦPSII, were recommended for use in screening mungbean varieties for drought tolerance. The varieties, AVTMB#1 and AVTMB#4, respectively achieved 39 and 38% greater seed yields relative to the commercial variety, Jade-AU, under terminal drought conditions.
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(This article belongs to the Special Issue Feature Papers in Genotype Evaluation and Breeding)
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Figure 1
Figure 1
<p>Trial set up of five mungbean varieties in lysimeter pots under two water treatments (well-watered and drought stress) in a glasshouse.</p> Full article ">Figure 2
<p>Time course of water used by five mungbean varieties in two water treatments: Well-watered (WW) = 100% water holding capacity (WHC) and drought stress (DS) = 40% WHC. Data are presented as means ± standard errors (SE) (<span class="html-italic">n</span> = 4 plants). The water in DS treatment was withheld from 10 DAS, with 40% water holding capacity achieved at 37 DAS.</p> Full article ">Figure 3
<p>Water-use efficiency (WUE; gL<sup>−1</sup>) of five mungbean varieties in two water treatments, Well-watered (WW) = 100% water holding capacity (WHC), drought stress (DS) = 40% WHC. The water in DS treatment was withheld from 10 DAS and 40% WHC was achieved at 37 DAS. Each vertical bar represents mean values (<span class="html-italic">n</span> = 6) and error bars indicate the standard errors. Letters above vertical bars indicate significant differences among varieties in drought stress and well-watered conditions.</p> Full article ">Figure 4
<p>Light-saturated photosynthetic rate (A<sub>sat;</sub> µmol CO<sub>2</sub> m<sup>−2</sup> s<sup>−1</sup>)—(<b>A</b>) and stomatal conductance (g<sub>s</sub>; mol H<sub>2</sub>O m<sup>−2</sup> s<sup>−1</sup>)—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS), well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p> Full article ">Figure 4 Cont.
<p>Light-saturated photosynthetic rate (A<sub>sat;</sub> µmol CO<sub>2</sub> m<sup>−2</sup> s<sup>−1</sup>)—(<b>A</b>) and stomatal conductance (g<sub>s</sub>; mol H<sub>2</sub>O m<sup>−2</sup> s<sup>−1</sup>)—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS), well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p> Full article ">Figure 5
<p>Ci (µmol mol<sup>−1</sup>)—(<b>A</b>) and iWUE—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Leaf internal carbon concentration (Ci, µmol mol<sup>−1</sup>)—(<b>A</b>) and Intrinsic water use of light-adapted leaves—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in; <a href="#app1-agriculture-14-01328" class="html-app">Supplementary Data: Table S2</a>.</p> Full article ">Figure 5 Cont.
<p>Ci (µmol mol<sup>−1</sup>)—(<b>A</b>) and iWUE—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Leaf internal carbon concentration (Ci, µmol mol<sup>−1</sup>)—(<b>A</b>) and Intrinsic water use of light-adapted leaves—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in; <a href="#app1-agriculture-14-01328" class="html-app">Supplementary Data: Table S2</a>.</p> Full article ">Figure 6
<p>PhiPS2 (ΦPSII)—(<b>A</b>) and Fv’/Fm’—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Light accounted for photochemistry PSII (ΦPSII)—(<b>A</b>) and photochemical efficiency of open PSII centres in light-adapted leaves Fv’/Fm’—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean of <span class="html-italic">n</span> = 4 plants with associated error bars representing standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p> Full article ">Figure 6 Cont.
