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Agriculture
Volume 13
Issue 12
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Agriculture, Volume 13, Issue 12 (December 2023) – 116 articles

Cover Story (view full-size image): Agricultural insurance loss is a mainstay for stabilizing food commodity systems in the United States. We examined wheat and apple insurance claims across the inland Pacific Northwest (iPNW) and their relationships with climatically driven damage causes from 2001 to 2022, which regionally generate over 70% of all farm production. Over time, we see losses correlating with climatic extremes (drought, heat, frost, freeze), with 2021 highlighted as the worst year by far. Using dimensionality reduction, we see the first two factor loadings account for over 90% of all wheat variance, with 60% for apples, suggesting that insurance loss analysis may serve as an effective barometer in gauging climatic influences. View this paper
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9 pages, 2157 KiB  
Communication
Rainwater: Harvesting and Storage through a Flexible Storage System to Enhance Agricultural Resilience
by Luigi Pari, Luca Cozzolino and Simone Bergonzoli
Agriculture 2023, 13(12), 2289; https://doi.org/10.3390/agriculture13122289 - 18 Dec 2023
Cited by 2 | Viewed by 1848
Abstract
Many climatic variables are projected to occur with more intense and frequent extreme events, possibly unpredictable patterns and negative feedback loops with other environmental processes. Agriculture has faced uncertainty regarding ground temperature and rainfall distribution during the last few years, making water availability [...] Read more.
Many climatic variables are projected to occur with more intense and frequent extreme events, possibly unpredictable patterns and negative feedback loops with other environmental processes. Agriculture has faced uncertainty regarding ground temperature and rainfall distribution during the last few years, making water availability one of the major concerns for farm management. In this scenario, rainwater harvesting could represent a powerful tool to mitigate this problem, and consequently, the research community has been fostering new technical solutions. On the other hand, a few studies on agronomic assessment of rainwater harvesting systems are present in scientific literature. The present study reports preliminary data of a long-term study on a Flexible Water Storage System (FWSS) evaluating the possibility of enhancing agriculture systems resilience, shifting from rainfed production to irrigated agriculture relying on excessive rainfall, collectible from extreme events. The idea of intercepting excess rainfall, which is generally lost, thanks to an innovative water harvesting system, and using it to mitigate drought stress for crops is in line with sustainable approaches aiming to improve the resilience of agricultural systems. The results highlighted that the system studied could potentially collect an annual average of 831.7 m3 of water, mitigating the excess of water in the ditch that can potentially cause flooding and storing fresh water to provide irrigation during dry periods. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Satellite image of the area at different scales.</p> Full article ">Figure 2
<p>(<b>a</b>) Schematic view of the FWSS. Numbers refer to the main components of both systems: (1) ditch and seasonal water stream, (2) loading system (including electric pump, pipes, and connections), (3) water storage system, (4) electric pump for water delivery, (5) water usage (e.g., irrigation system). (<b>b</b>) (A) Detail of the water extraction point; (B) automated extraction pump; (C) newly installed FWSS front view; (D) FWSS partially full.</p> Full article ">Figure 3
<p>Average monthly rainfall and temperatures of the area during the study period.</p> Full article ">
22 pages, 6759 KiB  
Article
Collaboration among Governments, Pesticide Operators, and Farmers in Regulating Pesticide Operations for Agricultural Product Safety
by Jing Gong, Hongyan Du and Yong Sun
Agriculture 2023, 13(12), 2288; https://doi.org/10.3390/agriculture13122288 - 18 Dec 2023
Cited by 4 | Viewed by 2029
Abstract
The regulation of pesticide operations still faces numerous challenges and issues. Conflicts of interest and power struggles among the government, pesticide operators, and farmers are crucial factors that impact the effectiveness of regulation. To enhance efficiency and ensure the quality and safety of [...] Read more.
The regulation of pesticide operations still faces numerous challenges and issues. Conflicts of interest and power struggles among the government, pesticide operators, and farmers are crucial factors that impact the effectiveness of regulation. To enhance efficiency and ensure the quality and safety of agricultural products through stakeholder cooperation, this paper presents a dynamic evolution model based on the theory of evolutionary games. The model incorporates the government, pesticide operators, and farmers and evaluates the stability and effectiveness of the stakeholder cooperation mechanism under different circumstances. The research findings indicate the following: The relationships between the government, pesticide-operating enterprises, and farmers are characterized by intricate dynamics of cooperation and competition, coordination and contradiction, reciprocity, and mutual detriment. The stability and effectiveness of the stakeholder cooperation mechanism vary depending on different parameters. Several factors influence the stability of the stakeholder cooperation mechanism, with regulatory supervision from the government, stringent penalties for non-compliant pesticide operations, and strong incentives for farmers’ oversight being the most significant. The stakeholder cooperation mechanism can establish an evolutionary stabilization strategy when these factors reach a certain threshold. This study contributes to understanding the operational mechanisms of stakeholder cooperation in pesticide operation regulation and offers decision support and policy recommendations to relevant stakeholders for advancing the sustainable development and optimization of pesticide operation regulation. Full article
(This article belongs to the Special Issue Sustainable Rural Development and Agri-Food Systems)
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<p>Evolutionary gaming system for operational regulation of pesticides.</p> Full article ">Figure 2
<p>Impact of initial behavioral strategies on the evolutionary game. Note: (<b>a</b>) impact of <span class="html-italic">x</span> variation on the evolutionary game; (<b>b</b>) impact of <span class="html-italic">y</span> variation on the evolutionary game; (<b>c</b>) impact of <span class="html-italic">z</span> variation on the evolutionary game; and (<b>d</b>) combined impact of simultaneous variations in <span class="html-italic">x</span>, <span class="html-italic">y</span>, and <span class="html-italic">z</span> on the evolutionary game.</p> Full article ">Figure 3
<p>Impact of behavioral parameters of governments on the evolutionary game. Note: (<b>a</b>) impact of <span class="html-italic">U<sub>g</sub></span> variation on the evolutionary game; (<b>b</b>) impact of <span class="html-italic">C<sub>g</sub></span> variation on the evolutionary game; (<b>c</b>) impact of <span class="html-italic">F</span> variation on the evolutionary game; (<b>d</b>) impact of <span class="html-italic">s</span> variation on the evolutionary game; and (<b>e</b>) impact of <span class="html-italic">A</span> variation on the evolutionary game.</p> Full article ">Figure 4
<p>Impact of behavioral parameters of pesticide operators on the evolutionary game. Note: (<b>a</b>) impact of <span class="html-italic">C<sub>c</sub></span> variation on the evolutionary game; (<b>b</b>) impact of <span class="html-italic">C<sub>n</sub></span> variation on the evolutionary game; (<b>c</b>) impact of <span class="html-italic">P</span> variation on the evolutionary game; (<b>d</b>) impact of <span class="html-italic">d</span> variation on the evolutionary game; (<b>e</b>) impact of <span class="html-italic">L<sub>e</sub></span> variation on the evolutionary game; and (<b>f</b>) impact of <span class="html-italic">P<sub>e</sub></span> variation on the evolutionary game.</p> Full article ">Figure 5
<p>Impact of behavioral parameters of farmers on the evolutionary game. Note: (<b>a</b>) impact of <span class="html-italic">U</span> variation on the evolutionary game; (<b>b</b>) impact of <span class="html-italic">C<sub>m</sub></span> variation on the evolutionary game; (<b>c</b>) impact of <span class="html-italic">M</span> variation on the evolutionary game; and (<b>d</b>) impact of <span class="html-italic">P<sub>f</sub></span> variation on the evolutionary game.</p> Full article ">Figure 6
<p>Institutional framework for pesticide management.</p> Full article ">
24 pages, 9320 KiB  
Article
Precision Corn Pest Detection: Two-Step Transfer Learning for Beetles (Coleoptera) with MobileNet-SSD
by Edmond Maican, Adrian Iosif and Sanda Maican
Agriculture 2023, 13(12), 2287; https://doi.org/10.3390/agriculture13122287 - 18 Dec 2023
Cited by 6 | Viewed by 2519
Abstract
Using neural networks on low-power mobile systems can aid in controlling pests while preserving beneficial species for crops. However, low-power devices require simplified neural networks, which may lead to reduced performance. This study was focused on developing an optimized deep-learning model for mobile [...] Read more.
Using neural networks on low-power mobile systems can aid in controlling pests while preserving beneficial species for crops. However, low-power devices require simplified neural networks, which may lead to reduced performance. This study was focused on developing an optimized deep-learning model for mobile devices for detecting corn pests. We propose a two-step transfer learning approach to enhance the accuracy of two versions of the MobileNet SSD network. Five beetle species (Coleoptera), including four harmful to corn crops (belonging to genera Anoxia, Diabrotica, Opatrum and Zabrus), and one beneficial (Coccinella sp.), were selected for preliminary testing. We employed two datasets. One for the first transfer learning procedure comprises 2605 images with general dataset classes ‘Beetle’ and ‘Ladybug’. It was used to recalibrate the networks’ trainable parameters for these two broader classes. Furthermore, the models were retrained on a second dataset of 2648 images of the five selected species. Performance was compared with a baseline model in terms of average accuracy per class and mean average precision (mAP). MobileNet-SSD-v2-Lite achieved an mAP of 0.8923, ranking second but close to the highest mAP (0.908) obtained by MobileNet-SSD-v1 and outperforming the baseline mAP by 6.06%. It demonstrated the highest accuracy for Opatrum (0.9514) and Diabrotica (0.8066). Anoxia it reached a third-place accuracy (0.9851), close to the top value of 0.9912. Zabrus achieved the second position (0.9053), while Coccinella was reliably distinguished from all other species, with an accuracy of 0.8939 and zero false positives; moreover, no pest species were mistakenly identified as Coccinella. Analyzing the errors in the MobileNet-SSD-v2-Lite model revealed good overall accuracy despite the reduced size of the training set, with one misclassification, 33 non-identifications, 7 double identifications and 1 false positive across the 266 images from the test set, yielding an overall relative error rate of 0.1579. The preliminary findings validated the two-step transfer learning procedure and placed the MobileNet-SSD-v2-Lite in the first place, showing high potential for using neural networks on real-time pest control while protecting beneficial species. Full article
(This article belongs to the Special Issue Computational, AI and IT Solutions Helping Agriculture)
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<p>The SSD-MobileNet architecture (adapted by [<a href="#B60-agriculture-13-02287" class="html-bibr">60</a>]).</p> Full article ">Figure 2
<p>Dataset file structure, following the Open Images format.</p> Full article ">Figure 3
<p>Dataset file structure following the Pascal VOC format requirements.</p> Full article ">Figure 4
<p>Assessing the performance of the two-step transfer learning procedure on SSD-MobileNet Networks; 2 step TL—two-step transfer learning; color scheme: orange-neural network models; blue-training procedures; green-datasets; grey-final results.</p> Full article ">Figure 5
<p>Loss variation in the MobileNet-SSD-v2-Lite network during the second retraining stage on the custom dataset.</p> Full article ">Figure 6
<p>Neural network performances in terms of mAP for each dataset class.</p> Full article ">Figure 7
<p>Performance gain of the MobileNet-SSD-v2-Lite model following the two-step transfer. learning procedure, against the baseline model.</p> Full article ">Figure 8
<p>Examples of accurate predictions made by MobileNet-SSD-v2-Lite, after a two-step transfer learning procedure, some of them in the following challenging conditions: (<b>a</b>,<b>c</b>): small to medium scale; (<b>b</b>) overlapping; (<b>d</b>) partially visible; (<b>e</b>) color blends with the environment; (<b>f</b>) high confidence for Ladybug; credit to the original, non-transformed images, (<b>a</b>–<b>f</b>): [<a href="#B79-agriculture-13-02287" class="html-bibr">79</a>,<a href="#B80-agriculture-13-02287" class="html-bibr">80</a>,<a href="#B81-agriculture-13-02287" class="html-bibr">81</a>,<a href="#B82-agriculture-13-02287" class="html-bibr">82</a>,<a href="#B83-agriculture-13-02287" class="html-bibr">83</a>,<a href="#B84-agriculture-13-02287" class="html-bibr">84</a>].</p> Full article ">Figure 8 Cont.
<p>Examples of accurate predictions made by MobileNet-SSD-v2-Lite, after a two-step transfer learning procedure, some of them in the following challenging conditions: (<b>a</b>,<b>c</b>): small to medium scale; (<b>b</b>) overlapping; (<b>d</b>) partially visible; (<b>e</b>) color blends with the environment; (<b>f</b>) high confidence for Ladybug; credit to the original, non-transformed images, (<b>a</b>–<b>f</b>): [<a href="#B79-agriculture-13-02287" class="html-bibr">79</a>,<a href="#B80-agriculture-13-02287" class="html-bibr">80</a>,<a href="#B81-agriculture-13-02287" class="html-bibr">81</a>,<a href="#B82-agriculture-13-02287" class="html-bibr">82</a>,<a href="#B83-agriculture-13-02287" class="html-bibr">83</a>,<a href="#B84-agriculture-13-02287" class="html-bibr">84</a>].</p> Full article ">Figure 9
<p>Examples of the following various types of errors: (<b>a</b>) misclassification; (<b>b</b>–<b>f</b>): types of failed detections; (<b>g</b>) double detection; (<b>h</b>) false and double detection. Credit to the original, non-transformed images, (<b>a</b>–<b>h</b>): [<a href="#B82-agriculture-13-02287" class="html-bibr">82</a>,<a href="#B82-agriculture-13-02287" class="html-bibr">82</a>,<a href="#B85-agriculture-13-02287" class="html-bibr">85</a>,<a href="#B85-agriculture-13-02287" class="html-bibr">85</a>,<a href="#B86-agriculture-13-02287" class="html-bibr">86</a>,<a href="#B87-agriculture-13-02287" class="html-bibr">87</a>,<a href="#B88-agriculture-13-02287" class="html-bibr">88</a>,<a href="#B89-agriculture-13-02287" class="html-bibr">89</a>].</p> Full article ">
17 pages, 3823 KiB  
Article
Influence of Grapevine Cultivar on Population Levels of Lobesia botrana (Lepidoptera: Tortricidae) and Effectiveness of Insecticides in Controlling This Pest
by Zahra Sepahvand, Masumeh Ziaee, Roshanak Ghorbani, Seyed Ali Hemmati and Jacek Francikowski
Agriculture 2023, 13(12), 2286; https://doi.org/10.3390/agriculture13122286 - 16 Dec 2023
Viewed by 1446
Abstract
The European grapevine moth, Lobesia botrana (Denis and Schiffermüller) (Lepidoptera: Tortricidae), is the most critical pest of vineyards. In the present study, pheromone-baited traps were applied in 2021 and 2022 to monitor the moth population dynamics and to determine the number of L. [...] Read more.
The European grapevine moth, Lobesia botrana (Denis and Schiffermüller) (Lepidoptera: Tortricidae), is the most critical pest of vineyards. In the present study, pheromone-baited traps were applied in 2021 and 2022 to monitor the moth population dynamics and to determine the number of L. botrana generations. The number of eggs and larvae was also counted in four vineyards with Askari, Yaghooti, Keshmeshi, and Fakhri cultivars. Moreover, the morphological properties of clusters were evaluated in different grape cultivars to find out the susceptible cultivar to L. botrana. In 2022, different insecticides were used in the Askari cultivar vineyard, and larval damage level was assessed. Three generations were recorded in all vineyards each year. The population of males was not affected by the cultivar. In contrast, the population density of eggs and larvae was significantly higher in Yaghooti than in other tested cultivars. It could be attributed to the cluster compactness and thin skin of berries in Yaghooti, which makes it more susceptible to L. botrana infestations. In contrast, the lowest eggs and larval population density was reported in the Fakhri cultivar indicating the tolerance of this cultivar compared to the other tested cultivars. The field trial showed that the application of insecticides in the second and third generations reduced the damage level of L. botrana. The rotation of insecticides with different modes of action in consecutive generations of L. botrana can be used to reduce damage levels. Full article
(This article belongs to the Special Issue Sustainable Pest Management in Agriculture)
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<p>Map of the experimental area with an indication of vineyard position.</p> Full article ">Figure 2
<p>Mean number (±SE) of <span class="html-italic">Lobesia botrana</span> adult males captured per trap in the untreated vineyards (2021).</p> Full article ">Figure 3
<p>Mean number (±SE) of <span class="html-italic">Lobesia botrana</span> adult males captured per trap in the untreated vineyards (2022).</p> Full article ">Figure 4
<p>Mean number (±SE) of <span class="html-italic">Lobesia botrana</span> eggs and larvae in the untreated vineyards (2021).</p> Full article ">Figure 5
<p>Mean number (±SE) of <span class="html-italic">Lobesia botrana</span> eggs and larvae in the untreated vineyards (2022).</p> Full article ">Figure 6
<p>Mean number (±SE) of eggs, and first instar larvae in different varieties in the experimental years. Means followed by the same lowercase letters for 2021, and uppercase letters for 2022 are not significantly different using the Tukey–Kramer (HSD) test at <span class="html-italic">p</span> = 0.05.</p> Full article ">Figure 7
<p>The mean temperature, and relative humidity in Saifabad village, Kamalvand district in 2021 and 2022.</p> Full article ">Figure 8
<p>Morphological properties (mean ± SE) of berry clusters of different grape cultivars. Means followed by the same lowercase letters are not significantly different using the Tukey–Kramer (HSD) test at <span class="html-italic">p</span> = 0.05.</p> Full article ">
16 pages, 4492 KiB  
Article
Compression Strength and Critical Impact Speed of Typical Fertilizer Grains
by Mingjin Xin, Zhiwen Jiang, Yuqiu Song, Hongguang Cui, Aiju Kong, Bowen Chi and Renbao Shan
Agriculture 2023, 13(12), 2285; https://doi.org/10.3390/agriculture13122285 - 16 Dec 2023
Cited by 1 | Viewed by 1419
Abstract
The application of fertilizer is necessary for the growth and yield of crops, especially for paddy rice. Precision application is important for the fertilizer utilization rate and sustainable development of agriculture. However, the crushing of fertilizer grains will reduce the quality of fertilization, [...] Read more.
The application of fertilizer is necessary for the growth and yield of crops, especially for paddy rice. Precision application is important for the fertilizer utilization rate and sustainable development of agriculture. However, the crushing of fertilizer grains will reduce the quality of fertilization, for the decrease in the size and mass of the fertilizer particles and the degree of crushing mainly depend on the physical and mechanical properties of the fertilizer grains. In this study, the compression strength and critical impact speed of four typical commonly used fertilizer grains, a compound fertilizer of nitrogen, phosphorus, and potassium (NPK compound fertilizer), organic fertilizer, large granular urea, and small granular urea, were measured and analyzed. The static compression test was carried out using a TMS-Pro texture analyzer and the results show that the four kinds of fertilizer grains are brittle materials, and their elastic moduli are 208 MPa, 233 MPa, 140 MPa, and 107 MPa, respectively; the theoretical impact model of fertilizer granules is established based on the compression test result and Hertz elastic contact theory, the theoretical formula for the critical impact speed of fertilizer grains is derived, and the theoretical critical impact strength and speed are worked out. An image capture system for the impact process of fertilizer grains was developed, and the impact test was conducted. The results show that the critical impact speed of the four kinds of fertilizer grains decreases with the increase in granule size, while the variance analysis shows that the effect is not significant. The comparison of the experimental results with the theoretical values shows that the theoretical formula could be used to predict the trends of the critical impact speed of fertilizer grains. The model was optimized with the MATLAB 2018 function fitting tool based on the test and analysis. The goodness of fit of the formula is 0.824, which is 13.43% greater than that of the original theoretical formula, indicating that the modified formula based on the compression test data might estimate the critical impact speed of the granular fertilizer with brittle material properties more accurately. The results may provide a reference for the parameter design of a precision fertilization machine. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Four kinds of fertilizer granules for testing.</p> Full article ">Figure 2
<p>Particle size distribution of four kinds of fertilizer particles.</p> Full article ">Figure 3
<p>Image capture and testing system for fertilizer particle state during impact process: 1. backboard; 2. jetting tube; 3. guide tube; 4. feeding tube 5. support 6. target board; 7. solenoid; 8. analyzer host; 9. controller; 10. air pipe; 11. air compressor; 12. high speed camera; 13. light source.</p> Full article ">Figure 4
<p>Fertilizer particle compression test: 1. computer; 2. texture analyzer; 3. indenter; 4. fertilizer particle.</p> Full article ">Figure 5
<p>Typical states of large granular urea impacted on 304 stainless steel board.</p> Full article ">Figure 6
<p>Compressive force-deformation curve of fertilizer granules.</p> Full article ">Figure 7
<p>Relationship between critical impact speed and fertilizer particle diameter.</p> Full article ">Figure 8
<p>Comparison between calculated value and measured value of critical impact speed of fertilizer particles.</p> Full article ">Figure 9
<p>Comparison between fitted and theoretical equations.</p> Full article ">
21 pages, 3198 KiB  
Article
Solid–Liquid Separation and Its Environmental Impact on Manure Treatment in Scaled Pig Farms—Evidence Based on Life Cycle Assessment
by Yijia Zhang, Qinqing Bo, Xintian Ma, Yating Du, Xinyi Du, Liyang Xu and Yadong Yang
Agriculture 2023, 13(12), 2284; https://doi.org/10.3390/agriculture13122284 - 16 Dec 2023
Cited by 1 | Viewed by 2698
Abstract
Recently, there has been a significant focus on the issue of pollution caused by livestock and poultry rearing, which is recognized as a prominent contributor to nonpoint source pollution in the agricultural sector. This study employed the life cycle assessment (LCA) methodology to [...] Read more.
Recently, there has been a significant focus on the issue of pollution caused by livestock and poultry rearing, which is recognized as a prominent contributor to nonpoint source pollution in the agricultural sector. This study employed the life cycle assessment (LCA) methodology to evaluate the environmental impact of several pig manure processing scenarios with the aim of determining the appropriate solid–liquid separation tool for large-scale pig farms. The findings indicate that the utilization of a screw extruder for solid–liquid separation in Scenario 2 has a lower environmental impact. In contrast to Scenario 1, Scenario 2 exhibits reduced environmental potential in the areas of global warming, human toxicity, acidification, and eutrophication. Specifically, the global warming, human toxicity, acidification, and eutrophication impacts decreased by 56%, 81%, 83%, and 273%, respectively, due to the implementation of solid–liquid separation. The type of solid–liquid separation equipment used during the processing of swine manure, as well as the subsequent treatment, have a significant impact on environmental emissions. Compared to Scenario 2, Scenario 3, which utilizes a centrifugal microfilter for solid–liquid separation, exhibits a lower environmental impact in terms of human toxicity, resulting in a reduction of 0.736 kg DCB-eq. In general, solid–liquid separation is a viable and environmentally friendly method for the disposal of waste from large-scale pig farms. The adoption of this method is highly recommended. During its implementation, careful consideration should be given to factors such as separation efficiency and pollution emissions. It is crucial to select appropriate equipment for solid–liquid separation to effectively process the waste. Full article
(This article belongs to the Special Issue Research on Resource Utilization of Green Agricultural Waste)
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<p>System boundaries for Scenario 1.</p> Full article ">Figure 2
<p>System boundaries for Scenario 2.</p> Full article ">Figure 3
<p>UASB—AO process handling process.</p> Full article ">Figure 4
<p>System boundaries for Scenario 3.</p> Full article ">Figure 5
<p>Life cycle assessment results of the (<b>a</b>) global warming potential in 3 scenarios, (<b>b</b>) eutrophication potential in 3 scenarios, (<b>c</b>) acidification potential in 3 scenarios, (<b>d</b>) abiotic depletion in 3 scenarios, and (<b>e</b>) human toxicity potential in 3 scenarios.</p> Full article ">
13 pages, 1041 KiB  
Article
Baseline Susceptibility to a Novel dsRNA-Based Insecticide across US Populations of Colorado Potato Beetle
by Samuel Pallis, Andrei Alyokhin, Brian Manley, Thais B. Rodrigues, Ethann Barnes and Kenneth Narva
Agriculture 2023, 13(12), 2283; https://doi.org/10.3390/agriculture13122283 - 16 Dec 2023
Cited by 1 | Viewed by 1358
Abstract
The Colorado potato beetle, Leptinotarsa decemlineata (Say) (Coleoptera: Chrysomelidae) is an insect defoliator of the potato, Solanum tuberosum L. This species thrives in agricultural environments because of its flexible and complex life history, as well as its ability to evolve insecticide resistance. As [...] Read more.
The Colorado potato beetle, Leptinotarsa decemlineata (Say) (Coleoptera: Chrysomelidae) is an insect defoliator of the potato, Solanum tuberosum L. This species thrives in agricultural environments because of its flexible and complex life history, as well as its ability to evolve insecticide resistance. As a result, it has become a widely distributed agricultural pest. Ledprona (trade name Calantha) is a recently developed novel double-stranded RNA (dsRNA) insecticide that controls populations of Colorado potato beetle through RNA interference (RNAi). Previous studies have demonstrated the efficacy of ledprona through laboratory, greenhouse, and field studies. Colorado potato beetles from geographically distinct populations are known to vary in their response to insecticides, including experimental compounds based on RNAi. We tested the mortality and foliage consumption of beetles from different areas in the US treated with ledprona and found significant variation in both parameters. The beetles originating from New York were significantly less susceptible to ledprona in leaf disc assays compared to other populations. However, currently there is no evidence of reduced performance of ledprona against that population under field conditions, possibly because intoxicated beetles cannot withstand multiple stressors present in the field. The results of this study confirmed that the ledprona efficacy differs among geographically distinct populations, which may have implications for managing Colorado potato beetles. Full article
(This article belongs to the Special Issue Application of RNA Interference (RNAi) in Agricultural Pest Management)
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<p>Mortality of Colorado potato beetles from geographically isolated populations in 2020 experiments. (<b>A</b>) Mean daily mortality on control potato leaves treated with distilled water. (<b>B</b>) Mean daily mortality on potato leaves treated with the low concentration (2.40 × 10<sup>−7</sup> g/L) of ledprona and corrected using Abbott’s formula. (<b>C</b>) Mean daily mortality on potato leaves treated with the high concentration (4.75 × 10<sup>−5</sup> g/L) of ledprona and corrected using Abbott’s formula. Error bars denote standard error about the mean.</p> Full article ">Figure 2
<p>Per beetle consumption of potato leaves by Colorado potato beetles from geographically isolated populations in 2020 experiments. (<b>A</b>) Control leaves treated with distilled water. (<b>B</b>) Leaves treated with the low concentration (2.40 × 10<sup>−7</sup> g/L) of ledprona, and (<b>C</b>) Leaves treated with the high concentration (4.75 × 10<sup>−5</sup> g/L) of ledprona. Error bars denote standard error about the mean.</p> Full article ">Figure 3
<p>Mortality of Colorado potato beetles from geographically isolated populations in 2021 experiments. (<b>A</b>) Mean daily mortality on control potato leaves treated with distilled water. (<b>B</b>) Mean daily mortality on potato leaves treated with the low concentration (5.56 × 10<sup>−7</sup> g/L) of ledprona and corrected using Abbott’s formula. (<b>C</b>) Mean daily mortality on potato leaves treated with the high concentration (6.62 × 10<sup>−1</sup> g/L) of ledprona and corrected using Abbott’s formula. Error bars denote standard error about the mean.</p> Full article ">Figure 4
<p>Per beetle consumption of potato leaves by Colorado potato beetles from geographically isolated populations in 2021 experiments. (<b>A</b>) Control leaves treated with distilled water. (<b>B</b>) Leaves treated with the low concentration (5.56 × 10<sup>−7</sup> g/L) of ledprona, and (<b>C</b>) Leaves treated with the high concentration (6.62 × 10<sup>−1</sup> g/L) of ledprona. Error bars denote standard error about the mean.</p> Full article ">
12 pages, 4457 KiB  
Article
Growing Patterns of the Branca Chicken Breed—Concentrate vs. Maize-Based Diet
by Laura Soares, Fernando Mata, Joaquim L. Cerqueira and José Araújo
Agriculture 2023, 13(12), 2282; https://doi.org/10.3390/agriculture13122282 - 16 Dec 2023
Cited by 1 | Viewed by 1184
Abstract
Local chicken breeds are threatened with extinction. They must be preserved in order to maintain genetic diversity. The best strategy to preserve these breeds is to understand how they can be made interesting in production systems. With this strategy in mind, this study [...] Read more.