<p>PhiPS2 (ΦPSII)—(<b>A</b>) and Fv’/Fm’—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Light accounted for photochemistry PSII (ΦPSII)—(<b>A</b>) and photochemical efficiency of open PSII centres in light-adapted leaves Fv’/Fm’—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean of <span class="html-italic">n</span> = 4 plants with associated error bars representing standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p> Full article ">Figure 7
<p>Fv/Fm—(<b>A</b>) and 1-qP)—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Quantum yield efficiency of dark-adapted leaves (Fv/Fm)—(<b>A</b>) and excitation pressure (1-qP)—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p> Full article ">Figure 8
<p>Leaf chlorophyll content (SPAD units) of five mungbean varieties under well-watered (WW = 100% WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p> Full article ">Figure 9
<p>Seed yield (g plant<sup>−1</sup>) of five mungbean varieties in two water treatments, well-watered (WW) = 100% water holding capacity (WHC), drought stress (DS) = 40% WHC. The water in DS treatment was withheld from 10 DAS and 40% WHC was achieved at 37 DAS. Each vertical bar represents mean values (<span class="html-italic">n</span> = 4) and error bars indicate the standard errors.</p> Full article ">Figure 10
<p>Correlations among studied parameters under drought stress. Correlations amongst A<sub>sat</sub>, g<sub>s</sub>, Ci, iWUE, ΦPSII (PhiPS2), Fv’/Fm’, Fv/Fm, 1-qP, leaf chlorophyll contents (SPAD units), leaf count (LC; #/plant), plant height (PH; cm), leaf dry weight (LDW; g DW/plant), stem dry weight (SDW; g DW/plant), pod dry weight (PDW; g DW/plant), above-ground biomass (AGB; g DW/plant), Root biomass (RB; g DW/plant), root:shoot ratio (R:S), seed yield (YLD; g/plant), 100-seed weight (100SW; g), and harvest index (HI) at flowering stage 37 DAS in drought stress (40% WHC) treatment.</p> Full article ">
<p>Trial set up of five mungbean varieties in lysimeter pots under two water treatments (well-watered and drought stress) in a glasshouse.</p> Full article ">Figure 2
<p>Time course of water used by five mungbean varieties in two water treatments: Well-watered (WW) = 100% water holding capacity (WHC) and drought stress (DS) = 40% WHC. Data are presented as means ± standard errors (SE) (<span class="html-italic">n</span> = 4 plants). The water in DS treatment was withheld from 10 DAS, with 40% water holding capacity achieved at 37 DAS.</p> Full article ">Figure 3
<p>Water-use efficiency (WUE; gL<sup>−1</sup>) of five mungbean varieties in two water treatments, Well-watered (WW) = 100% water holding capacity (WHC), drought stress (DS) = 40% WHC. The water in DS treatment was withheld from 10 DAS and 40% WHC was achieved at 37 DAS. Each vertical bar represents mean values (<span class="html-italic">n</span> = 6) and error bars indicate the standard errors. Letters above vertical bars indicate significant differences among varieties in drought stress and well-watered conditions.</p> Full article ">Figure 4
<p>Light-saturated photosynthetic rate (A<sub>sat;</sub> µmol CO<sub>2</sub> m<sup>−2</sup> s<sup>−1</sup>)—(<b>A</b>) and stomatal conductance (g<sub>s</sub>; mol H<sub>2</sub>O m<sup>−2</sup> s<sup>−1</sup>)—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS), well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p> Full article ">Figure 4 Cont.
<p>Light-saturated photosynthetic rate (A<sub>sat;</sub> µmol CO<sub>2</sub> m<sup>−2</sup> s<sup>−1</sup>)—(<b>A</b>) and stomatal conductance (g<sub>s</sub>; mol H<sub>2</sub>O m<sup>−2</sup> s<sup>−1</sup>)—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS), well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p> Full article ">Figure 5
<p>Ci (µmol mol<sup>−1</sup>)—(<b>A</b>) and iWUE—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Leaf internal carbon concentration (Ci, µmol mol<sup>−1</sup>)—(<b>A</b>) and Intrinsic water use of light-adapted leaves—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in; <a href="#app1-agriculture-14-01328" class="html-app">Supplementary Data: Table S2</a>.</p> Full article ">Figure 5 Cont.
<p>Ci (µmol mol<sup>−1</sup>)—(<b>A</b>) and iWUE—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Leaf internal carbon concentration (Ci, µmol mol<sup>−1</sup>)—(<b>A</b>) and Intrinsic water use of light-adapted leaves—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in; <a href="#app1-agriculture-14-01328" class="html-app">Supplementary Data: Table S2</a>.</p> Full article ">Figure 6
<p>PhiPS2 (ΦPSII)—(<b>A</b>) and Fv’/Fm’—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Light accounted for photochemistry PSII (ΦPSII)—(<b>A</b>) and photochemical efficiency of open PSII centres in light-adapted leaves Fv’/Fm’—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean of <span class="html-italic">n</span> = 4 plants with associated error bars representing standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p> Full article ">Figure 6 Cont.