Local chicken breeds are threatened with extinction. They must be preserved in order to maintain genetic diversity. The best strategy to preserve these breeds is to understand how they can be made interesting in production systems. With this strategy in mind, this study aimed to understand the growth patterns of the Branca breed, which is fed maize and commercial rations. A trial was conducted with N = 40 chickens, n = 10, in each of the combinations of gender and diet (cocks fed on ration, cocks fed on maize, hens fed on ration, and hens fed on maize). The first step was to determine the best nonlinear model to fit the growth data. After selecting the best fitting model, this was used to estimate the growth, relative growth rate, and instantaneous growth rate curves. The best fit was achieved with the Brody model. Ration-fed cocks grow faster and mature later, as the relative growth rate converges to zero later, while maize-fed hens show slower growth. Maize-fed cocks mature earlier as the relative growth rate converges to zero earlier. Maize-fed cocks and ration-fed hens show intermediate growth patterns compared to ration-fed cocks and maize-fed hens, and similar while comparing with each other. This is a slow-growing breed that reaches the slaughter-ready size at around the fifth month of age. Full article
(This article belongs to the Special Issue Effects of Dietary Interventions on Poultry Production)
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<p><span class="html-italic">Branca</span> breed. Cock and hen (Source: authors).</p> Full article ">Figure 2
<p>Models explaining the growths of hens and cocks fed on ration or maize. (<b>A</b>)—growth, (<b>B</b>)—relative growth rate (growth velocity), (<b>C</b>)—instantaneous growth rate (growth acceleration).</p> Full article ">Figure A1
<p>Brody model for female chickens ration-fed. (<b>A</b>) ordered residual plot, (<b>B</b>) residuals versus predicted value plot, (<b>C</b>) standardized residuals Q-Q plot.</p> Full article ">Figure A2
<p>Brody model for male chickens ration-fed. (<b>A</b>) ordered residual plot, (<b>B</b>) residuals versus predicted value plot, (<b>C</b>) standardized residuals Q-Q plot.</p> Full article ">Figure A3
<p>Brody model for female chickens maize-fed. (<b>A</b>) ordered residual plot, (<b>B</b>) residuals versus predicted value plot, (<b>C</b>) standardized residuals Q-Q plot.</p> Full article ">Figure A4
<p>Brody model for male chickens maize-fed. (<b>A</b>) ordered residual plot, (<b>B</b>) residuals versus predicted value plot, (<b>C</b>) standardized residuals Q-Q plot.</p> Full article ">Figure A5
<p>Gompertz model for female chickens ration-fed. (<b>A</b>) ordered residual plot, (<b>B</b>) residuals versus predicted value plot, (<b>C</b>) standardized residuals Q-Q plot.</p> Full article ">Figure A6
<p>Gompertz model for male chickens ration-fed. (<b>A</b>) ordered residual plot, (<b>B</b>) residuals versus predicted value plot, (<b>C</b>) standardized residuals Q-Q plot.</p> Full article ">Figure A7
<p>Gompertz model for female chickens maize-fed. (<b>A</b>) ordered residual plot, (<b>B</b>) residuals versus predicted value plot, (<b>C</b>) standardized residuals Q-Q plot.</p> Full article ">Figure A8
<p>Gompertz model for male chickens maize-fed. (<b>A</b>) ordered residual plot, (<b>B</b>) residuals versus predicted value plot, (<b>C</b>) standardized residuals Q-Q plot.</p> Full article ">Figure A9
<p>Logistic model for female chickens ration-fed. (<b>A</b>) ordered residual plot, (<b>B</b>) residuals versus predicted value plot, (<b>C</b>) standardized residuals Q-Q plot.</p> Full article ">Figure A10
<p>Logistic model for male chickens ration-fed. (<b>A</b>) ordered residual plot, (<b>B</b>) residuals versus predicted value plot, (<b>C</b>) standardized residuals Q-Q plot.</p> Full article ">Figure A11
<p>Logistic model for female chickens maize-fed. (<b>A</b>) ordered residual plot, (<b>B</b>) residuals versus predicted value plot, (<b>C</b>) standardized residuals Q-Q plot.</p> Full article ">Figure A12
<p>Logistic model for male chickens maize-fed. (<b>A</b>) ordered residual plot, (<b>B</b>) residuals versus predicted value plot, (<b>C</b>) standardized residuals Q-Q plot.</p> Full article ">
22 pages, 2799 KiB  
Article
Measuring Carbon Emissions from Green and Low-Carbon Full-Life-Cycle Feeding in Large-Scale Pig Production Systems: A Case Study from Shaanxi Province, China
by Qingsong Zhang, Haoling Liao, Honghong Yang, Mengmeng Liu, Suobin Jia and Hua Li
Agriculture 2023, 13(12), 2281; https://doi.org/10.3390/agriculture13122281 - 15 Dec 2023
Cited by 1 | Viewed by 1729
Abstract
In the pursuit of establishing a more environmentally sustainable and low-carbon hog farming system, the accurate quantification of emissions of greenhouse gas emanating from these systems, especially within the context of China, becomes imperative. Here, drawing insights from a life cycle approach, exhaustive [...] Read more.
In the pursuit of establishing a more environmentally sustainable and low-carbon hog farming system, the accurate quantification of emissions of greenhouse gas emanating from these systems, especially within the context of China, becomes imperative. Here, drawing insights from a life cycle approach, exhaustive field surveys, and context-specific analyses, we establish an emission measurement index system tailored to hog farming enterprises in China’s Shaanxi Province. Using this methodology, we probed the emission profiles and characteristics of three emblematic hog farming enterprises in the region. Our key findings are as follows: (1) The carbon dioxide emissions per kilogram of pork, factoring in feed cultivation, processing, and transportation, for Pucheng Xinliu Science and Technology, Baoji Zhengneng Farming, and Baoji Zhenghui Farming were quantified as 0.80298 kg, 1.52438 kg, and 0.81366 kg, respectively. (2) Presently, the methane emission coefficient due to enteric fermentation in large-scale hog farms in Shaanxi surpasses the default value set by the Intergovernmental Panel on Climate Change (IPCC). There appears to be a consistent underestimation of enteric methane emissions from live pigs in the province, as gauged against the IPCC metrics. Notably, the emission factor for fattening pigs averaged 2.61823 kgCH4/head/year, while that for breeding pigs stood at 2.96752 kgCH4/head/year. (3) When examining methane and nitrous oxide outputs from manure across various production stages, we observed that emissions from lactating pigs significantly outweigh those from other stages. Interestingly, nitrous oxide emissions from breeding pigs during fattening, finishing, and gestation remained nearly the same, regardless of the manure treatment method. (4) Under the management protocols followed by Pucheng and Baoji, the total carbon emissions from an individual fattening pig amounted to 328.5283 kg and 539.2060 kg, respectively, whereas for breeding pigs, these values were 539.2060 kg and 551.6733 kg, respectively. Further calculations showed that the average carbon footprint CF of large-scale pig farms in China was 3.6281 kgCO2/kg pork. In conclusion, optimizing feed cultivation and transportation logistics, promoting integrated breeding and rearing practices, refining feed formulation, and advancing manure management practices can collaboratively attenuate greenhouse gas emissions. Such synergistic approaches hold promise for steering the hog industry towards a greener, low-carbon, and sustainable trajectory. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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<p>System boundaries for hog farming.</p> Full article ">Figure 2
<p>Main feed ingredient ratios.</p> Full article ">Figure 3
<p>Manure treatment methods and percentages for three companies.</p> Full article ">Figure 4
<p>Calculation of methane emission factors for manure management.</p> Full article ">Figure 5
<p>Calculated nitrous oxide emission factors for manure management.</p> Full article ">Figure 6
<p>Total carbon emissions from fattening pigs in Shaanxi Province under two models.</p> Full article ">Figure 7
<p>Total carbon emissions from pig breeding in Shaanxi Province under two models.</p> Full article ">
28 pages, 1167 KiB  
Review
Development, Validation, and Application of Building Energy Simulation Models for Livestock Houses: A Systematic Review
by Andrea Costantino
Agriculture 2023, 13(12), 2280; https://doi.org/10.3390/agriculture13122280 - 15 Dec 2023
Cited by 5 | Viewed by 1505
Abstract
The need to improve the sustainability of intensive livestock farming has led to an increasing adoption of Building Energy Simulation (BES) models for livestock houses. However, a consolidated body of knowledge specifically dedicated to these models is lacking in literature. This gap represents [...] Read more.
The need to improve the sustainability of intensive livestock farming has led to an increasing adoption of Building Energy Simulation (BES) models for livestock houses. However, a consolidated body of knowledge specifically dedicated to these models is lacking in literature. This gap represents a significant obstacle to their widespread application and scalability in research and industry. The aim of this work is to pave the way for scaling the adoption of BES models for livestock houses by providing a comprehensive analysis of their application, development, and validation. For this aim, a systematic review of 42 papers—selected from over 795 results from the initial database query—is carried out. The findings underscored a growing body of research that involves BES models for different purposes. However, a common approach in both model development and validation is still lacking. This issue could hinder their scalability as a standard practice, especially in industry, also considering the limitations of BES models highlighted in this work. This review could represent a solid background for future research since provides an up-to-date framework on BES models for livestock houses and identifies future research opportunities. Moreover, it contributes to increasing the reliability of BES models for livestock houses by providing some recommendations for their validation. Full article
(This article belongs to the Special Issue Optimization of Livestock Housing Management)
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<p>Schematization of the systematic selection of the scientific papers considered within this review.</p> Full article ">Figure 2
<p>The annual publication count of the 42 journal papers included in this review throughout the considered timeframe (1998–2023). The sub-bars indicate the publication count related to the identified applications of the Building Energy Simulation models identified within the framework of this review.</p> Full article ">
26 pages, 1328 KiB  
Review
Applications of Ionic Liquids in the Field of Agriculture: A Review
by Zijun Wang, Xin Qin, Hongqiang Dong, You Liang, Zhongyang Huo, Kun Qian and Fengping Yang
Agriculture 2023, 13(12), 2279; https://doi.org/10.3390/agriculture13122279 - 15 Dec 2023
Cited by 1 | Viewed by 2484
Abstract
This review delves into the diverse applications of ionic liquids (ILs) in modern agriculture, focusing on their pivotal roles in the extraction of natural products and pesticides, as well as their substantial significance in sustainable pesticide delivery systems. The reported extraction methods include [...] Read more.
This review delves into the diverse applications of ionic liquids (ILs) in modern agriculture, focusing on their pivotal roles in the extraction of natural products and pesticides, as well as their substantial significance in sustainable pesticide delivery systems. The reported extraction methods include ILs and their modified materials as solvents in dispersive liquid–liquid microextraction, solid-phase dispersion, and solid-phase microextraction. The study categorizes ILs according to their utility as herbicides, microbicides, food repellents, and plant growth regulators. This review investigates the use of ILs as plant immunity inducers to elevate the systemic acquired resistance in crops, thereby augmenting their intrinsic ability to defend against plant pathogens. Furthermore, the review explores the application of ILs in pesticide delivery systems, emphasizing their ability to enhance efficacy while promoting environmental sustainability. The biodegradability and toxicity aspects of ILs are also discussed, shedding light on their potential as eco-friendly alternatives in agricultural practices. In conclusion, this comprehensive overview underscores the multifaceted contributions of ILs in agriculture, from efficient extraction methods to the development of innovative and sustainable pesticide delivery systems. As the agricultural landscape evolves towards environmentally conscious practices, the integration of ILs presents a promising avenue for enhancing productivity while minimizing ecological impact. As the agricultural industry seeks innovative and sustainable solutions, the nuanced exploration of ILs in this review highlights their potential to address multiple challenges in modern farming practices. Full article
(This article belongs to the Special Issue Feature Papers in Agricultural Product Quality and Safety)
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<p>Applications of ILs in agriculture.</p> Full article ">Figure 2
<p>The developmental history of pesticides based on ILs [<a href="#B15-agriculture-13-02279" class="html-bibr">15</a>,<a href="#B110-agriculture-13-02279" class="html-bibr">110</a>,<a href="#B111-agriculture-13-02279" class="html-bibr">111</a>,<a href="#B112-agriculture-13-02279" class="html-bibr">112</a>,<a href="#B113-agriculture-13-02279" class="html-bibr">113</a>,<a href="#B114-agriculture-13-02279" class="html-bibr">114</a>,<a href="#B115-agriculture-13-02279" class="html-bibr">115</a>,<a href="#B116-agriculture-13-02279" class="html-bibr">116</a>,<a href="#B117-agriculture-13-02279" class="html-bibr">117</a>].</p> Full article ">
22 pages, 3539 KiB  
Article
Changes in Polar Metabolites during Seed Germination and Early Seedling Development of Pea, Cucumber, and Wheat
by Joanna Szablińska-Piernik and Lesław Bernard Lahuta
Agriculture 2023, 13(12), 2278; https://doi.org/10.3390/agriculture13122278 - 15 Dec 2023
Cited by 4 | Viewed by 1905
Abstract
Seed-to-seedling transition plays a crucial role in plant vegetation. However, changes in the metabolome of crop seedlings during seed germination and early seedling development are mostly unknown and require a deeper explanation. The present study attempted to compare qualitative and quantitative changes in [...] Read more.
Seed-to-seedling transition plays a crucial role in plant vegetation. However, changes in the metabolome of crop seedlings during seed germination and early seedling development are mostly unknown and require a deeper explanation. The present study attempted to compare qualitative and quantitative changes in polar metabolites during the seed germination and early development of seedlings of three different and important crop types: pea, cucumber, and wheat. The application of gas chromatography coupled with a flame ionization detector, as well as gas chromatography coupled with mass spectrometry, identified 51 polar metabolites. During seed imbibition/germination, the rapid degradation of raffinose family oligosaccharides (RFOs) preceded a dramatic increase in the concentrations of intermediates of glycolysis and the TCA cycle in embryonic axes (of pea and cucumber) or embryos (of wheat), confirming the important role of RFOs in the resumption of respiration and seed-to-seedling transition. After germination, the metabolic profiles of the growing roots, epicotyl/hypocotyl/coleoptile, and cotyledons/endosperm changed according to fluctuations in the concentrations of soluble carbohydrates, amino acids, and organic acids along the timeline of seedling growth. Moreover, the early increase in species-specific metabolites justified their role in seedling development owing to their participation in nitrogen metabolism (homoserine in pea), carbon translocation (galactinol, raffinose, and stachyose), and transitory carbon accumulation (1-kestose in wheat). The obtained metabolic profiles may constitute an important basis for further research on seedling reactions to stress conditions, including identification of metabolic markers of stress resistance. Full article
(This article belongs to the Special Issue Seed Germination, Stress Tolerance and Aging: Physiological and Molecular Aspects)
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<p>Changes in the total concentration of identified polar metabolites in the embryonic axis, root, epicotyl (<b>A</b>), and cotyledons of pea (<b>D</b>); embryonic axis, roots, hypocotyl (<b>B</b>), and cotyledons of cucumber (<b>E</b>); and embryo, roots, shoot (<b>C</b>), and endosperm of wheat (<b>F</b>) during 7 days of seed germination. The same letters above the bars (separately for each part of the seedling) indicate no statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) based on ANOVA and Tukey’s post hoc test. *—hypocotyl was analyzed with root.</p> Full article ">Figure 2
<p>Changes in the concentrations of RFOs (raffinose, stachyose, and verbascose) in the embryonic axes of pea (<b>A</b>) and cucumber (<b>B</b>), and maltose and 1-ketose in the embryo of wheat (<b>C</b>) during 48 h of seed germination. The concentrations of hexose phosphates (Glc6P and Fru6P) in the embryonic axes (pea and cucumber) and embryos (wheat) are shown in (<b>D</b>–<b>F</b>). The same letters above the bars (separately for each metabolite) indicate no statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) based on ANOVA and Tukey’s post hoc test.</p> Full article ">Figure 3
<p>PCA score of polar metabolites in roots (<b>A</b>), shoots ((<b>B</b>) hypocotyl of cucumber; epicotyl of pea; and coleoptile of wheat) and storage tissues ((<b>C</b>) cotyledons of pea, cucumber, and endosperm of wheat) of developing seedlings (from the 3rd to 7th day of germination) of pea, cucumber, and wheat. Symbols: circles—pea; squares—cucumber; and diamonds—wheat. In cucumber seedlings, on the 3rd day of seed germination, the hypocotyl was analyzed together with the root, and data on changes in the hypocotyl started from the 4th day (<b>B</b>). Symbols for 3rd, 4th, 5th, 6th, and 7th day of germination are colored as follows: yellow, pink, green, navy blue, and sky blue.</p> Full article ">Figure 4
<p>PCA score (<b>A</b>,<b>C</b>,<b>E</b>) and loading (<b>B</b>,<b>D</b>,<b>F</b>) plots of polar metabolites in developing seedlings (from the 3rd to 7th days of germination) of pea (<span class="html-italic">Pisum sativum</span> L.), cucumber (<span class="html-italic">Cucumis sativus</span> L.), and wheat (<span class="html-italic">Triticum aestivum</span> L.). Symbols: circles—cotyledons of pea and cucumber or endosperm of wheat; squares—epicotyl of pea, hypocotyl of cucumber, or coleoptile of wheat; diamonds—roots. In cucumber, on the 3rd day of seed germination, the hypocotyl was analyzed together with the root, and data on changes in the hypocotyl started from the 4th day (<b>C</b>). Symbols for 3rd, 4th, 5th, 6th, and 7th day of germination are colored as follows: yellow, pink, green, navy blue, and sky blue.</p> Full article ">Figure 5
<p>The concentrations of glucose, fructose, and sucrose in roots (<b>A</b>–<b>C</b>), epicotyl of pea (<b>D</b>), hypocotyl of cucumber (<b>E</b>), shoot of wheat (<b>F</b>), and storage tissues—cotyledons (<b>G</b>,<b>H</b>) and endosperm (<b>I</b>)—of growing seedlings (between the 3rd and 7th day of seed germination) of pea (<b>A</b>,<b>D</b>,<b>G</b>), cucumber (<b>B</b>,<b>E</b>,<b>H</b>), and wheat (<b>C</b>,<b>F</b>,<b>I</b>). The same letters above the bars (separately for each metabolite) indicate no statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) based on ANOVA and Tukey’s post hoc test. *—hypocotyl was analyzed with root.</p> Full article ">Figure 6
<p>Changes in the concentrations of raffinose and stachyose in the roots (<b>A</b>), hypocotyl (<b>B</b>), and cotyledons (<b>C</b>) of growing cucumber seedlings. The same letters above the bars (separately for each metabolite) indicate no statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) based on ANOVA and Tukey’s post hoc test. *—hypocotyl was analyzed with root.</p> Full article ">Figure 7
<p>Changes in the concentrations of maltose and 1-kestose in roots (<b>A</b>), coleoptile (<b>B</b>), and endosperm (<b>C</b>) of growing wheat seedlings. The same letters above the bars (separately for each metabolite) indicate no statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) based on ANOVA and Tukey’s post hoc test.</p> Full article ">Figure 8
<p>Changes in the concentration of asparagine, glutamine, and glutamate in roots (<b>A</b>–<b>C</b>), epicotyl of pea (<b>D</b>), hypocotyl of cucumber (<b>E</b>), shoot of wheat (<b>F</b>), and storage tissues (cotyledons (G, H) and endosperm (I)) of growing seedlings (between the 3rd and 7th day of seed germination) of pea (<b>A</b>,<b>D</b>,<b>G</b>), cucumber (<b>B</b>,<b>E</b>,<b>H</b>), and wheat (<b>C</b>,<b>F</b>,<b>I</b>). The same letters above the bars (separately for each metabolite) indicate no statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) based on ANOVA and Tukey’s post hoc test. *—hypocotyl was analyzed with root.</p> Full article ">Figure 9
<p>Changes in the concentrations of citrate and malate in roots (<b>A</b>–<b>C</b>), epicotyl of pea (<b>D</b>), hypocotyl of cucumber (<b>E</b>), shoot of wheat (<b>F</b>), and storage tissues—cotyledons (<b>G</b>,<b>H</b>) and endosperm (<b>I</b>) of growing seedlings (between the 3rd and 7th day of seed germination) of pea (<b>A</b>,<b>D</b>,<b>G</b>), cucumber (<b>B</b>,<b>E</b>,<b>H</b>), and wheat (<b>C</b>,<b>F</b>,<b>I</b>). The same letters above the bars (separately for each metabolite) indicate no statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) based on ANOVA and Tukey’s post hoc test. *—hypocotyl was analyzed with root.</p> Full article ">
14 pages, 5593 KiB  
Article
A Simulation and Experiment on the Optimization Design of an Air Outlet Structure for an Air-Assisted Sprayer
by Shuaijie Jing, Longlong Ren, Yue Zhang, Xiang Han, Ang Gao, Baoyou Liu and Yuepeng Song
Agriculture 2023, 13(12), 2277; https://doi.org/10.3390/agriculture13122277 - 15 Dec 2023
Viewed by 1304
Abstract
In response to the issues of low-velocity zones and non-uniform jet velocity distribution in the airflow field of traditional air-assisted orchard sprayers, an arc-shaped air outlet suitable for axial-flow air-assisted systems is designed. This article employs the method of CFD numerical simulation and [...] Read more.
In response to the issues of low-velocity zones and non-uniform jet velocity distribution in the airflow field of traditional air-assisted orchard sprayers, an arc-shaped air outlet suitable for axial-flow air-assisted systems is designed. This article employs the method of CFD numerical simulation and experimental verification to compare and analyze the internal flow field of the air-assisted system and validates the reliability of the numerical simulation results through calculation error and chi-square test. The wind speed of the cross-section is measured at different distances from the outlet, and the distribution characteristics of the outflow field wind speed before and after the structural optimization of the air-assisted system are compared. The horizontal distribution of fog droplets is collected using a fog collection chamber. The experimental results show that the design of the arc-shaped outlet increases the average wind speed of the annular outlet from 14.95 m/s to 18.20 m/s and reduces the proportion of low-speed area from 20.83% to 0.71%. When the rounded corner radius of the air outlet is 50 mm, optimal parameters are attained. The maximum error between the simulated and experimental values is 9.52%. At a significance level of 0.05, the χ2 value is 0.252, indicating that the simulated values follow the distribution of the actual measurement values. On the cross-sections located at distances of 0.5, 0.75, 1, 1.25, and 1.5 m from the air outlet, the wind speed distribution with no arc-shaped air outlets exhibits a “low left and high right” type, tending to shift towards the right as a whole. Fog droplets also display a drift tendency towards the right side. The wind speed distribution with arc-shaped air outlets shows a symmetric “high in the middle and low on the sides” type. Fog droplets concentrate in the central position. The optimized air-assisted system can reduce the air field’s low-flow area, increase the airflow distribution uniformity, improve the average wind speed at the outlet, and decrease fog droplet drift. This provides a reference for the structural design of air-assisted systems in current orchard sprayers of the same type. Full article
(This article belongs to the Special Issue Advanced Technology for the Development of Agricultural Sprays)
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<p>Single-track remote-controlled self-propelled sprayer. 1—track, 2—track tractor, 3—control box, 4—arc-shaped air outlet, 5—air duct, 6—fan, and 7—spray tank.</p> Full article ">Figure 2
<p>Air displacement diagram of monorail self-propelled sprayer.</p> Full article ">Figure 3
<p>Simplified model of an air duct for an air-assisted system. (<b>a</b>) Before structural optimization. (<b>b</b>) After structural optimization.</p> Full article ">Figure 4
<p>Measurement distribution diagram of air outlet and external airflow field. (<b>a</b>) Schematic diagram of measuring points of the annular air outlet. (<b>b</b>) Punctuation diagram of measuring frame. (<b>c</b>) Measurement frame position distribution. (<b>d</b>) Field measurement frame distribution.</p> Full article ">Figure 5
<p>Fog collection chamber site layout diagram.</p> Full article ">Figure 6
<p>The streamlined diagram of the air duct in the air-assisted system. (<b>a</b>) Before structural optimization. (<b>b</b>) After structural optimization.</p> Full article ">Figure 7
<p>Velocity cloud diagrams at the annular air outlet and cross-section with different fillet radius.</p> Full article ">Figure 7 Cont.
<p>Velocity cloud diagrams at the annular air outlet and cross-section with different fillet radius.</p> Full article ">Figure 8
<p>Line chart depicting the variation in wind speed at the annular air outlet.</p> Full article ">Figure 9
<p>Proportion of low-speed area at the annular air outlet.</p> Full article ">Figure 10
<p>Comparison between simulated values and measured values.</p> Full article ">Figure 11
<p>The velocity variation curves of the external airflow field before and after optimization. (<b>a</b>) Before structural optimization. (<b>b</b>) After structural optimization.</p> Full article ">Figure 12
<p>Structure optimization before and after fog droplet distribution chart.</p> Full article ">
21 pages, 9966 KiB  
Article
Development of an Orchard Mowing and Sweeping Device Based on an ADAMS–EDEM Simulation
by Shuai Shen, Yichuan He, Zhihui Tang, Yameng Dai, Yu Wang and Jiaxin Ma
Agriculture 2023, 13(12), 2276; https://doi.org/10.3390/agriculture13122276 - 15 Dec 2023
Cited by 3 | Viewed by 1439
Abstract
In the context of cutting grass in orchards, the practice of leaving cut weeds in the orchard rows hinders the decomposition of the weeds and the absorption of nutrients by the fruit trees. To address this issue, a grass-cutting machine with an integrated [...] Read more.
In the context of cutting grass in orchards, the practice of leaving cut weeds in the orchard rows hinders the decomposition of the weeds and the absorption of nutrients by the fruit trees. To address this issue, a grass-cutting machine with an integrated sweeping disc was designed to remove weeds from orchard rows and sweep them to the roots of the trees to promote their absorption of nutrients. A coupled simulation platform was established using multi-body dynamics ADAMS and the discrete element method EDEM. The weed-shedding and sweeping device was dynamically analyzed through an ADAMS–EDEM collaborative simulation that enabled the use of a second-order regression orthogonal rotation experiment and response surface methodology. The optimal parameters for the cutting tools, cutter shaft speed, and the number of cutting tools included 23 cutting tools arranged in a single helical pattern for the cutting device, a cutter shaft speed of 728 rpm, and claw-shaped blades as the cutting tools. A prototype machine was built based on the optimized parameters and tested in the field. The results indicated that, when there were 250 m² of weeds, the cutting rate reached 92.96%. The machine was highly maneuverable, and the average remaining weed height in the orchard was 110 mm, which met the national standards and local agricultural requirements. The new orchard grass-cutting and sweeping device meets the technical demands of orchard grass operations in the Xinjiang region of China. Full article
(This article belongs to the Special Issue Application of Modern Agricultural Equipment in Crop Cultivation)
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<p>Orchard obstacle avoidance mowing and sweeping device, with the following components: 1. Sweeping disc; 2. mower casing; 3. water tank; 4. hammer claw blade; 5. inter-row tossing blade; 6. hydraulic oil tank; 7. reduction motor; 8. sweeping disc connecting frame; 9. connecting frame; 10. support wheel; 11. obstacle avoidance hydraulic cylinder; 12. hydraulic motor; and 13. obstacle avoidance disc.</p> Full article ">Figure 2
<p>The schematic diagram of power transmission for the machine, consisting of the following components: 1. input shaft; 2. input bevel gear I; 3. output bevel gear; 4. large pulley; 5. small pulley; 6. output bevel gear II; and 7. hydraulic motor.</p> Full article ">Figure 3
<p>Working principle of a smart mower for an orchard. (<b>a</b>) The identification-of-a-fruit-tree stage; (<b>b</b>) the hydraulic-pressure-system-working stage; (<b>c</b>) the machine-avoids-the-fruit-tree stage; and (<b>d</b>) the obstacle avoidance end stage.</p> Full article ">Figure 4
<p>Schematic diagram of the cutter structure. Note: <span class="html-italic">L</span><sub>1</sub> is the total length of the hammer claw knife, in mm; <span class="html-italic">L</span><sub>2</sub> is the width of the shank of the hammer claw knife, in mm; <span class="html-italic">L</span><sub>3</sub> is the width of the blade of the hammer claw knife, in mm; <span class="html-italic">R</span><sub>1</sub> is the radius of the shank shaft of the hammer claw knife, in mm; and <span class="html-italic">R</span><sub>2</sub> is the radius of the mounting hole of the hammer claw knife, in mm.</p> Full article ">Figure 5
<p>Schematic structure of the orchard inter-row mowing cutter shafts. (<b>a</b>) Main view; (<b>b</b>) side view. Note: <span class="html-italic">L</span><sub>11</sub> is the length of the blade shaft, in mm; <span class="html-italic">L</span><sub>12</sub> is the width of the blade holder, in mm; <span class="html-italic">L</span><sub>13</sub> is the distance from the left blade holder to the axis edge, in mm; <span class="html-italic">L</span><sub>14</sub> is the distance between two blade holders, in mm; <span class="html-italic">L</span><sub>15</sub> is the thickness of the blade holder, in mm; and <span class="html-italic">L</span><sub>16</sub> is the width of the blade holder, in mm.</p> Full article ">Figure 6
<p>Schematic diagram of the structure of the dump knife. Note: <span class="html-italic">L</span><sub>4</sub> is the total height of the Z-shaped blade, in mm; <span class="html-italic">L</span><sub>5</sub> is the length of the Z-shaped blade edge, in mm; <span class="html-italic">L</span><sub>6</sub> is the thickness of the blade edge, in mm; <span class="html-italic">L</span><sub>7</sub> is the width of the blade, in mm; <span class="html-italic">L</span><sub>8</sub> is the total length of the blade, in mm; <span class="html-italic">L</span><sub>9</sub> is the distance from the mounting hole to the blade handle, in mm; and <span class="html-italic">R</span><sub>3</sub> is the radius of the mounting hole, in mm.</p> Full article ">Figure 7
<p>Schematic diagram of the structure of the sweeping disk. Note: <span class="html-italic">R</span><sub>4</sub> is the radius of the fixed plate for the sweeping disc, in mm; <span class="html-italic">R</span><sub>5</sub> is the radius of the maximum sweeping area for the sweeping disc, in mm; <span class="html-italic">R</span><sub>6</sub> is the radius of the mounting flange for the sweeping disc, in mm.</p> Full article ">Figure 8
<p>Model of weed particles.</p> Full article ">Figure 9
<p>Discrete element model of the inter-row mowing device.</p> Full article ">Figure 10
<p>ADAMS simulation model.</p> Full article ">Figure 11
<p>Schematic of the mowing simulation. Note: (<b>a</b>) T = 0.1800021448 s; (<b>b</b>) T = 0.2000014839 s; (<b>c</b>) T = 0.3000008771 s; (<b>d</b>) T = 0.400002703 s; (<b>e</b>) T = 0.5000023611 s; and (<b>f</b>) T = 0.6000017543 s.</p> Full article ">Figure 12
<p>Simulation of the cleaning process. Note: (<b>a</b>) T = 4.00002 s; (<b>b</b>) T = 4.60001 s; (<b>c</b>) T = 5.80002 s; (<b>d</b>) T = 7.00003 s; (<b>e</b>) T = 7.60002 s; and (<b>f</b>) T = 8.00002 s.</p> Full article ">Figure 13
<p>Response surface analysis of the interaction factors affecting the number of broken bonding links. (<b>a</b>) Response surface plot of the effect of the interaction between the machine’s forward speed and the rotational speed of the cutter on the number of bond breaks. (<b>b</b>) Contour plot of the effect of the interaction between the machine’s forward speed and the rotational speed of the cutter on the number of bond breaks. (<b>c</b>) Response surface plot of the effect of the interaction between the machine’s forward speed and the number of cutters on the number of bond breaks. (<b>d</b>) Response surface plot of the effect of the interaction between the machine’s contour plot of the effect of the forward speed of the machine and the number of cutters on the interaction with the number of bond breaks. (<b>e</b>) Response surface plot of the effect of the rotational speed of the cutter and the number of cutters on the interaction with the number of bond breaks. (<b>f</b>) Contour plot of the effect of the rotational speed of the cutter and the number of cutters on the interaction with the number of bond breaks.</p> Full article ">Figure 14
<p>Response surface analysis of the interaction factors affecting the operating power. (<b>a</b>) Response surface plots of the effect of the machine’s forward speed and the cutter rotational speed on the interaction with the implemented power. (<b>b</b>) Contour plots of the effect of the machine’s forward speed and the cutter rotational speed on the interaction with the implemented power. (<b>c</b>) Response surface plots of the effect of the machine’s forward speed and the number of cutters on the interaction with the implemented power. (<b>d</b>) Contour plot of the effect of the machine’s forward speed and the number of cutters on the interaction with the implemented power. (<b>e</b>) Response surface plot of the effect of the rotational speed of the cutters and the number of cutters on the interaction with the implemented power. (<b>f</b>) Contour plot of the effect of the rotational speed of the cutters and the number of cutters on the interaction with the implemented power.</p> Full article ">Figure 14 Cont.