<p>PhiPS2 (ΦPSII)—(<b>A</b>) and Fv’/Fm’—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Light accounted for photochemistry PSII (ΦPSII)—(<b>A</b>) and photochemical efficiency of open PSII centres in light-adapted leaves Fv’/Fm’—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% water holding capacity; WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity was achieved at 37 DAS. Values are mean of <span class="html-italic">n</span> = 4 plants with associated error bars representing standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p> Full article ">Figure 7
<p>Fv/Fm—(<b>A</b>) and 1-qP)—(<b>B</b>) of five mungbean varieties under well-watered (WW) and drought stress (DS). Quantum yield efficiency of dark-adapted leaves (Fv/Fm)—(<b>A</b>) and excitation pressure (1-qP)—(<b>B</b>) of five mungbean varieties in two water treatments: well-watered (WW = 100% WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS, and 40% water holding capacity achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p> Full article ">Figure 8
<p>Leaf chlorophyll content (SPAD units) of five mungbean varieties under well-watered (WW = 100% WHC) and drought stress (DS = 40% WHC). The water in DS treatment was withheld from 10 DAS and 40% water holding capacity was achieved at 37 DAS. Values are mean (±SE) of <span class="html-italic">n</span> = 4 plants and error bars represent standard error. Statistics are reported in <a href="#app1-agriculture-14-01328" class="html-app">Table S2, Supplementary Data</a>.</p> Full article ">Figure 9
<p>Seed yield (g plant<sup>−1</sup>) of five mungbean varieties in two water treatments, well-watered (WW) = 100% water holding capacity (WHC), drought stress (DS) = 40% WHC. The water in DS treatment was withheld from 10 DAS and 40% WHC was achieved at 37 DAS. Each vertical bar represents mean values (<span class="html-italic">n</span> = 4) and error bars indicate the standard errors.</p> Full article ">Figure 10
<p>Correlations among studied parameters under drought stress. Correlations amongst A<sub>sat</sub>, g<sub>s</sub>, Ci, iWUE, ΦPSII (PhiPS2), Fv’/Fm’, Fv/Fm, 1-qP, leaf chlorophyll contents (SPAD units), leaf count (LC; #/plant), plant height (PH; cm), leaf dry weight (LDW; g DW/plant), stem dry weight (SDW; g DW/plant), pod dry weight (PDW; g DW/plant), above-ground biomass (AGB; g DW/plant), Root biomass (RB; g DW/plant), root:shoot ratio (R:S), seed yield (YLD; g/plant), 100-seed weight (100SW; g), and harvest index (HI) at flowering stage 37 DAS in drought stress (40% WHC) treatment.</p> Full article ">
Open AccessArticle
Correlation Analysis of Sitophilus oryzae (Linnaeus) Real-Time Monitoring and Insect Population Density and Its Distribution Pattern in Wheat Grain Piles
by
Zeyu Zhang, Guoxin Zhou, Cui Miao, Xin Du and Zhongming Wang
Agriculture 2024, 14(8), 1327; https://doi.org/10.3390/agriculture14081327 (registering DOI) - 9 Aug 2024
Abstract
The traditional manual sampling method for detecting stored grain insect pests is labor-intensive and time-consuming, often yielding non-representative samples. However, to achieve more accurate monitoring, it is necessary to understand the distribution patterns of different insect pests within grain silo and their correlation
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The traditional manual sampling method for detecting stored grain insect pests is labor-intensive and time-consuming, often yielding non-representative samples. However, to achieve more accurate monitoring, it is necessary to understand the distribution patterns of different insect pests within grain silo and their correlation with monitoring and sampling data. This study aimed to assess the population density and distribution of Sitophilus oryzae (rice weevil) in bulk wheat grain to predict insect dynamics effectively. Utilizing a probe trap in a wheat silo, adult insects were tracked across different population densities. The traps recorded captured pests, alongside temperature and humidity data. The correlation analysis revealed that rice weevils were active throughout the silo but less prevalent at the bottom, with the highest distribution near the upper surface. Temperature and humidity significantly influenced their activity, particularly within the 22 °C to 32 °C range. Higher population densities correlated with increased relative humidity, impacting weevil activity. Trapping data aligned with overall population density changes in the silo. This study will provide an accurate assessment of the population density of adult rice weevils in grain silos based on temperature changes in the upper part of the grain silo.