<p>Response surface analysis of the interaction factors affecting the operating power. (<b>a</b>) Response surface plots of the effect of the machine’s forward speed and the cutter rotational speed on the interaction with the implemented power. (<b>b</b>) Contour plots of the effect of the machine’s forward speed and the cutter rotational speed on the interaction with the implemented power. (<b>c</b>) Response surface plots of the effect of the machine’s forward speed and the number of cutters on the interaction with the implemented power. (<b>d</b>) Contour plot of the effect of the machine’s forward speed and the number of cutters on the interaction with the implemented power. (<b>e</b>) Response surface plot of the effect of the rotational speed of the cutters and the number of cutters on the interaction with the implemented power. (<b>f</b>) Contour plot of the effect of the rotational speed of the cutters and the number of cutters on the interaction with the implemented power.</p> Full article ">Figure 15
<p>Orchard test. Note: (<b>a</b>) orchard mowing site; (<b>b</b>) stubble height.</p> Full article ">Figure A1
<p>The three-dimensional drawing and physical drawings of the Orchard Mowing and Sweeping Device.</p> Full article ">
25 pages, 1908 KiB  
Article
Strategic Analysis for Advancing Smart Agriculture with the Analytic SWOT/PESTLE Framework: A Case for Turkey
by Deniz Uztürk and Gülçin Büyüközkan
Agriculture 2023, 13(12), 2275; https://doi.org/10.3390/agriculture13122275 - 15 Dec 2023
Cited by 2 | Viewed by 4053
Abstract
In the contemporary discourse, smart agriculture (SA) stands out as a potent driver for sustainable economic growth. The challenges of navigating SA transition are notably intricate in developing nations. To effectively embark on this transformative journey, strategic approaches are imperative, necessitating a thorough [...] Read more.
In the contemporary discourse, smart agriculture (SA) stands out as a potent driver for sustainable economic growth. The challenges of navigating SA transition are notably intricate in developing nations. To effectively embark on this transformative journey, strategic approaches are imperative, necessitating a thorough examination of the prevailing agricultural ecosystem. This study seeks to formulate strategies that advance Turkey’s agricultural sector. The primary research questions focus on optimizing the benefits of SA by aligning strengths and opportunities with diverse socio-economic and environmental factors, while also exploring effective strategies to mitigate the impact of weaknesses and threats within the agricultural landscape. To achieve this objective, the utilization of the 2-Tuple linguistic (2TL) model integrated DEMATEL (Decision-Making Trial and Evaluation Laboratory) methodology in conjunction with SWOT (Strengths, Weaknesses, Opportunities, and Threats) and PESTLE (Political, Economic, Social, Technological, Legal, Environmental) analyses is proposed. The integration of linguistic variables enhances the capacity to delve deeper into system analysis, aligning more closely with human cognitive processes. The research commences with SWOT and PESTLE analyses applied to Turkey’s agricultural sector. Subsequently, the 2TL-DEMATEL approach is employed to investigate interrelationships among analysis components. This inquiry aims to establish causal relations, facilitating the derivation of relevant strategies. The case study centers on Turkey, a developing country, with outcomes indicating that the highest-priority strategies revolve around addressing ‘environmental threats’ and ‘economic weaknesses’. The subsequent evaluation encompasses eight dimensions, resulting in the generation of fifteen distinct strategies, a process facilitated by collaboration with field experts. Importantly, both the results and strategies undergo rigorous validation, drawing upon insights from the recent literature and field experts. Significantly, these findings align seamlessly with the Sustainable Development Goals (SDGs), substantiating the study’s broader significance in fostering a sustainable future for Turkey. Full article
(This article belongs to the Section Digital Agriculture)
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<p>Proposed framework for strategy generation for the agriculture sector.</p> Full article ">Figure 2
<p>SWOT/PESTLE analysis factors for Turkish agriculture.</p> Full article ">Figure 3
<p>The 2TL-DEMATEL results for the SWOT/PESTLE factors for Turkish agriculture.</p> Full article ">Figure 4
<p>Comparative analysis of SWOT/PESTLE factor assessment.</p> Full article ">
11 pages, 1702 KiB  
Article
Cyclic Storage Chamber Ozonation as a Method to Inhibit Ethylene Generation during Plum Fruit Storage
by Natalia Matłok, Tomasz Piechowiak, Amanda Krempa, Czesław Puchalski and Maciej Balawejder
Agriculture 2023, 13(12), 2274; https://doi.org/10.3390/agriculture13122274 - 15 Dec 2023
Cited by 3 | Viewed by 1234
Abstract
This study presents a method for inhibiting ethylene production during the room-temperature storage of plum fruits, using gaseous ozone (O3). The proposed storage strategy involves the cyclic ozone treatment of fruits every 24 h with specified O3 doses. Throughout the [...] Read more.
This study presents a method for inhibiting ethylene production during the room-temperature storage of plum fruits, using gaseous ozone (O3). The proposed storage strategy involves the cyclic ozone treatment of fruits every 24 h with specified O3 doses. Throughout the storage period, cyclic analyses of the atmosphere composition in storage chambers were conducted, measuring ethylene and carbon dioxide levels. Several parameters describing changes in fruit quality and biochemical transformations were systematically monitored until the end of the storage process. The results clearly indicate that fruits subjected to cyclic ozone treatment with the highest O3 doses during storage exhibit the slowest ripening rate. This reduced ripening rate is primarily attributed to the downregulation of S-adenosylmethionine synthetase expression, leading to a lower ethylene concentration in the storage chambers. Other obtained results concerning soluble solid content, titratable acidity, total polyphenols, anthocyanins, and vitamin C content confirm the observations regarding the impact of ozone treatment in slowing down the fruit ripening process. The best outcomes were achieved by applying a cyclic ozone process with a 100 ppm dose for 30 min. Full article
(This article belongs to the Section Agricultural Product Quality and Safety)
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<p>The course of changes in C<sub>2</sub>H<sub>4</sub> concentration [ppm] in the storage chambers on different days of the experiment, both immediately before and after the ozone treatment, depending on its parameters. NOTE: Differences in the results between the dose of ozone; significant differences at the <span class="html-italic">p</span> &lt; 0.05 level; different lowercase letters indicate significant differences between variants (n = 9).</p> Full article ">Figure 2
<p>Expression of S-adenosulmethionine synthetase (SAMS) (<b>A</b>), MnSOD (<b>B</b>), and CAT (<b>C</b>) in plum fruits on the 5th day of storage, depending on the parameters of the cyclic ozone treatment. Photos of blots after chemiluminescent detection (<b>D</b>). NOTE: Differences in the results between the dose of ozone; significant differences at the <span class="html-italic">p</span> &lt; 0.05 level; different lowercase letters indicate significant differences between variants (n = 9).</p> Full article ">Figure 3
<p>ATP content in plum fruits on the 5th day of storage depending on the parameters of the cyclical ozonation process. NOTE: Differences in the results between the dose of ozone; significant differences at the <span class="html-italic">p</span> &lt; 0.05 level; different lowercase letters indicate significant differences between variants (n = 9).</p> Full article ">Figure 4
<p>The course of changes in the concentration of CO<sub>2</sub> [%] in the storage chambers on specific days of the experiment, immediately after the ozonation process, depending on its parameters.</p> Full article ">Figure 5
<p>Biochemical parameters of fruits on the 5th day of the experiment depending on the parameters of the cyclic ozone process. NOTE: Differences in the results between the dose of ozone; significant differences at the <span class="html-italic">p</span> &lt; 0.05 level; different lowercase letters indicate significant differences between variants (n = 9). (<b>A</b>)-The soluble solid substance, (<b>B</b>)-Titration acidity, (<b>C</b>)-Total polyphenols, (<b>D</b>)-Anthocyanin’s, (<b>E</b>)-Antioxidant activity, (<b>F</b>)-Vitamin C content.</p> Full article ">Figure 5 Cont.
<p>Biochemical parameters of fruits on the 5th day of the experiment depending on the parameters of the cyclic ozone process. NOTE: Differences in the results between the dose of ozone; significant differences at the <span class="html-italic">p</span> &lt; 0.05 level; different lowercase letters indicate significant differences between variants (n = 9). (<b>A</b>)-The soluble solid substance, (<b>B</b>)-Titration acidity, (<b>C</b>)-Total polyphenols, (<b>D</b>)-Anthocyanin’s, (<b>E</b>)-Antioxidant activity, (<b>F</b>)-Vitamin C content.</p> Full article ">
16 pages, 3030 KiB  
Article
New Insights into Polymorphisms in Candidate Genes Associated with Incidence of Postparturient Endometritis in Ossimi Sheep (Ovis aries)
by Fatmah A. Safhi and Ahmed Ateya
Agriculture 2023, 13(12), 2273; https://doi.org/10.3390/agriculture13122273 - 15 Dec 2023
Cited by 2 | Viewed by 1228
Abstract
This study examined the genes related to immunity, metabolism, and antioxidants that may interact with the prevalence of postpartum endometritis in Ossimi sheep. We used fifty endometritis-positive Ossimi sheep and fifty that appeared to be normal. For the purpose of taking blood samples, [...] Read more.
This study examined the genes related to immunity, metabolism, and antioxidants that may interact with the prevalence of postpartum endometritis in Ossimi sheep. We used fifty endometritis-positive Ossimi sheep and fifty that appeared to be normal. For the purpose of taking blood samples, each ewe had its jugular vein pierced. Nucleotide sequence differences for the immunological (alpha-2-macroglobulin, toll-like receptor 2, transforming growth factor beta, interleukin 1 receptor-associated kinase 3, high-mobility group box 1, Fc alpha and Mu receptor, and inducible nitric oxide synthase), metabolic (ADAM metallopeptidase with thrombospondin type 1 motif 20, potassium sodium-activated channel subfamily T member 2, Mitogen-activated protein kinase kinase kinase 4, FKBP prolyl isomerase 5, and relaxin family peptide receptor 1), and antioxidant (superoxide dismutase, catalase, NADH: ubiquinone oxidoreductase subunit s5, and Heme oxygenase-1) genes were found among sheep with endometritis and those in good condition utilizing PCR-DNA sequencing. Fisher’s exact test revealed a significant difference in the probability of dispersal of all significant nucleotide changes between ewe groups with and without endometritis (p ˂ 0.01). In endometritis ewes, there was a considerable up-regulation of the expression levels of A2M, TLR2, IRAK3, HMGB1, FCAMR, iNOS, ADAMTS20, KCNT2, MAP3K4, FKBP5, RXFP1, and HMOX1. Conversely, there was a down-regulation of the genes that encode TGF-β, SOD, CAT, and NDUFS5. The kind of marker and its frequency in postparturient endometrtits significantly impacted the transcript levels of the indicators under analysis. The results validate that nucleotide changes and gene manifestation outlines in these candidates are significant predictors of the prevalence of endometritis in sheep. Full article
(This article belongs to the Special Issue Welfare, Behavior and Health of Farm Animals)
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<p>Different transcript intensities of immune genes between normal and endometritis ewes. When <span class="html-italic">p</span> &lt; 0.05, the asterisk (*) indicates significance.</p> Full article ">Figure 2
<p>Different transcript intensities of metabolic genes between normal and endometritis ewes. When <span class="html-italic">p</span> &lt; 0.05, the asterisk (*) indicates significance.</p> Full article ">Figure 3
<p>Different transcript intensities of antioxidant genes between normal and endometritis ewes. When <span class="html-italic">p</span> &lt; 0.05, the asterisk (*) indicates significance.</p> Full article ">
24 pages, 2814 KiB  
Article
Optimizing Sustainability in Malting Barley: A Practical Approach to Nitrogen Management for Enhanced Environmental, Agronomic, and Economic Benefits
by Petros Vahamidis, Angeliki Stefopoulou and Vassilis Kotoulas
Agriculture 2023, 13(12), 2272; https://doi.org/10.3390/agriculture13122272 - 14 Dec 2023
Cited by 1 | Viewed by 2111
Abstract
Nitrogen (N) fertilisers used in barley production serve as the primary contributors to total greenhouse gas (GHG) emissions. Consequently, to lower the carbon footprint (CF) and GHG emissions, it is imperative to either reduce N fertiliser rates or enhance grain yield and improve [...] Read more.
Nitrogen (N) fertilisers used in barley production serve as the primary contributors to total greenhouse gas (GHG) emissions. Consequently, to lower the carbon footprint (CF) and GHG emissions, it is imperative to either reduce N fertiliser rates or enhance grain yield and improve nitrogen use efficiency (NUE). To address this challenge, we combined two strategies related to N: (1) a 34% reduction in the total N rate compared to the control (total N rate 108–110 kg N ha−1), and (2) testing two types of N fertilisers for topdressing against the control (common sulfur urea). These types included (a) a mixture comprising controlled-release fertiliser (CRF) combined with ammonium sulfate nitrate fertiliser in a 40:60 ratio (CRF + Nitro) and (b) ammonium sulfate nitrate (Nitro). Experiments were conducted in two distinct areas of Greece specialising in cereal production, aiming to unveil the effects of these strategies on all sustainability aspects of malting barley production. The results showed that although a 34% reduction in N rate did not result in yield penalties or a decrease in grain size, it did have a negative impact on grain protein content (GPC). CRF + Nitro not only reduced CF by approximately 30% compared to the control but also increased N agronomic efficiency by 51.5% and net profit by 7.1%. Additionally, it was demonstrated that the maximum achievable reduction in total GHG emissions and CF, by excluding N fertilisation from the crop system, ranged from 68.5% to 74.3% for GHG emissions and 53.8% to 67.1% for CF. Full article
(This article belongs to the Special Issue Agricultural Innovations for Sustainable Cereal Production under a Changing Climate)
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<p>Precipitation and air temperature (average, Tmin, and Tmax) profiles for the Larissa and Livadia experiments. Arrows denote key phenological stages: S = sowing; T = tillering; B = booting; A = anthesis. RGF represents the cumulative rainfall during the period spanning 6 days pre-anthesis to 20 days post-anthesis, identified as the primary environmental factor influencing plump grain (or retention fraction) determination [<a href="#B52-agriculture-13-02272" class="html-bibr">52</a>].</p> Full article ">Figure 2
<p>System boundary of barley production. The production of agricultural machines and crop seeds was not included in CF calculation. Processes modelled with primary data are represented with a grey background, while those relying on secondary data are depicted with a white background.</p> Full article ">Figure 3
<p>Effect of the N fertilization strategies on green canopy cover in Livadia and Larissa. The treatments labelled “CRF + Nitro” and “Nitro” were identical during the first observation, diverging only following the application of spring topdress nitrogen. Error bars represent the standard error of the mean (<span class="html-italic">n</span> = 4). Different letters indicate statistical significance differences within the same crop developmental stage and the same location according to L.S.D. test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Nitrogen agronomic efficiency (NAE) across the different N fertilisation strategies in Livadia and Larissa. Error bars represent the standard error of the mean (<span class="html-italic">n</span> = 4). Different letters indicate statistical significance differences within the same location according to L.S.D. test (<span class="html-italic">p</span> &lt; 0.05). Percentage change relative to the control is indicated within parentheses. Analysis of variance (ANOVA) is also shown. * F values significant at the <span class="html-italic">p</span> &lt; 0.05 probability levels. ** F values significant at the <span class="html-italic">p</span> &lt; 0.01 probability levels.</p> Full article ">Figure 5
<p>Total GHG emissions and carbon footprint across the different N fertilisation strategies in Livadia and Larissa. Error bars represent the standard error of the mean (<span class="html-italic">n</span> = 4). Different letters indicate statistical significance differences within the same location according to L.S.D. test (<span class="html-italic">p</span> &lt; 0.05). Percentage change relative to the control is indicated within parentheses. Analysis of variance (ANOVA) is also shown. * F values significant at the <span class="html-italic">p</span> &lt; 0.05 probability levels. *** F values significant at the <span class="html-italic">p</span> &lt; 0.001 probability levels.</p> Full article ">Figure 6
<p>Comparison of percentages of GHG emissions from different sources in barley production under different N fertilisation strategies in Larissa.</p> Full article ">Figure 7
<p>Comparison of percentages of GHG emissions from different sources in barley production under different N fertilisation strategies in Livadia.</p> Full article ">Figure 8
<p>Production cost and net profit across the different N fertilisation strategies in Livadia and Larissa. Error bars represent the standard error of the mean (<span class="html-italic">n</span> = 4). Different letters indicate statistical significance differences within the same location according to L.S.D. test (<span class="html-italic">p</span> &lt; 0.05). Percentage change relative to the control is indicated within parentheses. Analysis of variance (ANOVA) is also shown. * F values significant at the <span class="html-italic">p</span> &lt; 0.05 probability levels. *** F values significant at the <span class="html-italic">p</span> &lt; 0.001 probability levels.</p> Full article ">
10 pages, 263 KiB  
Article
Effects of Dietary Folic Acid Supplementation on Growth Performance and Immune Parameters in Weanling Piglets
by Qing Gao, Daiwen Chen, Xuemei Ding, Zhiwen Xu, Aimin Wu and Keying Zhang
Agriculture 2023, 13(12), 2271; https://doi.org/10.3390/agriculture13122271 - 14 Dec 2023
Viewed by 1206
Abstract
In order to study the effects of dietary folic acid (FA) supplementation on growth performance and immune status in weanling piglets, a single factorial randomized block design trial was conducted with six diets supplemented with FA at 0, 0.30, 3.00, 6.00, 9.00 or [...] Read more.
In order to study the effects of dietary folic acid (FA) supplementation on growth performance and immune status in weanling piglets, a single factorial randomized block design trial was conducted with six diets supplemented with FA at 0, 0.30, 3.00, 6.00, 9.00 or 15.00 mg/kg. A total of 108 crossbred (Landrace × Yorkshire) castrated weanling piglets (at 21 d of age) were allocated by body weight into 36 feeding cages (3 piglets/cage), which were allotted randomly into six dietary groups (six cages/group). Piglets were fed ad libitum for 24 days. Blood samples were collected on the 24th day. The growth performance and immune parameters were measured. Results showed that FA supplementation increased the serum FA level of weaned piglets (p < 0.01) and tended to increase the body weight (BW) at 45 d of age (p < 0.1) and the average daily gain (ADG) from 29 d to 45 d of age (p < 0.1). FA addition improved the feed efficiency (G/F) from 21 to 45 d of age (p < 0.01) with supplementary FA levels of 0.3, 3.0, and 9.0 mg/kg compared with the control group with no FA supplementation. FA supplementation showed a trend (p < 0.1) to increase the peripheral blood CD3+CD8+ lymphocyte subpopulation and a tendency (p < 0.1) to decrease the CD3+CD4+/CD3+CD8+ ratio; in particular FA supplementation of 0.3 and 3.0 mg/kg showed significant differences in comparison to the non-supplemented control group. Moreover, FA addition increased the serum interferon-γ (IFN-γ) level (p < 0.05) and tended to reduce the ratio of tumor necrosis factor-α to interleukin-4 (TNF-α/IL-4, p < 0.1) and immunoglobulin G (IgG, p < 0.1) in serum, but had no significant effect on serum IL-4, TNF-α, and nitric oxide. In conclusion, FA supplementation up to 3 mg/kg to the diet showed a tendency to improve immune function, while FA supplementation of up to 9 mg/kg improved feed efficiency, which resulted in a trend for higher growth in weaned piglets between 7 to 11 kg BW. Full article
(This article belongs to the Special Issue Effects of Dietary Interventions on Pig Production)
14 pages, 3289 KiB  
Article
Morphological and Physiological Mechanism of Activating Insoluble Inorganic Phosphorus of Different Peanut (Arachis hypogaea L.) Varieties under Low Phosphorus
by Zhen Tan, Fengzhen Liu, Yongshan Wan, Suqing Zhu, Jing Zhang, Kun Zhang and Lu Luo
Agriculture 2023, 13(12), 2270; https://doi.org/10.3390/agriculture13122270 - 14 Dec 2023
Viewed by 1226
Abstract
To reduce the application of phosphorus fertilizer and improve phosphorus efficiency in peanut (Arachis hypogaea L.) production, six peanut varieties with different phosphorus activation efficiencies were selected, and the root morphology, physiological indexes, and types and content of organic acids secreted were [...] Read more.
To reduce the application of phosphorus fertilizer and improve phosphorus efficiency in peanut (Arachis hypogaea L.) production, six peanut varieties with different phosphorus activation efficiencies were selected, and the root morphology, physiological indexes, and types and content of organic acids secreted were measured via a hydroponic experiment for 20 days. We analyzed the difference in calcium phosphate activation between peanut seedlings cultivated under low-phosphorus (LP, 0.01 mmol/L KH2PO4) and normal phosphorus (NP, 0.6 mmol/L KH2PO4) conditions and explored the physiological mechanisms of different peanut varieties on the activation efficiency of insoluble inorganic phosphorus. The results showed that under LP conditions, the root length, root surface area, root volume, root tip number, and root activity of the efficient P activation varieties were 18.31%, 17.50%, 15.23%, 20.00%, and 50.90% higher than those of the inefficient P activation varieties respectively. The reduction range of the nutrient solution pH of the high-efficiency varieties was 74.48% higher than that of the low-efficiency varieties under LP conditions. The total amount of organic acid secreted by the efficient P activation varieties increased by 236.07% on average under LP conditions compared with that under NP conditions. In comparison, the average increase in inefficient P activation varieties was only 16.36%. Under low P stress, the peanut varieties with high-efficiency P activation could increase the activation of insoluble inorganic P in the environment mainly by changing the root architecture and increasing the root-shoot ratio, root activity, and root proton and organic acid secretion. Full article
(This article belongs to the Section Crop Production)
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<p>Root morphology of six peanut varieties under NP and LP conditions.</p> Full article ">Figure 2
<p>Root–shoot ratio of six peanut varieties under NP and LP conditions. Note: “*” represents significant differences between the two treatments, while “ns” represents insignificant differences between the two treatments.</p> Full article ">Figure 3
<p>Root activity of six peanut varieties under NP and LP conditions. Note: The different lowercase letter indicates that the difference has reached a significant level (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>pH variations of nutrient solution after the addition of calcium phosphate under NP conditions. Note: the different lowercase letters in each column indicates that the difference has reached a significant level (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 5
<p>pH variations of nutrient solution after the addition of calcium phosphate under LP conditions. Note: the different lowercase letters in each column indicates that the difference has reached a significant level (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 6
<p>Correlation analysis of root morphology and root exudates of 6 peanut varieties.</p> Full article ">
22 pages, 1420 KiB  
Article
Investigating the Profitability of Government-Funded Small-Scale Broiler Projects in Northern KwaZulu-Natal, South Africa
by Sifiso Themba Clement Mdletshe and Ajuruchukwu Obi
Agriculture 2023, 13(12), 2269; https://doi.org/10.3390/agriculture13122269 - 13 Dec 2023
Viewed by 3424
Abstract
The frequent failures of government-funded broiler enterprises raise concerns about the viability and wisdom of government funding for smallholders. This study therefore investigates the scope for the profitability of the small-scale broiler production and the range of socio-demographic and production issues that are [...] Read more.
The frequent failures of government-funded broiler enterprises raise concerns about the viability and wisdom of government funding for smallholders. This study therefore investigates the scope for the profitability of the small-scale broiler production and the range of socio-demographic and production issues that are implicated. The study area was the Northern KwaZulu-Natal (KZN) Province of South Africa, where smallholder broiler production is popular. A total of 75 randomly selected, small-scale broiler producers from three districts in Northern KZN, namely, King Cetshwayo, uMkhanyakude, and Zululand, participated in the survey out of the 134 small-scale broiler producers supported by the government. The analyses employed diverse descriptive analysis and included the calculation of the gross margin to proxy broiler chicken profitability. Three models were fitted for the empirical analysis, namely, the OLS, the Two-Stage Least Squares, and the Stochastic Frontier models, to determine the factors influencing profitability, correcting for endogeneity, and computing the technical efficiency and inefficiency of the small-scale broiler production system. The results show that the primary production and marketing challenges were the lack of infrastructure (abattoirs and refrigeration) and the lack of formal markets, including the lack of market information and high transport costs. On average, the sampled government-funded small-scale broiler enterprises achieved a positive gross profit margin of 31 percent, which is relatively low when compared to the small-scale farmers that work for a large-scale enterprise—the Commercial Chicken Farm, near Pietermaritzburg. It was revealed that the profits are significantly influenced by gender, farmgate price, access to market information, and access to extension services. The production system was also shown to be operating at a reasonably high technical efficiency, which is strongly influenced by flock size, feeds, and labour input, while age, gender, and educational level contributed to technical inefficiency. The recent crisis that was experienced by the poultry industry in South Africa linked to the outbreak of the Avian Flu and its devastating consequences point up the urgency for more investment in infrastructure to enhance bird safety at affordable costs. Although the government-funded small-scale broiler enterprises in Northern KZN were found to be viable, it is evident that they can be better, possibly through more capacity building and collective action to take full advantage of the economies of scale. Full article
(This article belongs to the Special Issue Sustainable Rural Development and Agri-Food Systems)
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<p>Map showing the location of the selected districts in the KwaZulu-Natal Province in South Africa. Source: KZN municipalities.co.za (2022) [<a href="#B17-agriculture-13-02269" class="html-bibr">17</a>].</p> Full article ">Figure 2
<p>Illustration of the availability of and access to extension services by the small-scale broiler (source: data survey, 2021).</p> Full article ">Figure 3
<p>Availability and accessibility of small-scale broilers markets (Source: data survey, 2021).</p> Full article ">Figure 4
<p>Sources of market information for small-scale broiler producers.</p> Full article ">Figure 5
<p>Summary of the constraints faced by the sampled government-funded small-scale broiler producers in Northern KwaZulu-Natal.</p> Full article ">
17 pages, 880 KiB  
Article
Drivers for the Adoption of Organic Farming: Evidence from an Analysis of Chinese Farmers
by Maosen Xia, Pingan Xiang, Guo Mei and Zhizhen Liu
Agriculture 2023, 13(12), 2268; https://doi.org/10.3390/agriculture13122268 - 13 Dec 2023
Cited by 2 | Viewed by 2150
Abstract
Adoption decision is an important topic in organic farming research. In order to understand farmers’ decision-making, it is necessary to delve into the factors influencing their behavior. Some studies have used social psychology models to explore the adoption intention of farmers in specific [...] Read more.
Adoption decision is an important topic in organic farming research. In order to understand farmers’ decision-making, it is necessary to delve into the factors influencing their behavior. Some studies have used social psychology models to explore the adoption intention of farmers in specific locations regarding organic farming, but there is a lack of investigation into the differences in driving factors for adoption intention among farmers in the pre-organic conversion (conventional), mid-conversion (conversion), and post-conversion (certified) stages, as well as the examination of the relationship between intention and behavior. This study aims to address this issue by examining the driving factors of Chinese farmers’ adoption of organic farming practices. We established a theoretical framework based on the Theory of Planned Behavior (TPB) and applied Partial Least Squares–Structural Equation Modeling (PLS-SEM) to analyze intention data collected from 432 farmers and behavior data collected one year later. The study found that attitude, perceived behavioral control, subjective norms, and descriptive norms positively drive the intention to adopt organic farming. In addition to intention being a determinant of behavior, farm size also positively influences behavior. The strength of the impacts of subjective norms on intention and farm size on behavior differs between conventional farmers and conversion farmers. The common driving chain of “attitude → intention → behavior” exists in the organic adoption decision of conventional, conversion, and certified farmers. Our findings suggest that the public sector can attract conventional farmers to transition to organic and stabilize existing practitioners of organic agriculture practices by considering the differences in driving factors when formulating intervention policies. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The research framework. ATT = attitude; SN = subjective norm; DN = descriptive norm; PS = policy satisfaction; PBC = perceived behavioral control; INT = intention to adopt organic farming; FS = farm size; BEH = organic farming adoption behavior.</p> Full article ">Figure 2
<p>Research area.</p> Full article ">
11 pages, 764 KiB  
Article
Loss of Traditional Orchards and Its Impact on the Occurrence of Threatened Tree-Dwelling Bird Species
by Łukasz Kajtoch
Agriculture 2023, 13(12), 2267; https://doi.org/10.3390/agriculture13122267 - 13 Dec 2023
Cited by 1 | Viewed by 1491
Abstract
Horticulture is one of the land use types in agricultural landscapes, which is beneficial for nature if traditional ways of management are implemented. Orchards are affected by three negative transformations: abandonment that leads to afforestation; grubbing as a result of the cessation of [...] Read more.