Full article
(This article belongs to the Special Issue Grain Harvesting, Processing Technology, and Storage Management)
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<p>A real-time insect trap device based on photoelectric infrared sensors, which were jointly developed by Beijing University of Posts and Telecommunications and the Scientific Research Institute of the State Administration of Grain and Material Reserves.</p> Full article ">Figure 2
<p>Three-tonne capacity experimental silo (<b>a</b>), a schematic diagram of the silo size (<b>b</b>) and trap locations (<b>b</b>,<b>c</b>). OD = Outside Diameter; T = Top; M = Middle; B = Bottom; No. 1, 2, 3, 4 and 5 represent five entrapment points. Red colored cylinders represent probe traps.</p> Full article ">Figure 3
<p>Time series chart of the daily catch of 15 traps in three repeated experiments at 0.1 insect/kg, 1.0 insect/kg, and 5.0 insect/kg. R1, R2, and R3 represent the three repeated experiments that were conducted.</p> Full article ">Figure 3 Cont.
<p>Time series chart of the daily catch of 15 traps in three repeated experiments at 0.1 insect/kg, 1.0 insect/kg, and 5.0 insect/kg. R1, R2, and R3 represent the three repeated experiments that were conducted.</p> Full article ">Figure 4
<p>Three-dimensional scatter plots of daily average rice temperature, relative humidity, and trap capture quantity per probe at three different pest population densities: 0.1 (<b>a</b>), 1.0 (<b>b</b>), and 5.0 (<b>c</b>) adults/kg.</p> Full article ">
<p>A real-time insect trap device based on photoelectric infrared sensors, which were jointly developed by Beijing University of Posts and Telecommunications and the Scientific Research Institute of the State Administration of Grain and Material Reserves.</p> Full article ">Figure 2
<p>Three-tonne capacity experimental silo (<b>a</b>), a schematic diagram of the silo size (<b>b</b>) and trap locations (<b>b</b>,<b>c</b>). OD = Outside Diameter; T = Top; M = Middle; B = Bottom; No. 1, 2, 3, 4 and 5 represent five entrapment points. Red colored cylinders represent probe traps.</p> Full article ">Figure 3
<p>Time series chart of the daily catch of 15 traps in three repeated experiments at 0.1 insect/kg, 1.0 insect/kg, and 5.0 insect/kg. R1, R2, and R3 represent the three repeated experiments that were conducted.</p> Full article ">Figure 3 Cont.
<p>Time series chart of the daily catch of 15 traps in three repeated experiments at 0.1 insect/kg, 1.0 insect/kg, and 5.0 insect/kg. R1, R2, and R3 represent the three repeated experiments that were conducted.</p> Full article ">Figure 4
<p>Three-dimensional scatter plots of daily average rice temperature, relative humidity, and trap capture quantity per probe at three different pest population densities: 0.1 (<b>a</b>), 1.0 (<b>b</b>), and 5.0 (<b>c</b>) adults/kg.</p> Full article ">
Open AccessArticle
Research on UAV Downwash Airflow and Wind-Induced Response Characteristics of Rapeseed Seedling Stage Based on Computational Fluid Dynamics Simulation
by
Qilong Wang, Yilin Ren, Haojie Wang, Jiansong Wang, Guangsheng Zhou, Yang Yang, Zhiwei Xie and Xiaotian Bai
Agriculture 2024, 14(8), 1326; https://doi.org/10.3390/agriculture14081326 (registering DOI) - 9 Aug 2024
Abstract
Multi-rotor unmanned aerial vehicles (UAVs) are increasingly prevalent due to technological advancements. During rapeseed’s seedling stage, UAV-generated airflow, known as wind-induced response, affects leaf movement, tied to airflow speed and distribution. Understanding wind-induced response aids early rapeseed lodging prediction. Determining airflow distribution at
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Multi-rotor unmanned aerial vehicles (UAVs) are increasingly prevalent due to technological advancements. During rapeseed’s seedling stage, UAV-generated airflow, known as wind-induced response, affects leaf movement, tied to airflow speed and distribution. Understanding wind-induced response aids early rapeseed lodging prediction. Determining airflow distribution at various UAV heights is crucial for wind-induced response study, yet lacks theoretical guidance. In this study, Computational Fluid Dynamics (CFD) was employed to analyze airflow distribution at different UAV heights. Fluid–solid coupling simulation assessed 3D rapeseed model motion and surface pressure distribution in UAV downwash airflow. Validation occurred via wind speed experiments. Optimal uniform airflow distribution was observed at 2 m UAV height, with a wind speed variation coefficient of 0.258. The simulation showed greater vertical than horizontal leaf displacement, with elastic modulus inversely affecting displacement and leaf area directly. Discrepancies within 10.5% in the 0.5–0.8 m height range above the rapeseed canopy validated simulation accuracy. This study guides UAV height selection, leaf point determination, and wind-induced response parameter identification for rapeseed seedling stage wind-induced response research.
Full article
(This article belongs to the Section Agricultural Technology)
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Figure 1
<p>Research overall methodology and approach.</p> Full article ">Figure 2
<p>Rotor reverse modeling.</p> Full article ">Figure 3
<p>UAV model.</p> Full article ">Figure 4
<p>Four-rotor rotation direction.</p> Full article ">Figure 5
<p>Seedling-stage rapeseed model.</p> Full article ">Figure 6
<p>Rotating domain.</p> Full article ">Figure 7
<p>Static domain.</p> Full article ">Figure 8
<p>Fluid domain mesh: (<b>a</b>) Static domain mesh; (<b>b</b>) Rotating domain mesh.</p> Full article ">Figure 9
<p>Boundary condition setup.</p> Full article ">Figure 10
<p>Seedling-stage rapeseed-downwash air flow fluid-structure interaction simulation model.</p> Full article ">Figure 11
<p>Distribution of airflow test points under UAV.</p> Full article ">Figure 12
<p>XOZ plane velocity distribution.</p> Full article ">Figure 13
<p>Changes in airflow velocity along the <span class="html-italic">X</span>-axis in the XOZ plane at different heights from the ground: (<b>a</b>) Hovering height 1.6 m; (<b>b</b>) Hovering height 2.0 m; (<b>c</b>) Hovering height 2.4 m.</p> Full article ">Figure 14
<p>XOY plane airflow velocity distribution at different heights above the ground at a hovering height of 1.6 m.</p> Full article ">Figure 15
<p>XOY plane airflow velocity distribution at different heights above the ground at a hovering height of 2.0 m.</p> Full article ">Figure 16
<p>XOY plane airflow velocity distribution at different heights above the ground at a hovering height of 2.4 m.</p> Full article ">Figure 17
<p>The variation coefficient of XOY plane airflow velocity at 0.5~0.8 m above the ground at three hovering heights.</p> Full article ">Figure 18
<p>Distribution of airflow velocity components in each direction of the XOZ plane: (<b>a</b>) X-direction velocity component; (<b>b</b>) Y-direction velocity component; (<b>c</b>) Z-direction velocity component.</p> Full article ">Figure 19