Horticulture is one of the land use types in agricultural landscapes, which is beneficial for nature if traditional ways of management are implemented. Orchards are affected by three negative transformations: abandonment that leads to afforestation; grubbing as a result of the cessation of fruit plantation; or intensification with the use of chemicals. In this study, changes in orchard management and structure were examined over a decade (2014–2023) in southern Poland (the Carpathians). Additionally, changes in the distribution of Syrian woodpeckers were assessed—a rare species of special concern in the European Union being a major nest hole excavator in orchards. Over a decade, trees in nearly one-fourth of orchards were removed, 15% of orchards were overgrown by forests due to abandonment, and only 40% remained unchanged. The changes were most pronounced in already abandoned orchards and many traditionally used ones. Fruit trees were grubbed in orchards in areas with a high density of people and roads, whereas succession prevailed in orchards in the vicinity of forests. During the same period, around 40% of woodpecker territories vanished, and this phenomenon was associated with tree grubbing or succession by forests. As the Syrian woodpecker requires protection in Europe, it is recommended to preserve traditional horticulture. Moreover, conservative cultivation of traditional varieties of fruit trees and agro-tourism in traditional orchards could be implemented in synergy with nature conservation. Full article
(This article belongs to the Section Agricultural Systems and Management)
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<p>Pie charts presenting changes in orchards between 2014 and 2023 in three categories of orchards being formerly abandoned, traditionally and intensively used. Blue—abandoned, green—overgrown, red—grubbed, orange—transformed, yellow—unchanged, violet—eldered.</p> Full article ">Figure 2
<p>Differences of orchards (grouped according to changes observed in the last decade) in four selected landscape characteristics.</p> Full article ">Figure 3
<p>Plot showing the relationship between the occurrence of Syrian woodpecker territories with landscape characteristics in the examined area of southern Poland. Dots represent examined orchards assigned to three categories according to their type of management in 2014 (A—abandoned, E—traditionally managed, I—intensively farmed).</p> Full article ">
22 pages, 12086 KiB  
Article
A Korean Cattle Weight Prediction Approach Using 3D Segmentation-Based Feature Extraction and Regression Machine Learning from Incomplete 3D Shapes Acquired from Real Farm Environments
by Chang Gwon Dang, Seung Soo Lee, Mahboob Alam, Sang Min Lee, Mi Na Park, Ha-Seung Seong, Min Ki Baek, Van Thuan Pham, Jae Gu Lee and Seungkyu Han
Agriculture 2023, 13(12), 2266; https://doi.org/10.3390/agriculture13122266 - 12 Dec 2023
Viewed by 1966
Abstract
Accurate weight measurement is critical for monitoring the growth and well-being of cattle. However, the traditional weighing process, which involves physically placing cattle on scales, is labor-intensive and stressful for the animals. Therefore, the development of automated cattle weight prediction techniques assumes critical [...] Read more.
Accurate weight measurement is critical for monitoring the growth and well-being of cattle. However, the traditional weighing process, which involves physically placing cattle on scales, is labor-intensive and stressful for the animals. Therefore, the development of automated cattle weight prediction techniques assumes critical significance. This study proposes a weight prediction approach for Korean cattle using 3D segmentation-based feature extraction and regression machine learning techniques from incomplete 3D shapes acquired from real farm environments. Firstly, we generated mesh data of 3D Korean cattle shapes using a multiple-camera system. Subsequently, deep learning-based 3D segmentation with the PointNet network model was employed to segment 3D mesh data into two dominant parts: torso and center body. From these segmented parts, the body length, chest girth, and chest width of Korean cattle were extracted. Finally, we implemented five regression machine learning models (CatBoost regression, LightGBM, polynomial regression, random forest regression, and XGBoost regression) for weight prediction. To validate our approach, we captured 270 Korean cattle in various poses, totaling 1190 poses of 270 cattle. The best result was achieved with mean absolute error (MAE) of 25.2 kg and mean absolute percent error (MAPE) of 5.85% using the random forest regression model. Full article
(This article belongs to the Special Issue Intelligent Systems in Precision Agriculture: Data, Applications and Techniques)
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<p>Multiple-camera system. (<b>a</b>) System design; (<b>b</b>) real-world deployment.</p> Full article ">Figure 2
<p>Left infrared images from our capturing system.</p> Full article ">Figure 3
<p>Three-dimensional mesh data of two random animals (Korean cattle) after reconstruction.</p> Full article ">Figure 4
<p>Weight distribution of Korean cattle used in this study.</p> Full article ">Figure 5
<p>Overview structure diagram of proposed pipeline.</p> Full article ">Figure 6
<p>Three body dimensions of Korean cattle.</p> Full article ">Figure 7
<p>Torso segmentation (<b>left</b>) and center body segmentation (<b>right</b>).</p> Full article ">Figure 8
<p>PointNet Architecture for 3D segmentation [<a href="#B18-agriculture-13-02266" class="html-bibr">18</a>].</p> Full article ">Figure 9
<p>Simple schematic of regression machine learning for weight prediction.</p> Full article ">Figure 10
<p>Cross-sampling.</p> Full article ">Figure 11
<p>Torso segmentation training history plot.</p> Full article ">Figure 12
<p>Center body segmentation training history plot.</p> Full article ">Figure 13
<p>Torso segmentation results.</p> Full article ">Figure 14
<p>Centre body segmentation results.</p> Full article ">Figure 15
<p>Posture correction using PCA: (<b>a</b>) top view; (<b>b</b>) side view.</p> Full article ">Figure 16
<p>Extracting body length from the 3D-segmented torso.</p> Full article ">Figure 17
<p>Extracting chest girth and chest width from segmented center body.</p> Full article ">Figure 18
<p>Scatter plot of the relationship between three body dimensions and weight of Korean cattle: (<b>a</b>) body length and weight; (<b>b</b>) chest girth and weight; (<b>c</b>) chest width and weight.</p> Full article ">Figure 19
<p>Schematic of K-fold cross-validation with k = 10.</p> Full article ">Figure 20
<p>Average MAE results of 10 fold experiments.</p> Full article ">Figure 21
<p>Average MAPE results of 10 fold experiments.</p> Full article ">Figure 22
<p>Scatter plot of predicted weight values and measured weight values and the correlation coefficient (R) on different regression machine learning models: (<b>a</b>) CatBoost regression; (<b>b</b>) LightGBM regression; (<b>c</b>) polynomial regression; (<b>d</b>) random forest regression; (<b>e</b>) XGBoost regression.</p> Full article ">Figure 22 Cont.
<p>Scatter plot of predicted weight values and measured weight values and the correlation coefficient (R) on different regression machine learning models: (<b>a</b>) CatBoost regression; (<b>b</b>) LightGBM regression; (<b>c</b>) polynomial regression; (<b>d</b>) random forest regression; (<b>e</b>) XGBoost regression.</p> Full article ">
17 pages, 1255 KiB  
Article
Effect of Gender and Muscle Type on Fatty Acid Profile, Sanogenic Indices, and Instrumental and Sensory Analysis of Flemish Giant Rabbit Meat
by Gabriela Frunză, Marius-Mihai Ciobanu, Otilia Cristina Murariu, Roxana Nicoleta Rațu, Răzvan-Mihail Radu-Rusu, Cristina Simeanu and Paul-Corneliu Boișteanu
Agriculture 2023, 13(12), 2265; https://doi.org/10.3390/agriculture13122265 - 12 Dec 2023
Cited by 3 | Viewed by 1449
Abstract
The aim of this study was to represent quality characterization, by gender and muscle type, of rabbit meat from the Flemish Giant (FG) breed, following the fatty acid profile, sanogenic indices, and instrumental (color and texture) and sensory analysis. The biological material comprised [...] Read more.
The aim of this study was to represent quality characterization, by gender and muscle type, of rabbit meat from the Flemish Giant (FG) breed, following the fatty acid profile, sanogenic indices, and instrumental (color and texture) and sensory analysis. The biological material comprised 40 rabbits (20 females and 20 males) whose Longissimus dorsi (LD) and Semimembranosus (SM) muscles were sampled. Compared to female samples, the meat from males was more qualitative in terms of higher ratios of polyunsaturated vs. saturated fatty acids and proportions (+42%) of Essential and Desirable Fatty Acids (+21.6% EFA; +6.7% DFA). Also, the Atherogenic Index (AI) and Thrombogenic Index (TI) were better in males (−37.1% AI; −34.3% TI), as were the ratio of hypocholesterolemic/Hypercholesterolemic fatty acids (+27.8%) and the Nutritive Value Index (NVI, +11.6%). The Polyunsaturation Index (PI) was higher for females (+57.5%), with the widest differences in hind leg muscles (SM muscles), while the omega-6/omega-3 fatty acid ratio was also better (+11.3%). Female meat was more tender due to lower shear force (−6.2%… 9.3%) in both muscles. Female meat was less pigmented than that of males, while the overall sensory attributes were better scored in male samples (+3.1%… +7.1%) (p < 0.01). The meat of males proved to be more sanogenic (richer in EFA and DFA, with a better h/H ratio and NVI, while AI and TI were lower). We would recommend slaughtering 3–4 weeks earlier in females vs. males to avoid excessive fat deposition and, consequently, the development of unfavorable sanogenic indices for consumer health. Full article
(This article belongs to the Special Issue Animal Nutrition and Productions: Series II)
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<p>The sensory appreciation of LD muscles.</p> Full article ">Figure 2
<p>The sensory appreciation of SM muscles.</p> Full article ">
12 pages, 622 KiB  
Article
Monitoring of Chemical and Fermentative Characteristics during Different Treatments of Grape Pomace Silage
by Tea Sokač Cvetnić, Veronika Gunjević, Anja Damjanović, Anita Pušek, Ana Jurinjak Tušek, Tamara Jakovljević, Ivana Radojčić Redovniković and Darko Uher
Agriculture 2023, 13(12), 2264; https://doi.org/10.3390/agriculture13122264 - 12 Dec 2023
Cited by 1 | Viewed by 1549
Abstract
Grape pomace is a fibrous food with satisfactory quantities of residual sugars. It meets the desirable characteristics for conservation in the form of silage for later use in animal feed, mainly for ruminant herbivores. Fresh grape pomace was subdivided into three treatment groups: [...] Read more.
Grape pomace is a fibrous food with satisfactory quantities of residual sugars. It meets the desirable characteristics for conservation in the form of silage for later use in animal feed, mainly for ruminant herbivores. Fresh grape pomace was subdivided into three treatment groups: grape pomace as a control, grape pomace treated with an inoculum of lactic acid bacteria, and grape pomace treated with zeolite. The treatments were performed in micro-silos over 90 days. There was a significant change (p < 0.05) in the chemical characteristics, content of biologically active compounds, and fermentative characteristics during the silage of all treatments. After 30, 60 and 90 days of ensiling, silages treated with inoculum and zeolite had better fermentation quality indicated by significantly (p < 0.05) lower pH and ammonia-nitrogen contents compared with those of the control. Also, the additives have decreased the total polyphenols and tannins for 97% in average which confirmed that lactic acid bacteria and zeolite positively effect on the degradation of polyphenols and tannins in grape pomace silage. The Flieg score was calculated and the values were above 80% what refers to excellent silage. In conclusion, our results suggest that inoculant and zeolite supplementation improves the quality of grape pomace silage for later use in animal feed. Full article
(This article belongs to the Special Issue Feeding Strategies and Nutritional Quality of Animal Products—Volume II)
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<p>TP (<b>a</b>) and TT (<b>b</b>) content during 90 days of ensiling. Results are expressed as average values ± standard errors (CON-control; LAB-inoculum of lactic acid bacteria; ZEO-zeolite); n = 3. <sup>A–C</sup> The same superscript capital letters within a row denote no significant differences (<span class="html-italic">p</span> &gt; 0.05) between the values obtained for the different treatments regarding the control sample according to Tukey’s ANOVA. <sup>a–d</sup> The same superscript lowercase letters within a column denote no significant differences (<span class="html-italic">p</span> &gt; 0.05) between values obtained for different days of storage according to Tukey’s ANOVA.</p> Full article ">
18 pages, 992 KiB  
Article
Impact of Farmer Participation in Production Chain Outsourcing Services on Agricultural Output Level and Output Risk: Evidence from the Guanzhong Plain, China
by Shouhong Xie, Jizhou Zhang, Xiaojing Li, Zhe Chen, Xiaoning Zhang and Xianli Xia
Agriculture 2023, 13(12), 2263; https://doi.org/10.3390/agriculture13122263 - 12 Dec 2023
Cited by 1 | Viewed by 1834
Abstract
Shifting from a land-scale operation to a service-scale operation of agricultural production chain outsourcing services (APOS) is crucial to achieving innovation in agricultural-scale operation techniques. Using propensity score matching (PSM) and data from 1027 farm households in Guanzhong Plain, Shaanxi Province, we empirically [...] Read more.
Shifting from a land-scale operation to a service-scale operation of agricultural production chain outsourcing services (APOS) is crucial to achieving innovation in agricultural-scale operation techniques. Using propensity score matching (PSM) and data from 1027 farm households in Guanzhong Plain, Shaanxi Province, we empirically assessed the impact of APOS on agricultural output level and output risk. First, age, gender, health, education, training, number of outworkers, land tenure, land contiguity, and subsidy satisfaction had a substantial beneficial influence on the involvement of farm families in APOS. Second, involvement in APOS may greatly increase the amount of agricultural production and lower the risk associated with farm families’ agricultural output. Moreover, the participation in outsourcing services for agricultural machinery use and field management significantly increased agricultural output and decreased output risk, but the participation in agricultural machinery use outsourcing services increased yield and reduced risks more significantly. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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<p>Study area map.</p> Full article ">Figure 2
<p>Kernel density before and after matching.</p> Full article ">
17 pages, 7159 KiB  
Article
Phosphorus Utilization Efficiency and Status of Phosphorus Reuse in China from 1990 to 2019
by Yifan Wu, Jingyu Liu, Yong Geng and Dong Wu
Agriculture 2023, 13(12), 2262; https://doi.org/10.3390/agriculture13122262 - 11 Dec 2023
Viewed by 2071
Abstract
Phosphorus (P) is an essential element for supporting our life and is a non-renewable resource. This study applied dynamic material flow analysis to elucidate the phosphorus flow characteristics in China over the period from 1990–2019. Based on this, we developed a P resource [...] Read more.
Phosphorus (P) is an essential element for supporting our life and is a non-renewable resource. This study applied dynamic material flow analysis to elucidate the phosphorus flow characteristics in China over the period from 1990–2019. Based on this, we developed a P resource efficiency index system and further explored the potential reasons for the changes in different areas by analyzing the inflow, outflow, and reuse of P in various modules. Results show that the phosphorus utilization efficiency (PUE) in crop planting increased from 63% in 1990 to 72% in 2019, while this figure in feeding livestock increased from 35% in 1990 to 42% in 2019 due to the utilization of straw. The figure in aquaculture remained low at 9% in 2019. The total P amount used for human consumption increased to 2562 Gg in 2019 due to changes in dietary habits, and the overall P recycling rate (PRR) for various human activities jumped to 58% in 2019. Based upon these results, several policy suggestions are proposed from governance, technology, and economic instruments perspectives. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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<p>System boundary of the P flows in China.</p> Full article ">Figure 2
<p>The overall framework of the P cycle model in China in 2012 (<b>a</b>) and 2019 (<b>b</b>).</p> Full article ">Figure 3
<p>Soil P balance in China.</p> Full article ">Figure 4
<p>Dynamics of phosphorus resource reuse in China during the period of 1990–2019. (<b>a</b>) P recycling rate in straws as fertilizers; (<b>b</b>) Proportion of using recycled P from agricultural planting; (<b>c</b>) PUE of agricultural planting; (<b>d</b>) P outflow from chemical industry; (<b>e</b>) PUE of agricultural product processing; (<b>f</b>) PUE of aquaculture; (<b>g</b>) P recycling rate in human activities; (<b>h</b>) Proportion of using recycled P from aquaculture &amp; livestock poultry; (<b>i</b>) PUE of livestock &amp; poultry breeding.</p> Full article ">Figure 5
<p>Dynamic characteristics of phosphorus resources in China, 1949–2019. (<b>a</b>) Phosphorus ore mining volume and phosphorus outflow; (<b>b</b>) phosphorus outflow from chemical products; (<b>c</b>) phosphorus inflow to agricultural planting; (<b>d</b>) phosphorus flow characteristics of livestock and poultry breeding; (<b>e</b>,<b>f</b>) phosphorus flow characteristics of human consumption.</p> Full article ">Figure 6
<p>PUE of China in 1990, 2000, 2010, and 2019. (<b>a</b>) Livestock breeding; (<b>b</b>) agricultural product processing; (<b>c</b>) agriculture; (<b>d</b>) aquaculture.</p> Full article ">
23 pages, 5725 KiB  
Article
Impact of Cypermethrin (Arpon G) on Soil Health and Zea mays Growth: A Microbiological and Enzymatic Study
by Agata Borowik, Jadwiga Wyszkowska, Magdalena Zaborowska and Jan Kucharski
Agriculture 2023, 13(12), 2261; https://doi.org/10.3390/agriculture13122261 - 11 Dec 2023
Viewed by 1429
Abstract
In defining the research objective, consideration was given to the expanding range of applications of third-generation pyrethroids, including cypermethrin—the active substance in Arpon G preparation. The interest in cypermethrin is due to its high thermostability and photostability. This study verified the effect of [...] Read more.
In defining the research objective, consideration was given to the expanding range of applications of third-generation pyrethroids, including cypermethrin—the active substance in Arpon G preparation. The interest in cypermethrin is due to its high thermostability and photostability. This study verified the effect of Arpon G on both the soil condition and the growth and development of Zea mays. To this end, the alpha and beta diversity of bacterial and fungal communities were characterized using the NGS (Next Generation Sequencing) method, as was the response of soil enzymes. The positive response of Z. mays to the soil application of cypermethrin corresponded to higher soil microbial and biochemical activity. Sowing the soil with Z. mays moderated changes in the biodiversity of alpha- and beta-bacterial communities to a greater extent than cypermethrin. The influence of both parameters was less significant for fungi. Although bacteria belonging to the Actinobacteria phylum and fungi from the Ascomycota phylum dominated in the soil, the use of Arpon G reduced the abundance of unique nucleotide sequences in the mycobiome to a greater extent than in the bacteriobiome. The inhibitory potential of Arpon G was only evident for acid phosphatase (by 81.49%) and arylsulfatase (by 16.66%) in the soil sown with Z. mays. The activity of catalase, dehydrogenases, β-glucosidase, arylsulfatase, and alkaline phosphatase was most strongly associated with the abundance of bacteria, while dehydrogenases were correlated with the abundance of fungi at the genus level. Arpon G can, thus, be considered a safe insecticide for soil conditions and, consequently, for its productive function. Full article
(This article belongs to the Special Issue Chemical Pesticides and Soil Health: The Urgent Need for a New Concept in Agriculture)
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<p>The effect of cypermethrin on the biomass of aerial parts (g d.m. pot<sup>−1</sup>) (<b>a</b>), root biomass (g d.m. pot<sup>−1</sup>) (<b>b</b>), and the <span class="html-italic">Z. Mays</span> greenness index (SPAD) measured in the 4th leaf phase (<b>c</b>) and the 6th leaf phase (<b>d</b>). CS—soil sown with <span class="html-italic">Z. mays</span> without cypermethrin, CypS—soil sown with <span class="html-italic">Z. mays</span> with cypermethrin, SPAD—greenness index. Homogeneous groups denoted with letters (a, b) were calculated separately for the tested property.</p> Full article ">Figure 2
<p>Venn diagram illustrating unique and shared bacterial sequences in a soil sample. CS—soil sown with <span class="html-italic">Z. mays</span> without cypermethrin, CypS—soil sown with <span class="html-italic">Z. mays</span> with cypermethrin, CN—unsown soil without cypermethrin, CypN—unsown soil with cypermethrin.</p> Full article ">Figure 3
<p>The relative abundance of dominant bacterial types in soils, calculated using STAMP statistical analysis software, OTU ≥ 1%. CS—soil sown with <span class="html-italic">Z. mays</span> without cypermethrin, CypS—soil sown with <span class="html-italic">Z. mays</span> with cypermethrin, CN—unsown soil without cypermethrin, CypN—unsown soil with cypermethrin. *—statistical significance.</p> Full article ">Figure 4
<p>The relative abundance of the dominant bacterial genera in soils, calculated using STAMP statistical analysis software, OTU ≥ 1%. CS—sown soil with <span class="html-italic">Z. mays</span> without cypermethrin, CypS—sown soil with <span class="html-italic">Z. mays</span> with cypermethrin, CN—unsown soil without cypermethrin, CypN—unsown soil with cypermethrin. *—statistical significance.</p> Full article ">Figure 5
<p>Relative abundance of dominant bacterial genera in soils, presented as a principal component analysis (PCA). CS—sown soil with <span class="html-italic">Z. mays</span> without cypermethrin, CypS—sown soil with <span class="html-italic">Z. mays</span> with cypermethrin, CN—unsown soil without cypermethrin, CypN—unsown soil with cypermethrin.</p> Full article ">Figure 6
<p>A Venn diagram illustrating the unique and shared fungal sequences in soil samples. CS—sown soil with <span class="html-italic">Z. mays</span> without cypermethrin, CypS—sown soil with <span class="html-italic">Z. mays</span> with cypermethrin, CN—unsown soil without cypermethrin, CypN—unsown soil with cypermethrin.</p> Full article ">Figure 7
<p>The relative abundance of the dominant fungal types in soils, calculated using STAMP statistical analysis software, OTU ≥ 1%. CS—soil sown with <span class="html-italic">Z. mays</span> without cypermethrin, CypS—soil sown with <span class="html-italic">Z. mays</span> with cypermethrin, CN—unsown soil without cypermethrin, CypN—unsown soil with cypermethrin. *—statistical significance.</p> Full article ">Figure 8
<p>The relative abundance of the dominant fungal genera in soils, calculated using STAMP statistical analysis software, OTU ≥ 1%. CS—soil sown with <span class="html-italic">Z. mays</span> without cypermethrin, CypS—soil sown with <span class="html-italic">Z. mays</span> with cypermethrin, CN—unsown soil without cypermethrin, CypN—unsown soil with cypermethrin. *—statistical significance.</p> Full article ">Figure 9
<p>Relative abundance of dominant fungal genera in soils, presented as a principal component analysis (PCA). CS—soil sown with <span class="html-italic">Z. mays</span> without cypermethrin, CypS—soil sown with <span class="html-italic">Z. mays</span> with cypermethrin, CN—unsown soil without cypermethrin, CypN—unsown soil with cypermethrin.</p> Full article ">Figure 10
<p>Soil enzyme activity presented as a principal component analysis (PCA). CS—sown soil with <span class="html-italic">Z. mays</span> without cypermethrin, CS—sown soil with <span class="html-italic">Z. mays</span> without cypermethrin, CypS—sown soil with <span class="html-italic">Z. mays</span> with cypermethrin, CN—unsown soil without cypermethrin, CypN—unsown soil with cypermethrin; Deh—dehydrogenase, Cat—catalase, Ure—urease, AcP—acid phosphatase, AlP—alkaline phosphatase, Glu—<span class="html-italic">β</span>-glucosidase, Aryl—arylsulfatase.</p> Full article ">Figure 11
<p>Pearson’s correlation coefficients between the abundance of dominant bacterial genera and fungi are significant at <span class="html-italic">p</span> = 0.05, <span class="html-italic">n</span> = 12. *—statistical significance.</p> Full article ">Figure 12
<p>Pearson’s correlation coefficients between enzyme activity and the abundance of dominant bacterial genera. * significant at <span class="html-italic">p</span> = 0.05, <span class="html-italic">n</span> = 12, Deh—dehydrogenase, Cat—catalase, Ure—urease, AcP—acid phosphatase, AlP—alkaline phosphatase, Glu—<span class="html-italic">β</span>-glucosidase, Aryl—arylsulfatase. *—statistical significance.</p> Full article ">Figure 13
<p>Simple correlation coefficients between enzyme activity and the abundance of dominant fungal genera. * significant at <span class="html-italic">p</span> = 0.05, <span class="html-italic">n</span> = 12. Deh—dehydrogenase, Cat—catalase, Ure—urease, AcP—acid phosphatase, AlP—alkaline phosphatase, Glu—<span class="html-italic">β</span>-glucosidase, Aryl—arylsulfatase. *—statistical significance.</p> Full article ">
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Article
Glyphosate-Based Herbicide Formulations and Their Relevant Active Ingredients Affect Soil Springtails Even Five Months after Application
by Anna Altmanninger, Verena Brandmaier, Bernhard Spangl, Edith Gruber, Eszter Takács, Mária Mörtl, Szandra Klátyik, András Székács and Johann G. Zaller
Agriculture 2023, 13(12), 2260; https://doi.org/10.3390/agriculture13122260 - 11 Dec 2023
Cited by 3 | Viewed by 2245
Abstract
Glyphosate is the most widely used active ingredient (AI) in glyphosate-based herbicides (GBHs) worldwide and is also known to affect a variety of soil organisms. However, we know little about how the effects of glyphosate AIs differ from those of GBHs that also [...] Read more.
Glyphosate is the most widely used active ingredient (AI) in glyphosate-based herbicides (GBHs) worldwide and is also known to affect a variety of soil organisms. However, we know little about how the effects of glyphosate AIs differ from those of GBHs that also contain so-called inert co-formulants. We conducted a greenhouse experiment using the model cover crop white mustard (Sinapis alba) to investigate the effects of three GBHs (Roundup PowerFlex, Roundup LB Plus, and Touchdown Quattro) and their respective glyphosate AIs (glyphosate potassium, isopropylamine, and diammonium salt) on epedaphic springtails (Sminthurinus niger; Collembola) activity in soils with low (3.0%) or high (4.1%) organic matter content (SOM). Springtail activity was assessed using pitfall traps. Most GBHs and AIs reduced springtail activity compared to mechanical removal of mustard in the short-term and even up to 5 months after application. GBHs and AIs differed considerably in their effects on springtail activity, and effects were modified by SOM content. Our results highlight the need to (i) distinguish between the effects of glyphosate AIs and commercial GBH formulations, (ii) disclose all ingredients of GBHs, as co-formulants also affect non-target organisms, and (iii) include soil properties in ecotoxicological risk assessments for soil organisms to better characterize the situation in the field. Full article
(This article belongs to the Special Issue Feature Papers in Crop Protection, Diseases, Pests and Weeds)
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Figure 1

Figure 1
<p>Springtail activity under low (3.0%) and high (4.1%) soil organic matter (SOM) levels after the application of weed control measures to a model crop. CO…control, mechanical removal of mustard; GBH…application of one glyphosate-based herbicide (Roundup PowerFlex, Roundup LB Plus, Touchdown Quattro), AI…application of one respective glyphosate active ingredient (potassium, isopropylamine, diammonium salt). Legacy effects from application of the same measures 4 months ago; short-term effects after new application on day 42 (treatment). Means ± SD, <span class="html-italic">n</span> = 5.</p> Full article ">Figure 2
<p>Legacy effects over 150 days on springtail activity in soil with low (3.0%) or high (4.1%) soil organic matter levels (SOM) where different weed control measures have been applied. CO…control, mechanical removal of mustard; Roundup PowerFlex (PF) or its AI glyphosate potassium salt (po); Roundup LB Plus (LB) or its AI isopropylamine salt (is); Touchdown Quattro (TQ) or its AI diammonium salt (am). Means ± SE of four sampling dates, <span class="html-italic">n</span> = 5. Significant differences between CO and individual GBHs or AIs, and between GBHs and associated AIs are indicated with asterisks; *** <span class="html-italic">p</span> &lt; 0.001. See <a href="#agriculture-13-02260-t003" class="html-table">Table 3</a> for the statistical results of all mean comparisons.</p> Full article ">Figure 3
<p>Cumulative springtail activity under low (3.0%) and high (4.1%) soil organic matter (SOM) levels after the application of weed control measures to a model crop. CO…control, mechanical removal of mustard; GBH…application of one glyphosate-based herbicide (Roundup PowerFlex, Roundup LB Plus, Touchdown Quattro), AI…application of one respective glyphosate active ingredient (potassium, isopropylamine, diammonium salt). Legacy effects from application of the same measures 150 days ago; short-term effects after new application on day 42 (treatment). Means ± SD, <span class="html-italic">n</span> = 5.</p> Full article ">Figure 4
<p>Short-term effects over 35 days on springtail activity in soil with low (3.0%) or high (4.1%) soil organic matter levels (SOM) where different weed control measures have been applied. CO…control, mechanical removal of mustard; Roundup PowerFlex (PF) or its AI glyphosate potassium salt (po); Roundup LB Plus (LB) or its AI isopropylamine salt (is); Touchdown Quattro (TQ) or its AI diammonium salt (am). Means ± SE of four sampling dates, <span class="html-italic">n</span> = 5. Significant differences between CO and individual GBHs or AIs, and between GBHs and associated AIs are indicated with asterisks; *** <span class="html-italic">p</span> &lt; 0.001. See <a href="#agriculture-13-02260-t005" class="html-table">Table 5</a> for the statistical results of all mean comparisons.</p> Full article ">
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