<p>Fluid–structure interaction simulation results: (<b>a</b>) Distribution of downwash airflow around rapeseed at seedling stage; (<b>b</b>) Surface pressure distribution of rapeseed at seedling stage.</p> Full article ">Figure 20
<p>Displacement variation of rapeseed leaf tips under different mechanical properties: (<b>a</b>) M1; (<b>b</b>) M2; (<b>c</b>) M3.</p> Full article ">Figure 21
<p>Simulation values and experimental values of UAV downwash airflow velocity: (<b>a</b>) Rotor1; (<b>b</b>) Rotor2; (<b>c</b>) Rotor3; (<b>d</b>) Rotor4.</p> Full article ">
<p>Research overall methodology and approach.</p> Full article ">Figure 2
<p>Rotor reverse modeling.</p> Full article ">Figure 3
<p>UAV model.</p> Full article ">Figure 4
<p>Four-rotor rotation direction.</p> Full article ">Figure 5
<p>Seedling-stage rapeseed model.</p> Full article ">Figure 6
<p>Rotating domain.</p> Full article ">Figure 7
<p>Static domain.</p> Full article ">Figure 8
<p>Fluid domain mesh: (<b>a</b>) Static domain mesh; (<b>b</b>) Rotating domain mesh.</p> Full article ">Figure 9
<p>Boundary condition setup.</p> Full article ">Figure 10
<p>Seedling-stage rapeseed-downwash air flow fluid-structure interaction simulation model.</p> Full article ">Figure 11
<p>Distribution of airflow test points under UAV.</p> Full article ">Figure 12
<p>XOZ plane velocity distribution.</p> Full article ">Figure 13
<p>Changes in airflow velocity along the <span class="html-italic">X</span>-axis in the XOZ plane at different heights from the ground: (<b>a</b>) Hovering height 1.6 m; (<b>b</b>) Hovering height 2.0 m; (<b>c</b>) Hovering height 2.4 m.</p> Full article ">Figure 14
<p>XOY plane airflow velocity distribution at different heights above the ground at a hovering height of 1.6 m.</p> Full article ">Figure 15
<p>XOY plane airflow velocity distribution at different heights above the ground at a hovering height of 2.0 m.</p> Full article ">Figure 16
<p>XOY plane airflow velocity distribution at different heights above the ground at a hovering height of 2.4 m.</p> Full article ">Figure 17
<p>The variation coefficient of XOY plane airflow velocity at 0.5~0.8 m above the ground at three hovering heights.</p> Full article ">Figure 18
<p>Distribution of airflow velocity components in each direction of the XOZ plane: (<b>a</b>) X-direction velocity component; (<b>b</b>) Y-direction velocity component; (<b>c</b>) Z-direction velocity component.</p> Full article ">Figure 19
<p>Fluid–structure interaction simulation results: (<b>a</b>) Distribution of downwash airflow around rapeseed at seedling stage; (<b>b</b>) Surface pressure distribution of rapeseed at seedling stage.</p> Full article ">Figure 20
<p>Displacement variation of rapeseed leaf tips under different mechanical properties: (<b>a</b>) M1; (<b>b</b>) M2; (<b>c</b>) M3.</p> Full article ">Figure 21
<p>Simulation values and experimental values of UAV downwash airflow velocity: (<b>a</b>) Rotor1; (<b>b</b>) Rotor2; (<b>c</b>) Rotor3; (<b>d</b>) Rotor4.</p> Full article ">
Open AccessArticle
The Carpathian Agriculture in Poland in Relation to Other EU Countries, Ukraine and the Environmental Goals of the EU CAP 2023–2027
by
Marek Zieliński, Artur Łopatka, Piotr Koza and Barbara Gołębiewska
Agriculture 2024, 14(8), 1325; https://doi.org/10.3390/agriculture14081325 - 9 Aug 2024
Abstract
This study discusses the issue of determining the direction and strength of changes taking place in the structure of agricultural land in the mountain and foothill areas of the Carpathians in Poland in comparison with Slovakia, Romania and Ukraine. The most important financial
[...] Read more.
This study discusses the issue of determining the direction and strength of changes taking place in the structure of agricultural land in the mountain and foothill areas of the Carpathians in Poland in comparison with Slovakia, Romania and Ukraine. The most important financial institutional measures dedicated to the protection of the natural environment in Polish agriculture in the Areas facing Natural and other specific Constraints (ANCs) mountain and foothill in the first year of the CAP 2023–2027 were also established. Satellite data from 2001 to 2022 were used. The analyses used the land use classification MCD12Q1 provided by NASA and were made on the basis of satellite imagery collections from the MODIS sensor placed on two satellites: TERRA and AQUA. In EU countries, a decreasing trend in agricultural areas has been observed in areas below 350 m above sea level. In areas above 350 m, this trend weakened or even turned into an upward trend. Only in Ukraine was a different trend observed. It was found that in Poland, the degree of involvement of farmers from mountain and foothill areas in implementing financial institutional measures dedicated to protecting the natural environment during the study period was not satisfactory.
Full article
(This article belongs to the Special Issue Sustainable Agriculture and Food Supply: Scientific, Economic and Policy Aspects)
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![](https://pub.mdpi-res.com/agriculture/agriculture-14-01325/article_deploy/html/images/agriculture-14-01325-g001-550.jpg?1723198397)
Figure 1
Figure 1
<p>Scheme of the analysis of agriculture within separate groups of communes due to the fact and nuisance of ANCs mountain and foothill in Poland. Source: own study.</p> Full article ">Figure 2
<p>Distribution of communes with different shares of ANCs mountain and foothill in Poland. Source: own study ISSPC SRI; IAFE NRI.</p> Full article ">Figure 3
<p>Land use in the Carpathians in 2001 and 2022. Source: own study based on MODIS.</p> Full article ">Figure 4
<p>Trends in the percentage share [%] of the total agricultural area and cropland in the total area of land in the Carpathians in 2001–2022. Source: own study based on MODIS.</p> Full article ">Figure 5
<p>Number of farms participating in practices under eco-schemes, in organic and agri–environment–climate measures in communes with different shares of ANCs mountain and foothill in Poland in 2023. Source: own study based on ARMA.</p> Full article ">Figure 6
<p>Share of [%] farms with eco-schemes in total number of farms in communes with ANCs mountain and foothill in 2023. Source: own study based on ARMA.</p> Full article ">Figure 7
<p>Share of [%] farms with organic and agri–environmental–climate measure in total number of farms in communes with ANCs mountain and foothill in 2023.</p> Full article ">Figure 8
<p>Agricultural area covered by practices under eco-schemes, ecological and agri–environment–climate measures in communes with different shares of ANCs mountain and foothill in Poland in 2023. Source: own study based on ARMA.</p> Full article ">Figure 9
<p>Share [%] of UAA in farms with eco-schemes in total UAA in communes with ANCs mountain and foothill in 2023.</p> Full article ">Figure 10
<p>Share [%] of UAA covered by organic and agri–environmental–climate measures in total UAA in communes with ANCs mountain and foothill in 2023.</p> Full article ">
<p>Scheme of the analysis of agriculture within separate groups of communes due to the fact and nuisance of ANCs mountain and foothill in Poland. Source: own study.</p> Full article ">Figure 2
<p>Distribution of communes with different shares of ANCs mountain and foothill in Poland. Source: own study ISSPC SRI; IAFE NRI.</p> Full article ">Figure 3
<p>Land use in the Carpathians in 2001 and 2022. Source: own study based on MODIS.</p> Full article ">Figure 4
<p>Trends in the percentage share [%] of the total agricultural area and cropland in the total area of land in the Carpathians in 2001–2022. Source: own study based on MODIS.</p> Full article ">Figure 5
<p>Number of farms participating in practices under eco-schemes, in organic and agri–environment–climate measures in communes with different shares of ANCs mountain and foothill in Poland in 2023. Source: own study based on ARMA.</p> Full article ">Figure 6
<p>Share of [%] farms with eco-schemes in total number of farms in communes with ANCs mountain and foothill in 2023. Source: own study based on ARMA.</p> Full article ">Figure 7
<p>Share of [%] farms with organic and agri–environmental–climate measure in total number of farms in communes with ANCs mountain and foothill in 2023.</p> Full article ">Figure 8
<p>Agricultural area covered by practices under eco-schemes, ecological and agri–environment–climate measures in communes with different shares of ANCs mountain and foothill in Poland in 2023. Source: own study based on ARMA.</p> Full article ">Figure 9
<p>Share [%] of UAA in farms with eco-schemes in total UAA in communes with ANCs mountain and foothill in 2023.</p> Full article ">Figure 10
<p>Share [%] of UAA covered by organic and agri–environmental–climate measures in total UAA in communes with ANCs mountain and foothill in 2023.</p> Full article ">
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